Afferent motor feedback determines the perceived location of tactile stimuli in the external space presented to the moving arm.

People make systematic errors when localizing a brief tactile stimulus in the external space presented on the index finger while moving the arm. Although these errors likely arise in the spatiotemporal integration of the tactile input and information about arm position, the underlying arm position information used in this process is not known. In this study, we tested the contributions of afferent proprioceptive feedback and predictive arm position signals by comparing localization errors during passive vs. active arm movements. In the active trials, participants were instructed to localize a tactile stimulus in the external space that was presented to the index finger near the time of a self-generated arm movement. In the passive trials, each of the active trials was passively replayed in randomized order, using a robotic device. Our results provide evidence that the localization error patterns of the passive trials are similar to the active trials and, moreover, did not lag but rather led the active trials, which suggests that proprioceptive feedback makes an important contribution to tactile localization. To further test which kinematic property of this afferent feedback signal drives the underlying computations, we examined the localization errors with movements that had differently skewed velocity profiles but overall the same displacement. This revealed a difference in the localization patterns, which we explain by a probabilistic model in which temporal uncertainty about the stimulus is converted into a spatial likelihood, depending on the actual velocity of the arm rather than involving an efferent, preprogrammed movement. We show that proprioceptive feedback of arm motion rather than efferent motor signals contributes to tactile localization during an arm movement. Data further show that localization errors depend on arm velocity, not displacement per se, suggesting that instantaneous velocity feedback plays a role in the underlying computations. Model simulation using Bayesian inference suggests that these errors depend not only on spatial but also on temporal uncertainties of sensory and motor signals.