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.

中文翻译：

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学习运动技能的动态调节以供其招募

人类通过实践和经验学习运动技能 (MS)，然后可以保留它们以供招募，这对于新环境的快速反应是有效的。对于要在新环境中招募的 MS，必须扩大其招募范围。在解决这个问题时，我们假设 MS 是根据反馈上下文动态调制的，以将其招募范围扩展到新的上下文中，这不涉及 MS 的学习。考虑以下两个子问题。我们之前已经证明，学习到的 MS 可以通过其调制在新的上下文中招募，这是通过根据反馈上下文动态调节肌肉之间的协同冗余来驱动的。然而，这种调制是在 MS 学习环境下的动态训练中进行的。在特定条件下学习 MS 在新环境中执行 MS 时，自然会导致运动偏离所需状态。我们假设可以通过对 MS 进行额外调制来减少这种偏差，该调制通过使用由与所需状态的偏差驱动的反馈增益信号来调整 MS 产生的肌肉活动。基于这一假设，我们提出了一个学习 MS 的反馈增益信号驱动调整模型，以实现其稳健的招募。该模型基于皮质基底节回路中的神经生理学结构，其中 MS 在学习时可能会被保留，然后通过根据反馈上下文调整其肌肉控制信号来招募。本研究通过计算模拟，