Department of Psychology, University of California, Berkeley, California.
Helen Wills Neuroscience Institute, University of California, Berkeley, California.
J Neurophysiol. 2021 Jan 1;125(1):12-22. doi: 10.1152/jn.00493.2020. Epub 2020 Nov 25.
Sensorimotor adaptation is influenced by both the size and variance of error information. In the present study, we varied visual uncertainty and error size in a factorial manner and evaluated their joint effect on adaptation, using a feedback method that avoids inherent limitations with standard visuomotor tasks. Uncertainty attenuated adaptation, but only when the error was small. This striking interaction highlights a novel constraint for models of sensorimotor adaptation. Sensorimotor adaptation is driven by sensory prediction errors, the difference between the predicted and actual feedback. When the position of the feedback is made uncertain, motor adaptation is attenuated. This effect, in the context of optimal sensory integration models, has been attributed to the motor system discounting noisy feedback and thus reducing the learning rate. In its simplest form, optimal integration predicts that uncertainty would result in reduced learning for all error sizes. However, these predictions remain untested since manipulations of error size in standard visuomotor tasks introduce confounds in the degree to which performance is influenced by other learning processes such as strategy use. Here, we used a novel visuomotor task that isolates the contribution of implicit adaptation, independent of error size. In two experiments, we varied feedback uncertainty and error size in a factorial manner. At odds with the basic predictions derived from the optimal integration theory, the results show that uncertainty attenuated learning only when the error size was small but had no effect when the error size was large. We discuss possible mechanisms that may account for this interaction, considering how uncertainty may interact with the relevance assigned to the error signal or how the output of the adaptation system in terms of recalibrating the sensorimotor map may be modified by uncertainty. Sensorimotor adaptation is influenced by both the size and variance of error information. In the present study, we varied visual uncertainty and error size in a factorial manner and evaluated their joint effect on adaptation, using a feedback method that avoids inherent limitations with standard visuomotor tasks. Uncertainty attenuated adaptation but only when the error was small. This striking interaction highlights a novel constraint for models of sensorimotor adaptation.
感觉运动适应受误差信息的大小和方差的影响。在本研究中，我们以因子的方式改变视觉不确定性和误差大小，并使用一种避免标准视觉运动任务固有局限性的反馈方法来评估它们对适应的联合影响。不确定性会减弱适应，但只有在误差较小时才会减弱。这种显著的相互作用突出了感觉运动适应模型的一个新约束。感觉运动适应是由感觉预测误差驱动的，即预测的反馈和实际反馈之间的差异。当反馈的位置变得不确定时，运动适应会减弱。在最优感觉整合模型的背景下，这种效应归因于运动系统对噪声反馈的折扣，从而降低了学习率。在其最简单的形式中，最优整合预测不确定性将导致所有误差大小的学习减少。然而，这些预测仍然未经检验，因为在标准视觉运动任务中对误差大小的操纵会在多大程度上影响其他学习过程（如策略使用）对性能的影响方面引入混淆。在这里，我们使用了一种新的视觉运动任务，该任务可以将隐含适应的贡献与误差大小隔离开来。在两项实验中，我们以因子的方式改变反馈不确定性和误差大小。与最优整合理论得出的基本预测相悖的是，结果表明，只有当误差较小时，不确定性才会减弱学习，但当误差较大时，不确定性没有影响。我们讨论了可能的机制，这些机制可能会考虑不确定性如何与误差信号的相关性相互作用，或者不确定性如何修改适应系统在重新校准感觉运动图方面的输出。
