title: Neural mechanisms for integrating prior knowledge and likelihood in value-based probabilistic inference.
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.
authors: Institute of Neuroscience and.
authors: Chih-Chung
authors: CC
authors: Ting
authors: School of Medicine, Taipei Medical University, Taipei, 110 Taiwan, and.
authors: Chia-Chen
authors: CC
authors: Yu
authors: Department of Psychology, Center for Neural Science, and Institute for the Interdisciplinary Study of Decision Making, New York University, New York, New York 10003.
authors: Laurence T
authors: LT
authors: Maloney
authors: Institute of Neuroscience and Brain Research Center, National Yang-Ming University, Taipei, 112 Taiwan, Institute for the Interdisciplinary Study of Decision Making, New York University, New York, New York 10003 swwu@ym.edu.tw.
authors: Shih-Wei
authors: SW
authors: Wu
mesh_terms: D000328:Adult; D001499:Bayes Theorem; D001921:Brain / Q000098:blood supply / Q000502:physiology*; D001931:Brain Mapping; D003657:Decision Making / Q000502:physiology*; D003662:Decision Theory; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D019359:Knowledge; D016013:Likelihood Functions; D016015:Logistic Models; D008279:Magnetic Resonance Imaging; D008297:Male; D015233:Models, Statistical; D010100:Oxygen / Q000097:blood; D055815:Young Adult
chemical_list: D010100:Oxygen
keywords: Bayesian decision theory; Bayesian integration; decision making; judgment under uncertainty; medial prefrontal cortex
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