Journal
PSYCHOLOGICAL REVIEW
Volume 117, Issue 1, Pages 197-209Publisher
AMER PSYCHOLOGICAL ASSOC
DOI: 10.1037/a0017808
Keywords
classical conditioning; renewal; latent inhibition; Bayesian; hippocampus
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Funding
- Microsoft Research
- NATIONAL INSTITUTE ON DRUG ABUSE [T90DA022770] Funding Source: NIH RePORTER
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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.
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