Context-dependent decision-making: a simple Bayesian model

Kevin Lloyd, David S Leslie

Research output: Contribution to journalArticle (Academic Journal)

16 Citations (Scopus)

Abstract

Many phenomena in animal learning can be explained by a context-learning process whereby an animal learns about different patterns of relationship between environmental variables. Differentiating between such environmental regimes or ‘contexts’ allows an animal to rapidly adapt its behaviour when context changes occur. The current work views animals as making sequential inferences about current context identity in a world assumed to be relatively stable but also capable of rapid switches to previously observed or entirely new contexts. We describe a novel decision-making model in which contexts are assumed to follow a Chinese restaurant process with inertia and full Bayesian inference is approximated by a sequential-sampling scheme in which only a single hypothesis about current context is maintained. Actions are selected via Thompson sampling, allowing uncertainty in parameters to drive exploration in a straightforward manner. The model is tested on simple two-alternative choice problems with switching reinforcement schedules and the results compared with rat behavioural data from a number of T-maze studies. The model successfully replicates a number of important behavioural effects: spontaneous recovery, the effect of partial reinforcement on extinction and reversal, the overtraining reversal effect, and serial reversal-learning effects.
Original languageEnglish
Article number20130069
JournalJournal of the Royal Society Interface
Volume10
Issue number82
DOIs
Publication statusPublished - 20 Feb 2013

Keywords

  • Bayesian decision-making
  • spontaneous recovery
  • reversal learning
  • Chinese restaurant process
  • Thompson sampling

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    Making decisions in an unstable world

    Gilchrist, I. D., Ludwig, C. J. H. & Leslie, D. S.

    1/10/1129/02/16

    Project: Research, Parent

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