A sensorimotor paradigm for Bayesian model selection
Identifieur interne : 002625 ( Main/Merge ); précédent : 002624; suivant : 002626A sensorimotor paradigm for Bayesian model selection
Auteurs : Tim Genewein [Allemagne] ; Daniel A. Braun [Allemagne]Source :
- Frontiers in Human Neuroscience [ 1662-5161 ] ; 2012.
Abstract
Sensorimotor control is thought to rely on predictive internal models in order to cope efficiently with uncertain environments. Recently, it has been shown that humans not only learn different internal models for different tasks, but that they also extract common structure between tasks. This raises the question of how the motor system selects between different structures or models, when each model can be associated with a range of different task-specific parameters. Here we design a sensorimotor task that requires subjects to compensate visuomotor shifts in a three-dimensional virtual reality setup, where one of the dimensions can be mapped to a model variable and the other dimension to the parameter variable. By introducing probe trials that are neutral in the parameter dimension, we can directly test for model selection. We found that model selection procedures based on Bayesian statistics provided a better explanation for subjects' choice behavior than simple non-probabilistic heuristics. Our experimental design lends itself to the general study of model selection in a sensorimotor context as it allows to separately query model and parameter variables from subjects.
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DOI: 10.3389/fnhum.2012.00291
PubMed: 23125827
PubMed Central: 3486689
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Braun</name><affiliation wicri:level="1"><nlm:aff id="aff1"><institution>Max Planck Institute for Biological Cybernetics</institution><country>Tübingen, Germany</country></nlm:aff><country xml:lang="fr">Allemagne</country><wicri:regionArea></wicri:regionArea><wicri:regionArea># see nlm:aff region in country</wicri:regionArea></affiliation><affiliation wicri:level="1"><nlm:aff id="aff2"><institution>Max Planck Institute for Intelligent Systems</institution><country>Tübingen, Germany</country></nlm:aff><country xml:lang="fr">Allemagne</country><wicri:regionArea></wicri:regionArea><wicri:regionArea># see nlm:aff region in country</wicri:regionArea></affiliation></author></analytic><series><title level="j">Frontiers in Human Neuroscience</title><idno type="eISSN">1662-5161</idno><imprint><date when="2012">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Sensorimotor control is thought to rely on predictive internal models in order to cope efficiently with uncertain environments. Recently, it has been shown that humans not only learn different internal models for different tasks, but that they also extract common structure between tasks. This raises the question of how the motor system selects between different structures or models, when each model can be associated with a range of different task-specific parameters. Here we design a sensorimotor task that requires subjects to compensate visuomotor shifts in a three-dimensional virtual reality setup, where one of the dimensions can be mapped to a model variable and the other dimension to the parameter variable. By introducing probe trials that are neutral in the parameter dimension, we can directly test for model selection. We found that model selection procedures based on Bayesian statistics provided a better explanation for subjects' choice behavior than simple non-probabilistic heuristics. Our experimental design lends itself to the general study of model selection in a sensorimotor context as it allows to separately query model and parameter variables from subjects.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Akaike, H" uniqKey="Akaike H">H. Akaike</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Bishop, C M" uniqKey="Bishop C">C. M. Bishop</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Braun, D A" uniqKey="Braun D">D. A. Braun</name></author><author><name sortKey="Aertsen, A" uniqKey="Aertsen A">A. Aertsen</name></author><author><name sortKey="Wolpert, D M" uniqKey="Wolpert D">D. M. Wolpert</name></author><author><name sortKey="Mehring, C" uniqKey="Mehring C">C. Mehring</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Braun, D A" uniqKey="Braun D">D. A. 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