Cortical hierarchies perform Bayesian causal inference in multisensory perception.

To form a veridical percept of the environment, the brain needs to integrate sensory signals from a common source but segregate those from independent sources. Thus, perception inherently relies on solving the "causal inference problem." Behaviorally, humans solve this problem optimally as...

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Autores principales:	Tim Rohe, Uta Noppeney
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2015
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/91512fc0ec5e487ca92cc9228a95e6a3
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id	oai:doaj.org-article:91512fc0ec5e487ca92cc9228a95e6a3
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spelling	oai:doaj.org-article:91512fc0ec5e487ca92cc9228a95e6a32021-12-02T19:54:41ZCortical hierarchies perform Bayesian causal inference in multisensory perception.1544-91731545-788510.1371/journal.pbio.1002073https://doaj.org/article/91512fc0ec5e487ca92cc9228a95e6a32015-02-01T00:00:00Zhttps://doi.org/10.1371/journal.pbio.1002073https://doaj.org/toc/1544-9173https://doaj.org/toc/1545-7885To form a veridical percept of the environment, the brain needs to integrate sensory signals from a common source but segregate those from independent sources. Thus, perception inherently relies on solving the "causal inference problem." Behaviorally, humans solve this problem optimally as predicted by Bayesian Causal Inference; yet, the underlying neural mechanisms are unexplored. Combining psychophysics, Bayesian modeling, functional magnetic resonance imaging (fMRI), and multivariate decoding in an audiovisual spatial localization task, we demonstrate that Bayesian Causal Inference is performed by a hierarchy of multisensory processes in the human brain. At the bottom of the hierarchy, in auditory and visual areas, location is represented on the basis that the two signals are generated by independent sources (= segregation). At the next stage, in posterior intraparietal sulcus, location is estimated under the assumption that the two signals are from a common source (= forced fusion). Only at the top of the hierarchy, in anterior intraparietal sulcus, the uncertainty about the causal structure of the world is taken into account and sensory signals are combined as predicted by Bayesian Causal Inference. Characterizing the computational operations of signal interactions reveals the hierarchical nature of multisensory perception in human neocortex. It unravels how the brain accomplishes Bayesian Causal Inference, a statistical computation fundamental for perception and cognition. Our results demonstrate how the brain combines information in the face of uncertainty about the underlying causal structure of the world.Tim RoheUta NoppeneyPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Biology, Vol 13, Iss 2, p e1002073 (2015)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Tim Rohe Uta Noppeney Cortical hierarchies perform Bayesian causal inference in multisensory perception.
description	To form a veridical percept of the environment, the brain needs to integrate sensory signals from a common source but segregate those from independent sources. Thus, perception inherently relies on solving the "causal inference problem." Behaviorally, humans solve this problem optimally as predicted by Bayesian Causal Inference; yet, the underlying neural mechanisms are unexplored. Combining psychophysics, Bayesian modeling, functional magnetic resonance imaging (fMRI), and multivariate decoding in an audiovisual spatial localization task, we demonstrate that Bayesian Causal Inference is performed by a hierarchy of multisensory processes in the human brain. At the bottom of the hierarchy, in auditory and visual areas, location is represented on the basis that the two signals are generated by independent sources (= segregation). At the next stage, in posterior intraparietal sulcus, location is estimated under the assumption that the two signals are from a common source (= forced fusion). Only at the top of the hierarchy, in anterior intraparietal sulcus, the uncertainty about the causal structure of the world is taken into account and sensory signals are combined as predicted by Bayesian Causal Inference. Characterizing the computational operations of signal interactions reveals the hierarchical nature of multisensory perception in human neocortex. It unravels how the brain accomplishes Bayesian Causal Inference, a statistical computation fundamental for perception and cognition. Our results demonstrate how the brain combines information in the face of uncertainty about the underlying causal structure of the world.
format	article
author	Tim Rohe Uta Noppeney
author_facet	Tim Rohe Uta Noppeney
author_sort	Tim Rohe
title	Cortical hierarchies perform Bayesian causal inference in multisensory perception.
title_short	Cortical hierarchies perform Bayesian causal inference in multisensory perception.
title_full	Cortical hierarchies perform Bayesian causal inference in multisensory perception.
title_fullStr	Cortical hierarchies perform Bayesian causal inference in multisensory perception.
title_full_unstemmed	Cortical hierarchies perform Bayesian causal inference in multisensory perception.
title_sort	cortical hierarchies perform bayesian causal inference in multisensory perception.
publisher	Public Library of Science (PLoS)
publishDate	2015
url	https://doaj.org/article/91512fc0ec5e487ca92cc9228a95e6a3
work_keys_str_mv	AT timrohe corticalhierarchiesperformbayesiancausalinferenceinmultisensoryperception AT utanoppeney corticalhierarchiesperformbayesiancausalinferenceinmultisensoryperception
_version_	1718375914296836096

Cortical hierarchies perform Bayesian causal inference in multisensory perception.

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