Department of Psychology, University of California, Berkeley, Berkeley, United States.
Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, United States.
Elife. 2022 Oct 5;11:e75801. doi: 10.7554/eLife.75801.
Traditional associative learning tasks focus on the formation of associations between salient events and arbitrary stimuli that predict those events. This is exemplified in cerebellar-dependent delay eyeblink conditioning, where arbitrary cues such as a tone or light act as conditioned stimuli (CSs) that predict aversive sensations at the cornea (unconditioned stimulus [US]). Here, we ask if a similar framework could be applied to another type of cerebellar-dependent sensorimotor learning - sensorimotor adaptation. Models of sensorimotor adaptation posit that the introduction of an environmental perturbation results in an error signal that is used to update an internal model of a sensorimotor map for motor planning. Here, we take a step toward an integrative account of these two forms of cerebellar-dependent learning, examining the relevance of core concepts from associative learning for sensorimotor adaptation. Using a visuomotor adaptation reaching task, we paired movement-related feedback (US) with neutral auditory or visual contextual cues that served as CSs. Trial-by-trial changes in feedforward movement kinematics exhibited three key signatures of associative learning: differential conditioning, sensitivity to the CS-US interval, and compound conditioning. Moreover, after compound conditioning, a robust negative correlation was observed between responses to the two elemental CSs of the compound (i.e. overshadowing), consistent with the additivity principle posited by theories of associative learning. The existence of associative learning effects in sensorimotor adaptation provides a proof-of-concept for linking cerebellar-dependent learning paradigms within a common theoretical framework.
传统的联想学习任务侧重于形成突出事件与任意刺激之间的关联，这些刺激预测这些事件。这在小脑依赖性延迟眨眼条件反射中得到了例证，其中任意线索（如声音或光线）充当条件刺激（CS），预测角膜（非条件刺激[US]）的不愉快感觉。在这里，我们询问是否可以将类似的框架应用于另一种类型的小脑依赖性运动学习——运动适应。运动适应的模型假设，环境干扰的引入会产生一个错误信号，该信号用于更新运动规划的感觉运动图的内部模型。在这里，我们朝着整合这两种小脑依赖性学习形式的方向迈出了一步，研究了联想学习核心概念对运动适应的相关性。我们使用视觉运动适应任务，将与运动相关的反馈（US）与中性听觉或视觉上下文线索配对，这些线索充当 CS。前馈运动运动学的逐次变化表现出联想学习的三个关键特征：差异条件作用、对 CS-US 间隔的敏感性和复合条件作用。此外，在复合条件作用后，观察到复合的两个基本 CS 之间的反应之间存在稳健的负相关（即遮蔽），这与联想学习理论提出的加性原则一致。运动适应中联想学习效应的存在为在共同理论框架内联系小脑依赖性学习范式提供了一个概念验证。
