Department of Neurology
Department of Neurology.
J Neurosci. 2021 Mar 24;41(12):2747-2761. doi: 10.1523/JNEUROSCI.2125-20.2021. Epub 2021 Feb 8.
The human motor system can rapidly adapt its motor output in response to errors. The prevailing theory of this process posits that the motor system adapts an internal forward model that predicts the consequences of outgoing motor commands and uses this forward model to plan future movements. However, despite clear evidence that adaptive forward models exist and are used to help track the state of the body, there is no definitive evidence that such models are used in movement planning. An alternative to the forward-model-based theory of adaptation is that movements are generated based on a learned policy that is adjusted over time by movement errors directly ("direct policy learning"). This learning mechanism could act in parallel with, but independent of, any updates to a predictive forward model. Forward-model-based learning and direct policy learning generate very similar predictions about behavior in conventional adaptation paradigms. However, across three experiments with human participants ( = 47, 26 female), we show that these mechanisms can be dissociated based on the properties of implicit adaptation under mirror-reversed visual feedback. Although mirror reversal is an extreme perturbation, it still elicits implicit adaptation; however, this adaptation acts to amplify rather than to reduce errors. We show that the pattern of this adaptation over time and across targets is consistent with direct policy learning but not forward-model-based learning. Our findings suggest that the forward-model-based theory of adaptation needs to be re-examined and that direct policy learning provides a more plausible explanation of implicit adaptation. The ability of our brain to adapt movements in response to error is one of the most widely studied phenomena in motor learning. Yet, we still do not know the process by which errors eventually result in adaptation. It is known that the brain maintains and updates an internal forward model, which predicts the consequences of motor commands, and the prevailing theory of motor adaptation posits that this updated forward model is responsible for trial-by-trial adaptive changes. Here, we question this view and show instead that adaptation is better explained by a simpler process whereby motor output is directly adjusted by task errors. Our findings cast doubt on long-held beliefs about adaptation.
人类运动系统可以迅速适应其运动输出以响应错误。这一过程的主流理论假设，运动系统会适应一个内部的前向模型，该模型预测传出运动指令的后果，并使用该前向模型来规划未来的运动。然而，尽管有明确的证据表明适应性前向模型存在并被用于帮助跟踪身体状态，但没有确凿的证据表明这些模型被用于运动规划。适应性的前向模型理论的替代理论是，运动是基于随时间调整的学习策略生成的，该策略通过运动错误直接进行调整（“直接策略学习”）。这种学习机制可以与预测前向模型的任何更新并行，但独立于其更新。基于前向模型的学习和直接策略学习在传统适应范式中对行为产生非常相似的预测。然而，在三个有人类参与者参与的实验中（n=47，26 名女性），我们表明，这些机制可以根据镜像反转视觉反馈下的隐性适应的特性来区分。虽然镜像反转是一种极端的扰动，但它仍然会引起隐性适应；然而，这种适应会放大而不是减少错误。我们表明，随着时间的推移和跨目标的这种适应模式与直接策略学习一致，但与基于前向模型的学习不一致。我们的发现表明，需要重新审视适应性的前向模型理论，而直接策略学习为隐性适应提供了更合理的解释。大脑能够根据错误来调整运动是运动学习中研究最广泛的现象之一。然而，我们仍然不知道错误最终导致适应的过程。众所周知，大脑会维护和更新一个内部的前向模型，该模型预测运动指令的后果，而运动适应的主流理论假设，这个更新的前向模型负责逐次自适应变化。在这里，我们质疑这种观点，并表明，通过一个更简单的过程，即通过任务错误直接调整运动输出，可以更好地解释适应。我们的发现对长期以来关于适应的信念提出了质疑。
