Neural Mechanisms for Integrating Prior Knowledge and Likelihood in Value-Based Probabilistic Inference

Chih Chung Ting, Shih Wei Wu, Shih Wei Wu, Chia Chen Yu, Laurence T. Maloney, Laurence T. Maloney, Laurence T. Maloney

Research output: Contribution to journalArticle

Abstract

In Bayesian decision theory, knowledge about the probabilities of possible outcomes is captured by a prior distribution and a likelihood function. The prior reflects past knowledge and the likelihood summarizes current sensory information. The two combined (integrated) form a posterior distribution that allows estimation of the probability of different possible outcomes. In this study, we investigated the neural mechanisms underlying Bayesian integration using a novel lottery decision task in which both prior knowledge and likelihood information about reward probability were systematically manipulated on a trial-by-trial basis. Consistent with Bayesian integration, as sample size increased, subjects tended to weigh likelihood information more compared with prior information. Using fMRI in humans, we found that the medial prefrontal cortex (mPFC) correlated with the mean of the posterior distribution, a statistic that reflects the integration of prior knowledge and likelihood of reward probability. Subsequent analysis revealed that both prior and likelihood information were represented in mPFC and that the neural representations of prior and likelihood in mPFC reflected changes in the behaviorally estimated weights assigned to these different sources of information in response to changes in the environment. Together, these results establish the role of mPFC in prior-likelihood integration and highlight its involvement in representing and integrating these distinct sources of information.

Original languageEnglish (US)
Pages (from-to)1792-1805
Number of pages14
JournalJournal of Neuroscience
Volume35
Issue number4
DOIs
StatePublished - Jan 28 2015

Fingerprint

Prefrontal Cortex
Reward
Decision Theory
Likelihood Functions
Sample Size
Magnetic Resonance Imaging
Weights and Measures

Keywords

  • Bayesian decision theory
  • Bayesian integration
  • Decision making
  • Judgment under uncertainty
  • Medial prefrontal cortex

ASJC Scopus subject areas

  • Neuroscience(all)

Cite this

Neural Mechanisms for Integrating Prior Knowledge and Likelihood in Value-Based Probabilistic Inference. / Ting, Chih Chung; Wu, Shih Wei; Wu, Shih Wei; Yu, Chia Chen; Maloney, Laurence T.; Maloney, Laurence T.; Maloney, Laurence T.

In: Journal of Neuroscience, Vol. 35, No. 4, 28.01.2015, p. 1792-1805.

Research output: Contribution to journalArticle

Ting, Chih Chung ; Wu, Shih Wei ; Wu, Shih Wei ; Yu, Chia Chen ; Maloney, Laurence T. ; Maloney, Laurence T. ; Maloney, Laurence T. / Neural Mechanisms for Integrating Prior Knowledge and Likelihood in Value-Based Probabilistic Inference. In: Journal of Neuroscience. 2015 ; Vol. 35, No. 4. pp. 1792-1805.
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