Structure Learning in Bayesian Sensorimotor Integration.
Previous studies have shown that sensorimotor processing can often be described by Bayesian learning, in particular the integration of prior and feedback information depending on its degree of reliability. Here we test the hypothesis that the integration process itself can be tuned to the statistica...
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2015-08-01
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doaj-20ec38c293b3466ab1b313e7ddc03e492020-11-25T01:44:39ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582015-08-01118e100436910.1371/journal.pcbi.1004369Structure Learning in Bayesian Sensorimotor Integration.Tim GeneweinEduard HezZeynab RazzaghpanahDaniel A BraunPrevious studies have shown that sensorimotor processing can often be described by Bayesian learning, in particular the integration of prior and feedback information depending on its degree of reliability. Here we test the hypothesis that the integration process itself can be tuned to the statistical structure of the environment. We exposed human participants to a reaching task in a three-dimensional virtual reality environment where we could displace the visual feedback of their hand position in a two dimensional plane. When introducing statistical structure between the two dimensions of the displacement, we found that over the course of several days participants adapted their feedback integration process in order to exploit this structure for performance improvement. In control experiments we found that this adaptation process critically depended on performance feedback and could not be induced by verbal instructions. Our results suggest that structural learning is an important meta-learning component of Bayesian sensorimotor integration.http://europepmc.org/articles/PMC4549275?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Tim Genewein Eduard Hez Zeynab Razzaghpanah Daniel A Braun |
spellingShingle |
Tim Genewein Eduard Hez Zeynab Razzaghpanah Daniel A Braun Structure Learning in Bayesian Sensorimotor Integration. PLoS Computational Biology |
author_facet |
Tim Genewein Eduard Hez Zeynab Razzaghpanah Daniel A Braun |
author_sort |
Tim Genewein |
title |
Structure Learning in Bayesian Sensorimotor Integration. |
title_short |
Structure Learning in Bayesian Sensorimotor Integration. |
title_full |
Structure Learning in Bayesian Sensorimotor Integration. |
title_fullStr |
Structure Learning in Bayesian Sensorimotor Integration. |
title_full_unstemmed |
Structure Learning in Bayesian Sensorimotor Integration. |
title_sort |
structure learning in bayesian sensorimotor integration. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS Computational Biology |
issn |
1553-734X 1553-7358 |
publishDate |
2015-08-01 |
description |
Previous studies have shown that sensorimotor processing can often be described by Bayesian learning, in particular the integration of prior and feedback information depending on its degree of reliability. Here we test the hypothesis that the integration process itself can be tuned to the statistical structure of the environment. We exposed human participants to a reaching task in a three-dimensional virtual reality environment where we could displace the visual feedback of their hand position in a two dimensional plane. When introducing statistical structure between the two dimensions of the displacement, we found that over the course of several days participants adapted their feedback integration process in order to exploit this structure for performance improvement. In control experiments we found that this adaptation process critically depended on performance feedback and could not be induced by verbal instructions. Our results suggest that structural learning is an important meta-learning component of Bayesian sensorimotor integration. |
url |
http://europepmc.org/articles/PMC4549275?pdf=render |
work_keys_str_mv |
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