Department of Psychology, University of California, Berkeley, Berkeley, United States.
Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, United States.
Elife. 2019 Apr 29;8:e39882. doi: 10.7554/eLife.39882.
Recent studies have demonstrated that task success signals can modulate learning during sensorimotor adaptation tasks, primarily through engaging explicit processes. Here, we examine the influence of task outcome on implicit adaptation, using a reaching task in which adaptation is induced by feedback that is not contingent on actual performance. We imposed an invariant perturbation (rotation) on the feedback cursor while varying the target size. In this way, the cursor either hit or missed the target, with the former producing a marked attenuation of implicit motor learning. We explored different computational architectures that might account for how task outcome information interacts with implicit adaptation. The results fail to support an architecture in which adaptation operates in parallel with a model-free operant reinforcement process. Rather, task outcome may serve as a gain on implicit adaptation or provide a distinct error signal for a second, independent implicit learning process.
This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).
最近的研究表明，任务成功信号可以调节感觉运动适应任务中的学习，主要是通过参与显式过程。在这里，我们研究了任务结果对隐性适应的影响，使用了一种伸手任务，其中适应是通过与实际表现无关的反馈来诱导的。我们在反馈光标上施加不变的扰动（旋转），同时改变目标大小。这样，光标要么击中目标，要么错过目标，前者显著削弱了隐性运动学习。我们探索了不同的计算架构，以解释任务结果信息如何与隐性适应相互作用。结果不支持一种适应与无模型的操作性强化过程并行运作的架构。相反，任务结果可能作为隐性适应的增益，或者为第二个独立的隐性学习过程提供不同的误差信号。
本文经过编辑过程，作者决定如何处理同行评审期间提出的问题。审稿人的评估是所有问题都已得到解决（见评审意见）。
