Risk-Sensitivity in Sensorimotor Control
Identifieur interne : 002A20 ( Main/Merge ); précédent : 002A19; suivant : 002A21Risk-Sensitivity in Sensorimotor Control
Auteurs : Daniel A. Braun [Royaume-Uni, États-Unis] ; Arne J. Nagengast [Royaume-Uni] ; Daniel M. Wolpert [Royaume-Uni]Source :
- Frontiers in Human Neuroscience [ 1662-5161 ] ; 2011.
Abstract
Recent advances in theoretical neuroscience suggest that motor control can be considered as a continuous decision-making process in which uncertainty plays a key role. Decision-makers can be risk-sensitive with respect to this uncertainty in that they may not only consider the average payoff of an outcome, but also consider the variability of the payoffs. Although such risk-sensitivity is a well-established phenomenon in psychology and economics, it has been much less studied in motor control. In fact, leading theories of motor control, such as optimal feedback control, assume that motor behaviors can be explained as the optimization of a given expected payoff or cost. Here we review evidence that humans exhibit risk-sensitivity in their motor behaviors, thereby demonstrating sensitivity to the variability of “motor costs.” Furthermore, we discuss how risk-sensitivity can be incorporated into optimal feedback control models of motor control. We conclude that risk-sensitivity is an important concept in understanding individual motor behavior under uncertainty.
Url:
DOI: 10.3389/fnhum.2011.00001
PubMed: 21283556
PubMed Central: 3028548
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Braun</name><affiliation wicri:level="1"><nlm:aff id="aff1"><institution>Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge</institution><country>Cambridge, UK</country></nlm:aff><country xml:lang="fr">Royaume-Uni</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>Computational Learning and Motor Control Laboratory, Department of Computer Science, University of Southern California</institution><country>Los Angeles, CA, USA</country></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea></wicri:regionArea><wicri:regionArea># see nlm:aff region in country</wicri:regionArea></affiliation></author><author><name sortKey="Nagengast, Arne J" sort="Nagengast, Arne J" uniqKey="Nagengast A" first="Arne J." last="Nagengast">Arne J. Nagengast</name><affiliation wicri:level="1"><nlm:aff id="aff1"><institution>Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge</institution><country>Cambridge, UK</country></nlm:aff><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea></wicri:regionArea><wicri:regionArea># see nlm:aff region in country</wicri:regionArea></affiliation><affiliation wicri:level="1"><nlm:aff id="aff3"><institution>Department of Experimental Psychology, University of Cambridge</institution><country>Cambridge, UK</country></nlm:aff><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea></wicri:regionArea><wicri:regionArea># see nlm:aff region in country</wicri:regionArea></affiliation></author><author><name sortKey="Wolpert, Daniel M" sort="Wolpert, Daniel M" uniqKey="Wolpert D" first="Daniel M." last="Wolpert">Daniel M. Wolpert</name><affiliation wicri:level="1"><nlm:aff id="aff1"><institution>Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge</institution><country>Cambridge, UK</country></nlm:aff><country xml:lang="fr">Royaume-Uni</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="2011">2011</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Recent advances in theoretical neuroscience suggest that motor control can be considered as a continuous decision-making process in which uncertainty plays a key role. Decision-makers can be risk-sensitive with respect to this uncertainty in that they may not only consider the average payoff of an outcome, but also consider the variability of the payoffs. Although such risk-sensitivity is a well-established phenomenon in psychology and economics, it has been much less studied in motor control. In fact, leading theories of motor control, such as optimal feedback control, assume that motor behaviors can be explained as the optimization of a given expected payoff or cost. Here we review evidence that humans exhibit risk-sensitivity in their motor behaviors, thereby demonstrating sensitivity to the variability of “motor costs.” Furthermore, we discuss how risk-sensitivity can be incorporated into optimal feedback control models of motor control. 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