To integrate or not to integrate: Temporal dynamics of hierarchical Bayesian causal inference.

To form a percept of the environment, the brain needs to solve the binding problem-inferring whether signals come from a common cause and are integrated or come from independent causes and are segregated. Behaviourally, humans solve this problem near-optimally as predicted by Bayesian causal inferen...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Máté Aller, Uta Noppeney
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2019
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/cb9113431177494193834bee652b8874
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:cb9113431177494193834bee652b8874
record_format	dspace
spelling	oai:doaj.org-article:cb9113431177494193834bee652b88742021-12-02T19:54:41ZTo integrate or not to integrate: Temporal dynamics of hierarchical Bayesian causal inference.1544-91731545-788510.1371/journal.pbio.3000210https://doaj.org/article/cb9113431177494193834bee652b88742019-04-01T00:00:00Zhttps://doi.org/10.1371/journal.pbio.3000210https://doaj.org/toc/1544-9173https://doaj.org/toc/1545-7885To form a percept of the environment, the brain needs to solve the binding problem-inferring whether signals come from a common cause and are integrated or come from independent causes and are segregated. Behaviourally, humans solve this problem near-optimally as predicted by Bayesian causal inference; but the neural mechanisms remain unclear. Combining Bayesian modelling, electroencephalography (EEG), and multivariate decoding in an audiovisual spatial localisation task, we show that the brain accomplishes Bayesian causal inference by dynamically encoding multiple spatial estimates. Initially, auditory and visual signal locations are estimated independently; next, an estimate is formed that combines information from vision and audition. Yet, it is only from 200 ms onwards that the brain integrates audiovisual signals weighted by their bottom-up sensory reliabilities and top-down task relevance into spatial priority maps that guide behavioural responses. As predicted by Bayesian causal inference, these spatial priority maps take into account the brain's uncertainty about the world's causal structure and flexibly arbitrate between sensory integration and segregation. The dynamic evolution of perceptual estimates thus reflects the hierarchical nature of Bayesian causal inference, a statistical computation, which is crucial for effective interactions with the environment.Máté AllerUta NoppeneyPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Biology, Vol 17, Iss 4, p e3000210 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Máté Aller Uta Noppeney To integrate or not to integrate: Temporal dynamics of hierarchical Bayesian causal inference.
description	To form a percept of the environment, the brain needs to solve the binding problem-inferring whether signals come from a common cause and are integrated or come from independent causes and are segregated. Behaviourally, humans solve this problem near-optimally as predicted by Bayesian causal inference; but the neural mechanisms remain unclear. Combining Bayesian modelling, electroencephalography (EEG), and multivariate decoding in an audiovisual spatial localisation task, we show that the brain accomplishes Bayesian causal inference by dynamically encoding multiple spatial estimates. Initially, auditory and visual signal locations are estimated independently; next, an estimate is formed that combines information from vision and audition. Yet, it is only from 200 ms onwards that the brain integrates audiovisual signals weighted by their bottom-up sensory reliabilities and top-down task relevance into spatial priority maps that guide behavioural responses. As predicted by Bayesian causal inference, these spatial priority maps take into account the brain's uncertainty about the world's causal structure and flexibly arbitrate between sensory integration and segregation. The dynamic evolution of perceptual estimates thus reflects the hierarchical nature of Bayesian causal inference, a statistical computation, which is crucial for effective interactions with the environment.
format	article
author	Máté Aller Uta Noppeney
author_facet	Máté Aller Uta Noppeney
author_sort	Máté Aller
title	To integrate or not to integrate: Temporal dynamics of hierarchical Bayesian causal inference.
title_short	To integrate or not to integrate: Temporal dynamics of hierarchical Bayesian causal inference.
title_full	To integrate or not to integrate: Temporal dynamics of hierarchical Bayesian causal inference.
title_fullStr	To integrate or not to integrate: Temporal dynamics of hierarchical Bayesian causal inference.
title_full_unstemmed	To integrate or not to integrate: Temporal dynamics of hierarchical Bayesian causal inference.
title_sort	to integrate or not to integrate: temporal dynamics of hierarchical bayesian causal inference.
publisher	Public Library of Science (PLoS)
publishDate	2019
url	https://doaj.org/article/cb9113431177494193834bee652b8874
work_keys_str_mv	AT matealler tointegrateornottointegratetemporaldynamicsofhierarchicalbayesiancausalinference AT utanoppeney tointegrateornottointegratetemporaldynamicsofhierarchicalbayesiancausalinference
_version_	1718375927735386112

To integrate or not to integrate: Temporal dynamics of hierarchical Bayesian causal inference.

Ejemplares similares