Context, Learning, and Extinction

Samuel J. Gershman, David M. Blei, Yael Niv

Research output: Contribution to journalArticlepeer-review

238 Scopus citations


A. Redish et al. (2007) proposed a reinforcement learning model of context-dependent learning and extinction in conditioning experiments, using the idea of "state classification" to categorize new observations into states. In the current article, the authors propose an interpretation of this idea in terms of normative statistical inference. They focus on renewal and latent inhibition, 2 conditioning paradigms in which contextual manipulations have been studied extensively, and show that online Bayesian inference within a model that assumes an unbounded number of latent causes can characterize a diverse set of behavioral results from such manipulations, some of which pose problems for the model of Redish et al. Moreover, in both paradigms, context dependence is absent in younger animals, or if hippocampal lesions are made prior to training. The authors suggest an explanation in terms of a restricted capacity to infer new causes.

Original languageEnglish (US)
Pages (from-to)197-209
Number of pages13
JournalPsychological Review
Issue number1
StatePublished - Jan 2010

All Science Journal Classification (ASJC) codes

  • General Psychology


  • Bayesian
  • classical conditioning
  • hippocampus
  • latent inhibition
  • renewal


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