Bayesian models: The structure of the world, uncertainty, behavior, and the brain

Iris Vilares, Konrad Kording

Research output: Contribution to journalReview articlepeer-review

88 Scopus citations

Abstract

Experiments on humans and other animals have shown that uncertainty due to unreliable or incomplete information affects behavior. Recent studies have formalized uncertainty and asked which behaviors would minimize its effect. This formalization results in a wide range of Bayesian models that derive from assumptions about the world, and it often seems unclear how these models relate to one another. In this review, we use the concept of graphical models to analyze differences and commonalities across Bayesian approaches to the modeling of behavioral and neural data. We review behavioral and neural data associated with each type of Bayesian model and explain how these models can be related. We finish with an overview of different theories that propose possible ways in which the brain can represent uncertainty.

Original languageEnglish (US)
Pages (from-to)22-39
Number of pages18
JournalAnnals of the New York Academy of Sciences
Volume1224
Issue number1
DOIs
StatePublished - Apr 2011

Keywords

  • Bayesian models
  • Graphical models
  • Neural representations
  • Psychophysics
  • Uncertainty

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