On the origins of suboptimality in human probabilistic inference.

Humans have been shown to combine noisy sensory information with previous experience (priors), in qualitative and sometimes quantitative agreement with the statistically-optimal predictions of Bayesian integration. However, when the prior distribution becomes more complex than a simple Gaussian, suc...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Luigi Acerbi, Sethu Vijayakumar, Daniel M Wolpert
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/c22d2741c6a3487ea50fa411c0d5c53d
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:c22d2741c6a3487ea50fa411c0d5c53d
record_format	dspace
spelling	oai:doaj.org-article:c22d2741c6a3487ea50fa411c0d5c53d2021-11-11T05:52:06ZOn the origins of suboptimality in human probabilistic inference.1553-734X1553-735810.1371/journal.pcbi.1003661https://doaj.org/article/c22d2741c6a3487ea50fa411c0d5c53d2014-06-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24945142/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Humans have been shown to combine noisy sensory information with previous experience (priors), in qualitative and sometimes quantitative agreement with the statistically-optimal predictions of Bayesian integration. However, when the prior distribution becomes more complex than a simple Gaussian, such as skewed or bimodal, training takes much longer and performance appears suboptimal. It is unclear whether such suboptimality arises from an imprecise internal representation of the complex prior, or from additional constraints in performing probabilistic computations on complex distributions, even when accurately represented. Here we probe the sources of suboptimality in probabilistic inference using a novel estimation task in which subjects are exposed to an explicitly provided distribution, thereby removing the need to remember the prior. Subjects had to estimate the location of a target given a noisy cue and a visual representation of the prior probability density over locations, which changed on each trial. Different classes of priors were examined (Gaussian, unimodal, bimodal). Subjects' performance was in qualitative agreement with the predictions of Bayesian Decision Theory although generally suboptimal. The degree of suboptimality was modulated by statistical features of the priors but was largely independent of the class of the prior and level of noise in the cue, suggesting that suboptimality in dealing with complex statistical features, such as bimodality, may be due to a problem of acquiring the priors rather than computing with them. We performed a factorial model comparison across a large set of Bayesian observer models to identify additional sources of noise and suboptimality. Our analysis rejects several models of stochastic behavior, including probability matching and sample-averaging strategies. Instead we show that subjects' response variability was mainly driven by a combination of a noisy estimation of the parameters of the priors, and by variability in the decision process, which we represent as a noisy or stochastic posterior.Luigi AcerbiSethu VijayakumarDaniel M WolpertPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 10, Iss 6, p e1003661 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Luigi Acerbi Sethu Vijayakumar Daniel M Wolpert On the origins of suboptimality in human probabilistic inference.
description	Humans have been shown to combine noisy sensory information with previous experience (priors), in qualitative and sometimes quantitative agreement with the statistically-optimal predictions of Bayesian integration. However, when the prior distribution becomes more complex than a simple Gaussian, such as skewed or bimodal, training takes much longer and performance appears suboptimal. It is unclear whether such suboptimality arises from an imprecise internal representation of the complex prior, or from additional constraints in performing probabilistic computations on complex distributions, even when accurately represented. Here we probe the sources of suboptimality in probabilistic inference using a novel estimation task in which subjects are exposed to an explicitly provided distribution, thereby removing the need to remember the prior. Subjects had to estimate the location of a target given a noisy cue and a visual representation of the prior probability density over locations, which changed on each trial. Different classes of priors were examined (Gaussian, unimodal, bimodal). Subjects' performance was in qualitative agreement with the predictions of Bayesian Decision Theory although generally suboptimal. The degree of suboptimality was modulated by statistical features of the priors but was largely independent of the class of the prior and level of noise in the cue, suggesting that suboptimality in dealing with complex statistical features, such as bimodality, may be due to a problem of acquiring the priors rather than computing with them. We performed a factorial model comparison across a large set of Bayesian observer models to identify additional sources of noise and suboptimality. Our analysis rejects several models of stochastic behavior, including probability matching and sample-averaging strategies. Instead we show that subjects' response variability was mainly driven by a combination of a noisy estimation of the parameters of the priors, and by variability in the decision process, which we represent as a noisy or stochastic posterior.
format	article
author	Luigi Acerbi Sethu Vijayakumar Daniel M Wolpert
author_facet	Luigi Acerbi Sethu Vijayakumar Daniel M Wolpert
author_sort	Luigi Acerbi
title	On the origins of suboptimality in human probabilistic inference.
title_short	On the origins of suboptimality in human probabilistic inference.
title_full	On the origins of suboptimality in human probabilistic inference.
title_fullStr	On the origins of suboptimality in human probabilistic inference.
title_full_unstemmed	On the origins of suboptimality in human probabilistic inference.
title_sort	on the origins of suboptimality in human probabilistic inference.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/c22d2741c6a3487ea50fa411c0d5c53d
work_keys_str_mv	AT luigiacerbi ontheoriginsofsuboptimalityinhumanprobabilisticinference AT sethuvijayakumar ontheoriginsofsuboptimalityinhumanprobabilisticinference AT danielmwolpert ontheoriginsofsuboptimalityinhumanprobabilisticinference
_version_	1718439535722889216

On the origins of suboptimality in human probabilistic inference.

Ejemplares similares