Computational Approaches to Motor Control

The act of making a movement involves solving four kinds of problems: (1) We need to learn the costs that are associated with our actions as well as the rewards that we may experience; (2) we need to learn how our motor commands produce changes in state of our body and our environment; (3) given the cost structure of the task and the expected outcome of motor commands, we need to find those motor commands that minimize the costs and maximize the rewards; and (4) finally, as we execute the motor commands, we need to integrate our predictions about sensory outcomes with the actual feedback from our sensors to update our belief about our state. In this framework, the function of basal ganglia appears related to learning costs and rewards associated with our sensory states. The function of the cerebellum is to predict sensory outcome of motor commands and correct motor commands through internal feedback. Reward-driven optimal feedback control theory appears the most consistent framework to explain a number of disorders in human motor control.