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Learning Optimal Adaptation Strategies in Unpredictable Motor Tasks

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83827

Braun,  DA
Research Group Sensorimotor Learning and Decision-Making, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Braun, D., Aertsen A, Wolpert, D., & Mehring, C. (2009). Learning Optimal Adaptation Strategies in Unpredictable Motor Tasks. Journal of Neuroscience, 29(20), 6472-6478. doi:10.1523/​JNEUROSCI.3075-08.2009.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-C4D7-3
Zusammenfassung
Picking up an empty milk carton that we believe to be full is a familiar example of adaptive control, because the adaptation process of estimating the carton's weight must proceed simultaneously with the control process of moving the carton to a desired location. Here we show that the motor system initially generates highly variable behavior in such unpredictable tasks but eventually converges to stereotyped patterns of adaptive responses predicted by a simple optimality principle. These results suggest that adaptation can become specifically tuned to identify task-specific parameters in an optimal manner.