Efficient Coding in Motor Planning
A paramount challenge for the brain is to precisely model the world and control behavior within the confines of limited encoding capacities. Efficient coding theory posits a unified framework for understanding how neural systems enhance encoding accuracy by tuning to environmental statistics. While this theory has been thoroughly explored within the perceptual realm, it is less clear how efficient coding applies to the motor system. Here, we probe the core principles of efficient coding theory through center-out reaching tasks. Our results reveal novel sequential effects in motor planning: The current movements are biased in a direction opposite to that of recent movements, and individual differences in this repulsive bias are associated with variability within the motor system. Furthermore, we observed that movement variance escalates with the angular divergence between successive movements. These findings are consistent with the prediction of an efficient coding model for motor planning and contrast with alternative models for sequential motor effects such as Bayesian models or repeated suppression.