location). The perceptual variables may be either discrete (e.g., presence/absence) or continuous (e.g., altitude), and they may be of variable quality, but the key point is to ensure that there is some sort of perceptual model that intervenes between the sensory cues available to the human and the subsequent situation assessment processing that integrates and abstracts those cues. Otherwise, there will be a tendency for the human behavior representation to be omniscient, so that downstream situation assessment processing is likely to be error free and nonrepresentative of actual human assessment behavior. By explicitly modeling perceptual limitations in this fashion, we will be more likely to capture inappropriate assessments of the situation that occur with a "correct" assessment strategy, but an "incorrect" perceptual knowledge base.

A variety of perceptual models could be used to drive a situation awareness model. One that has been used in several modeling efforts conducted by Zacharias and colleagues (1992, 1996) is based on the perceptual estimator submodel of the optimal control model of the human operator developed by Kleinman and colleagues (Kleinman et al., 1971). This is a quantitative perceptual model that transforms continuous sensory cues (e.g., an altimeter needle) into continuous perceived states (e.g., estimated altitude), using a Kalman filter to extract an optimal state estimate from the noisy sensory signal. 11

Several ongoing modeling efforts could benefit from this estimation theory approach to perceptual modeling. For example, Reece and Wirthlin (1996) introduce the notion of "real" entities, "figments," and "ghosts'' to describe concepts that are well understood in the estimation community and would be better handled using conventional estimation theory. Likewise, Tambe et al. (1995) introduce the notion of a "persistent internal state" in Soar; what they mean in estimation theory parlance is simply "state estimate." We suspect that the incorporation of this type of well-founded perceptual model could significantly enhance the realism of Soar's behavior in the face of a limited number of intermittent low-precision cues.

There are, however, at least two problems with this type of perceptual model. The first has to do with its focus on continuous, rather than discrete, states. The continuous-state estimates generated by this type of perceptual model are well suited to modeling human behavior in continuous-control tasks, but not well suited to generating the discrete events typically required by procedure-oriented tasks in which human decision makers engage. Early efforts to solve this problem (e.g., Baron et al., 1980) used simple production rules to effect a continuous-to-discrete transform. More recently, these continuous-to-discrete event transfor-


There is considerable treatment of this type of perceptual model in the manual control literature (starting with Kleinman et al., 1971), with extensive validation against empirical data. In addition, several studies and modeling efforts have been directed at accounting for attention sharing across a set of available sensory cues (e.g., separate instruments on a display panel), using the OCM as the basis for postulating an optimal attention-sharing or visual-scanning strategy (Kleinman, 1976).

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