Adaptive Perspectives on Human-Technology Interaction: Methods and Models for Cognitive Engineering and Human-Computer Interaction

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20
Reflections from a Cognitive
Engineering and Human
Factors Perspective

Kim J. Vicente

Both organism and environment will have to be seen as systems, each with properties of its
own, yet both hewn from basically the same block.

—Brunswik (1957, p. 5)

What psychology needs is the kind of thinking that is beginning to be attempted in what is
loosely called systems theory.

—Gibson (1979, p. 2)

By adopting a systems approach that focuses on the organism–environment interaction, Egon Brunswik and James Gibson developed a version of psychology that dovetails seamlessly with the practical aims of systems design. Nevertheless, it took decades before this connection was noticed and explored by human factors and cognitive engineering researchers and practitioners. The design relevance of Gibsonian psychology was marked a decade ago by the publication of a pair of companion monographs (Flach et al., 1995; Hancock et al., 1995). With the publication of this long overdue volume, we now begin to see how Brunswikian psychology can also impact critical issues in the design of human–technology interaction. The research compiled here exhibits a number of positive features. Many of the chapters begin with a practical problem and use its characteristics to dictate the choice of theory and methods. To take but one example, Bisantz et al. (chapter 3) describe the demands faced by Anti-Air Warfare Coordinators and then go on to argue how the lens model equation (LME) provides a suitable means for studying that phenomenon. This problem-driven approach contrasts with much psychological and human factors research that has the tail wagging the dog by letting methodology take primacy over phenomena (Vicente, 2000). Many of the chapters also focus on developing insights that have important implications for systems design. In fact, the number of design interventions that are addressed— displays, alerts, decision aids, training, and evaluation —is impressive and shows the breadth of applicability of a Brunswikian approach to human factors and cognitive engineering. The complexity of the tasks investigated in this volume is also laudable, including sophisticated laboratory simulations and even naturalistic operational environments. The inclusion of dynamic tasks—a comparatively infrequently investigated aspect of judgment and decision making (Hogarth, 1981)—is particularly important for reasons that I will address later. The diversity of methods adopted is also important.

Traditionally, Brunswikian research has been equated with lens model research (Vicente, 2003), and although many of the chapters still adopt the LME, they also illustrate creative and sophisticated variations on that theme, variations that begin to reflect the complexity of the practical problems being investigated (e.g., by taking multiple time slices, by adopting an n-dimensional approach, by adopting a multilevel approach, and by adopting a hybrid hierarchical/n-system approach).

Other researchers bring in a breath of fresh air by dropping the LME altogether and adopting other

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