Dynamic Modulation of a Learned Motor Skill for Its Recruitment

Abstract

Humans learn motor skills (MSs) through practice and experience and may then retain them for recruitment, which is effective as a rapid response for novel contexts. For an MS to be recruited for novel contexts, its recruitment range must be extended. In addressing this issue, we hypothesized that an MS is dynamically modulated according to the feedback context to expand its recruitment range into novel contexts, which do not involve the learning of an MS. The following two sub-issues are considered. We previously demonstrated that the learned MS could be recruited in novel contexts through its modulation, which is driven by dynamically regulating the synergistic redundancy between muscles according to the feedback context. However, this modulation is trained in the dynamics under the MS learning context. Learning an MS in a specific condition naturally causes movement deviation from the desired state when the MS is executed in a novel context. We hypothesized that this deviation can be reduced with the additional modulation of an MS, which tunes the MS-produced muscle activities by using the feedback gain signals driven by the deviation from the desired state. Based on this hypothesis, we propose a feedback gain signal-driven tuning model of a learned MS for its robust recruitment. This model is based on the neurophysiological architecture in the cortico-basal ganglia circuit, in which an MS is plausibly retained as it was learned and is then recruited by tuning its muscle control signals according to the feedback context. In this study, through computational simulation, we show that the proposed model may be used to neurophysiologically describe the recruitment of a learned MS in novel contexts.