Semi-rational models of conditioning: The case of trial order

Nathaniel D. Daw, Aaron C. Courville, Peter Dayan

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This chapter considers the question of how learning adapts to changing environments, with particular reference to animal studies of operant and classical conditioning. It discusses a variety of probabilistic models, with different assumptions concerning the environment; and contrasts this type of model with a model by Kruschke (2006) which carries out local, approximate, Bayesian inference. It further suggests that it may be too early to incorporate mechanistic limitations into models of conditioning - enriching the understanding of the environment, and working with a 'pure' Bayesian rational analysis for that environment, may provide an alternative, and perhaps theoretically more elegant, way forward.

Original languageEnglish (US)
Title of host publicationThe Probabilistic Mind
Subtitle of host publicationProspects for Bayesian cognitive science
PublisherOxford University Press
ISBN (Electronic)9780191695971
ISBN (Print)9780199216093
DOIs
StatePublished - Mar 22 2012

Keywords

  • Animal studies
  • Bayesian inference
  • Conditioning
  • Environment
  • Kruschke
  • Learning

ASJC Scopus subject areas

  • Psychology(all)

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  • Cite this

    Daw, N. D., Courville, A. C., & Dayan, P. (2012). Semi-rational models of conditioning: The case of trial order. In The Probabilistic Mind: Prospects for Bayesian cognitive science Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199216093.003.0019