RI-Small: Cognitive Modeling of Human Motor Skill Acquisition

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Theoretical psychology has not, until recently, been in position to have much to say about how humans learn in dynamic, multidegree-of-freedom manual control domains. While applied concerns have led to successful training programs for manual control tasks (e.g., most people learn to steer automobiles in reasonable time), we currently lack the ability to predict how well people will do in these domains or how rapidly they will learn. This limits our ability to train people in these domains, where we presently rely on expensive one-on-one tutoring or similar intensive methods.The project studies human performance and acquisition of sensorimotor tasks in real and virtual environments. Human motion data and performance of various skills by high performers and low performers, who exhibit linear performance gains, will be analyzed and compared to data for subjects who rapidly acquire skill and exhibit nonlinear performance gains. This data will inform the development of more accurate models of sensorimotor skill acquisition that can be expressed in ACT-R, and doing this should lead to improved understanding of training methods in human motor learning domains.