Motor expertise facilitates the accuracy of state extrapolation in perception

Predicting the behavior of objects in the environment is an important requirement to overcome latencies in the sensorimotor system and realize precise actions in rapid situations. Internal forward models that were acquired during motor training might not only be used for efficiently controlling fast motor behavior but also to facilitate extrapolation performance in purely perceptual tasks. In this study, we investigated whether preceding virtual cart-pole balancing training facilitates the ability to extrapolate the virtual pole motion. Specifically, subjects had to report the expected pole orientation after an occlusion of the pole of 900ms duration. We compared a group of 10 subjects, proficient in performing the virtual cart-pole balancing task, to 10 naïve subjects without motor experience in cart-pole balancing task. Our results demonstrate that preceding motor training increases the accuracy of pole movement extrapolation, although extrapolation is not trained explicitly. Additionally, we modelled subjects’ behaviors and show that the difference in extrapolation performance can be explained by individual differences in the accuracy of internal forward models. When subjects are provided with feedback about the true orientation of the pole after the occlusion in a second phase of the experiment, both groups improve rapidly. The results indicate that the perceptual capability to extrapolate the state of the cart-pole system accurately is implicitly trained during motor learning. We discuss these results in the context of shared representations and action-perception transfer.