Probabilistic Mechanisms in Sensorimotor Control

  1. Derek J. Chadwick,
  2. Mathew Diamond Organizer and
  3. Jamie Goode
  1. Konrad P. Körding and
  2. Daniel M. Wolpert

Published Online: 7 OCT 2008

DOI: 10.1002/9780470034989.ch15

Percept, Decision, Action: Bridging the Gaps: Novartis Foundation Symposium 270

Percept, Decision, Action: Bridging the Gaps: Novartis Foundation Symposium 270

How to Cite

Körding, K. P. and Wolpert, D. M. (2006) Probabilistic Mechanisms in Sensorimotor Control, in Percept, Decision, Action: Bridging the Gaps: Novartis Foundation Symposium 270 (eds D. J. Chadwick, M. Diamond and J. Goode), John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470034989.ch15

Author Information

  1. Sobell Department of Motor Neuroscience and Movement Disorders, Institute of Neurology, University College London, Queen Square, London WC1N 3BG, UK

  1. This paper was presented at the symposium by Daniel Wolpert, to whom correspondence should be addressed.

Publication History

  1. Published Online: 7 OCT 2008
  2. Published Print: 13 JAN 2006

Book Series:

  1. Novartis Foundation Symposia

Book Series Editors:

  1. Novartis Foundation

ISBN Information

Print ISBN: 9780470012338

Online ISBN: 9780470034989

SEARCH

Keywords:

  • human sensorimotor control;
  • sensorimotor control probabilistic framework;
  • Bayesian decision theory framework;
  • state estimation and Kalman controllers;
  • Bayesian integration for probabilistic tasks

Summary

Uncertainty constitutes a fundamental constraint on human sensorimotor control. Our sensors are noisy and do not provide perfect information about all the properties of the world. Moreover, our muscles generate noisy outputs and many tasks we perform vary in an unpredictable way. Here we review the computations that the CNS uses in the face of such sensory, motor and task uncertainty. We show that the CNS reduces the uncertainty in estimates about the state of the world by using a Bayesian combination of prior knowledge and sensory feedback. It is shown that these mechanisms generalize to state estimation of ones own body during movement. We review how the CNS optimizes decisions based on these estimates, examining the error criterion that people optimize when performing targeted movements. Finally, we describe how signal-dependent noise on the motor output places constraints on performance. Goal-directed movement arises from a model in which the statistics of our actions are optimized. Together these studies provide a probabilistic framework for sensorimotor control.