Structure Learning in Bayesian Sensorimotor Integration
Identifieur interne : 000533 ( Main/Merge ); précédent : 000532; suivant : 000534Structure Learning in Bayesian Sensorimotor Integration
Auteurs : Tim Genewein [Allemagne] ; Eduard Hez [Allemagne] ; Zeynab Razzaghpanah [Allemagne] ; Daniel A. Braun [Allemagne]Source :
- PLoS Computational Biology [ 1553-734X ] ; 2015.
Abstract
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:
DOI: 10.1371/journal.pcbi.1004369
PubMed: 26305797
PubMed Central: 4549275
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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.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Rao, Rp" uniqKey="Rao R">RP Rao</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Knill, Dc" uniqKey="Knill D">DC Knill</name></author><author><name sortKey="Pouget, A" uniqKey="Pouget A">A Pouget</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Kording, Kp" uniqKey="Kording K">KP Körding</name></author><author><name sortKey="Wolpert, Dm" uniqKey="Wolpert D">DM Wolpert</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Wolpert, Dm" uniqKey="Wolpert D">DM Wolpert</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Kording, K" uniqKey="Kording K">K Körding</name></author></analytic></biblStruct><biblStruct><analytic><author><name 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uniqKey="Griffiths T">TL Griffiths</name></author><author><name sortKey="Kemp, C" uniqKey="Kemp C">C Kemp</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Sato, Y" uniqKey="Sato Y">Y Sato</name></author><author><name sortKey="Toyoizumi, T" uniqKey="Toyoizumi T">T Toyoizumi</name></author><author><name sortKey="Aihara, K" uniqKey="Aihara K">K Aihara</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Kemp, C" uniqKey="Kemp C">C Kemp</name></author><author><name sortKey="Tenenbaum, Jb" uniqKey="Tenenbaum J">JB Tenenbaum</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Kemp, C" uniqKey="Kemp C">C Kemp</name></author><author><name sortKey="Tenenbaum, Jb" uniqKey="Tenenbaum J">JB Tenenbaum</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Tenenbaum, Jb" uniqKey="Tenenbaum J">JB Tenenbaum</name></author><author><name sortKey="Kemp, C" uniqKey="Kemp C">C Kemp</name></author><author><name sortKey="Griffiths, Tl" uniqKey="Griffiths T">TL Griffiths</name></author><author><name sortKey="Goodman, Nd" uniqKey="Goodman N">ND Goodman</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Genewein, T" uniqKey="Genewein T">T Genewein</name></author><author><name sortKey="Braun, Da" uniqKey="Braun D">DA Braun</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Genewein, T" uniqKey="Genewein T">T Genewein</name></author><author><name sortKey="Braun, Da" uniqKey="Braun D">DA Braun</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Bishop, Cm" uniqKey="Bishop C">CM Bishop</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Murphy, Kp" uniqKey="Murphy K">KP Murphy</name></author></analytic></biblStruct></listBibl></div1></back></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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