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Optimality, stochasticity, and variability in motor behavior

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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.

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Authors and Affiliations

  1. INSERM U742, ANIM, Université Pierre et Marie Curie (UPMC Paris 6), 9, quai Saint-Bernard, 75005, Paris, France

    Emmanuel Guigon

  2. Centre de Neurosciences Cognitives, CNRS UMR 5229, 67, Bd Pinel, 69675, Bron, France

    Pierre Baraduc & Michel Desmurget

Authors
  1. Emmanuel Guigon
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  2. Pierre Baraduc
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  3. Michel Desmurget
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Correspondence to Emmanuel Guigon.

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Action Editor: Frances K. Skinner

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Guigon, E., Baraduc, P. & Desmurget, M. Optimality, stochasticity, and variability in motor behavior. J Comput Neurosci 24, 57–68 (2008). https://doi.org/10.1007/s10827-007-0041-y

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  • DOI: https://doi.org/10.1007/s10827-007-0041-y

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