Pattern Recognition for Cognitive Performance Modeling

Craig Haimson, Stacy Lovell

Cognitive performance modeling captures patterns of observable behavior that reflect the operation of underlying cognitive activities. These behavioral patterns can be linked to training requirements in order to derive automated measures of cognitive performance. We are currently developing the Team Coaching Assistant for Simulation-Based Training (T-CAST), a cognitive performance modeling system designed to support assessment of team performance in simulated environments. T-CAST maps competency-based performance requirements to data signatures of actions executed within a massively multiplayer online role-playing game. Through standard rule-based inferencing methods and spatial reasoning techniques, T-CAST produces lists of significant game-playing events from which aggregate measures of individual and team performance may be computed. These events are then fed back to the game for use in indexing simulation replay and supporting instructor-led after action review (AAR). In this paper we describe the design and development of T-CAST and suggest extensions of cognitive performance modeling to other domains and applications.

Subjects: 1.7 Expert Systems; 4. Cognitive Modeling


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