A Dual-Pathway Neural Network Model of Control Relinquishment in Motor Skill Learning
Ashish Gupta, David C. Noelle
Cognitive psychologists have long recognized that the acquisition of a motor skill involves a transition from attention-demanding controlled processing to more fluent automatic processing. Neuroscientific studies suggest that controlled and automatic processing rely on two largely distinct neural pathways. The controlled pathway, which includes the prefrontal cortex, is seen as acquiring declarative representations of skills. In comparison, the automatic pathway is thought to develop procedural representations. Automaticity in motor skill learning 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 automaticity. This model offers a dual-pathway neurocomputational account of the translation of declarative knowledge into procedural knowledge during motor learning. In support of the model, we review some previously reported human experimental results involving the learning of a sequential key pressing task, and we demonstrate, through simulation, how the model provides a parsimonious explanation for these results.