The Binding of Learning to Action in Motor Adaptation

In motor tasks, errors between planned and actual movements generally result in adaptive changes which reduce the occurrence of similar errors in the future. It has commonly been assumed that the motor adaptation arising from an error occurring on a particular movement is specifically associated with the motion that was planned. Here we show that this is not the case. Instead, we demonstrate the binding of the adaptation arising from an error on a particular trial to the motion experienced on that same trial. The formation of this association means that future movements planned to resemble the motion experienced on a given trial benefit maximally from the adaptation arising from it. This reflects the idea that actual rather than planned motions are assigned ‘credit’ for motor errors because, in a computational sense, the maximal adaptive response would be associated with the condition credited with the error. We studied this process by examining the patterns of generalization associated with motor adaptation to novel dynamic environments during reaching arm movements in humans. We found that these patterns consistently matched those predicted by adaptation associated with the actual rather than the planned motion, with maximal generalization observed where actual motions were clustered. We followed up these findings by showing that a novel training procedure designed to leverage this newfound understanding of the binding of learning to action, can improve adaptation rates by greater than 50%. Our results provide a mechanistic framework for understanding the effects of partial assistance and error augmentation during neurologic rehabilitation, and they suggest ways to optimize their use.

在运动任务中，计划运动与实际运动之间的偏差通常会引发适应性改变，从而降低未来同类错误的发生概率。此前学界普遍认为，某一次运动产生的偏差所引发的运动适应性，仅与该次运动的计划动作相关联。本研究表明，这一认知并不成立。取而代之的是，我们证实了某一试次中偏差所引发的运动适应性，会与该试次中个体实际感知到的动作绑定。这种关联的形成意味着，若未来某次运动的计划动作与某一试次中实际感知到的动作相似，则该运动将从该试次引发的适应性中获得最大化收益。这一结果印证了“运动错误的归因应归于实际运动而非计划运动”的观点——从计算层面而言，最大程度的适应性反应应与被归因于该错误的状态相关联。我们通过分析人类手臂伸达运动中，针对新型动态环境的运动适应性泛化模式，对这一过程进行了研究。研究发现，这些泛化模式与“适应性与实际运动而非计划运动相关”的理论预测高度吻合，且在实际运动聚集的区域，泛化程度达到峰值。基于上述发现，我们进一步设计了一种新型训练范式，该范式利用了“学习与动作绑定”这一新认知，可使运动适应性的提升速率提升50%以上。本研究结果为理解神经康复过程中部分辅助与错误强化的作用机制提供了理论框架，并为优化其临床应用提供了可行思路。