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.
Document type :
Journal articles
Acta Biotheoretica, Springer Verlag, 2010, 58 (2-3), pp.191-216. 〈10.1007/s10441-010-9101-1〉
Liste complète des métadonnées

Cited literature [69 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-00530356
Contributor : Julien Diard <>
Submitted on : Thursday, October 28, 2010 - 4:21:25 PM
Last modification on : Monday, February 28, 2011 - 3:18:13 PM
Document(s) archivé(s) le : Friday, October 26, 2012 - 12:31:25 PM

File

colas10_hal_.pdf
Files produced by the author(s)

Identifiers

Collections

INRIA | LIG | UGA | LPNC

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〉

Share

Metrics

Record views

503

Document downloads

274