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内隐适应补偿人类运动学习中不稳定的外显策略。

Implicit adaptation compensates for erratic explicit strategy in human motor learning.

机构信息

John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.

Department of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China.

出版信息

Nat Neurosci. 2020 Mar;23(3):443-455. doi: 10.1038/s41593-020-0600-3. Epub 2020 Feb 28.

Abstract

Sports are replete with strategies, yet coaching lore often emphasizes 'quieting the mind', 'trusting the body' and 'avoiding overthinking' in referring to the importance of relying less on high-level explicit strategies in favor of low-level implicit motor learning. We investigated the interactions between explicit strategy and implicit motor adaptation by designing a sensorimotor learning paradigm that drives adaptive changes in some dimensions but not others. We find that strategy and implicit adaptation synergize in driven dimensions, but effectively cancel each other in undriven dimensions. Independent analyses-based on time lags, the correlational structure in the data and computational modeling-demonstrate that this cancellation occurs because implicit adaptation effectively compensates for noise in explicit strategy rather than the converse, acting to clean up the motor noise resulting from low-fidelity explicit strategy during motor learning. These results provide new insight into why implicit learning increasingly takes over from explicit strategy as skill learning proceeds.

摘要

运动中充满了策略,但教练经验法则经常强调“静心”、“相信身体”和“避免过度思考”,以说明在依赖低水平的隐含运动学习而不是高水平的明确策略方面的重要性。我们通过设计一种感测运动学习范式来研究明确策略和隐含运动适应之间的相互作用,该范式会驱动某些维度的适应性变化,但不会驱动其他维度的适应性变化。我们发现,在驱动维度中,策略和隐含适应协同作用,但在非驱动维度中,它们实际上相互抵消。基于时间滞后、数据中的相关结构和计算建模的独立分析表明,这种抵消是因为隐含适应有效地补偿了明确策略中的噪声,而不是相反,在运动学习过程中,它会清除由于低保真度明确策略而产生的运动噪声。这些结果为为什么随着技能学习的进展,隐含学习越来越取代明确策略提供了新的见解。

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