Did We Get Sensorimotor Adaptation Wrong? Implicit Adaptation as Direct Policy Updating Rather than Forward-Model-Based Learning
Did We Get Sensorimotor Adaptation Wrong? Implicit Adaptation as Direct Policy Updating Rather than Forward-Model-Based Learning
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DOI：
10.1523/jneurosci.2125-20.2021
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发表时间：
2021-03-24
影响因子：
5.3
通讯作者：
Haith, Adrian M.
中科院分区：
文献类型：
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作者：
Hadjiosif, Alkis M.;Krakauer, John W.;Haith, Adrian M.
人类的马达系统可以迅速调整其马达输出，以响应错误。这一过程的流行理论假设，电机系统采用了一个内部向前模型，该模型预测了发出的电机命令的结果，并使用该向前模型来规划未来的动作。然而，尽管有明确的证据表明自适应向前模型的存在并被用来帮助跟踪身体的状态，但没有明确的证据表明这种模型被用于运动规划。基于前向模型的适应理论的另一种选择是，运动是基于学习的策略产生的，随着时间的推移，该策略直接由运动错误调整(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，但独立于预测前向模型的任何更新。基于前向模型的学习和直接政策学习对传统适应范式中的行为产生了非常相似的预测。然而，通过对人类参与者(N=47，26名女性)的三次实验，我们发现这些机制可以基于镜像反转视觉反馈下的内隐适应特性而分离。虽然镜像颠倒是一种极端的干扰，但它仍然引起内隐顺应；然而，这种顺应的作用是放大而不是减少错误。我们表明，这种适应随着时间的推移和跨目标的模式与直接政策学习是一致的，但不是基于向前模型的学习。我们的发现表明，基于前向模型的适应理论需要重新审视，直接的策略学习为内隐适应提供了更可信的解释。我们大脑对错误做出反应的动作适应能力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，大脑维持和更新一个内部前向模型，该模型预测运动命令的结果，而流行的运动适应理论假设，这个更新的前向模型负责一次又一次的适应性变化。在这里，我们质疑这一观点，相反，我们表明，适应更好地解释为一个更简单的过程，即运动输出直接由任务错误来调整。我们的发现让人们对长期以来关于适应的看法产生了怀疑。
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 (N = 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.& nbsp;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.