A mixture of delta-rules approximation to bayesian inference in change-point problems.

Error-driven learning rules have received considerable attention because of their close relationships to both optimal theory and neurobiological mechanisms. However, basic forms of these rules are effective under only a restricted set of conditions in which the environment is stable. Recent studies...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Robert C Wilson, Matthew R Nassar, Joshua I Gold
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/319536d444224beeac36e201a42ed281
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:319536d444224beeac36e201a42ed281
record_format	dspace
spelling	oai:doaj.org-article:319536d444224beeac36e201a42ed2812021-11-18T05:53:42ZA mixture of delta-rules approximation to bayesian inference in change-point problems.1553-734X1553-735810.1371/journal.pcbi.1003150https://doaj.org/article/319536d444224beeac36e201a42ed2812013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23935472/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Error-driven learning rules have received considerable attention because of their close relationships to both optimal theory and neurobiological mechanisms. However, basic forms of these rules are effective under only a restricted set of conditions in which the environment is stable. Recent studies have defined optimal solutions to learning problems in more general, potentially unstable, environments, but the relevance of these complex mathematical solutions to how the brain solves these problems remains unclear. Here, we show that one such Bayesian solution can be approximated by a computationally straightforward mixture of simple error-driven 'Delta' rules. This simpler model can make effective inferences in a dynamic environment and matches human performance on a predictive-inference task using a mixture of a small number of Delta rules. This model represents an important conceptual advance in our understanding of how the brain can use relatively simple computations to make nearly optimal inferences in a dynamic world.Robert C WilsonMatthew R NassarJoshua I GoldPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 9, Iss 7, p e1003150 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Robert C Wilson Matthew R Nassar Joshua I Gold A mixture of delta-rules approximation to bayesian inference in change-point problems.
description	Error-driven learning rules have received considerable attention because of their close relationships to both optimal theory and neurobiological mechanisms. However, basic forms of these rules are effective under only a restricted set of conditions in which the environment is stable. Recent studies have defined optimal solutions to learning problems in more general, potentially unstable, environments, but the relevance of these complex mathematical solutions to how the brain solves these problems remains unclear. Here, we show that one such Bayesian solution can be approximated by a computationally straightforward mixture of simple error-driven 'Delta' rules. This simpler model can make effective inferences in a dynamic environment and matches human performance on a predictive-inference task using a mixture of a small number of Delta rules. This model represents an important conceptual advance in our understanding of how the brain can use relatively simple computations to make nearly optimal inferences in a dynamic world.
format	article
author	Robert C Wilson Matthew R Nassar Joshua I Gold
author_facet	Robert C Wilson Matthew R Nassar Joshua I Gold
author_sort	Robert C Wilson
title	A mixture of delta-rules approximation to bayesian inference in change-point problems.
title_short	A mixture of delta-rules approximation to bayesian inference in change-point problems.
title_full	A mixture of delta-rules approximation to bayesian inference in change-point problems.
title_fullStr	A mixture of delta-rules approximation to bayesian inference in change-point problems.
title_full_unstemmed	A mixture of delta-rules approximation to bayesian inference in change-point problems.
title_sort	mixture of delta-rules approximation to bayesian inference in change-point problems.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/319536d444224beeac36e201a42ed281
work_keys_str_mv	AT robertcwilson amixtureofdeltarulesapproximationtobayesianinferenceinchangepointproblems AT matthewrnassar amixtureofdeltarulesapproximationtobayesianinferenceinchangepointproblems AT joshuaigold amixtureofdeltarulesapproximationtobayesianinferenceinchangepointproblems AT robertcwilson mixtureofdeltarulesapproximationtobayesianinferenceinchangepointproblems AT matthewrnassar mixtureofdeltarulesapproximationtobayesianinferenceinchangepointproblems AT joshuaigold mixtureofdeltarulesapproximationtobayesianinferenceinchangepointproblems
_version_	1718424656493412352

A mixture of delta-rules approximation to bayesian inference in change-point problems.

Ejemplares similares