Individual Differences: Tools for Improving HF Research?

Members of the human factors discipline have expended considerable energies investigating the impact of hardware characteristics, training systems, environmental influences, and operational protocols on human performance. Each of these classes of variables has direct, indirect, and interactive effects on performance. Relatively scant attention has been paid to the influence of operator characteristics, despite the fact that other fields of psychology have demonstrated powerful influences of individual differences on human behavior and performance in applied contexts (Hunter & Hunter, 1984; Ree & Earles, 1992; Ackerman, 1987, 1988; Ackerman, Kanfer, & Goff, 1995; Hochwarter, Amason, & Harrison, 1995; Russ & McNeilly, 1995). Why has so little attention been paid to individual differences in human factors research? Few human factors theories recognize individual differences as fundamental determinants of performance. As a consequence, human factors studies that include such variables tend to be sporadic and unrelated. Little programmatic research has been performed in this area. Research involving aging and human performance is the exception. Many recent articles published in Human Factors have addressed age-related.performance and safety issues (Rogers, Cabrera, Gilbert, & Fisk, 1996; Gilbert & Rogers, 1996; Morrow, Leirer, Andrassy, Tanke, & Stine-Morrow, 1996; Dingus, Hulse, Mollenhauer, Fleischman, McGehee & Manakkal, 1997; Walker, Fain, Fisk, & McGuire, 1997). While this work is valuable and timely, age-related changes in performance capability are not the only individual differences relevant to the design of humanmachine systems. Cronbach (1957) described applied psychology as “divided against itself’. He saw personnel psychology and engineering psychology as disciplines with competing goals and methods. Personnel psychologists attempted to maximize performance through proper selection and classification. Their studies were designed to maximize individual variability while minimizing group differences. Engineering psychologists attempted to maximize performance by simplifying the operator’s task. Their research was designed to control individual variability in order to highlight treatment effects. Success for either discipline would have obviated the need for the other. In reality, Cronbach recognized that neither discipline could achieve great success alone. Little progress has been made over the last forty years toward an integration of the two fields. Personnel psychology is still pre-occupied with individual differences constructs and with selection and classification for fixed tasks. Human factors specialists are still preoccupied with information-processing models of human functioning which leave little room for human variability as they strive to improve performance for the “average” human (as if such a creature existed). Little cross-training is provided to new scientists in either of these areas. Bramble (1997) revisited Cronbach’s call for a federation of the two disciplines, pointing out the importance of considering individual differences when conducting human factors research. Incorporation of individual differences constructs and measures increases the generalizability of human factors research. It offers three practical benefits for the researcher as well: (a) increased statistical power for detecting treatment effects through the use of covariates; (b) detection of aptitude-treatment interactions; and (c) comparison of the relative influence of individual differences versus other classes of system variables. While methods for conceptualizing tradeoffs between individual differences and system design variables remain largely undeveloped, some useful ideas have been proposed. Jones and Kennedy (1996) pointed out the potential applicability of a representation used in psychophysics and economics. “Isoperformance” curves are two-dimensional line graphs depicting tradeoffs between two variables for predicting a fixed value on a third variable, the criterion. For example, tradeoffs between aptitude and experience can be displayed to indicate combinations of the two variables, which result in roughly equivalent levels of performance (e.g., error rate on an assembly task). Such tools could improve the utility of human factors data for system designers, perhaps alleviating some of the shortcoming noted by Meister (1997). The Individual Differences in Performance (IDIP) Technical Group intends to highlight the role played by individual differences in human-machine systems. Specifically, the group wishes to promote interest in individual differences among human factors researchers and to disseminate the results of research in this exciting area to our colleagues in the Human Factors and Ergonomics Society. The following papers were invited with these purposes in mind. An attempt was made to include work performed in a variety of contexts. Papers with an individual differences bent were solicited from four major application areas: (a) assessment of environmental influences on performance, (b)