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dc.contributor.authorEmken, Jeremy L.
dc.contributor.authorBenítez Iglesias, Raúl
dc.contributor.authorSideris, Athanasios
dc.contributor.authorBobrow, James E.
dc.contributor.authorReinkensmeyer, David J.
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.identifier.citationEmken,J.L.; Benitez,R.; Sideris,A.; Bobrow, J.E.; Reinkensmeyer, D.J.(2007). Motor adaptation as a greedy optimization of error and effort. Journal of Neurophysiology, 97(6): 3997-4006. ISSN:0022-3077
dc.description.abstractthought of as a process in which the nervous system learns to anticipate the environmental forces to eliminate kinematic error. Here we show that motor adaptation can more generally be modeled as a process in which the motor system greedily minimizes a cost function that is the weighted sum of kinematic error and effort. The learning dynamics predicted by this minimization process are a linear, auto-regressive equation with only one state, which has been identified previously as providing a good fit to data from force-field-type experiments. Thus we provide a new theoretical result that shows how these previously identified learning dynamics can be viewed as arising from an optimization of error and effort. We also show that the coefficients of the learning dynamics must fall within a specific range for the optimization model to be valid and verify with experimental data from walking in a force field that they indeed fall in this range. Finally, we attempted to falsify the model by performing experiments in two conditions (repeated exposure to a force field, exposure to force fields of different strengths) for which the single-state, auto-regressive equation might be expected to not fit the data well. We found however that the equation adequately captured the pattern of errors and thus conclude that motor adaptation to a force field can be approximated as an optimization of effort and error for a range of experimental conditions.
dc.publisherThe American Physiological Society
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Investigació operativa::Optimització
dc.subject.lcshMachine theory
dc.subject.othergreedy algorithm
dc.titleMotor adaptation as a greedy optimization of error and effort
dc.subject.lemacMàquines, Teoria de
dc.contributor.groupUniversitat Politècnica de Catalunya. NOLIN - Física No-Lineal i Sistemes Fora de l'Equilibri
dc.description.peerreviewedPeer Reviewed
dc.rights.accessRestricted access - publisher's policy
dc.description.versionPostprint (updated version)

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