Probabilistic models in human sensorimotor control

Abstract

Sensory and motor uncertainty form a fundamental constraint on human sensorimotor control. Bayesian decision theory (BDT) has emerged as a unifying framework to understand how the central nervous system performs optimal estimation and control in the face of such uncertainty. BDT has two components: Bayesian statistics and decision theory. Here we review Bayesian statistics and show how it applies to estimating the state of the world and our own body. Recent results suggest that when learning novel tasks we are able to learn the statistical properties of both the world and our own sensory apparatus so as to perform estimation using Bayesian statistics. We review studies which suggest that humans can combine multiple sources of information to form maximum likelihood estimates, can incorporate prior beliefs about possible states of the world so as to generate maximum a posteriori estimates and can use Kalman filter-based processes to estimate time-varying states. Finally, we review Bayesian decision theory in motor control and how the central nervous system processes errors to determine loss functions and select optimal actions. We review results that suggest we plan movements based on statistics of our actions that result from signal-dependent noise on our motor outputs. Taken together these studies provide a statistical framework for how the motor system performs in the presence of uncertainty.

Fig. 1

Bayesian estimation. A cursor appears briefly at locations on each trial defined by the prior probability (thin dashed line). Because of sensory noise the likelihood of the visual location is given by the thick dashed line. The application of Bayes rule leads to a posterior (solid line) whose peak is located between the peak of the prior and the peak of the likelihood. Note that the posterior is the likelihood multiplied point-wise by the prior and then normalised to have area 1.