Common bayesian models for common cognitive issues

Abstract : How can an incomplete and uncertain model of the environment be used to perceive, infer, decide and act efficiently? This is the challenge that both living and artificial cognitive systems have to face. Symbolic logic is, by its nature, unable to deal with this question. The subjectivist approach to probability is an extension to logic that is designed specifically to face this challenge. In this paper, we review a number of frequently encountered cognitive issues and cast them into a common Bayesian formalism. The concepts we review are ambiguities, fusion, multimodality, conflicts, modularity, hierarchies and loops. First, each of these concepts is introduced briefly using some examples from the neuroscience, psychophysics or robotics literature. Then, the concept is formalized using a template Bayesian model. The assumptions and common features of these models, as well as their major differences, are outlined and discussed.
Type de document :
Article dans une revue
Acta Biotheoretica, Springer Verlag, 2010, 58 (2-3), pp.191-216. 〈10.1007/s10441-010-9101-1〉
Liste complète des métadonnées

Littérature citée [69 références]  Voir  Masquer  Télécharger

https://hal.archives-ouvertes.fr/hal-00530356
Contributeur : Julien Diard <>
Soumis le : jeudi 28 octobre 2010 - 16:21:25
Dernière modification le : mercredi 17 janvier 2018 - 10:44:41
Document(s) archivé(s) le : vendredi 26 octobre 2012 - 12:31:25

Fichier

colas10_hal_.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

Citation

Francis Colas, Julien Diard, Pierre Bessiere. Common bayesian models for common cognitive issues. Acta Biotheoretica, Springer Verlag, 2010, 58 (2-3), pp.191-216. 〈10.1007/s10441-010-9101-1〉. 〈hal-00530356〉

Partager

Métriques

Consultations de la notice

529

Téléchargements de fichiers

306