Motor Learning Without Moving: Hand Localization after Passive Training

Abstract

An accurate estimate of limb position is necessary for movement. Where we localize our unseen hand after a reach depends on felt hand position, or proprioception, but often only predicted sensory consequences based on efference copies of motor commands are considered. Both signals should contribute, so here we use passive training with rotated visual feedback of hand position to prevent updates of predicted sensory consequences, but still recalibrate proprioception. After this training we measure participants' hand location estimates based on both efference-based predictions and afferent proprioceptive signals with self-generated hand movements as well as based on proprioception only with robot-generated movements. The changes in hand localization are equally large after training with robot- and self-generated hand movements. Both motor and proprioceptive changes are only slightly smaller as those after training with self-generated movements, confirming that recalibrated proprioception contributes to motor learning.