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Challenges to the Modularity Thesis Under the Bayesian Brain Models

Modularity assumption is central to most theoretical and empirical approaches in cognitive science. The Bayesian Brain (BB) models are a class of neuro-computational models that aim to ground perception, cognition, and action under a single computational principle of prediction-error minimization. I...

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Autores principales: George, Nithin, Sunny, Meera Mary
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6796786/
https://www.ncbi.nlm.nih.gov/pubmed/31649518
http://dx.doi.org/10.3389/fnhum.2019.00353
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author George, Nithin
Sunny, Meera Mary
author_facet George, Nithin
Sunny, Meera Mary
author_sort George, Nithin
collection PubMed
description Modularity assumption is central to most theoretical and empirical approaches in cognitive science. The Bayesian Brain (BB) models are a class of neuro-computational models that aim to ground perception, cognition, and action under a single computational principle of prediction-error minimization. It is argued that the proposals of BB models contradict the modular nature of mind as the modularity assumption entails computational separation of individual modules. This review examines how BB models address the assumption of modularity. Empirical evidences of top-down influence on early sensory processes is often cited as a case against the modularity thesis. In the modularity thesis, such top-down effects are attributed to attentional modulation of the output of an early impenetrable stage of sensory processing. The attentional-mediation argument defends the modularity thesis. We analyse this argument using the novel conception of attention in the BB models. We attempt to reconcile classical bottom-up vs. top-down dichotomy of information processing, within the information passing scheme of the BB models. Theoretical considerations and empirical findings associated with BB models that address the modularity assumption is reviewed. Further, we examine the modularity of perceptual and motor systems.
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spelling pubmed-67967862019-10-24 Challenges to the Modularity Thesis Under the Bayesian Brain Models George, Nithin Sunny, Meera Mary Front Hum Neurosci Human Neuroscience Modularity assumption is central to most theoretical and empirical approaches in cognitive science. The Bayesian Brain (BB) models are a class of neuro-computational models that aim to ground perception, cognition, and action under a single computational principle of prediction-error minimization. It is argued that the proposals of BB models contradict the modular nature of mind as the modularity assumption entails computational separation of individual modules. This review examines how BB models address the assumption of modularity. Empirical evidences of top-down influence on early sensory processes is often cited as a case against the modularity thesis. In the modularity thesis, such top-down effects are attributed to attentional modulation of the output of an early impenetrable stage of sensory processing. The attentional-mediation argument defends the modularity thesis. We analyse this argument using the novel conception of attention in the BB models. We attempt to reconcile classical bottom-up vs. top-down dichotomy of information processing, within the information passing scheme of the BB models. Theoretical considerations and empirical findings associated with BB models that address the modularity assumption is reviewed. Further, we examine the modularity of perceptual and motor systems. Frontiers Media S.A. 2019-10-10 /pmc/articles/PMC6796786/ /pubmed/31649518 http://dx.doi.org/10.3389/fnhum.2019.00353 Text en Copyright © 2019 George and Sunny. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Human Neuroscience
George, Nithin
Sunny, Meera Mary
Challenges to the Modularity Thesis Under the Bayesian Brain Models
title Challenges to the Modularity Thesis Under the Bayesian Brain Models
title_full Challenges to the Modularity Thesis Under the Bayesian Brain Models
title_fullStr Challenges to the Modularity Thesis Under the Bayesian Brain Models
title_full_unstemmed Challenges to the Modularity Thesis Under the Bayesian Brain Models
title_short Challenges to the Modularity Thesis Under the Bayesian Brain Models
title_sort challenges to the modularity thesis under the bayesian brain models
topic Human Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6796786/
https://www.ncbi.nlm.nih.gov/pubmed/31649518
http://dx.doi.org/10.3389/fnhum.2019.00353
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