Optimality, stochasticity, and variability in motor behavior.

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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Article dans une revue
Journal of Computational Neuroscience, Springer Verlag, 2008, 24 (1), pp.57-68. 〈10.1007/s10827-007-0041-y〉
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Contributeur : Emmanuel Guigon <>
Soumis le : mardi 22 janvier 2008 - 17:39:27
Dernière modification le : jeudi 29 mars 2018 - 09:36:05
Document(s) archivé(s) le : jeudi 15 avril 2010 - 02:04:26

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Emmanuel Guigon, Pierre Baraduc, Michel Desmurget. Optimality, stochasticity, and variability in motor behavior.. Journal of Computational Neuroscience, Springer Verlag, 2008, 24 (1), pp.57-68. 〈10.1007/s10827-007-0041-y〉. 〈inserm-00212327〉

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