  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.
