Structure learning in a sensorimotor association task.

Learning is often understood as an organism's gradual acquisition of the association between a given sensory stimulus and the correct motor response. Mathematically, this corresponds to regressing a mapping between the set of observations and the set of actions. Recently, however, it has been s...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Daniel A Braun, Stephan Waldert, Ad Aertsen, Daniel M Wolpert, Carsten Mehring
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c608baaa83a24812a21bef3bf9d37bfe
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:c608baaa83a24812a21bef3bf9d37bfe
record_format	dspace
spelling	oai:doaj.org-article:c608baaa83a24812a21bef3bf9d37bfe2021-11-25T06:26:16ZStructure learning in a sensorimotor association task.1932-620310.1371/journal.pone.0008973https://doaj.org/article/c608baaa83a24812a21bef3bf9d37bfe2010-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20126409/?tool=EBIhttps://doaj.org/toc/1932-6203Learning is often understood as an organism's gradual acquisition of the association between a given sensory stimulus and the correct motor response. Mathematically, this corresponds to regressing a mapping between the set of observations and the set of actions. Recently, however, it has been shown both in cognitive and motor neuroscience that humans are not only able to learn particular stimulus-response mappings, but are also able to extract abstract structural invariants that facilitate generalization to novel tasks. Here we show how such structure learning can enhance facilitation in a sensorimotor association task performed by human subjects. Using regression and reinforcement learning models we show that the observed facilitation cannot be explained by these basic models of learning stimulus-response associations. We show, however, that the observed data can be explained by a hierarchical Bayesian model that performs structure learning. In line with previous results from cognitive tasks, this suggests that hierarchical Bayesian inference might provide a common framework to explain both the learning of specific stimulus-response associations and the learning of abstract structures that are shared by different task environments.Daniel A BraunStephan WaldertAd AertsenDaniel M WolpertCarsten MehringPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 5, Iss 1, p e8973 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Daniel A Braun Stephan Waldert Ad Aertsen Daniel M Wolpert Carsten Mehring Structure learning in a sensorimotor association task.
description	Learning is often understood as an organism's gradual acquisition of the association between a given sensory stimulus and the correct motor response. Mathematically, this corresponds to regressing a mapping between the set of observations and the set of actions. Recently, however, it has been shown both in cognitive and motor neuroscience that humans are not only able to learn particular stimulus-response mappings, but are also able to extract abstract structural invariants that facilitate generalization to novel tasks. Here we show how such structure learning can enhance facilitation in a sensorimotor association task performed by human subjects. Using regression and reinforcement learning models we show that the observed facilitation cannot be explained by these basic models of learning stimulus-response associations. We show, however, that the observed data can be explained by a hierarchical Bayesian model that performs structure learning. In line with previous results from cognitive tasks, this suggests that hierarchical Bayesian inference might provide a common framework to explain both the learning of specific stimulus-response associations and the learning of abstract structures that are shared by different task environments.
format	article
author	Daniel A Braun Stephan Waldert Ad Aertsen Daniel M Wolpert Carsten Mehring
author_facet	Daniel A Braun Stephan Waldert Ad Aertsen Daniel M Wolpert Carsten Mehring
author_sort	Daniel A Braun
title	Structure learning in a sensorimotor association task.
title_short	Structure learning in a sensorimotor association task.
title_full	Structure learning in a sensorimotor association task.
title_fullStr	Structure learning in a sensorimotor association task.
title_full_unstemmed	Structure learning in a sensorimotor association task.
title_sort	structure learning in a sensorimotor association task.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/c608baaa83a24812a21bef3bf9d37bfe
work_keys_str_mv	AT danielabraun structurelearninginasensorimotorassociationtask AT stephanwaldert structurelearninginasensorimotorassociationtask AT adaertsen structurelearninginasensorimotorassociationtask AT danielmwolpert structurelearninginasensorimotorassociationtask AT carstenmehring structurelearninginasensorimotorassociationtask
_version_	1718413769212690432

Structure learning in a sensorimotor association task.

Ejemplares similares