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Sensorimotor Recalibration Depends on Attribution of Sensory Prediction Errors to Internal Causes

Overview of attention for article published in PLOS ONE, January 2013
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Title
Sensorimotor Recalibration Depends on Attribution of Sensory Prediction Errors to Internal Causes
Published in
PLOS ONE, January 2013
DOI 10.1371/journal.pone.0054925
Pubmed ID
Authors

Carlo Wilke, Matthis Synofzik, Axel Lindner

Abstract

Sensorimotor learning critically depends on error signals. Learning usually tries to minimise these error signals to guarantee optimal performance. Errors can, however, have both internal causes, resulting from one's sensorimotor system, and external causes, resulting from external disturbances. Does learning take into account the perceived cause of error information? Here, we investigated the recalibration of internal predictions about the sensory consequences of one's actions. Since these predictions underlie the distinction of self- and externally produced sensory events, we assumed them to be recalibrated only by prediction errors attributed to internal causes. When subjects were confronted with experimentally induced visual prediction errors about their pointing movements in virtual reality, they recalibrated the predicted visual consequences of their movements. Recalibration was not proportional to the externally generated prediction error, but correlated with the error component which subjects attributed to internal causes. We also revealed adaptation in subjects' motor performance which reflected their recalibrated sensory predictions. Thus, causal attribution of error information is essential for sensorimotor learning.

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Mendeley readers

The data shown below were compiled from readership statistics for 112 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
Germany 2 2%
France 1 <1%
United Kingdom 1 <1%
Poland 1 <1%
Unknown 104 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 24%
Researcher 23 21%
Student > Master 12 11%
Student > Doctoral Student 10 9%
Professor 6 5%
Other 17 15%
Unknown 17 15%
Readers by discipline Count As %
Psychology 32 29%
Neuroscience 17 15%
Engineering 10 9%
Agricultural and Biological Sciences 9 8%
Medicine and Dentistry 7 6%
Other 16 14%
Unknown 21 19%