As described by Goldstein in chapter 2, Brunswik's approach to the study of perception drew heavily on the distinction between proximal cues, and the distal environmental objects and events associated with those cues. The chapters in this section demonstrate how this same distinction can be usefully coopted to shed light on a quite different unit of cognitive analysis: human interaction with an environment mediated by a technological interface. In particular, the authors of the following chapters use the proximal-distal distinction to distinguish between the sources of information proximally available from an information display (e.g., a blip on a radar screen) and a distal environmental object or event represented by that information (e.g., an aircraft that may be beyond the bounds of normal perception).
Although this analogy between the two situations (perception versus technologically mediated cognition) seems direct enough, the following chapters reveal that the dynamism and complexity of today's technological work environments often require that investigators seeking to use Brunswikian theory and methods must augment their analysis and modeling in a variety of ways. In chapter 3, for example, Bisantz and colleagues had to deal with two features of dynamic, interactive environments that make a standard application of the lens model problematic. Because people often have at least some freedom to determine when they will make judgments, in a dynamic environment it is unlikely that any two participants in a study will make their judgments on the basis of identical cue sets, because the latter are perpetually and autonomously changing. Additionally, in interactive environments, people may also have the opportunity to seek out information actively. Because people may differ in this regard as well, interactivity again increases the possibility that cue sets will be specific to the individual studied. A central contribution of their work is the development of a technique to create idiographic (performer-specific) models to use as the basis for lens model analysis in dynamic tasks.
In chapter 4, Adelman and coauthors present a study of how distributed teams respond to time pressure. Their research demonstrated how the lens model could be extended to a multilevel form using path modeling to describe flows of information among team members and how the team responded to time stress. Adelman and colleagues found that as the tempo of the dynamic task increased, the effectiveness of information flow among team members suffered, with measurable (and independent) effects on both knowledge and consistency of performance.