ACT-R Modeling of Rapid Motor Learning Based on Schema Construction

The environment surrounding organisms changes dynamically, and humans acquire motor skills by improving the prediction of such environmental changes. The research on cognitive architectures has so far proposed several mechanisms explaining the process of human motor learning. Adaptive Control of Thought-Rational (ACT-R), one of the representative cognitive architectures, has perceptual and motor modules for interaction with the external environment. However, the performance of these modules is insufficient for real-time environments, especially in terms of learning speed. This study proposes a method to simulate human-level rapid motor learning using a pre-trained motor learning module. We assume that in a novel perceptual-motor task, a pre-trained motor schema is rediscovered/recalled. In the simulations, we trained the motor learning module in advance and conducted a simulation where difficulties of rediscovering schemata were manipulated. As a result, we confirmed that the pre-trained phase increased the human-model fitting in motor learning.