Cargando…

A Goal-Directed Bayesian Framework for Categorization

Categorization is a fundamental ability for efficient behavioral control. It allows organisms to remember the correct responses to categorical cues and not for every stimulus encountered (hence eluding computational cost or complexity), and to generalize appropriate responses to novel stimuli depend...

Descripción completa

Detalles Bibliográficos
Autores principales: Rigoli, Francesco, Pezzulo, Giovanni, Dolan, Raymond, Friston, Karl
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5360703/
https://www.ncbi.nlm.nih.gov/pubmed/28382008
http://dx.doi.org/10.3389/fpsyg.2017.00408
_version_ 1782516633472860160
author Rigoli, Francesco
Pezzulo, Giovanni
Dolan, Raymond
Friston, Karl
author_facet Rigoli, Francesco
Pezzulo, Giovanni
Dolan, Raymond
Friston, Karl
author_sort Rigoli, Francesco
collection PubMed
description Categorization is a fundamental ability for efficient behavioral control. It allows organisms to remember the correct responses to categorical cues and not for every stimulus encountered (hence eluding computational cost or complexity), and to generalize appropriate responses to novel stimuli dependant on category assignment. Assuming the brain performs Bayesian inference, based on a generative model of the external world and future goals, we propose a computational model of categorization in which important properties emerge. These properties comprise the ability to infer latent causes of sensory experience, a hierarchical organization of latent causes, and an explicit inclusion of context and action representations. Crucially, these aspects derive from considering the environmental statistics that are relevant to achieve goals, and from the fundamental Bayesian principle that any generative model should be preferred over alternative models based on an accuracy-complexity trade-off. Our account is a step toward elucidating computational principles of categorization and its role within the Bayesian brain hypothesis.
format Online
Article
Text
id pubmed-5360703
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-53607032017-04-05 A Goal-Directed Bayesian Framework for Categorization Rigoli, Francesco Pezzulo, Giovanni Dolan, Raymond Friston, Karl Front Psychol Psychology Categorization is a fundamental ability for efficient behavioral control. It allows organisms to remember the correct responses to categorical cues and not for every stimulus encountered (hence eluding computational cost or complexity), and to generalize appropriate responses to novel stimuli dependant on category assignment. Assuming the brain performs Bayesian inference, based on a generative model of the external world and future goals, we propose a computational model of categorization in which important properties emerge. These properties comprise the ability to infer latent causes of sensory experience, a hierarchical organization of latent causes, and an explicit inclusion of context and action representations. Crucially, these aspects derive from considering the environmental statistics that are relevant to achieve goals, and from the fundamental Bayesian principle that any generative model should be preferred over alternative models based on an accuracy-complexity trade-off. Our account is a step toward elucidating computational principles of categorization and its role within the Bayesian brain hypothesis. Frontiers Media S.A. 2017-03-22 /pmc/articles/PMC5360703/ /pubmed/28382008 http://dx.doi.org/10.3389/fpsyg.2017.00408 Text en Copyright © 2017 Rigoli, Pezzulo, Dolan and Friston. 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) or licensor 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 Psychology
Rigoli, Francesco
Pezzulo, Giovanni
Dolan, Raymond
Friston, Karl
A Goal-Directed Bayesian Framework for Categorization
title A Goal-Directed Bayesian Framework for Categorization
title_full A Goal-Directed Bayesian Framework for Categorization
title_fullStr A Goal-Directed Bayesian Framework for Categorization
title_full_unstemmed A Goal-Directed Bayesian Framework for Categorization
title_short A Goal-Directed Bayesian Framework for Categorization
title_sort goal-directed bayesian framework for categorization
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5360703/
https://www.ncbi.nlm.nih.gov/pubmed/28382008
http://dx.doi.org/10.3389/fpsyg.2017.00408
work_keys_str_mv AT rigolifrancesco agoaldirectedbayesianframeworkforcategorization
AT pezzulogiovanni agoaldirectedbayesianframeworkforcategorization
AT dolanraymond agoaldirectedbayesianframeworkforcategorization
AT fristonkarl agoaldirectedbayesianframeworkforcategorization
AT rigolifrancesco goaldirectedbayesianframeworkforcategorization
AT pezzulogiovanni goaldirectedbayesianframeworkforcategorization
AT dolanraymond goaldirectedbayesianframeworkforcategorization
AT fristonkarl goaldirectedbayesianframeworkforcategorization