Motor expertise facilitates the precision 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 pole motion. We compared a group of 10 subjects, proficient in performing the cart-pole balancing task, to 10 naive subjects. Our results demonstrate that preceding motor training increases the precision 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 pole movement in a second phase, 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.