Task-relevant and task-irrelevant variability causally shape error-based motor learning

Recent studies of motor learning show dissociable roles of reward- and sensory-prediction errors in updating motor commands by using typical adaptation paradigms where force or visual perturbations are imposed on hand movements. Such classic adaptation paradigms ignore a problem of redundancy inherently embedded in the motor pathways where the central nervous system has to find a unique solution in the high-dimensional motor command space. Computationally, a possible way of solving such a redundancy problem is exploring and updating motor commands based on the learned knowledge of the structures of both the motor pathways and the tasks. However, the effects of task-irrelevant motor command exploration in structure learning and its effects on reward-based and error-based learning have yet to be examined. Here, we used a redundant motor task where participants manipulated a cursor on a monitor screen with their hand gesture movements and then analyzed single-trial motor learning by fitting models consisting of reward-based and error-based learning contributions. We found that the error-based learning rate positively correlated with both task-relevant and task-irrelevant variability, likely reflecting the effect of motor exploration in structure learning. Further modeling results show that the effects of both task-relevant and task-irrelevant variability are simultaneous, and not mediated by one another. In contrast, the reward-based learning rate correlated with neither task-relevant nor task-irrelevant variability. Thus, although not having a direct influence on the task outcome, exploration in the task-irrelevant space late in training has a significant effect on the learning of a task structure used for error-based learning. This suggests that motor exploration, in both task-relevant and task-irrelevant spaces, has an essential role in error-based motor learning in a redundant motor mechanism. (C) 2021 The Author(s). Published by Elsevier Ltd.