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Modeling Human and Organizational Behavior: Application to Military Simulations
types of process models described in Chapter 7. In particular, we describe how moderator parameters could modulate a perception model built on modern estimation theory, and how such parameters could also be used to modulate situation awareness models built around, respectively, the technologies of expert systems, case-based reasoning, and belief networks.6
Modern Estimation Models of Perception
The discussion earlier in this chapter of perceptual models that might feed into a situation awareness model outlines a hybrid model, consisting of a state estimator and an event detector, that (1) generates a current state estimate of any continuous dynamic variables and (2) identifies current task-relevant event and features (see Chapter 7 for a full discussion). The event detector component of this model can be thought of as a cue extraction mechanism that combines both data-directed and goal-directed processing to identify salient perceptual features and important events in the incoming data stream (i.e., the event detector module will be more efficient at extracting features for which it has been trained). The state estimator can be implemented as a Kalman filter (Kleinman et al., 1971) containing an internal model that captures the dynamic properties of each object in the environment. The event detector can incorporate a rule-based event classification mechanism with a fuzzy-logic front end, allowing for manipulations of the certainty of the detected events and cues. Stressor/moderator parameters of this model could include the following:
Content parameters—sophistication and accuracy of the internal estimator model, number of events that can be detected, preference for particular events
Process parameters—number of variables that can be processed simultaneously, speed and accuracy of state prediction and event detection
The primary individual differences that could be represented in the state estimator and event detector include training, skill level, and individual history, which affect both the number and type of state variables predicted and events detected (i.e., sophistication of the commander's internal model) and the efficiency of prediction and detection (e.g., well-trained events are detected more readily, requiring lower signal strength or fewer triggering cues). This approach could also be used to represent the documented effect of anxiety on interpretive functions (MacLeod, 1990) by biasing the interpretation of certain neutral stimuli as threatening.
We recognize that there are many other submodel architectures and technologies that could be used in these examples. Those cited here are used to illustrate how moderator effects could be modeled within a “process” context, and not simply as an add-on “performance” moderator.