Multisensory oddity detection as bayesian inference.

A key goal for the perceptual system is to optimally combine information from all the senses that may be available in order to develop the most accurate and unified picture possible of the outside world. The contemporary theoretical framework of ideal observer maximum likelihood integration (MLI) ha...

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Autores principales:	Timothy Hospedales, Sethu Vijayakumar
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2009
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4b0eb27af8ee4b2080d2920ee527bc65
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spelling	oai:doaj.org-article:4b0eb27af8ee4b2080d2920ee527bc652021-11-25T06:17:45ZMultisensory oddity detection as bayesian inference.1932-620310.1371/journal.pone.0004205https://doaj.org/article/4b0eb27af8ee4b2080d2920ee527bc652009-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/19145254/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203A key goal for the perceptual system is to optimally combine information from all the senses that may be available in order to develop the most accurate and unified picture possible of the outside world. The contemporary theoretical framework of ideal observer maximum likelihood integration (MLI) has been highly successful in modelling how the human brain combines information from a variety of different sensory modalities. However, in various recent experiments involving multisensory stimuli of uncertain correspondence, MLI breaks down as a successful model of sensory combination. Within the paradigm of direct stimulus estimation, perceptual models which use Bayesian inference to resolve correspondence have recently been shown to generalize successfully to these cases where MLI fails. This approach has been known variously as model inference, causal inference or structure inference. In this paper, we examine causal uncertainty in another important class of multi-sensory perception paradigm--that of oddity detection and demonstrate how a Bayesian ideal observer also treats oddity detection as a structure inference problem. We validate this approach by showing that it provides an intuitive and quantitative explanation of an important pair of multi-sensory oddity detection experiments--involving cues across and within modalities--for which MLI previously failed dramatically, allowing a novel unifying treatment of within and cross modal multisensory perception. Our successful application of structure inference models to the new 'oddity detection' paradigm, and the resultant unified explanation of across and within modality cases provide further evidence to suggest that structure inference may be a commonly evolved principle for combining perceptual information in the brain.Timothy HospedalesSethu VijayakumarPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 4, Iss 1, p e4205 (2009)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Timothy Hospedales Sethu Vijayakumar Multisensory oddity detection as bayesian inference.
description	A key goal for the perceptual system is to optimally combine information from all the senses that may be available in order to develop the most accurate and unified picture possible of the outside world. The contemporary theoretical framework of ideal observer maximum likelihood integration (MLI) has been highly successful in modelling how the human brain combines information from a variety of different sensory modalities. However, in various recent experiments involving multisensory stimuli of uncertain correspondence, MLI breaks down as a successful model of sensory combination. Within the paradigm of direct stimulus estimation, perceptual models which use Bayesian inference to resolve correspondence have recently been shown to generalize successfully to these cases where MLI fails. This approach has been known variously as model inference, causal inference or structure inference. In this paper, we examine causal uncertainty in another important class of multi-sensory perception paradigm--that of oddity detection and demonstrate how a Bayesian ideal observer also treats oddity detection as a structure inference problem. We validate this approach by showing that it provides an intuitive and quantitative explanation of an important pair of multi-sensory oddity detection experiments--involving cues across and within modalities--for which MLI previously failed dramatically, allowing a novel unifying treatment of within and cross modal multisensory perception. Our successful application of structure inference models to the new 'oddity detection' paradigm, and the resultant unified explanation of across and within modality cases provide further evidence to suggest that structure inference may be a commonly evolved principle for combining perceptual information in the brain.
format	article
author	Timothy Hospedales Sethu Vijayakumar
author_facet	Timothy Hospedales Sethu Vijayakumar
author_sort	Timothy Hospedales
title	Multisensory oddity detection as bayesian inference.
title_short	Multisensory oddity detection as bayesian inference.
title_full	Multisensory oddity detection as bayesian inference.
title_fullStr	Multisensory oddity detection as bayesian inference.
title_full_unstemmed	Multisensory oddity detection as bayesian inference.
title_sort	multisensory oddity detection as bayesian inference.
publisher	Public Library of Science (PLoS)
publishDate	2009
url	https://doaj.org/article/4b0eb27af8ee4b2080d2920ee527bc65
work_keys_str_mv	AT timothyhospedales multisensoryodditydetectionasbayesianinference AT sethuvijayakumar multisensoryodditydetectionasbayesianinference
_version_	1718413922824880128

Multisensory oddity detection as bayesian inference.

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