Acta Biotheoretica

, Volume 58, Issue 2–3, pp 191–216 | Cite as

Common Bayesian Models for Common Cognitive Issues

  • Francis ColasEmail author
  • Julien Diard
  • Pierre Bessière
Regular Article


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.


Mixture Model Bayesian Network Bayesian Model Conditional Independence Hide Variable 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.ASL, ETH ZurichZurichSwitzerland
  2. 2.LPNC, CNRS, UPMF, Bâtiment Sciences de l’Homme et MathématiqueGrenoble Cedex 9France
  3. 3.E-Motion, LIG, CNRSMontbonnot Saint-IsmierFrance

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