Journal of Computational Neuroscience

, Volume 24, Issue 1, pp 57–68 | Cite as

Optimality, stochasticity, and variability in motor behavior

  • Emmanuel Guigon
  • Pierre Baraduc
  • Michel Desmurget
Article

Abstract

Recent theories of motor control have proposed that the nervous system acts as a stochastically optimal controller, i.e. it plans and executes motor behaviors taking into account the nature and statistics of noise. Detrimental effects of noise are converted into a principled way of controlling movements. Attractive aspects of such theories are their ability to explain not only characteristic features of single motor acts, but also statistical properties of repeated actions. Here, we present a critical analysis of stochastic optimality in motor control which reveals several difficulties with this hypothesis. We show that stochastic control may not be necessary to explain the stochastic nature of motor behavior, and we propose an alternative framework, based on the action of a deterministic controller coupled with an optimal state estimator, which relieves drawbacks of stochastic optimality and appropriately explains movement variability.

Keywords

Motor control Noise Model 

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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Emmanuel Guigon
    • 1
  • Pierre Baraduc
    • 2
  • Michel Desmurget
    • 2
  1. 1.INSERM U742, ANIMUniversité Pierre et Marie Curie (UPMC Paris 6)ParisFrance
  2. 2.Centre de Neurosciences CognitivesCNRS UMR 5229BronFrance

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