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  Sensorimotor learning and decision-making in complex environments

Braun, D. A. (2014). Sensorimotor learning and decision-making in complex environments. Talk presented at 4th Joint Spring School Multisensory Perception for Action. Wildbad Kreuth, Germany.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0001-33DE-C Version Permalink: http://hdl.handle.net/21.11116/0000-0001-39E3-F
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 Creators:
Braun, Daniel A.1, 2, Author              
Affiliations:
1Research Group Sensorimotor Learning and Decision-Making, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497809              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Recent advances in movement neuroscience suggest that sensorimotor control can be considered as a continuous decision-making process in complex environments in which uncertainty and task variability play a key role. Leading theories of motor control assume that the motor system learns probabilistic models and that motor behavior can be explained as the optimization of payoff or cost criteria under the expectation of these models. Here we discuss first how the motor system exploits task variability to build up efficient models and then discuss evidence that humans deviate from Bayes optimal behavior in their movements, because they exhibit effects of model uncertainty. Furthermore, we discuss in how far model uncertainty can be considered as a special case of a general decision-making framework inspired by statistical physics and thermodynamics.

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 Dates: 2014-06
 Publication Status: Published in print
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Title: 4th Joint Spring School Multisensory Perception for Action
Place of Event: Wildbad Kreuth, Germany
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Title: 4th Joint Spring School Multisensory Perception for Action
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 7 Identifier: -