Bayesian Models of Motor Control

Abstract

Motor control is fundamental to the nervous system: only through our movements do we interact with the world. Successful motor control requires the integration of a myriad of pieces of information. Yet all this information is of an uncertain nature because we only obtain noisy information in a changing world. We are thus faced with the problem of integrating many uncertain pieces of information into a relatively precise estimate of the properties of our bodies and the surrounding world. Bayesian models of motor control formalize the problem of how uncertain information should be integrated and make predictions that often well describe human movement behavior. © 2009 Elsevier Ltd All rights reserved.