A neurocomputational approach to automaticity in motor skill learning

Cognitive agents physically interacting with the world can best adapt to their task environments if they are able to learn motor skills from experience. Many cognitive architectures have focused on a single learning mechanism to accomplish such adaptation. Behavioral studies with humans, however, have shown that the acquisition of a motor skill generally occurs in two stages. In the initial stage, acquisition is performed via attention-demanding neural processes, producing a high cognitive load. This is followed by more fluent automatic processing, requiring less deliberation. Neuroscientific studies have since identified two relevant interacting neural systems, suggesting that the acquisition of a motor skill involves a transition from heavy dependence on a system involving cognitive control to only weak dependence on such a system. This cognitive control system, which includes the prefrontal cortex, is thought to be responsible for acquiring and manipulating declarative representations of skills. This frontal system is seen as modulating processing in a more automatic neural pathway, which develops procedural representations over time. In this paper, we propose a biologically plausible computational model of motor skill automaticity. This model offers a neurocomputational account of the translation of declarative into procedural knowledge during learning. In support of the model, we review some previously reported experimental results, and we demonstrate, through simulation, how the model provides a parsimonious explanation for these results. The model is seen as exemplifying a novel approach to motor skill learning in artificial agents.