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10.1371/journal.pcbi.1004369 http://scihub22266oqcxt.onion/10.1371/journal.pcbi.1004369 C4549275!4549275 !26305797     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\26305797 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       PLoS+Comput+Biol 2015 ; 11 (8 ): e1004369 Nephropedia Template TP {{tp|p=26305797 |t=2015. Structure Learning in Bayesian Sensorimotor Integration |pdf=|usr=}}{{26305797 }} gab.com Text Structure Learning in Bayesian Sensorimotor Integration JO: PLoS Comput Biol http://www.kidney.de/pget.php?&tw=1&pmid=26305797 #FOAvip 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. Twit Text FOAVip Structure Learning in Bayesian Sensorimotor Integration ????PLoS Comput Biol http://www.kidney.de/pget.php?&tw=1&pmid=26305797 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26305797 #FOAvip English Wikipedia <ref>{{Cite pmid|26305797 }}</ref> Structure Learning in Bayesian Sensorimotor Integration #MMPMID26305797 Genewein T ; Hez E ; Razzaghpanah Z ; Braun DA PLoS Comput Biol 2015[Aug]; 11 (8 ): e1004369 PMID26305797 show ga 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. |Bayes Theorem [MESH] |Computational Biology [MESH] |Feedback, Sensory/*physiology [MESH] |Female [MESH] |Hand/physiology [MESH] |Humans [MESH] |Learning/*physiology [MESH] |Male [MESH]Structure Learning in Bayesian Sensorimotor Integration (epmc).pdf DeepDyve Pubget Overpricing