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Second Class postage paid at Washington, DC and additional mailing offices. Journal of the Washington Academy of Sciences, Volume 83, Number 1, Pages 1-8, March 1993 Human Remains from Hospital San Juan de Dios, Quito, Ecuador Douglas H. Ubelaker Department of Anthropology, National Museum of Natural History, Smithsonian Institution, Washington, D.C. and Agnes Rousseau Paris, France ABSTRACT Excavations conducted in 1988 and 1989 at the earliest formal hospital in Ecuador (founded.in 1565) revealed two samples of human remains. This analysis focuses on second- ary, disarticulated remains found within the church and skulls found within a related os- suary. The church remains reveal all ages and both sexes, with little evidence of skeletal disease and moderate levels of dental disease. The ossuary skulls also reveal minimal evi- dence of disease, with males showing higher frequencies of dental caries than females. Since 1973, the excavation and analysis of human remains from archeological contexts in Ecuador has revealed a great deal about biological patterns of change within human populations of that area. Initially this work focused on Indian populations dating prior to the arrival of the Spanish (Ubelaker, 1980a; 1980b; 1981; 1983a; 1983b; 1988a; 1988b). More recently, research has expanded to include colonial period remains from the city of Quito (Ubelaker, 1992; 1994). Information gleaned from skeletal analysis supplements the historic record with unique information on demography, disease and other variables that normally would not be available. Corresponding author: D. H. Ubelaker, Department of Anthropology, NMNH, MRC 112, Smithsonian Institution, Washington D.C. 20560. telephone 202 786 2505, fax 202 357-2208. 1 2 UBELAKER AND ROUSSEAU From November 1988 to June 1989, with support from the Instituto Ecuato- riano de Obras Sanitarias (Section of the Health Ministry), excavations were conducted at the “Hospital San Juan de Dios” in the central part of Quito, Ecuador. The excavations were initiated in conjunction with architectural stud- ies and restoration of the structure of the hospital. The Hospital San Juan de Dios represents the first formal hospital established in Ecuador. The hospital was founded on March 9, 1565, and was in use in the old central part of Quito until about 1974. The facility included infirmaries and storage rooms distributed around a northern and southern patio. From 1705 to 1736, the hospital was expanded by construction of a church, immediately east of the northern patio. The church was built over the remains of a house which belonged to a Spanish merchant named Pedro de Ruanes which itself was con- structed on pre-colonial ruins. Stone walls with Inca characteristics were found beneath the church floor in no stratigraphical relation to the burial pits. The size of these walls suggests the existence of a large structure at this site. The ceramic material associated with them was representative of the ““domestic-Inca” type ware. The church was built by the Bethlemitas, a religious order created in 1650 in Guatemala by Father José de Betancourt, to administer hospitals and care for the sick. Prior to their arrival, the hospital was managed by a charitable brother- hood. Due to conflict among the brotherhood members, the hospital had many problems. The Real Audencia President approached the Bethlemitas to become involved to help solve these problems. A cemetery is associated with the hospital and dates from about 1565 to about - 1800. Excavations clearly identified two burial areas within the complex: an ossuary located outside the church and a group of individual skeletons within the church. The ossuary was located within one of the storage rooms (Bodega D) immediately west of the church, between it and an area of the hospital complex called the northern patio. The bones were concentrated within the general fill of this area. Archeological evidence suggests that the ossuary was filled in with the bones, bricks, soil, and other construction materials after the wall of the north- ern patio was destroyed by the earthquake of 1755. It is likely that the bones originated from skeletons that had been buried within the same space between 1705 (date of initial construction of the church) and 1755. Presumably, the areas in and around the church must have represented privileged sites for burial. Two radiocarbon dates are available for the ossuary. Both were collected at the time of excavation and sent, with support from the Smithsonian Institution, to Beta Analytic Inc. of Miami, Florida. A sample of human bone was dated at 60 years plus or minus 60 years (essentially modern) and thus was obviously HUMAN REMAINS FROM SAN JUAN DE DIOS 3 contaminated. A more reliable charcoal sample was dated at 160 years, plus or minus 50 years. While the charcoal date should be relatively reliable, it likely represents a terminal date for use of the ossuary. All of the remains were disarti- culated which indicates they had been removed from another location (probably in the same area) and reburied. The evidence suggests that the remains represent not pre-contact Indians, but early colonial period individuals, most likely dating between 1705 and 1755. No diagnostic artifacts were found in association with the bones. The remains were generally poorly preserved and fragmented upon removal. All bones were disarticulated, i.e., no bones were in anatomical order. The individuals had been located in the primary repository long enough for all soft tissue to decompose, allowing the bones to disarticulate during the transfer to the ossuary. No cut marks or other indicators of defleshing were noted. Excavation of the floor of the church also revealed six roughly rectangular pits containing skeletons. The pits were located around the periphery of the church floor and were arranged symmetrically in line with the axis of the church. All of these pits contained at least one articulated skeleton (one contained two). Disar- ticulated bones were found in the fill above the skeletons within four of the pits. Many immature individuals were present. Since not all of the area along the walls inside the church was excavated, possibly other pits and skeletons remain. The skeletons likely date between 1710 (the supposed date of completion of construction of the church) and 1810, the date of the War of Independence and the conversion of the hospital to a public facility. The similar positions of the articulated skeletons within the church (one hand on the opposite shoulder and the other one on the stomach area) and their small number suggest they may have been high-ranking Bethlemitas. Unfortunately, historical information about their burial customs is lacking. No remains of wooden coffins or other associated artifacts were recovered. The origin of the disarticulated bones found within the fill of the pits remains unknown. Burial of the general public associated with the hospital was in the main cemetery, now located beneath recent construction outside the southern part of the church. In contrast to the bones of the ossuary, those from the church were well preserved. All were disarticulated and removed for analysis except one, which was preserved intact and removed within a block of soil for exhibition purposes. In August of 1990, many of the bones were washed and dried in preparation for analysis. Due to limitations of time, only the secondary samples from the church and the crania recovered from the ossuary were studied. Most of the crania were not sufficiently intact to allow reliable assessment of population origins. How- ever, one elderly male ossuary skull (number two) appeared to be of European origin. A UBELAKER AND ROUSSEAU Table 1.—Measurements and observations, Church Male Female Auricular height 106 Porion-bregma 105 Cranial length 170 Cranial breadth 143 137 Basion-bregma 124 Minimum frontal breadth 96 Nasal height 53 Nasal breadth 21 Maximum alveolar length 50 Maximum alveolar breadth 60 Palatal length 45 Palatal breadth 39 Bigonial breadth 94 Height of ascending ramus 67 Minimum breadth of ascending ramus 26 Secondary Sample from the Church The bones from the secondary sample in the church represented at least 18 individuals, four adults and 17 immature individuals. Three adults are indicated by the presence of three right radii, left and right femora, left fibulae, nght temporals, gladioli of the sternum, innominates, and calvaria. Two individuals are represented by the humeri, left radius, both ulnae, right clavicle, left tem- poral, both maxillae, mandibles, patellae, thoracic vertebrae, sacra, left greater multangulars of the hand, right first and fifth metacarpals, the second, third, and fourth metacarpals, proximal hand phalanges, right calcanea and right ribs. A single individual is represented by the tibiae, right fibula, scapulae, manubrium, second cervical, other cervicals, the hand naviculars, right lunate, right capitate, left first and fifth metacarpal, middle hand phalanges, tali, and the first through fourth metatarsals. Morphology of the innominates indicates that at least one male and two females are present. Since morphology of the three calvaria indicates the likely presence of one female and two males, at least four adults are probably present, two males and two females. Morphological assessment of the symphyseal surfaces of the pubic bones suggests ages at death of 40 to 50 years and 50 to 70 years for the males and 40 to 50 years and 38 to 45 years for the females. At least 15 immature individuals are indicated by the left humerus, 14 by the femora, 12 by the right humerus, 10 by the left side of the mandible, eight by the right scapula, left temporal and right mandible, seven by the right ilium, six by the night ulna, left scapula and the left ilium, five by the left radius, left ulna, left tibia, both clavicles, and left maxilla, four by the right radius, left fibula and ribs, HUMAN REMAINS FROM SAN JUAN DE DIOS 5 Fig. 1. The ossuary at San Juan de Dios. three by the right fibula and vertebrae, two by the left ischium and right pubis, and one by the right maxilla, carpal and tarsal bones. Ages estimated from long bone lengths indicate that 17 immature individuals are present. Their estimated ages are four to five years, three years, 1.5 years, two at one year, two at about 10 months, one at six months, four at about three months, and five at newborn. Table 2.—Measurements and observations, Ossuary Male Female Mean _ s.d. Range n Mean sd. Range 5 Auricular height 3 119 2 111-124 1 113 113 Porion-bregma 3 123 16.1 110-141 1 110 110 Cranial length 5 183 11.9 164-197 2 172 3.5 169-174 Cranial breadth 4 137 0.5 136-138 2 130 2.8 128-132 Basion-bregma 2 133 14.1 123-143 Minimum frontal breadth 6 96 2.3 92-98 Nasal height 4 33 1.0 52-54 1 44 44 Nasal breadth 4 26 1.4 24-27 Maximum alveolar length 3 33 13 53-56 1 51 51 Maximum alveolar breadth 4 62 Paes 63-68 1 61 61 Palatal length 3 47 2.6 44-49 | 40 40 Palatal breadth 3 42 2.0 40-44 1 38 38 Height of ascending ramus 1 64 64 Minimum breadth of ascending ramus _ 1 32 32 6 UBELAKER AND ROUSSEAU The only evidence of bone pathology in this sample is a well remodeled periosteal lesion on the posterior surface of the distal end of an adult night femur. The affected area is located about 64 mm from the distal end. The distal articu- lar surface also appears to be involved since it displays irregular bone deposits and pits. Infection is the likely cause of the bony changes. Four cervical vertebrae show stage one osteophytosis. Of 11 thoracic verte- brae, eight are stage one and three are stage two. All six lumbar vertebrae show stage one. These changes likely indicate normal age changes rather than disease. Living stature can be estimated only for three adult individuals from femoral lengths. Left femoral lengths of 473 mm and 490 mm (estimated) suggest living statures of 174 cm and 178 cm respectively. One right femoral length of a different adult suggested a stature of 166 cm. All of the femora likely originated from males and all statures were calculated using Trotter’s formulae for White males (Trotter, 1970; Ubelaker, 1989). Only 32 adult teeth from three individuals were recovered from the church sample. Of 70 observations of the presence or absence of permanent teeth, 38 (54 percent) were absent antemortem. Only two (3 percent) of 30 teeth were carious. The two carious lesions were located on the mesial surface of a first maxillary molar and on the mesial surface of a second maxillary molar. One (3 percent) of 33 teeth showed associated alveolar abscesses. The large percentage of teeth lost antemortem reflects the cumulative effect of untreated caries, peri- odontal disease and trauma. Dental calculus was concentrated more on the lingual surfaces of the teeth than on the buccal surfaces. Of the buccal surfaces examined, five showed no deposits, 24 slight, one moderate, and two large. Lingual surfaces showed four absent, 20 slight, five moderate and three large. Only one tooth, a maxillary right central incisor, displayed linear enamel hypoplasia. This lesion was located about 7 mm from the crown root junction and likely formed about the age of three years. Only seven deciduous teeth are present, maxillary left and right canines, left maxillary first and second molars, and the left mandibular canine, and first and second molars. None of these teeth show evidence of caries, alveolar abscess, calculus or enamel hypoplasia. Cranial and mandibular measurements are summarized in Table 1. Since measurements were possible for only one male and one female, summary statis- tics are not needed. Evidence of cranial deformation was not detected. Observations on discrete cranial and mandibular traits were possible primar- ily on one male and one female. Frontal grooves were present on one male and absent on one female. All other observations were negative. “HUMAN REMAINS FROM SAN JUAN DE DIOS 7 The Ossuary Sample Of the ossuary material, nine crania were available for analysis. Additional remains had been recovered but had not been thoroughly cleaned and prepared for data collection. All of the crania originated from adults: seven males and two females, all undeformed. Ages estimated for the males are 23 to 28, 55 to 60, 40 to 50, 50 to 60, and three between 30 and 35. Ages for the females are 30 to 40 and 20 to 30. The mean age of males in the cranial sample is 40 years, but only 30 years for females. Ages were estimated from the extent of cranial suture closure and dental observations. Table 2 summarizes measurements of the os- suary crania. Dental data are available for only one female cranium, No. 5. Eight maxillary teeth reveal no carious lesions and no alveolar abscesses. All teeth show slight — calculus on both the buccal and lingual surfaces. No hypoplasia was noted. The seven male crania presented 59 teeth of which 10 (16.9 percent) were carious. All carious teeth were molars. Of 98 observations on teeth lost antemor- tem, five (5 percent) had been lost. Of 97 observations on alveolar abscess, only three teeth (3 percent) had associated abscesses. Calculus was minimally present. Scores for buccal tooth surfaces were 14 absent, 35 slight, 11 moderate, and no large. Lingual scores were 14 absent, 45 slight, one moderate and no large. No examples of hypoplasia were noted. Summary Although the human remains from the ossuary and the later secondary church sample are small, they add new perspective to the historic information already available about human biology of the colonial populations in Ecuador. Comparisons between the samples reveal slightly higher adult life expectancy for both males and females in the church sample. This greater life expectancy 1s accompanied by greater tooth loss in the church sample (54 percent vs 5 percent). The church sample also reveals a lower caries rate (3 percent vs 10 percent) and similar rates of dental abscess (3 percent). No hypoplastic teeth were found in the small ossuary dental sample, but three percent of the church teeth were hypoplastic. In general, the dental disease frequencies fall within the range previ- ously reported for skeletal populations within Ecuador and perhaps suggest greater diversity during the historic period than previously thought. Comparison between these two samples is complicated by lack of detailed information about the populations they represent. In general, the hospital and 8 UBELAKER AND ROUSSEAU church serviced the underprivileged populations of urban Quito. Those buried directly within the church as single interments may represent higher status indi- viduals, perhaps those with administrative positions within the church. The secondary deposits found within the church and reported here are of unknown population origins. Although the sizes of these two samples are small, they contribute to the growing information on biological change within ancient Ecuador. It is hoped that these data can be augmented through future analysis of the additional remains already excavated, and from new excavations at San Juan de Dios and other historic mortuary sites in the area. References Trotter, M. (1970). Estimation of stature from intact limb bones. In T. D. Stewart (Ed.), Personal Identifica- tion in Mass Disasters, (pp. 71-83). Washington: Smithsonian Institution. Ubelaker, D. H. (1980a). Human Skeletal Remains from Site OGSE-80, A Pre-ceramic Site on the Sta. Elena Peninsula, Coastal Ecuador. J. Wash. Acad. Sci., 70, No. 1, 3-24. Ubelaker, D. H. (1980b). Prehistoric Human Remains from the Cotocollao Site, Pichincha Province, Ecua- dor. J. Wash. Acad. Sci., 70, No. 2, 59-74. Ubelaker, D. H. (1981). The Ayalan Cemetery: A Late Integration Period Burial Site on the South Coast of Ecuador. Smithsonian Contributions to Anthropology 29. Washington, D.C. Ubelaker, D. H. (1983a). Human Skeletal Remains from OGSE-172, an Early Guangala Cemetery Site on the Coast of Ecuador. J. Wash. Acad. Sci., 73, No. 1, 16-26. Ubelaker, D. H. (1983b). Prehistoric Demography of Coastal Ecuador. National Geographic Society Research Reports, 15, 695-703. Ubelaker, D. H. (1988a). Human Remains from OGSE-46, La Libertad, Guayas Province, Ecuador. J. Wash. Acad. Sci., 78, No. 1, 3-16. Ubelaker, D. H. (1988b). Prehistoric Human Biology at La Tolita, Ecuador, A Preliminary Report. J. Wash. Acad. Sci., 78, No. 1, 23-37. Ubelaker, D. H. (1989). Human Skeletal Remains. Excavation, Analysis, Interpretation, second edition. Wash- ington: Taraxacum. Ubelaker, D. H. (1992). (abstract) Patterns of Biological Change in Ancient Ecuador. American Journal of Physical Anthropology, Suppl. 14, 165. Ubelaker, D. H. (1994). The Biological Impact of European Contact in Ecuador. In Larsen & Milner (Eds.) In the Wake of Contact: Biological Responses to Conquest. New York: Wiley-Liss. Journal of the Washington Academy of Sciences, Volume 83, Number |, Pages 9-31, March 1993 Work Efficiency vs. Complexity: Introduction to Ergodynamics' Valery F. Venda Department of Mechanical and Industrial Engineering, University of Manitoba, Winnipeg, Canada, R3T 2N2 ABSTRACT Ergodynamics is proposed as a theoretical foundation and practical method for studying and improving work efficiency in dynamic environments. Ergodynamics is based upon three laws termed: 1) mutual adaptation; 2) plurality of functional work structures; and 3) trans- formations. Work efficiency and complexity are interpreted as opposite criteria measured with similar units. Analysis of correlations between criteria and factors of efficiency and complexity may help to model work functional structures and to predict dynamics of trans- formations between the structures. Recommendations to increase both work efficiency, and the practical usefulness of the laboratory testing of products and workstations are given. Ways to avoid losses and increase profits while upgrading software and technology are suggested. The Britannica World Language Dictionary (1954, p. 419) defines efficiency as “1. The character of being efficient, effectiveness; 2. The ratio of the work done by an organism or machine to the amount of food or fuel consumed and to the energy expended. “The same Dictionary (p. 277) defines a complexity as “the state of being complex; something complex”; and complex as “1. Consist- ing of various parts or elements; composite; 2. Complicated; involved; intricate, something composite or complicated”. The book by Streufert and Swezey (1986) became a classic in studies and teaching theory of complexity in organiza- tional psychology and management. We have used the theory by these authors as a basis for analysis of complexity dynamics in decision making processes (Venda and Venda, 1994). "This paper is dedicated to fond memory of Yuri V. Venda (1969-1991) who discovered the Law of Transformations. Author appreciates the fruitful discussions and editing of this paper by Dr. John J. O'Hare and Dr. Robert W. Swezey. These studies were supported by Northern Telecom Ltd., Bell-Northern Research, and Natural Sciences and Engineering Research Council of Canada. 10 VENDA The terms “efficiency” and “complexity” are often combined, as in the saying “this task is very simple for you (me), thus you (I) can do it easy, quickly, effectively.”’ Or in another case, “‘this task 1s too complicated, and that is why you spent so much time and made so many mistakes.” Time spent and mistakes made are used as criteria of complexity: if more time and mistakes, then higher complexity. Productivity, work tempo, and the number of tasks solved in a certain amount of time are used as criteria of efficiency. An inverse correlation occurs between efficiency (what one has obtained) and complexity (what one has spent). For example, efficiency may be measured as the number of a student’s correct answers on an exam and as the number of wrong answers. Thus the complexity criterion value (wrong answers) will supplement the efficiency crite- rion value (correct answers). Higher complexity leads to lower efficiency; higher efficiency leads to lower complexity. This concept of efficiency and complexity has been applied successfully in many ergonomic and psychological studies and projects (Lomov and Venda, 1977; Savelyev and Venda, 1989; Venda, 1975, 1980, 1990). The concept offers an operational method for measuring and comparing efficiency and complexity using the same units. Although it may be convenient in design and ergonomic practice to increase efficiency, but there are many situations when it is easier to find what causes complexity and influences on complexity, for instance by optimizing the use of information displays, hard- ware, and software (Venda, 1975, 1982). In industry, higher efficiency is gener- ally considered better (i.e. more products and higher quality). In science and education, higher efficiency may be of special interest as a way to success in research, understanding of a new field, and/or acquisition of skills and knowl- edge. Karwowski and Ayoub (1984) have suggested application of fuzzy set theory to assess the stress and complexity of manual lifting tasks. Karwowski and Mital (1986) have expanded application of fuzzy set theory to the main areas of ergonomics; and Karwowski, Marek, and Noworol (1988), and Kar- wowski (1991) have worked out a general approach to the theory of ergonomics and complexity based upon fuzzy set theory and categories of entropy and ergonomic incompatibility. In our own work, we have tried to work out an operational theory of efficiency and complexity of human work as well as human-machine-environment mu- tual adaptation (Venda, 1975, Venda and Venda, 1991). In this view, complex- ity depends on internal functional structures (work skills) and their mutual adaptation with the external environment. Loss of efficiency, when a work task and environment are constant, may mean there have been changes in the hu- man internal state. Grandjean (1988) stated that fatigue is invariably associated STUDIES OF EFFICIENCY AND COMPLEXITY 11 with ‘“‘a loss of efficiency and a disinclination for any kind of effort’ (p. 156). This loss of efficiency is considered to be the result of mutual dysadaptation of human internal components (subsystems, organs), and thus increases work complexity, while efhciency and complexity are characteristics of human-en- vironment mutual adaptation (Venda, 1975). Before the issue of efficiency and complexity is addressed, users, goals, and functional structures need to be deter- mined. If there is no goal, then complexity may not exist. Further, the same goal (task) may be associated with different complexity criterion values for different users using different functional work structures. Work is regarded generally, as any kind of goal oriented human performance. For example, if students or conference attendees are shown a control board for a power plant, and asked whether it is complex, the question is improper. When persons do not have some concrete task for reference, they cannot determine complexity. Ifa student is not interested in watching the control board and no human-board contact happens, there is no complexity. However, the same control board may present the power-plant operator an emergency task of very high complexity; and task complexity in that situation could be assessed as an obstacle to reaching maxi- mal efficiency of the human-machine system. The higher the complexity (the greater the obstacle on the way to a goal) the lower the work efficiency. Higher complexity leads to efficiency losses, and thus to lower real efficiency. The need to define human goals in all studies of the human-environment interaction has been analyzed and demonstrated for many situations by Leontiev (1971), and by the many Russian psychologists who were followers of the psychological theory of human goal-oriented activity. Work Efficiency and Complexity Criteria Any work-output level to be increased may be used as a criterion of efficiency. The criteria of efficiency may not only be engineering result, but work satisfac- tion, health protection, and even happiness can also be considered as efficiency criteria. The ergodynamics approach applies both to qualitative and quantita- tive analyses of work processes and to the functioning of complex systems. Efficiency is a positive measure of work and living processes. In an efficient system, obstacles, difficulties, errors, and deviations from optimal work pro- cesses and algorithms are minimal. These negative aspects (preferably measured with quantitative measures) are criteria of functional complexity. Functional efficiency and complexity criteria are opposites. If higher work efficiency is attained, it automatically means that complexity has been lowered. If some part of potential efficiency is lost, it means that functional complexity 12 VENDA has become higher. Typical criteria used to assess complexity are extra time spent for a given amount of work, number of errors committed, frequency of defective products, and probability of a wrong decision (failure). If one measures the time spent on the creation of a product, and determines the productive part of that time, the remaining time defines a criterion of complexity. Functional complexity may be measured by any criterion reflecting losses to be minimized, and may be converted into efficiency, because less complexity is more eff- ciency, and vice versa. For example, if the probability of correct decisions is p,,, (aS a criterion of efficiency), then the probability of wrong decisions p,,,, 1S Pyro = 1 — Door. Thus, Q.e + Cre = 1, where Q,,; is a relative criterion of efficiency and C,,, is a relative criterion of complexity. In absolute values, total productivity is Q,,, (number of items produced), the effective productivity is Q (a number of quality items), and the complexity of production is C (the number of defective items). Obviously, Q fae Sa OD Q/ Qtot ai C/ Qtot 7" Qrot/ One Ox + Cre 7 i because Q/ Qrot is a relative efficiency, Q,., and C/Q,,, is a relative complexity C,,,. Instead of addition of efficiency and complexity criteria values, multiplication is used in many cases. For example, productivity (a number of items produced) in a time unit (effi- ciency, Q) could be multiplied on time spent on production of one item (com- plexity, C), thus Q k C = 1, Q = 1/C, C= 1/Q. Qualitative and Quantitative Analysis of Efficiency and Complexity Some authors limit ergonomic analysis of human-machine-environment in- teraction to qualitative methods, for example as skill-based, rule-based, and knowledge-based behaviors (Rasmussen, 1986). However, a combined qualita- tive-quantitative approach is supported by many leading scientists (Hendrick, 1992; Sheridan, 1992). Streufert and Swezey (1985) have shown the advantages of a combined descriptive and predictive methodology, and mathematical mod- els based on complexity theory for analysis of dynamic management decisions. They stressed that full dedication of any scientist to only quantifiable predic- tions limits the possibility of success to a relatively narrow area in real manage- ment problem-solving processes and cases. If descriptive methodology is used without a strong theory of development dynamics, quantification is like looking through rear-view mirrors. One cannot drive safely using this kind of informa- tion when the road makes sharp zigzags and becomes crowded. Thus, attempts to utilize previous dynamics for the prediction of future events, based exclu- sively on simple linear, or monotonic exponential models not only do not help in organizational and technological control decisions, but may lead to serious STUDIES OF EFFICIENCY AND COMPLEXITY 13 mistakes. In spite of the partial successes of mathematical decision-making theory applications, described for example by Dickson (1983), there are many negative results in practical use of quantitative predictive models in organiza- tional spheres. There is a very important difference between problem-solving processes in organizational, management activities, and in human operator performance. Even for the most complex technological control-system, in either a case of emergency or in a normal situation, a human operator can make a successful decision if the operator’s psychological model is adequate to the state and dy- namics of the real object. If the operators at the Three Mile Island Nuclear Power Plant during that famous accident were able to synthesize an adequate model of the current events, in the short time allowed by quick dynamics of the control processes, elimination of the emergency would have been a trivial task of manipulating several control buttons and handles. All information needed, for successful decision making for any technological control object, virtually exists in the object. The problem is to find that information, extract its most important features, display 1t to the human operators in a volume and structure appro- priate for their knowledge, skills, psychophysiological state (i.e. possible high stress), and cognitive strategies. Decision makers in organizational systems, typically have more time than do decision makers in emergency situations, but they, in principle, do not typically have access to large portions of the information needed for a decision, because it is often located outside the organization, among competitors, world market, political institutions, etc. Despite those difficulties, Streufert and Swezey (1985), have created both a strong theory and concrete practical methods for observing, quantifying, and measuring structural characteristics of successful organiza- tional decisions. They noticed that rational decisions need not always be based on mathematical models or arithmetic calculations. Streufert and Swezey (1985) proved that rationality and irrationality of decisions can be understood more widely than in a dictionary definition. In Webster’s Dictionary (1976), the term rational, (p. 1885), means (1) having reason or understanding, (2) of, relating to, or based upon reason, (3) involving only multiplication, division, addition and subtraction and only a finite number of times, (4) agreeable to reason: intelligent, sensible, (5) capable of being measured in terms of mora in Greek and Latin prosody: having the normal ration between argis and thesis. Some authors may have considered this meaning when they suggested that many corporations have, on occasion, reached great success via irrational deci- sions (Streufert and Swezey, 1985). This is an important difference between technological control-decisions during emergencies and organizational, manage- ment, economic decisions when relating to competition. More detailed surveys 14 VENDA on decision making complexity are found in these reports (Bodrov and Venda, 1992), (Streufert and Swezey, 1985), (Strickland, 1991), and (Venda and Venda, 1994) ). Even when a technological control-decision is not possible and accept- able, there is some family of appropriate decisions, and every decision should be compared with the real state and the dynamics of the control system, in order to be adequate, and understandable for all participants (and, where appropriate, to members of the commission that will assess the decisions if damages occur). Of course every manager should be able to explain a decision to the Board of Directors, Chairman, and shareholders. But the explanation occurs later in time. Irrationality of an organizational decision means it should be sudden and therefore unpredictable to competitors. If competitors can predict decisions and responses on their actions (e.g. implementation of a new product and model, or decreasing prices at 15, 25, and 40%), and take strong preventive steps, a deci- sion will be wrong and may lead to bankruptcy. This scientific problem is named conflict between systems or conflicting structures (Lefebr, 1971). Our discussions herein, is limited to formalizing, quantitatively assessing efhiciency and complexity, and examining the processes of problem solving and decision making in technological control-systems and in industrial companies. With the creation of a practical theory and methods of modeling organiza- tional decisions, successful and experienced managers could use it to predict and parry competitors’ decisions. Decisions that cannot be directly predicted and explained on the basis of well-known theory may be qualified as “‘irrational”’ and may be successful with a higher probability than “‘rational” decisions. To attain higher success, managers may waive traditional methods of organi- zational decisions, but operators, on the contrary, typically need to follow stan- dard technological control-decisions based on the current state and dynamics of the control system. This difference could serve to stimulate quantitative predic- tive decision-making theories in the human-machine-environment systems but, in certain aspects, hamper similar studies in the organizational sphere. There are not many studies on this problem, either in organization and man- agement, or in ergonomics, human factors and engineering psychology. Studies by Streufert and Swezey (1985), are among the few oriented to complex qualita- tive and quantitative analyses of organizational decisions. Although one can find statements in the literature about various problems associated with mathe- matical decision theories (see very fundamental analysis and survey by Streufert and Swezey, 1985), only a few scientists have studied control and technological decisions using the complexity-based approach. Unfortunately, some of them have met with difficulties, and therefore have concentrated their attention on a single method. For example, after many years of quantitative studies of manual control and decision making, J. Rasmussen (1986, 1989) has limited his studies STUDIES OF EFFICIENCY AND COMPLEXITY 15 to strict qualitative analysis of skill-based, rule-based and knowledge-based (SRK-models) human-operator behaviors. Qualitative models are not sufficient even for “‘irrational’’ organizational decisions. But to improve efficiency, and especially the safety of technological objects like nuclear power plants, decisions concerning control, design and training, and on the processes of mutual adapta- tion in human-machine-environment systems, should be based upon both de- tailed and quantitative models, and mandatory qualitative analyses in order to avoid principal mistakes. Another difference between organizational and technological control-deci- sions is the problem of repetitiveness. When an emergency situation recurs then the same decision that was made earlier can sometimes be used. However, an organizational decision that earlier led to success could lead to undesirable outcomes even though the situation is the same. Hendrick (1986a,b) has studied relations among cognitive complexity and optimal organizational and work system design. Harvey, Hunt, and Schroder (1961), and Harvey (1963), have also found that cognitive complexity levels underlie differences in how persons conceptualize reality and, hence, strategies for reacting to changing or novel situations. Barrif and Lusk (1977) have related cognitive complexity levels of operators to the (ergonomic) design of management information systems. Stamp (1981) has related human-complexity level to job-complexity level, and suggested guidelines for joint optimization. These studies were continued by Hendrick (1986a,b) with hotel managers. Hendrick (1992) has suggested that success of any complex human-machine system is contingent on its ability to adapt to its external environment. In open-system terms, organizations require monitoring and feedback mecha- nisms to follow and sense changes in their relevant task environments, as well as the capacity to make responsive adjustments. For many organizations, telecom- munications systems, and the adequacy of their ergonomic design are critically important components of this adaptability. Of particular importance is the fact that specific task environments vary along two dimensions that strongly influ- ence the effectiveness of an organization’s macroergonomic design, i.e., their degrees of environmental change and the environmental factors which affect their human performance-complexity. Degree of change refers to the extent to which a specific task environment is dynamic or remains stable over time; degree of complexity refers to the number of relevant task environments. In combination, these two dimensions determine the environmental uncertainty of the system. In general, the greater the environmental uncertainty, the greater is the need for work system design and related human-machine interfaces to allow for, and support, operator flexibility within different functional structures. Many interesting studies on the interdependence of work factors and eff- 16 VENDA ciency criteria have been conducted in both former Soviet, and current Russian, psychology and ergonomics fields (see surveys by Bodrov and Venda, 1992, Strickland, 1991, Venda, 1990, Zarakovski et al., 1977, and Zinchenko and Munipov, 1989). A Paradox of Complexity Many authors have emphasized the complexity of environment, object, and information display systems, independent of human goals, functions, and work processes. This perspective, however, is in error. Every real object has an endless number of parts and particles, and therefore complexities of real objects cannot be compared with real environments. From the point of view of a scientific paradigm, if something cannot be observed, measured, or compared, it does not exist as a subject of scientific interest. Therefore, a paradox exists 1.e. since real environment complexity is endless, an environment itself does not possess com- plexity. 2 Complexity is defined as a characteristic of human activities and attitudes, toward achieving concrete goals. An environment could be friendly and support- ive or hostile and complex, depending on the human goal, task, functional structure, or generally speaking, the process of human-environment mutual adaptation. Efficiency and complexity thus are not viewed as characteristics of the environment, but of human performance, interaction, and adaptation with the environment. Therefore, the proper scientific focus is on environmental factors of eficiency and complexity, instead of environmental complexity, per se. Also, if one cannot establish a criterion of efficiency in any system, it is useless to try to find a criterion of complexity, and vice versa. If it is imprecise to say “environmental efficiency” then the term “environmental complexity” should also be avoided. It is, however, very important to study the environmen- tal factors of eficiency and complexity in human performance. These factors are critical parameters of the mutual adaptation human-environment processes relevant to human-performance goals. The complexity and efficiency of software, hardware, or graphic information displays cannot be assessed if human functional-structures and tasks are un- known. The same information structure may be effective (less complex) in a normal control situation, but ineffective (more complex) in an emergency-con- trol situation. The satisfying sight of a rain forest, full of trees and bushes, can be reduced if one worries about the overall destiny of rain forests. When the com- plexity of a task is low, an environment requires a small investment of time. The same forest could increase in complexity should one attempt to cross it at night. STUDIES OF EFFICIENCY AND COMPLEXITY 17 This example is of special interest, because it demonstrates the ease of assessing complexity criteria such as expenditures of time, money, and human and tech- nical resources; and the difficulty of agreeing on criteria of efficiency. If criteria of complexity exist, however, there also exist criteria of efficiency (e.g. productiv- ity, savings of money and human resources, probability of successful solutions at limited time intervals, etc.). In theory, complexity can be measured as the inverse of efficiency, which may include: (1) a total productivity; (2) percentage of a quality product; (3) number of correct actions; (4) probability of successful decision in a limited time; or (5) profit. Respective measures of complexity would include: (1) loss of productiv- ity; (2) percentage of a non-quality product; (3) number of errors (incorrect actions); (4) probability of failure; or (5) monetary losses. Quantitative Measures of Efficiency and Complexity Measuring efficiency and complexity with the same units is convenient for ergonomic and psychological practice, and for the improvement of work envi- ronments, tools, and skills. This also means that the same factors of human-en- vironment mutual adaptation may be considered as factors of efficiency and complexity. Indeed, a bell-shaped curve has two sides. When factor values are less than optimal for a certain functional structure, increasing those factor val- ues leads to increased efficiency. In other words, the correlation between such factors and efficiency criteria is positive. It is therefore logical to consider such factors as measures of efficiency. If factor values increase above an optimal value for this functional structure, then efficiency will decrease and complexity will increase. Thus, factor values yield a negative correlation with efficiency, but a positive correlation with complexity. Such factors could thus be considered, in this case, as factors of complexity. So we may view these factors as indicators of efficiency-complexity or of mutual adaptation. Since decreasing complexity translates to increasing efficiency, in certain practical situations, it may be convenient to maximize one or the other. If work productivity is simply defined and measured, then an ergonomists’ attention may be concentrated on increasing efficiency. However, if losses of a product occurred, due to human error, then decreasing complexity (number of errors) may be more easily accomplished rather than increasing the total number of products, decisions, or actions. Correlation coefficients between practical (industrial) criteria of work efh- ciency-complexity and ergonomic factors of efficiency-complexity are asso- ciated with the quality of factors (that is, which factors were chosen, the magni- 18 VENDA tude of their correlation coefficients with criteria, etc), quantity of factors (how many factors were analyzed), and methods for the measurement of the factors. The range of factors of eficiency-complexity could thus be extended to find the desired level of statistically-valid activity descriptions (Venda, 1990). Fundamentals of Ergodynamics In a paper dedicated to fundamental theoretical problems of ergonomics W. Karwowski (1991) recalled that Wojciech Yastrzebowski, established in 1857 this name combining two Greek words (for work and natural laws), hoping that future generations would discover the laws of the prospective science. The infa- mous Russian psychologist and psychiatrist Vladimir Bekhterev organized the first conference on ergonomics in 1921, (he named it “‘ergologia’’) and stressed the necessity to study the laws of work and ergonomics (Zinchenko and Muni- pov, 1989). However, to date we have been unable to find in the ergonomic literature, any attempts to specifically state laws of ergonomics. After publication of our paper on transformation dynamics theory and et of transformations (Venda and Venda, 1991), and presenting an address at the 36th Annual Meeting of Human Factors and Ergonomics Society (Atlanta, GA, October 1992), many valuable suggestions on this issue were received from the colleagues. The main suggestions were: (1) the theory should be based on three principles (fundamental laws) of transformation dynamics; (2) use simple ex- periments to test and demonstrate the laws; (3) apply transformation dynamics specifically to work-dynamics (ergodynamics) analysis and optimization, to en- able more practitioners to benefit from it; and (4) carefully analyze basic ergo- dynamics categories such as efficiency and complexity of work. We began our efforts by reviewing the scientific work analysis history. In 1908, Taylor (1971), began to study work efficiency as a function of the dy- namics of the work environment. In one effort, he manipulated shovel weights systematically in order to achieve maximal work-productivity. Taylor’s discov- ery was that work productivity, Q., is a bell-shaped function of the work environ- ment, F (in his case, shovel weight). The function Q.(F) is modeling a work functional structure, S, = Q,(F). He found that the work functional structure, Q,(F), was different for small, Q.(F), middle Q,,(F), and big, Q,(F) men (Figure 1). Taylor organized a process for mutually adapting workers and shovels. Big- ger workers were supplied with heavier shovels and smaller workers were moti- vated to train themselves to use larger shovels, and thereby to achieve higher productivity and pay. The weight of the shovel (with its material) was thus, a factor of mutual adaptation between the worker and work environment (task, STUDIES OF EFFICIENCY AND COMPLEXITY 19 F, F,opt=9 FmPt=10 Fpopt=12 F Fig. 1. Work functional structures Q,(F) for shoveling by three groups of men: small, Q,, middle, Q,,, and big, Q,, (after Taylor, 1911). Q—relative productivity of shoveling, F—weight of the shovel with material (kg), Fp: represents optimal weight of shovel. tool, machine). Productivity of shoveling was used as the criterion of work efficiency. This suggested that maximal work efficiency could be reached by mutually adapting between the human-work functional structure (by professional selec- tion, training, motivation), and the work environment (by ergonomic design of machine, workstation, interior, or software). Yerkes and Dodson (1908), and later, other researchers, also confirmed the bell-shaped function of work effh- ciency on work factors (Freivalds, 1987; Konz, 1990; Tinker, 1963; Venda, 1975, 1986; Woodworth, 1938). In another investigation on this issue, Warren (1984) studied the influence of stair riser height on human climbing efficiency. He found that the “inverse efficiency” (complexity (C), in our terms) of climbing (that is, energy expended per step cycle/work done per step cycle) was a U-shaped curve function of riser height (Figure 2). In Figure 2, we have added a curve displaying efficiency, Q, of climbing (work done per step cycle/energy expended per step cycle). This is a function of the factor, F, of mutual adaptation between the climber and the stairs. Warren found a unique factor as a ratio of riser height/leg length (F = RH/LL). He discussed this factor as intrinsic, internal, and evolutionary. The efficiency, Q, is a bell-shaped curve of F, with constant optimum F,,, = 0.25 for the groups of short climbers (the solid line at Figure 2), and tall climbers (the broken line). Prior to actual climbing, Warren asked participants to visually assess which riser height would be best for them. Arrows show visual-riser prefer- ence prior to climbing. Dots on the curves show the location of F,,, for Qa, and C,nin» Obtained in the experiments. 20 VENDA 0.1 0.2 Fopto.3 0.4 F Fig. 2. Functional structure of climbing efficiency-complexity during visual evaluation. Efficiency, Q, mea- sured as the stair height climbed per one calorie spent by the climber (m/cal). Complexity, C, measured as a number of calories spent per one meter of stairs. Factor, F, is a riser height (m) (adapted from (Warren, 1984) ). Subscripts for tall (T) and short (S) individuals. F,,, represents optimal stair height. Secondly, an experiment similar to Taylor’s, but using a ““modern shovel’’, a notebook computer, was conducted. In the Ergonomics laboratory in the Uni- versity of Manitoba, students typed text in a sitting position. Chair height was kept constant at 39 cm. Desk height was systematically changed. Typing produc- tivity was found to generate a skewed bell-shaped function versus desk height (Figure 3). The equation Q,., = Qua + Ca (where Q,,., 18 maximal possible efficiency found as a peak point of the bell-shaped curve and Q,,, and C,,, are actual efficiency and complexity values), represents the way in which complex- ity, and each individual’s efficiency level, interact to comprise the maximal possible efficiency. These results can be summarized as Ergodynamics Law 1, (The Law of Mu- tual Adaptation): ““Work efficiency is a bell-shaped function of the factor of mutual adaptation between human work structure and its environment.” Fig- STUDIES OF EFFICIENCY AND COMPLEXITY 21 75 100 125ee = 150 F F OPt Fact = 120 Fig. 3. Work efficiency Q (typing productivity of characters per 3 minutes) as a function of the factor of human-environment mutual adaptation F (desk height, cm). F°"' represents optimal desk height. Q*“—an actual efficiency value Q** = 505 char./3 min. is shown for one particular F value: F = 120 cm. C** = Q™ — Q** = 550 — 505 = 45 char./3 min. ures 1-3 demonstrate that Law. There are two coordinates: efficiency, Q; and the factor of human-environment (machine, person) mutual adaptation, F. In the practice of work design and optimization, it is necessary to find an equation or curve, QF), for each actual work-structure, S,. This will determine Foor, When Q; = Qimax; and F,,;, and F,.,, when Q;O. The goal of the notebook computer study was to find different work func- tional structures S(F) that could be used for the same work task. The experi- ment was extended so the students typed in a sitting (S,), as well as in a standing (S,) position, with different desk-heights. Two different functional structures for every student were presented. Prior studies on the processes of reading and the perception of control board information have also resulted in functional structures (cognitive strategies) that can be modeled using bell-shaped, Q.(F), curves (Stishkovskaya, Venda et al., 1993; Venda, 1980, 1986, 1990). Results of these studies can be used to suggest Ergodynamics Law 2 (The Law of Work Structures Plurality): “Every work task can be done with different work structures modeled as a family of respective bell-shaped functions’. The third law of ergodynamics was worded and analyzed in a previous paper (Venda and Venda, 1992). It is known as the Law of Transformations: ““Trans- formations between different structures of the system and interaction between different systems’ structures are maximally effective if they go through a state common and equal for the structures.”» The common state 1s modeled as the intersect point of the respective Q(F) curves of the two structures (Figure 4). 22 VENDA Fig. 4. Functional structures of typing in sitting (S,) and standing (S,) positions. Q—typing productivity (characters per minute); F—desk height as a factor of human-environment mutual adaptation. F°™ represents optimal desk height when typing person and work environment are mutually adapted and thus Q = Q™. Common and equal states for the structures S, and S, occur when both struc- tures have equal efficiency, that is, when F = F,, and Q,(F, 5) = Q)(F,2). The third law of ergodynamics may be illustrated using results from the previously discussed typing experiment. When the desk height is changed from minimal (F = 50 cm) to maximal (F = 140 cm), a variable total productivity is obtained at all heights, and it is important to find the optimal height for the transformation from a sitting (S,) posture (work structure), to a standing one (S,). Three trans- formation-heights are compared in the nght hand side of Figure 5: F = 100 cm, F = 107 cm (intersection point for sitting and standing), and F = 120 cm. It is seen in Figure 5, that a maximal integral productivity for the two consecutive functional structures S, and S, with their transformation S, S, was obtained when the transformation was made at F = 107 cm (solid line at the right side). Transformations at F = 100 cm and F = 120 cm (see broken lines at Figure 5) led to lower integral efficiency values. In addition to graphs Q(F) and Q(T), graph F(T) is shown at Figure 5. F(T) shows dynamics of the factor of mutual human-environment adaptation, F. If one knows functional structures S, = Q,(F), and S, = Q,(F), as well as dynamics F(T), then the dynamics of work efficiency, Q(T), for different trajectories of the transformations S, ~ S, and S, ~ S, may be predicted and optimized. Various work structures may belong to a single individual, or may be used sequentially. Work structures may also belong to different individuals (or hu- man-machine linkages) interacting in work processes. Science (ergonomics, economics and psychology included) theoretically de- scribes only the following types of development (i.e. progress, learning): 1) step functions, where efficiency increases in stages; 2) linear increases in efficiency; 3) STUDIES OF EFFICIENCY AND COMPLEXITY 23 Fig. 5. Visual image of the third law of ergodynamics shown on right side of graph. If F changes gradually from Fmin = 50 cm to Fmax = 140 cm, and vice versa, forward and backward transformations between two functional structures S, and S,, (S, S,), which go through a common and equal state with the coordinates Q, >, = 522 char./3 min. F,,. = 107 cm at the trial #12, lead to the bigger integral productivity (as a sum of Q; at all heights, F) than transformations at any other state, e.g. at F = 100 (trial #10) and F = 120 cm (trial #13) as shown with broken lines. T = time (number of trials, consecutively from F = 50 cm to F = 140 cm with difference between next to each other trials DF = 5 cm). monotonic, or exponential increases in efficiency (Ebbinghaus, 1885), and 4) increases in efficiency with intermediate plateaus (Bryan and Harter, 1899). Using the Law of Transformations, we have previously explained a new type of development dynamics, as a transformation having a wavy image (Venda and Venda, 1991). For this purpose, human operator behavior was studied in emer- gency situations, and information signal-flow characteristics were noted and used to create simulations for a training experiment. Twelve engineering stu- dents were asked to perform a compensatory tracking task, where dynamic signals were presented simultaneously on several (from one to six) measurement instruments, with the student controlling an equal number of switches (from one to six). The signal flow, programmed as a Poisson process with its intensity parameter in the range 0.04 to 1.50 signals per sec., initiated deviations of a 24 VENDA dynamic model of simple control objects, and thus deflections in the needle on displays. A computer-generated Poisson process, with intensity \ = 0.04-0.09 signals per sec., were fed into control objects with time constants from 10 to 20 sec. The outputs were fed to the measurement instruments (displays). The stu- dents’ task was to adjust the control corresponding to the display in order to counteract the induced deflection. Due to the inertia of the control system, the student was required to make a sequence of regulating responses in both direc- tions, much like controlling a vehicle. The signals were given four levels of priority. The first had absolute priority; the others were relatively less important. When the student did not have time to regulate all signals generated by the computer, some were held in computer memory. Most of the learning curves that resulted were non-monotonic in shape. Dif- ferent cognitive and sensory-motor strategies and transformations could be found between them (Venda, 1986). The criterion of efficiency (Q, number of signals adjusted in a minute) as a function of a number of measurement instru- ments presented simultaneously (n = 1-6), and training time (T—number of training sessions), is shown in Figure 6a. The criterion of complexity (C, time spent to adjust one signal) as a function of n and T, is shown at Figure 6b. The efficiency and complexity values are as opposites to one another. Increasing efhiciency and decreasing complexity is a general tendency of the training pro- cess, but the result is not always monotonic. Sometimes, complexity of perfor- mance increases when the functional structures of performance are trans- formed. In these situations, temporary disadaptation of the functional structure occurs to free components of the previous structure, so that they mutually adapt each to other in new order in accordance with a new functional structure (Venda and Venda, 1991). This oscillation between efficiency and complexity produces wave-like dynamics of development, learning, and progress. Ergostatics and Ergodynamics If the independent factor has a constant value, F = Const, and Q = Const then the result will be functional statics. But if the factor is changed in time and efficiency, and analyzed as a function of both the factor and of time Q(F,T), then the outcome will be functional dynamics. The main advantage of the mutual- adaptation approach is an interrelated analysis and visual representation of the functional statics (particularly ergostatics) and functional dynamics (ergodyna- mics). Particularly ergostatics and ergodynamics emphasizes that this approach is useful in the functional analysis of all human-machine-environment system components. In reality, several factors may be essential for studying STUDIES OF EFFICIENCY AND COMPLEXITY 25 sigznals/ 3.0 ei), jit ye Soniye Typ WRT i atays he CPR Ara 'y Sy at ATS livys Fig. 6. (a). Efficiency, Q, as a speed of signal tracking (signals/minute) (b). Complexity, C, as time spent to track one signal (sec). Compensatory tracking as the function of number of measurement instruments pre- sented (n = 1-6) and number (days) of training (T). efficiency statics Q(F,,...,F,) and dynamics Q(F,,...,F,, T), thus, instead of plain graphs, spatial and hyper-spatial models are frequently needed. _Ergodynamics and Laboratory Testing of Workstations and Products The second law of ergodynamics describes how ergonomic projects may fail when they are tested in a laboratory by subjects using only one functional structure (S,,,), but are implemented in the workplace where operators use different strategies (S,,). Several intervals of work factor F may be used to test the system (Figure 7). Testing data obtained by subjects in the interval F,,,""" — F,,™", are useless (U) because operators do not work in that interval. Usually, such tasks are of very low complexity and are irrelevant in practice. The interval F,,™" — F,,°, is slightly relevant (SR); whereas, the interval F,,°' — F,,,™™, 1s very relevant (VR) and helpful because, in this interval, the correlation between subjects’ and opera- tors’ work efficiency is positive. Thus, changes in system design which increase subjects’ efficiency in that interval will also increase operators’ work efficiency. The interval between F,,,°"' — F,,°P', seems to be most helpful because the maxima of efficiency for the laboratory subjects and real operators are very close. But an unexpected paradox exists. In reality, all data obtained in the 26 VENDA Feupmin FeqpoPt Fs,q FopSPt Fopmax Fig. 7. Work functional structures of real operators (S,,) and of laboratory individuals (S,,,,). Q—criterion of efficiency (productivity); F—work factor; intervals of F for operators: U—useless, SR—slightly relevant, H—harmful, VR—very relevant, I—inaccessible for the laboratory participants. interval F,,°°' — F,,°", are harmful (H). Q,,, and Q,, have a negative correla- tion. This means that when designers apply laboratory data to improve a system, and the subjects’ efficiency increases, then the same changes will decrease the operators’ work efficiency in the real system. The interval F,,,"" — F,,™*, 1s inaccessible (I) for the laboratory subjects. The tasks in that interval may be too complicated or the conditions were not modeled in the laboratory. It is likely that all other intervals outside F,,,"" — F,,™™, are irrelevant for the operational system and laboratory experiments. The second law of ergodynamics explains why many practitioners do not trust ergonomic laboratory testing to real systems and prefer to rely on their experi- ence and common sense. The paradox is that improvement for the people with one functional structure (professional skills, individual abilities) may cause losses for the people with other functional structures. In other words, better performance may occur in the laboratory, but worse in reality, when functional structures are not identical. Practical use of the first and second laws of ergodynamics can be illustrated in the following example. A company is not satisfied with work productivity and invites an ergonomist as a consultant. The consultant finds a main factor of human-environment mutual adaptation (or work efficiency factor) F. The con- sultant determines the direction of change for the work factor that will increase STUDIES OF EFFICIENCY AND COMPLEXITY hal efficiency (or decrease complexity). If the current factor values are at the left side of the bell-shaped curve of the work functional structure, the factor value would be increased to improve efficiency. If they are on the right side of the curve then the factor value would be decreased to improve work efficiency. If the consultant could model the functional structure, or conduct experi- ments with the most successful employees, then the data would reveal the maxi- mal efficiency, and the optimal factor value directly. Optimizing the work factor would then become a relatively simple task. On the other hand, an experiment, changing the factor to either a larger or smaller value, would demonstrate the direction required for maximal work-efficiency to occur. If both factor changes led to declines in efficiency, it would suggest that the factor value was optimal. If optimal, the factor should be fixed; it would not be a source of additional work improvement if the functional structure was retained. If changes in the factor to both directions were to lead to higher efficiency-cri- terion values, then the consultant should consider other possible work-struc- tures and the transformations among them. Ergodynamics: Practical Applications Ergodynamics can help to answer many questions important to ergonomic practice. The following are some examples: 1. How may human work efficiency be increased when a single work structure or several different ones are used? The first law and Figure 1 provide an answer. 2. How can the quality and productivity of work be improved? Human-environment mutual adaptation may be an effective way. 3. Can the usability level (user’s work efficiency) of a new technology or workstation be predicted? When should the technology be upgraded to profit from such upgrad- ing? The third law of ergodynamics can help to predict a level of efficiency during transformation period. 4. Can the efficiency data of any new product, software, or information display in the laboratory, based on naive subjects, be transferred to industry, for example to a power plant control room? Figure 7 provides an answer. One should notice that similarity of functional structures, identity of Fopt for both subjects and workers, is much more important than identity of their efficiency levels. 5. What do the performance-efiiciency dynamics look like when the performance functional-structure is constant and the environmental factor F reverses in both directions? A wavy-like dynamics of efficiency may ensue. 6. Will human performance and efficiency stay constant when its structure is constant and F = F,,, = Const? No, they will change as a result of such events as fatigue, boredom, overexertion, and repetitive strain injuries (cumulative trauma de- sorders). 7. What should an ergonomist recommend when the same work site characteristic (for example, speed or volume of information input) affects one worker’s efficiency 28 VENDA Fy=F,opt12 Fa Fax F4opt PF fom Tylyacr) Tale ep Fig. 8. Predicting dynamics of firm’s efficiency (profit) Q as a result of upgrading software. S,;—work functional structure when initial software S, was used by the firm; S,, S;, S,—software packages successively available. F = efficiency-complexity factor. Shadowed areas on the right side of the graph depict the losses of work efficiency during transformations relative to efficiency when first software S, was optimally used. T— transformational complexity. positively but another’s negatively? Provisions for individual adaptation of the work site characteristic for different people should be introduced. 8. Can efficiency dynamics in the workplace be predicted and observed when new technologies, management, and products are introduced? Again, the third law will help. Ergodynamics suggests that similar structures can be transformed without essential transformation dips; and that monotonic exponential dynamics will per- mit slow, continuous improvements. 9. Can the usability level (user’s work efficiency) of new software be predicted theoreti- cally when the user’s functional structure is S,, but the new software was designed for structure S,? Figure 5 provides an answer. The user, having work structure S,, will use the new software requiring S,, with maximal initial efficiency being Q, >. 10. How often should software be upgraded to obtain a maximal profit from upgrad- ing? Software companies’ sales persons may promise clients immediate profit from upgrading software, but typically they do not allow for inevitable losses during the transformation periods. Analysis of work-functional structures and transforma- tions can help to find a reasonable basis for frequency of upgrading software. IfS,, S;, and S, represent different software packages that become available, then upgrad- ing the firm’s existing software S, to S,, S, and S,, should be carefully analyzed. Work functional-structures, S,, are shown on the left side of Figure 8 and their transformations are shown on the right side. Many different processes and consequences are possible in this situation: a) When upgrading is made directly to S;, or especially to S,, a significant decrease in efficiency could occur. b) When upgrading occurs too often, then losses of efficiency following the transformation of S, into S,, S, into S,, S; into S,, are bigger than the increases in efficiency. There may not be enough time between STUDIES OF EFFICIENCY AND COMPLEXITY 29 upgradings to reach maximal efficiency for each new product generation. Thus upgrading could not only cost money, but also lead to large losses in productiv- ity. Shadowed areas (Figure 8) depict losses at the periods between T, and T,. c) When maximal complexity (measured as a factor F value) of the initial tasks is F nax> then the relative efficiency of using the most advanced (and usually the most expensive) software S,, is low. The optimal software at that time would be S;, although S, would be acceptable, considering the predicted losses for trans- formation of S, into S;. Conclusion Ergodynamics is an application of the theory of Transformation Dynamics to the analysis and improvement of work in the dynamic environment. It is being based on the three laws of: 1) mutual adaptation, 2) plurality of functional work structures, and 3) transformations. Along with methodological analysis of en- tropy, fuzziness and incompatibility in human-machine systems, and with stud- ies ON Macroergonomics, ergodynamics may help to build a general theory and methodology of ergonomics. Work functional efficiency and complexity are interpreted as an opposing criteria. This concept produces an operational method for measuring and comparing efficiency and complexity using the same or similar units. Efficiency and complexity depend on both internal functional structure (work skills) and mutual adaptation-disadaptation with the external environment. Functional efficiency and complexity are always addressed to a specific hu- man function, operator, and goal. Functional complexity can be assessed using any criterion reflecting the decrements that are to be minimized and converted into efficiency, because less complexity results in more efficiency, and vice versa. Goals and functions may be used to determine criteria for efficiency, com- plexity, and work strategy. For instance, in order to attain the highest success levels, managers may wish to avoid traditional methods for organizational deci- sions and instead, follow technological control decision methods when the current state and dynamics of the control system are known. Such an approach can, in turn, stimulate quantitative analyses of transformational dynamics in decision making. Every real object has an infinite number of parts and particles. This mitigates against direct comparisons among the complexities of real objects and environ- ments. From the point of view of a scientific paradigm, if something cannot be observed, measured, compared, it does not exist. Therefore, a paradox arises: 30 VENDA complexity of real environments are not directly measurable, and thus, an envi- ronment itself does not have complexity. Instead, complexity is a characteristic of human activities, such as attitudes towards achieving concrete goals by inter- acting with and adaptating to the environment. The environment can be friendly and supportive or more hostile and complex, depending upon the hu- man goal, task, and/or functional structure under investigation. Therefore, it is necessary to focus on the environmental factors of efficiency and complexity rather than on environmental complexity per se. To measure efficiency and complexity with the same units, is convenient for ergonomic and psychological practice in improving the work environment, tools, and skills. This would sug- gest that the factors involved in human-environment mutual adaptation may be considered as factors of eficiency and complexity. Maximal work-efficiency can be achieved using mutual adaptation between human work-functional struc- tures (by professional selection, training, motivation), and work environments (by ergonomic design of machines, workstations, interiors, and software). The laws proposed to describe these conditions are useful not only for work dynamics specifically, but they are general in nature. The dynamics of any complex system of laws may be summarized as follows: The first law of transfor- mation dynamics: Functioning and development of any system is a process of mutual adaptation between inner components of the system, and between the system and its environment. The second law: Every complex system may have several different functional structures. The third law: Transformations between functional structures of the complex system move, with minimal losses, through common and equal states for prior and new structures. References Bariff, M. L., & Lusk, E. J. (1977). Cognitive and personality tests for the design of management information systems. Management Science, 23:820-837. Bodrov, V. A., & Venda, V. F. (eds.) (1992). System approach in engineering and work psychology. Moscow: Russian Academy of Science. 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Rogers American Trucking Associations Foundation, Trucking Research Institute, Alexandria, Virginia ABSTRACT Literatures concerning fourteen human abilities are reviewed with respect to changes which occur with aging, and in the context of driving performance. Particular emphasis is devoted to relationships between the abilities and commercial vehicle driving. The ability literatures reviewed are: static visual acuity, dynamic visual acuity, contrast sensitivity, use- ful field of vision, field dependence, depth perception, glare sensitivity, night vision, reaction time, multilimb coordination and physical proficiency, control precision, decision-making, selective attention, and attention sharing. Introduction In the United States, it has been recently reported that 22 percent of the licensed drivers are 55 and older, while 10 percent are 65 and older (Rothe, ' This work was conducted under Federal Highway Administration Contract No. DTFH61-93-C-00088 with the American Trucking Associations Foundation, Trucking Research Institute. The authors would like to acknowledge the important contributions of Mr. Robert Davis, Ms. Teresa Doggett, and Mr. Nathan Root of the Federal Highway Administration (FHWA) Office of Motor Carriers, Dr. Harold Van Cott of Van Cott and Associates, Dr. Peter Hancock of the University of Minnesota, and Mr. David Willis of the AAA Foundation for Traffic Safety, for providing information documents and reviewing drafts; and Ms. Lisa Swezey of ISA for the research and administrative support she provided. Finally, we wish to acknowledge the important contri- butions of Dr. Mark Barnes, formerly of InterScience America, who developed the earlier review document (Barnes, Llaneras, Swezey, Brock, and Rogers, 1994) from which this article was adapted. That comprehensive report is available from the ATA Foundation, 2200 Mill Road, Alexandria, VA, 22314-4677, USA. 32 _ ABILITIES, AGE, AND DRIVING PERFORMANCE 33 1990). Rothe has also estimated that by the year 2000, approximately 28 percent of drivers will be 65 and older. The number of older drivers 1s increasing in part because of medical advances; more people are living longer and enjoying better health later in life (Santrock, 1985); however, other reasons related to social changes are also contributing to this trend. A greater proportion of persons in younger generations have learned to drive. People now have more leisure time than previously, and thus may have an increased desire to continue driving (Jette and Branch, 1992). In short, driving remains an important facet of the older person’s lifestyle. However, a concern also exists that older drivers pose safety problems to both themselves and to others. There is evidence that: a) older drivers are overrepresented in certain types of accidents, and b) older people tend to overrate their driving abilities (Fox, 1989). The highway safety industry is thus, faced with finding ways to allow older drivers to continue having driving privileges, while at the same time ensuring road safety. Studies that have investigated age and accident characteristics have shown that older drivers tend to be disproportionately involved in accidents involving head-on collisions, left turns, parking or backing, and right angles (Maleck and Hummer, 1986). In addition, older drivers have been found to be overly repre- sented in accidents in urban settings, and accidents involving right-of-way, im- proper turning, and the disregard of signals (Maleck and Hummer, 1986; All- gier, 1965, Huston and Janke, 1986). Accidents involving older drivers are more apt to occur at slower speeds, at intersections, involve multiple vehicles, and occur under good weather conditions, as compared to younger drivers (Waller, House, and Stewart, 1977). When taking into account the number of miles driven per year however, accident rates increase with drivers in their late 50s, and accelerate beyond that (Cerelli, 1989; Waller, 1991; Williams and Carsten, 1989). This increase occurs despite indications that many older drivers inten- tionally avoid driving at night, in heavy traffic, and in other demanding condi- tions (Yee, 1985). Although information such as this provides a snapshot of the accident patterns associated with older drivers, a shortcoming is that it does not provide much insight into what is actually causing the accidents. One approach to identifying potential causal factors, is to identify human performance abilities that are associated with accidents. This logic presumes that accidents are related to particular ability deficits. Briefly, the premise un- derlying the human ability approach to analyzing driving safety, is that success- ful driving requires a combination of perceptual, psychomotor, and cognitive abilities. Drivers must continuously monitor the environment in order to avoid obstacles and to ensure lateral control. Coordination among arms, legs, neck, and head are needed for steering and shifting. Strength, in combination with quickness, is needed for circumstances involving manual shifting or sudden 34 LLANERAS ET AL. braking movements. Drivers must maintain attention, shift and/or share atten- tion, and constantly make decisions. The extent to which drivers rely on particu- lar abilities depends upon demands from both the highway environment and vehicle. Successful driving hinges on the individual’s ability to meet both these demands, and the consequences that result when they are not met. This article summarizes research which addresses how changes in perceptual, psychomotor, and cognitive abilities influence driving performance. It is a por- tion of a much larger and more comprehensive report (Barnes, Llaneras, Swe- zey, Brock, and Rogers, 1994). Of particular interest were findings related to abilities needed to safely operate commercial trucks. As discussed in the review, however, research pertaining specifically to the commercial truck driving in- dustry is sparse as compared to that related to conventional vehicle drivers generally. Therefore, the literature reviewed herein was not restricted to articles involving commercial vehicle driving. Literature which addresses the following fourteen driving-related abilities are reviewed herein. These abilities were selected on the basis of their known rela- tionships with driving performance. Perceptual Abilities — static visual acuity — dynamic visual acuity — contrast sensitivity — useful field of vision — field dependence — depth perception — glare sensitivity — night vision Psychomotor Abilities — reaction time — multilimb coordination and physical proficiency — control precision Cognitive Abilities — decision-making — selective attention — attention sharing Although these abilities are commonly discussed in the literature as indepen- dent, realistically they are interrelated. That is, there are common underlying mechanisms that cause them to change in similar ways (Greene and Madden, 1987). Much of this is due to the fact that abilities reflect different aspects of a common physical structure and sensory system. Consequently, changes that occur within one sensory system (e.g., the visual system) will impact several ‘ABILITIES, AGE, AND DRIVING PERFORMANCE 35 abilities. Furthermore, changes to the central nervous system could potentially have an impact on all abilities. Some normative changes to the central nervous system and sensory systems occur with advancing age, which in turn affect abilities. Neural processing becomes less efficient as a result of neural atrophy and slowing (Birren, 1965). In addition, reduced blood flow results in a decline of muscle and sensory function. In essence, many of the ability changes that are discussed in this review can be traced back to these fundamental changes. Discussion of Driving-related Abilities Perceptual Abilities Some normal age-related changes to the structure of the eye result in broad changes to all visual abilities (Weale, 1963). For example, the cornea, which acts to focus images onto the retina, becomes increasingly more rigid and yellow as new cellular layers are added. As a result, older adults are less able to bring close objects into focus; the average 60 year old person is unable to focus on objects closer than 40 inches (Helander, 1987). Because of yellowing, less light pene- trates the cornea, and that which does, undergoes some degree of scattering. By age 50, the amount of light reaching the retina is reduced by about 50% (Weale, 1963). Consequently, older adults are less able to see in the presence of dim illumination or to cope well with glare (Fozard, Wolf, Bell, McFarland, and Podolsky, 1977). In addition, they require greater contrast between a target and its background, as compared to young persons (Blackwell and Blackwell, 1980). Also, the blood supply to the retina diminishes in older adults (Weale, 1963). As a result, visual receptors are slower to regenerate, peripheral vision becomes restricted, and the size of the blind spot increases. Shinar and Schieber (1991) have offered some general remarks regarding the effect of the normal aging process on visual abilities. They concluded that: 1) all visual functions deteriorate with increasing age, 2) the amount, rate, and onset of deterioration vary widely between individuals and functions, 3) significant deterioration in static acuity does not appear before the age of 60, but more complex processes begin to decline earlier, and 4) performance differences be- tween individuals increase with age. Arguably, the most important perceptual abilities associated with driving are visual abilities. Drivers are required to con- tinuously scan the roadway, focus on relevant objects, and make distance judg- ments in the presence of varying illumination levels. It has been estimated that 85 to 95 percent of the sensing clues in the driving task are visual (Malfetti and Winter, 1986). 36 LLANERAS ET AL. Static Visual Acuity Static visual acuity has traditionally been defined as the ability to resolve details of a stationary target in a well illuminated environment (Sturr, Kline, and Taub, 1990). Visual acuity varies for different locations on the retina; it is best near the straight-ahead or foveal field (near 0 degrees), but drops off rapidly toward the periphery (Schmidt and Connolly, 1966). The area of best acuity corresponds with the highest concentration of cone receptors, and extends less than ten degrees away from the foveal center (Coran, Porak, and Ward, 1984). Although visual acuity declines in all persons under dim illumination, re- search has indicated that significant age-related declines in static visual acuity become evident around age 45 (Decina and Staplin, 1993), and rapidly acceler- ates after the age of 60 (Burg, 1966; Pitts, 1982; Laux and Brelsford, 1990). For example, although 85% of individuals age 35 to 44 have acuity levels 20/20 or better, only 32% of individuals age 65 to 74 have similar acuity levels (U. S. Department of Health, Education, and Welfare, 1977). Fortunately, static vi- sual acuity can be easily corrected by contact lenses or glasses. Coincidentally, it is also the one measure that all state licensing agencies depend upon to detect visual deficiency (National Highway Traffic Safety Administration, 1986). Relationships to driving performance. Static acuity has been found to have weak, but consistent relationships to traffic accidents and conviction rates (Burg, 1971; Burg, 1964; Burg, 1967; Henderson and Burg, 1974; Shinar, 1977). For example, Burg (1967) reported that three static visual tests considered as a composite had the second strongest relationship with accidents behind dynamic visual acuity. The three correlations, based on a sample of 17,000 drivers, —.129 (screen static acuity), —.076 (Ortho-rater), and —.053 (Snellen) were small but statistically significant. In other studies using fewer subjects, Henderson and Burg (1974) found a significant relationship between static acuity and accident rates only for drivers aged 25-49. Hoffstetter (1976; cited in Bailey and Sheedy, 1988) provided evidence that drivers with poor static acuity were more likely to be accident prone; that is, they were involved in multiple accidents over short time periods. Drivers in the lowest quartile of static acuity measurements were twice as likely to have had three accidents, and 50 percent more likely to have two accidents in the previous twelve months, than those with measurements above the median. Rogers and Janke (1992) compared driving records of heavy-vehicle drivers having substandard static acuity with those that were unimpaired. Substandard static acuity subjects were categorized into two groups: moderately impaired (corrected acuity between 20/40 and 20/200 in the worse eye, 20/40 or better in the good eye), and severely impaired (corrected acuity worse than 20/200 in the worse eye, 20/40 or better in the good eye). Results showed that as a group, ABILITIES, AGE, AND DRIVING PERFORMANCE 37 visually impaired drivers had a higher incidence of accidents and convictions than did unimpaired drivers. However, the incidence of accidents between mod- erately impaired and unimpaired drivers did not differ significantly, regardless of age. Older drivers in this sample had /ower conviction and accident rates, despite having poorer visual acuity on average. Although age ranges and correla- tion coefficients were not reported, this would appear to contradict other data that reports poorer vision and higher accident rates for older drivers. In another study which included 236 truck and bus drivers, Henderson and Burg (1973) failed to find any significant relationships between static acuity and three acci- dent measures. Other studies have linked visual acuity to non-accident driving performance measures. Relationships have been demonstrated between acuity and improper lookout behavior (Shinar, McDonald, and Treat, 1978), and the ability to detect the distance of an object on the road in the presence of oncoming car headlights. However, Kline, Ghali, Kline, and Brown (1990) found no difference in the distance at which young, middle-aged, and elderly drivers were able to see high- way signs, regardless of illumination levels. Because it is easy to measure and relevant to driving (TRB, 1988), static acuity is routinely checked by all states before issuing an initial driver's license (Bailey and Sheedy, 1988). However, its importance applies primarily to instances in which the vehicle is stopped or moving at a slow rate, such as at intersections or in parking lots. Unlike real visual scenes, the stimuli used to measure static visual acuity are typically small and of high contrast (Ginsburg, 1980; Owsley, Sekuler, and Boldt, 1981; both cited in Evans and Ginsburg, 1985). It is not, therefore, surprising to find wide variations, among the research describing the effects of static visual acuity on driving performance and accident involvement rates. Dynamic Visual Acuity Dynamic visual acuity is the ability to resolve the details of a moving target. Static acuity determines the upper potential of dynamic acuity, since dynamic acuity also relies on foveal vision (Shinar, 1977). Most studies, however, report little or no correlation between these measures (Burg, 1968; Henderson and Burg, 1974). The weak association between static and dynamic visual acuity has been found to decrease with advancing age (Burg, 1968). Four major points concerning dynamic visual acuity as summarized by Burg (1964) are that: 1) Acuity for a moving target deteriorates with increased target velocity, 2) It is improved by increasing exposure time, illumination, and by practice, 3) It is better when the target is foveal rather than peripheral, and 4) It varies widely between individuals with essentially the same static visual acuity. 38 LLANERAS ET AL. In general, all visual acuity deteriorates rapidly as the image moves away from the foveal region of the retina (Shinar, 1977). Declines begin when the lateral rate of the target approaches 20 degrees/second. Above 30 degrees/second, smooth eye pursuit movements lag behind the target and more complex eye movements (e.g., saccadic movements) are needed to keep the image in the foveal region. With respect to aging, dynamic visual acuity has been found to decline at an earlier age than does static acuity, and this deterioration accelerates more rapidly after age 50 (Burg, 1966). Shinar (1976; cited in Shinar, 1977) has placed the onset age of rapid decline to be slightly later, at about age 55. Age-re- lated changes are greater as the angular velocity exceeds about 30 degrees/se- cond. At this speed, acuity becomes dependent on more complex perceptual and neural processing as well as fine ocular movements, which partially explains why most studies have found weak (Burg and Hulbert, 1961) or no correlation (Henderson and Burg, 1974) between dynamic and static acuity. Since older persons demonstrate a systematic reduction in the ability to execute smooth pursuit eye movements (Sharpe and Sylvester, 1978), these additional demands may determine why age-related declines in dynamic acuity begin earlier than for static acuity. Relationships to driving performance. Dynamic visual acuity has been found to be positively associated with the average number of miles a person drives (Retchin, Cox, Fox, and Irwin, 1988); and has been the ability most related to accident involvement in several correlational studies (Burg, 1968; Shinar, 1977; Laux and Brelsford, 1990). As with static acuity, however, these relationships have been consistent, but weak. The reasons for this are also simi- lar; dynamic acuity is a constant and important ability used when driving, but is not the only important visual ability involved in driving. Shinar, Mayer, and Treat (1975) noted that drivers found recently to be at fault in an accident had poorer dynamic visual acuity than a group of persons who had not been in an accident for two years. Henderson and Burg (1974) found that among profes- sional drivers over 50 years of age, the best 10% with respect to dynamic visual acuity had a lower accident rate than the mean, while the worst 10% had a higher accident rate. As indicated by the literature, driving-related activities that appear to rely on dynamic acuity include reading street signs while in motion, locating road boundaries when negotiating a turn, and making lateral lane changes. In these situations, greater speeds are associated with poorer acuity and narrowing of the high acuity portion of the visual field. As the visual field becomes more complex and dynamic, other abilities are increasingly important. This is especially true when drivers must detect objects in instances where: a) the relevant visual field area is large, b) many relevant objects are present in the visual field, and c) ABILITIES, AGE, AND DRIVING PERFORMANCE 39 angular velocities of objects within the visual field are high. Under these condi- tions, other abilities such as head and neck flexibility, field dependence, and attentional factors take on greater importance. Most studies in this area have relied upon accident data as a criterion, which, because many variables contrib- ute to accidents, does not result in strong conclusions concerning the impor- tance of individual factors. Although dynamic visual acuity has been consis- tently related to increased accident involvement, the magnitude of this relationship has not been strong enough to change licensing policy (Bailey and Sheedy, 1988). Leibowitz, et al. (1993) have speculated that although dynamic visual acuity is important to performance, stronger relationships with driving performance must be established before policy decisions can be made with confidence. Contrast Sensitivity Contrast sensitivity is the ability to detect variation in sine-wave patterns (i.e., adjacent light and dark regions) as a function of closeness of the neighboring regions (Decina, Breton, and Staplin, 1991). Contrast sensitivity tests measure both the response to sharply defined black-on-white targets and those with grayer, less distinct edges. By measuring the full range of spatial frequencies and regional contrasts, a comprehensive measurement of visual capability can be assessed, allowing performance to be summarized as a plot relating target sensi- tivity across the spatial frequency ranges (Evans and Ginsburg, 1985). Perfor- mance with respect to higher spatial frequencies (measured in cycles per degree) has been found to predict real-world target detection, such as detecting simu- lated aircraft targets (Ginsburg, Evans, Sekular, and Harp, 1982) and reading road signs (Evans and Ginsburg, 1985). Response to low spatial frequencies has been linked to visual performance under poor viewing conditions (Ginsburg, Easterly, and Evans, 1983; Ginsburg, 1980; Ginsburg, 1981). Evans and Ginsburg (1985) reported a comparison of contrast sensitivity between a group of 19-30 year-olds and a group of 55-79 year olds. The data indicated a significant decrement for the older group in the middle and upper frequency ranges (i.e., more detailed stimuli). Similarly, Schieber (1988) re- ported that sensitivity in these same ranges begins after age 40. Age-related declines have also been found for dynamic contrast sensitivity (Scialfa, Garvey, Gish, Deering, Leibowitz, and Goebel, 1988; Scialfa, Garvey, Tyrrell, and Lei- bowitz, 1992), sensitivity under low levels of illumination (Sloane, Owsley, and Alvarez, 1988), and sensitivity in the presence of glare (Leibowitz, Tyrrell, Andre, Eggers, and Nicholson, 1993). Relationships to driving performance. Although these data make a case for the importance of contrast sensitivity with respect to driving visual require- 40 LLANERAS ET AL. ments, lack of confirming data has limited its potential usefulness as a predictor of driving performance. During the course of this review, only one study was identified which reported data concerning relationships between contrast sensi- tivity and driving performance. Decina and Staplin (1993) failed to find a rela- tionship between contrast sensitivity measures and accident rates in a 3.67 year period following the measurement. However, a composite measure combining a broad contrast sensitivity measure, binocular visual acuity, and horizontal vi- sual field measurement, was related to crash involvement for drivers aged 66 and older. The authors asserted that including contrast sensitivity in visual measures used for licensing, could contribute to the identification of the highest risk older drivers. Testing practices regarding contrast sensitivity face challenging issues. De- cina, Breton, and Staplin (1991), have asserted that full ranges of contrast sensi- tivity testing require more time than is typically available. In the case of licens- ing, added administration time and cost would be undesirable (Bailey and Sheedy, 1988). Further, a lack of normative data raises problems with specifying the criterion level that separates normal from abnormal performance, deter- mining the size of sample measures needed for accurate testing, and establishing expected reliability ranges (Legge and Rubin, 1986). Useful Field of Vision A measure of the visual field which has received much recognition 1n recent research is the useful field of vision (UFOV). This has been defined as the area of the visual field that is useful for acquiring information during a brief glance (Sanders, 1970). UFOV is determined by measuring the peripheral field surrounding a straight-ahead fixation point, in which a subject can detect stim- uli (in the periphery) while simultaneously performing a task in the forward view. It is believed to accurately represent performance of typical search behav- iors, since the individual’s attention remains in the forward view. One factor that contributes to the size of the useful field of vision, is the size and sensitivity of the peripheral field—an area which tends to become less sensitive with increasing age (Wolf, 1967; Haas, Flammer, and Schnieder, 1986; Drance, Berry, and Hughes, 1967; Johnson and Keltner, 1983). These studies have shown that when parts of the visual field are lost as a result of normal aging, it is the peripheral area that is affected first. Although peripheral vision is an important component of UFOV, the latter measure is believed to be more useful for predicting visual performance in real activities, since it relies somewhat on attentional ability (Walker, Sedney, Wochinger, Boehm-Davis, and Perez, 1993). UFOV has been shown to decline with advancing age. Changes have been ABILITIES, AGE, AND DRIVING PERFORMANCE 41 found for extrafoveal acuity (Cerrella, 1985), determining locations of stimuli in the presence of distracting stimuli (Sekular and Ball, 1986), and for identifying letters (Scialfa, Kline, and Lyman, 1987). In addition, Ball, Beard, Roenker, Miller, and Griggs (1988), found that increasing the difficulty of the task in the straight-ahead field had a detrimental effect only on a group of older subjects. Scialfa, et al. (1987) concluded that older adults appear to take smaller percep- tual samples from the visual scene and scan these samples more slowly than do young adults. As a result, the size or intensity of stimuli presented in the periph- ery needs to be increased if older adults are to see them as quickly. In summary, research indicates that the UFOV is influenced by: a) the size of the visual field, and b) ability to make use of information within the visual field. Relationships to driving performance. Several attempts have been made to link aspects of the visual field to driving performance. Regarding visual field size, Johnson and Keltner (1983) found that drivers with visual field loss in both eyes had accident and conviction rates more than twice as high as those with no significant loss. Others have also found associations, but only in similar circum- stances where persons had field size losses greater than would be expected with normal aging (Burg, 1968; Shinar, 1977; Council and Allen, 1974). Walker, et al. (1993), have pointed out that one reason peripheral vision has been only weakly associated with driving performance, is that peripheral target detection has typically been measured as a primary task. It has only been in cases of extreme abnormalities in the size of the visual field that influences on driving performance have been detected. The UFOV measure has shown promise as a better predictor of driving safety than field size alone. Walker, et al. (1993), have found similar patterns with the UFOV and driving performance as have been found by other researchers study- ing non-driving tasks. Using a part-task driving simulator, they found that an older driver group (range = 60-65 years old) showed a narrowing effect on UFOV as a result of increased difficulty of the primary task, as indicated by reaction times. Ball, Owsley, Sloane, Roenker, and Bruni (1993) examined the relationship between several measures of visual processing (e.g., eye health, visual function, mental status, and UFOV) and the number of crashes that each participant was responsible for in a five year period. They reported a correlation of .46 between UFOV and accidents. Based on the set of correlations between all variables, they suggested a model for predicting crash frequency. According to their model, central vision, eye health, peripheral vision, and mental status, all affect UFOV directly. The size of the UFOV in turn, had direct effects on crash frequency. Mental status had a direct effect on both UFOV and crash frequency. Although age was included as a predictor variable in the study, the change of UFOV asa 42 LLANERAS ET AL. function of age was not reported. The Ball, et al. (1993) study represents a different approach to examining accident precursors. The majority of other studies have treated each ability as being independent and as having a direct effect on driving performance. In this case, the model reflects partialled effects and interactions among abilities. It is possible that this method may depict more accurate relationships between abilities and performance. Field Dependence Field dependence refers to the ability of a person to perceive relevant targets within an embedded context (Shinar, McDowell, Rackoff, and Rockwell, 1978). It reflects a type of stable cognitive style—a way of perceiving which is domi- nated by the overall organization of the field. Field independence, on the other hand, reflects a style in which parts of the field are perceived as discrete from the organized background (Witkin, Lewis, Hertzman, Machover, Meissner, and Wapner, 1954). This ability is typically measured by having a subject scan a complex figure and detect a prescribed target. Persons who are best able to extract salient information from complex backgrounds are characterized as field independent, while those less able to do so are field dependent. Field dependence is distinct from UFOV in that it involves more than target detection. Rather, it requires the use of scanning strategies and making sense of multiple information sources in the visual scene (Shinar, et al., 1978). Most research indicates that people become more field dependent with increasing age (Shinar, et al., 1978; Ranney and Pulling, 1990; Manivannan, Czaja, Drury, and Ip, 1993). Loo (1978) reported a small, but insignificant relationship between age and field dependency. Relationships to driving performance. Field dependency measures have demonstrated some predictive value with respect to driving performance mea- sures such as recognizing hazards, recognizing road signs, controlling skidding vehicles, and driving in high speed and high density trafic (Goodenough, 1976). In driving studies that did not consider age differences, field-independent drivers have been found to have lower accident rates (Harano, 1970), have quicker braking reaction times (Olson, 1974; Barrett and Thornton, 1968), have higher deceleration rates during simulated emergency situations (Barrett, Thornton, and Cabe, 1969), and show better headway maintenance patterns, and better control a skid-prone car after an initial trial (Olson, 1974), than field dependent drivers. Arthur, Barrett, and Alexander (1991), included field depen- dence as part of a meta-analysis examining the roles of cognitive, personality, and demographic factors on vehicular accident involvement. Their research resulted in twelve correlation coefficients related to field dependence. The mean overall correlation was .151, while 65.07 percent of the variance in these studies ABILITIES, AGE, AND DRIVING PERFORMANCE 43 was left unaccounted for by moderating variables. These authors determined field dependence to be a weak predictor of accident involvement, and attributed this to be due in part to the three different assessment tests that were used across studies (e.g., the Group Embedded Figures Test, the Portable Rod-and-Frame Test, and the Hidden Figures Test). We are aware of two studies that have examined age differences in conjunc- tion with field dependency and driving performance. Mihal and Barrett (1976), examined the relationships between several perceptual and information-process- ing abilities and accident rates of utility company drivers. One ability was field dependence, measured by the Portable Rod and Frame Test and the Embedded Figures Test. Both measures were significantly correlated with the number of accidents over a five year period. As an additional statistical manipulation, the sample was divided into a younger group (25 to 43 years) and an older group (45 to 64 years). For every significant predictor variable (i.e., Rod and Frame, Em- bedded Figures, complex reaction time, selective attention), the relationship with accidents was greater for the older group. Shinar, McDowell, Rackoff, and Rockwell (1978) examined the relationship between field dependence and on-the-road visual search behavior. They noted an age-related decline in time needed to identify information. Field dependent drivers were characterized as needing longer eye fixations to gather relevant information. In addition, they were less able to adapt to changing perceptual requirements involved in curve negotiation. Although there appears to be some support for a relationship between field dependence and driving performance, the small number of studies in this area limit the confidence of this assertion. Depth Perception Depth perception is defined as the ability to judge the distance, and changes in distance, of an object (Burg, 1964). Depth perception cues can be one of two types: physiological and environmental (Coran, Porak, and Ward, 1984). Most physiological measures stem from information about the shape of the eye. When shifting one’s gaze to a closer or further distance, the lens changes shape to focus the image. The muscular tension used to change the curvature of the lens pro- vides a cue as to the distance of an object. When objects are distant however (50 feet and beyond), the shape of the lens does not change dramatically, and is therefore less helpful as a measure. In addition, because the lens hardens and ocular muscles weaken in older adults, physiological measures of depth become less effective with age. The second source of depth perception cues are those present in the visual field. When judging the distance of stationary objects, cues such as surface texture gradients, relative heights of objects, linear angles, and retinal disparity are used (Goldstein, 1989). For judging the distance of objects 4 LLANERAS ET AL. in motion, cues in the form of changes to the relative size of the images (i.e., expansion or contraction) and stimulation to adjacent receptor cells also contrib- ute to depth judgments (Shinar, 1977). In general, individuals are more sensitive (i.e., threshold levels are lower) to targets that are in the presence of photopic illumination levels (Rock, 1953; cited in Henderson and Burg, 1974) when they are allowed unlimited viewing time (Rock, 1953; Leibowitz and Lomont, 1954; Leibowitz, 1955; each cited in Hen- derson and Burg, 1974), and that take up less than 2—3 degrees of space on the retina (Shinar, 1977). Furthermore, sensitivity to central movement in-depth (i.e., targets moving directly at an observer from the front) is greater than periph- eral movement in-depth (i.e., targets moving toward an observer from a side angle) for all ages. Nevertheless, the ability to make accurate depth judgments and detect changes in depth, decreases with advancing age (Bell, Wolf, and Bernholtz, 1972; Henderson and Burg, 1973, 1974; Shinar, 1976). This pattern holds true for both peripherally and centrally presented targets (Shinar, 1976). Further, glare sensitivity and visual acuity under dim illumination have both been shown to limit the ability to detect depth (Yanik, 1986; Rock, 1953; cited in Henderson and Burg, 1974). Both of these abilities also deteriorate with age and are important when driving at night. Age differences in depth perception begin to be noticeable before age 40, increase significantly by age 50, and con- tinue to increase thereafter (Bell, Wolf, and Bernholtz, 1972). These patterns were replicated by Henderson and Burg (1974). Other research related to angu- lar movement detection has been sparse, but existing data suggests that the range of individual differences is large (Henderson and Burg, 1974). This, combined with intuitive relevance to driving tasks, indicate that these measures may serve as a good discriminator for driving performance. Relationships to driving performance. The ability to judge the distance be- tween one’s vehicle and other vehicles moving at approximately the same rate is one of the most critical driving abilities (Shinar, 1977). Central angular detec- tion ability is important when steering to follow a desired path and avoiding vehicles in a traffic string (Henderson and Burg, 1974). Detection of angular motion in the periphery may be the first clue that other vehicles are close or that hazards are appearing from the side. Boyar, Couts, Joshi, and Klein (1985) found that following too closely was the second most common mistake that led to commercial vehicle accidents in 1983. Similar conclusions have been reached for conventional vehicles (Barrett, Alexander, and Forbes, 1973). Judging dis- tance through angular movement may be a critical contributor to these types of incidents. Judging in-depth motion is made difficult by the fact that when no lateral displacement occurs, the primary depth cue is the expansion or contraction in _ ABILITIES, AGE, AND DRIVING PERFORMANCE 45 the image size of other vehicles (Hills, 1980). It appears that older drivers have difficulty controlling vehicles in this type of situation (Ranney and Pulling, 1990). Using a battery of closed-course driving and laboratory tests, Ranney and Pulling found that older drivers (74 to 83 years old) made more gap execution errors (1.e., struck objects/excessively slow speed) and more gap judgment errors than did a group of younger drivers (30 to 51 years old). In Henderson and Burg’s (1973) study of truck drivers, those who had central movement in-depth thresholds greater than 16 minutes of arc/second for large targets, and greater than 12 minutes of arc/second for small targets had higher accident rates than the population mean. However, Henderson and Burg (1974) failed to replicate these findings. In the 1974 study, only ability to detect depth of a large expand- ing target (1.e., simulated movement of a car on a collision course) was asso- ciated with accident rates for adults over 25 years of age. The ability to perceive depth where lateral movement occurs is believed to be an important determinant in driving tasks involving traffic merging and cross- ing through intersections (Staplin and Lyles, 1992). To assess age-related depth/ motion perception ability changes, Staplin and Lyles (1992) had subjects esti- mate how long it would take them to reach specified points in their path given a constant speed (time-to-collision). In one study, a 100 percent threshold eleva- tion was found for 70-75 year old drivers, as compared to those 20-29 years old. This was assessed using a simulation task requiring drivers to identify car pass- ing events based on tail light positions. Hills (1975) found age-related decre- ments with motion perception when vehicles were moving closer, but no age differences when vehicles were moving away. A second study by Hills and John- son (1980) found that older drivers were likely to underestimate the speeds of oncoming vehicles. Also, judgments by older drivers about the “‘last safe point” at which to make a left turn in front of an oncoming vehicle, were found to correspond to a constant distance, whereas younger drivers took into account the speed of the oncoming vehicle. Staplin and Lyles concluded that an increase in angular velocity is an important clue needed for motion detection and that this ability declines with age. Other data also suggest that this may be true, since older drivers report that they have problems making left turns against oncoming traffic, and merging into a traffic stream (Malfetti and Winter, 1987). Depth perception does indeed appear to be a promising variable for predicting driving performance, as indicated by both simulated driving tasks and correla- tional studies. Examining the tasks that have been studied, it appears that depth perception is an important contributor of information leading to decisions. One promising finding in the literature is that practice, with or without feedback, was able to improve ability to detect movement of angular targets (Johnson and Leibowitz, 1974; cited in Shinar, 1977). The effects of practice were still detected Ad LLANERAS ET AL. three months later. A test-retest study by Henderson and Burg (1974), con- firmed this finding. Thus, depth perception may be an ability that declines with age, but may be rejuvenated via remedial attention. Glare Sensitivity Glare has been defined as brightness within the field of vision that is sufh- ciently greater than the luminance to which the eyes are adapted (McCormick and Sanders, 1982). Glare is mostly debilitating to the region within 10 degrees of the line of sight, since this region is instrumental for seeing detail (Henderson and Burg, 1974). In the driving environment, two types of glare, veiling glare and spot glare, are relevant (Shinar, 1977). Veiling glare is a uniform luminance masking, while spot glare is characterized by a region(s) of concentrated lumi- nance (e.g., headlights). Due to yellowing of the lens and clouding of the intraocular fluid, older people tend to experience a veiling luminance that imposes upon the retinal image when in bright environments (Drance, Berry, and Hughes, 1967). As a result, they are less tolerant of glare, and consequently are less apt to detect stimuli in the visual field in the presence of imposing bright illumination. Leibowitz, Tyrrell, Andre, Eggers, and Nicholson (1993) examined age differences in both static and dynamic contrast sensitivity in the presence and absence of glare. Glare significantly reduced performance for both static and dynamic measures across all spatial frequencies. In addition, performance was degraded across all ages. However, glare caused a greater detriment for a group of older adults (mean age of 68.3 years) than for a group of younger adults (mean age of 25.7 years). Other researchers have confirmed age-related declines for contrast sensitivity and static acuity in the presence of glare. However, age differences vary some- what across studies, making a determination of an onset age unclear. Allen and Vos (1967), and Burg (1967), examined contrast sensitivity in the presence of glare and determined that performance remained little effected for various tar- get sizes and background illuminations until the mid to late 40s, after which decrements began to accelerate. In addition, Burg reported that the time needed to recover from glare exposure follows a similar pattern. Shinar (1976) measured acuity in the presence of both veiling and spot glare. In contrast, this data suggested that significant changes do not begin to appear until around age 65. Further evidence that glare effects are not significant across age groups was provided by Finlay and Wilkinson (1984). They found no differences in glare- related performance effects for an older group (ages 43 to 56 years) as compared to a young group (19 to 24 years). The lack of consistency between studies may be heightened by large individual differences in glare sensitivity. While patholo- ABILITIES, AGE, AND DRIVING PERFORMANCE 47 gies such as cataracts and glaucoma may have only a small impact on acuity (depending on their location), they still may increase light scattering in the eye (Shinar, 1977). Relationships to driving performance. Although proper lighting can be ef- fective in increasing visibility for all drivers (Mortimer, 1988), older drivers require extra illuminance and contrast to be able to see adequately (Sivak, Olson, and Pastalan, 1981). Furthermore, older drivers have less ability to toler- ate glare, such as that produced by automobile headlights (Mortimer, 1988). Henderson and Burg (1974) found that 50 year-old drivers who had the lowest 10 percent of visual acuity in the presence of veiling and spot glare, had accident rates higher than that of the population mean. Pulling, Wolf, Sturgis, Vaillan- court, and Dolliver (1980), studied the acceptable level of oncoming headlight illuminance using a simulated driving task. Prior to age 70, performance slowly decreased and individual differences were large, but after age 70 the ability to tolerate glare decreased rapidly. This problem is maximized at night, when illuminance from headlights reduces the contrast of objects to their background (Attwood, 1979). Despite evidence that glare sensitivity and recovery are important age-related visual changes, few studies have investigated the relationship between glare and actual driving performance. Headlight glare was attributed as a potential ‘“‘envi- ronmental” causal factor in approximately 2.3% of night accidents in one inves- tigative study (Indiana University, 1975; cited in Mortimer, 1988). In another, glare was mentioned as a factor in 3 of 30 nighttime incidents where drivers were run off the road (Boyce, Hochmuth, Meneguzzer, and Mortimer, 1987; cited in Mortimer, 1988). Further, glare was named as a contributing factor in 13 per- cent of accidents in which an adverse environment was involved (Sabey and Stoughton, 1975; cited in Mortimer, 1988). The time needed to recover from glare was included in Burg’s (1971) study, and was found to correlate weakly with accident rates. Other studies, however, have failed to find a direct relation- ship between glare sensitivity measures and driving performance (Shinar, 1977; Wolbarsht, 1977; Burg, 1967). One possible reason that glare is not implicated as a significant factor more often is that only drivers with the poorest tolerance to glare are affected to the point where accidents occur, as Henderson and Burg (1974) found. A second explanation might be that glare is not a constant part of the driving environ- ment. In other words, abilities such as static and dynamic acuity are always relied upon, and thus are more likely to be associated with accident occurrence. Because glare sensitivity and recovery are only salient in the presence of glare within about 10 degrees of the line of sight, and because sources of glare are not always present, the relationship with all accidents is understandably weak. 48 LLANERAS ET AL. Night Vision Visual degradation resulting from lower illumination levels has been found to increase with advancing age (Laux and Brelsford, 1990; Forbes and Vanosdall, 1973; Shinar, 1976; Henderson and Burg, 1974). Further, these studies indicate that the decline in static acuity under reduced illumination conditions, occurs earlier in life and progresses more quickly than does declines due to acuity in the presence of daytime illumination levels. Sturr, Kline, and Taub (1990), for example, tested static acuity of young (18-25 years) and older (60-87 years) persons under six luminance levels, ranging from photopic (daytime) to me- sopic (nighttime). In this study, very little differentiation between age groups occurred under the highest level of illumination (245.50 cd/m). However, under lower illumination levels, distinctions appeared. Richards (1977) esti- mated that the average nighttime illumination level on urban roads was about 1.03 cd/m?. At a level just above this (2.45 cd/m7), large differences exist be- tween the 60-64 group and older groups. Based upon these data, Sturr, Kline, and Taub concluded that 65 1s a critical point, after which visual acuity becomes increasingly poorer under low illumination. In the case of nighttime vision, acuity depends upon adaptation, the ability of the eye to adapt its sensitivity to incident light changes (Haig, 1941). During dark adaptation, the size of the pupil and sensitivity of the retina change in order to prevent underlighting and overlighting of the retina. Acuity improves rapidly after one moves to a darkened state. This initial response pattern is due to rapid adaptation by the cone receptors and pupillary expansion (Wald, 1968). The slower, but more dominant shift, reflects adaptation by the rod receptors, lo- cated toward the periphery of the retina. These receptors, and associated neural processing, produce visual responses useful for detecting general light patterns, as opposed to fine detail (Barlow, 1982). Approximately 5—8 minutes are needed before the light-adapted eye switches from resolving visual information with cones to the more sensitive rods (Shinar, 1977). Rods then continue to slowly gain sensitivity. The shape of the adaptation function does not change significantly with age; however, the extent of sensitivity achieved appears to decrease as one ages (McFarland, et al, 1960). As a consequence, older people have less sensitivity at any given time during adaptation. It is therefore more beneficial to older persons to avoid environments requiring complete adaptation or large contrasts in illu- mination. The ideal situation would be to always have at least low-level illumina- tion present and no sources of bright light which require the visual receptors to readapt to darkened environments. Relationships to driving performance. Visual abilities that are important during daytime driving are also important during nighttime driving. Although _ ABILITIES, AGE, AND DRIVING PERFORMANCE 49 rods are most sensitive in low illumination, adaptation of foveal vision is critical when performing everyday activities where objects must be detected, such as when walking into a darkened room (Shipley, 1974). The adaptation of the rods become important when circumstances require peripheral vision (e.g., detecting pedestrians). Adaptation is required frequently within the nighttime driving environment due to extreme changes in illumination and to the presence of headlights from other vehicles. Henderson and Burg (1974) made the point that dark adaptation proceeds at a slower rate than the natural decline in outdoor illumination after sunset. During this transitionary period, peripheral vision is most affected. This is one reason that twilight is a dangerous time. for driving, especially when driving into sunlight. As with photopic static visual acuity, studies examining acuity under mesopic conditions have revealed weak relationships between acuity and driving perfor- mance. No relationship was found between low illumination acuity (Henderson and Burg, 1973) or dark adaptation (Burg, 1964) and accident involvement. Henderson and Burg (1974) found no overall relationship between low illumina- tion acuity and accident rates. However, by separating the data by various accident risk levels, they determined that acuity under low illumination was a good measure for discriminating drivers with poor overall vision who experi- enced higher rates of accidents. Shinar (1975), compared low illumination acuity scores for drivers judged to have committed recognition errors while involved in an accident, with a group that did not commit recognition errors. As a group, drivers committing errors attained poorer acuity scores. In a survey of pedestrian accidents, Hazlett and Allen (1968; cited in Shinar, 1977) found that drivers did not see the pedestrian that they struck significantly more at night (87%), than during the day (11.8%). Weak relationships between night vision abilities and accident rates may result from failing to distinguish between accidents occurring during the night- time versus daytime hours, or from the tendency of drivers with night vision problems to drive less often or more cautiously (Shinar, 1977). Of the studies mentioned, only Hazlett and Allen’s took into account the time of day that the accident took place. Without controlling for this, the chance of finding strong relationships between nighttime acuity and driving performance is severely re- stricted. In a study that distinguished between daytime and nighttime accidents, Shinar (1977), found that acuity under dim illumination conditions was specifi- cally associated with nighttime accidents and was one of the best predictors of accident involvement with respect to older drivers. Sivak, Olson, and Pastalan (1981), found age differences in sign reading distances under scotopic condi- tions. Sixty year-old drivers needed to be up to 35 percent closer to read signs as 50 LLANERAS ET AL. compared to 25 year old drivers with equal photopic acuity. These researchers and others (Sturr, Kline, and Taub, 1990) have claimed that a separate testing of acuity under low levels of illumination is warranted for licensing, based upon the weak relationship between photopic and scotopic acuity. Summary of Perceptual Abilities Sensory and perceptual changes appear to be an inevitable part of aging. Different abilities change at different rates and changes vary widely across indi- viduals; some abilities decline steadily throughout the life-span, while others appear to change at critical ages. Distinct patterns begin to emerge as early as forty years of age, and by age fifty an assortment of declining perceptual abilities can be identified. Table 1 provides a synopsis of studies examining perceptual abilities in association with driving performance. In early studies, weak relation- ships were found between ability variables and accidents or convictions. Dy- namic visual acuity tended to be associated with these measures most consis- tently, with some associations found for static visual acuity, glare recovery, and field size. According to Burg (1971) potential explanations as to why weak relationships have generally resulted from this type of research are that: @ Many factors influence driving performance, @ There may be disparity between an individual’s capability and the degree to which it is used, @ Tests may measure characteristics that are not necessarily related to functions used in driving, and @ Studies may have other shortcomings related to sampling and performance measures. Although Burg made these remarks in connection with visual abilities, in essence they apply to all abilities covered in this review. Driving depends upon a myriad of factors, making it difficult to attribute performance to any single ability. This does not imply that each individual ability is unimportant. In fact, some evidence of performance effect was found for each visual ability. With this in mind, many studies have attempted to make importance comparisons by including visual measurements. In some cases, the number of abilities included in these studies were limited to the abilities that devices were capable of measur- ing. In others, it was limited by the goals of the researchers. Data from early studies, along with advances in testing engineering, have lead to improved func- tional measures, such as contrast sensitivity, depth perception, and useful field of vision. These measures have been studied less often, but the strength of results obtained with these measures, suggests that they may be increasingly useful for predicting driving performance. ABILITIES, AGE, AND DRIVING PERFORMANCE 51 Table 1.—Summary of Perceptual Abilities and Driving Performance Ability/Author(s) Static Visual Acuity Burg (1964; 1967), Henderson & Burg (1974) and Shinar (1977) Henderson & Burg (1974) Hoffstetter (1976) Burg (1971) Rogers and Janke (1992) Henderson & Burg (1973) Shinar, McDonald, and Treat (1978) Kline, Ghali, Kline, and Brown (1990) Dynamic Visual Acuity Retchin, Cox, Fox, and Irwin (1988) Burg (1968), Shinar (1977) and Laux & Brelsford (1990) Shinar, Mayer, and Treat (1975) Henderson & Burg (1974) Contrast Sensitivity Decina & Staplin (1993) Useful Field of Vision (UFOV) Johnson & Keltner (1983) Burg (1968), Shinar (1977) and Council & Allen (1974) Walker, Sedney, Wochinger, Boehm-Davis, and Perez (1993) Ball, Owsley, Sloane, Roenker, and Bruni (1993) Owsley, Ball, Sloane, Roenker and Bruni (1991) Field Dependence Harano (1970) Research Findings Found consistent, but weak, relationships between static acuity and traffic accident and conviction rates. Identified significant relationships between static visual acuity and accident rates occurred for a restricted population: drivers aged 25 to 49. Found that drivers with poor static acuity were more likely to be involved in multiple accidents. Indicated that significant relationships between static acuity and conviction rates occurred only for female drivers. Reported that drivers with poor static visual acuity had significantly higher conviction rates, but no differences in overall accident rates. Failed to identify any significant relationship between static acuity and accident rates. Found a significant relationship between static visual acuity and improper lookout behavior. Failed to find differences in the ability to detect highway signs, regardless of illumination levels, across young, middle aged, and elderly drivers. Found a significant relationship between dynamic visual acuity and the number of miles driven. Indicated that dynamic visual acuity was the ability most highly correlated with accident involvement. Indicated that drivers found to be at fault in accidents are more likely to have poorer dynamic visual acuity than comparable groups of drivers. Found that professional drivers who were over age 50, and among the top 10 percent with respect to dynamic visual acuity, had lower than average accident rates, while the bottom 10 percent had higher than average accident rates. Found no significant relationships between contrast sensitivity and accident rates; however, when included as part of a composite measure, contrast sensitivity was related to the incidence of accidents in drivers age 66 and older. Found that accident and conviction rates for drivers with visual field loss were approximately double those of individuals with no significant loss. Found that accident and conviction rates were associated with UFOV only when the loss was greater than would be expected with normal aging. Found that increasing task difficulty significantly reduced the useful field of vision of older drivers (age 60 to 65). Indicated that UFOV was significantly related (r = .46) to crash frequency. Found a correlation of .36 between UFOV and accidents in a sample of 53 older drivers. Found field independent drivers had lower accident rates than field dependent drivers (age differences not considered). 52 Table 1 .—Continued Ability/Author(s) LLANERAS ET AL. Research Findings Olson (1974) and Barrett & Thornton (1968) Barrett, Thornton, and Cabe (1969) Olson (1974) Mihal & Barrett (1976) Shinar, McDowell, Rackoff, and Rockwell (1978) Depth Perception Henderson & Burg (1973) Ranney & Pulling (1990) Hills & Johnson (1980) Staplin & Lyles (1992) Hills (1975) Glare Sensitivity Sivak, Olson, and Pastalan (1981) Mortimer (1988) and Attwood (1979) Henderson & Burg (1974) Shinar (1977), Wolbarsht (1977) and Burg (1967) Burg (1971) Night Vision Henderson & Burg (1973; 1974) Burg (1964) Hazlett & Allen (1968) Shinar (1977) Found braking reaction times were significantly correlated with field dependence; field independent drivers had quicker braking reaction times than field dependent drivers. Found that while driving during simulated emergency situations, field independent drivers were able to decelerate significantly faster than field dependent drivers. Indicated that field independent drivers were able to maintain better headway patterns and control a skid prone car than field dependent drivers. Found that field dependency, in a sample of utility company drivers, was significantly correlated with accident frequency over a five year period. This relationship was greater for older drivers (age 45 to 64) than younger drivers (age 25 to 43). Indicated that field dependent drivers required longer eye fixations to gather relevant information, and demonstrated less adaptive eye fixation patterns in response to changing roadway conditions than field independent drivers. Found that truck drivers with in-depth thresholds greater than 16 minutes of arc/sec for large targets, and 12 minutes of arc/sec for small targets had higher than average accident rates. Indicated that when forced to rely on a single depth cue (the expansion and contraction of the image size) older drivers (age 74 to 83) made more gap execution and judgment errors than a group of younger drivers (age 30 to 51). Found that older drivers were more likely to underestimate the speed of oncoming vehicles, and base the last safe point at which to make a turn in front of an oncoming vehicle on a constant distance rather than the speed of the oncoming vehicle. Found older driver’s (age 70 to 75) time-to-collison estimates were elevated by a magnitude of 100 percent as compared to younger drivers (age 20 to 29); older drivers significantly over-estimated the time-to-collison with an oncoming vehicle. Found age-related decrements in the ability to detect motion when vehicles were moving closer, but no age differences in this ability when the vehicles were moving away. Indicated that older drives required extra illuminance and contrast in order to see adequately. Found that older drivers are less able to tolerate glare (such as that produced by automobile headlights) than younger drivers. Found that 50 year-old drivers who had poor visual acuity (in the lower 10 percent range) in the presence of glare had higher accident rates. Failed to find relationships between glare sensitivity measures and driving performance. Found glare sensitivity to correlate weakly with female accident rates. Found no relationship between low illumination acuity and accident rates. Failed to find a relationship between dark adaption and accident involvement. Found that the majority of drivers involved in pedestrian accidents failed to detect pedestrians more at night; 87 percent failed to detect pedestrians at nighttime versus 12 percent during the daytime. Found that acuity under low illumination levels was a leading predictor of older driver nighttime accident involvement. ‘ABILITIES, AGE, AND DRIVING PERFORMANCE 53 Psychomotor Abilities Psychomotor abilities change throughout adulthood in much the same way as perceptual abilities. In general, psychomotor abilities decline with advancing age, but these changes vary widely, both across abilities and among individuals. Some changes to physical structures of the body underlie changes to individual psychomotor abilities. Fundamentally, older adults are smaller in most aspects of body size than younger adults (Stoudt, 1981). In addition to changes in the neural systems, changes also occur to musculoskeletal and cardiovascular sys- tems. As a result, older adults have less muscle strength and are less able to maintain maximum muscular effort (Santrock, 1985). States (1985), has sug- gested that some age-related changes to bones, cartilage, ligaments, and muscles impair the musculoskeletal system’s capability to perform driving activities, and _ that licensing standards should be developed for strength and joint range-of- motion. Several psychomotor abilities appear to be important for driving. Most nota- bly, multilimb coordination and control precision are necessary when maneu- vering, braking, and manually shifting (Stelmach and Nahom, 1992). These “sets” of movements in turn require many individual movements. Essential musculoskeletal skills needed to perform driving movements include ankle and plantar flexion, knee extension, hip flexion and extension, hand grip strength, wrist flexion and extension, and shoulder flexion and extension (Stock, Light, Douglass, and Burg, 1970). These physical proficiency abilities are sometimes measured individually, but for the sake of brevity, they will be discussed here, only to the extent that they contribute to more general movements. Further, reaction time is important when responding to potentially hazardous situations, such as wind gusts or objects appearing in the roadway (Wierwille, Casali, and Repa, 1983; Cox, 1989). Reaction Time One of the most widely studied aspects of motor performance is reaction time (Kausler, 1991). It is most often defined as the time elapsed between the appear- ance of a signal and the execution of a person’s response movement. The num- ber of studies investigating reaction time is enormous and it is impractical to include all of them in this review (see Welford, 1977). As an example of age-re- lated changes found in reaction time studies, Hodgkins (1962), measured the reaction time of 400 females between the ages of six and eighty-four. Reaction time improved from childhood until about the age of twenty, remained constant until about age twenty-six, then gradually declined. Between the twenties and seventies there was a 43 percent increase in reaction time. Researchers have distinguished between phases comprising reactive move- ments. For this discussion, it is appropriate to distinguish between premotor 54 LLANERAS ET AL. activities and motor activities. Premotor activities refer to response preparation, selection, and programming (Stelmach and Nahom, 1992). These activities are largely cognitive in nature, and are intimately related to decision-making. Mo- tor activities are the actual physical movements that occur. Stelmach and Na- hom (1992) reported a sample of studies which illustrate simple and choice reaction time differences between young and older adults (Clarkson, 1978; Jor- dan and Rabbitt, 1977; Larish and Stelmach, 1982; Szafran, 1951; Weiss, 1965). Each study reported slower reaction times for the older group of adults. Across studies, the older groups averaged approximately 21 percent greater simple reac- tion times and approximately 38 percent greater choice reaction times com- pared to young groups. Some studies made separate measurements for premotor and motor phases and reported a similar trend; older adults took longer to complete both phases. Some alternative explanations have been offered to account for these age-re- lated changes. Stelmach and Goggin (1989), proposed that changes in reaction time go beyond the simple process of neural slowing. Rather, they contended that older adults are less able to utilize automated motor programs, and instead must rely on feedback control processes. As a result, reactive movements may be more deliberate for older individuals than for younger ones. Consequently, responses become more variable as well as slower. Salthouse (1985) added that older adults may be more concerned about avoiding mistakes, and therefore reaction time is slowed to ensure better accuracy. Alternatively, Rabbitt (1979) concluded that age-related slowing is related to loss of precise control over the speed at which responses can be made, or to loss of fine differentiation between fast’ and ’slow’ responses (1.e., larger intra-individual variance). In summary, the aging research is fairly consistent in the view that reaction time declines with increasing age. Some evidence suggests that the pace of the aging process can be altered somewhat. First, physical activity may slow the aging effect. Spirduso (1982), found exactly this, when comparing the reaction times of age-matched physically trained older subjects with physically untrained older subjects. Reaction times have also been improved through training by imposing time limitations on responses (Baron and Mattila, 1989). Finally, it has been noted by several researchers that allowing older persons to anticipate having to react, may help to speed responses. Relationships to driving performance. Individual experiments studying age- related differences in reaction time during driving situations have provided a wide range of results. Much research has focused on reaction times involving braking movements. Olson and Sivak (1986) examined brake reaction/move- ment time in an on-the-road driving task. Response latencies were greater for an older driver group (ages 50-84), than for a younger group (ages 18-40) when ' ABILITIES, AGE, AND DRIVING PERFORMANCE a5 braking in response to a light attached to the front hood of the car. However, no response differences were found when subjects responded to an object suddenly appearing in the roadway. Lerner (1993), compared on-the-road brake perception-reaction times for drivers in three age groups: 20-40, 65-69, and over 70 years old. Subjects drove their own vehicles on actual roads and were required to make an emergency response to a barrel appearing in the roadway. No age difference in braking time was found, although only about one-half of the subjects used braking as part of their response. Thus, assuming that no accidents occurred, it is questionable whether this task truly represented an emergency event. However, other studies have also failed to find braking reaction time differences between younger and older drivers under conditions of unexpected roadway hazards (Korteling, 1990; Olson and Sivak, 1986). Without discounting the prevailing evidence that infor- mation processing slows with age, Korteling (1990) suggested that age differ- ences are moderated by longitudinal driving practice. Retchin, Cox, Fox, and Irwin (1988) addressed the role of practice and driving reaction time by comparing reaction times of older persons who were either frequent drivers, infrequent drivers, or nondrivers. In a simulated task, subjects released an accelerator control and applied a brake control in response to a traffic light change. They found that nondrivers took significantly more time to make these movements than other groups, but that all three groups performed slower than a group of younger subjects. Practice has been shown to improve response selection ability in older adults in non-driving tasks. Clark, Lanphear, and Riddick (1987) had a group of older adults play a video game for two hours per week for seven weeks. This group later performed better on a response selection task when they were required to press a button in response to a stimu- lus using the opposing hand (i.e., incompatible response). Since younger persons were not included in the study, it is not known whether the practice effects were related to age. In the above discussion of age-related reaction time changes, it was noted that older drivers might benefit by being able to anticipate having to make actions. Studying reaction time under cued conditions, Staplin, Janoff, and Decina (1985) found that driving movement responses are only slightly slower for older drivers when responses are preplanned. In the Olson and Sivak (1986) experi- ment, no significant age differences in perception-reaction times occurred when drivers knew that they would soon need to brake. One potential problem with the majority of these past studies, is that reaction time has been examined as an isolated event. Considering the limited input and responses, simple reaction time studies are likely to be a poor predictor of the complex driving task (Mihal and Barrett, 1976). Although some studies used 56 LLANERAS ET AL. contrived driving situations, it is unlikely that each of these situations demanded quick reactions. In a real driving environment, drivers are continually required to react to many sources of environmental stimuli. Existing driving reaction time research does not provide a great deal of evidence as to whether older drivers are more likely to be hindered when faced with multiple choices of action, or whether they are less likely to recover from mistakes made during response initiation, as shown in non-driving situations. Multilimb Coordination and Physical Proficiency Multilimb coordination is the ability to coordinate movements of two or more limbs, such as in moving equipment controls (Fleishman and Quaintance, 1984). In the case of driving a vehicle, coordinated movements are needed for shifting, steering, braking, and accelerating (Cox, 1989). Especially demanding are circumstances where several movements must be performed simultaneously or in rapid sequence. Flexibility of the trunk and neck also contribute to coordi- nated movements, since actions such as merging, changing lanes, and backing require the driver to scan a large portion of the 360 degree visual field (Hancock, Caird, and White, 1990). Multilimb coordination is actually a combination of several more molecular abilities. It requires some cognitive effort in the form of monitoring and feed- back processes (Godthelp, Milgram, and Blaaw, 1984), as well as the physical actions that make up the set of movements. In addition to limb movements, strength and flexibility of the trunk and neck may also affect coordination while driving (Stelmach and Nahom, 1992). Some biomechanical measures, which contribute to coordinated driving movements have been shown to decline with age. Larsson, Grimby, and Karlsson (1979) found that maximum knee extensor isometric strength, extension velocity, and dynamic strength all increase through the age of 29 years, remain stable through the age of 40, and then decline beyond that age. A slightly different pattern was observed by Murray, Gardner, Molinger, and Sepic (1980). In their sample, static and dynamic strength for both knee flexion and extension movements declined in men from age 20 onward. Laux and Brelsford (1990) reported the following relationships between age of a sample of active drivers and anthropometric measures believed to be driving-related: Measure Correlation with age Grip strength (left hand) nate | Grip strength (right hand) —.42 Neck flexibility (left side) me Neck flexibility (right side) —.44 Torso flexibility (left side) 2 Torso flexibility (right side) Ou | ABILITIES, AGE, AND DRIVING PERFORMANCE 57 Taken together, these data indicate that the maximum efforts that older per- sons are able to produce deteriorate. Maximum strength will rarely be required while driving, except in abnormal situations, such as a tire blowout or stopping abruptly (Sanders, 1981). The effect of age-related strength loss is unclear, but probably not critical for most driving tasks. Flexibility measures, on the other hand, may indicate a more pertinent problem for driving. Coordinated move- ments that require the driver to exceed comfortable movement boundaries (reaching, turning) may be adversely affected (Ostrow, Shaffron, and McPher- son, 1992). In general, data suggest that older adults may have some coordina- tion deficit when tasks are demanding (e.g., require a great deal of speed). Under normal circumstances, or when persons are experienced, however, these differ- ences may not surface. Relationships to driving performance. Despite the logical connection be- tween multilimb movements and driving, few studies have investigated it for- mally. In those that have, small or nonexistent age-performance relationships have resulted. Most studies have included measurements of strength or flexibil- ity, rather than coordination. Cox (1989), included measures for range of mo- tion, muscle strength, head and trunk control, grip strength, reaction time, proprioception, and light touch and localization in a test battery. None of these measures were found to be useful for predicting actual in-car driving perfor- mance for a group of adults over 65 years old. Laux and Brelsford (1990) compared grip strength, trunk flexibility, and neck flexibility measures with a set of self-reported driving performance measures. Poorer grip strength for both hands was significantly related to higher frequen- cies of bumping into something with the front bumper, running over a curb, and getting honked at by other drivers (interpreted as a possible driving norm viola- tion). Height was also related to each of these measures, indicating that shorter and weaker subjects were reporting more driving problems. Interestingly, grip strength has been found to correlate with the number of miles an adult drives in two separate studies (Laux and Brelsford, 1990; Retchin, Cox, Fox, and Irwin, 1988). In the Laux and Brelsford study, the ability to turn one’s head to the left, important when checking the blind spot caused by a vehicles’ door frame, was also related to being honked at. Other researchers have also noted that some older drivers have the habit of failing to look to the rear when changing lanes or backing up (Malfetti and Winter, 1986). One likely contributor to this habit is that older persons compensate for chronic stiffness or pain in the upper torso and neck by not making this critical movement. Indeed, data shows that older drivers may have more problems making these movements. In one sample of older drivers, 35% reported problems with arthritis, and 21% found it difficult to turn their heads in order to look to the rear when driving (Yee, 1985). In addi- tion, McPherson, Ostrow, Shaffron, and Yeater (1988), found that older drivers 58 LLANERAS ET AL. (60-75 years) had less shoulder and torso/neck flexibility than younger drivers (20-35 years). Thus, although only slight relationships between abilities related to making coordinated movements and accident involvement are apparent, there is some indication that these abilities could potentially limit movements necessary for safe driving. In summary, evidence of age differences in making coordinated driving move- ments is inconclusive. In the case of normal driving situations, physical limita- tions probably play a minor role, since steering, braking, and accelerating move- ments become well learned with experience (Staplin, Janoff, and Decina, 1985; cited in Stelmach and Nahom, 1992). It is unclear whether coordination plays a more important role in demanding circumstances, where cognitive demands are also great (e.g., making sharp turns, turning at intersections). Control Precision Control precision refers to the ability to accurately adjust the controls of a machine or vehicle. This involves the degree to which controls can be moved quickly and repeatedly to exact positions (Fleishman and Quaintance, 1984). In the case of driving, the gear shift, clutch, steering wheel, brake pedal, and acceler- ator must all be moved to more or less exact locations. Some movements may require quickness or force; thus, control precision 1s closely related to coordina- tion and reaction time. Most research addressing coordinated movement and control precision has measured performance in terms of reaction time. Past research has provided few clues as to how coordinated control movements, as required for driving a vehi- cle, change with age. Two experiments studied age differences in the ability to move a lever. Singleton (1955), studied rapid lever movement patterns and found age-related differences during separate phases of movement. The task required subjects to move a lever rapidly from side to side in an 18-inch slot. Overall, older subjects made movements 29 percent slower than younger sub- jects. No age differences were evident during the first quarter of movement, suggesting that age was not a factor in initial acceleration. During the last quarter of movement, older subjects actually had faster movement. The overall slowing was a result of older subjects taking longer to make movements at the middle and for changing direction at the ends. Thus, the older subjects reached slower maximum movement times and used the end stops to arrest movements. In contrast, younger subjects made more graded decelerations which resulted in overall quicker responses. Another experiment by Singleton (1954), employed a decision-making task requiring movements similar to those used when manually shifting gears in a vehicle. Subjects sat with a joystick located between their knees. At the appear- ABILITIES, AGE, AND DRIVING PERFORMANCE 59 ance of a signal, they were to pull the joystick straight back. At the end of this movement another signal indicated whether to move the Joystick to the left or right. Both time to move the joystick and time spent at points where the joystick changed direction increased with age. Thus, quickness of control movements was shown to decline somewhat with increasing age. Relationships to driving performance. Little evidence exists to indicate the effects of age-related differences with respect to making precise movements of vehicle controls. The experimental tasks used by Singleton (1954, 1955) may offer some generalizability. In these experiments, older adults were found to move levers and joysticks in a less graduated manner; they had slower move- ment times and relied on end stops of the path to arrest their movement. It would be useful to know if older drivers also demonstrate cumbersome move- _ ment when shifting gears, pressing brake pedals, etc. Staplin, Janoff, and Decina (1985; cited in Stelmach and Nahom, 1992) compared the driving skills of young and old adults, and determined that older drivers perform movements only slightly slower than young drivers when they were preplanned. They con- cluded that when driving activities are well learned, the effects of the aging process are minimal. Sanders (1981), included a task related to driving that required a combination of control precision and strength. Truck and bus drivers applied peak and sus- tained isometric forces to a truck steering wheel using three different hand positions. The purpose was to determine how many current drivers could apply the force needed to control the vehicle in the case of a tire blowout. It was estimated that seven percent of the drivers would not have been able to apply enough force to maintain control. Because measurements were taken under expected conditions and subjects were not required to brake, it is likely that the actual number of control failures could be higher. Age was not considered in this study, however, in light of evidence that arm and grip strength decline with age (Johnson, 1982; Forbes and Reina, 1970; MacLennan, Hall, Timothy, and Robinson, 1980; Laux and Brelsford, 1990), it is possible that fewer older drivers would be able to maintain control. In brief, any age-related changes concerning control precision probably play a minor role in driving performance. Drivers with practice may show only slight decrements in the ability to make correct movements. Demands for making quick and forceful movements have been lessened by some design innovations. Finally, it appears that the consequences associated with making poor control movements may be less severe relative to poor performance with other abilities (e.g., reaction time). Failing to move the transmission smoothly or exhibiting poor braking or steering patterns may reflect poor driving skill, but are less likely to result in significant numbers of incidents. 60 LLANERAS ET AL. Summary of Psychomotor Abilities In summary, a variety of psychomotor abilities are obviously needed to drive a vehicle, and some data suggest that they decline with age. The influence of psychomotor changes on age-related accident patterns, however, is minor rela- tive to the influence of decision-making, perception, and cognitive abilities (Welford, 1977). On a positive note, one reason that physical abilities are less critical, is that physical demands have been reduced by the introduction of innovations such as automatic transmissions, power steering and power brakes. As a result, fewer controlling movements (e.g., shifting) are required and less force is necessary to make those that do exist. Indeed, older drivers may take advantage of such innovations. As Lerner (1993) noted, almost all of the older drivers’ vehicles studied were equipped with automatic transmissions as com- pared to about two-thirds of the younger drivers’ vehicles. Table 2 provides a synopsis of studies that examined psychomotor abilities in relation to driving performance. The majority of studies centered around reac- tion time, due to its critical nature. Although little driving research addressed control precision and multilimb coordination, these abilities were included in this review because they are relied on so frequently, and because they may represent a critical distinction between abilities needed for driving conventional vehicles and those needed for driving commercial vehicles. Psychomotor de- mands could be greater when driving trucks or buses due to differences in physical features of these vehicles. The majority of commercial vehicles are equipped with standard transmissions, which require frequent repetitive shift- ing. Head, neck, and trunk movements may be more substantial due to the need to monitor larger viewing angles. Reactive steering and braking movements may also require more limb movement and strength. Finally, anticipation and reaction time is liable to be even more critical when driving larger vehicles, since it takes longer to stop a larger vehicle’s forward motion. On the other hand, psychomotor demands associated with driving commercial vehicles may be offset by driver experience. For example, Godthelp (1986), concluded that expe- rienced drivers are able to use their knowledge of vehicle handling to lessen their reliance on visual feedback. Also, Staplin, et al., (1985) found that braking, steering, and accelerating activities may become well learned and show resis- tance to age-related reaction time slowing. Because of this uncertainty, psycho- motor abilities have been included in the scope of this review. Cognitive Abilities Cognitive abilities are an integral part of performance of everyday complex activities. Cognition has been defined as the ability to know and understand the ABILITIES, AGE, AND DRIVING PERFORMANCE 61 Table 2.—Summary of Psychomotor Abilities and Driving Performance Ability/Author(s) Reaction Time Olson & Sivak (1986) Lerner (1993) Korteling (1990) Retchin, Cox, Fox, and Irwin (1988) Staplin, Janoff, and Decina (1985) Multilimb Coordination & Physical Proficiency Cox (1989) Laux & Brelsford (1990) Retchin, Cox, Fox, and Irwin (1988) and Laux & Brelsford (1990) McPherson, Ostrow, Shaffron, and Yeater (1988) Malfetti & Winter (1986) and Yee (1985) Control Precision Singleton (1955) Singleton (1954) Research Findings Found that response latencies for older drivers (age 50 to 84) were significantly greater than for younger drivers (age 18 to 40) when braking in response to a light attached to the front hood of a car. However, no age-related differences in reaction time were found when drivers knew they would soon need to brake. Found no age differences in braking times in a comparison of on-the-road brake perception-reaction times for drivers in three age groups—20 to 40, 65 to 69, and over 70. Failed to find differences in braking reaction times between younger and older drivers under conditions of unexpected roadway hazards. Indicated that older non-drivers took significantly more time to release an accelerator control and apply a brake control in response to traffic light changes than older frequent or infrequent drivers. However, older drivers, regardless of driving experience, performed slower than a group of younger adults. Found that movement responses are only slightly slower for older drivers when responses are preplanned. Found no relationship between actual in-car driving performance for a group of adults over 65 and measures of range of motion, muscle strength, head and trunk control, grip strength, reaction time, proprioception, and touch and sound localization. Found poor grip strength was related to higher incidence of degraded driving performance; including bumping into something with the front bumper, running over a curb, and getting honked at (all self-reports). Found grip strength was correlated with the number of miles driven by adult drivers. Found older drivers (age 60 to 75) had less shoulder and torso/neck flexibility than younger drivers (age 20 to 35). Found older drivers experienced difficulty and/or failed to turn their heads and look in the rear when changing lanes or backing-up. Found age was not a factor in initial acceleration during rapid lever movements; however, older subjects had slow maximum movement times and used end points to arrest their movements. Found that the speed associated with control movements in a simulated gear shifting task declined with age; older subjects took longer to move a joystick and spent more time at points where the joystick changed directions than younger subjects. demands of safe driving, and to be able to react to situations in an appropriate manner (Irwin, 1989). Driving performance, in particular, is maintained through a constant stream of small decisions and less frequent larger decisions (Decina, Breton, and Staplin, 1991). In order to make decisions, drivers must be able to focus and divide attention on information sources. In this respect, atten- tion has an intimate relationship with perceptual abilities; attention is needed to 62 LLANERAS ET AL. focus on relevant information, but in turn will be limited by the quality of the information that the sensory systems provide (Foley and Moray, 1987). Like perceptual and psychomotor abilities, cognitive abilities undergo age- related changes. Cognitive difficulty, often referred to as workload, will be high- est when there are uncertainties about the environment and when tasks are physically difficult (Hancock, Caird, and White, 1990). Investigations of acci- dents and fatalities of older drivers show that cognitive factors, namely, errors of omission (failing to take some action), and inattention are significant contribut- ing factors (Malfetti and Winter, 1986). This appears to be largely a problem of failing to commit undivided, concentrated attention to the driving task. Fell (1976), has estimated that 60 percent of crashes involving older drivers occur as a result of cognitive factors; while Shinar (1978), has estimated that 25%-50% of accidents are a result of driver inattention. Inattention is a likely culprit in many rear end collisions, since drivers have better sensitivity to movement toward them as opposed to away from them, indicating a reduced role of perceptual abilities (Staplin and Lyles, 1992). Decision-Making Closely related to choice reaction time is decision-making—the ability to judge when a situation requires action and to take appropriate action. As dis- cussed, older adults show slower performance when faced with reaction time tasks involving multiple response alternatives (Singleton, 1954, 1955). Differ- ences in response selection ability may be an important contributor to age- related declines on highly reactive decision-making tasks (Kausler, 1991). Mak- ing decisions while driving involves some degree of selective attention, since drivers must process some perceptual information while ignoring other infor- mation (Staplin, 1990; cited in Staplin and Fisk, 1991). Age-related decrements related to perceptual abilities will thus effect the speed and accuracy of the information intake (e.g., highway geometry, traffic signs, other vehicles), and therefore will also slow decision-making (Staplin and Fisk, 1991). Under uncertain conditions, cognitive effort referred to as control processing, is needed to process incoming perceptual information and compare it to knowl- edge and decision rules stored in memory in order to determine an action to take (Schneider and Shiffrin, 1977). Control processing is slow, effortful, and is ad- versely affected by demanding circumstances (Hancock, Caird, and White, 1990). Relationships to driving performance. If, in fact, older adults suffer signifi- cant declines in perception and cognitive processing (Salthouse, 1990a), it should follow that driving performance will be poorer in situations involving greater decision-making. Research indicates that situations that require decision et ' ABILITIES, AGE, AND DRIVING PERFORMANCE 63 making, namely left-turns, parking and backing, and right-angle turns, are prob- lematic for older drivers (Maleck and Hummer, 1986). In addition, older drivers have been found to have difficulty with high-density intersections (Drury and Clement, 1978), despite the fact that they avoid driving at night, in heavy traffic, and in unfamiliar situations where perceptual performance is more difficult (Shinar and Schieber, 1991; Laux and Brelsford, 1990). Thus, it seems reason- able to expect that declining decision-making ability contributes to the problems of the older driver in demanding situations such as signalized intersections (Staplin and Fisk, 1991). As mentioned, one driving situation that older drivers have particular diffi- culty with is making left-hand turns through intersections. When making a left-hand turn, drivers must pre-plan the path of the turn, perceive relevant traffic signs and/or lights, determine the right-of-way status of other vehicles, visually monitor the status of moving vehicles, make a decision to actuate the turn, and monitor the visual scene while making the turn. This represents one of the most demanding driving tasks and one of the few situations where signifi- cant response planning is required. Staplin and Fisk (1991) conducted two experiments where younger (ages 18-49), and older (ages 65-80), drivers made decisions about the right-of-way status as quickly as possible in several simulated left-hand turn driving situa- tions. In the first experiment, older drivers made slower decisions than younger drivers in each type of situation, but no differences occurred with respect to decision accuracy. The second experiment included a dynamic presentation with cued response, intended to make the task more complex. Under these conditions, older drivers also made relatively more mistakes. The introduction of advanced information before the intersection aided performance of both groups equally. Thus, adding additional information ahead of the intersection did not help to alleviate age differences. In the case of decision-making when merging or turning at intersections, there is a fair amount of uncertainty in the form of visually hidden information, changing right-of-way status, and the intent of other drivers. Sivak, Flannagan, and Olson (1987) examined reaction time in relation to uncertainty (likelihood of having to make a simulated braking response). Although reaction times were slowest during conditions of relatively high uncertainty, no significant differ- ences resulted with respect to age comparisons. Other studies have investigated decision-making away from intersections and found age-related differences. van Wolffelaar, Rothengatter, and Brouwer (1990; cited in Korteling, 1990) found that older drivers needed 50% more time than young drivers to observe and decide whether they could safely merge into traffic on a road. Flint, Smith, and Rossi (1988) found that older persons do well 64 LLANERAS ET AL. when staying 1n one lane, but that they make more inappropriate responses and have greater response delays when having to make quick decisive reactions. Practice may offer some potential for improving cognitive performance while driving. Lucas, Heimstra and Spiegel (1973) demonstrated improvement in making judgments about the last safe moment for passing a lead vehicle in the face of an oncoming vehicle, by providing feedback during a training session. Age differences were not reported, but it seems that training could be an effec- tive measure for reducing age-related decision-making decrements in driving situations. Salthouse (1990b) summarized two general findings in the effects of practice on cognitive functioning. First, it appears that both young and old adults improve their performance with additional experience. Thus, if it is desir- able to improve the absolute level of functioning, then experience may be bene- ficial. Second, there is no evidence that age differences can be eliminated after all individuals have received comparable amounts of practice or training. Thus, given equal amounts of experience, age differences in cognitive performance will persist. In summary, the driving literature reveals several situations that require deci- sion-making ability (e.g., turning at intersections, merging into traffic). Al- though decision-making is not a continuous activity, it is fairly critical, since mistakes can easily result in collisions. Few studies have examined age-related decision-making in the context of driving, but those that have, provide consis- tent evidence that older drivers have difficulty making roadway decisions quickly and accurately when tasks are demanding. Selective Attention Attention can be considered as a type of psychological energy required to perform effortful mental work. Evidence suggests that people possess a limited attentional capacity (Hasher and Zacks, 1979; Kahneman, 1973), and that it becomes more limited with increasing age (Hasher and Zacks, 1979; Craik and Simon, 1980). Selective attention, sometimes referred to as focused attention, 1s the ability to concentrate on a task one is performing, despite boredom or distracting stimuli (Fleishman and Quaintance, 1984). It includes focusing on relevant stimuli while ignoring that which is irrelevant, and sometimes involves switching attentional resources to appropriate stimuli at appropriate moments. Although research has provided evidence for age-related declines in atten- tional capacity (e.g., memory), findings related specifically to selective attention are less clear. Results are mixed, and vary depending upon the type of task studied. Rabbitt (1965), demonstrated an age-related decline in the ability to ignore irrelevant stimuli in a card sorting task. Manivannan, Czaja, Drury, and Ip (1993), Plude and Hoyer (1986), and Farkas and Hoyer (1980) have found ABILITIES, AGE, AND DRIVING PERFORMANCE 65 similar changes using letter and symbol detection tasks. On the other hand, Madden and Nebes (1980) found no age difference in the ability to perform a visual search task under cued (not requiring selective attention) or noncued (requiring selective attention) conditions. Other studies have also addressed auditory selective attention and found age changes for some conditions. Panek and Rush (1981), employed a dichotic- listening task, in which stimuli were presented to each ear simultaneously. Sub- jects repeated the target messages (e.g., numbers and letters) as soon as they heard them, while ignoring irrelevant stimuli. The number of errors increased for older adults. In conclusion, it appears that older adults are adversely affected by irrelevant stimuli when visual search is required or when irrelevant stimuli must be processed along with relevant stimuli. Data stemming from non-search tasks and auditory tasks are inconclusive, but suggest potential age-related de- cline. Relationships to driving performance. Selective attention is likely important for making constant speed and position adjustments and remaining prepared to make reactive movements. It is also important whenever a driver must direct attention to changes in the roadway. In this role, attentional limitations may exacerbate the effect of declining sensory skills (Sekuler, Kline, and Dismukes, 1982). Older drivers have been noted to make mistakes of omission, such as failing to yield and running red lights (Planek and Fowler, 1971), and not mak- ing avoidance responses before an accident (Sussman, Bishop, Madnick, and Walter, 1982). Planek and Fowler (1971) have claimed that many of the acci- dents involving older drivers are directly attributable to reduced ability to ignore irrelevant stimuli in older adults. Selective attention was found to be the cognitive/human characteristic vari- able most related to vehicle accident involvement in Arthur, Barrett, and Alex- ander’s (1991) meta-analysis. It was deemed to be a “‘moderately favorable” predictor; the overall mean correlation was .257, while 42.52 percent of the variance remained unaccounted for. Again, these authors attributed some pre- dictive weakening to the fact that two different measures were used across stud- ies (e.g., the Auditory Selective Attention Test and the Dichotic Listening Test). Arthur and Doverspike (1992) found a significant correlation between auditory selective attention in an investigation of personal characteristics and self- reported accident rates. Kahneman, Ben-Ishai, and Lotan (1973) investigated the relationship between an auditory selective attention test and accident rates of professional bus drivers. The test required the listener to monitor concurrent messages delivered simultaneously to both ears. After the presentations, a tone was presented to one of the ears indicating which of the two messages to report. The ability to reorient attention to relevant stimuli was believed to reflect an 66 LLANERAS ET AL. important attentional component to driving. Data showed a significant rela- tionship between the number of errors on the test and accidents during that year. Mihal and Barrett (1976) included the same auditory selection measure in a study of utility company driver accident rates. They also found a moderate correlation between selective attention and accidents. The relationship between attentional deficit and accident rate was greater for older drivers as compared to younger drivers. Avolio, Kroeck, and Panek (1985) gathered three measures of visual and auditory selective attention (omission errors, intrusion errors, and switching errors), as well as a field dependency measure, and correlated these with 10-year accident experience for commercial drivers from a utility firm. Correlations between all of the attention measures except visual (intrusion errors) were signif- icant. Correlations among the attention measures were also high, suggesting that they may be measuring some common aspects of the same fundamental ability. In summary, the number of driving studies which include measures of selec- tive attention is small. However, the strength of the correlations in studies relat- ing selective attention to accidents is encouraging. The data are especially im- pressive considering that it has been difficult for any ability to be strongly associated with accidents. In the case of selective attention, it is important to examine associations with other abilities. As mentioned, attention in general has intimate ties with other abilities, most notably the useful field of vision and depth perception. Attention Sharing Attention sharing (also commonly referred to as divided attention) is the ability to shift one’s attention back and forth between two or more sources of information (Fleishman and Quaintance, 1984). Attention sharing decrements stem from an inability to process all important information that is present in a situation. One of the clearest results in the aging literature prior to around 1980, was the finding that older subjects are more penalized when they must divide their attention (Craik, 1977). However, these early divided attention studies failed to properly control for irrelevant age differences. Specifically, age-related differences in emphasis people place on each task, and ability to perform individ- ual tasks of varying complexity were not controlled. In dual-task situations, performance on both tasks typically exhibit performance decrements (Salt- house, 1982). It is uncertain, however, as to how each individual is allocating attentional resources between tasks. Some recent studies have controlled the effects of emphasis and complexity by using Performance Operating Curves (POCs) in which performance on one task is plotted against performance on another task across several conditions ABILITIES, AGE, AND DRIVING PERFORMANCE 67 involving relative emphasis of the two tasks (Somberg and Salthouse, 1982; Salthouse, Rogan, and Prill, 1984; Ponds, Brouwer, and van Wolffelaar, 1988; McDowd and Craik, 1988). In each study, emphasis was controlled by manipu- lating the level of reward for performance on each task, while complexity was controlled in all but the first study by including tasks of varying difficulty. Somberg and Salthouse (1982) reported no age differences in dual-task perfor- mance involving two simple keypressing tasks. On the other hand, McDowd and Craik (1988) using auditory monitoring and visual identification tasks, Salthouse et al., (1984) using two memory tasks, and Ponds, et al., (1988) using simulated road display and visual counting tasks, found significant age-related declines. The common conclusion from these controlled” studies is that age- related differences in ability to shift attention between tasks occurs when great emphasis is placed on one of the tasks and when at least one of the tasks is complex. Relationships to driving performance. With respect to driving, it is assumed that maintaining visual attention in the forward field is typically the primary task. Attention will sometimes be shared with other tasks, such as routine steer- ing and shifting, listening to conversations or radios, visually scanning for haz- ards, or avoiding vehicles entering into the driving path. The amount of effort required to perform tasks in tandem depend upon: a) demands placed on percep- tual, physical, or cognitive resources, b) compatibility of tasks (Kantowitz, 1974), and c) experience performing the tasks (Shiffrin and Schneider, 1977). Ponds, Brouwer, and van Wolffelaar (1988) studied divided attention in a simulated driving task, using POCs to control for both task difficulty and empha- sis. Subjects guided the path of an automobile along a simulated roadway in the presence of obstacles. A secondary task required them to determine the number of dots that appeared in a visual display. The results indicated that the detrimen- tal effect caused by dividing attention between tasks was greater for a group of older adults (mean age = 68.6 years) as compared to young and middle-aged adults. No difference occurred between the young and middle-aged adults. Brouwer, Waterink, van Wolffelaar, and Rothengatter (1991), studied di- vided attention using dual visual tasks in a driving simulator. One task was a compensatory lane-tracking task, while the other was a self-paced visual analysis task. POCs were not used to control for emphasis; however, an attempt to adjust for individual differences was made by representing each person’s dual-task score as a function of their performance on an adaptive single-task score. Older adults showed a decreased ability to divide attention, as shown by lane tracking performance and accuracy with respect to the visual analysis task. The impair- ment with respect to visual analysis task accuracy was less when responses were made verbally, as opposed to manually; indicating that requiring persons to 68 LLANERAS ET AL. make two responses using the same body parts may contribute to performance decrements for older persons. McKnight and McKnight (1991) studied the effects of performing several common secondary tasks during a simulated driving task. Subjects responded to traffic situations while placing a cellular phone call, carrying on a simple cellular phone conversation (simply talking), carrying on a complex cellular phone con- versation (solving problems), tuning a radio, or driving with no distractions. All of the distractions led to significant increases in both situational response fail- ures and response time. Complex conversations were most detrimental, while simple conversations were least detrimental. Response failures and response times approximately doubled for a group of older adults (ages 51-80), as com- pared to younger adults when making calls or having simple conversations. In this experiment, drivers were required to perform the secondary task. Thus, it appears that drivers sacrificed driving performance in response. Staplin and Fisk (1991), however, determined that drivers ignored signs if their messages were not of sufficient conspicuity and legibility to be perceived and understood at a glance. Speculatively, if Staplin and Fisk's suggestions are correct, and age-related perceptual deterioration is taken into consideration, it may be pre- dicted that older drivers would increasingly ignore environmental stimuli that they were unable to easily process. This illustrates the importance of examining dual-task performance across levels of emphasis. In summary, the literature provides some fairly conclusive evidence that older adults have difficulty sharing attention between two tasks, especially when at least one task is complex or greatly emphasized. The few studies using simulated driving tasks provide some indication that this pattern may also apply to driving situations. It should be expected that when driving demands are light, the sec- ondary task can easily be attended. It is in circumstances where driving demands become great, that drivers, and especially older drivers, are likely to fail to process all important information. Urban settings often require the use of di- vided attention, and older drivers have been shown to be overrepresented in these accidents (Maleck and Hummer, 1986). Summary of Cognitive Abilities Table 3 provides a synopsis of studies examining the relationship between decision-making, selective attention, and divided attention with driving perfor- mance. In summary, both the aging and older driver literature for decision-making, selective attention, and attention sharing, indicate that capacity limitations exist and are more limited in older adults. Age-related performance differences are even more substantial under demanding circumstances. ABILITIES, AGE, AND DRIVING PERFORMANCE 69 Table 3.—Summary of Cognitive Abilities and Driving Performance Ability/Author(s) Decision-Making Singleton (1954) Staplin & Fisk (1991) van Wolffelaar, Rothengatter, and Brouwer (1990) Flint, Smith, and Rossi (1988) | Sivak, Flannagan, and Olson (1987) Lucas, Heimstra and Spiegel (1973) Selective Attention Planek & Fowler (1971) and Sussman, Bishop, Madnick, and Walter (1982) Kahneman, Ben-Ishai, and Lotan (1973) Mihal & Barrett (1976) Avolio, Kroeck, and Panek (1985) Attention Sharing McKnight & McKnight (1991) Brouwer, Waterink, van Wolffelaar, and Rothengatter (1991) Ponds, Brouwer, and van Wolffelaar (1988) Research Findings Found that older adults made slower decisions at points where directional movements were required in a lever moving task. Examined right-of-way and left-hand turn situations and found that older drivers (age 65 to 80) took significantly longer than younger drivers (age 18 to 49) to make decisions in both situations, but detected no differences in terms of accuracy. When the tasks were increased in difficulty, however, older drivers made relatively more mistakes than younger drivers. Indicated that older drivers needed 50 percent more time than younger drivers to observe and decide whether they could safely merge into traffic. Found that older drivers make more mistakes when the task requires them to make quick decisions as compared to situations where these responses are not necessary (e.g., staying in a single lane). Found no significant age-related differences in the ability to make decisions in the presence of uncertainty as measured by braking reaction times. Indicated that feedback improves decision-making, as demonstrated in improved judgments about the last safe moment for passing a lead vehicle in the presence of oncoming traffic. (Age effects were not investigated) Found older drivers tend to make mistakes of omission, such as failing to yield and running red lights, and failing to make avoidance responses before an accident. Found a significant relationship between auditory selective attention and accident rates of professional bus drivers. Indicated that the relationship between auditory selective attention and accident rates of utility company drivers was significantly greater for older drivers than younger drivers. Found significant relationships between measures of visual and auditory selective attention and accident rates of commercial drivers over a ten year period. In a study investigating the effects of common secondary tasks (talking, tuning the ratio, etc.) on simulated driving performance, these researchers found significant increases in response failures and response times as a consequence of these distractions. Deficiencies were approximately doubled for older drivers (age 51 to 80) than for younger drivers when making calls or having simple conversations. Found older adults showed a decreased ability to divide attention, as measured by lane tracking performance and visual accuracy. Indicated that when guiding a vehicle along a simulated roadway in the presence of obstacles, older drivers (mean age 69 years) had significantly more difficulty dividing their attention between the roadway and a secondary visual display than younger drivers. 70 LLANERAS ET AL. The strength of effects related to driving performance support the call for additional basic research on how driving performance is affected by attention, information processing, and problem solving across the population of drivers (TRB, 1988). However, two problems were apparent in the attentional studies, and these must be overcome in order to be able to generalize the data properly. First, the relative contributions of separate measures must be assessed. For instance, several selective attention measures were claimed to have been related to accidents in Avolio, et al.’s (1985) study. However, without knowing what the associations between these measures were, it is difficult to know their unique predictability. In other words, they may be (and likely are) measuring some of the same process. Second, emphasis and complexity must be controlled in dual- task situations. Without knowing how individuals are placing emphasis on each task, it is difficult to generalize findings to a real situation, such as driving. Conclusions One theme that has been reiterated throughout this review is that driver abilities are a critical component of the vehicle-driver-roadway system. The requirements to perceive, think, and take action must meet the demands of each driving circumstance in order to ensure safety. The reviewed literature has pro- vided a considerable amount of evidence that older drivers are less able than younger drivers to meet those demands. Although it is convenient to classify driver data by an individual’s age, not all individuals will perform consistently with an age group’s norm; in fact, much of the literature reviewed here reported that variability in performance increased with advancing age. Thus, although perceptual, psychomotor, and cognitive functions tend to deteriorate with in- creasing age, the amount, rate, and onset of these degradations vary widely among individuals and functions. As driving-related abilities decline, risks associated with driving become greater. Older drivers in the general population tend to compensate for these degradations by changing their driving habits; they drive fewer miles, and avoid driving at night, at high speeds, and in bad weather. Commercial truck drivers, however, are less apt to have the flexibility to choose the circumstances under which they drive. Thus, it is conceivable that ability deficits may have a larger impact on commercial driving performance than conventional driving. On a positive note, older commercial drivers may possess greater experience levels than does the general driving population. Consequently, they may be more efficient in terms of acquiring environmental information and acting on it, than less experienced drivers; in effect, countering ability degradations. . | | ‘ ABILITIES, AGE, AND DRIVING PERFORMANCE 71 One potential solution for bridging the gap between driver abilities and driv- ing demands is to introduce interventions which counteract the gap. In order to produce effective interventions, it appears logical to address areas where they may lead to the greatest performance improvement. To do this, interventions must therefore address abilities that (1) decline with age, and (2) are important to driving. Ideally, it would be helpful to make definitive conclusions about the relative contribution of each ability reviewed toward performance problems of older drivers. 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(SEE * New Processes * Communication ° Conflict Resolution Decision Meking © Problem Solving TEAM * Boundary Spanning INDIVIDU AL ee PERFORMANCE CHARACTERISTICS CHARACTERISTICS * Task ESAs © General Abilities * Power Distribution ° Motivation * Member Homogensity ° Err Attitudes Team Resources i * Persomality ° Climate - Team TEAM ° Me. * Cohesiv ntel Models eaz INTERVENTIONS INDIVIDU AL CHANGES © Imfivid uel T mining * Team Tmining ° Team Building ° Task ESAs e Attitudes * Motivation ° Mental Models Feedback Fig. 3. Integrated model of team performance and training (adapted from Tannenbaum, Beard, & Salas, 1992) contended that team interdependence influenced team task performance (Nieva et al., 1978), and a meta-analysis of team research found that task difficulty accounted for a significant amount of variance in team performance (Tannen- baum, Dickinson, Salas, & Converse, 1990). Individual characteristics include task knowledge, skills and abilities, in addition to general abilities, motivation, attitudes, personality and mental models. As we have already indicated, it has been shown repeatedly that individual abilities and proficiencies account for some degree of team performance (Tannenbaum et al., 1990). Additionally, personality may factor into team performance. For example, Driskell, Salas, & Hogan (1987) argued that teams composed of certain personality traits will perform best on certain tasks. Both task characteristics and individual characteristics influence work struc- ture, which includes work assignment, team norms, and communication struc- ture. For example, who is allowed or required to speak with whom can influence team performance (Naylor & Dickinson, 1969). In addition, teams build norms regarding the way work is to be performed, and these norms can exert a large influence on team processes and subsequent performance (Hackman, 1987). Task and individual characteristics also affect team characteristics, which in- clude power distribution, member homogeneity, team resources, team climate, TEAM PERFORMANCE AND TRAINING RESEARCH 93 and cohesiveness. The makeup of a team, such as a team’s homogeneity, has been shown to be related to team performance (Gunderson & Ryman, 1967). In addition, team cohesiveness reflects a team’s sense of belongingness and sense of teamness and has been recognized as a critical team characteristic (Tannen- baum et al., 1990). Task characteristics, individual characteristics, work struc- ture, and team characteristics make up the input stage of the model. Although not explicitly stated by the Tannenbaum et al. (1992) model, we contend that a number of other influences exist between task characteristics, individual characteristics, team characteristics and work structure. First, we argue that task characteristics can also influence individual characteristics. Fur- ther, we argue that team characteristics can influence work structure, and that individual characteristics can affect task characteristics. In addition, we also - argue that team characteristics and individual characteristics mutually influ- ence each other. In sum, we contend that the four input components (team characteristics, individual characteristics, task characteristics, and work struc- ture) all interact, and the result of those interactions, in turn, influences the throughput and output stages. Future work should focus on these consider- ations. The result of the interaction between task, individual, and team characteris- tics, and work structure produces a combined effect on the throughput stage of the model affecting team processes. These are the intragroup and intergroup actions that transform resources into a product (Gladstein, 1984). Team pro- cesses include coordination, communication, conflict resolution, decision mak- ing, problem solving, and boundary spanning. Considerable research has identi- fied a number of processes that factor into team performance (see Tannenbaum et al., 1992). In addition, team interventions such as individual training, team training, and team building also affect team processes. Training interventions can focus on the development of individual skills or on the development of team skills (Sundstrom, Perkins, George, Futrell, & Hoffman, 1990). Team building can include goal-setting, interpersonal training, and role negotiation. The results of team processes feed into the output stage. The output stage includes team changes (e. g., new norms, new rules, new communication pat- terns, new processes), team performance (e. g., quality, quantity, time, errors, costs), and individual changes (e. g., task KSAs, attitudes, motivation, and men- tal models). While team performance is the primary output, Tannenbaum et al. (1992) also consider changes in the team and changes in the individual team members as outputs. For example, team changes may include greater or lessor cohesiveness. Individual changes may include improved or decreased skills, attitudes, or motivation. Finally, the team’s performance can serve as feedback 94 SALAS, CANNON-BOWERS, AND BLICKENSDERFER and subsequently affects the input block of task, individual, team characteris- tics, and work structure—the cycle begins once again. Measurement One challenge of team research has been to develop powerful, reliable, and valid measurement techniques for measuring team performance. This has not been an easy task. The distinction between individual and team tasks creates one measurement challenge unique to team training. This is exacerbated by the division between behavioral processes and performance outcomes. Another dif- ficulty is that team performance evaluation thus far has been largely subjective with the usual assessment technique being observation based appraisals of team processes (Baker & Salas, 1992). Thus, the search for objective performance measurement has been of particular importance. A full discussion of measurement issues is beyond our scope here. Therefore, we will focus briefly on the major issues relevant to team performance measure- ment. The goal of team performance measurement must be to assess the process that a team employs in task performance, as well as to assess the outcome of that process. This is an important, but often overlooked distinction. While it is important to measure the outcomes of team effort, it 1s equally essential to understand the specific behavior that led to those outcomes. That is, measure- ment must provide information that indicates why processes occurred as they did and how those processes are linked to particular outcomes. Also, team performance measures must consider performance at the individ- ual level as well as at the subteam (i.e., teams within a team) and overall team levels. It is important to make this distinction to emphasize the nature of the individual skill versus team skill contribution to overall performance. For feed- back purposes, it is essential to understand whether particular aspects of perfor- mance can be attributed to individual or team behavior. In addition to these requirements, a comprehensive system to measure perfor- mance should serve several purposes if it is to be useful in training and managing team performance (Cannon-Bowers, Salas, & Grossman, 1991). First of all, team performance measures must be able to describe team performance accu- rately; that is, measures must be sensitive enough to document the moment-to- moment interactions and changes in performance. While this may seem rather obvious, in many team situations the ability of the measurement system to describe performance is complicated due to the dynamic, interactive tasks and environments characteristic of team situations. Team performance measures must also provide an evaluation of team perfor- mance. Specifically, measures must distinguish between effective and ineffec- tive processes, strategies, and teamwork behaviors. The implication of distin- , ’ P | TEAM PERFORMANCE AND TRAINING RESEARCH 95 guishing between effective and ineffective is that a standard or index of perfor- mance can be developed as a means to gauge the performance of a given team. Also, team performance measures must capture the quality of teamwork skills displayed by the team. Finally to be most useful, team performance measures must be diagnostic. As mentioned earlier, the measurement tools must provide information that indi- cates why processes occurred as they did and how particular processes are linked to certain outcomes. If these objectives can be achieved, it will be possible for researchers to understand fully the nature of team performance, and practi- tioners will be able to provide teams with feedback necessary to improve future performance. Given these multiple purposes, it is likely that a team performance measurement system will consist of several components. These measurement components or “tools” can be developed and employed individually or in com- bination in order to address specific research questions or practical problems (e.g., providing feedback, assessing training effectiveness, and etc.). In sum, it can be seen that an ideal team performance measurement system must have the following characteristics: 1) provide measurement at both team and individual levels; 2) assess the quality of team processes as well as outcomes; 3) focus attention on teamwork skills (as an adjunct to individual competen- cies); 4) provide data that can be used to describe, evaluate, and diagnose team performance (Cannon-Bowers, Salas, & Grossman, 1991). Given such stringent requirements, it is clear that a team performance measurement system will actually consist of several measurement tools, each of which assesses a subset of the dimensions of team performance noted above. One such effort addressed the subset dimensions of the team process issues. Glickman, McIntyre, and their colleagues have worked at defining teamwork components and developing teamwork measures (Dickinson et al., 1992; McIn- tyre & Salas, in press; Morgan et al., 1986). The latest effort from this group resulted in a conceptual framework for developing team process measures of decision making performance (Dickinson et al., 1992). Briefly, Dickinson and his colleagues first generated components of teamwork: team orientation, team leadership, communication, monitoring, feedback, backup behavior, and coor- dination. Based on these components, three techniques for teamwork measures were presented: behavioral observation scale, behavioral summary scale, and behavioral event format. The behavioral observation scale is used to rate the frequency of teamwork behaviors exhibited by a particular team and its members. The format consists of seven scales. Each scale represents a particular component of teamwork and incorporates 6-12 items which reflect that component. Each item is rated on a 5-point scale according to its frequency of occurrence (Almost Always to Almost 96 SALAS, CANNON-BOWERS, AND BLICKENSDERFER Never). Teamwork behaviors between any two or more members are included. Thus, the sheer number of teamwork behaviors displayed is rated. The behav- ioral summary scale, on the other hand, can also be used to rate the degree of teamwork displayed, but these scales do not contain multiple items. Instead, the observer rates each component of teamwork only once. The observer rates the team’s skill level on each respective component according to a 5 point scale ranging from “Hardly Any Skill” to ““Complete Skill’. The third measurement technique from Dickinson et al. (1992), the behav- ioral event format, is used to observe and code team performance in structured environments. Critical events must be first identified by subject matter experts. Because of this, the behavioral event format is scenario specific such that for each critical event in the scenario there are expected behaviors. The observer then checks whether the expected and appropriate teamwork behaviors have occurred. The observer also lists any additional behaviors that appear and indi- cate the teamwork component of those behaviors. While at present we cannot do more, we recommend that some future effort be directed at developing measurement methods that could be used for both “‘real’’, on-going perfor- mance as well as for training purposes. Overall, developing team performance measures has been a difficult area, but progress has been made. Numerous issues remain to be addressed, including: the most appropriate level of analysis (i. e., the individual or team level), the quality of outcomes and processes associated with team performance, and the search for tools that describe, evaluate, and diagnose team performance. Team Training Techniques Perhaps most important to the on-going quest of understanding team perfor- mance is the goal of designing effective team training strategies. Cannon- Bowers, Salas, & Grossman (1991) argued that tactical decision making teams in complex environments are faced with scenarios characterized by rapidly un- folding events, multiple plausible hypotheses, high information ambiguity, se- vere time pressure, sustained operations, and severe consequences for errors. In addition to military teams, surgical, fire fighting, and even astronaut teams face scenarios with similar characteristics. To perform effectively in the high stress environment, team members must learn to coordinate their action so that they can gather, process, integrate, and communicate information in a timely and effective manner (Hall, Dwyer, Cannon-Bowers, Salas, & Volpe, 1993). There- fore, team training interventions must maximize the use of instructional designs that will allow teams to maintain performance under stressful conditions (Hall et al., 1993). ‘TEAM PERFORMANCE AND TRAINING RESEARCH 97 With both individual skills and team skills being part of team performance, a need for both individual and team training exists (Salas et al., 1992). However, a number of authors have noted that team training usually emphasizes instruction of individual skills within a team setting, regardless of the nature of the team task (Briggs & Johnston, 1967; Converse, Dickinson, Tannenbaum, & Salas, 1988; Meister, 1976; Salas et al., 1992). One reason behind the lack of teamwork skill training may be that researchers have not yet given team training practitioners proven tools with which teamwork skills can be trained. However, recent research has taken a step in this direction with an increased focus on specific team training interventions. Smith and Salas (1991), for exam- ple, developed a training technique with which to train one behavioral team- work skill, assertiveness. Smith and Salas (1991) found that subjects who re- ceived assertiveness training including lecture with modeling and role play practice exhibited significantly more assertive behavior in the team task than did subjects who received training including only lecture or lecture with modeling. Most importantly, subjects who received assertiveness training that included role play were the only subjects who differed significantly from the no-training control group. Thus, role play appears necessary in training the team skill asser- tiveness. The Smith and Salas training method appears ready for use by practi- tioners. Employing cross-training using positional rotation as a team training tech- nique has also received a growing amount of attention. Positional rotation can be conceptualized as a type of job rotation among team members. This method of cross-training provides team members with an understanding of the basic knowledge necessary to successfully perform the tasks, duties, and/or positions of the other team members. Additionally, cross-training gives team members an overall framework of the team task and how each particular individual’s task is important to the team task (Travillian, Volpe, Cannon-Bowers, & Salas, 1993). Cross-trained teams have achieved team process ratings and team outcome scores higher than those teams without such training (Travillian et al., 1993). In sum, cross-training appears to be a viable instructional strategy for teams. Other recent research has focused on summarizing and extracting team train- ing guidelines from the empirical research and ensuring that those guidelines are accessible for use by both researchers and practitioners. For instance, Swezey and Salas (1992) presented a comprehensive listing of team training guidelines, while Burgess, Salas, and Cannon-Bowers (1993) presented a listing of tech- niques for effective team leadership. The growing accessibility of such guidelines should help to increase the training of teamwork skills. Research emphasis on specific team training techniques, in addition to results summarized into straight-forward guidelines, should help practitioners begin to 98 SALAS, CANNON-BOWERS, AND BLICKENSDERFER integrate teamwork skill training into their own training programs. Cross-train- ing and modeling/role-playing are two recently emphasized team training tech- niques that should help practitioners to begin integrating teamwork skill train- ing into team training programs. Other Team Training Issues A recurrent issue in team training design concerns the sequence of team training. That is, trainers must consider whether mastery of individual skills should occur first followed by mastery of team skills or vice-versa. Research generally advocates that team training is most effective and efficient when indi- vidual skill mastery is completed before team training (Biggs & Johnston, 1967; Daniels, Alder, Kanarick, Gray & Feuge, 1972; Denson, 1981; Johnston, 1966; Klaus & Glaser, 1970). The rationale for training individual skills first is that if individual skills are not developed fully, the team may not be able to perform the task successfully no matter how effective their team skills may be. In addition, if the taskwork skills are not developed prior to the introduction of the teamwork skills, training for teamwork skills may interfere with team members’ learning of taskwork skills. 3 An issue in team training that has not yet been resolved is performance feed- back for team tasks. Although team research has acknowledged the importance of feedback (Dyer, 1984), many questions regarding feedback in team training exist. Feedback in a team environment should 1) enable each team member to perform his/her individual task, 2) demonstrate the contribution of an individ- ual’s performance to the performance of other members and, 3) demonstrate the contribution of an individual’s performance to the performance of the team as a whole. Because of the multiple levels that team feedback can address, giving team feedback is not entirely straightforward. For instance, feedback enhances performance on the aspect of performance about which feedback 1s provided. In team tasks, this translates to feedback focusing on the team skills or on the task skills, and the trainer must decide where to focus the feedback (Alexander & Cooperband, 1965; Chapman, Kennedy, Newell, & Biel, 1959). In addition, problems arise when giving overall team outcome feedback. For example, if high and low performing individuals receive the same positive team feedback, the low performers will likely not realize that their performance needs improve- ment (Nadler, 1979). An unintended consequence of giving team level feedback without respect to the relationship of individual performance to the team perfor- mance is that incorrect behaviors may be reinforced. This, in turn, may result in no improvement and may well wash out the impact of team feedback on the performance of both the individual and team overall. One issue inherent in team feedback is sequence—when, what type, and how ‘TEAM PERFORMANCE AND TRAINING RESEARCH 99 much feedback should be given (Salas et al., 1992). Briggs and Johnston (1967) found that providing feedback on one aspect of the task during early training sessions and increasing this feedback to refer to several aspects as training pro- gresses helped teams focus on different aspects of the task. Klaus and Glaser (1970) also emphasized timing of feedback. They suggested that individual per- formance feedback should be given during early training and that overall team performance feedback should be given during later phases of training. While it is easy to understand that feedback will likely need to change focus at different points in team training, the most effective uses of feedback in team training tasks is still not well understood and numerous issues have yet to be addressed. Clearly, effective team training is an important goal of team performance research. Researchers agree that individual skills should be trained before team skills. However, questions still exist on the most effective team training tech- niques, and how to use feedback as a team training tool. Principles of Team Performance and Training We define “principle” as an underlying truth about a human phenomenon. In team performance and training research, it is important not only to elicit principles but also to communicate these principles to both the research commu- nity and practitioners. After the review of team training and performance, a number of principles can be derived. Principles of Team Performance Principle 1: Teamwork skills are distinct from taskwork (individual) skills (Mor- gan et al., 1986). As McIntyre and Salas (in press) summarized, two “tracks” of skill exist in team tasks: the “‘taskwork”’ track involves those skills necessary for individuals to perform their own task or function. On the other hand, “teamwork” skills are those necessary to be an effective team member. Principle 2: Teamwork consists of a series of related behaviors (McIntyre & Salas, in press). After separating taskwork from teamwork, teamwork then divides into re- lated behaviors. McIntyre and Salas (in press) argued that these include, for example, monitor own performance, perform self-correction of errors, provide task and motivational reinforcement, adapt to unpredictable situations, predict each others behavior, and use closed-loop communication (see also Oser et al., 1989). Effective teams exhibit these behaviors more frequently than do less effective teams. 100 SALAS, CANNON-BOWERS, AND BLICKENSDERFER Principle 3: Teams evolve (mature) over time (Glickman et al., 1987). As team members learn about each other and the task, team members pro- gress from working as individuals into working as a team (Glickman et al., 1987). Effective feedback parallels this evolution. That is, at certain times in team training, individually focused feedback may be the most useful, while at other points in the training process general team feedback may be the most beneficial. Principle 4: “Mature” teams have members who anticipate each others’ needs (Glickman et al., 1987). When team members become familiar with each other’s knowledge, skills, abilities, attitudes, motivation, preferences, style, etc., they are able to better anticipate the task, informational, and interpersonal needs of teammates. Ac- cording to Cannon-Bowers et al. (1993), this is the basis of shared team mental models. 3 Principle 5: Effective teams have a strong sense of “teamness”’ (Glickman et al., 1987). Experienced teams realize when their team performs an operation which utilizes teamwork behaviors, and experienced teams can identify operations which will require teamwork behaviors. Principle 6: “Mature” teams do not need to rely on overt communicate as much to perform effectively (Orasanu, 1990; Rouse et al., 1993). Teams with shared mental models don’t need to communicate as much under high workload as do teams with less developed shared knowledge. Communica- tion will decrease when teams have a higher amount of shared information; they have shared expectations and intentions and can anticipate each other’s behav- ior. This has been referred to as “‘implicit”’ coordination (see Kleinman & Ser- faty, 1989). | Principle 7: Effective teams can adjust their strategy under stress (Kleinman & Serfaty, 1989). According to Kleinman & Serfaty (1989), effective teams employ “implicit” coordination strategies under high workload conditions. This is an example of how teams might change their behavior in response to task demands. Other such strategies include: load balancing, performance monitoring, feedback (see Can- non-Bowers et al., in press). Principle 8: Some teamwork skills are generic (Cannon-Bowers et al., in press). Although tasks may require certain task specific skills, some basic teamwork skills appear to be a part of effective teamwork across situations. Therefore, it may be possible to improve team performance by training team members in “generic” teamwork skills. However, more specific (task-related) team training is probably necessary in many cases (Cannon-Bowers et al., in press). TEAM PERFORMANCE AND TRAINING RESEARCH 101 Principle 9: Effective teams optimize resources (Cannon-Bowers et al., in press). Teams working effectively learn to self-correct. At the same time, team members can also compensate for each other. That is, if one member does not perform well, the team will learn to work around the problem. The emphasis for effective teams is on teamwork process—working well as a team. Principle 10: 4 number of external and internal factors influence team perfor- mance (Salas et al., 1992). Teams do not operate in isolation. The organizational and situational con- text, task characteristics, work structure, individual characteristics, team char- acteristics, and team processes all influence team effectiveness (Tannenbaum et al., 1992). It is the interaction among the external and internal factors that make each team unique. These principles are a necessary step in understanding team training. How- ever, once principles are delineated, we must operationalize those principles into specific training techniques. Principles of Team Training Principle 1: Individual proficiency must precede team proficiency (Stout, Salas, & Carson, 1994). If team members do not learn the task skills, team performance will remain low. Training time must be allowed for members to master their individual task skills. Principle 2: Team training must diagnose and remediate team performance (Cannon-Bowers et al., 1991). In order to effect a behavioral change, a necessary first step is to identify the deficiencies in team members’ skills, knowledge, and attitudes. To accomplish this, measurement and diagnostic mechanisms must exist that allow team in- structors to identify and attribute team performance problems. Once having done this, training interventions can be used to ameliorate the particular perfor- mance problems. Principle 3: Team training must allow for: information exchange, demonstration of teamwork behaviors, practice, and feedback (Prince et al., 1992). As with all types of training, team training must follow the basic requirements of sound training design (Salas, Burgess, & Cannon-Bowers, in press). For team training, these phases of team training should be based on a careful analysis of the teamwork demands of the task. Principle 4: Team training must emphasize the nature of interdependency (Swe- zey & Salas, 1992). Without interaction, team skills will likely remain undeveloped. Scenarios structured around mutual dependency promote the use of team skills. More- 102 SALAS, CANNON-BOWERS, AND BLICKENSDERFER over, feedback regarding teamwork skills is impossible if the task does not de- mand sufficient interdependency among team members. Principle 5: Team training must emphasize teamwork skills. This relates directly to the previously mentioned teamwork/taskwork divi- sion (Morgan et al., 1986). Team training that emphasizes only individual skills will not ultimately improve performance. Team skills are part of team perfor- mance and must be addressed in training. Principle 6: Team training must create systematic opportunities to practice (sce- nario development) (Prince et al., 1992). Prince, Oser, Salas, and Woodruff (1993) summarized that the findings of multiple researchers which suggested that team training requires the develop- ment of structured scenarios which provide the opportunity to perform and receive feedback about-critical team actions. Further, Hall et al. (1993) pre- sented a strategy for developing team scenarios with varying degrees of stress. Future Research While team research has made progress in the area of team performance and team training over the past few decades, much remains to be done. In particular, the following research questions need to be addressed: What is the nature and content of shared mental models or knowledge struc- tures? As mentioned earlier, in efforts to identify the subtleties behind team pro- cesses, recent work has turned to the mental model construct. However, at this time, no concrete evidence for shared mental models has appeared. Researchers must now focus on providing evidence of shared mental models as well as techniques designed to measure the degree of similarity of the knowledge struc- tures in team members’ minds. Can reliable, systematic, usable team performance and diagnostic measures be designed and developed? Until team researchers have satisfactory measurement tools, team perfor- mance and training research will never reach its full potential. Once developed, such a “‘tool-box’’ could be used by researchers and practitioners alike and would add considerable authority to team research and training. What is the impact of selected individual and organizational factors on team performance? As presented earlier, team performance models acknowledge the significant impact that individual and organizational factors have on team performance. However, the models do not explain specifically which influences will affect TEAM PERFORMANCE AND TRAINING RESEARCH 103 which aspects of team performance. Further, the models do not explain benefi- cial versus detrimental influences on team performance. Summary and Conclusion Since the 1960s, the area of team performance and training research has evolved dramatically. Early studies focused on the importance of individual abilities and skills to the overall team task. These studies were followed by efforts focused on identifying a difference between task skills and team skills (e.g., the taskwork versus teamwork and evolution of teams concepts described previ- ously) and the focus on trainable skills (e.g., the team skill dimensions). Only recently have theorists emphasized the realm of both individual and organiza- tional factors that influence team performance. Current research reflects this emphasis, and there are an increasing number of efforts aimed at explicating the relationship between team performance and a variety of concepts (e.g., organiza- tional climate, leadership, and efficacy issues). Another potentially fruitful research area which has only recently come into focus concerns cognitive aspects of team performance such as shared knowledge structures among team members and complex team skills such as team decision making. In addition to the need for further theoretical underpinnings this area also demands investigation of more refined measurement techniques. If team research is to benefit from the mental model construct, valid and reliable mea- sures for tapping these structures are necessary. At present, the literature is beginning to provide useful guidelines for manag- ing and training teams. Specifically, a solid base of empirically based research is available. In addition, team researchers have begun to present their findings in the form of explicit recommendations and guidelines of team training tech- niques useful to practitioners. This strongly suggests that the field has matured significantly over the last 20 years. Finally, the team performance and training literature appears to be coming into focus on the ultimate mission: improving team performance. That is, re- searchers and practitioners alike have begun integrating laboratory results with real world environments. 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Graphical, tabular, and verbal forms of a thesaurus data base were constructed, along with questions that required users to modify the data base. Three question formats, each compatible with one of the forms of the data bases, were designed — graphical, tabular, and verbal. The data indicate that users are faster and more accurate in modifying the data base when the format of the information in the data base matches the format of the information in the modification instructions. While the importance of matching data base format to likely modifications may seem obvious, it would appear that the designers of most current data base systems have not taken this into account. Since the advent of the information age, increasing amounts of information have come to be stored in data bases. However, research into the best ways of storing and presenting the information in the data base has not followed at the same pace. Although research has been conducted on this issue, there is no consensus on the effects of specific information presentation formats. In the domains of problem solving and decision-making, most of the research has focused on comparing the speed and accuracy of performance using tabular versus graphic formats (e.g. see Lalomia & Coovert, 1987; and Powers, Lashley, Sanchez, & Shneiderman, 1984). Investigators, however, have failed in their attempts to clearly demonstrate the general superiority of one format over an- other. For example, after reviewing 29 studies, DeSanctis (1984) reported that 12 studies found tables superior to graphs, 7 found graphs superior to tables, and 10 found no difference between the two formats. She concluded that the best ' Requests for reprints should be sent to Deborah A. Boehm-Davis, George Mason University, Psychology Department, Fairfax, VA 22030-4444. 107 108 BOEHM-DAVIS, HOLT, AND PETERS method of displaying information may be a function of the task to be per- formed. Unfortunately, knowing that the task is an important constraint on the form of information display does not provide, a priori, a method for deciding what form information presentation should take in a particular instance. That is, none of the studies discussed so far addresses the more theoretical question of why one might expect a particular form of information presentation to be better than another in a given situation. Recently, Vessey and Galletta (1991) have proposed the notion of “‘cognitive fit’, which they describe as “resulting from matching the characteristics of the problem representation to those of the task’’. In this paper, Vessey and Galletta propose a problem solving model that takes as input the problem representation, the problem solving task, and the problem solving skill of the user. Their argument is that to the extent that the representa- tion of the problem (in the interface) matches the characteristics of the problem solving task, performance will be improved. Although their recent research (Sinha & Vessey, 1991; Vessey, 1991; and Vessey & Galletta, 1991) has only partially supported the model, it provides a comprehensive view in which pre- vious research can be accomodated. It also suggests the importance of matching the format of input information to the format in which the information must be interpreted. This general notion has been supported by a number of previous studies in the literature (Bennett, 1987; Boehm-Davis, Holt, Koll, Yastrop, & Peters, 1989; Durding, Becker, & Gould, 1977; Peters, Yastrop, & Boehm-Da- vis, 1987; Wickens & Scott, 1983; and Wright & Fox, 1972). The studies by Durding, Becker, and Gould (1977) and by Bennett (1987) provide specific evidence for the importance of stimulus-response compatibil- ity. Durding, Becker, and Gould examined how people organize information. They presented people with sets of data organized into schemes that were either consistent or inconsistent with their natural structure. People were generally faster and more accurate at remembering and organizing information when they were presented with a structure in which to put their responses that matched the original organization of the words than when they were presented with a less compatible structure. These researchers argue that the conceptual structure of a data base should conform to the semantic relationships among the data elements. This argument is supported by Bennett (1987) who examined transfer-of- training performance of users working with a perceptual data base system. His results showed that training with a display that is consistent with the target display facilitated performance and that training with an inconsistent display inhibited performance. The research by Wickens and Scott (1983), Wright and Fox (1972), Peters, DATA BASE FORMATS AND INFORMATIONAL RETRIVAL 109 Yastrop and Boehm-Davis (1987) and Boehm-Davis, Holt, Koll, Yastrop and Peters (1989) speak more directly to the importance of matching the format of information presentation to the characteristics of the task to be performed. Wickens and Scott examined the performance of students on a complex deci- sion task requiring the integration of separate pieces of information. Their re- sults showed that performance was best when the displays showed the data in an integral form which could be used directly as the basis for judgment. Wright and Fox, using different forms of information display in tables, also found that performance was best when the table contained integral information that could be used directly in the decision-making task. Boehm-Davis, Holt, Koll, Yastrop, and Peters (1989), using both database format (spatial, tabular, or verbal) and question type (spatial, tabular, or verbal) as independent variables, found that users were faster and more accurate at information retrieval when the format of the information in the database matched the type of information needed to answer the question. In addition, an item analysis of the retrieval tasks revealed an interaction between type of re- trieval task and data base format. For example, searching for the number of occurrences of a specific item was facilitated by using a tabular database, while searching for the shortest route between two cities was facilitated by using a graphic database. This finding was replicated using a modified version of the original data base (Peters, Yastrop, & Boehm-Davis, 1987). The present study was designed to extend the previous work in this area. Our last study (Boehm-Davis, Holt, Koll, Yastrop, & Peters, 1989) focused on searching through a data base to locate information. However, most data bases are dynamic, not static and users enter the new information into the data base. We therefore felt it important to examine the effect of display format on the ability of users to modify information in the data base. This study addresses that issue by asking users to modify information in a data base of a given format when the information to be modified is presented in a format that is either consistent or inconsistent with the format of the data base itself. Method Design The experimental design used in this experiment was a 3 X (3 X 2 X 3 X 2) design. The between-subjects factor was the format of the data base the subject used (graphic, tabular, or verbal). The within-subjects factors were format of the question (graphic, tabular, or verbal), item modified (word or relationship), type 110 BOEHM-DAVIS, HOLT, AND PETERS of modification (add, modify, or delete), and time (whether it was the first or second time they made a particular type of modification). Participants The participants in this study were 36 students at George Mason University. The students received either payment or course credit for their participation in the study. Unfortunately, after data collection was complete, the data from one subject were lost; the final analyses in this paper are based on the remaining 35 subjects. Materials Data Bases. A thesaurus data base developed by Boehm-Davis, Holt, Koll, Yastrop, & Peters (1989) was used in this study. The thesaurus data base con- tained relational information for 21 words, including broader terms, narrower terms, and related terms to the target word. Three versions of the data base were used — a graphic version, a tabular version, and a verbal version. Portions of each of the three versions of the data base are illustrated in Figure 1. Questions. A set of questions was written for use in this study. These questions required modification of some type of information contained in the data base. These questions required the subjects to make a change (add, modify or delete) to an item (word or relationship) in the data base. This resulted in six types of questions: adding a word, modifying a word, deleting a word, adding a relation- ship, modifying a relationship, and deleting a relationship. Nine examples of each type of question were generated to create a set of fifty-four questions. Examples of the six types of questions are shown in Table 1. 7 The questions consisted of two parts: (1) computer-based questions that al- lowed the change information to be entered into the data base and (2) paper- based descriptions of the changes to be made. The computer-based questions had to be modified slightly for use with each of the three data base formats; however, care was taken to keep the questions as similar as possible. For exam- ple, a sample question modifying a relationship, tailored for each type of data base, 1s shown in Table 2. The accompanying paper documentation for the changes also had to be modified to represent each of the three question formats (1.e., graphic, tabular, and verbal). The three question formats are shown in Figure 2 for modifying a relationship. Procedure Experimental sessions were conducted on an IBM PC. Initially, the partici- pants were introduced to the data base with which they would be working. Each participant was given a paper copy of one format of the data base (graphic, DATA BASE FORMATS AND INFORMATIONAL RETRIVAL 111 (a) Spatial Form of the Data Base. Heavy line connects Broader Term (above) to Narrow Term (below); Thin line connects Related Terms. Key: communication sychology cybernetic homeostasis advertising (b) Tabular Form of the Data Base. Key: _B: Term is above Broader Term for term on left; N: Term is above Narrower Term for term on left; R: Term above is Related Term to term on left. communication (c) Verbal Version of the Data Base. Key: BT: Broader Term (s) for the key term; NT: Narrower Term (s) for the key term; RT: Related Term (s) to the key term. communication NT: advertising, cybernetics, language, non_verbal_com RT: feedback, language arts, linguistics, media, speech. cybernetics BT: communication NT: feedback RT: homeostasis feedback BT: cybernetics, homeostasis, psychology RT: communication Fig. 1. Portions of the graphical, tabular, and verbal versions of the thesaurus data base. tabular, or verbal) to examine. Then, the subjects were presented with three blocks of trials (one for each format of question, presented in random order). Within each block, the first 6 questions were practice trials; they represented one 112 BOEHM-DAVIS, HOLT, AND PETERS Table 1.—Sample questions in the verbal format Add Word Modify Word Delete Word Add Relationship Modify Relationship Delete Relationship Add to the data base the word that is circled on your change form. To add the word, you will only need to indicate where it should be placed into the data base. Enter the number of the word that the new word follows. In the data base, replace the word that is crossed out with the word shown next to it. To replace the word, you will only have to give the location of the old word. Enter the number of the word to be replaced: Delete the circled word from the data base. Enter the number of the word to be deleted: Add to the data base the relationship shown by the circles on your entry form. To add this relationship, you will need to give the numbers of the two words and the type of relationship. Enter the number corresponding to the key term: Enter the row number for the kind of relationship you are adding: Enter the position number showing which word you will insert the new word after (from the list of words to the right of the key term): In the database, change the relationship between the two words indicated to the relationship shown within the circle. To change this relationship, you only need to give the numbers of the two words and the type of relationship. Enter the number corresponding to the key term: Enter the number corresponding to the word it is related to: Enter the choice number that reflects the modified relationship between the two words: (1) BT (2) NT (3)RT: Delete from the data base the relationship that is crossed out on your entry form. To delete this relationship, you will only need to give the numbers of the two words that are related. Enter the number corresponding to the key term: Enter the location of the word it 1s related to: modification of each type (1.e., adding, modifying, or deleting either a word ora relationship). Following the six practice questions, twelve experimental ques- tions were presented, two of each type of modification. An interactive data collection system recorded the participants’ responses throughout the session, and the time required for each response. After all of the questions had been answered, the participants completed a questionnaire. Results Performance Measures The two major performance measures in this study were the time or latency of the response and the accuracy of the response. Response latencies were mea- sured in hundredths of a second. Where multiple questions had to be answered in order to implement a modification (i.e., when modifying relationships), the average time required to answer the questions was calculated for use in the analyses. Both of these dependent variables were analyzed bya3 X (3 X2X3X DATA BASE FORMATS AND INFORMATIONAL RETRIVAL 113 Table 2.—Verbal, graphic, and tabular formats of the modify relationship question Modify Relationship (Graphic Format) In the database, change the relationship between the two words indicated to the relationship shown within the circle. To change this relationship, you only need to give the numbers of the two words and the type of relationship. — Enter the location of the first word: — Enter the location of the word it is related to: — Enter the choice number that reflects the modified relationship between the two words: (1) = = = = (2) --------- : Modify Relationship (Tabular Format) In the database, change the relationship between the two words indicated to the relationship shown within the circle. To change this relationship, you only need to give the numbers of the two words and the type of relationship. — Enter the row number of the key term: —- Enter the column number of the word it is related to: —- Enter the choice number that reflects the modified relationship between the two words: (1) B (2)N (3)R: Modify Relationship (Verbal Format) In the database, change the relationship between the two words indicated to the relationship shown within the circle. To change this relationship, you only need to give the numbers of the two words and the type of relationship. — Enter the number corresponding to the key term: — Enter the number corresponding to the word it is related to: — Enter the choice number that reflects the modified relationship between the two words: (1) BT (2) NT (3)RT: 2) repeated measures analysis of variance. The between-subjects factor was the format of the data base (graphic, tabular, and verbal). In addition to format of question (graphic, tabular, or verbal), the type of item modified (word or rela- tionship), type of modification (add, modify, or delete) and time (first or second presentation of the type of modification) formed additional within-subjects fac- tors. Response Latency. There was a main effect for data base format (F (2, 33) = 6.17, p< 0.01). The average latency of response for the graphic data base (13.56 sec) was shortest, with the tabular data base (14.62 sec) taking somewhat longer, and the verbal data base taking the longest (19.01 sec), as can be seen in Figure 3. This suggests that the graphic data base was “‘easiest”’ to use overall. The main effects for question format and time were not significant at the 0.01 level (F'(2, 66) = 2.22, F (1,33) = 0.99). Main effects were significant for the type of modification (add, modify, or delete) being made (F (2,66) = 27.52, p < 0.01) and for the item (word or relationship) being modified (F (1,33) = 35.67, p < 0.01); see Figures 4 and 5. Making an addition to the data base required the most time (19.32 sec), followed by making a modification (15.09 sec) and making a deletion (12.78 sec). Modifying a word (average time per response = 18.00 sec) took longer than modifying a relationship (average time per response = 13.46 sec). This result may have been an artifact of our data reduction methodology. The relationship questions required answering two or three questions while the 114 BOEHM-DAVIS, HOLT, AND PETERS communication non_verbal_com ——DJ advertising (a) | Graphic Form of the Modify Relationship Question (relationship shown on this paper is the new relationship between the words). _advertising non_verbal_com psychology (b) Tabular Form of the Modify Relationship Question (relationship shown on this paper is the new relationship between the words). nanes non_verbal_con Ba NT: RT: (“advertising > psychology (c) Verbal Form of the Modify Relatonship Question (relationship shown on this paper is the new relationship between the words). Fig. 2. Sample paper-based questions for modifying a relationship using graphical, tabular, and verbal format of questions. word questions required answering only one question. It may be that answering the first question takes more time than answering the subsequent questions; this would lead to a shorter time overall for the modifications that required answer- ing several questions (i.e., the relationship questions). DATA BASE FORMATS AND INFORMATIONAL RETRIVAL 115 TIME TO COMPLETE MODIFICATION TASK 20 O 3] Ly 18 ea] Ss ke a 16 z e) I ea = fx] 14 4 12 GRAPHIC TABULAR VERBAL TYPE OF DATABASE Fig. 3. Main effect of data base format (in seconds). The critical interaction between data base format and type of question, which can be seen in Figure 6, was also significant (F (4,66) = 2.68, p < 0.04). In addition, several of the other two-way interactions were found to be significant. The item (word or relationship) by type of modification (add, modify, or delete) TIME TO COMPLETE MODIFICATION TASK 20 18 O w y isa) S 16 e=! ~ 2) eae 3 12 ADD MODIFY DELETE TYPE OF CHANGE Fig. 4. Main effect of type of modification (add, modify, or delete) in seconds. 116 BOEHM-DAVIS, HOLT, AND PETERS TIME TO COMPLETE MODIFICATION TASK 20 18 16 14 REACTION TIME (SEC) 12 WORD RELATION TYPE OF MODIFICATION Fig. 5. Main effect of type of item modified (word or relationship), in seconds. interaction (Figure 7), was significant (F (2,66) = 34.53, p < 0.01). This interac- tion showed that the amount of time required to modify a relationship was constant across types of modification (add, modify, or delete, mean time = TIME TO COMPLETE MODIFICATION TASK BY TYPE OF DATABASE 24 22 % 20 2 ¢ H 18 a (e) | G 16 2 TASK * GRAPHIC 14 4 TABULAR @ VERBAL 12 GRAPHIC TABULAR VERBAL TYPE OF DATABASE Fig. 6. Interaction between data base format and format of question, in seconds. DATA BASE FORMATS AND INFORMATIONAL RETRIVAL 117 TIME TO COMPLETE MODIFICATION TASK 30 IME (SEC) N wn N So 4 WORD @ RELATIONSHIP REACTION - un So ADD MODIFY DELETE TYPE OF CHANGE Fig. 7. Interaction between type of item modified (word or relationship) and type of modification made (add, modify, or delete), in seconds. 13.46 sec); however, the time required to add a word (25.34 sec) to the data base was greater than that required to either modify (16.88 sec) or delete (11.78) a word. Accuracy. Data base format had an effect on accuracy (F (2,33) = 26.18, p < 0.01). As can be seen in Figure 8, performance on the graphic (99.3%) and ACCURACY BY TYPE OF DATABASE 100 a ie 9s eal < = UO inte 39.0 85 GRAPHIC TABULAR VERBAL TYPE OF DATABASE Fig. 8. Main effect of data base format for accuracy. 118 BOEHM-DAVIS, HOLT, AND PETERS ACCURACY OF MODIFICATION TASK BY TYPE OF DATABASE Ve) 18) ACCURACY (%) We) oO TASK * GRAPHIC 85 4 TABULAR @® VERBAL 80 GRAPHIC TABULAR VERBAL TYPE OF DATABASE Fig. 9. Interaction between data base format and format of question for accuracy data. tabular (99.1%) data bases was almost perfect, while performance on the verbal data base was reduced (90.3%). However, this main effect must be interpreted with caution. The poorer overall performance of the verbal data base arises from the combination of verbal data base with a graphic question, as can be seen in Figure 9. This interpretation is supported by the significant interaction between data base format and question format (F (4,66) = 4.51, p < 0.01). The critical interaction also supported the main hypothesis that accuracy would be higher when the form of the data base matched the form of the question. — None of the other main effects were significant for the accuracy variable. However, several two-way and three-way interactions were significant. An in- teraction (Figure 10) was found between type of modification (add, modify, or delete) and format of question (F (4,132) = 2.49, p < 0.05). For the verbal and tabular questions, modifying an item (word or relationship) was more error- prone than adding or deleting an item. For the graphical questions, adding an item was much more error-prone than either modifying or deleting an item. As in the latency data, the item (word or relationship) by modification type (add, modify, or delete) interaction was significant (F(2,66) = 10.10, p < 0.01); however, the form of the interaction was slightly different (see Figure 11). This analysis showed that more errors were made when adding a word than when adding a relationship; however, more errors were made when modifying a rela- tionship than when modifying a word. When deleting words and relationships, an equal number of errors was made. DATA BASE FORMATS AND INFORMATIONAL RETRIVAL 119 ACCURACY BY TYPE OF CHANGE AND TASK TYPE 100 oo eakels: a s > UO U c 90 TASK * GRAPHIC 4 TABULAR @® VERBAL 85 ADD MODIFY DELETE TYPE OF CHANGE Fig. 10. Interaction between type of modification (add, modify, or delete) and format of question for accuracy data. A three-way interaction between data base format, question format and type of modification (add, modify, or delete, F (8,132) = 2.54, p < 0.02) suggests that when working with a graphic data base, modifications and deletions are more ACCURACY BY TYPE OF CHANGE 100 95 beet > 3 Sr Tne5 U cH 80 4 WORD @ RELATIONSHIP 75 ADD MODIFY DELETE TYPE OF CHANGE Fig. 11. Interaction between type of item (word or relationship) modified by type of modification (add, modify, or delete) for accuracy data. 120 BOEHM-DAVIS, HOLT, AND PETERS difficult when working from a verbal question; when working with a tabular data base, additions are more difficult than modifications and deletions when working from a verbal question for all types of questions; when working with a verbal data base, adding an item (word or relationship) is extraordinarily error- prone when working from a graphic question. An examination of the three-way interaction of data base format, modifica- tion type (add, modify, or delete), and item modified (word or relationship, F (4,66) = 5.05, p < 0.01) suggests that the two-way interaction seen in Figure | 1 is due primarily to the pattern of results obtained with the verbal data base. For the graphic and tabular data bases (Figure 10), the accuracy scores are all close to ceiling; however, for the verbal data base, the pattern of results is similar to that in Figure 11. That is, adding a word is more error-prone than adding a relation- ship; modifying a relationship 1s more error-prone than modifying a word; and deleting words and relationships are equally error-prone. Discussion Several basic results emerge from this investigation into the effect of data base format on the ability to modify information in that data base. First, the results support the hypothesis that the nature of the modification task to be performed exerts a significant influence on the best form of information display. Perfor- mance, as measured both by response time and by percent correct, was best when the format of the data base presentation matched the type of information to be modified. Second, the graphic data base was generally easier to use than either the tabular or verbal data bases. Comparing these results with the results for accu- racy, we find that participants were more often correct with the graphic and tabular forms of the data base than with the verbal form of the data bases. Unlike our previous study (Boehm-Davis, Holt, Koll, Yastrop and Peters, 1989) where the forms of the data base that facilitated the quickest responses were not neces- sarily the same forms as those that produced more accurate responses, here the data base that produced the quickest responses was also the one that produced the most accurate modifications. These combined results suggest that for an interactive data base task with this type of content (relational data about words), the graphic form was best. Finally, the results suggest that specific types of modifications may be easier or more difficult to make. The latency data showed a main effect of type of modifi- cation (add, modify, or delete) which suggested that adding items to a data base is more difficult than modifying or deleting an item. This is interesting in light of DATA BASE FORMATS AND INFORMATIONAL RETRIVAL 121 the fact that adding an item to our data base did not require any more entries than deleting an item, and that modifying an item took more steps (although the data were calculated on the average time per input, not on total time). Further, the interaction between the format of the question (graphic, tabular, or verbal) and type of modification (add, modify, or delete) in the accuracy data suggests that there are specific task factors at work. This would not be surprising accord- ing to Vessey’s (1991) notion of “‘cognitive fit’, if different tasks tap different underlying representations. However, it is not immediately apparent from an examination of our three types of modifications why they would be suited to different forms of external representation, or what the underlying variables accounting for these results might be. In summary, the data suggest that users are faster and more accurate in modi- fying information in the data base when the modification to be made is pre- sented in the same format as that of the information in the data base. However, the interactions found in this study also suggest that the picture is not that simple. These interactions suggest that there are task-specific variables that influ- ence the general pattern of results. This suggests that, in future research, we broaden the types of tasks examined, and perhaps look for dimensions underly- ing tasks that might be used to predict performance. Implications for Data Base Design These results suggest some implications for designing data bases. First, the interaction between question format and data base format suggests that the nature of the searches that are likely to be performed on the data base should be considered when choosing an interface format. Specifically, the results suggest that designers should conduct task analyses to determine the nature of a typical retrieval situation. However, it is likely that more than one type of question will be asked in any given data base. One solution would be to take an averaging approach, that is, to choose the type of data base format that serves the average type of retrieval question. However, the overall speed-accuracy tradeoff in the search task sug- gests that one format for all question types may not be the best route to take, at least for static data bases. Another possible solution would be to use multiple display formats. However, that leaves open the question of who would choose the format to be displayed. One alternative would be to give users the option. This assumes users know their best options. Although one of our earlier studies (Boehm-Davis, Holt, Koll, Yastrop, & Peters, 1989) suggested that this may be the case, another study (Peters, Yastrop, & Boehm-Davis, 1988), and earlier research by Vicente, Hayes, and Williges (1987) suggested that more basic cognitive and spatial fac- 122 BOEHM-DAVIS, HOLT, AND PETERS tors are more important. The alternative would be to have the computer decide which format to display. This could be done either by determining what task is being performed or by following a user profile. Each of these options comes with a cost and the benefit to be derived from tailoring data bases. If a builder has a captive audience, that is, one that must use the system, the tradeoff appears to be between the increased cost to develop the system and the risk of increased errors, response time, and reduced user satisfac- tion in using the system. Although users may not have a choice of which system to use, they always have the choice of whether or not to use the system at all. For users who do have a choice of which system to use, the tradeoff appears to be between the increased design cost and later loss of business. Thus, it may be more important for developers to design a flexible interface when they are in a competitive market place. However, for both groups of users, total system per- formance is related to usability. That is, system performance can be defined as a function of the ability to make effective use of a system multiplied by how much someone actually uses the system. Using this metric, performance is related to the amount of effort that goes into the original design, whether or not the user has a choice about when to use the system. In examining the value of this research, it is important to remember that the data show that spatial skills are an important component of performance, even for small data bases. When one moves to larger data bases, where the task of navigating to the right location in the data base is added to the task of locating information locally, it seems likely that the problem of individual differences would be greatly magnified. In summary, the research suggests that designers need to consider the use to which a data base will be put during system design, and to recognize that individual differences play a great role in information retrieval tasks. Acknowledgements This research was supported by the Office of Naval Research, Engineering Psychology Group (Contract #N00014-85-K-0243). The views expressed in this paper are not necessarily those of the Office of Naval Research or the Depart- ment of Defense. REFERENCES Bennett, K. B. (1987). Mental models, conceptual model, and the design of graphic displays. Unpublished manuscript, personal communication. Boehm-Davis, D., Holt, R., Koll, M., Yastrop, G., and Peters, R. (1989). Effects of different data base formats on information retrieval. Human Factors, 31:579-592. DATA BASE FORMATS AND INFORMATIONAL RETRIVAL 123 DeSanctis, G. (1984). Computer graphics as decision aids: Directions for research. Decision Sciences, 15:463- 487. Durding, B. M., Becker, C. A., and Gould, J. D. (1977). Data organization. Human Factors, 19:1-14. Lalomia, M. J., & Coovert, M. D. (1987). A comparison of tabular and graphical displays in four problem- solving domains. SIGCHI Bulletin, 19:49-54. Peters, R., Yastrop, G., & Boehm-Davis, D. A. (1988). Predicting information retrieval performance. In Proceedings of the Human Factors Society Annual Meeting, 32:301-305. Powers, M., Lashley, C., Sanchez, P., & Shneiderman, B. (1984). An experimental comparison of tabular and graphic data presentation. /nternational Journal of Man-Machine Studies , 20:545-566. Sinha, A., & Vessey, I. (1991). Cognitive fit: An empirical study of recursion and iteration. Pennsylvania State University. 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Ergonomics, 15:173-187. | A eld AR MA yar viel (rats reese * ies ae Oe C ue | Li: a sean ‘ hai vm Peek, 2 OV Vea +4 phe Le tats fry spat Gt) © (ya > a3 Sa ay varie ft " ¥¢ Creeley: : eer nt } } j : ’ b A 3 5) % 1 r iy ' * i PY | fe ory 4 Pin, a LAE Ib Oe { ‘ phan ; Wa hp4 Bes Dina a * A eee + aura rm is ity iat : ; by # i Bo 0%, Ck ) WFR Hi Minit Are ron dioptabet. 4 88 On! ‘a é ye { ? i) is Journal of the Washington Academy of Sciences, Volume 83, Number 2. Pages 125-131, June 1993 No Arrow of Time Joe Rosen! Department of Physics, The Catholic University of America, Washington, DC 20064 and Department of Physics and Astronomy, University of Central Arkansas, Conway, AR 720357 ABSTRACT The fallacy of “‘the flow of time” and “the arrow of time” is pointed out, and a better notion of time, in terms of becoming, is offered. Introduction Much, indeed very much, has been written and said about the various aspects of time, ranging through the philosophical, the psychological, the artistic, the social, and so on to the technical physical. For a sampling one might refer to the published proceedings of the International Society for the Study of Time, whose eighth conference was held in the summer of 1992. For a taste of the technical physical approach see Zeh (1989), for example. The present article is offered as an addition to that print deluge, because many of us who are occupied with science, but are not devoting special thought to time, are being misled by much of the more publicized expositions about time. And that is especially acute with regard to the notion expressed by the terms “arrow of time,” “direction of time,”’ and expressions in similar vein, the notion that time could “flow one way” rather than “‘flow the opposite way.” Let us start then with a brief discussion of “‘the flow of time.” ' On leave from the School of Physics and Astronomy, Tel-Aviv University, 69978 Tel-Aviv, Israel. ? Present address. 125 126 ROSEN The Flow of Time We perceive change in ourselves and in our environment. (Perception itself involves change.) We use the term “‘time’”’ to indicate that change in general. We might (and I do) define time as the dimension of change (Rosen, 1991, Chapter 7), or as the possibility, or potentiality, or capacity, of nature for change. Now, it is very common to liken time to a flow. One view is passive: events flow toward us from the future, we perceive them in the present, then they recede from us into the past. The “‘motion”’ is from the future to the past. A simile is a river of events flowing past a stationary perceiver standing on the shore. Another view is active: we flow from the past to the future, experiencing events along the way. The “motion” here is from the past to the future. A corresponding simile is the perceiver in a boat, floating along with the river current past the stationary events along the shore. That might remind physicists of the “‘block universe” picture, in which one’s consciousness and sense of present is supposed to creep along one’s world line. Time in Physics Physics is completely lacking a flow of time. Our sense of time’s passage has no representation in physics. Time in physics is a parameter, commonly de- noted by ¢, that can take on a range of values. But there is nothing in physics that makes f¢ take any specific value or any sequence of values. Thus we can “run” our mathematical models “‘forward”’ in time, by consider- ing their behavior as ¢ takes on an increasing sequence of values, or “backward” in time, as ¢ assumes a decreasing sequence. That brings us to time reversal symmetry for models. Time Reversal for Models A model is said to possess time reversal symmetry if its set of behaviors for increasing sequences of ¢ values is the same as its set for decreasing sequences. That can be checked for equations by changing the sign of ¢ and of all odd-order time derivatives and seeing whether the resulting equation possesses the same set of solutions as the original one. For example, Newtonian mechanics with elastic forces 1s a time reversal sym- metric model. Any process obtained for a decreasing sequence of t values can be obtained, with a suitable choice of initial conditions, for an increasing sequence. The diffusion equation, on the other hand, is not time reversal symmetric. The process of concentration that results for a decreasing sequence of ¢ values is not a behavior obtained for an increasing sequence. (It could be obtained by NO ARROW OF TIME 127 changing the sign of the diffusion coefficient, but the sign of the diffusion coefh- cient is an ingredient of the model.) Time Reversal for Nature That was time reversal symmetry (or asymmetry) for models. Time reversal symmetry for nature operates closer to the real world. (1) First, let TS denote the state that is the time reversal transform of state S, where TS is obtained from S$ by changing the sign of all generalized velocities or of the quantum phase, as the case may be. If a state S is not characterized by any generalized velocity or quantum phase, then TS and S will be identical. (2) Second, consider any pro- cess of some physical system by which an initial state J evolves into a final state F, I — F. (3) Third, consider the process that develops from the state TF, the - time reversal transform of final state F, when TF is taken as the initial state of the same physical system. (4) Finally, if state TF evolves into the time reversal transform of state J, T/, 1.e., if TF — TJ, for all states J of the system, then the system is said to possesses time reversal symmetry. (The situation is actually more complicated. See Rosen (1994b).) Time reversal symmetry of a physical system can thus be expressed as the validity of the following diagram for all states J of the system (Rosen, 1983, Chapter 6): Time reversal I Tl Natural — Natural Evolution Evolution | Time reversal TF Or in everyday terms, a physical system is time reversal symmetric, if for any process of the system a movie of that process projected in reverse depicts a possible process of the system. An example of a time reversal symmetric system is any isolated system that does not involve neutral kaons (or K mesons), when the system is considered microscopically. On the other hand, such a system that does involve neutral kaons is not time reversal symmetric. A sufficiently complex system, when considered macroscopically (where ma- crostates are equivalence classes of microstates), can be time reversal asymmet- ric. That is macroscopic irreversibility and is related to thermodynamic consid- erations. 128 ROSEN As for the Universe as a whole, since physics deals only with the single Uni- verse we have and the Universe is expanding, it seems physically meaningless to consider whether contraction is a possible process for the Universe. Arrows of Time The term “arrow of time” is used to refer to any phenomenon that is time reversal asymmetric. Here are extremely brief descriptions of some arrows of time. The most obvious arrow of time is the psychological arrow of time, our sub- jective time sense. We remember the past, are intensely aware of the present, and anticipate the future. We do not remember the future and do not anticipate the past. | Then there is the thermodynamic arrow of time, the time reversal asymmetry of sufficiently complex systems considered macroscopically. This arrow of time can by coupled with the psychological one by the claim that the stable recording of memory traces is an entropy increasing process, so that we cannot but per- ceive entropy increase in our surroundings. The submicroscopic world does not offer an arrow of time, except through the neutral kaon. But that effect is generally considered too small to have significant effect on the ordinary scale. The cosmic arrow of time is the expansion of the Universe, in the sense that the Universe is in fact expanding and not contracting, independent of any con- sideration of whether contraction is possible for it. Some deem this to be the master arrow of time from which all others follow, but that approach is not universally subscribed to. Other arrows of time have been pointed out (Zeh, 1989). As commonly understood, what seems to be the message of all this is that, except for phenomena involving neutral kaons, the submicroscopic world pos- sesses no arrow of time, and one direction of time is just as valid as the other. At larger scales differentiation of temporal directions enters the picture, and a fortiori our observation of the world around us imposes sharp discrimination between directions of time. Some would add that it is the expansion of the Universe that underlies all that and fundamentally endows time with a direc- tion. No Arrow of Time That appears to be what is commonly understood. But as I wrote in the Introduction, that is misleading. Time has no arrow, no direction. The very notion of directionality has no relevance for time. While “the flow of time” is a NO ARROW OF TIME 129 beautiful metaphor, it must not be stretched to the extent of thinking of time as flowing one “way” rather than another. I think the arrow fallacy is a result of conceptual spatialization of time’s “flow,” against which Capek (1961, Chapter 11), among others, strongly warns us. After all, although a river actually flows in one direction, it can be imagined flowing in the opposite direction. So when the “flow” of time is likened, as it so commonly is, to the flow of a river, it is all too easy to fall into the fallacy of ascribing to time a direction of flow and the possibility of flow in the opposite direction. And so it is also with the “‘block universe” picture; if we imagine we “move” along world lines, then why not “move” in the opposite direction? But time, as I mentioned in Section 2, is nature’s dimension, or possibility, or potentiality, or capacity, of change. That is it. Simple, in a way. Events occur; _ things change. The undoing of a change is also a change, an event or sequence of events. A time reversed process is also a process. None of that is time “running in reverse.’ Time does not run anywhere. Events occur; things change. That is time. Indeed, we quantify time and find it useful to represent time on an axis in 4-dimensional space-time. But nevertheless time is not a spatial dimension. Even the theories of relativity do not completely unify time with space. In fact they actually emphasize the distinction of time from space; the light-cone struc- ture of space-time, reflected in the indefinite metric, assigns to time a different character from that of space. Just because we can move along the x axis and can move just as well in the negative x direction as in the positive, does not imply that we must then necessarily ““move’”’ in time or, moreover, that it is necessarily meaningful to ““move”’ in one “‘direction”’ rather than in the other. Becoming Well, if time does not run anywhere, does not really flow, and has no direc- tion, how then might we better grasp the notion of time? We can do very well by focusing on change, as I stated in Section 2. And I think the following reasoning iS persuasive. We human beings have evolved to be very well adapted to the world we live in, since we are on the whole thriving quite successfully. Thus the way we perceive and conceive of the world cannot be terribly mismatched with what is really going on there, at least at the ordinary scales of lengths, time intervals, speeds, ete. Now, our temporal conception is of an ordered sequence of events, of changes, involving: (1) the past, consisting of those events that have happened, some of which we remember; (2) the present, involving the events currently happening; (3) and the future, consisting of whatever will occur. Of the three, the 130 ROSEN present is the only perceived reality. The past is our mental construct of what we remember to have happened together with whatever else might have happened even if we do not remember it. The future is our anticipation of more events to occur after whatever is happening now. We perceive a present of occurring events, changes, which is well termed “becoming.” Our temporal perceived reality is of becoming. Normally we are constantly aware of the world’s becoming, including our own becoming and our awareness as part of that becoming. (I assume that the self-referential circularity of our awareness of our being aware is deeply involved in our time sense.) So that should reflect what is really going on in the world, at least for ordinary scale time intervals (sufficiently longer than, perhaps, the Planck time of about 10°* s and sufficiently shorter than, say, the Hubble time of some 10!° y—I strongly suspect that our intuition is at least as poorly adapted to the extra large scale as it is to the extra small). Thus time should be thought of as a universal wave of becoming, the combined becomings at all locations in space. Our con- sciousness, as basically a physical phenomenon, participates in the universal wave of becoming, and, one might say, “rides the wave.” The universal wave of becoming is not to be understood as a universal wave of simultaneity! We know better, from the special theory of relativity. Simultaneity is a convention that is operationally defined and observer dependent. The uni- versal wave of becoming is not operational and is observer independent. It is a good way of thinking about time that is less likely to lead to the fallacy of an arrow, or a direction, for time. Besides its much better representing what time is all about, this view of time in terms of (nondirectional) becoming, of change, rather than in terms of direc- tional flow, has the following additional advantage. Many questions about time, all those questions concerned with the issue of time’s “‘arrow,” time’s “‘direc- tion,” become nonquestions, and the picture simplifies enormously. It is not that the questions are swept under the rug. Rather, they are validly dismissed as irrelevant. The situation is very much like that of astronomical epicycles. When the motions of the planets were grasped in terms of ellipses rather than circles, all questions involving epicycles went straight to the trash heap of irrelevancy. And similarly for questions about the composition, form, and color of ghosts, when, as long as there 1s no objective evidence for them, ghosts are generally assumed not to exist. In my holistic moods I like to think of the becomings at the various locations in space, which all together constitute the universal wave of becoming, as some- how linked, perhaps coordinated, via a deeper, nontemporal and nonspatial level of reality underlying space-time. That level might be partially accessible via the quantum. I have elaborated on such ideas elsewhere (Rosen, 1994a). NO ARROW OF TIME 131 Acknowledgments I would like to express my deep thanks to Lawrence Fagg and to Avshalom Elitzur for many interesting and useful discussions about time. References Capek, M. (1961). The Philosophical Impact of Contemporary Physics. Van Nostrand: Princeton, NJ. Rosen, J. (1983)..A Symmetry Primer for Scientists. Wiley: New York, NY. Rosen, J. (1991). The Capricious Cosmos. Macmillian: New York, NY. Rosen, J. (1994a). Time, c, and nonlocality: a glimpse beneath the surface? Physics Essays, (in press). Rosen, J. (1994b). Universal microirreversibility and indeterminism in classical dynamical systems. J. Wash. Acad. Sci., (in press). Zeh, H.-D. (1989). The Physical Basis of the Direction of Time. Springer-Verlag: Berlin, Germany. was ‘nee RG) Picue Lan aS Tey s ‘2 thes 4 ee A “ 1% r 6 M ia _ " x i ‘ ’ ee | x > os Vi wre Lass * e DELEGATES TO THE WASHINGTON ACADEMY OF SCIENCES, REPRESENTING THE LOCAL AFFILIATED SOCIETIES Piilasopiaical: SOGGY OF WASHINGION, 22. icc. ac cc ee cece ceed dee daewes Thomas R. Lettieri PEnopelofical SOctety Of WaSHINGLOM: 26)... 6.6 ss sc ccc e ee dees eesnetececes Jean K. Boek Bralomical SOciety Of WasmINGtOM 6.5 Mb. L.ncicecc ee cee cae eee dase deasescues Kristian Fauchald erred SOCIETY OF VV aSMINPIOM 2262 so. eda Sees eo eicibn was beaaboenceuss Elise A. B. Brown Entomological Society of Washington ..... 2.2.02... ce eee eee e ee ele F. 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Second Class postage paid at Washington, DC and additional mailing offices. Journal of the Washington Academy of Sciences, Volume 83, Number 3, Pages 133-141, September 1993 Universal Microirreversibility and Indeterminism in Classical Dynamical Systems Joe Rosen!’ Department of Physics, The Catholic University of America, Washington, DC 20064 and Department of Physics and Astronomy, University of Central Arkansas, Conway, AR 720357 ABSTRACT In order to help make the ideas of classical microirreversibility and chaos more accessible to interested nonspecialists, a narrative style discussion is presented. It is shown, based on an operational understanding of time reversal symmetry, that irreversibility, i1.e., time reversal asymmetry, is a property of the microevolution of classical dynamical systems in general. Yet approximate microreversibility may be exhibited for sufficiently short durations. The cause underlying universal microirreversibility is the generally divergent evolution of classi- cal dynamical systems and the consequent sensitivity of the evolution to initial conditions. Divergent evolution is responsible also for the indeterministic, i.e., chaotic, behavior of those systems. . Introduction This article offers some thoughts in narrative form on determinism, indeter- minism, chaos, and macroscopic and microscopic reversibility and irreversibil- ity. No essentially new results are presented. But this way of looking at things, and especially the symmetry considerations involved in this way of looking at things, helps clarify what is going on and makes the ideas more accessible to. the interested nonspecialist. ' On leave from the School of Physics and Astronomy, Tel-Aviv University, 69978 Tel-Aviv, Israel. ? Present address. 133 134 JOE ROSEN The main aim of this article is to show, based on an operational understand- ing of time reversal symmetry, that irreversibility, 1.e., time reversal asymmetry, is a property of the microevolution of classical dynamical systems in general. Even so, approximate microreversibility, 1.e., approximate time reversal sym- metry of microevolution, may be exhibited for sufficiently short durations of evolution. The cause underlying universal microirreversibility is the generally divergent character of the evolution of dynamical systems and the evolution’s consequent sensitivity to initial conditions. Divergent evolution is responsible also for the indeterministic, 1.e., chaotic, behavior of those systems. The presentation proceeds as follows. In the next section we look into the meaning of time reversal symmetry in general. In the section entitled ““Macro- irreversibility” there is a succinct discussion of the macroirreversibility of dynamical systems possessing an equilibrium macrostate. We see how the gener- ally divergent evolution of dynamical systems brings about their indeterminis- tic, 1.e., chaotic, behavior in the section entitled ““‘Determinism Lost’’. That such systems may nevertheless exhibit approximate determinism for sufficiently short durations is shown in the section entitled ““cDeterminism Approximately Regained’’. After the discussion of macroirreversibility in the beginning of the article as preparation, we see in the last section, entitled ““Microirreversibility”’ that the criterion for time reversal symmetry, understood operationally, cannot be met in general even for microevolution, whatever the dynamics of the system. Thus microirreversibility is a universal feature of dynamical systems. However, approximate microreversibility may be exhibited for sufficiently short dura- tions. | For the purpose of our discussion we consider the following model dynamical system: a large classical, ostensibly deterministic system, isolated from its surroundings, describable both in macroscopic and in microscopic terms. As an example, think of an isolated quantity of ideal gas. For simplicity of presentation we usually take the natural evolution of the system to be discrete: initial state > final state. We assume that the system possesses a unique equilibrium macro- state, which is a certain macrostate into which the system evolves from any initial macrostate. An equilibrium macrostate evolves, of course, into itself. A macrostate corresponds to a class of microstates. I.e., if the system is in any microstate of such a class, it will be in the corresponding macrostate. Thus microstate space decomposes into classes, of which each class but one corre- sponds to a unique macrostate, while the exceptional class contains all micro- states to which no macroscopic description is appropriate. Of microevolution and macroevolution the former is the more fundamental, giving rise to the latter through course-graining, i.e., through our ignoring cer- tain details of microstates and considering only equivalence classes of them. Just UNIVERSAL MICROIRREVERSIBILITY AND INDETERMINISM 135 how that comes about in technical detail 1s, it seems, still under investigation by the experts in the field. Time Reversal Symmetry We start by defining the time reversal transformation of states, denoted T: For any classical (macro or micro) state S, the corresponding time reversed state TS is obtained from S' by reversing (1.e., changing the sign of) all generalized veloci- ties (i.e., the time derivative of generalized coordinates, such as the ordinary velocity of a point particle, the angular velocity of a rigid body, the time deriva- tive of a field intensity, etc.) characterizing S, while leaving all other properties of S unchanged. (Thus the time reversal transform of a time reversal transform is the original state itself; TT.S = S.) If state Sis not characterized by any general- _ 1zed velocity, then TS and S will be identical. Now, for any classical dynamical system consider a general evolution, whereby initial (macro or micro) state J evolves into final state F. Consider the time reversal transform of final state F, TF, as an initial state. If TF evolves into the time reversal transform of initial state J, TY, 1.e.,if TF — TY, for all states J of the system, then the system’s evolution possesses time reversal symmetry and the system is said to be reversible (Rosen, 1983, Section 6.1). Note that a necessary condition for reversibility is that the evolution be non- convergent, 1.e., that different states always evolve into different states. To see that, assume that different states J and J’ both evolve into the same state F. Now consider the time reversal transform of state F, TF, as an initial state. In a deterministic system TF will evolve into a unique final state, which thus cannot be both T/ and TI’, since those states are different, as J and /’ are different by assumption. Therefore reversibility is precluded by nonconvergent evolution. Macroirreversibility Thus whether or not the microevolution of our model dynamical system is assumed to be reversible, its macroevolution is irreversible, as follows from the assumed convergence property, that every initial macrostate evolves into the same (equilibrium) macrostate, where the latter evolves into itself. The simulta- neous possession of both microreversibility and macroirreversibility is not for- bidden by nature, since there indeed exist physical systems that possess both microreversibility and macroirreversibility, such as a sample of gas. That has been taken as a dilemma, a contradiction, a problem. How can reversible mi- croevolution give rise to irreversible macroevolution (Balescu, 1975, Section ee)? In order to understand how that comes about, let us rephrase the question in the light of our above discussion: How does course-graining give rise to conver- 136 JOE ROSEN gent macroevolution, even when the fundamental microevolution is noncon- vergent? The resolution of that depends crucially on the fact that for our model to represent the real world we must also course-grain 1n time, 1.e., our observa- tions must each be of truly macroscopic duration, must be time-smoothing. What happens is the following. Let us consider the system continuously in time and microscopically. Let it start in some microstate belonging to the class corresponding to some nonequi- librium macrostate. As time proceeds the system “‘wanders”’ through microstate space, from microstate to microstate to microstate.. . . Each microstate along the trajectory belongs to the class corresponding to one or another macrostate or possibly to no macrostate. As we well know, statistical analysis shows that the population of the equilibrium macrostate class is overwhelmingly greater than that of all the other classes combined. Thus, as the system evolves, it spends overwhelmingly more time in its equilibrium macrostate class than in any other macrostate class or in the class of no macrostate (Balescu, 1975, Chapter 4). Since the system starts in a nonequilibrium macrostate class, some time must pass before it enters the equilibrium macrostate class for the first time. The characteristic time duration for that to happen 1s called the “relaxation time” of the system. The relaxation time for an ordinary sample of gas, for example, is about 107'! s (Balescu, 1975, Section 13.2). So after a time interval of the order of the relaxation time the system is in the equilibrium macrostate class and stays there for a long time before wandering out. The characteristic time duration for remaining in the equilibrium macrostate class once entering it can be called the “excitation time” of the system. The excitation time for an ordinary sample of gas, for example, is probably longer than the age of the universe. (““You should live so long,”’ Boltzmann is supposed to have told Zermelo (Lebowitz, 1983).) When the system finally does ““wander”’ out of the equilibrium macrostate class, it will stay out for a time interval of the order of the relaxation time, only to return to the equilibrium class and remain there again for a time interval of the order of the excitation time. Thus the fraction of time the system spends out of macroscopic equilibrium is of the order of the ratio of its relaxation time to its excitation time. That fraction is less than about 10°”? for an ordinary sample of gas, for example. It follows then that a macroscopic duration for observations is any duration that is much longer than the relaxation time. For such observations the evolving system is always found to be in equilibrium, with average relative error of the order of the above ratio. Determinism Lost Until fairly recently the commonly held conception of the character of the evolution of isolated classical dynamical systems was that the evolution is in UNIVERSAL MICROIRREVERSIBILITY AND INDETERMINISM 137 general nondivergent. This means that for a given system similar initial states, 1.€., initial states that are close to each other in state space, generally evolve into similar (close) final states, where “similar” and “‘close”” have whatever signifi- cance is appropriate to the system under consideration. Or, considered continu- ously, evolution trajectories in state space that start off close to each other generally remain close for their entire duration. It had traditionally been considered safe in the context of classical physics to assume that it is possible in principle to set up an isolated classical system in any of its possible states. At the same time it had been recognized that in practice this is not exactly true, due to uncontrollable residual imprecision. (And not merely in practice, since quantum theory sets limits on the precision of setting up classical states, such as states characterized by both position and momentum.) _ But, in line with the assumed generally nondivergent evolution, that impreci- sion had been taken as inconsequential, since it had been assumed that even if we did not manage to set up exactly the state we wanted but only a very similar one, the system would anyway evolve into a state very similar to the one it ideally would have evolved into. Yet fairly recent developments in the field of dynamical systems have led to the belief among those familiar with the field that the evolution of isolated classical dynamical systems is generally characterized by divergence rather than by nondivergence. Similar initial states of the same system, even if they differ merely on the order of quantum uncertainty, do not in general evolve into similar final states but into widely differing ones. Trajectories in state space generally diverge in time (Horton, Reichl, & Szebehely, 1983, especially Lebo- witz, 1983, and Misra and Prigogine, 1983, there, and Prigogine and Stengers, 1984). That effect is also variously called “instabilities,” “‘sensitivity to initial conditions,” or “stochasticity of the motion” (Lebowitz, 1983). Nondivergent evolution might occur in special cases. An important implication of that for physics is that determinism, and hence also predictability, are dead. And we are talking about classical physics! It is true that laws and theories predict results and so imply determinism. But physics is concerned first and foremost with phenomena. And in the operational dealing with phenomena we are forced to realize that determinism is no longer with us. How is that? Determinism means that, with suitable definition of state, the initial state of any isolated dynamical system uniquely determines its final state. Operationally this means that every time we set up a dynamical system in the same initial state / it will evolve into the same final state F. But we cannot set up a system in any initial state J more than once (and even then it will not be exactly in the state we might have intended to set up). The best we can do is to set up a system in a sequence of similar initial states J, J’, 7”, . . . , which evolve into 138 JOE ROSEN final states F, F’, F”,. . . , respectively. As long as nondivergent evolution was assumed, we thought we could confirm determinism by checking whether final states F, F’, F”,. . . were similar. But it now appears that, as similar as initial states J, I’, I”, . . . are made to be, even if they differ merely on the order of quantum uncertainty, final states F, F’, F”,. . . willin general be widely differ- ent. How, then, determinism? Thus classical trajectories in state space are in general unobservable (Prigogine and Stengers, 1984, p. 264). And even more than that. We know that an isolated system is but an idealiza- tion; we know there are influences that cannot be totally screened out (and perhaps also those that cannot even be attenuated by distance). So I prefer the term “‘quasi-isolated”’ to “isolated,” where quasi-isolated means isolated as best we can. Then, even if it were possible to set up a quasi-isolated dynamical system in precisely the same initial state more than once, random influences from the surroundings, as weak as they might be, generally would, due to the amplifying effect of divergent evolution, preclude the system’s evolving into even similar final states. Hence indeterminism again. From the preceding discussion in this Section it follows that chaotic behavior is the general fate of quasi-isolated dynamical systems. By Poincaré’s theorem a truly isolated dynamical system will eventually evolve into a state as similar to its initial state as desired. If the evolution were nondivergent, the system would then go through a cycle of evolution very similar to the previous one, since it would then be starting off again in a state very similar to its initial state. Hence its characteristic behavior would in general be quasi-periodic, exhibiting Poincaré recurrences (Lebowitz, 1983, and Balescu, 1975, Section 3.3). Indeed, a truly isolated dynamical system’s behavior might even be exactly periodic. That would happen if after some finite time it returned to its initial state exactly. The evolution, however, is generally divergent, and similar is just not good enough. Even when the system eventually evolves into a state very similar to its initial state, its ensuing evolution will in general bear no resemblance to the previous cycle. Furthermore there do exist anti-isolatory factors. Hence, no periodicity, no quasi-periodicity, no Poincaré recurrences—only chaos. Determinism Approximately Regained If determinism is indeed dead, and predictability along with it, how is it that we can do science? (Do not forget that reproducibility and predictability form the cornerstones of the foundations of science.) And actually it seems very reasonable that without determinism in nature living beings, and a fortiori intel- ligent beings such as scientists, could not even exist. Consider the degree of determinism that is necessary for our survival as individuals and as the human race, for us to have stable, reliable memories as individuals and as a society, and UNIVERSAL MICROIRREVERSIBILITY AND INDETERMINISM 139 so on. Indeterminism implies lack of predictability. And without predictability we could not reliably control our bodies or anything else and could not survive our interactions with our unpredictable environment and with each other. Yet we exist and we do science. How does that accord with the discussion of the preceding section? Just as for the course-graining that brings about macroirreversibility, time is the essential issue here. Although initially close trajectories in microstate space will in general eventually diverge, some time may pass before they actually do so appreciably. That time interval can be called the “chaotization time.” Thus the chaotization time of a dynamical system is the characteristic time duration from the start of its evolution until the onset of its chaotic behavior. A system’s chaotization time might depend, even very strongly, on the values of system parameters, such as flow speed of a fluid or temperature difference, for example. (Nondivergent evolution is characterized by infinite chaotization time.) For time intervals sufficiently shorter than its chaotization time, a dynamical sys- ~ tem’s behavior is deterministic to a sufficiently good approximation. Thus the determinism we find in nature indicates that there do exist dynamical systems whose chaotization times are very long compared with the durations of our observations. An additional source of determinism could also lie with the ability of open systems far from macroscopic equilibrium to remain far from equilibrium while generating order within themselves (i.e., decreasing their entropy, but at the expense of increased entropy of their surroundings) (Prigogine and Stengers, 1984, Chapter 6, and Prigogine, 1980). Merely maintaining order, let alone producing it, requires deterministic evolution. So such systems seem to be con- currently “generating” determinism, actively extending their chaotization times, presumably at the expense of reduced chaotization times of their surroundings. Living beings are such systems. Microirreversibility Our discussion of macroirreversibility in the section bearing that title does not present anything new. It does show succinctly how large dynamical systems possessing a macroscopic equilibrium state are found to be irreversible when considered macroscopically (by course-graining, in time as well). And that is valid whether the microevolutions of the systems are reversible or not. The discussion of macroirreversibility is intended to serve as preparation for our discussion of microirreversibility, where we will see that in a certain sense mi- croevolution, too, is irreversible whether it is reversible or not. Our presentation of time reversal symmetry in the section of that title should 140 JOE ROSEN be understood operationally as saying: If we have a process of a dynamical system whereby state J evolves into state F, ] > F, we should set up state TF, the time reversal transform of state F (by reversing all generalized velocities asso- ciated with state F), and let it evolve, TF — _X. If state X is the same as state T/, the time reversal transform of state J, the process is time reversal symmetric, or reversible. If that is found to hold true for more and more states J of the system, we suspect, then gain confidence in, time reversal symmetry of the evolution and thus reversibility of the system. That is independent of whether we have a law or theory for the system’s evolution. If we do have a law in the form of a set of equations, time reversal symmetry is expressed by invariance of the set of solutions of the equations under the substitution of —t for t, —d/dt for d/dt, —v for v, etc. Now, what we will see is that the operational criterion for time reversal sym- metry cannot be met in general. And that is true even if there exists a time reversal symmetric law or theory of microevolution, such as the classical theory of electrodynamics. That is the meaning of the above oxymoronic declaration that microevolution is irreversible whether it is reversible or not. Traditionally, assuming nondivergent evolution for classical dynamical sys- tems, we had thought it inconsequential for time reversal symmetry that, as mentioned in the section “Determinism Lost”’’, states cannot be set up precisely to specification. Thus, by the operational understanding of time reversal sym- metry, even if we could not precisely set up state TF for the purpose of letting it evolve and comparing the result with state TJ, as long as we set up a state sufficiently similar to TF, it would evolve into a state sufficiently similar to the ideal result so that its comparison with state TJ would be meaningful. But it now appears that evolution is divergent in general. So setting up a state merely similar to TF is no longer good enough, since the result of its evolution will in general be a state bearing no resemblance to the state that would have resulted if TF itself had evolved. Thus, whatever the underlying dynamics, the operational criterion for time reversal symmetry cannot in general be met. Try as we may to get things running in reverse, the system will never go back again. Nevertheless, the possibility of approximate determinism allows the possibil- ity of approximate time reversal symmetry. As long as we limit our observations of a dynamical system to durations sufficiently shorter than its chaotization time, we will be able to meaningfully apply the operational criterion for time reversal symmetry. Thus, only for durations sufficiently shorter than its chaoti- zation time can a dynamical system exhibit even approximate microreversi- bility. UNIVERSAL MICROIRREVERSIBILITY AND INDETERMINISM 141 References Balescu, R. (1975). Equilibrium and Nonequilibrium Statistical Mechanics. Wiley: New York, NY. Horton, Jr., C. W., Reichl, L. E., & Szebehely, V. G. (eds.) (1983). Long-Time Prediction in Dynamics. Wiley: New York, NY. Lebowitz, J. L. (1983). Microscopic dynamics and macroscopic laws. Jn Horton, Jr., C. W., Reichl, L. E., & Szebehely, V. G. (eds.) Long-Time Prediction in Dynamics. (pp. 3-19). Wiley: New York, NY. Misra, B., & Prigogine, I. (1983). Time, probability, and dynamics. Jn Horton, Jr., C. W., Reichl, L. E., & Szebehely, V. G. (eds.) Long-Time Prediction in Dynamics. (pp. 21-43). Wiley: New York, NY. Prigogine, I. (1980). From Being to Becoming. Freeman: San Francisco, CA. Prigogine, I., & Stengers, I. (1984). Order Out of Chaos. Bantam: New York, NY. Rosen, J. (1995). Symmetry in Science: An Introduction to the General Theory. Springer: New York, NY. Journal of the Washington Academy of Sciences, Volume 83, Number 3, Pages 143-160, September 1993 Interpreting the Language of Informational Sound! James A. Ballas Navy Center for Applied Research in Artificial Intelligence Naval Research Laboratory, Washington, DC ABSTRACT Sound offers advantages for information systems, in the delivery of alerts, duration infor- mation, for encoding of rapidly incoming information, for reaction time enhancement, for background monitoring, and for representing position in 3-D space around the person. To assist in utilizing these advantages, background information on auditory capabilities and design guidelines are available. This paper discusses ways of conveying information using non-speech audition, including the limitations of present applications of auditory signals, the basis of these limitations, recent developments in the field including encoding of ur- gency, presenting 3-D audio and using sounds of real events in computer systems. In order to conceptualize the use of informational sound, analogies to language are presented and de- scribed. While these analogies have clear limitations, they provide a useful framework. Spe- cifically, sounds are used analogously as exclamations, for deictic reference both to place and to entities, as simile and metaphor, and for symbolic reference. The incorporation of every- day sounds as symbols for computer processes is examined in detail. Issues in this applica- tion include the integration of the sound with a concurrent visual stimulus, and the identifia- bility of the sound. Recent research on causal ambiguity of everyday sounds is presented. Introduction Modern computer, aircraft, process control, and C? systems have information available that cannot be delivered to the operator without careful consideration to the encoding of this information including the modality for the information and the formatting and sequencing of information within the modality. The modality that is of interest here is audition. Auditory signals can be used to present a variety of information including status of equipment, status of a dy- namic process, and initiation or completion of events. A primary application is "Presented at the mid-year meeting of Division 21 of the American Psychological Association, March 5, 1992. Preparation of this paper was supported in part by the Naval Research Laboratory. The author’s research on sound identification was supported by the Office of Naval Research through the Perceptual Sciences Program. 143 144 JAMES A. BALLAS as an alerting signal to direct attention to critical information. Auditory signals are also used to deliver information through another channel, increasing the amount of information that can be delivered concurrently. In using auditory signals, it is helpful to recognize applications in which sound has potential advantages over visual presentation of information. Applications in which sound is at an advantage including alerting information (Posner, Nissen, and Klein, 1976), encoding of rapid incoming information (Posner, 1967), reaction time enhancement (Colavita, 1974), information monitored in the background, and information intended to represent position in 3-D space around the opera- tor. Jenkins (1985) summarizes the benefits of acoustic information over visual information, particularly in natural settings. The advantages include unobtru- sive monitoring, no requirement for an external energy source if natural events are producing the sound, provision of information about the cause of the sound and its source in space, and interrupt capability because sound does not require oriented receptors for effective delivery of the information. In order to imple- ment these applications, guidelines for the use of complex sound in systems design have been presented by Sorkin (1987) and by Mulligan, McBride, and Goodman (1985), and general information about audition is available in Haw- kins and Presson (1986), Scharf and Houtsma (1986), Green (1988) and Hirsh (1988). Both Sorkin (1987) and Mulligan, McBride, and Goodman (1985) describe the characteristics of the auditory channel and guidelines for its usage. Sorkin (1987) addresses factors that must be considered in establishing the level, pitch, duration, shape, and temporal pattern of the sound. In addition, he covers the design of binaural sounds and complex coding for sounds. Mulligan, McBride, and Goodman (1985) provide algorithms that assist the designer in designing auditory signals especially in ways to enhance detectability of signals in noise and to increase loudness without increasing signal level. Coverage of audition is available in several sources, often from a particular perspective. Hawkins and Presson (1986) focus on topics related to the capacity to process auditory information including attention and memory, and factors that mediate processing capacity such as noise and aging. Green (1988) and Scharf and Houtsma (1986) cover psychophysical performance in detection and discrimination of intensity and frequency, sound localization, and perception of loudness and pitch. Hirsh (1988) organizes his coverage of audition into single sounds, sound sequences, and speech, covering the important perceptual attrib- utes of each type of sound. These sources provide extensive and excellent cover- age of auditory perception. This paper is an overview of how sound is used in systems to convey informa- tion. As used here, the term informational sound refers to the broader meaning INFORMATIONAL SOUND 145 of information rather than the precise meaning from information theory. Readers interested in the latter approach should consult the early work by Pol- lack and Ficks (1954), Sumby, Chambliss and Pollack (1958), and Chaney and Webster (1966). Here the term means any non-speech sound that conveys infor- mation relevant to the completion of a task, a notion presented by Burrows (1960). The approach here is to present examples of different types of sound and explore the benefits and limitations of these sounds. The objective is to docu- ment the wide range of sound that has been put to use in systems including recent developments. Broader coverages of sound including speech, music, natu- ral sound and sounds of modern life are available in Truax (1984) and Schafer (1977). The focus here is on sound in human-machine systems that typically interest applied-experimental psychologists. The need to document sound usage derives in part from the ephemeral nature of sound. In contrast to visual displays and text, which are routinely printed, distributed, and saved, sound is transient and must be recorded as it occurs. Even when recorded, it is not distributed conveniently to others. Thus the usage of sound is known sometimes only through anecdote. A good example is the often told story of the expert auto mechanic who can diagnose engine problems. Although the story has probably been around since the invention of the auto- mobile, it is only recently that descriptions of the sounds such a mechanic might use and tape recordings of those sounds for training purposes have been avail- able (Home Mechanix, 1986). Even with this example, the information is not in the scientific literature. Analogies to Linguistic Elements Analogies are useful devices to present relationships in a meaningful manner. In order to present a framework for the variety of non-speech sound that is used in information systems, I have adopted analogies from linguistics. The approach is certainly not new. Comparisons between speech and music have a long his- tory, and a similar description for sounds used in computer interfaces was pre- sented by Gaver (1989), although the terminology I use is somewhat different. He proposed that the relationship between a sound and its meaning could be symbolic, metaphorical, or iconic. He defined symbolic relationships as being arbitrary. Metaphorical relationships rely on similarities across the different domains. Iconic relationships are based upon physical causation. In this paper, using analogies to linguistic units, I classify sounds in terms of functionality rather than on the basis of relationships. However, an important aspect of func- tionality is the type of relational information that is provided by the sound, and so there is overlap between the scheme that I use and the one presented by Gaver. For example, I discuss sounds that function as similes and metaphors, 146 JAMES A. BALLAS Table 1.—Summary of Linguistic Analogies to Informational Sound Linguistic Element Informational Sound Example Exclamation Alerting tone _Deictic reference to place 3-D audio tone Deictic reference to an entity Indicator tone Onomatopoeic Sign Auditory icon Simile Earcon Metaphor Audio monitor Polysemous word Ambiguous sound and sounds that designate an event by simulating the actual sound of the event. Two sources that offer extended discussion of the relationships between speech, music and natural sounds are Handel (1989) and Truax (1984). The analogies I use are only for illustrative purposes. Analogies at another level are discussed in Ballas and Howard (1987). A summary of the analogies is presented in Table 1. The following definitions of linguistic elements are offered: _ Exclamation is a sudden, vehement utterance or outcry. Deictic reference is a demonstrative, a pointing-out device, such as the pro- nouns “there,” “here,” “‘this,”’ “those.” Reference can be made to a place, an entity, or a time. Interpretation of the reference requires knowledge of the speaker’s position and time. Simile is a figure of speech in which one thing is likened to another, dissimilar thing. Metaphor is a figure of speech in which one thing is likened to another, dissimilar thing by being spoken of as if it were that thing. Dead metaphors are figures of speech which have lost their metaphoric function and are used to denote a particular concept, such as “ship of state,’ ““cold person.” Polysemy means multiplicity of meanings. A sign is a unit of language such as a word that means, stands for, designates, or denotes something to an interpreter. Onomatopoeic means imitative or echoic. A symbol is something that stands for or suggests something else by reason of relationship, association, convention, or accidental but not intentional refer- ence. An index is a sign whose specific character is causally dependent on the object to which it refers but independent of an interpretant. Sounds as Exclamations The general alerting tone is a type of exclamation functioning similarly to the word “‘hey!”’. As exclamation, the alert works well. However, designs that lead to INFORMATIONAL SOUND 147 high false-alarm rates may have adverse consequences on overall system perfor- mance (Sorkin and Woods, 1985). An important point about the meaning of exclamations is that they are uninterpretable out of context. The same applies to alert tones. Although alerts serve a critical function, there are some problems in their usage. A disadvantage of simpler types of signals is that they are subject to noise masking and do not have the spectral complexity or redundancy of speech that can help to offset the effects of noise masking. Doll and Folds (1986) reported that some of the signals being used would be hard to discriminate in high workload and stressful conditions and recommend research on enhancing the distinctiveness and masking resistance of auditory signals. | A problem with alerts in commercial aircraft has been the increase in the number of alerts (Veitengruber, 1978). Ironically, the subsystem that has seen the greatest growth in alerts is the automatic flight control system (AFCS). According to Veitengruber, the number of alerts in this subsystem increased at about twice the rate of any other subsystem between 1965 and 1970. He also found that pilots agreed unanimously that any further increase in the number of alerts would be unacceptable. The increasing reliance on alerting signals in systems that had introduced automation is needed to direct attention to the status of systems that were not under continuous operator control. The recent development of techniques to encode urgency may alleviate some of the problems in implementing alerting tones. Edworthy, Loxley and Dennis (1991) examined the role of both spectral and temporal parameters in conveying urgency. They identified nine parameters that contribute to perceived urgency and showed how selected combinations of these parameters could convey varied levels of urgency. The parameters include spectral and envelop properties of sound bursts as well as temporal and melodic patterns across several bursts that are joined to form an urgency alarm. The important contribution from their findings is they have shown that urgency need not be encoded by increasing amplitude, an approach which has several drawbacks. It should be kept in mind that the encoding they have used provides relative levels of urgency. The signals may not be identified as alerts, especially the low urgency ones, unless there is prior exposure and learning. Sounds for Deictic Reference to Entities The alerting tone can also be designed as an indicator for a specific problem, process, or subsystem. In this usage, it provides deictic reference to an entity. Ideally, the tone should be quickly and easily identifiable. Unfortunately, there are problems in achieving this goal. Doll and Folds (1986) compared the audi- tory signals used in a variety of aircraft and found no standardization. They found also that a relatively large number of signals were being used, making it 148 JAMES A. BALLAS difficult for the crew to recall the meanings of the messages. These problems arise not only because guidelines are not being applied, but also because stan- dard signals do not exist. Design guidelines discuss general principles for audi- tory signal design, especially the design of tones that vary in loudness or pitch. Unfortunately the reliance on loudness and pitch changes in designing a signal limits the application and/or requires substantial training to learn the arbitrary relationship between the signal and its meaning. Therefore, the guidelines gener- ally recommend audio signals for simple, short messages, and especially for alerting signals. Design guidelines generally suggest limiting the sounds to a few levels that are highly discriminable in one dimension. As a result, the usage of signals is limited. The increased usage of tones in systems has resulted in the proliferation of auditory signals and produced auditory information systems that ironically re- move the very advantage of the auditory modality in presenting alerts. With just a few tonal signals, the meaning of the tones can be determined quickly assum- ing adequate training on the signals. However, with the proliferation of alerting tones, the advantage of the tone as a specific alert is lost. With proliferation, the tones become generic alerting signals, unable to generate a unique interpreta- tion. For example, at Three Mile Island, 150 alarms went off in the first few seconds of the accident. These alarms were not coded for priority and the opera- tors could only acknowledge them with a single switch. Sounds for Deictic Reference to Place The recent development of techniques to present spatial audio to a user effec- tively and relatively simply introduces the possibility of putting tones in 3-D space and using them to function similarly to demonstrative pronouns such as “here” or “there.” The device that is cited most commonly is the Convolvotron (Wenzel, Stone, Fisher, and Foster; 1990), so called because it uses mathemati- cal convolution to filter a sound digitally in the same way that the pinnae do. This filtering includes the effects of interaural differences in time and amplitude that accompany changes in the spatial location of a sound source. The effect is a perception of a sound source originating outside the head even though the sound is delivered through headphones, which would normally produce source origina- tion within the head. The convolution is specific to the user’s pinnae shape and is determined empirically. Although the Convolvotron is limited in the number of sources that can be presented, and may not produce some of the subtleties of free-field sound such as reverberant fields, the sounds of self-movement sound (the sound of clothing as you walk and turn your head, etc.), and the filtering effects of objects in the environment, the perception of spatial audio is dramatic. The technique has INFORMATIONAL SOUND 149 been verified for static source localization especially for source azimuth. Best results are obtained if the digital filtering is based upon an individualized func- tion, although Wenzel et al. (1990) suggest that results with a generalized transfer function are sufficiently accurate. The Convolvotron will change the stimulus ‘“‘on the fly’ depending on the listener’s current head position in order to maintain a fixed position in space for the source of the sound. Sorkin, Wightman, Kistler, and Elvers (1989) examined the effect of the movement-correlated sound for localization, comparing this condition to localization with the head position fixed and to localization with head movement required but the sound is maintained at a fixed position relative to the head. The last condition employed sound that had cues supporting the perception of an external source, but the location of the source was fixed relative to the head. They found that localization in azimuth was best when the sound cues were coupled to head movement and the source remained fixed to an external location. There was no difference in the three conditions for elevation location. The effectiveness of spatial cues has been demonstrated recently for detection tasks by Perrott, Sadralodabai, Saberi and Strybel (1991). They presented visual targets for detection with and without a concurrent sound located at the same position as the visual stimulus. They found that the positional cue improved joint detection and identification of the visual stimulus and that the improve- ment increased as the stimulus became more peripheral and as the number of distractor stimuli increased. A concurrent sound even provides some improve- ment when the stimulus appears in the line of sight. A different approach to produce deictic reference to place was used by Ed- wards (1989) to communicate the position of the cursor relative to objects on a computer screen. As the cursor moved from one object to another, the tone changed in pitch increasing from left to right and from bottom to top. In addi- tion, the edges of the screen were marked by a distinctive tone. The user could get a speech message about the currently-picked object with a mouse click. Edwards found that blind users did not use the pitch level to locate the cursor but did count the tone changes to locate the cursor. Sounds as Similes There are many examples of sounds used as similes in systems. To act as a simile, the sound must be used as a comparison to some property or parameter, and the basis of this comparison should not be completely arbitrary. One of the better known examples of sound simile that is effective is the portable radiation monitor which is similar to the geiger counter. Contrary to what you might expect, most modern radiation monitors employ a visual dis- 150 JAMES A. BALLAS play and even those that add an auditory signal emphasize the use of the visual display (Tzelgov, Srebro, Henik, and Kushelevsky, 1987). However, Tzelgov et al. found that the auditory signal was better than the visual display or the dual- mode system in a search task. In a detection task, there were no differences between the single modes and no differences between single modes and dual mode. They interpreted the results in terms of a visual bias effect which directed the operator’s attention from monitoring other aspects of the task. Simile is also effective in the design of devices for the visually impaired. Bindal, Saksena and Singh (1983) developed a sonic weighting balance which uses Changes in both frequency and amplitude to indicate the scale level and the point of balance, respectively. In order to weigh an item, the user rotates a dial producing both an increase in frequency and amplitude. At the point of balance, there is a sharp drop in amplitude for the current frequency. As the user moves away from the point of balance, the frequency continues to increase, as well as the amplitude (from the lower level). Tests with a small group of subjects indi- cated that most errors with this device were less than 1%. Sometimes the simile is indirect but still effective. An example is the acoustic traffic signal developed by Poulsen (1982) for blind pedestrians which delivers WAIT and WALK signals. The requirements for a pedestrian signal include good localization, discrimination from other street sounds, being audible to elderly people with hearing impairment, low annoyance, not disorienting to guide dogs, attenuated by windows, and reliable performance. These require- ments led to the development and implementation of two sounds that capture some aspects of the difference between walking and waiting. The walk signal uses a shorter sound and repeats it more rapidly (200 ms pulsed square or sawtooth wave at 880 Hz repeated at 2.5 Hz, with a 200 ms gap between sounds) compared to the wait signal (400 ms wave at the same frequency repeated at 0.5 Hz, with a 1.6 sec gap between sounds). The simile can also be very elaborate. Blattner, Sumikawa, and Greenberg (1989) have developed a system of representing messages to the user through short musical sequences. They introduce the concept of audio icons, which they call earcons. These are auditory signals that provide information and feedback to the computer user about the status and functioning of the computer system. Blattner et al. restrict the audio cues to tones and tone sequences that change in pitch and loudness. Their system includes a method of representing the hierar- chical structure implicit in computer messages. For example, they suggest that message families such as errors, prompts, system messages, and editing messages can be signaled by a family sequence followed by a sequence that represents the specific message within the family. The focus of their approach is clearly on INFORMATIONAL SOUND 151 musical tones, but they mention the possibility of using natural sounds to repre- sent events and processes in the computer system. Effective earcons ought to be based upon commonly understood metaphors for sound. Walker (1987) investigated the choices of visual metaphors for sound parameters. In the sound domain he looked at frequency, waveform, amplitude, and duration. Choices that the subjects were given in the visual domain to match changes in these auditory parameters included size, shape, pattern, and vertical or horizontal position. He found consistent support for four matches: frequency with vertical placement, waveform with pattern, amplitude with size, and duration with horizontal length. However, consistency of these matches was related to musical training and age, and lesser so to cultural and environmental factors. Sound simile must also consider the effect of psychophysical transformations. Mappings between aural dimensions and visual dimensions, and between aural dimensions and data should be founded on the established literature on cross- modality matching (e.g., Baird and Noma, 1978). The effectiveness of sounds that rely on simile is still an open issue. A recent study by Barfield, Rosenberg and Levasseur (1991) found that the addition of earcons to commands did not improve performance with either command- based or iconic-based menus. They measured three aspects of performance: time to complete the command, memorability of icons or commands, and memorability of the top-level branch for specific menu items. They used tones for sounds, and modified the pitch of the tone to indicate the menu level for the executed command or icon. The tones sounded for about one-half second. This implementation is consistent with the general guidelines proposed by Blattner, Sumikawa and Greenberg (1989). However, Barfield et al. point out that the pitch of the tones represented the menu level, not the individual commands, and thus was not indicative of individual command content. Furthermore, he also points to the problem discussed by Gaver of designing auditory sounds for functions that do not have a representative sound. An intriguing example of sound simile are the sounds used in a children’s drawing program called Kid Pix. The drawing program provides an assortment of drawing tools to the child, and there is usually a sound associated with the employment of each tool. For exam- ple, drawing with the pencil produces a scratching sound. As the pencil width increases, the scratching sound is increased. Other tools produce visual effects, and the sounds reflect the visual effect. The visual effect and the sound seem compatible on an intuitive level. It would be interesting to analyze the visual effects and the sounds to determine the basis of the compatibility. I expect that we would be able to explain very little of the apparent compatibility. 152 JAMES A. BALLAS Sound as Metaphor A metaphor is an extension of the simile to an identity relationship. Rather than saying that one thing 1s like another, a metaphor states that the first thing is the other. Metaphor can also be used as a class inclusion statement (Glucksberg and Keysar, 1990), but that is not how it is being used here. The reason that metaphor is introduced here is that there are some examples of sounds that take simile beyond its normal usage. These sounds convey infor- mation about a hidden process in a way that supports an identity between the sound and the process. The result is that the listener 1s provided with informa- tion about the process itself. The two examples I cite both involve an extension of hearing beyond the normal audible range. In contrast, when sounds are used as simile, the listener receives information about a value of a variable or parame- ter from the level of the sound within a particular dimension. In metaphor, the way I am using it, the perception 1s focused on the process being represented; in simile the perception is focused on the acoustic parameters. Two examples can be cited. The first is the use of Doppler ultrasonic monitor- ing to diagnose decompression sickness (Butler, Robinson, Fife, and Sutton, 1991). When a diver decompresses too rapidly, bubbles become more prevalent in the bloodstream and begin to increase in size. Ultrasonic waves are reflected by these bubbles and the echo is transferred into the audible range. Skilled listeners can assess not only the increase in the number of bubbles, but also the increase in the size. The sound delivered to the listener is artificial, but is deter- mined by the physical properties of the bubbles reflecting the waves. The sound is present ultrasonically and the equipment brings it into the audible range. The second example comes from Beizer (1984) who recommends the use of an audio monitor to assess software performance. The audio monitor is simply a radio (AM) placed near the computer to pick up electromagnetic emissions. Algo- rithms running within the machine produce different types of emissions and thus different types of sound. For example, loops will produce pitched sounds with higher pitches coming from tighter loops. Computer load will change the intensity of the sound. Updating the CRT display produces a noticeable change in the sound. In both of these examples auditory perception involves an interpretation of acoustic parameters as is the case in interpreting sounds that act as similes. The difference is that with these metaphorical sounds the interpreter is listening to sounds that are causally related to the physical properties in a nonarbitrary manner. In fact, the relationship between the sound and the phenomena is determined by the physical processes in the phenomena, not by a scheme of matching specific data values to specific acoustic parameters. INFORMATIONAL SOUND 153 Onomatopoeic Sounds as Signs of Events One way to offset the limitations of tonal signals, but take advantage of the benefits of an auditory signal, is to use sounds that imitate real sounds of events. There are many examples of real sounds in computer systems that provide information about events. These include the sounds made by a disk drive, both by the movement of the read/write head and the movement of the disk. Any computer user who has heard a disk “thrashing” (often caused by excessive swapping of data between memory and disk) will not forget the sound, and the initial worry that something serious is wrong with the disk. Formatting of a disk produces a distinctive sound, which can change as the formatting proceeds and the head is accessing different sectors. Finally, a disk drive read/write failure produces a distinctive sound if the disk driver software has been programmed to make several attempts. Experienced users probably come to rely upon the infor- mation in these sounds to monitor the status of the disk system. But these sounds and the meaning of them are not typically documented even though they provide useful information. Two types of information are available through sound: unseen activity and the status of components. The examples of how sound indicates the status of a disk drive are all examples of sound revealing the status of unseen activity. Real sounds can also be used in system evaluations. An example of how sound has been used to analyze an accident comes from Air Florida flight 90. The flight recorder recorded the sound of the throttles being pushed up to full power seconds before the plane hit the 14th Street bridge in Washington, DC. This determination was based upon an analysis of the blade passing frequency (Vance, 1986). The pilots had the engine throttle set at 75% power because of faulty instrument readings. Recent research into everyday sound perception has provided insights into the usage of event-based sounds in systems design. Gaver (1989) developed a sonic interface that uses sounds called auditory icons in a computer interface. These sounds have an intuitive basis to their meaning. These are sounds of everyday events and are used to represent the same event in the computer system. For example, in a windowed computer display, the user must often move objects around the display from one window to another. An everyday sound that suggests this event directly is the sound of a object being dragged across a surface. Gaver concluded that a prototype interface using auditory icons was effective in increasing the user’s flexibility to gain information about the system and in directly engaging the user in the system. Auditory icons may work better than current signals because they would naturally represent the intended meaning. As with simile, good examples of sound usage are found in educational soft- 154 JAMES A. BALLAS ware. For example, a software program called The Playroom presents the sounds of a bird chirping, an animal thumping its tail on the floor, clothes rustling, and the sound of a drawer opening when the child selects the appropriate object on the computer display. These sounds mimic the actual sounds that would be produced by the movements of the objects. However, the implementation of auditory icons would be limited to sounds that have a direct or metaphorical relationship to the events being represented. The advantage of auditory icons is that intuitive or natural knowledge is the basis for the user’s interpretation of the sound. This advantage would exclude conveying system messages that have no equivalent sound or which do not have a counterpart event in the everyday world. Gaver (1989) suggests that in these cases a sound be constructed which seems related to the system message either by analogy or through metaphor. Although this solution would help, there would still be system messages that could not be signaled by an auditory icon because an equivalent, analogous, or metaphorical sound is unavailable. Sound Ambiguity An issue that arises in using onomatopoeic sounds is how well the sounds can be identified, and what conditions influence the accuracy of identification. Un- fortunately, the meaning of real sounds can be ambiguous even with experi- enced operators. For example, in the crash of Delta Flight 1141 in Dallas, August 31, 1988, the pilots heard the sound of an engine compressor stall (which is like a car backfiring) several times. They interpreted this as an indicator of engine failure. The cause was a disruption of airflow to the engines, because of the unusually high pitch of the aircraft. They may have hesitated engaging full power because of the misdiagnosis of the cause of these sounds. A series of studies by myself and colleagues have examined factors related to the identification of single sounds presented in isolation and the identification of single sounds presented within a sequence of other sounds. The identification of sounds presented in isolation is related to a number of factors. In a study of 41 brief everyday sounds, Ballas (1993) found that identification time was corre- lated with causal uncertainty and was also related to ecological frequency and certain acoustic properties. Furthermore, the degree to which the sound matched a mental stereotype also affected identification time. When sounds were placed in sequences of other sounds to assess context effects, the interpretation of ambiguous sounds was influenced in expected direc- tions by the context (Ballas and Mullins, 1991). In a signal detection analyses, we found that the context produced consistent effects on response bias but had little effect on measures of sensitivity. The magnitude of the response bias was increased when a free identification paradigm was used, compared to a forced INFORMATIONAL SOUND 155 choice paradigm, but the direction of the response bias was consistent in both paradigms. These results support the importance of considering sound ambigu- ity when using everyday sounds to convey information. One way to illustrate sound ambiguity is to cluster sounds on the basis of response similarity. The clustering would reveal alternative identifications that might occur from a sound. Using data from the identification of 41 brief sounds (Ballas, 1993), a hierarchical cluster analysis was conducted using an index of causal similarity calculated from a matrix of overlapping identification re- sponses. Specifically, the identification responses for the 41 sounds were com- bined and sorted by similar response and by sound. Altogether, 1795 identifica- tion responses were sorted into categories of similar events. Restricting the categories to those that occurred for at least two sounds resulted in a total of 66 categories. A matrix was formed of 66 response categories by the 41 sounds, with the entries a binary notation of the occurrence of an event category used to identify a sound. From this matrix, a response similarity matrix (half of a 41 by 41 matrix) was generated by counting the number of event categories that pairs of sounds had in common. Response similarity was computed as follows: Si, = 1/(e; + 1) S;; = response similarity for sound 7 and sound j é,;, = number of events cited in common for sounds / and j when / is not equal to 7, number of events cited for a sound when 7 is equal to /. These data were used as similarity data in a cluster analysis. Both single linkage and complete linkage solutions produced two large clusters of the sounds, one composed mostly of impact sounds and the other composed of water, signaling, and continuous sounds. The complete linkage solution is shown in Figure |. In both solutions, the first four clusters formed are identical. The sounds within clusters in both solutions have obvious acoustic similarities. In order to determine whether ~ octave profiles capture this similarity, the 5 octave spectral values for a sound were treated as vectors, normalized, and the cosine between every pair of octave vectors computed and correlated with the distance measure defined above. The correlation was significant because of the large number of pairs (r = —0.21, p < 0.01, m = 820), but the variability in the response overlap data accounted for by octave similarity is less than 5%, R* = 0.04. Thus the similarity between sounds in this clustering is only weakly related to spectral properties. Temporal properties would probably be more important. Clustering produced groupings of water sounds and impact sounds and re- vealed identification confusions. Greater confusions would be expected within clusters that are formed to the left of the figure, where similarities are high (e.g., 156 Gunshot indoors Telephone hung up Cork pop Tree chop File drawer closed Door latched Gunshot outdoors Fireworks Stapler Automatic rifle Car backfire Electric lock Door closed Door opened Jail door closed Footsteps Door knock Hammering Clog footsteps Clock ticking Bugle Car horn Foghorn Bell buoy Toilet flush Bacon frying Water bubbling Oar rowing Water drip Church bell Touch tone Doorbell Telephone ring Light switch Boat whistle Car ignition Lawn mower Power saw Sub dive horn Sawing Cigarette lighter JAMES A. BALLAS eececes Maximum Distance Between Clusters Fig. 1. Complete linkage clustering of identification similarity data for 41 sounds. tree chop and a corkpop). These types of confusions would be expected. How- ever, according to the cluster analysis, the sound of hanging up a phone may be confused with the sound of a gunshot indoors, which would not be expected. Although this clustering is limited to the set of 41 sounds, it does illustrate the types of sounds that might be confused. Combining Functionality Several of the most recent developments in aural delivery of information combine the functions described above, especially in combining spatial refer- ence with another function. For example, the improved detection and identifi- cation of visual stimuli found by Perrott et al. (1991) is actually achieved by a combination of an exclamation and a sound providing reference to place. An example of combining simile and spatial reference was developed by Smith, Bergeron and Grinstein (1990). Their system maps data parameters to aural dimensions and provides stereo separation capability, so that the sound can be INFORMATIONAL SOUND 157 moved horizontally between the speakers, as well as cues for apparent distance. Moreover, they integrate the aural presentation of information with visual dis- plays that provide “texture” cues related to the data parameters, and thus use sound to complement and extend the simile presented visually. This example raises important questions about how aural information is integrated with con- current visual information, when both modalities are employing simile or meta- phor. Modality Integration The last issue to be discussed concerns the integration of sound with other media, particularly visual images. In any type of system except a high-fidelity simulation in which the aural and visual information is veridical with the true _ system, there will be a conceptual relationship between the stimulus properties and the information to be represented. This relationship can be described from several perspectives. Concept formation is involved, and if the stimulus cues are highly abstract, then traditional literature on concept formation may be rele- vant. However, the more typical case is one in which the mappings in the aural and visual modalities are not arbitrary but based upon effective simile. Even then, there are several issues involved. Often the information to be displayed is dynamic and represented by events and objects. Treisman (1986) suggests that fundamental differences between vision and audition exist in the definition of objects and events. Visual objects are physical structures; visual events involve some movement or change in the physical structure. Auditory objects are not defined as easily. They could be thought of as the source of the sound, the sound itself, or properties of the sound. Furthermore, the source could be thought of as either the events or the objects producing the sound. The difficulty in defining an aural object and the lack of a clear parallel between the concept of object and event in the two modalities may not arise if the intention is to simply put a sound out that is redundant with the visual image. This can be assured only when there is a unequivocal ecological linkage between the image and the sound. Unfortunately, for many of the events that occur in using computer systems, there is no ecologically valid sound and/or image. For example, compilation of code is a process for which no visual image or sound exists. In these cases, the similes used in the two modalities would have to be integrated. Treisman (1986) suggests that cross-modal integration occurs in two ways. First, the representations within each dimension could be trans- lated into separate unimodal objects which would then be integrated. For exam- ple, the image could represent the three dimensional structure of an object and the sound could represent its resonance when struck. The two object representa- tions could be combined to indicate whether the object is a block of wood or 158 JAMES A. BALLAS steel. Alternatively, the representations could be integrated prior to the defini- tion of an object. For example, the height of a bar and the pitch of a tone could be integrated to indicate relative position, which would then be used to represent the value of a parameter in a spreadsheet. There is limited information available on the integration of information from different modalities that is relevant to the design of informational sound (see Welch and Warren, 1986 for an overview). Vision may dominate in spatial orientation and shape and size perception. Audition may dominate in temporal perceptions such as duration. When a sound is simply added to complement a visual image, it may function as a label and have the potentially negative effects that have been found with labeling. For example, Rankin (1963) found that labeling a set of abstract figures was detrimental to the performance of some tasks such as drawing the figures from memory and fitting the figures together as a jigsaw puzzle. Labeling did improve the serial recall of the figures. Conclusion Although there are still formidable issues in the application of sound in mod- ern systems, advances 1n presenting sound have been made in a number of areas. The development of systematic approaches to encoding urgency may lead to the development of standardized alerting sounds. The development of 3-D audio technology that has a firm scientific basis should lead to enhanced methods of representing events and objects in space in a wide variety of applications. The addition of sounds to computer interfaces has produced new ways of using sound and stimulated analyses and research on the symbolic effectiveness of different types of sound. These developments have broadened the concept of how sound can convey information. Analogies to linguistic devices help to convey the variety of informational functions that non-speech sound can sup- port. There is much more to be learned. The scattered examples of innovative uses of sound presented in this paper are suggestive of a large variety of interest- ing, effective ways of using sound. Further documentation of sound usage would be helpful to both designers and researchers. Part of the documentation should address how sound is being integrated with other modalities, especially in newer computer interfaces and virtual reality systems which have flexibility in the design of audio and video stimuli. References Baird, J. C., & Noma, E. (1978). Fundamentals of scaling and psychophysics. New York: Wiley. 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Liggett Cockpit Integration Division Wright-Patterson AFB, Ohio ABSTRACT Rapid developments in display technologies and artificial intelligence software are dramat- ically affecting cockpit design. Beginning with the F-18 and continuing through the F-15E, cockpits have changed from presenting information on mostly electro-mechanical instru- ments to utilizing electro-optical controls and displays for the presentation of information required by the pilot. With the advent of large screen flat panel displays, the entire instru- ment panel may some day consist of one display surface where both pictorial and alphanu- meric formats will be displayed. It is very possible that some of the information will be displayed in three dimensions. The future may also bring the virtual cockpit where pilots have a 360 degree look around capability, and the instrument panel, as it is known today, is replaced by images of the instruments projected on the pilot’s visor. The purpose of this paper is to trace the history of cockpit displays, to discuss the potential impact that several emerging display concepts will have on future cockpits, and to cite the results of several experiments involving some emerging display concepts. In order to appreciate how information will be displayed in future aircraft, it is necessary to understand the type of information that is normally or traditionally displayed in aircraft cockpits, as well as the past and present methods of display- ing this information. Performing a very top level task analysis of the pilot’s job in a single seat, military aircraft will provide us with an understanding of the type of information to be displayed. Many aircraft have more than one crewmember, but it is felt that the most difficult job of information portrayal is in single seat aircraft. History of Cockpits The primary role of pilots is to control the aircraft, although in commercial aircraft this job is often taken over by the flight management system. Control information includes attitude and power indicators. Even if pilots are not man- ually controlling the aircraft, they still need to know the flight status; therefore, performance information is of prime importance. An aircraft in flight is always 161 162 REISING, EMERSON AND LIGGETT proceeding to a particular destination so navigation information is also crucial to pilots. Pilots are also concerned with the health of the systems aboard the aircraft. While the systems are performing normally, system status information is of prime importance; if problem situations occur, emergency information is crucial. For military aircraft, additional information is required to keep pilots abreast of the enemy and the threat that they may pose to them. This knowledge is called tactical situation information. The challenge for the cockpit designer is to present all of this information in a manner which does not overload pilots’ information processing capability. In addition, the manner of information pre- sentation is often affected by the particular technologies (controls and displays) available at the time. For the purposes of this short historical account, cockpit technology will be broken into two areas—the electro-mechanical (E-M) and the electro-optical (E-O). Electro-mechanical Instrument Provided Information The use of E-M instruments can be traced to some very early days of flight, roughly 1920, and a Cathode Ray Tube (CRT) E-O display was flight tested by United Airlines in 1937. However, the “era” of E-M cockpits is generally ac- cepted to be from the introduction of vertical tape instruments seen in Figure 1, (late 1950s), until the Navy F-18, with its multi-purpose CRTs, was introduced in the early 1980s. E-M instruments are in extensive use today, but are gradually being replaced by E-O displays. In most cases, the E-M instruments are powered by electricity and have a display driver that is a combined electrical and me- chanical device. The driver converts an electrical signal to movement of a dis- play element, such as a pointer, over the display surface or to movement of the entire display surface, such as an attitude ball. A small servomechanism is often employed as the driver. Many E-M instruments are single purpose or dedicated to the display of a single flight parameter, such as engine RPMs. The tape displays referred to earlier are dedicated displays, although they use one instru- ment case to contain several drive mechanisms for several different parameters. For example, in Figure 1, the vertical tape instrument on the left has individual drivers for mach number, airspeed, and acceleration, while the tape instrument on the right displays rate-of-climb, vernier altitude, and gross altitude. Similarly attitude and navigation instruments display more than one parameter. Impact of the Electro-Optical Cockpit There has been a dramatic reduction in the use of single-purpose controls and displays in modern aircraft crewstations. For example, the Boeing 747-400 (the latest version of this aircraft) contains 365 lights, gages and switches; the pre- vious models of the 747 contain 971 of these devices (Avionics, 1988). The DISPLAYING INFORMATION 163 AIRSPEED RATE OF CLIMB MACH NUMBER VERNIER ALTITUDE ACCELERATION GROSS ALTITUDE ¢ mM ro) ll Serres ae it > “Taal \ q 5 a 3 ripe on zn COMMAND MACH KNOTS 110 COMMAND 2992 32300 = TARGET & : (39300 ot e J Wes Fig. 1. Electro-mechanical Flight Displays. primary cause for this reduction was the substitution of multifunction E-O displays, which can include many pages of information, for the single purpose displays. The reason multifunction displays were required was that the spare cockpit real estate was already very scarce in the earlier cockpit versions, and with an increase in the number of cockpit monitoring/controlling systems that needed to be included in the cockpit, a new display design philosophy was needed. Also, with the entire cockpit real estate filled with single purpose dis- plays and controls, the time for the pilot’s scan of the displays and controls can become excessive. The incorporation of E-O displays and controls in the cockpit can help solve the limited real estate problem with the use of multifunctional displays. The 747-400 contains six 8-inch color CRTs and three multifunction control/display units. This same trend has become evident in military cockpits as well, especially in fighter aircraft. A view of Figure 2, an F-111 E-M cockpit, and Figure 3, an F-18 cockpit with three multifunction displays, illustrate the trend from single-purpose displays and controls to their multifunctional coun- terparts. When the Navy introduced the F-18 into the inventory, the aircraft had a 164 REISING, EMERSON AND LIGGETT Fig. 2. F-111 Cockpit. profound impact on cockpit control/display (C/D) research. With the multi- functional capability, emphasis in cockpit research shifted into the area of infor- mation processing, specifically, how to best “‘package”’ display formats and con- trol menus so that pilots don’t drown in an overflow of data or get lost in the bowels of a very complicated control logic structure. In addition, there was an ability to change the way information could be presented since the flexibility of the CRTs allowed many different variations of the information formats—a capability not available with E-M instruments. Early Electro-Optical Display Provided Information In the early military and civilian E-O cockpits, the format of the information presented was, by and large, the same as that presented on the E-M instruments. There are several reasons for this. The first is that the introduction of E-O displays into the cockpit was a radical departure from previous designs; there- fore, in order to prevent “‘culture shock’’, the decision was made to emulate the same picture on the CRT that the pilot was used to seeing on his E-M instru- DISPLAYING INFORMATION 165 dose wow, / @-s e ‘ es $ Fig. 3. F-18 Cockpit. ments. Another reason that more advanced display formats were not used 1s that the symbol generator (the computer that draws the format) was not capable of drawing more complicated pictures. However, current developments in air- borne graphics generators will make more detailed or advanced formats possi- ble. Still another reason why the E-O formats didn’t differ from the E-M instru- ments is that the advanced formats had not been developed to the stage where pilots had complete confidence in them. As pilots become more experienced with E-O displays, cockpit designers can explore their potential by developing formats that represent a total paradigm shift from replicating the E-M instru- ments on the CRT. ; Types of Advanced Technology and their Potential Payoff Before discussing in detail the computer-generated formats which will appear in future aircraft, it is necessary to briefly review the hardware and software 166 REISING, EMERSON AND LIGGETT advances which will affect both the type of information presented and the man- ner in which it is shown. The most important technological developments are flat panel displays and artificially intelligent software. Flat Panel Displays The term “flat panel’? comes about because these devices occupy a much smaller relative depth than does a CRT. Another term, “matrix addressed dis- plays’, is used because of the manner in which the individual picture elements (pixels) are activated. The pixels are arranged in an X, Y matrix, as are the electrical connections (drivers) which activate them. When an electrical signal is sent across a particular row and column, the pixel at the intersection of the row and column is activated (Haralson, Reising, & Ghrayeb, 1989). When matrix addressed displays reach maturity, they are expected to offer several advantages over CRTs. The most publicized advantage is in the area of reliability; the displays are expected to be an order of magnitude more reliable when compared to CRTs. While the reliability of airborne CRTs is often in the hundreds of hours, flat panel displays are expected to be in the thousands. Matrix addressed displays are different from the CRT in one fundamental aspect, which is very important for information presentation in the cockpit— the surface size of the display, through continued development, has the potential to increase until one display covers the entire instrument panel (300 sq. in.). The largest CRTs in fighter aircraft have a surface area of approximately 40 sq. in. (Olson, Arbak, & Jauer, 1991). Therefore, by using flat panels, the canvass on which to paint the display picture becomes an order of magnitude larger, and the designer can now contemplate display formats which were not possible with older technology. Artificial Intelligence In recent years, a number of authors have introduced the concept of providing assistance to the pilot through expertise residing in a combination of conven- tional and artificially intelligent software. (Small, Lizza, & Zenyuh, 1989). This software combination has been referred to as an electronic flight engineer, a pilot’s associate, and an electronic crewmember (EC). Regardless of what it is called, this concept is importanf to the display of cockpit information since it will have a major impact on both what is displayed and how the information is presented. Hereafter we will refer to the concept as the EC. EC enhancement of pilot’s situation awareness. Situation awareness (SA) can be defined as the crew’s knowledge of both the internal and external states of the aircraft, as well as the environment in which it is operating. The internal state of the aircraft refers to the “health” of its utility systems, such as hydraulics, electri- DISPLAYING INFORMATION 167 cal, and fuel; and to its mission equipment, such as radar and weapons. In order for pilots to be aware of systems’ status, the systems have to be monitored. If pilots have to do this, it can require a significant amount of their time, and it can also cause boredom since it is not a very challenging task. However, the EC can do this very efficiently and keep pilots well informed as to the situation by presenting status information upon pilot request or automatically in the case of failure—and the EC never gets bored. The EC, through sensors located in the aircraft’s skin and at the flight control surfaces, will also have in-depth knowledge of the external states of the aircraft. Because of the EC’s tremendous processing throughput, it can update its knowl- edge base orders of magnitude faster than can pilots. For example, the EC can monitor the systems assigned to it at a 30 Hz rate, something impractical for _ pilots. It can determine if flight control surfaces have been damaged and decide if flight control reconfiguration is required to give pilots the best performance possible. The impact of the damage on mission success can also be displayed to pilots. The aircraft’s external environment is especially important since it directly affects safety (location of terrain or threats) and mission success (weather condi- tions or target location). The EC’s sensors will play a crucial role in providing a clear picture of this environment. The EC, through the use of digital data bases combined with pictorial graphics and stored photographs, will be able to provide pilots with previews of their waypoints and target areas. In addition, this capabil- ity will allow pilots to look ahead and preview the run-in to the target long before they actually arrive in the area. This kind of knowledge is crucial to giving pilots the SA they need to stay ahead of the mission. EC fused data. The EC could easily inundate the pilot with data since it has such a high processing rate. In order to avoid this problem, the EC must com- bine (fuse) data into higher level information packages. It is at this higher level that the EC will communicate its information to pilots; the form of the commu- nication could be verbal through a voice interactive system, or visual through computer generated, pictorial formats. The form of the communication will depend on which of the pilot’s processing resources are least loaded at the time, and the type of information to be conveyed. The fused information is crucial to SA because it allows pilots to operate at an executive level and deal with only the most important mission-level decisions. Specific examples of formats which will display fused information are discussed in the following section. Advanced Graphic Formats The formats reviewed in this section are related to the previously discussed information needed by pilots. Specifically, control and limited performance 168 REISING, EMERSON AND LIGGETT FLIGHT PATH MARKER HEADING INDICATOR FOLLOW-ME AIRCRAFT | ALTIMETER AIRSPEED ON me INDICATOR e°%e SS oo te Pca Cea ; P e 1756 e r) @ e e DME 12.8 on Fig. 4. Pathway-in-the-Sky Display. information is provided by the Pathway-in-the-Sky; navigation and tactical situ- ation information is provided by the Tactical Situation Display; and systems’ status and emergency information is provided by the Crew Alerting and Sys- tems’ Status Display. An additional feature which has been added to a number of these display formats is 3-D stereo which will provide the formats with added realism. Pathway-in-the-Sky The heart of the advanced flight display is the Pathway-in-the-Sky (Hoover, Cronauer, & Shelly, 1985). The Pathway can take the place of both the flight path angle scale and the flight path marker symbology currently used on head up displays (HUDs). The Pathway consists of a series of blocks configured to resem- ble a highway (Figure 4). In addition, a “follow me” aircraft appears at a particu- lar distance above the path and acts as both a speed and altitude cue when pilots fly in formation with it. The advantage of the path is that it gives pilots a means of determining what their 3-dimensional route will be like in the future and how to maintain their commanded airspeed in a very natural manner, by flying in formation with the follow me aircraft. Thus pilots can view the path in the distance and anticipate the turns, climbs, and dives; whereas, today’s HUD displays only depict the route location at the present time and do not provide knowledge about future maneuvers. DISPLAYING INFORMATION 169 Fig. 5. Tactical Situation Display. Tactical Situation Display (TSD) The TSD (Figure 5) portrays to pilots the EC’s fused data regarding both navigation and tactical information. Through the use ofa pictorial format which chunks the data, the TSD can reduce information overload and aid decision making. The TSD combines, in a perspective view, the aircraft symbol with terrain data and threat data. It gives pilots a look at the overall tactical situation as it is developing before them out to the horizon, e.g., 20 miles away. There is a pathway extending ahead of the aircraft which shows where the aircraft will go if pilots allow it to follow the pre-planned path. One of the issues faced in design- ing a perspective display is the elevation and angle of the viewpoint (McCleary, Jenks, & Ellis, 1991). The viewpoint chosen for initial perspective displays was | mile behind and 1000 feet above the pilot’s own aircraft; the lookdown angel was 30 degrees (Way, Martin, Gilmour, Hornsby, & Edwards, 1987). However, in future displays, operators will have a continuously adjustable viewpoint. The 170 REISING, EMERSON AND LIGGETT | Mission Critical | ' ands ! Flight Safety ! Fig. 6. Crew Alerting and Systems Status Display. (From Way, Hobbs, Qualy-White, and Gilmour, 1990). continuous elevation adjustment is analogous to riding in an elevator. The continuous lookdown angle is similar to standing on an observation tower look- ing straight down to the earth, and then slowly raising your head until your gaze is level with the horizon. All of these views will be available to pilots. The terrain can be color-coded to portray additional information to pilots. For instance, the ground, which is below the current flight level, can be green, and the ground at or above the current flight level can be brown. In addition, ground-based threats, such as surface to air missile (SAM) and radar-directed anti-aircraft artillery (AAA) sites can be shown in perspective view and are also color-coded. The area of greatest potential lethality to the aircraft can be shown in red and the area of lesser lethality can be shown in yellow. Additional infor- mation can be given to the pilot about the status of each threat by further coding dimensions: threat sites with known locations, but not active, can be shown as outline polygons only; sites which are actively searching can be shown as filled- in solid polygons; sites which are tracking the aircraft can be connected to your aircraft symbol with a vector; and a site which has launched a weapon against the aircraft can be connected to your aircraft symbol by a blinking vector. In the last two conditions, a circle can be shown around the aircraft symbol, filled in yellow if on-board countermeasures are effectively countering the threat, and filled in red if countermeasures are not being effective. Crew Alerting and System Status Format An example of a new type of graphics display which gives the pilot the overall health of his primary systems is the Crew Alerting and System Status (CASS) format (Figure 6). “CASS had several purposes: it provided full time dynamic DISPLAYING INFORMATION 171 display of fuel quantity and engine thrust; it alerted the pilot to system malfunc- tions; and it identified mission or flight safety implications of those malfunc- tions.” (Way, Hobbs, Qualy-White, & Gilmour, 1990, p. 73-74). One of the unique aspects of this display is that 1t could not only show which system had failed, e.g., the left engine, but it could also show the mission impact. The mission impact is the overall effect of the particular failure on the successful completion of the mission. In the case of the engine failure, for example, the impact would be on the speed/performance aspect of mission performance. An additional display would then show, for example, the particular restrictions in speed/performance and how it would impact times over target. Three Dimensional Stereo Formats The Tactical Situation Display previously discussed utilizes formats that can - provide the pilot with the SA needed for successful mission completion. SA is especially important for fighter/attack aircraft which have very demanding mis- sions and have a crew of only one or two people. Mission success and survivabil- ity in such an environment are dependent upon the crew’s knowledge of surrounding aircraft, ground targets and threats, and topographical layout; therefore, the crew must create a 3-dimensional (3-D) model of their environ- ment in order to obtain adequate SA. However the perspective view, sometimes called 2--D, does not totally map onto the 3-D mode. In order to provide the most veridical information to the crew, researchers have begun to utilize com- puter generated display formats incorporating not only monocular depth cues typical of most displays today, but also containing binocular cues which allow the displaying of a true 3-D situation. However, since the display formats will contain both monocular and binocular depth cues, a brief discussion of them is in order. Cues to depth. There are two basic types of cues by which humans judge depth: monocular and binocular. Monocular cues are those characteristics of a given scene from which perceived depth information can be derived using only one eye. Monocular cues include linear perspective, interposition, familiar ob- ject size, texture gradients, shadow patterns, etc. (Goldstein, 1984). The binocular cues, on the other hand, require the use of both eyes. The two binocular cues are convergence and stereopsis. Convergence can be thought of as range-finder cue to depth; ““. . . the eyes pivot inwards for viewing near objects, and distance is signaled to the brain by this angle of convergence” (Gregory, 1973, p. 51). The other binocular depth cue, stereopsis, involves “‘dif- ferences in depth stimulated by retinal disparity” (Schor, 1991, p. 547). Because the eyes are separated, the image on each eye is slightly different. The integration of these two images within the visual system results in the perception of depth. 172 REISING, EMERSON AND LIGGETT The binocular cue of stereopsis can now be presented on computer generated displays through the use of display systems which present a different picture to each eye. Several studies employing stereo 3-D, which will be described next, utilized a display system consisting of: 1) a graphic display controller, 2) liquid crystal display shuttering goggles made by the Stereographics Corporation, and 3) a Hitachi high resolution red-green-blue (RGB) monitor modified to run with a vertical scan rate of 120 Hz. The faster vertical scan rate allows the nght and left views of the object to be displayed alternately, resulting in a 60 Hz rate for each view (Opp, Reising, & Zenyuh, 1988). The result is a flicker free, dynamic 3-D display. By utilizing this type of equipment, it 1s possible to create computer generated displays which have a potential large payoff to pilots of future aircraft. 3-D stereo and the TSD. A perspective view, stereo 3-D map offers great promise to pilots by unburdening them from translating information from the 2-D TSD display and constructing a representational 3-D, dynamically chang- ing world. However, once such a map is created, how does the pilot mark items of interest in stereo 3-D space? Marking items is a common task on a map display and the inclusion of stereo 3-D resulted in needed research that focused on how to solve this potential problem. This study explored the use of two types of continuous cursor controllers and one discrete controller to manipulate a cursor in stereo 3-D space in order to designate targets on a map. The continuous controllers were a multi-axis joy- stick and an ultrasonic hand tracker. A voice control system was the discrete controller. Based on previous research in this area (Reising, Liggett, Rate, & Hartsock, 1992) it was determined that the use of aiding techniques with continuous controllers could enhance the pilot’s performance when designating targets. Therefore, this research also investigated two types of aiding. Contact aiding consisted of providing the subjects with position feedback information via a color change of the target once the cursor came in contact with it (Figure 7). This type of aiding eliminates some of the precise positioning necessary when using the cursor to designate targets. Proximity aiding removed precise positioning completely by using the Pythagorean Theorem to calculate the distance between the cursor and all other targets on the screen (Osga, 1991). To aid the user, the target in closest proximity to the cursor was automatically selected (Figure 7). These two types of aiding applied to the joystick and hand tracker devices only. The display formats consisted of a perspective view map which contained typical features, targets, and terrain. The targets could be presented in different depth volumes within the stereo 3-D scene. Four depth volumes were used for this study and they included Front (perceived as 1-7 inches in front of the screen volume), Screen (a 1 inch area at the screen plane), Behind (perceived as 1-7 DISPLAYING INFORMATION 173 CONTACT PROXIMITY Fig. 7. Types of Aiding (Solid circle indicates selected target). inches behind the screen volume), and Far Behind (perceived as 7-14 inches behind the screen volume) (Figure 8). Results showed that the dominant factor turned out to be type of aiding. Recall that proximity aiding was coupled with the two continuous controllers. Subjects designated targets significantly faster with proximity aiding than with contact aiding. Proximity aiding was also significantly faster than voice. (Figure 9). When using a continuous controller, there are two components to position- ing: gross and precise movements. The addition of proximity aiding to both continuous controllers greatly reduced gross positioning and eliminated precise positioning. Contact aiding, on the other hand, did not affect gross positioning but decreased the amount of precise positioning. There was a second feature of proximity aiding which contributed to its superiority over contact aiding. Since FAR BEHIND DEPTH VOLUME 7-14 INCHES BEHIND Y DEPTH VOLUME 1-7 INCHES CREEN PLANE 1 INCH FRONT DEPTH VOLUME 1-7 INCHES Fig. 8. Depth Volumes within the 3-D Scene. VIEW 174 REISING, EMERSON AND LIGGETT HT - HAND TRACKER JS - JOYSTICK P - PROXIMITY C - CONTACT Total Task Time (Sec.) HT/P JS/P VOICE HT/C JS/C Combination Fig. 9. Total Target Designation Time by Device/Aiding Combination. the proximity algorithm was continually computing the distance to the targets, as soon as one target was designated, the cursor would automatically jump to the next target. The voice control system, being a discrete controller, had no positioning error. Therefore, the voice system performed faster than the continuous con- trollers coupled with contact aiding. Voice was slower than the proximity aided conditions because the subjects had to recite each target and it’s identifying number before the cursor would move. Because there was no automatic jump- ing of the cursor in the voice condition, there was a time delay. The reported research demonstrated that there are many methods for aiding the pilot in the use of these types of displays. Specifically, the hand tracker or joystick coupled with proximity aiding was most effective continuous controller for the task of designating multiple targets on a stereo 3-D perspective map display. The voice system, while taking somewhat longer than the continuous controllers with proximity aiding, also appears to have significant potential as a control mechanism in glass cockpits—especially when the pilot cannot take his hands off of the stick and throttle. 3-D stereo and the Pathway-in-the-Sky. Stereo 3-D has been shown to en- hance pilot performance when added to formats which attempt to portray spa- tially related objects, such as a series of aircraft in flight (Reising & Mazur, 1990). An additional research issue concerned the payoff of adding stereo 3-D to displays already possessing a very powerful monocular cue, for example, the cue of linear perspective that makes a roadway or railroad track disappear in the distance. This question was addressed in a study (Reising, Barthelemy, & Hart- sock, 1989) conducted to examine the addition of stereo 3-D to the Pathway-in- the-Sky. Recall that one of the key features of the Pathway is that pilots would be DISPLAYING INFORMATION 175 HRMS FEET HUD TWO-D : THREE-D DISPLAY TYPE Fig. 10. Horizontal Root Mean Square Error for Three Display Formats. able to preview the path ahead, and, therefore, anticipate changes in altitude and/or heading. Adding stereo 3-D depth cues to the path should further aid pilots in obtaining SA by showing how far out in space the path will turn or change altitude. The purpose of this study was to evaluate the effectiveness of a two-dimensional pathway, a three-dimensional pathway, and a two-dimen- sional HUD when flying a preprogrammed route. The results showed that pilots performed significantly better when using either the 2-D or the stereo 3-D path than they did when using the HUD; however, there was no significant difference in performance between the two versions of the path (Figure 10). Performance with the stereo 3-D path was not significantly better than with the 2-D path. A possible interpretation of this fact is that the 2-D path provided sufficient mon- ocular depth cues through the use of linear perspective, relative motion, and interposition. For instance, through the use of linear perspective the portion of the pathway seen in the distance appeared smaller and seemed to converge at the horizon. Since perspective is the most important monocular cue (Kaufman, 1974), it will contribute greatly to the perception of depth. The pathway also possessed the monocular depth cue of interposition, which Kaufman (1974, p. 230) states“. . . isan extraordinarily potent cue to relative distance.” Since the 2-D path possessed both of these very powerful monocular depth cues, there was little for stereo to add. Therefore, the 2-D version was virtually as intuitive as the 3-D path. 176 REISING, EMERSON AND LIGGETT Conclusion The computer graphics revolution has removed the constraints on display designers, and they are limited primarily by their own creativity in providing display formats for future aircraft cockpits. It is the coupling of the high resolu- tion flat panel displays with advanced graphics generators that will enable the crew station designer to produce formats which will dramatically improve the pilot’s ability to obtain clearer, yet more complete, SA data in the tactical arena than is presently possible. These color, pictorial formats will also enable pilots to “stay ahead” of their mission and allow them to act as a fast-time information processor. With the increased exposure of the general population to computer-oriented products and particularly with the younger generations’ “unquestioning” accep- tance of, and skill acquisition in, playing video games, it is fairly certain that pilots of the future will readily adapt to using similar appearing advanced tech- nology in the cockpit. In fact, during many of the recent experiments to test advanced technology applications in the cockpit, the authors have noted that there is much greater acceptance of new technology among pilots today than there was even a few years ago. The reasons may be as subtle as technology “creep” —the gradual acclimation of people (pilots) to an expanding technologi- cally-oriented environment, or they may be as striking as the Gulf War—which demonstrated the advantages technology can provide for minimizing personnel and equipment losses. Whatever those reasons, it is crucial that the same kinds of technology continue to be used and improved, and that this technology be adapted to the unique requirements of pilots. Through the development and testing of computer graphics formats now, along with being cognizant of ad- vances in display hardware and artificially intelligent software, the smooth tran- sitioning of the next generation pilot can be assured. References Avionics. (1988). 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Grunwald (Eds.), Pictorial communication in virtual and real environments. (pp. 76-96). London: Taylor & Francis. Olson, J. L., Arbak, C. J., & Jauer, R. A. (1991). Panoramic cockpit control and display system. volume 2: DISPLAYING INFORMATION 177 pecads 2000. (Tech. Report AFWAL-TR-88-1038). Wright-Patterson Air Force Base, OH: Flight Dynamics Laboratory. Opp, R. O., Reising, J. M., & Zenyuh, J. P. (1988). Stereo 3-d displays for cockpits. In Proceedings of the eighth digital avionics conference. San Jose, CA: DASC. Osga, G. A. (1991). Using enlarged target area and constant visual feedback to aid cursor positioning tasks. In Proceedings of the human factors society 35th annual meeting. (pp. 369-373). Santa Monica, CA: Human Factors Society. Reising, J. M., Barthelemy, K. K., & Hartsock, D.C. (1989). Pathway-in-the-sky evaluation. In Proceedings of the fifth symposium on aviation psychology. Columbus, Ohio: Ohio State University. Reising, J. M., Liggett, K. K., Rate, C., & Hartsock, D. C. (1992). 3-D target designation using two control devices and an aiding technique. In Proceedings of the SPIE/SPSE symposium on electronic imaging science and technology. Bellingham, WA: SPIE. Reising, J. M. & Mazur, K. M. (1990). 3-D displays for cockpits: where they payoff. In Proceedings of the SPIE/SPSE symposium on electronic imaging science and technology. Santa Clara, CA: SPIE. Schor, C. (1991). Spatial constraints of stereopsis in video displays. In S. R. Ellis (Ed.), Pictorial communica- tion in virtual and real environments. (pp. 546-557). London: Taylor & Francis. Small, R. L., Lizza, C. S., & Zenyuh, J. P. (1989). The pilot’s associate: today and tomorrow. In The human- electronic crew: can they work together? (pp. 133-138). (Tech. Report WRDC-TR-89-7008) Wright-Patter- son Air Force Base, OH: Flight Dynamics Laboratory. _ Way, T. C., Hobbs, R. E., Qualy-White, J., & Gilmour, J. D. (1990). 3-D imagry cockpit display development. (Tech. Report WRDC-TR-90-7003) Wright-Patterson Air Force Base, OH: Flight Dynamics Laboratory. Way, T. C., Martin, R. L., Gilmour, J. D., Hornsby, M. E., & Edwards, R. E. (1987). Multi-crew pictorial format display evaluation. (Tech. Report AFWAL-TR-87-3047) Wright-Patterson Air Force Base, OH: Flight Dynamics Laboratory. e Le 7 Hell Ndaar ok’ i ‘ oN < uy a ae : : gvAct 2 A ' Rafe A ew 'T ti ; ‘e 7 } pe aaa, , I Py ‘ ‘i ayy Peg tay Saye ‘ f i VES orm 3 En Tee f Ax A DPT 2 ae how ; Ce 20, : # mf ? an Spr Ler 2 whine”, Ly oe i NA a i ty, . -_ f ; ; . permleraesiscninwnateisnrg rr ih reopuenpionnr pep aerie oro ‘i < 3 ‘i Ns i x ‘ 7 way HAS tee) aN 19 ANN ee " 4 ¥- OK a < = ee , ‘eae , n F| ¢ oa MAGEE LS yeh 32 ng 1 Ag Lt Sado i P We Er, c ; % } Kat i vplae Bia sinh PN, an vataonse: Sch me aE DELEGATES TO THE WASHINGTON ACADEMY OF SCIENCES, REPRESENTING THE LOCAL AFFILIATED SOCIETIES PSPC) SOCICLY OL WASMINSION 25 slic teen senses ccene wrap eecene Thomas R. 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Second Class postage paid at Washington, DC and additional mailing offices. Journal of the Washington Academy of Sciences, Volume 83, Number 4, Pages 179-201, December 1993 Cognitive Factors in Display Design Christopher D. Wickens University of Illinois at Urbana-Champaign Aviation Research Laboratory Savoy, Illinois ABSTRACT I discuss three generic approaches to using advanced technology to improve the layout and configuration of multiple displays for complex systems. All are based upon appreciation of the linkage between the perception of the displayed material (dictated by its display format), and the cognitive understanding of that material, necessary for task performance. I first address the form of the display array, by describing the proximity compatibility principle, which focuses on making physically (and perceptually) similar, those displays whose infor- mation needs to be integrated. Within this section, I also address an emerging tradeoff between the consistency of information format and the flexibility of that format, and present a proposed way of addressing this tradeoff through the principle of visual momentum. I next consider the particular problems associated with “hiding” displayed information in elec- tronic space. Finally I address ways in which adherence to various display design principles discussed earlier, can be satisfied through computational models of display layout and organi- zation. Cognitive Factors in Display Design The overall purpose of a display is to present information to the human operator at the time that it is needed (when), at a location that requires little effort to access (where), and in a format in which it can be understood correctly with little cognitive effort (how). The conventional, historical approach to dis- play design has not always been consistent with these goals, as it has often presented information all in one (or a restricted number) of formats, dictated by mechanical constraints (e.g., round dial “steam gauges’), and on dedicated display panels (Figure 1). Implicitly or explicitly it has been assumed that the operator’s mental model would be the guide for when each display needed to be sampled and where it was located. Training would be the guide for interpreting Send correspondence to Dr. Chris Wickens, Aviation Research Laboratory, University of Illinois, One Airport Road, Savoy, Il 61874 179 180 WICKENS Fig. 1. Illustrates multiple display indicators of a single uniform, type. the meaning of the values and trends on the indicators. Information access was obtained via visual scanning and head movement. The costs of this approach, however, became readily apparent. First, in many systems, such as the aircraft or the nuclear power plant, there was an exponential increase in the amount of information that designers felt needed to be displayed, thereby creating the real estate problem (Figure 2). Secondly, even well trained operators were found to make mistakes in interpreting such information, hence suggesting that even extensive training would not fully address problems of interpretation. The classic study by Fitts, et al., (1950), revealing how well trained pilots misinterpreted the altitude as depicted on round dial altimeters, is an example here. Three general categories of solutions to these problems will be discussed here. First, display technology can be used to enhance the FORM with which infor- mation is presented. Such technology includes the judicious use of color, three- dimensionality and display integration. Secondly, software developments have the potential of enhancing the flexibility of information presented—what is presented where, when—and hence addressing the problem of cluttered, crowded real estate through the development of multifunction displays. Finally, COMPUTATIONAL MODELS, based upon task and information analysis can guide the LOCATION of information in a way that best serves the user’s mental model. Yet these three solutions, and particularly the first and second, must be 1m- plemented with caution. There is a clear need for data-driven principles to COGNITIVE FACTORS 181 140 Displays are defined as dial and CONCORDE @ pointer instruments, digital read- . outs and cathode-ray tubes. LOCKHEED L-1011 @ DOUGLAS DC-9 BOEING 707 @ LOCKHEED CONSTELLATION NUMBER OF DISPLAYS ~“N oO @- DOUGLAS DC-3 _-® FORD TRI-MOTOR 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 SOURCE: Lockheed Corp. YEAR Fig. 2. Illustrates the growth in number of displays in commercial air transport, causing the “real estate” problem. address the display-cognitive interface when a user interacts with a system. That is, how the form in which the information is rendered can best serve the content, necessary to carry out the task, in a way that maximizes the interpretability and minimizes cognitive effort, allowing the operator to update his/her mental model of the system, in order to carry out the task. In the following pages, I will talk in detail about three principles that deal with this match between form and content, the proximity compatibility principle, the consistency-flexibility trade- off and visual momentum, all having in common the fact that they address the display representation of multiple channels of information. Principles of Multielement Displays The Proximity Compatibility Principle (PCP) Display layout and display integration both call for bringing certain sources of information “close” to each other (and therefore, by implication, farther away 182 WICKENS from others). Which sources of information should be grouped? How close? and by what means? The proximity compatibility principle (Wickens, 1992a) in its most basic form says that, to the extent that two information sources must be integrated mentally (close mental proximity), they should be displayed close together physically (close display, or perceptual proximity); to the extent that the two sources should be processed independently, such that the information in one has no bearing on the appropriate response to the other, then they can, and perhaps should be presented at more distant proximity from each other. This compatibil- ity of close mental proximity with close display proximity defines an interaction as a prescription for optimal display configuration, as shown in Figure 3, an interaction which can actually take on several different specific forms. There are, of course, several different ways of creating psychological proxim- ity or similarity between a pair or set of displays, and we shall consider five of these here; objectness, dimensional integrality, emergent features, color and space. Objectness. Objects have two general characteristics. Their parts are con- nected (generally by contours), and they have a certain “‘rigidness”’ of their parts across transformations. In addition, theories of perception suggest that configu- ration of separate dimensions as parts of a single object bring them “‘psychologi- cally close’ (Kahneman and Treisman, 1984), in a way that meets the criterion of close perceptual or display proximity in the PCP. Indeed three examples suggest the advantages of configuring object displays to facilitate information integration. First, the classic attitude display indicator in aviation renders the pitch and bank as two dimensions (vertical position and angle) of a single object (the artificial horizon). Such design, while not guided explicitly by the PCP, is highly consistent with it, since these two parameters must often be integrated by the pilot (close mental proximity) in coordinated turns. Secondly, in Figure 4 we see that the line graph in the lower panel, presents a greater psychological sense of relatedness (proximity) between the various points, than does its counterpart, the bar graph at the top. This object integra- tion by the line graph allows more effective integration of the information in the task of trend analysis, than does the separation of the bar graph, a finding empirically validated by Schutz (1961) and Carswell (1992). (See also Goettl, et al., (1991) for similar conclusions). At the same time, the more distant proxim- ity of the bar graph may allow more effective focusing of attention on single values. Thirdly, a ‘““mesh”’ surface, across separated points in a 3-D graph, such as that shown at the bottom of Figure 5, allows more effective integration of the overall pattern of the surface, than that provided by the separated display at the top COGNITIVE FACTORS 183 INDEPENDENT GOOD FOCUSED ATTENTION, DUALTASK O Low Cx -- O TASK PROXIMITY ao ee Y HIGH @————— “ INTEGRATION POOR CLOSE DISTANT (SIMILAR) DISPLAY PROXIMITY (DIFFERENT) CLOSE DISPLAY PROX DISTANT CLOSE DISPLAY PROX DISTANT CLOSE DISPLAY PROX DISTANT CLOSE DISPLAY PROX DISTANT Fig. 3. Possible interactions predicted by the proximity compatibility principle. 184 WICKENS Production Units Production ® Factory A Units 1984 1985 1986 1987 1988 oa Factory B Fig. 4. Separate bar graph (top) versus integrated line graph (bottom) (from Wickens, 1992). which, in turn, supports more effective reading of the height of specific points. (Liu and Wickens, 1992). The mesh 1s the three-dimensional object counterpart of the two-dimensional line 1n the line graph. Dimensional integrality. Another way of creating a more “‘proximal” rela- tion between different sources of information is to combine them in higher dimensional representations. Thus two one-dimensional bar graphs can be combined as a single two- dimensional point (a single object). A data point in 3D Euclidian space, can be represented as either two points, on each of two planar graphs (XY and ZY), or as a single point in a 3D volume. Indeed each of these means of attaining close proximity through “‘dimensional integrality” was exam- ined; comparing bar graphs with point plots (Goettl, et al., 1991), and compar- ing planar with volume representation (Wickens, et al., 1994). In each case, the higher dimensional representation was found to provide selective advantages when, and only when, information needed to be integrated across data points. Figure 6 shows the displays used by Wickens, et al., (1994) for a task resembling COGNITIVE FACTORS 185 Random-line display Clustered-line display Random-surface display Clustered-surface display Fig. 5. Separated bar graph (top) versus mesh (bottom) (from Liu & Wickens, 1992). that confronting scientists who are visualizing their data. The 3D rendering was most advantageous only for those questions that required integration across data points and across dimensions. The two- and three-dimensional formats shown in Figure 7 are displays for flight path guidance compared by Haskell and Wickens (1993). In the first panel, the three dimensions of aircraft motion are shown in separate forward looking, top view and side view displays. In the second integrated view, the aircraft motion is shown in a single perspective display. The contrast between these views also revealed an advantage of the integrated 3D format for the integrative task of flight path control; which requires integration across all three axes of space, but this advantage was selective, in that it was not found for the more separated task of airspeed control, which requires more focused attention along a single axis. It is important to note that there are many aspects of three-dimensional dis- play technology that are not incorporated in the considerations of the proximity compatibility principle (Wickens et al., 1989; Reising et al., 1993). Emergent features. Sometimes the perceptual “belongingness” of two or more elements results because a “‘feature’’ emerges out of their particular collo- cation and orientation, that would not exist if they were displayed differently. For example, combining two indicators of linear extent as the height and width of a rectangle (rather than, say two bar graphs) creates two emergent features of area and shape, which would simply not exist in their more separate representa- tion. In fact, many of the advantages to object displays result because the objects DN Z. fa) se = 5 186 Ln Fig. 6. Separated 2 planar display (top) versus integrated perspective display (bottom) for data visualization (from Wickens, Merwin, & Lin, '994). COGNITIVE FACTORS 187 are created in such a way that their emergent features map directly on to task integration requirements (Barnett and Wickens, 1988; Wickens and Andre, 1990; Cole, 1986). For example, think of all of the situations in which two dimensions combine multiplicatively to create a third dimension that is also relevant to the display user: distance = velocity X time; amount = rate X time; volume = pressure X temperature; information value = diagnosticity X reliabil- ity. In these, and many other cases, an object display that portrays the two terms as the height and width, thereby allows the product to be directly perceived as the emergent feature—-the area. In essence this transforms the cognitive integra- tion of the two terms into a perceptual one, a process which humans can often do more rapidly and sometimes more accurately (Vicente and Rasmussen, 1992). It is important to emphasize that if object displays create emergent features that are not mapped to integration task demand, then they will serve little benefit (Bennett and Flach, 1992). In addition, emergent features do not need to come only from object displays. Sanderson et al. (1990) for example demon- strated how a separated bar graph display, representative of that which might be used to display the status of four engines on a multiengined aircraft (Figure 8), could provide an emergent feature that facilitated the integration of information across the four display elements. In this case the feature would be the imagined line connecting their top. This would be directly mapped to an important cogni- tive state: that all engines are running at an equivalent level. An important new direction in complex display design, called “‘ecological interface design” (Vicente and Rasmussen, 1992), directs itself, in part, to analy- sis of how critical parameter sets that need to be integrated by the user can be represented in a display in such a way that emergent features can be perceived, thereby alleviating the need for cognitive computation. Color. When two display elements share a common color, which is distinct from both background color, and the color of other display elements, they will share a psychological proximity, one which makes integration of their informa- tion content an easier enterprise (Wickens and Andre, 1990). It is likely that the source of this advantage is simply the reduced visual search effort that is re- quired for the focus of attention to traverse from one to the other, given the great benefits of unique color in search tasks. Correspondingly the unique color of a SINGLE indicator (more “distant” display proximity) will facilitate the user’s ability to focus attention on its content and be unperturbed by the content of other differently colored indicators in the surround. Space. Space is a dominant dimension in our visual experience, and it has several profound influences on our ability to process multiple channels of infor- mation. It appears that close proximity in space of two (or more) indicators — 188 WICKENS Situational Awareness Support Wire Current First Aircraft Command Symbol Path Box Situational Awareness Support Vector Artificial Horizon Predictor Command Command Path Boxes Path Boxes celinireile Situational Predictor Airspeed Bar Awareness Support Wire Command Airspeed Bars Fig. 7. Separated planar displays (top) versus integrated perspective display for flight path guidance (from Haskell & Wickens, 1993). creates distinctive advantages by reducing the information access cost required to navigate between them (Wickens, 1992b). By information access cost, we refer to the combined effects of eye movement, head movement and movement of an internal “attention pointer” (the latter can be studied independently of eye and head movement, by assessing the role of space in very brief exposures that are too short for either the eye or the head to move). The data appear to suggest that close proximity in space reduces information access cost to the extent that: (a) there is other clutter in the display between the two elements (Wickens and Andre, 1990), COGNITIVE FACTORS 189 Command Path Boxes Predictor Situational Awareness Support Wire CurrentTime Symbol Situational Awareness Support Vector Command Speed Bars Situational Awareness Support Wire Fig. 7. —Continued Engine power 1 2 3 4 Engine number Fig. 8. Illustrates an emergent feature on a separated bar graph display. The feature is the imaginary line connecting the top of all three graphs. 190 WICKENS (b) there is some uncertainty of the location of one or the other elements (Liu & Wickens, 1992b), (c) the two elements need to be integrated (close mental proximity). Two examples illustrate this role of spatial proximity. First, it should be evi- dent that the design of good graphs 1s facilitated when the graph label is close to the line to which it refers (note the bottom in contrast to the top panel of Figure 4; Milroy and Poulton, 1978). Second, the guidance of close spatial proximity for information integration has been followed in the design of the head-up display for aircraft (Weintraub and Ensing, 1992). Here information that is conformal with the outside world (that is, information which is a direct analog representation of, and overlays information in the pilot’s view beyond the wind- screen), 1s presented in close spatial proximity (in fact, is superimposed), in order to facilitate the task of integrating information between the two. This superimposition is helpful to the task of flying (Weintraub and Ensing 1992; Larish and Wickens, 1991; Wickens and Long, 1994). It is however, also important to realize that there is a cost to close spatial proximity of information sources that are not directly conformal (i.e., ““cookie cutter’ images). The cost is one of clutter and, if the images actually overlap (as they often do in the head up display), there is an added cost of confusion. The cost is one that may lead to high perceptual effort to ““mentally segregate” the two images that are not physically segregated. This cost will be particularly damaging if the two images are to be processed independently; but may be present even when integration 1s required (Wickens, 1992a; Wickens and Cars- well, in press). : The overall role of spatial proximity in the visual field has been incorporated into a rough model of multichannel processing shown in Figure 9 (Wickens, 1992b). The model identifies various “‘forces,” actually embodied as principles of optimal display design, that act on information sources to either pull them together or, if they are too close, to push them apart. The figure depicts the attractive force between two displays (vertical axis) as a function of their separa- tion from each other. At the left, there is little need for attraction since the two displays are already in foveal vision. At the center, there is a greater attractive force (need for close proximity) when visual scanning is required. At the right, there is a still greater attractive force when the displays are far enough apart that head movement is required. The bottom shows how the attractive force is greater (and grows more rapidly with separation) in a cluttered display. Models of this sort are of considerable importance for the computational models that we discuss in part three of this chapter. | In summary, the proximity compatibility principle aggregates a number of psychological mechanisms (emergent features, information access effort, fo- ee — COGNITIVE FACTORS 191 Open Display 17) fe) O i¢p) w) S Uncertain oO Break Point < | | | | Head Field S © Eye - Field E Fovea L = (No - Scan) 0° 20 - 25° Display - Separation Cluttered Display 3 O vp) wn ® oO Oo ire | Y yr) ; 4 f i ‘ r } ‘hee nity irri tea cata ioe TER / tare toe A Te i P oe Ts ie veh WT , c fe 4; RR EN yc RE ey chp ag f qi 4 Journal of the Washington Academy of Sciences. Volume 83. Number 4, Pages 203-208, December 1993 President’s Report to the Membership for the Year 1992-1993 Dr. Stanley G. Leftwich, P.E. Mount Vernon, Virginia Once again, thanks to a dedicated, outstanding group of supporters and active members of the Academy, the Academy program which emphasized “Increase Productivity” from June 1992 through May 1993, paid off with dividends both in immediate and future terms. The program year got off to a successful start on July 4th with the Second Annual Reception for the Past Presidents at Belle Rive, the home of President Stan and his wife Mickey Leftwich in Alexandria on the beautiful Potomac River. The Theme “‘An Independence Day Celebration on the Potomac” was a huge success. More than two dozen past presidents were in attendance. The refreshments and food were outstanding. Past president, Dr. Al Forziati was honored as the oldest former president in attendance. Past president, Dr. Mary Louise Robbins, was honored for traveling the greatest distance to attend last years meeting. She came all the way from Tokyo, Japan and delighted the attendees by agreeing to be on the program, sharing her experiences about Japan where she lived and worked for a number of years. This year’s reception was very well received and all of the past presidents in attendance expressed a desire to have the Annual Reception for Past Presidents continue as an annual event. The September Program was planned to attract the Science Teachers, Science Supervisors, Students, Principals and interested parents from the 16 surround- ing jurisdictions of the Washington, D.C., Maryland and Virginia School Sys- tems. The theme was “Productivity in Science Education Encouragement Pro- grams.” The Chairman of the Joint Board on Science and Engineering Education and the President of the D.C. Council of Engineering and Architec- Send correspondence to: Bruce F. Hill, Ph.D., Editor, Washington Academy of Sciences, 2100 Foxhall Road, NW, Washington, DC 20007-1199 203 204 LEFTWICH tural Societies and the Vice President of the Junior Academy of Sciences were all invited and all were in attendance with the exception of the DCCEAS. A well thought-out program was presented: First: Mentors: their role and their worth to the education process Second: Science Project Guidelines Third: Mock Science Fair techniques Last: Research Participation: JASON SEAP etc. Although the program was technically sound, the attendance could have been better. Refreshments were served and everyone enjoyed themselves at the Uni- versity of the District of Columbia location. Thanks are extended to Anna Belle Darwin, Betty and Corson Long, Grover and Margaret Sherlin, Jim Powell, Mary Thomas, Dr. & Mrs. John Proctor, and Marylin Krupsaw. The October program looked at “Productivity Improvement in Alternative Energy Sources, Environment and Natural Habitat.” Mr. William Taylor, P.E., a member of the Board of Managers, led an interesting discussion on a study project concerning an alternative energy source for the island of St. Lucia. The project would allow St. Lucia to realize a relatively inexpensive, clean and efficient energy source at a great economic savings. Dr. Carolyn Brown of the National Oceanic and Atmosphere Administration, presented a number of in- teresting alternative environmental approaches the agency is currently promot- ing. Dr. Phil Williams, also of the National Oceanic and Atmospheric Adminis- tration, looked at Habitat Enhancement projects along coastal areas, including the habitat for the Green Sea Turtle. His discussion included a lengthy question and answer session on saving endangered species and enhancing the coastal habitat. : Credit is due to Mr. Robert McCracken, a Past President of WAS and for- merly head of National Capital Astronomers, for a successful November pro- gram. Mr. McCracken invited Dr. Jeffrey Hayes to discuss the topic, ““Hubble Telescope Revisited: Past Accomplishments and Increasing Productivity for the Future.”’ Dr. Hayes pointed out that the Hubble telescope has already been a resounding success. It has accomplished many things which were impossible before it was launched. Interestingly, the Hubble’s vision and focus were 1m- proved tremendously during several scheduled space efforts. Dr. Hayes pointed out that the focus port will be replaced by a pay-telephone sized unit which would be relatively easy to install. The prospects for the future productivity for the Hubble Space Telescope after this correction are excellent. Dr. Hayes’ pre- diction that humankind can expect more spectacular successes from the Hubble PRESIDENT’S REPORT 205 in the future has been shown in the recent in-space repair of the Hubble tele- scope. 3 Although a Board Meeting was held in December, it was felt that a technical program in December would be in conflict with the busy holiday season and a big program for the New Year in January was planned. Dr. John Proctor, President-elect, moderated the topic for January, ““Enhanc- ing Innovation and Productivity” with an outstanding panel of Dr. John Sanders, Chairman and CEO of TechNews Inc.; Dr. Stan Settles, Assistant Director of Industrial Technology, White House Office of Science and Technol- ogy; and Dr. Jack Simon, Technology Liaison Manager for General Motors Corporation. Each speaker made a short presentation followed by a lively dis- cussion period. The event which drew over 120 people was jointly sponsored by five afhliated organizations, The American Society of Quality Control, Institute of Industrial Engineers, American Institute of Aeronautics and Astronautics, American Society of Mechanical Engineers and the Association for Science, Technology and Innovation and by the Washington Academy of Sciences. One of the major issues in the recent presidential political campaign was the econ- omy and ways to improve it. This issue was a concern for all of the presidential candidates and each promised to focus actions to improve the economy early in his term as president. One of the ways that is sure to be employed is to develop strategies that will make the goods and services produced by the United States more competitive in the global market place. This is not to say that the U.S. is not competitive, but our negative balance of trade must be corrected. One measure of the deterioration of the U.S. position that has taken place over time can be seen in the percentage of machine tools exported. In 1970, the U.S. had 12% of the export market. Today it has 3% of the market. Enhancing innovation by providing the means and incentives is a major strategy that must be employed. Some actions to enhance innovation are: provi- sions of venture capital, sharing or joint research ventures, changing govern- ment policies to encourage innovation and greater funding for research and development. Many thanks for a successful program go to Neal Schmeidler, and Herbert Fockler, both members of the Board of Managers, Mrs. Jennifer Mainardi, Mr. Jim Powell and President-elect, Dr. John Proctor. “Congressional Reports on Science and Technology,” the February program, was handled by Mr. Herbert Fockler, member of the Board of Managers. He arranged for Mr. Frank Murray, Research Director for the U.S. House of Repre- sentatives’ Committee on Science, Space and Technology to be our speaker. Mr. Murray’s interesting and informative presentation included topics about legisla- 206 LEFTWICH tion and policy pertaining to energy, atomic power and related science and technology. A lively question and answer session followed with refreshments concluding the program. The March program, “Selling Our National Security,” once again found Mr. Herbert Fockler acting as our Program Chairman. Mr. Fockler had invited Dr. Susan Tolchin, a professor at George Washington University, and her husband Martin Tolchin, a reporter with the New York Times, to discuss their book, “Selling Our Security.”” However, due to an emergency at the last minute, they were unable to be present. Mr. Fockler very ably pinch hit in their absence. His lecture described the sale of hundreds of United States scientific and technology companies to Japan and other foreign countries. This is a large loss for the U.S. manufacturing industry and potentially a grave danger to U.S. National Secu- rity. An interesting question and answer session followed, with refreshments concluding this interesting program. The April program, ‘“‘Global Issues: the Push of Science and Technology—the Pull of Cultural Diversity and Human Values,” was the highlight of the year. It was the result of the close cooperation of the Washington Academy of Sciences, The Russian Academy of Sciences, and the World Academy of Art and Science. Thanks to a generous underwriting by the Barbara Gauntlet Foundation and contributions of the World Man Fund, the World Academy of Art and Science and the support of the Washington Academy of Sciences, this outstanding inter- national conference came to fruition. For over a year of planning work, our thanks go to the Washington Academy of Sciences Program Committee chaired by Dr. John Proctor, President-elect; Dr. Stanley Leftwich, President of WAS; Dr. Charles Wynn, Former President; Mr. Grover Sherlin; Mr. Charles Sills; Mrs. Marylin Krupsaw; Mr. Tom Doeppner; Dr. Ed Finn; Dr. Nancy Flournoy; Mr. Herbert Fockler; Dr. Bruce Hill; Dr. John Honig; and Rev. Frank Haig. The objectives of the international program were: |. to increase the awareness of global problems and opportunities: 2. to stimulate the interest of young people toward careers in science and art: 3. to strengthen the foundations for joint effort and new combinations of talent and resources. The forum was moderated by Professor Lincoln Bloomfield of the Massachu- setts Institute of Technology before an audience of several hundred adults and students from the Junior Academy of Sciences. Video and audio recordings were made concurrent with English and Russian translations. Dr. Serge Kapitza of Moscow and Cambridge University led off first discussing his topic ““A Global View of the Planet to the Year 2050” with particular emphasis on population growth. Dr. Richard Benedict, Former Ambassador and now Senior Fellow of the World Wildlife Fund, also chose to discuss the critical problem of human population growth currently and projected for the next fifty years. ““The Push of PRESIDENT’S REPORT 207 Science and Technology” was the topic of the talk by the Academician Igor Makarov, Chief Scientific Secretary of the Russian Academy of Sciences. ““The Pull of Cultural Diversity” was the topic of Professor Harlan Cleveland, Former Ambassador and President of the World Academy of Art and Science. The topic, ““Human Values,” was discussed by Professor Michael Reisman of Yale University Law School and Academician Yu S. Osipov, Mathematician and President of the Russian Academy of Sciences. And finally, the field of Biotechnology and Biomedicine was explored by Dr. Rita Colwell, Professor of Microbiology, University of Maryland and Academician Rem Petrov, Immu- nologist and Vice President of the Russian Academy of Sciences. The question and answer session which followed was chaired by WAS Presi- dent-elect Dr. John Proctor and gave the Junior Academy of Sciences students a chance to ask questions of some of the world’s foremost thinkers. A reception was held afterward in Georgetown University’s Healy Hall for press and photo opportunities. Refreshments were served to all the Junior Acad- emy of Sciences members and their families. Beautiful plaques commemorating the event were presented to each forum participant by Dr. Stanley Leftwich and his lovely wife, Mickey Leftwich. A monograph containing the written papers of the forum participants was adapted in English by Dr. John Proctor and in Russian by Dr. Rem Petrov. A thirty minute sound and color video of the forum was prepared and copies produced by the Washington Academy of Sciences for use by our Junior Acad- emy of Sciences. Videos and monographs were made available to Moscow Tele- vision and middle schools by the Russian Academy of Sciences and the World Academy of Art and Science. In May, the Annual Academy Awards Dinner and Ceremony was very effec- tively arranged and conducted by Dr. Cyrus Creveling at Fort McNair. Hearty congratulations are extended to the two “Presidential Awardees,”’ Mr. Herbert Fockler and Dr. Bruce Hill. For the list of the Awardees see Dr. Creveling’s report elsewhere in this issue. Iam pleased to add my sincere congratulations to all the winners. With the help of a select group of fine people, a by-laws change was initiated to enhance our Academy’s membership by adding “‘Sustaining Membership”’ for large organizations. It was submitted to the membership and passed by a healthy margin. In order for the Academy to get ready for the 100th Anniversary Celebration in 1998, a Centennial Committee was organized to plan and establish appro- priate goals for celebrating this epic event. As a cost cutting measure and to improve efficiency, the Academy head- quarters was moved to the Mount Vernon College at 2100 Foxhall Road, N.W. 208 LEFTWICH Many scientists and others who attended Academy functions at the College for the first time expressed favorable impressions. In conclusion, competent and effective volunteerism is essential for a large organization such as the Academy to operate. I must thank the following individ- uals for their dedication and support: Dr. Bruce Hill of Mount Vernon College who helped immeasurably in facilitating the Headquarter’s move, and for his dedicated service in securing meeting rooms and equipment, and especially for his invaluable service as Editor of the Journal of the Washington Academy of Sciences; Mr. Grover Sherlin, our Past President, for his unmatched and stoic- like, superlative service to the Academy, for the many, many times he has shown up to help with untold mailings and overall for his general support and enthusi- asm; Mrs. Margaret Sherlin, for her many occasions of help with mailings and her general support; Mr. Charles Thomas, Grover Sherlin’s son-in-law, for his help with the headquarter’s move and mailings; Mr. Andrew Davis, for his unyielding support with mailings and help-with the headquarters move; Mr. Herb Fockler, for his superlative work as program chairman and his stellar work as an Academy appointee with the Joint Board on Science and Engineering Education; Dr. John Proctor, for his exceptional work on both the January and April programs; (The International program in April stands out as an example of what the Academy can do if we work at it); Mr. Robert McCracken, Past President, for his leadership on the Centennial Planning Committee, his support in chairing the excellent October program on the Hubble telescope, and his overall service to the Academy; Mr. Tom Doeppner for his stellar work in helping to reorganize the A fhliate Affairs Function (under his leadership as Vice President of Afhliate Affairs much progress was made); Mr. Neal Schmeidler, for his chairmanship of a very successful program in January; Mr. Jim Powell, for his leadership and hard work as Chairman of the Joint Board of Science and Engineering Education; Dr. Cyrus Creveling, for his leadership as Vice Presi- dent of Membership and for conducting a superlative awards program; and finally many, many thanks to my wife, Mickey Leftwich, for her support and understanding. Step-by-step the Academy is being brought up to a level of strength at which its voice and programs for science and engineering will be very significant. But to continue this development, each of us must work and participate in Academy Affairs. It has been my privilege to serve as president of the Academy during this important development period. I sincerely thank you for my chance at the helm and I also thank you for your support in the process. Journal of the Washington Academy of Sciences, Volume 83, Number 4, Pages 209-214, December 1993 The 1993 Washington Academy of Sciences Awards Program for Scientific Achievement C. R. Creveling National Institute of Diabetes, Digestive, and Kidney Diseases Bethesda, MD One of the many ways by which The Washington Academy of Sciences con- tributes to the growth and recognition of scientists in the Washington Metropoli- tan area is through the awards program of the Academy. Each year the Academy recognizes such persons for scientific endeavors of merit and distinction. Awards are made for outstanding contributions in Mathematics and Computer Sciences, Behavioral and Social Sciences, Engineering Sciences, Biological and Physical Sciences. In addition the Academy makes an award designated as the “Distinguished Career in Sciences Award” to recognize a person who has made distinguished and life-long contributions to science. The Academy in recognition of the primary responsiblities for the well being of society is in the teaching of science to young persons. In keeping with this goal the Academy presents the Leo Schubert Award for excellence in college teaching of science and the Bernice Lambertson Award for excellence in the teaching of science in high school. Those receiving awards are selected from those persons nominated by either Academy members or the public, by panels of experts in each of the respective fields. The selections of the Awards Committee are then approved by the Board of the Academy. The Awards were presented on Thursday, May 13, 1993 at a ceremony, held at the Officers Club at Fort Lesley J. McNair, Washington DC. The 1993 awardees were: Dr. Maurice Mandel Shapiro Distinguished Career in Science Dr. Marc M. Sebrechts Behavioral and Social Science 209 210 CREVELING Dr. Larry K. Keefer Biological Science Dr. David J. Fry Engineering Science Dr. Thomas DiBerardino Physical Science Dr. Hans Joseph Lugt Mathematics and Computer Science Mr. Edward L. McIntosh Bernice Lamberton Awards for the Ms. Elaine Kilbourne Teaching of Science in High School Distinguished Career in Science Dr. Maurice Mandel Shapiro was selected to receive the “Distinguished Ca- reer in Science Award” for his scholarly contributions to knowledge of our cosmic environment and his outstanding international leadership in promoting cooperation in the sciences. Dr. Shapiro received his doctorate in physics from the University of Chicago in 1942. A few highlights of his illustrious career include: Group leader at the Los Almos Science Laboratory; Senior physicist, Oak Ridge National Laboratory; Chief Scientist for cosmic-ray physics, United States Naval Research Laboratory; Chairman of the International Geophysical Year for Interdisciplinary Research; Principal investigator for the Gemini cos- mic-ray experiments, Skylab and the Long-duration Exposure Facility; and Di- rector of the International School for Cosmic-ray Astrophysics, Italy. He has been a visiting professor many universities including: University of Maryland, George Washington University, the E. Fermi International School of Physics, Verenna, Italy; Wiezmann Institute, Rehovoth, Israel; the Institute for Mathe- matics and Sciences, Madras, India; Northwestern University, Evanston, IIli- nois; the University of Bonn, Germany; the Max Planck Institute fur Astrophy- sik, Munich, Germany; and the University of California. He has been a consultant and advisor both for industry, the United States and foreign govern- ments. Dr. Shapiro is the recipient of many honors and awards including the 1978 Medal of Honor from the Societe d’Encoragement au Progress by the French Republic and the 1982 Alexander von Humboldt award. Dr. Shapiro was nominated by Dr. Robert McCracken. The Award was accepted by Dr. Shapiro’s son, Joel N. Shapiro. Behavioral and Social Science Dr. Marc M. Sebrechts was selected for the award in the Behavioral and Social Sciences for innovative work on the application of the theory and principles of cognitive science to the design of computer based systems. His work has contrib- uted to the development of “‘user-friendly’” computer systems, expert systems, WAS AWARDS PROGRAM 211 and intelligent tutoring systems while simultaneously advancing cognitive theory in the areas of human memory and problem solving. Dr. Sebrechts was nominated by Dr. Sue Bogner. Biological Science Dr. Larry K. Keefer was selected for the award in the Biological Sciences for his innovative and fundamental studies on the chemistry and biology of nitric oxide. Dr. Keefer was among the first to recognize the importance of nitric oxide in biomedical research. Exploiting his experience in the chemistry of N-nitroso compounds he launched a major effort in the chemistry and biology of nitric oxide. He demonstrated that a series of N-nitroso compounds, the nitric oxide/ nucleophile complexes or NONOates capable of releasing nitric oxide in aqueous systems and most importantly that such compounds were able to dilate the isolated rabbit aorta. Since this report many studies on the multifaceted pharmacological effects centering around nitric oxide as a neurotransmitter with blood pressure lowering actions, anti-thrombotic activity, an ability to inhibit the growth of human melanoma cells and others. Dr. Keefer has also shown that nitric oxide 1s capable of inducing point mutations by deamination of the bases in DNA leading to carcinogenesis. As a result of Dr. Keefer’s discov- eries there is now an intense international research interest in the possible nitric oxide genotoxicity in humans. Dr. Keefer was nominated by Dr. Joy A. Barron- Ctle. Engineering Science Dr. David J. Fry was selected for the award in the Engineering Sciences for his development of innovative flow measurement and control systems for velocity fields in support of ship hull and propulsor design and analysis. He applied these measurement systems to pioneering determinations of surface ship wakes, wind tunnel propulsor-hull interaction, and ship wake/ambient ocean wave interac- tions. His work has been invaluable in providing archival experimental data bases for unraveling the physics of complex flows and permitting the evaluation of theoretical/numerical computational models. Dr. Fry was nominated by Captain D. K. Kruse, USN. Physical Science Dr. Thomas DiBerardino was selected for the award in the Physical Sciences for his outstanding research on polymers possessing intrinsic electrical conduc- 212 CREVELING tivity. These materials have found potential as electromagnetic absorbers and in high temperature applications. Dr. Diberardino’s research pioneered the inves- tigation of these materials at a molecular level by solid state spectroscopy. His studies of the curing process resulted in improved processing via modification of the starting material through synthetic chemistry. It should be emphasized that Dr. DiBerardino’s multidisciplinary research style was responsible for recogni- tion of applications of conductive organic materials which normally would have gone unnoticed. Dr. DiBerardino was nominated by Captain D. Kruse, USN. Mathematics and Computer Science Dr. Hans Joseph Lugt was selected for the award in Mathematics and Com- puter Sciences for his outstanding record of scientific and technical achieve- ments and leadership in the analysis and numerical simulation of basic fluid flow phenomena, and his investigation and solution of significant aerodynamic and hydrodynamic problems of naval importance. Dr. Lugt has made a large number of exceptionally significant contributions to the field of viscous fluid flow thorough the use of sophisticated mathematical analysis and computer simulation. His basic research in fluid dynamics has resulted not only in the solution of specific problems, but has generated new insight into basic flow phenomena, such as the development, shedding propagation and decay of vor- tices, flow separation, and free-surface behavior. Dr. Lugt instituted a series of biennial International Conferences Numerical Hydrodynamics. The proceed- ings of these conferences have become a major source of information in the field of hydrodynamics. Dr. Lugt was nominated by Captain D. K. Kruse, USN. Bernice Lamberton Awards for the Teaching of Science in High School Mr. Edward L. McIntosh received the Bernice Lamberton Award for the Teaching of Science at the Montgomery Blair High School. Mr. McIntosh has been a model of dedication in the implementation of the Minority Access to Research Careers and the Minority Engineering Organization. He has also been a moving force in the Gifted Student’s Program in Science and Mathematics. Mr. McIntosh was nominated by Dr. Marylin Krupsaw. Ms. Elaine Kilbourne also received the Bernice Lamberton Award for the Teaching of Science at the Thomas S. Wootton High School. Ms. Kilbourne has for many years been outstanding in her innovative and inspirational teaching of the Science of Chemistry. Ms. Kilbourne was nominated by Dr. Marylin Krup- saw. WAS AWARDS PROGRAM 213 Following the Awards presentation the Academy heard an entertaining but informative lecture by Dr. J. Thomas Ratchford from the White House Science Advisory Office. Mr. Ratchford brought greetings from Jack Gibbons, the science advisor to President Clinton, who was unable to be present but offered his personal congratulations to the Awardees. Dr. Ratchford began by describing his initiation into science advising in gov- ernment with “How I got into the policy world’’. He was on a leave of absence as a professor of physics with an internship as a Science Fellow to Congress. He was first assigned to a “Liberal” congressman then later to a ““Conservative” Sena- tor. The latter was on the all important appropriations Committee. This Senator referred to Dr. Ratchford or “Tom” as his “token intellectual’? and further defined an intellectual as ““someone educated beyond their ability”. The difh- culties of being a scientist in government is illustrated by the reference to the Plumb book which lists the sixty most difficult science positions in government. By insiders this is refereed to as the “Prune Book”’. Dr. Ratchford went on to explain that one of the initial difficulties that scien- tists encounter in dealing with government has to do with their “habit of truth” and approach to problem solving. He illustrated this fundamental disadvantage by telling the following story: “During the French revolution three prisoners were being Guillotined. The first victim, a cleric-down came the knife—which stopped just before the victims neck. The crowd reacted ’’A sign from God!* and the cleric was released. With the second victim, a lawyer, the same thing happened and the lawyer was re- leased. The next victim was a scientist, who after placing his neck below the knife—and looked up and said “Oh, I see the problem, indicating a bent flaw in the guillotine track—oops”’. In a more serious vein Dr. Ratchford summarized the history of the role of the White House science advisor. Following the successful launch in 1957 of “Sput- nik” by the USSR, President Eisenhower asked Jim Killian to come to the White House as a science advisor. For the next decade, 1958 to 1978 the White House science advisors played a somewhat inconstant role. In 1973 President Nixon was reported to be “unhappy” with his science advisor, Dr. Edward David. Dr. David apparently testified on science policy contrary to the position of the Administration. President Nixon abolished the science advisory position. In 1976 President Ford reestablished advisory role. Soon after Congress passed Science and Technology Priority Act thus legislatively establishing the White House Science Advisory role. Dr. Steven Press served a Science Advisor under President Carter. The office was expanded under Presidents Reagan and Bush when Dr. Allen Bromley was appointed as Assistant to the President for Science and Technology. President Clinton appointed Jack Gibbons as Science Advisor 214 CREVELING a role which now clearly includes concerns of both basic science and technology. The purview of the Science Advisor now includes: the Space Council; the Na- tional Critical Materials Repository: Redesign of Space Station Project: Super Conductor Project; and Human Genom Project. With the present administration another major change has occurred with the assignment of Vice President Gore to the committee for Public Policy on Science and Technology, the so called FIXIT Committee. Vice President Gore is both interested in and knowledgeable about both science and technology. The stated three long term goals of the current administration are economic growth, more effective government and maintenance of world leadership in science, mathematics and technology. It is recognized that industrial growth depends upon new technology. Further Dr. Ratchford clearly implied that new technology depends upon well educated personnel in math and science. It is the intention of the Clinton Science Policy to include no reduction in the support of “Basic Science” and the concept of “Stable Funding”’. Dr. Ratchford indicated that Research and Development funding represents 3% of our gross national product. However nearly 30% of the support is still utilized for military and defense research. Efforts are being made thru the “Transfer of Technology” from various National Defense Laboratories to in- crease the “Social Return” of the previous investment in defense research. Dr. Ratchford concluded his presentation with a comment on the concept of stable funding and a recognition that Government often appears fickle as re- gards long term support. He acknowledged that a lack of sustained support is very expensive both in terms of trained personnel and program hardware. Ef- forts are in progress to address the question of stable funding. Journal of the Washington Academy of Sciences, Volume 83, Number 4, Pages 215-228, December 1993 The Bylaws of the Washington Academy of Sciences’ ARTICLE I. OBJECTIVES Section 1. The objectives of the Washington Academy of Sciences (hereinafter called the Academy) shall be: (a) to stimulate interest in the sciences, both pure and applied; and (b) to promote their advancement and the development of their philosophical aspects by the Academy membership and through coopera- tive action by the Affiliated Societies. Section 2. These objectives may be attained by, but are not limited to: (a) publishing a periodical, occasional scientific monographs and such other books or pamphlets as may be deemed desirable; (b) conducting public lectures of broad scope and interest in the fields of science; (c) sponsoring a Washington Junior Academy of Sciences (WJAS); (d) promoting science education and a professional interest in science among people of high school and college age; (e) accepting or making grants of funds to aid special research projects; (f) conven- ing symposia, both formal and informal, on any aspects of science; (g) calling scientific conferences; (h) organizing or assisting in scientific assemblies or bod- ies; (1) cooperating with other academies and scientific organizations; (j) award- ing prizes and citations for special merit in science; (k) maintaining an office and staff to aid in carrying out the objectives of the Academy. ARTICLE I. MEMBERSHIP Section 1. The Academy shall be comprised of individuals, Affiliated Societies and Sustaining Associates. Throughout this document when reference is made to individuals, the use of “‘he” shall be interpreted as “‘he or she.” ' The revised Bylaws of the Washington Academy of Sciences dated May 1982 were replaced by a March 1984 edition. The 1984 version was found to have many imperfections. The effort to correct resulted in a April 1, 1988 version, followed quickly by a April 29, 1988 version which took away the vote of representatives of Affiliated Societies. The subsequent May 1989 version returned the vote of the afhliates but other problems came to the forefront. A proposed version dated May 24, 1990 was mailed to the membership for consider- ation with a cut-off date of August 9, 1990. The revisions were approved by majority vote of the Membership. The December 1993 version of the Washiongton Academy of Sciences Bylaws includes revision approved by the membership since the Bylaws were published in Decenber 1991. 215 216 WASHINGTON ACADEMY OF SCIENCES Section 2. Members shall be individuals who are interested in and will support the objectives of the Academy and who are otherwise acceptable to at least two-thirds of the Committee on Membership. A letter or application form re- questing membership and signed by the applicant may suffice for action by Committee; approval by the Committee constitutes election to membership. Section 3. Fellows shall be individuals who by reason of original research or other outstanding service to the sciences, mathematics, or engineering are deemed worthy of the honor of election to Academy fellowship. Section 3(a). Nominations of fellows shall be presented to the Committee on Membership on a form approved by the Committee. The form shall be signed by the sponsor (a Fellow who has knowledge of the nominee’s field), and shall be endorsed by at least one other Fellow. An explanatory letter from the sponsor and supporting material shall accompany the completed nomination form. Section 3(b). Election to fellowship shall be by vote of the Board of Managers upon recommendation of the Committee on Membership. Final action on nom- inations shall be deferred at least one week after presentation to the Board of Managers, where two-thirds of the vote cast shall be necessary to elect. The election process shall be completed upon submission of the processed nomina- tion forms to the Vice President for Membership Affairs. Section 3(c). Each individual (not already a Fellow) who has been chosen to be the recipient of an Academy Award for Scientific Achievement shall be consid- ered nominated for immediate election of fellowship. The election process shall be completed upon submission of the standard nomination forms to the Vice President for Membership Affairs, thus obviating the procedures of Sections 3(a) and 3(b) of this Article. Section 3(d). Any fellow of the Academy may recommend in writing that an individual of national eminence be considered for immediate election to fellow- ship by the Board of Managers, without the necessity of compliance with the procedures of Sections 3(a) and 3(b) of this Article. Following approval by the Board of Managers, the election process shall be completed upon submission of the standard nomination forms to the Vice President for Membership Affairs. Section 4. Patrons. Members or fellows who have given to the Academy not less than one thousand dollars, or its equivalent in property or tangible assets, shall be eligible for election by the Board of Managers as Patrons of the Academy (for life). Following approval by the Board of Managers, the election process shall be completed when suitable documentation has been submitted to the Vice Presi- dent for Membership Affairs. LS or ee ee ACADEMY BYLAWS 217 Section 5. Life Members or Life Fellows shall be those individuals who have made a single payment in accordance with Article II, Section II(a) in lieu of annual dues. Section 6. Members or fellows in good standing who are retired and are no longer engaged in regular gainful employment may be placed in emeritus status. Individuals in emeritus status shall be designated Emeritus Memberor Emeritus Fellow as appropriate. Upon request to the Vice President for Membership Affairs for transfer to this status, they shall be relieved of further payment of dues, beginning with the following January first; shall retain rights to hold office and attend meetings; shall receive notices of meetings without charge; and at their request, shall be entitled to receive the Academy periodical at cost. This transfer shall be completed when the Treasurer and the Vice President for Ad- ministrative Affairs have been so notified. : Section 7. Members or fellows living more than 50 miles from the White House, Washington, DC shall be classed as Nonresident Members or Nonresi- dent Fellows. Section 8. An election to any dues-paying class of membership shall be void if the candidate does not within three months thereafter pay his dues or satisfacto- rily explain his failure to do so. Section 9. Former members or fellows who resigned in good standing may be reinstated upon application to the Vice President for Membership Affairs for approval by the Board of Managers. No reconsideration of the applicant’s quali- fications need be made by the Membership Committee in these cases. Section 10. Dues. Annual dues for each member class shall be fixed by the Board of Managers. No dues shall be paid by Emeritus members, Emeritus fellows, Life members, Life fellows, Patrons, or Afhliated Societies. Section 10(a). Members and fellows in good standing may be relieved of further payment of dues by making a single payment that has a value equal to ten years of dues current at the time of payment. (see Article II, Section 5) Such persons are to be identified as Life Members or Life Fellows as appropriate. Income from this source shall be identified as the Life Membership Endowment Fund (LMEF) and shall be monitored in perpetuity by three 7rustees who are resident Life Members or resident Life Fellows. The Trustees shall direct the investment of the Fund (LMEF) in a conservative action and turn over to the Treasurer all interest from such investments. Trustees shall serve for the duration of life or until the change to nonresident status or the onset of permanent disability or resignation. | 218 WASHINGTON ACADEMY OF SCIENCES Section 10(b). Individuals whose dues are in arrears for one year (counting from the ’’dues payable date“ on the latest dues payment bill) shall neither be entitled to receive Academy publications nor to vote in Academy elections. Section 10(c). Individuals whose dues are in arrears for twenty-four (24) months (counting from the ’’dues payable date“ on the latest dues payment bill) shall be dropped from the rolls of the Academy, upon notice to the Board of Managers, unless otherwise directed. Those who have been dropped from membership for nonpayment of dues may be reinstated upon approval of the Board of Managers and upon payment of back dues for two years together with dues for the year of reinstatement. Section 11. Affliated Societies. Bona fide scientific societies may apply for afhli- ation with the Academy_by furnishing to the Secretary of the Academy an outline of their objectives, organizational structure and requirements for mem- bership in their society. The Secretary will transmit the application to the Policy and Planning Committee for review and recommendation for action by the Board of Managers. Section 1l(a). Each Affiliated Society shall select one of its members who is also a member or fellow of the Academy to serve as its representative to the Board of Managers. The representative shall serve until replaced by his society. Section 11(b). Each Affiliated Society shall cooperate with the Academy in sponsoring joint meetings of general scientific interest. Section 12. Sustaining Associates. Any association, corporation, firm, institu- tion or subdivision thereof, which has an interest in promoting the objectives of the Academy may be invited by the President of the Academy with approval of the Board of Managers to become a Sustaining Associate for the purpose of supporting the Academy and its programs. The names of the Sustaining Asso- ciates shall be listed annually in the Journal of the Washington Academy of Sciences. Section 12(a). Each Sustaining Associate shall designate a person to serve as liaison to the Washington Academy of Sciences. This individual will receive the Journal of the Washington Academy of Sciences and all mailings regarding upcoming technical meetings. The position shall be non-voting unless the liai- son 1s concurrently an individual Member or Fellow of the Academy. ARTICLE II. ELECTED OFFICERS and BOARD MEMBERS Section 1. Officers of the Washington Academy of Sciences shall be President, President-Elect, Vice President for Administrative Affairs, Vice President for ae ACADEMY BYLAWS 219 Membership Affairs, Vice President for Affliate Affairs, Vice President for WJAS Affairs, Secretary, and Treasurer. All shall be chosen from resident fel- lows of the Academy. Section 2. The President shall appoint all committees and such nonelective officers as are needed unless otherwise directed by the Board of Managers or provided in the bylaws. He (or his substitute: the President-Elect, the Vice Presi- dent for Administrative Affairs, the Vice President for Membership Affairs, the Vice President for Affiliate Affairs, the Vice President for WJAS Affairs, the Secretary, or the Treasurer, in that order) shall preside at all meetings of the Academy, the Board of Managers and the Executive Committee. Section 3. The President-Elect shall succeed to the office of President following one term as President-Elect. The President-Elect shall serve as Chair of the Program Planning Committee to arrange speakers and meeting places for the following year (the year in which the President-Elect succeeds to President) Other duties may be assigned by the Board of Managers. Section 4. The Vice President for Membership Affairs shall have general respon- sibilities for committees related to membership: the Membership Committee, the Membership Promotion Committee, and the Committee on Awards for Scientific Achievement. Other duties may be assigned by the Board of Man- agers. Section 5. The Vice President for Administrative Affairs shall have general re- sponsibility for operation of the Business Office of Academy and the Journal of the Washington Academy of Sciences, and such other duties as assigned by the Board of Managers. He shall oversee the activities of the Editorial Advisory Committee, the Ways and Means Committee, and the Office Manager. Section 6. The Vice President for Affiliate Affairs shall serve as Chair of the Affiliated Society Representatives. He shall carry out such other duties as as- signed by the Board of Managers. Section 7. The Vice President for WJAS Affairs shall have general responsibility for the committees relating to organizing and maintaining the Junior Academy (WJAS). He shall interface with the Joint Board on Science and Engineering Education, and shall carry out such other duties as assigned by the Board of Managers. Section 8. The Secretary shall record and distribute the minutes of the meetings of the Board of Managers and such. other meetings as the Board of Managers may direct. He shall conduct all correspondence relating thereto, except as 220 WASHINGTON ACADEMY OF SCIENCES otherwise provided, and shall be the custodian of the Corporate Seal of the Academy. Section 9. In cooperation with the Vice Presidents for the functional areas described in Sections 4, 5, 6, and 7, above, the Treasurer shall be responsible for preparing the Budget of the Academy and submitting it to the Board of Man- agers for approval. The Treasurer shall also be responsible for distributing to the Board of Managers in a timely manner records of funds received and expended. The Treasurer shall be responsible for maintaining records of funds deposited in banks or other savings instruments. The Treasurer and/or other designated persons shall sign checks for disbursements of funds as directed by the Board of Managers. The Treasurer shall prepare annual reports as required by the Inter- nal Revenue Service, the U.S. Postal Service, etc. He shall deposit and disburse funds of the Washington Junior Academy of Sciences. Section 10. The President and the Treasurer, as directed by the Board of Man- agers, shall jointly assign securities belonging to the Academy and endorse finan- cial and legal papers necessary for the uses of the Academy, except those relating to current expenditures authorized by the Board of Managers and those under cognizance of the Life Membership Endowment Fund Trustees. In case of dis- ability or absence of the President or Treasurer, the Board of Managers may designate the President-Elect or another elected officer as Acting President and/ or another elected officer of the Academy as Acting Treasurer, who shall per- form the duties of these offices during such disability or absence. Section 11. Two members or fellows of the Academy shall be elected each year to serve a three-year term as Members of the Board of Managers. To initiate staggered terms or to fill vacancies, additional Members of the Board of Man- agers may be selected in the annual election. Section 12. The newly elected officers and Members of the Board of Managers shall take office at the close of the annual meeting, when the President-Elect of the previous year becomes President. ARTICLE IV. APPOINTED OFFICERS Section 1. An Office Manager shall be appointed by the Board of Managers. The Office Manager shall be responsible for the routine business operation of the Academy. The Board of Managers shall determine the type of business activity (volunteer workers or contract workers) and the amount of funds to be allocated to the business office. ACADEMY BYLAWS 221 Section 2. An Editor for the Journal of the Washington Academy of Sciences shall be appointed by the Board of Managers. The Editor shall be responsible to the Vice President for Administrative Affairs for administrative policy and re- lated activities. Section 3. An Archivist may be appointed by the President. If appointed he shall maintain the permanent records of the Academy, including important records which are no longer in current use by the Secretary, Treasurer or other officer, and such other documents and material as the Board of Managers may direct. ARTICLE V. BOARD OF MANAGERS Section 1. The activities of the Academy shall be guided by the Board of Man- agers, consisting of the President, the President-Elect, the immediate Past Presi- dent, the four Vice Presidents, the Secretary, Treasurer, the six Members of the Board of Managers, and one representative from each of the Affiliated Societies. The elected officers of the Academy shall hold like offices on the Board of Managers. Section 2. The Board of Managers shall set the dues for individual members and the minimum contribution for Patrons. For prolonged, diligent and well-docu- mented service in the administrative work of the Academy the Board of Man- agers may recognize such service of a member or fellow by citation including dues paid for Life. Section 3. The Board of Managers shall transact all business of the Academy not otherwise provided for. A quorum of the Board shall be one third of the membership of the Board of Managers. To be eligible to vote the officer or member of the Board of Managers must be in good standing, casting one vote only regardless of the number of offices or Affiliated Societies that he may represent. Section 4. The Board of Managers may provide for such standing and special committees as it deems necessary. Section 5. The Board of Managers shall have power to fill all vacancies in its elected membership until the next election. This does not apply to the offices of the President and Treasurer or to representatives of Affiliated Societies. ARTICLE VI. COMMITTEES Section 1. An Executive Committee shall have cognizance of Academy finances by reviewing the Treasurer’s monthly reports of budgeted expenses and antici- 222 WASHINGTON ACADEMY OF SCIENCES pated income, and by reviewing the status of several internal accounts: the Life Membership Endowment Fund, the I.R.S. Form 990 accounts, the Postal Ac- counts, the WJAS Account, etc. Section 2. The Executive Committee shall meet one-half hour prior to the scheduled meeting of the Board of Managers to anticipate and obviate budge- tary imbalances. The results of such actions shall be reported to the Board of Managers following the Treasurer’s report. Section 3. The Executive Committee shall consist of the incumbent elected officers of the Board of Managers plus two non-elected members of the Board of Managers chosen by the Board of Managers. Section 4. Committees under the cognizance of the President are the Executive Committee, the Nominating Committee, the Policy and Planning Committee, the Audit Committee, and such other committees as shall be determined by the Board of Managers. Section 5. Committees under the cognizance of the President-Elect are the Program Planning Committee and such other committees as shall be deter- mined by the Board of Managers. | Section 6. Committees under the cognizance of the Vice President for Member- ship Affairs are the Membership Committee, the Membership Promotion Com- mittee, the Committee on Awards for Scientific Achievement, and such other committees as shall be determined by the Board of Managers. Section 7. Committees under the cognizance of the Vice President for Admin- istrative Affairs are the Editorial Advisory Committee, the Ways and Means Committee, and such other committees as shall be determined by the Board of Managers. Section 8. Committees under the cognizance of the Vice President for WJAS Affairs are the Committee on the Encouragement of Science Talent, Committee on Grants-in-Aid for Scientific Research, and such other committees as shall be determined by the Board of Managers. Section 9. The President shall appoint from the Academy membership such committees as are authorized by the Board of Managers and such special com- mittees as necessary to carry out its functions. Committee appointments shall be staggered as to term whenever it is determined by the Board of Managers to be in the interest of continuity of committee affairs. Section 10. The President, with the approval of the Board of Managers, shall appoint a Nominating Committee of six fellows of the Academy, (see Article VI, ACADEMY BYLAWS 223 Section 4) at least one of whom shall be a Past-President of the Academy, and at least three of whom shall have served as representatives of Affiliated Societies for at least one year. Section 11. The Nominating Committee shall prepare a slate listing one or more persons for each of the offices of President-Elect, the four Vice Presidents, Secretary, Treasurer, and four or more persons for the two Members of the Board of Managers whose terms expire after three years and at least two persons for each vacant unexpired term of such position (see ARTICLE III, Section 11 ). The slate shall be presented for approval at the meeting in December. Not later than December 15, the Vice President for Administrative Affairs shall forward to each Academy member and fellow an announcement of the election, the Committee’s nomination for the offices to be filled, and a list of incumbents. Additional candidates for such offices may be proposed by any member or fellow in good standing by letter received by the Vice President for Administra- tive Affairs not later than January 3. The letter shall include the concurrence of the nominees and the names of 25 members or fellows making the proposal. Upon verification by the nominating committee the names shall be entered on the ballot. The Board shall remind members and fellows of the foregoing option with the distribution of the preliminary slate. Section 12. Not later than February 15, the Vice President for Administrative Affairs shall prepare and mail ballots to members and fellows. Independent nominations shall be included on the ballot, and the names of the nominees shall be arranged in alphabetical order. When more than two candidates are nominated for the same office, the voting shall be by preferential ballot in a manner prescribed by the Board of Managers. The ballot shall contain a notice to the effect that votes not received by the Vice President for Administrative Affairs before the first Thursday of March, and votes of individuals whose dues are in arrears for one year or more, will not be counted. The Committee of Tellers shall count the votes and report the results at the April Meeting of the Board of Managers. ARTICLE VII. MEETINGS OF THE ACADEMY Section 1. The annual meeting of the Academy shall be held each year in May. It shall be held on the third Thursday of the month unless otherwise directed by the Board of Managers. At this meeting, the reports of the President-Elect and the several Vice Presidents, the Secretary, the Treasurer, and the Committee of Tellers shall be presented. 224 WASHINGTON ACADEMY OF SCIENCES Section 2. Meetings of the Board of Managers shall be held as called by the President, or in his absence by the Secretary, or within ten days after a written request by six members of the Board of Managers has been sent to all members of the Board of Managers. Regular meetings of the Board of Managers shall be set preferably for a fixed place, hour, day of week, and sequence of months excepting July and August. Section 3. Other meetings may be held at such time and place as the Board of Managers may determine. Section 4. The rules contained in ’’Robert’s Rules of Order Revised“ shall govern the Academy in all cases to which they are applicable, and in which they are not inconsistent with the bylaws or special rules of order of the Academy. ARTICLE VIT. REMOVAL FROM OFFICE Section 1. Members of the Board of Managers and the Executive Committee shall assure that all business of the Academy is conducted in the highest spirit of ethics and integrity. This includes the absence of a conflict of interest, which is defined as the acceptance of positions or contracts with the Academy which would result or give the appearance of resulting in a profit or other material advantage to an officer of the Academy. NOTE: Article V, Section 2 is consid- ered a service citation by the Academy and as such is an exception. Section 2. If any member of the Board of Managers or the Executive Commit- tee is found by a vote of two-thirds of the Board of Managers to have violated the spirit of ethics and integrity or the conflict of interest requirements, he or she shall be removed from office. Section 3. The position vacated by such removal shall be filled temporarily by appointment by the Board of Managers until the next scheduled election or regular appointment to the affected position. Section 4. When for approved Academy obligations, circumstances necessitate payment by persons other than the Academy officers who sign checks, reim- bursement to such persons shall be made only when appropriate documentation is submitted to the Treasurer of the Academy. ARTICLE IX. COOPERATION Section 1. The term “‘Affiliated Societies” in their order of seniority (see Article II, Section 10) shall be held to cover the: Philosophical Society of Washington; ACADEMY BYLAWS 225 Anthropological Society of Washington; Biological Society of Washington; Chemical Society of Washington; Entomological Society of Washington; National Geographic Society; Geological Society of Washington; Medical Society of the District of Columbia; Columbia Historical Society; Botanical Society of Washington; Society of American Foresters, Washington Section; Washington Society of Engineers; Institute of Electrical and Electronics Engineers, Washington Section; American Society of Mechanical Engineers, Washington Section; Helminthological Society of Washington; American Society for Microbiology, Washington Branch; Society of American Military Engineers, Washington Post; American Society of Civil Engineers, National Capital Section; Society for Experimental Biology and Medicine, District of Columbia Section; American Society for Metals, Washington Chapter; American Association for Dental Research, Washington Section; American Institute of Aeronautics and Astronautics, National Capital Section; American Meteorological Society, District of Columbia Chapter; Insecticide Society of Washington, now Pest Science Society of Washington; Acoustical Society of America, Washington Chapter; American Nuclear Society, Washington Section; Institute of Food Technologists, Washington Section; American Ceramic Society, Baltimore-Washington Section; Electrochemical Society, National Capital Section; Washington History of Science Club; American Association of Physics Teachers, Chesapeake Section; Optical Society of America, National Capital Section; American Society of Plant Physiologists, Washington Area Section; Washington Operations Research Council, now Washington Operations Re- search and Management Science Council; Instrument Society of America, Washington Section; American Institute of Mining, Metallurgical, and Petroleum Engineers, Wash- ington Section; National Capital Astronomers; Maryland-District of Columbia- Virginia Section of the Mathematical Associa- tion of America; 226 WASHINGTON ACADEMY OF SCIENCES District of Columbia Institute of Chemists; District of Columbia Psychological Association; Washington Paint Technical Group; American Phytopathological Society, Potomac Division; Society for General Systems Research, Metropolitan Washington Chapter; Human Factors Society, Potomac Chapter; American Fisheries Society, Potomac Chapter; Association for Science, Technology and Innovation; Eastern Sociological Society; Institute of Electrical and Electronics Engineers, Northern Virginia Chapter; Association for Computing Machinery, Washington Chapter; Washington Statistical Society; Institute of Industrial Engineers; Society of Manufacturing Engineers; and such others as may be hereafter recommended by the Board of Managers and elected by two-thirds of the members of the Academy voting, the vote being taken by correspondence. A society may be released from afhliation on recom- mendation of the Board of Managers, and the concurrence of two-thirds of the members of the Academy voting. Section 2. The Academy may assist the afhliated scientific societies of Washing- ton in any matter of common interest, as in joint meetings, or in the publication of a joint directory; provided it shall not have power to incur for or in the name of one or more of these societies any expense or liability not previously autho- rized by said society and societies, nor shall it without action of the Board of Managers be responsible for any expenses incurred by one or more of the Afhli- ated Societies. Section 3. No Afhliated Society shall be committed by the Academy to any action in conflict with the charter, constitution, or bylaws, of said society, or its parent society. Section 4. The Academy may establish and assist a Washington Junior Acad- emy of Sciences for the encouragement of interest in sclence among students in the Washington area of high school and college age. ARTICLE X. AWARDS AND GRANTS-IN-AID Section 1. The Academy may award medals and prizes or otherwise express its recognition and commendation of scientific work of high merit and distinction -in the Washington area. Such recognition shall be given only on approval by the ACADEMY BYLAWS 227, Board of Managers of a recommendation by the Committee on Awards for Scientific Achievement. Section 2. The Academy may receive or make grants to aid scientific research in the Washington area. Grants shall be received or made only on approval by the Board of Managers of a recommendation by the Committee on Grants-in-Aid for Scientific Research. ARTICLE XI. AMENDMENTS Section 1. Amendments to these bylaws shall be proposed by the Board of Managers and submitted to the members of the Academy in the form of a mail ballot accompanied by a statement of the reasons for the proposed amendment. A two-thirds majority of those members voting is required for adoption. At least two weeks shall be allowed for the ballots to be returned. Section 2. Any Afhliated Society or any group of ten or more members may propose an amendment to the Board of Managers in writing. The action of the Board of Managers in accepting or rejecting this proposal to amend the bylaws shall be by a vote on roll call, and the complete roll call shall be entered in the minutes of the meeting. ARTICLE XII. DISTRIBUTION OF FUNDS ON DISSOLUTION In the event of a liquidation, dissolution, termination or winding up of the Washington Academy of Sciences (whether voluntary, involuntary, or by oper- ation of law) the total assets of the Washington Academy of Sciences shall be distributed by the Board of Managers, provided that none of the property or assets of the Washington Academy of Sciences shall be made available in any way to any individual, corporation or other organization, except to one or more corporations, or other organizations which qualify as exempt from federal in- come tax under Section 501 (c)(3) of the U.S. Internal Revenue Code of 1954, as may be from time to time amended. ARTICLE XIII. PURPOSE The Washington Academy of Sciences is organized exclusively for charitable, educational, and scientific purposes, including, for such purposes, the making of distributions to organizations that qualify as exempt organizations under Sec- 228 WASHINGTON ACADEMY OF SCIENCES tion 501(c)(3) of the U.S. Internal Revenue Code (or the corresponding provi- sion of any future United States Internal Revenue Law.). ARTICLE XIV. CONTROL OF FUNDS, ACTIVITIES No part of the net earnings of the Washington Academy of Sciences shall inure to the benefit of, or be distributable to its members, trustees, officers, or other private persons, except that the Washington Academy of Sciences shall be autho- rized and empowered to pay reasonable compensation for services rendered, and to make payments and distributions in furtherance of the purposes set forth in ARTICLEXII hereof. No substantial part of the activities of the Washington Academy of Sciences shall be the carrying on of propaganda, or otherwise at- tempting to influence legislation, and the Washington Academy of Sciences shall not participate in, or intervene in (including the publishing or distribution of statements) any political campaign on behalf of any candidate for public office. Notwithstanding any other provision of these Articles, the Washington Academy of Sciences shall not carry on any other activities not permitted to be carried on (a) by an association exempt from Federal income tax under Section 501 (c)(3) of the Internal Revenue Code of 1954 (or the corresponding provision of any future United States Internal Revenue Law) or (b) by an association, contributions to which are deductible under Section 1 70(c)(2) of the U.S. Inter- nal Revenue Code of 1954 (or the corresponding provision of any future United States Internal Revenue Law). Journal of the Washington Academy of Sciences, Volume 83, Number 4, Pages 229-242, December 1993 1993 Washington Academy of Sciences Membership Directory M = Member; F = Fellow; LF = Life Fellow; LM = Life Member; EM = Emeritus Member; EF = Emeritus Fellow; NRF = Non-Resident Fellow. ABDULNUR, SUHEIL F. (Dr) 5715 Glenwood Road, Bethesda, MD 20817 (F) ABELSON, P. H. (Dr) 4244 50th Street, NW, Washington, DC 20016 (F) ABRAHAM, GEORGE (Dr) 3107 Westover Drive, SE, Washington, DC 20020 (LF) ACHTER, MEYER R. (Dr) 417 Sth Street, SE, Washington, DC 20003 (EF) ADAMS, ALAYNE A. (Dr) 8436 Rushing Creek Court, Springfield, VA 22153 (F) ADAMS, CAROLINE L. (Dr) 242 N. Granada Street, Arlington, VA 22203 (EM) AFFRONTI, LEWIS F. (Dr) 5003 Woodland Way, Annadale, VA 22033 (F) ALDRIDGE, MARY H. (Dr) 7904 Hackamore Drive, Potomac, MD 20854-3825 (EF) ALEXANDER, BENJAMIN H. (Dr) 1608 Dexter Avenue, Cincinnati, OH 45206 (EF) ALEXANDER, DONALD H. (Mr) 16912 Olde Mill Run, Derwood, MD 20855 (M) ALEXANDER, KERMIT L. (Mr) 8812 Flower Avenue, Silver Spring, MD 20901 (M) ALICATA, J. E. (Dr) 1434 Punahou Street, #736, Honolulu, HI 96822 (EF) ALLEN, J. FRANCES (Dr) P.O. Box 284, Roxbury, NY 12474 (NRF) ANDRUS, EDWARD D. (Mr) 2497 Patricia Court, Falls Church, VA 22043 (M) ARMANET, FRANCOIS (Dr) 4909 Elsmere Avenue, Bethesda, MD 20814 (F) ARONSON, CASPER J. (Mr) 3401 Oberon Street, Kensington, MD 20895 (EM) ARSEM, COLLINS (Mr) 10821 Admirals Way, Potomac, MD 20854 (M) ARVESON, PAUL T. (Mr) 10205 Folk Street, Silver Spring, MD 20902 (F) ARY, T. S. (Mr) 3301 North Nottingham Street, Arlington, VA 22207 (M) AXELROD, JULIUS (Dr) LCB-M. H. IRP-NIMH, Room 3A15A, Bldg. 36, National Institute of Mental Health, Bethesda, MD 20892 (EF) AXILROD, BENJAMIN M. (Dr) 9216 Edgewood Drive, Gaithersburg, MD 20877 (EF) BAILEY, CLIFTON R. (Dr) 6507 Divine Street, McLean, VA 22101-4620 (LF) BAKER, ARTHUR A. (Dr) 5201 Westwood Drive, Bethesda, MD 20816-1849 (EF) BAKER, LEONARD (Dr) 4924 Sentinel Drive, Bethesda, MD 20816 (F) BALLARD, LOWELL D. (Mr) 7823 Mineral Springs Drive, Gaithersburg, MD 20877 (F) BARBOUR, LARRY L. (Mr) Rural Route 1, Box 492, Great Meadows, NJ 07838 (M) BARRETT, TERENCE WILLIAM (Dr) 1453 Beulah Road, Vienna, VA 22182 (M) BARTFELD, CHARLES I. (Dr) 6007 Kirby Road, Bethesda, MD 20817 (EM) BARWICK, W. ALLEN (Dr) 13620 Maidstone Lane, Potomac, MD 20854-1008 (F) BATAVIA, ANDREW I. (Mr) 700 Seventh St., SW, Apt #813, Washington, DC 20024 (LF) BAUMANN, ROBERT C. (Mr) 9308 Woodberry Street, Seabrook, MD 20706 (F) BEACH, LOUIS A. (Dr) 1200 Waynewood Blvd., Alexandria, VA 22308-1842 (F) BEALE, CALVIN L. (Mr) 1960 Biltmore Street, NW, Washington, DC 20009 (F) BECKER, EDWIN D. (Dr) Bldg. 5, Room 124, N.I.H., Bethesda, MD 20892 (F) BECKMANN, ROBERT B. (Dr) 10218 Democracy Lane, Potomac, MD 20854 (F) BEKEY, IVAN (Mr) 4624 Quarter Charge Drive, Annandale, VA 22003 (F) 229 230 WASHINGTON ACADEMY OF SCIENCES BENDER, MAURICE (Dr) 16518 NE Second Place, Bellevue, WA 98008-4507 (EF) BENESCH, WILLIAM M. (Dr) 4444 Linnean Avenue, NW, Washington, DC 20008 (LF) BENJAMIN, CHESTER R. (Dr) 315 Timberwood Avenue, Silver Spring, MD 20901 (EF) BENNETT, JOHN A. (Mr) 7405 Denton Road, Bethesda, MD 20814 (F) BENSON, WILLIAM M. (Dr) 636 Massachusetts Avenue, NE, Washington, DC 20002 (F) BERGER, HENRY (Dr) 7135 Groveton Gardens Road, Alexandria, VA 22306 (M) BERGMANN, OTTO (Dr) George Washington Univ., Dept. of Physics, Washington, DC 20052 (F) BERKSON, HAROLD (Dr) 12001 Whippoorwill Lane, Rockville, MD 20852 (EM) BERNSTEIN, BERNARD (Mr) 7420 Westlake Terr, Apt #608, Bethesda, MD 20817 (M) BETTS, ALLEN W. (Mr) 2510 South Ivanhoe Place, Denver, CO 80222-6226 (M) BICKLEY WILLIAM E. (Dr) 6516 Fortieth Ave, University Park, Hyattsville, MD 20782 (EF) BLANK, CHARLES A. (Dr) 255 Massachusetts Avenue, Apt. #607, Boston, MA 02115 (NRF) BLUNT, ROBERT F. (Dr) 5411 Moorland Lane, Bethesda, MD 20814 (F) BOEK, HEATHER (Dr) Corning Incorporated, SP-FR-5-1, Corning, NY 14831 (NRF) BOEK, JEAN K. (Dr) National Graduate University, 1101 N. Highland St, Arlington, VA 22201 (LF) BOEK, WALTER E. (Dr) 501 1-Lowell Street, NW, Washington, DC 20016 (F) BOGNER, MARILYN SUE (Dr) 9322 Friars Road, Bethesda, MD 20817-2308 (LF) BONEAU, C. ALAN (Dr) 6518 Ridge Drive, Bethesda, MD 20816-2636 (F) BOURGEOIS, LOUIS D. (Dr) 8701 Bradmoor Drive, Bethesda, MD 20817 (EF) BOURGEOIS, MARIE J. (Dr) 8701 Bradmoor Drive, Bethesda, MD 20817 (F) BOWMAN, THOMAS E. (Dr) 13210 Magellan Avenue, Rockville, MD 20853 (F) BOYD, WENDELL J. (Mr) 6307 Balfour Drive, Hyattsville, MD 20782 (M) BRADSHAW, SARA L. (Ms) 5405 Duke Street, Apt #312, Alexandria, VA 22304 (M) BRANCATO, EMANUEL L. (Dr) 7370 Hallmark Road, Clarksville, MD 21029 (EF) BRENNER, ABNER (Dr) 7204 Pomander Lane, Chevy Chase, MD 20815 (F) BRIER, GLENN W. (Mr) 12127 Cathedral Drive, Lake Ridge, VA 22192 (LF) BRIMMER, ANDREW F. (Dr) 4400 MacArthur Blvd., Suite 302, NW, Washington, DC 20007 (F) BRISKMAN, ROBERT D. (Mr) 6728 Newbold Drive, Bethesda, MD 20817 (F) BRITZ, STEVEN JOHN (Dr) USDA Climate Stress Lab, B-046A BARC-W, Beltsville, MD 20705 (F) BROADHURST, MARTIN G. (Dr) 116 Ridge Rd, Box 163, Washington Grove, MD 20880 (F) BROWN, ELISE A. B. (Dr) 6811 Nesbitt Place, McLean, VA 22101-2133 (LF) BROWN, ERNESTO (Mr) 9810 Dairyton Court, Gaithersburg, MD 20879-1101 (M) BRYAN, MILTON M. (Mr) 3322 North Glebe Road, Arlington, VA 22207-4235 (M) BUOT, FELIX A. (Dr) Code 6864 Naval Research Laboratory, Washington, DC 20375 BURAS, EDMUND M.., JR. (Mr) 824 Burnt Mills Ave, Silver Spring, MD 20901-1492 (EF) BURNS, EDGAR J. (Mr) 3718 Thornapple Street, Chevy Chase, MD 20815 (F) BUTTERMORE, DONALD O. (Mr) 34 West Berkeley St, Uniontown, PA 15401-4241 (LF) CAMPBELL, LOWELL E. (Mr) 14000 Pond View Road, Silver Spring, 20905 MD (F) CANNON, EDWARD W. (Dr) 18023-134th Avenue, Sun City West, AZ 85375 (M) CANTELO, WILLIAM W. (Dr) 11702 Wayneridge Street, Fulton, MD 20759 (F) CARR, DANIEL B. (Dr) 9930 Rand Drive, Burke, VA 22015 (F) CARROLL, WILLIAM R. (Dr) 4802 Broad Brook Drive, Bethesda, MD 20814-3906 (EF) CERRONI, MATTHEW J. (Mr) 12538 Browns Ferry Road, Herndon, VA 22070 (M) CETINBAS, MEHMET A. (Mr) CTL/MAC Eng., 8480 D Tyco Rd., Vienna, VA 22182 (M) CHAMBERS, RANDALL M. (Dr) 2704 Winstead Circle, Wichita, KS 67226 (NRF) CHAPLIN, HARVEY R., JR. (Dr) 1561 Forest Villa Lane, McLean, VA 22101 (F) CHAPMAN, ROBERT D. (Dr) 10976 Swansfield Road, Columbia, MD 21044 (F) CHEEK, CONRAD H. (Dr) 4334 H. Street, SE, Washington, DC 20019 (F) CHEZEM, CURTIS G. (Dr) 3378 Wisteria Street, Eugene, OR 97404 (EF) MEMBERSHIP DIRECTORY 231 CHOI, KYU YONG (Prof) Dept. of Chem. Eng., Univ. of Maryland, College Park, MD 20742 (F) CHRISTIANSEN, MERYL N. (Dr) 610 T-Bird Drive, Front Royal, VA 22630 (EF) CLAIRE, CHARLES N. (Mr) 4403 14th Street, NW Apt #31, Washington, DC 20011 (EF) CLARK, GEORGE E., JR. (Mr) 4022 N. Stafford Street, Arlington, VA 22207 (F) CLEVEN, GALE W. (Dr) 2413 S. Eastern #245 Las Vegas, NV 89104 (EF) CLINE, THOMAS LYTTON (Dr) 13708 Sherwood Forest Dr, Silver Spring, MD 20904 (F) CLORE, GIDEON MARIUS (Dr) Lab of Chemical Physics, Bldg 5, Room 132 NIDDK, National Institute of Health, Bethesda, MD 20892 (F) COATES, JOSEPH F. (Mr) 3738 Kanawha Street, NW Washington, DC 20015 (F) COFFEY, TIMOTHY P. (Dr) Naval Research Laboratory, Code 1001, Washington, DC 20375-5000 (F) COHEN, MICHAEL P. (Dr) 555 New Jersey Avenue, NW Washington, DC 20208-5654 (M) COLWELL, RITA R. (Dr) Biotechnology Inst. 4321 Hartwick Rd., Suite 550, University of Maryland, College Park, MD 20742 (LF) COMAS, JAMES (Dr) NIST, Bldg 255, Rm A-305 Bureau Dr, Gaithersburg, MD 20899 (F) CONDELL, WILLIAM J., JR (Dr) 4511 Gretna Street, Bethesda, MD 20814 (F) CONNELLY, EDWARD McD. (Mr) 11915 Cheviot Dr., Herndon, VA 22070 (F) COOK, RICHARD K. (Dr) 4111 Bel Pre Road, Rockville, MD 20853 (F) COOPER, KENNETH W. (Dr) 4497 Picacho Drive, Riverside, CA 92507-4873 (EF) CORLISS, EDITH L. R. (Mrs) 2955 Albemarle Street, NW, Washington, DC 20008 (LF) COSTRELL, LOUIS (Mr) 15115 Interlachen Drive, Apt #621, Silver Spring, MD 20906-5641 (F) CREVELING, CYRUS R. (Dr) 4516 Amherst Lane, Bethesda, MD 20814 (F) CROSBY, DAVID S. (Dr) Dept. Math & Stat., American Univ., 4400 Mass. Ave., NW Washington, DC 20016 (M) CRUM, JOHN K. (Dr) 1155 16th Street, NW Washington, DC 20036 (F) CURRIE, CHARLES L., S. J. (Rev) Rector, Jesuit Community, St. Joseph’s University, 5600 City Ave., Philadelphia, PA 19131 (M) D’ANTONIO, WILLIAM V. (Dr) 3701 Connecticut Ave, NW Apt. 818, Washington, DC 20008 (EF) DAVIS, MARION MACLEAN (Dr) Crosslands, Apt. 100, Kennett Square, PA 19348 (LF) DAVIS, ROBERT E. (Dr) 1793 Rochester Street, Crofton, MD 21114 (F) DAVISON, MARGARET C. (Mrs) 2928 N. 26th Street, Arlington, VA 22207 (M) DAVISSON, JAMES W. (Dr) 400 Cedar Ridge Road, Oxon Hill, MD 20745 (EF) DEAHL, KENNETH L. (Dr) USDA-ARS-BARC WEST, Beltsville, MD 20705 (F) DEAL, GEORGE E. (Dr) 6245 Park Road, McLean, VA 22101 (EF) DeBERRY, MARIAN B. (Mrs) 3608 17th Street, NE, Washington, DC 20018 (EM) DEDRICK, ROBERT L. (Dr) 1633 Warner Avenue, McLean, VA 22101 (F) DeLANEY, WAYNE R. (Mr) 602 Oak Street, Farmville, VA 23901-1118 (M) DEMING, W. EDWARDS (Dr) 4924 Butterworth Place, NW, Washington, DC 20016 (EF) DEMUTH, HAL P. (Cdr) 118 Wolfe Street, Winchester, VA 22601 (NRF) DESLATTES, RICHARD D., JR. (Dr) 610 Aster Blvd., Rockville, MD 20850 (F) DEUTSCH, STANLEY (Dr) 7109 Laverock Lane, Bethesda, MD 20817 (EF) DeWIT, RONALD (Dr) 11812 Tifton Drive, Rockville, MD 20854 (F) DIBERARDINO, THOMAS (Dr) Code 2844 Naval Surface Warfare Center, Annapolis, MD 21402 (F) DICKSON, GEORGE (Mr) 415 Russell Ave. Apt #11116, Gaithersburg, MD 20877 (F) DIMOCK, DAVID A. (Mr) 4291 Molesworth Terrace, Mt. Airy, MD 21771 (EM) DOCTOR, NORMAN (Mr) 6 Tegner Court, Rockville, MD 20850 (F) DOEPPNER, THOMAS W. (Col) 8323 Orange Court, Alexandria, VA 22309 (LF) DONALDSON, EVA G. (Ms) 3941 Ames Street, NE, Washington, DC 20019 (F) DONALDSON, JOHANNA B. (Mrs) 3020 North Edison Street, Arlington, VA 22207 (F) 232 WASHINGTON ACADEMY OF SCIENCES DONNERT, HERMANN J. (Dr) 5217 Terra Hights Drive, Manhattan, KS 66502 (NRF) DOOLING, ROBERT J. (Dr) 13615 Straw Bale Lane, Darnestown, MD 20878 (F) DOUGLAS, THOMAS B. (Dr) 3031 Sedgwick Street, NW, Washington, DC 20008 (EF) DRAEGER, HAROLD R. (Dr) 1201 North 4th Street, Tucson, AZ 85705 (EF) DUBEY, SATYA D. (Dr) 7712 Groton Road, West Bethesda, MD 20817 (EF) DUFFEY, DICK (Dr) Chem-Nuclear Engineering Dept., University of Maryland, College Park, MD 20742 (LF) DUKE, JAMES A. (Mr) 8210 Murphy Road, Fulton, MD 20759 (LF) DUNCOMBE, RAYNOR L. (Dr) 1804 Vance Circle, Austin, TX 78701 (NRF) DuPONT, JOHN E. (Mr) P.O. Box 358, Newtown Square, PA 19073 (NRF) EDINGER, STANLEY E. (Dr) 5901 Montrose Road, Apt. 404-N, Rockville, MD 20852 (F) EDMUND, NORMAN W. (Mr) 407 NE 3rd Avenue, Ft. Lauderdale, FL 33301 (M) EISENHART, CHURCHILL (Dr) 9629 Elrod Road, Kensington, MD 20895 (EF) EISNER, MILTON P. (Dr) 1565 Hane Street, McLean, VA 22101-4439 (F) EL KHADEM, HASSAN (Dr) Dept. of Chemistry, American Univ., Washington, DC 20016-8014 (F) EL-BISI, HAMED M. (Dr) 258 Bishops Forest Drive, Waltham, MA 02154 (M) ENDO, BURTON Y. (Dr) 1010 Jigger Court, Annapolis, MD 21401 (F) ENTLEY, WILLIAM J. (Mr) 5707 Pamela Drive, Centreville, VA 22020 (F) ESTRIN, NORMAN F. (Dr) BA 9109 Copenhaver Drive, Potomac, MD 20854 (M) ETTER, PAUL C. (Mr) 16609 Bethayres Road, Rockville, MD 20855-2043 (F) EWERS, JOHN C. (Mr) 4432 26th Road North, Arlington, VA 22207 (EF) FALK, JAMES E. (Dr) 11201 Leatherwood Drive, Reston, VA 22091 (F) FARLEE, CORALIE (Dr) 389 O Street, SW, Washington, DC 20024 (F) FARMER, ROBERT F.. (Dr) c/o Akzo Chem, | Livingstone Ave., Dobbs Ferry, NY 10522-3401 (NRF) FAULKNER, JOSEPH A. (Mr) 2 Bay Drive, Lewes, DE 19958 (NRF) FAUST, WILLIAM R. (Dr) 5907 Walnut Street, Temple Hills, MD 20748-4843 (F) FAY, ROBERT E. (Dr) 6425 Cygnet Drive, Alexandria, VA 22307 (F) FEARN, JAMES E. (Dr) 374 North Drive, Severna Park, MD 21146 (EF) FEINGOLD, S. NORMAN (Dr) 1511 K Street, NW, Suite #541, Washington, DC 20005 (F) FERRELL, RICHARD A. (Dr) 6611 Wells Parkway, University Park, MD 20782 (EF) FINKELSTEIN, ROBERT (Mr) Robotic Technology, Inc. 10001 Crestleigh Lane, Potomac, MD 20854 (M) FISHER, JOEL L. (Dr) 4033 Olley Lane, Fairfax, VA 22032 (M) FLINN, DAVID R. (Dr) 9714 Wild Flower Circle, Tuscaloosa, AL 35405 (NRF) FLORIN, ROLAND E. (Dr) 7407 Cedar Avenue, Takoma Park, MD 20912 (EF) FLOURNOY, NANCY (Dr) 4712 Yuma Street, NW Washington, DC 20016-2048 (F) FOCKLER, HERBERT H. (Mr) 10710 Lorain Avenue, Silver Spring, MD 20901 (M) FONER, SAMUEL N. (Dr) 11500 Summit West Blvd, No. 15B, Temple Terr, FL 33617 (EF) FOOTE, RICHARD H. (Dr) HC 75, Box 166 L.O.W., Locust Grove, VA 22508 (NRF) FORZIATI, ALPHONSE F. (Dr) 15525 Prince Fredrick Way, Silver Spring, MD 20906-1318 (F) FORZIATI, FLORENCE H. (Dr) 15525 Prince Fredrick Way, Silver Spring, MD 20906-1318 (F) FOURNIER, ROBERT O. (Dr) 108 Paloma Road, Portola Valley, CA 94028 (M) FOX, WILLIAM B. (Dr) 1813 Edgehill Drive, Alexandria, VA 22307 (F) FRANKLIN, JUDE E. (Dr) 7616 Carteret Road, Bethesda, MD 20817-2021 (F) FREEMAN, ANDREW F. (Mr) 5012 33rd Street North, Arlington, VA 22207-1821 (EM) FRIEDMAN, MOSHE (Dr) 4511 Yuma Street, NW, Washington, DC 20016 (F) FRUSH, HARRIET L. (Dr) 4912 New Hampshire Ave, NW, Apt #104, Washington, DC 20011-4151 (M) MEMBERSHIP DIRECTORY 233 FRY, DAVID J. (Dr) 15149 Winesap Drive, Gaithersburg, MD 20878 (F) FURUKAWA, GEORGE T. (Dr) 1712 Evelyn Drive, Rockville, MD 20852 (F) GAGE, WILLIAM W. (Dr) 10 Trafalgar Street, Rochester, NY 14619-1222 (NRF) GALLER, SIDNEY R. (Dr) 6242 Woodcrest Avenue, Baltimore, MD 21209 (EF) GANEFF, IWAN (Mr) 5944 W. Wrightwood Avenue, Chicago, IL 60639 (EM) GARVIN, DAVID (Dr) 18700 Walker’s Choice Rd., No. 807, Gaithersburg, MD 20879 (EF) GAUNAURD, GUILLERMO C. (Dr) 4807 Macon Road, Rockville, MD 20852-2348 (F) GHAFFARI, ABOLGHASSEM (Dr) 7532 Royal Dominion Dr, West Bethesda, MD 20817 (LF) GIST, LEWIS A. (Dr) 1336 Locust Road, NW, Washington, DC 20012 (EF): GLASER, HAROLD (Dr) 1346 Bonita Street, Berkeley, CA 94709 (EF) GLASGOW, AUGUSTUS R., JR., (Dr) 4116 Hamilton Street, Hyattsville, MD 20781-1805 (EF) GLOVER, ROLFE E., III (Prof) 7006 Forest Hill Drive, Hyattsville, MD 20782 (EF) GLUCKMAN, ALBERT G. (Mr) 11235 Oakleaf Dr, No 1619, Silver Spring, MD 20901-1305 (F) GLUCKSTERN, ROBERT L. (Dr) 10903 Wickshire Way, Rockville, MD 20852 (F) GOESSMAN, ROBERT C. (Mr) 9357 Birchwood Court, Manassas, VA 22110 (M) GOFF, JAMES F. (Dr) 3405-34th Place, NW Washington, DC 20016 (F) GOLDEN, MORGAN A. (Dr) 9110 Drake Place, College Park, MD 20740 (F) GOLUMBIC, CALVIN (Dr) 6000 Highboro Drive, Bethesda, MD 20817 (EM) GONET, FRANK (Dr) 4007 N. Woodstock Street, Arlington, VA 22207-2943 (EF) GOODE, ROBERT J. (Mr) 2402 Kegwood Lane, Bowie, MD 20715 (EF) GORDON, RUTH E. (Dr) American Type Culture Collection, 12301 Parklawn Drive, Rockville, MD 20852 (EF) GRAY, IRVING (Dr) 5450 Whitley Park Terrace, Apt. 802, Bethesda, MD 20814-2060 (F) GREENOUGH, M. L. (Mr) Greenough Data Assc, 616 Aster Blvd, Rockville, MD 20850 (F) GRONENBORN, ANGELA M. (Dr) 5503 Lambert Road, Bethesda, MD 20814 (F) GROSS, DONALD (Mr) 3530 North Rockingham Street, Arlington, VA 22213 (F) GROSSLING, BERNARDO F. (Dr) 10903 Amherst Ave, #241, Silver Spring, MD 20902 (F) GRUNTFEST, IRVING (Dr) 140 Lake Carol Drive, West Palm Beach, FL 33411-2132 (EF) HACSKAYLO, EDWARD (Dr) P.O. Box 189, Port Republic, MD 20676 (F) HAENNI, EDWARD O. (Dr) 7907 Glenbrook Road, Bethesda, MD 20814-2403 (F) HAGN, GEORGE H. (Mr) 4208 Sleepy Hollow Road, Annadale, VA 22003 (LF) HAIG, FRANK R. SJ (Rev) Loyola College, 4501 North Charles St, Baltimore, MD 21210 (F) HAINES, KENNETH A. (Mr) 900 N. Taylor Street, #1231, Arlington, VA 22203-1855 (F) HAMER, WALTER J. (Dr) 407 Russell Avenue, #305, Gaithersburg, MD 20877-2889 (EF) HANEL, RUDOLPH A. (Dr) 31 Brinkwood Road, Brookeville, MD 20833 (EF) HANFORD, WILLIAM E., JR., (Mr) 5613 Overlea Road, Bethesda, MD 20816 (M) HARR, JAMES W. (Mr) 9503 Nordic Drive, Lanham, MD 20706 (M) HARRINGTON, FRANCIS D. (Dr) 4600 Ocean Beach Blvd., Apt. 204, Cocoa Beach, FL 32931 (NRF) HARRINGTON, MARSHALL C. (Dr) 10450 Lottsford Road #2207, Mitcheville, MD 20721 (EF) HARTLEY, JANET W. (Dr) Bldg. 7, Room 302, National Institutes of Health, Bethesda, MD 20892 (F) HARTMANN, GREGORY K. (Dr) 10701 Keswick St, Box 317, Garrett Park, MD 20896 (EF) HASKINS, CARYL P. (Dr) 1545 18th Street, NW, Suite 810, Washington, DC 20036 (EF) HASS, GEORG H. (Dr) 7728 Lee Avenue, Alexandria, VA 22308-1003 (F) HAUGE, SHARON K. (Dr) Math Department, UDC, 4250 Connecticut Ave., NW, Washington, DC 20008 (M) HAUPTMAN, HERBERT (Dr) The Medical Foundation of Buffalo, 73 High Street, Buffalo, NY 14203-1196 (NRF) 234 WASHINGTON ACADEMY OF SCIENCES HAYDEN, GEORGE A. (Dr) 1312 Juniper Street, NW, Washington, DC 20012 (EM) HAYNES, ELIZABETH D. (Mrs) 4149 25th Street, North, Arlington, VA 22207 (M) HEIFFER, MELVIN H. (Dr) 11107 Whisperwood Lane, Rockville, MD 20852 (F) HERKENHAM, MILES (Dr) 11705 Cherry Grove Drive, Gaithersburg, MD 20878 (F) HERMACH, FRANCIS L. (Mr) 2201 Colston Drive, #311, Silver Spring, MD 20910 (F) HERMAN, ROBERT (Dr) 8434 Antero Drive, Austin, TX 78759 (EF) HEYER, W. RONALD (Dr) Amphibian and Reptile, M.S. 162, Smithsonian, Washington, DC 20560 (F) HIBBS, EUTHYMIA D. (Dr) 7302 Durbin Terrace, Bethesda, MD 20817 (M) HILL, BRUCE F. (Dr) Mount Vernon College, 2100 Foxhall Road, NW, Washington, DC 20007 (F) HILLABRANT, WALTER J. (Dr) 1927 38th Street, NW, Washington, DC 20007 (M) HILSENRATH, JOSEPH (Mr) 9603 Brunett Avenue, Silver Spring, MD 20901 (F) HOBBS, ROBERTS B. (Dr) 7715 Old Chester Road, Bethesda, MD 20817 (EF) HOFFELD, J. TERRELL (Dr) 11307 Ashley Drive, Rockville, MD 20852-2403 (F) HOGE, HAROLD J. (Dr) 65 Grove Street, Apt. 148, Wellesley, MA 02181 (EF) HOLLINSHEAD, ARIEL (Dr) 3637 Van Ness St, NW, Washington, DC 20008-3130 (EF) HOLSHOUSER, WILLIAM L. (Mr) P.O. Box 1475, Banner Elk, NC 28604 (NRF) HONIG, JOHN G. (Dr) 7701 Glenmore Spring Way, Bethesda, MD 20817 (F) HOOVER, LARRY A. (Mr) 1541 Stableview Drive, Gastonia, NC 28056 (M) HOPP, THEODORE H. (Dr) 303 Kent Oaks Way, Gaithersburg, MD 20878-5617 (M) HORNSTEIN, IRWIN (Dr) 5920 Byrn Mawr Road, College Park, MD 20740 (EF) HOROWITZ, EMANUEL (Dr) 14100 Northgate Drive, Silver Spring, MD 20906 (F) HOWARD, DARLENE V. (Dr) 10550 Mackall Road, St. Leonard, MD 20685 (F) HOWARD, JAMES H., JR. (Dr) 10550 Mackall Road, St. Leonard, MD 20685 (F) HOYT, JAMES A., JR. (Mr) 3717 Thoroughbred Lane, Owings Mills, MD 21117 (M) HUDSON, COLIN M. (Dr) 143 S. Wildflower Road, Asheville, NC 28804 (EF) HUHEFY, JAMES E. (Dr) 6909 Carleton Terrace, College Park, MD 20740 (LF) HUMMEL, LANI S. (Ms) 1400 Smokey Wood Drive, #806, Pittsburgh, PA 15218 (M) HUMMEL, JOHN N. (Mr) P.O. Box 1263, Newington, VA 22122 (M) HURDLE, BURTON G. (Dr) 6222 Berkley Road, Alexandra, VA 22307 (F) HURTT, WOODLAND (Dr) 7302 Parkview Drive, Frederick, MD 21702 (M) IKOSSI-ANASTASIOU, KIKI (Dr) 2245 College Drive, #200, Baton Rouge, LA 70808 (M) IRVING, GEORGE W., JR (Dr) 4601 North Park Ave, Apt 613, Chevy Chase, MD 20815 (LF) IRWIN, GEORGE R. (Dr) 7306 Edmonston Avenue, College Park, MD 20740 (F) JACKSON, JO-ANNE A. (Dr) 14711 Myer Terrace, Rockville, MD 20853 (LF) JACOX, MARILYN E. (Dr) 10203 Kindly Court, Gaithersburg, MD 20879 (F) JAMES, HENRY M. (Mr) 6707 Norview Court, Springfield, VA 22152 (M) JEN, CHIH K. (Dr) 10203 Lariston Lane, Silver Spring, MD 20903 (EF) JENSEN, ARTHUR S. (Dr) 5602 Purlington Way, Baltimore, MD 21212-2950 (LF) JERNIGAN, ROBERT W. (Dr) 14805 Clavel Street, Rockville, MD 20853 (F) JOHNSON, DANIEL P. (Dr) P.O. Box 359, Folly Beach, SC 29439 (EF) JOHNSON, EDGAR M. (Dr) 5315 Renaissance Court, Burke, VA 22015 (LF) JOHNSON, PHYLLIS T. (Dr) 4721 East Harbor Drive, Friday Harbor, WA 98250 (EF) JOHNSTON, ALLEN B. (Mr) 31 S. Aberdeen Street, Arlington, VA 22204 (M) JONES, DANIEL B. (Mr) 11612 Toulone Drive, Potomac, MD 20854 (M) JONES, HOWARD S., JR (Dr) 3001 Veazey Terr, NW Apt 1310, Washington, DC 20008 (LF) JONES, JOANNE M. (Dr) 13184 Larchdale Road, Apt. 13, Laurel, MD 20708 (F) JONG, SHUNG-CHANG (Dr) American Type Culture Collection, 12301 Parklawn Drive, Rockville, MD 20852-1776 (LF) MEMBERSHIP DIRECTORY 235 JORDAN, GARY BLAKE (Dr) 13392 Fallenleaf Road, Poway, CA 92064 (LM) JOYCE, PRISCILLA G. (Ms) 605 N. Emerson Street, Arlington, VA 22203 (M) KAHNE, STEPHEN J. (Dr) 2430 Brussels Court, Reston, VA 22091 (F) KAISER, HANS E. (Dr) 433 Southwest Drive, Silver Spring, MD 20901 (M) KANTOR, GIDEON (Dr) 10702 Kenilworth Avenue, Garrett Park, MD 20896-0553 (M) KAPETANAKOS, C. A. (Dr) 4431 MacArthur Blvd., Washington, DC 20007 (F) KARP, SHERMAN (Dr) 10205 Couselman Road, Potomac, MD 20854-5023 (F) KARR, PHILLIP R. (Dr) 1200 Harbor CR N, Oceanside, CA 92054-1051 (EF) KEEFER, LARRY (Dr) 7016 River Road, Bethesda, MD 20817 (F) KEISER, BERNHARD E. (Dr) 2046 Carrhill Road, Vienna, VA 22181 (F) KESSLER, KARL G. (Dr) 5927 Anniston Road, Bethesda, MD 20817 (EF) KILBOURNE, ELAINE G. (Ms) Thomas S. Wooten High School, 2100 W. Ritchie Parkway, Rock- ville, MD 20850 (F) KIRK, KENNETH L. (Dr) National Institutes of Health, Building 8, Room B1A-02, Bethesda, MD 20892 (F) KLINGSBERG, CYRUS (Dr) 1318 Deerfield Drive, State College, PA 16803 (NRF) KLOPFENSTEIN, REX C. (Mr) 4224 Worcester Drive, Fairfax, VA 22032 (M) | KNOX, ARTHUR S. (Mr) 2006 Columbia Road, NW, Washington, DC 20009 (M) KOPP, WALTER H. (Mr) 5040 Cliffhaven Drive, Annandale, VA 22003-4345 (M) KROLL, MARTIN G. (Dr) 14070 Saddle River, North Potomac, MD 20878 (M) KROP, STEPHEN (Dr) 7908 Birnam Wood Drive, McLean, VA 22102-2711 (EF) KROWNE, CLIFFORD M. (Mr) 3810 Maryland Street, Alexandria, VA 22309 (F) KRUGER, JEROME (Dr) 619 Warfield Drive, Rockville, MD 20850 (F) KRUPSAW, MARYLIN (Mrs) 10208 Windsor View Drive, Potomac, MD 20854 (LF) KUZETSOV, VLADIMAR (Dr) 2424 Pennsylvania Ave. NW, Apt. 814, Washington, DC 20037 (M) LANG, MARTHA E. C. (Mrs) 3133 Connecticut Ave. NW, Apt. 625, Kennedy-Warren, Washington, DC 20008 (EF) LANG, SCOTT W. (Mr) 3640 Dorshire Court, Pasadena, MD 21122-6469 (M) LANG, TERESA C. (Ms) 3640 Dorshire Court, Pasadena, MD 21122-6469 (M) LAWSON, ROGER H. (Dr) 10613 Steamboat Landing, Columbia, MD 21044 (F) LEE, RICHARD H. (Dr) 5 Angola by the Bay, Lewes, DE 19958 (EF) LEFTWICH, STANLEY G. (Dr) 3909 Belle Rive Terrace, Alexandria, VA 22309 (LF) LEIBOWITZ, LAWRENCE M. (Dr) 3903 Laro Court, Fairfax, VA 22031 (F) LEINER, ALAN L. (Mr) 850 Webster Street, Apt. 635, Palo Alto, CA 94301-2837 (EF) LEJINS, PETER P. (Dr) 7114 Eversfield Dr, College Heights Estates, Hyattsville, MD 20782-1049 (F) LENTZ, PAUL LEWIS (Dr) 5 Orange Court, Greenbelt, MD 20770 (EF) LETTIERI, THOMAS R. (Dr) 14313 Duvall Hill Court, Burtonsville, MD 20866 (F) LEVY, SAMUEL (Mr) 2279 Preisman Drive, Schenectady, NY 12309 (EF) LEWIS, A. D. (Mr) 3476 Mt. Burnside Way, Woodbridge, VA 22192 (M) LEY, HERBERT L., JR (Dr) 4816 Camelot Street, Rockville, MD 20853 LIBELO, LOUIS F. (Mr) 9413 Bulls Run Parkway, Bethesda, MD 20817 (LF) LIEBLEIN, JULIUS (Dr) 1621 East Jefferson Street, Rockville, MD 20852 (EF) LIEBOWITZ, HAROLD (Dr) George Washington Univ., 2021 K Street, NW, Room 710, Washington, DC 20052 (F) LING, LEE (Mr) 1608 Belvoir Drive, Los Altos, CA 94024 (EF) LINK, CONRAD B. (Dr) 407 Russell Ave., #813, Gaithersburg, MD 20877 (F) LIST, ROBERT J. (Mr) 1123 Francis Hammond Parkway, Alexandria, VA 22302 (EF) LOCKARD, J. DAVID (Dr) University of Maryland, Botany Dept, College Park, MD 20742 (F) 236 WASHINGTON ACADEMY OF SCIENCES LONG, BETTY JANE (Mrs) 416 Riverbend Road, Fort Washington, MD 20744 (F) LOOMIS, TOM H. W. (Mr) 11502 Allview Drive, Beltsville, MD 20705 (M) LUGT, HANS J. (Dr) 10317 Crown Point Court, Potomac, MD 20854 (F) LUSTIG, ERNEST (Dr) Rossittenweg 10, D-3340 Wolfenbuttel, West Germany (EF) LUTZ, ROBERT J. (Dr) 17620 Shamrock Drive, Olney, MD 20832 (F) LYNN, JEFFERY W. (Prof) 13128 Jasmine Hill Terrace, Rockville, MD 20850 (F) LYON, HARRY B. (Mr) 7722 Northdown Road, Alexandria, VA 22308-1329 (M) LYONS, JOHN W. (Dr) 7430 Woodville Road, Mt. Airy, MD 21771 (F) MADDEN, ROBERT P. (Dr) National Institute of Standards and Technology, A-251 Physics Bldg., Gaithersburg, MD 20899 (NRF) | MAKAROV, IGOR M. (Acad) Chief Scientific Secretary, Russian Academy of Sciences, 14 Leninski Prospect, 11790, GSP1 Moscow, V-71 Russia CIS (F) MANDERSCHEID, RONALD W. (Dr) 10837 Admirals Way, Potomac, MD 20854-1232 (LF) MARTIN, ROY E. (Mr) National Fisheries Institute, 1525 Wilson Blvd., Suite 500, Arlington, VA 22209 (F) MARTIN, P. E. EDWARD J. (Dr) 7721 Dew Wood Drive, Derwood, MD 20855 (M) MASON, HENRY LEA (Dr) 3440 S Jefferson St, #823, Falls Church, VA 22041-3127 (EF) MAYOR, JOHN R. (Dr) 3308 Solomons Court, Silver Spring, MD 20906 (F) McBRIDE, GORDON W. (Mr) 8100 Connecticut Avenue, Apt. 506, Chevy Chase, MD 20815-2813 (EF) McCRACKEN, ROBERT H. (Mr) 5120 Newport Avenue, Bethesda, MD 20816-3025 (LF) MICNTOSH, EDWARD L. (Mr) Montgomery Blair High School, 313 Wayne Ave., Silver Spring, MD 20910 (F) McKENZIE, LAWSON M. (Mr) 1719 North Troy, #394, Arlington, VA 22201 (F) McNESBY, JAMES R. (Dr) 13308 Valley Drive, Rockville, MD 20850 (EF) MEADE, BURFORD K. (Mr) 5903 Mt. Eagle Dr, Apt 404, Alexandria, VA 22303-2523 (EF) MEARS, FLORENCE M. (Dr) 8004 Hampden Lane, Bethesda, MD 20814 (EF) MEARS, THOMAS W. (Mr) 2809 Hathaway Terrace, Wheaton, MD 20906 (F) MEBS, RUSSELL W. (Dr) 6620 32nd Street, North, Arlington, VA 22213-1608 (F) MELMED, ALLEN J. (Dr) 732 Tiffany Court, Gaitherburg, MD 20878 (F) MENZER, ROBERT E. (Dr) 90 Highpoint Drive, Gulf Breeze, FL 32561-4014 (NRF) MESSINA, CARLA G. (Mrs) 9800 Marquette Drive, Bethesda, MD 20817 (F) MILLER, CARL F. (Dr) P.O. Box 127, Gretna, VA 24557 (EF) MILLER, LANCE A. (Dr) P.O. Box 58 Snickersville Pike, Middleburg, VA 22117 (F) MINTZ, RAYMOND D. (Dr) Office of Enforcement, U.S. Customs Service, $305, 1301 Constitution Ave. NW, Washington, DC 20229 (F) MITTLEMAN, DON (Dr) 80 Parkwood Lane, Oberlin, OH 44074-1434 (EF) MIZELL, LOUIS R. (Mr) 8122 Misty Oaks Blvd., Sarasota, FL 34243 (EF) MOLNAR, JOSEPH A. (Mr) 8809 Woodland Meadows Ct., Annandale, VA 22003 (M) MORRIS, J. ANTHONY (Dr) 23E Ridge Road, Greenbelt, MD 20770 (M) MORRIS, P. E., ALAN (Dr) 5817 Plainview Road, Bethesda, MD 20817 (F) MORSE, ROBERT A. (Mr) St. Albans School, Washington, DC 20016 (M) MOSTOFI, F. K. (MD) 7001 Georgia Street, Chevy Chase, MD 20815 (F) MOUNTAIN, RAYMOND D. (Dr) 5 Monument Court, Rockville, MD 20850 (F) MUESEBECK, CARL F. W. (Mr) 18 North Main Street, Elba, NY 14058 (EF) MUMMA, MICHAEL J. (Dr) 210 Glen Oban Drive, Arnold, MD 21012 (F) MURDAY, JAMES S. (Dr) 7116 Red Horse Tavern Lane, West Springfield, VA 22153 (M) MURDOCH, WALLACE P. (Dr) 65 Magaw Avenue, Carlisle, PA 17013-7618 (EF) MEMBERSHIP DIRECTORY 237 NAESER, CHARLES R. (Dr) 6654 Van Winkle Drive, Falls Church, VA 22044 (EF) NAMIAS, JEROME (Dr) Scripps Inst of Oceanography, A-024, La Jolla, CA 92093 (NRF) NASHED, NASHAATT (Mr) 261 Congressional Lane #714, Rockville, MD 20852 (M) NEF, EVELYN S. (Mrs) 2726 N Street, NW, Washington, DC 20007 (M) NEUBAUER, WERNER G. (Dr) 4603 Quarter Charge Drive, Annandale, VA 22003 (F) NEUENDORFFER, J. A. (Dr) 911 Allison Street, Alexandria, VA 22302 (EF) NEUPERT, WERNER M. (Dr) Goddard Space Flight Center, Code 680, N.A.S.A., Greenbelt, MD 20771 (F) NEWMAN, MORRIS (Dr) 1050 Las Alturas Road, Santa Barbara, CA 93103 (NRF) NOFFSINGER, TERRELL L. (Dr) 5785 Bowling Green Road, Auburn, KY 42206 (EF) NORENBURG, JON L. (Dr) 1440 Q Street, NW, Washington, DC 20009 (F) NORRIS, KARL H. (Mr) 11204 Montgomery Road, Beltsville, MD (EF) O’HARE, JOHN J. (Dr) 4601 O’Connor Court, Irving, TX 75062 (EF) O’HERN, ELIZABETH M. (Dr) 633 G Street, SW, Washington, DC 20024 (EF) O’KEEFE, JOHN A. (Dr) Goddard Space Flight Center, Code 681 N.A.S.A., Greenbelt, MD 20771 (F) OBERLE, E. MARILYN (Ms) 58 Parklawn Road, West Roxbury, MA 02132 (M) OEHSER, PAUL H. (Mr) 7130 Southside Blvd., #380, Jacksonville, FL 32256-7086 (EF) OKABE, HIDEO (Dr) 6700 Old Stage Road, Rockville, MD 20852 (F) OLIPHANT, MALCOLM W. (Dr) 1606 Ulupii Street, Kailua, HI 96734 (EF) OLIPHANT, SUSIE V. F. (Dr) 910 Luray Place, Hyattsville, MD 20783 (M) ORDWAY, FRED (Dr) 5205 Elsmere Avenue, Bethesda, MD 20814-5732 (F) OSER, HANS J. (Dr) 8810 Quiet Stream Court, Potomac, MD 20854-4231 (F) OSIPOV, YURIS. (Acad) President, Russian Academy of Sciences, 14 Leninski Prospect, 11790, GSP 1 Moscow, V-71 Russia CIS (F) OSTAFF, WILLIAM ALLEN (Mr) 10208 Drumm Ave, Kensington, MD 20895-3731 (EM) PANCELLA, JOHN R. (Dr) 1209 Veirs Mill Road, Rockville, MD 20851 (F) PARASURAMAN, RAJA (Dr) Catholic University, Dept of Psychology, Washington, DC 20064 (F) PARMAN, GEORGE K. (Mr) 4255 Donald Street, Eugene, OR 97405 (NRF) PARSONS, HENRY McILVAINE (Dr) Human Resources Research Organization, 66 Canal Center Plaza, Alexandria, VA 22314 (F) PATY, ALMA (Ms) 1920 N Street NW, Suite 300, Washington, DC 20036 (M) PAZ, ELVIRA L. (Dr) 172 Cook Hill Road, Wallingford, CT 06492 (EF) PELCZAR, MICHAEL J. (Dr) Avalon Farm, P.O. 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(Dr) Chief, Laboratory of Cell Biology and Genetic Biology, Bldg 8, Room 403, National Institutes of Health, Bethesda, MD 20892 (F) PONNAMPERUMA, CYRIL (Dr) Department of Chemistry, University of Maryland, College Park, MD 20742-2714 (F) 238 WASHINGTON ACADEMY OF SCIENCES POST, MILDRED A. (Miss) 8928 Bradmoore Drive, Bethesda, MD 20817 (F) PRINCE, JULIUS S. (Dr) 7103 Pinehurst Parkway, Chevy Chase, MD 20815 (F) PRINZ, DIANNE K. (Dr) 1704 Mason Hill Drive, Alexandria, VA 22307 (F) PRO, MAYNARD J. (Mr) 7904 Falstaff Road, McLean, VA 22102 (EF) PROCTOR, JOHN H. (Dr) 308 East Street, NE, Vienna, VA 22180 (F) PRYOR, C. NICHOLAS (Dr) 3715 Prosperity Avenue, Fairfax, VA 22031 (F) PURCELL, ROBERT H. (Dr) 17517 White Grounds Road, Boyds, MD 20841 (F) PYKE, THOMAS N. JR., (Mr) NOAA, FB #4, Room 2069, Washington, DC 20233 (F) QUIROZ, RODERICK S. (Mr) 4520 Yuma Street, NW, Washington, DC 20016 (F) RABINOW, JACOB (Mr) 6920 Selkirk Drive, Bethesda, MD 20817 (F) RADER, CHARLES A. (Mr) Gillette Research Inst., 401 Professional Dr., Gaithersburg, MD 20879 (F) RADO, GEORGE T. (Dr) 818 Carrie Court, McLean, VA 22101 (F) RAMAKER, DAVID E. (Dr) 6943 Essex Avenue, Springfield, VA 22150 (F) RAMSAY, MAYNARD J. (Dr) 3806 Viser Court, Bowie, MD 20715 (F) RANSOM, JAMES R. (Mr) 107 E. Susquehanna Avenue, Towson, MD 21286 (M) RAUSCH, ROBERT L. (Dr) P.O. Box 85477, University Station, Seattle, WA 98145-1447 (NRF) RAVITSKY, CHARLES (Mr) 1505 Drexel Street, Takoma Park, MD 20912 (EF) REDISH, EDWARD F. (Prof) 6820 Winterberry Lane, Bethesda, MD 20817 (F) REED, WILLIAM DOYLE (Mr) 1330 Massachusetts Ave. NW, Apt. 624, Washington, DC 20005 (EF) REHDER, HARALD A. (Dr) 3900 Watson Pl, NW, Apt 2G-B, Washington, DC 20016 (F) REINER, ALVIN (Mr) 11243 Bybee Street, Silver Spring, MD 20902 (F) RESWICK, JAMES S. (Dr) 1003 Dead Run Drive, McLean, VA 22101 (F) RHYNE, JAMES J. (Dr) 2704 Westbrook Way, Columbia, MO 65203 (NRF) RICE, ROBERT L. (Mr) 15504 Fellowship Way, North Potomac, MD 20878 (M) RICE, SUE ANN (Dr) 6728 Fern Lane, Annadale, VA 22003 (M) RIEL, GORDEN K. (Dr) Naval Surface Warfare Center, Dahlgren Division, Code R36, White Oak, Silver Spring, MD 20903-5640 (LF) RITT, PAUL E. (Dr) 36 Sylvan Lane, Weston, MA 02193 (NRF) ROBBINS, MARY LOUISE (Dr). Tatsuno House A-23, 2-1-8 Ogikubo, Suginami-Ku, Tokyo 167 Japan (EF) | ROBERTSON, A. F. (DR) 4228 Butterworth Place, NW, Washington, DC 20016 (EF) ROBERTSON, EUGENE C. (Dr) 922 National Center, USGS, Reston, VA 22092 (M) ROBSON, CLAYTON W. (Mr) 2504 Woodland Drive, Eugene, OR 97403 (M) RODNEY, WILLIAM S. (Dr) Georgetown University, Physics Dept, Washington, DC 20057 (F) ROSCHER, NINA M. (Dr) 10400 Hunter Ridge Drive, Oakton, VA 22124 (F) ROSE, WILLIAM K. (Dr) 10916 Picasso Lane, Potomac, MD 20854 (F) ROSENBLATT, DAVID (Dr) 2939 Van Ness St, NW, Apt 702, Washington, DC 20008 (F) ROSENBLATT, JOAN R. (Dr) 2939 Van Ness St, NW, Apt 702, Washington, DC 20008 (F) ROSENFELD, AZRIEL (Dr) 847 Loxford Terrace, Silver Spring, MD 20901 (F) ROSSI, PETER H. (Prof) 34 Stagecoach Road, Amherst, MA 01002 (EF) ROTHMAN, RICHARD B. (Dr) 1510 Flora Court, Silver Spring, MD 20910 (F) ROTKIN, ISRAEL (Mr) 11504 Regnid Drive, Wheaton, MD 20902 (EF) RUBLE, BRUCE L. (Mr) 4200 Davenport Street, NW, Washington, DC 20016 (M) RUTNER, EMILE (Dr) 34 Columbia Avenue, Takoma Park, MD 20912 (M) SAAD, ADNAN A. (Dr) 8647 Oak Chase Drive, Fairfax Station, VA 22033 (M) SAENZ, ALBERT W. (Dr) 6338 Old Town Court, Alexandria, VA 22307 (F) MEMBERSHIP DIRECTORY 239 SALVINO, ROBERT E. (Dr) 4329 Thistlewood Terrace, Burtonsville, MD 20866 (M) SANDERSON, JOHN A. (Dr) B-206 Clemson Downs, 150 Downs Blvd, Clemson, SC 29631 (EF) SANK, VICTOR J. (Dr) 5 Bunker Court, Rockville, MD 20854-5507 (F) SASMOR, ROBERT M (Dr) 4408 North 20th Road, Arlington, VA 22207 (F) SAVILLE, THORNDIKE JR., (Mr) 5601 Albia Road, Bethesda, MD 20816-3304 (LF) SCHACHNER, STEPHEN H. (Dr) 7 Corners Medical Building, 6305 Castle Place, No. 3-A, Falls Church, VA 22044 (F) SCHALK, JAMES M. (Dr) 7 Oakland Drive, Patchogue, NY 11772 (NRF) SCHINDLER, ALBERT I. (Dr) 6615 Sulky Lane, Rockville, MD 20852 (F) SCHLAIN, DAVID (Dr) 2A Gardenway, Greenbelt, MD 20770 (EF) SCHMEIDLER, NEAL F. (Mr) 7218 Hadlow Drive, Springfield, VA 22152 (M) SCHMIDT, CLAUDE H. (Dr) 1827 North 3rd Street, Frago, ND 58102-2335 (EF) SCHNEIDER, SIDNEY (Mr) 239 N. Granada Street, Arlington, VA 22203-1321 (EM) SCHNEPFE, MARIAN M. (Dr) Potomac Towers, Apt. 640, 2001 N. Adams Street, Arlington, VA 22201 (EF) SCHOOLEY, JAMES F. (Dr) 13700 Darnestown Road, Gaithersburg, MD 20878 (EF) SCHULMAN, JAMES H. (Dr) 4615 North Park Ave, #1519, Chevy Chase, MD 20815 (EF) SCHULTZ, WARREN W. (Dr) 4056 Cadle Creek Road, Edgewater, MD 21037-4514 (LF) SCOTT, DAVID B. (Dr) 9100 Belvoir Woods Parkway, Apt. 209, Fort Belvoir, VA 22060 (EF) SCRIBNER, BOURDON F. (Mr) 123 Peppercorn Place, Edgewater, MD 21037 (EF) SEABORG, GLENN T. (Dr) 1154 Glen Road, Lafayette, CA 94549 (NRF) SEBREAHTS, MARC M. (Dr) 7012 Exeter Road, Bethesda, MD 20814 (F) SEITZ, FREDERICK (Dr) Rockefeller University, 1230 York Ave, New York, NY 10021 (NRF) SHAFRIN, ELAINE G. (Mrs) 800 4th Street, No. N702, Washington, DC 20024 (F) SHAPIRO, MAURICE M. (Prof) 205 Yoakum Parkway, #2-1414, Alexandria, VA 22304 (F) SHAPIRO, GUSTAVE (Mr) 3704 Munsey Street, Silver Spring, MD 20906 (F) SHEPARD, HAROLD H. (Dr) 2701 South June Street, Arlington, VA 22202-2252 (EF) SHERESHEFSKY, J. LEON (Dr) 4530 Connecticut Ave, NW, Washington, DC 20008 (EF) SHERLIN, GROVER C. (Mr) 4024 Hamilton Street, Hyattsville, MD 20781 (LF) SHIER, DOUGLAS R. (Dr) 416 Westminster Dr, Pendleton, SC 29670 (NRF) SHRIER, STEFAN (Dr) 624A South Pitt Street, Alexandria, VA 22314-4138 (F) SHROPSHIRE, W., JR. (Dr) Omega Laboratory, P.O. Box 189, Cabin John, MD 20818-0189 (M) SILLS, CHARLES F. (Mr) 1200 N. Nash Street, Apt. 552, Arlington, VA 22209 (F) SILVER, DAVID M. (Dr) Applied Physics Lab, 1110 John Hopkins Rd, Laurel, MD 20723-6099 (M) SILVERMAN, BARRY G. (Dr) George Washington Univ., 2021 K Street, NW, Suite 710, Washing- ton, DC 20006 (F) SIMHA, ROBERT (Dr) Case-Western Reserve University, Department of Macromolecular Science, Cleveland, OH 44106-7202 (EF) SIMPSON, MICHAEL M. (Dr) 4602 Duncan Drive, Annandale VA 22003-4610 (LM) SINDEN, STEVEN LEE (Dr) 35-K Ridge Road, Greenbelt, MD 20770 (F) SLACK, LEWIS (Dr) 27 Meadow Bank Road, Old Greenwich, CT 06870-2311 (EF) SLAWSKY, MILTON M. (Dr) 8803 Lanier Drive, Silver Spring, MD 20910 (EF) SLAWSKY, ZAKA I. (Dr) 4701 Willard Avenue, Apt. 318, Chevy Chase, MD 20815 (EF) SMITH, BLANCHARD D., JR. (Mr) 2509 Ryegate Lane, Alexandria, VA 22308 (F) SMITH, EDWARD L. (Mr) 11027 Earlgate Lane, Rockville, MD 20852 (F) SMITH, LLOYD MARK (Dr) 11110 Forest Edge Drive, Reston, VA 22090 (F) SMITH, MARCIA S. (Ms) 6015 N. Ninth Street, Arlington, VA 22205 (LM) SMITH, REGINALD C. (Mr) 7731 Tauxemont Road, Alexandria, VA 22308 (M) SODERBERG, DAVID L. (Mr) 403 West Side Dr, Apt 102, Gaithersburg, MD 20878 (M) SOLAND, RICHARD M. (Dr) George Washington Unv, SEAS, Washington, DC 20052 (LF) SOLOMON, EDWIN M. (Mr) 3330 N. Leisure World Blvd, Apt 222, Silver Spring, MD 20906 (EM) 240 WASHINGTON ACADEMY OF SCIENCES SOMMER, HELMUT (Dr) 9502 Hollins Court, Bethesda, MD 20817 (EF) SORROWS, HOWARD E. (Dr) 8820 Maxwell Drive, Potomac, MD 20854 (F) SOUSA, ROBERT J. (Dr) 56 Wendell Road, Shutesbury, MA 01072 (NRF) SPATES, JAMES E. (Mr) 8609 Irvington Ave, Bethesda, MD 20817 (LF) SPECHT, HENIZ (Dr) Fairhaven, C-135, 7200 3rd Ave, Sykesville, MD 21784 (EF) SPERLING, FREDERICK (Dr) 5902 Mt. Eagle Drive, #407, Alexandria, VA 22303 (F) SPIES, JOSEPH R. (Dr) 507 North Monroe Street, Arlington, VA 22201 (EF) SPILHAUS, A. F., JR. (Dr) American Geophysical Union, 2000 Florida Ave. NW, Washington, DC 20009 (F) SPRAGUE, GEORGE F. (Dr) 494 West 10th Ave., Apt. 208, Eugene, OR 97401-2880 (EF) STANLEY, WILLIAM (Mr) 10494 Graeloch Road, Laurel, MD 20723 (M) STEGUN, IRENE A. (Ms) 62 Leighton Avenue, Yonkers, NY 10705 (NRF) STERN, KURT H. (Dr) 103 Grant Avenue, Takoma Park, MD 20912-4636 (F) STEWART, T. DALE (Dr) 1191 Crest Lane, McLean, VA 22101 (EF) STIEF, LOUIS J. (Dr) N.A.S.A. Goddard Space Flight Ctr, Code 691 Greenbelt, MD 20771 (F) STIEHLER, ROBERT D. (Dr) 3234 Quesada Street, NW, Washington, DC 20015-1663 (F) STILL, JOSEPH W. (Dr) 1408 Edgecliff Lane, Pasadena, CA 91107 (EF) STOETZEL, MANYA B. (Dr) Systematic Entomology Lab, Rm 100, Bldg. 046, Barc-West, Beltsville, MD 20705 (F) STOWE, LARRY L. (Dr) NOAA NESDIS WWB-RM 711, Washington, DC 20233 (F) STRAUSS, SIMON W. (Dr) 4506 Cedell Place, Camp Springs, MD 20748 (LF) SVOBODA, JAMES A. (Mr) 13301 Overbrook Lane, Bowie, MD 20715 (M) SWEZEY, ROBERT W. (Dr) Clarks Ridge Rd, Route 3, Box 142, Leesburg, VA 22075 (F) SYKES, ALAN O. (Dr) 304 Mashie Drive, Vienna, VA 22180 (M) TAEUBER, CONRAD (Dr) 10 Allds St, Apt 150, Nashua, NH 03060 (NRF) TASAKI, ICHIJI (Dr) 5604 Alta Vista Road, Bethesda, MD 20817 (F) TATE, DOUGLAS R. (Mr) Carolina Meadows Villa #257, Chapel Hill, NC 27514-8526 (NRF) TAYLOR, BARRY N. (Dr) 11908 Tallwood Court, Potomac, MD 20854 (F) TAYLOR, LURISTON S. (Dr) 10450 Lottsford Rd, #3011, Mitchellville, MD 20721-2734 (EF) TAYLOR, WILLIAM DOUGLAS (Mr) 7025 Quander Road, Alexandria, VA 22309 (M) TAYLOR, WILLIAM B. (Mr) 4001 Bell Rive Terrace, Alexandria, VA 22309 (M) TERMAN, MAURICE J. (Mr) 616 Popular Drive, Falls Church, VA 22046 (EM) THOMPSON, F. CHRISTIAN (Dr) 4255 South 35th Street, Arlington, VA 22206 (LF) TOLL, JOHN S. (Dr) 6609 Boxford Way, Bethesda, MD 20817 (F) TOUSEY, RICHARD (Dr) 10450 Lottsford Road, #231, Bowie, MD 20721-2742 (EF) TOUSIMIS A. J. (Dr) Tousimis Research Corp, 2211 Lewis Ave, Rockville, MD 20851 (M) TOWNSEND, CHARLES E. (Dr) 3529 Tilden Street, NW, Washington, DC 20008-3194 (F) TOWNSEND, LEWIS R. (Dr) 8906 Liberty Lane, Potomac, MD 20854 (F) TOWNSEND, MARJORIE R. (Mrs) 3529 Tilden St, NW, Washington, DC 20008-3194 (LF) TRAUB, ROBERT (Col. Ret) 5702 Bradley Boulevard, Bethesda, MD 20814 (EF) TUNELL, GEORGE (Dr) 300 Hot Springs Rd, #124, Montecito, CA 93108 (EF) TURNER, JAMES H. (Dr) 509 South Pinehurst Ave, Salisbury, MD 21801-6122 (EF) TYLER, PAUL E. (Dr) 1023 Rocky Point Court NE, Albuquerque, NM 87123-1944 (NRF) UBELAKER, DOUGLAS H. (Dr) Dept. of Anthropology, National Museum of Natural History, Smithsonian Institution, Washington, DC 20560 (F) UBERALL, HERBERT (Dr) 5101 River Road, Apt. 1417, Bethesda, MD 20816 (F) UHLANER, J. E. (Dr) 4258 Bonavita Drive, Encino, CA 91436 (EF) UTZ, JOHN P. (Dr) Georgetown University Medical Center, 3900 Reservoir Road, NW, Washington, DC 20007 (F) MEMBERSHIP DIRECTORY 241 VAISHNAV, MARIANNE P. (Ms) P.O. Box 2129, Gaithersburg, MD 20879 (LF) VAN COTT, HAROLD P. (Dr) 8300 Still Spring Court, Bethesda, MD 20817 (EF) VAN DERSAL, EVA P. (Dr) 8101 Greenspring Avenue, Baltimore, MD 21208-1908 (M) VAN TUYL, ANDREW (Dr) 1000 W. Nolcrest Drive, Silver Spring, MD 20903 (F) VANARSDEL, WILLIAM C., III (Dr) 1000 Sixth St, SW, Apt 301, Washington, DC 20024 (M) VARADI, PETER F (Dr) 4620 North Park Ave, Apt. 1606W, Chevy Chase, MD 20815 (F) VAVRICK, DANIEL J. (Dr) 3905 Beltsville Road, No. 3, Beltsville, MD 20705 (M) VEITCH, FLETCHER P., JR (Dr) P.O. Box 513, Lexington Park, MD 20653 (NRF) VENKATESHAN, C. N. (Dr) P.O. Box 30219, Bethesda, MD 20824 (M) VILA, GEORGE J. (Mr) 5517 Westbard Avenue, Bethesda, MD 20816 (F) VON ARB, CHRISTOP (Dr) Embassy of Switzerland, 2900 Cathedral Avenue, NW, Washington, DC 20008 (M) VON HIPPEL, ARTHUR (Dr) 265 Glen Road, Weston, MA 02193 (EF) WAGNER, A. JAMES (Mr) 7568 Cloud Court, Springfield, VA 22153 (F) WALDMANN, THOMAS A. (Dr) 3910 Rickover Road, Silver Spring, MD 20902 (F) WALKER, CHRISTOPHER W. (Dr) Lake Road, Box 2087, Middleburg, VA 22117 (M) WATSON, ROBERT B. (Dr) 1176 Wimbledon Drive, McLean, VA 22101 (EM) WAYNANT, RONALD W. (Dr) 13101 Claxton Drive, Laurel, MD 20708 (F) WEBB, RALPH E. (Dr) 21-P Ridge Road, Greenbelt, MD 20770 (F) WEGMAN, EDWARD J. (Dr) 157 Science-Technology II, Ctr Computational Stat, George Mason University, Fairfax, VA 22030 (LF) WEIDMAN, SCOTT T. (Mr) 4915 41st Street, NW, Washington, DC 20016 (M) WEINBERG, HAROLD P. (Mr) 11410-1B-314 Strand Drive, Rockville, MD 20852 (F) WEINER, JOHN (Dr) 8401 Rhode Island Avenue, College Park, MD 20740 (F) WEINTRAUB, ROBERT L. (Dr) 407 Brooks Avenue, Raleigh, NC 27607 (EF) WEISS, ARMAND B. (Dr) 6516 Truman Lane, Falls Church, VA 22043 (LF) WEISSLER, PEARL (Mrs) 5510 Uppingham Street, Chevy Chase, MD 20815 (EF) WEISSLER, ALFRED (Dr) 5510 Uppingham Street, Chevy Chase, MD 20815 (F) WELLES, MARILYN T. (Ms) P.O. Box 95, Cabin John, MD 20818 (M) WELLMAN, FREDERICK L. (Dr) 501 E. Whitaker Mill Road, Whitaker Glen 105-B, Raleigh, NC 27608 (EF) WENSCH, GLEN W. (Dr) 413 S Rising Road, Champaign, IL 61821 (EF) WERGIN, WILLIAM P. (Dr) 10108 Towhee Avenue, Adelphi, MD 20783 (F) WERTH, MICHAEL W. (Mr) 14 Grafton Street, Chevy Chase, MD 20815 (EM) WESTWOOD, USN (Ret) JAMES T. (LCDR) 3156 Cantrell Lane, Fairfax, VA 22031 (M) WHITE, HOWARD J. JR (Dr) 8028 Park Overlook Drive, Bethesda, MD 20817 (F) WHITELOCK, LELAND D. (Mr) 2320 Brisbane St, Apt 4, Clearwater, FL 34623 (NRF) WHITTEN, CHARLES A. (Mr) 9606 Sutherland Road, Silver Spring, MD 20901 (EF) WIENER, ALFRED A. (Mr) 550 W 25th Place, Eugene, OR 97405 (NRF) WIESE, WOLFGANG L. (Dr) 8229 Stone Trail Drive, Bethesda, MD 20817 (F) WIGGINS, PETER F. (Dr) 1016 Harbor Drive, Annapolis, MD 21403 (F) WILHELMSEN, GUNNAR (Dr) 7303 Hooking Road, McLean, VA 22101 (M) WILMOTTE, RAYMOND M. (Dr) 2512 Que Street, NW, Washington, DC 20007 (LF) WILSON, WILLIAM K. (Mr) 1401 Kurtz Road, McLean, VA 22101 (LF) WISTORT, ROBERT L. (Mr) 11630 35th Place, Beltsville, MD 20705 (EM) WITTLER, RUTH G. (Dr) 2103 River Cresent Drive, Annapolis, MD 21401-7271 (EF) WOLFF, EDWARD A. (Dr) 1021 Cresthaven Drive, Silver Spring, MD 20903 (F) WUERKER, ANNE K. (Dr) 887 Gold Spring Pl, Westlake Village, CA 91361-2024 (NRF) WULF, OLIVER R. (Dr) 557 Berkeley Avenue, San Marino, CA 91108 (EF) 242 WASHINGTON ACADEMY OF SCIENCES WYNNE, RONALD D. (Dr) 3128 Brooklawn Terrace, Chevy Chase, MD 20815 (F) YAPLEE, BENJAMIN S. (Mr) 8 Crestview Court, Rockville, MD 20854 (F) YODER, HATTEN S. JR. (Dr) Geophysical Lab, 5251 Broad Branch Rd, NW, Washington, DC 20015 (EF) YOUMAN, CHARLES (Mr) 4419 N. 18th Street, Arlington, VA 22207 (M) ZELENY, LAWRENCE (Dr) 4312 Van Buren Street, University Park, MD 20782 (EF) ZIEN, TSE-FOU (Dr) Code R44, Naval Surface Warfare Center, Silver Spring, MD 20903-5000 (F) MEMBERSHIP DIRECTORY 243 ~ Necrology The following fellows/members of the Academy deceased since the last publication of the WAS mem- bership directory. Dr. W. V. Loebenstein Mr. Ralph I. Cole Dr. K. C. Emerson Dr. Philip S. Klebanoff Mr. R. H. Nelson Dr. Galen Schubauer Dr. William W. Walton Sr. Dr. Lawrence A. Wood Member Category Fellow Non-Resident Fellow Emeritus Fellow Life Fellow Member Emeritus Member Life Member Mr. Casper J. Aronson Dr. James Comas Prof. Rolfe E. Glover Dr. John H. Proctor Mr. Douglas R. Tate Deceased Life Fellows/Members Deceased Fellows/Members Mrs. Dorothy K. Culbert Dr. Louis S. Hansen Dr. George A. Moore Dr. Randall M. Robertson Dr. Edwin L. Shotland Mr. Bruce L. Wilson Membership Distribution % Geographic Location N % 40.4 Maryland 278 48.2 6.6 Virginia 12 21.0 24.8 Other States 93 16.1 71.6 District of Columbia 80 13.9 ha Foreign 5 0.9 2.9 0.5 1993 Benefactors Mr. Glenn W. Brier Dr. James E. Fearn Mr. Robert H. McCracken Mr. Israel Rotkin cB aa es 61 ra é fad ich : pay Tis nmi fsa ‘ fn ; is seta leew aus (PLR Bes ris N. Fai he RO WR ai a Mae Non Mi pe ; * Z i | OE VA E NO PRL a Be aeeetaiad ® q Ms yang ‘ined 5th V3 . phe nebatA Enenasions AW : , ie Wy tal eo Teka ape Kyat Set Uy PEEL EY . enh ris evi an me ahh sae nyrnegoe hearty axhdiland dipadiaperiahine 2 Tn GL San wy A tan linge ea el oti BEMITCL CUE a Eee Hos yeas, ; Mey kt te % al Vy ; ; Mies Mae aPe y OTOL OME CUMIN ap ee AUN ENE enter edu pH si, pane be ninenee “rth | ry ti ng ef ei ame bie Ai 7 at SAORI: Gta weet 7 fix lonctarneneey Fee a i oN ea | ie im i A fo y | y PETS TD coe \ DELEGATES TO THE WASHINGTON ACADEMY OF SCIENCES, REPRESENTING THE LOCAL AFFILIATED SOCIETIES Elnosopliical Society Ob WashiInetOn 2 .))4...650c.2.05.02 08s ewes eee eee eae Thomas R. Lettieri Pparopolocical Society Of WashiMgtoOm .. 22... 6.5... 60. cece ede anes ees ee eas Jean K. Boek mI ete SOCICIN Ol VW ASMIMELOM 2.644 celiy.e Gee cisit eo sls cece de cei bce nts soba be Kristian Fauchald Micamied Society OF WaShiNStOM 1.28. e.iiade seed ce cee ceed ceeveuvecccene Elise A. B. Brown Pimamelorical Society of Washinston .....4..6. 22.0.0... 0cc eck ae eens F. Christian Thompson ea AT COPEADMIC SOCICLY 2.2.5 ob. koe ede denne eee case dese eseveees Stanley G. Leftwich Pe eeeGeSOGIeLy Ol WaSmINGtON ..<.02 046 226i shed cdo ek ves eee ce oe oeeGeeaseeees VACANT miedieamsaciety of the District of Columbia ............ 020.0000 cccuseecssudevees John P. Utz mmnicam society Of Washington, DC «2%... bc eee ce ec lea a ewes cece cece ees ces VACANT MMmCemasNOCIely OF WaSHINStON 555. ...0. cisco es ede eee bee ss dev evesscuedues Muriel Poston Sacer on American Foresters, Washington Section ..................000000085 Eldon W. Ross SERIE SOCICtyY Of ENGINCETS 6... 6 6. e eee e Sac eclee eee cee ee eee eet es Alvin Reiner Institute of Electrical and Electronics Engineers, Washington Section ........ George Abraham American Society of Mechanical Engineers, Washington Section ............ Daniel J. Vavrick facimiimnbaolarical Society of Washington .............-..-...0ccccccseeeesseeuceees VACANT ‘American Society for Microbiology, Washington Branch ..................000.000005 Ben Tall Society of American Military Engineers, Washington Post ................. William A. Stanley American Society of Civil Engineers, National Capital Section ..................... VACANT Society for Experimental Biology and Medicine, DC Section .............. Cyrus R. Creveling Psrimcmmanonal, Washineton Chapter ............6.....000 cece cee ewww ee wes Richard Ricker American Association of Dental Research, Washington Section ............. J. Terrell Hoffeld _ American Institute of Aeronautics and Astronautics, National Capital Mena soe ales eee A uaetalsa is dp Del oa dv bolle Gael etad Reginald C. Smith imenmcan Wieteorological Society, DC Chapter ................00 ccc eee ees A. James Wagner Parmer Society Of WaSMINGtON ... 2... cones eee n ese ceo seeeesecanwun To be determined Acoustical Society of America, Washington Chapter ........................ Richard K. Cook mimenean Nuctear society, Washington Section ..............-....0.0000s eee ee eee Kamal Araj Institute of Food Technologists, Washington Section ...........0........00000. Roy E. Martin American Ceramic Society, Baltimore-Washington Section .................. Curtis A. Martin eM IIMS CICUY ein cic sc ie id oo os a wRiole wislepoe twa Sel vh evs ceucaecaceswme Regis Conrad wasiimenon tistory of Science Club:........5.5...00.060cccccne eens sence Albert G. Gluckman American Association of Physics Teachers, Chesapeake Section ............. Robert A. Morse Optical Society of America, National Capital Section ...................... William R. Graver American Society of Plant Physiologists, Washington Area Section ............. Steven J. Britz Washington Operations Research/Management Science Council .............. John G. Honig insimument Society of America, Washington Section ...................000000ee cues VACANT American Institute of Mining, Metallurgical and Petroleum Engineers, “SUE SVL (SCLC) 0 eee ne a Anthany Commarota Jr. On AlM@amital ASIKONOMELS 6... cc Geislee ccc ccc ecw bce e cases eens Robert H. McCracken Mathematics Association of America, MD-DC-VA Section ................. Sharon K. Hauge Piimcwon Columbia Institute of Chemists ................50..-00 0000 eens William E. Hanford District of Columbia Psychological Association ...............00.0.0 00 eee Marilyn Sue Bogner Pasmmnatonreamt lechnology Group, 0.0.6. cece s seca dee cee ee ce eee eccucees Lloyd M. 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