knowledge might be applied in representing human behavior in computational models.
This section reviews various attempts to incorporate behavior moderators into computational models of human behavior. The first is the Army's work on the improved performance research integration tool (IMPRINT) (Archer and Lockett, 1997; Allender et al., 1997a), to which physical environmental stressors, mental workload, personnel characteristics, and training are added to make the human component more realistic. A primary use of this model is to provide human factors input for models designed to test and compare the performance capabilities of various systems considered for acquisition. We then review efforts to develop models that incorporate affective variables into synthetic agents (Moffat, 1997; Elliot, 1994; Cesta et al., 1996). The focus of this work has been twofold: to understand human behavior and to build realistic synthetic actors for entertainment. We then examine an approach to the specific problem of incorporating emotion into the simulation of command decision making proposed by Hudlicka (1997). Finally, we describe some alternative approaches to modeling the effects of intrinsic moderator variables on perception and situation awareness. We include these examples to show that even with imprecise definitions and weak quantification, some progress has been made toward incorporating such variables in models of human behavior.
As described by Allender et al. (1995, 1997a, 1997b) and Lockett and Archer (1997), IMPRINT operates as an event-based task network in which a mission is decomposed into functions that are further decomposed into tasks. The tasks are linked together in a network that represents the flow of events. Task performance time and accuracy, along with expected error rates and failure to perform, are entered for each task. These data, obtained from military research studies (field tests, laboratory tests, and subject matter experts), are assumed to be representative of average performance under typical conditions.
In addition to basic task network simulation, IMPRINT also provides the capability to incorporate the effects of training, personnel characteristics, cognitive workload, and various environmental stressors (see, e.g., Kuhn, 1989). Stressors degrade performance, whereas training can both increase and degrade performance.
The stressors include protective clothing (mission-oriented protective posture, or MOPP), heat, cold, noise, and hours since sleep. Each of these stressors can have an impact on one or more of several classes of human behavior, such as visual, numerical, cognitive, fine motor, and gross motor (referred to as taxons). Taxons are basic