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A Dual-Pathway Neural Network Model of Control Relinquishment in Motor Skill Learning

A Dual-Pathway Neural Network Model of Control Relinquishment in Motor Skill Learning,Ashish Gupta,David C. Noelle

A Dual-Pathway Neural Network Model of Control Relinquishment in Motor Skill Learning  
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Cognitive psychologists have long recognized that the acquisition of a motor skill involves a transi- tion from attention-demanding controlled process- ing to more fluent automatic processing. Neurosci- entific studies suggest that controlled and automatic processing rely on two largely distinct neural path- ways. The controlled pathway, which includes the prefrontal cortex, is seen as acquiring declarative representations of skills. In comparison, the au- tomatic pathway is thought to develop procedural representations. Automaticity in motor skill learn- ing involves a reduction in dependence on frontal systems and an increased reliance on the automatic pathway. In this paper, we propose a biologically plausible computational model of motor skill auto- maticity. This model offers a dual-pathway neuro- computational account of the translation of declar- ative knowledge into procedural knowledge during motor learning. In support of the model, we re- view some previously reported human experimen- tal results involving the learning of a sequential key pressing task, and we demonstrate, through simula- tion, how the model provides a parsimonious expla- nation for these results.
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