A Computational Model of Complex Skill Learning in Varied-Priority Training

Abstract

We developed a computational model that captures the differential effects of Fixed Priority (FP) and Varied Priority (VP) training on complex skill learning. The model is developed based on learning mechanisms associated with the modular circuits linking Basal Ganglia, the prefrontal association cortex, and the pre-motor cortex. Two forms of learning occur simultaneously. In discrimination learning, goal-directed actions are selected through recognition of external stimuli through the connections between the frontal cortex and the striatum, and is mediated by dopaminergic signals through a reinforcement learning mechanism. With practice, skill learning shifts from discrimination learning to Hebbian learning, which directly associates stimuli to responses by strengthening the connection between the prefrontal and pre-motor cortex. The model explains why VP training benefits lower performance participants more, and why learning was more strongly correlated with the size of the striatum in VP than FP training.


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