1. Neural Mechanisms for Integrating Prior Knowledge and Likelihood in Value-Based Probabilistic Inference
- Author
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Chih-Chung Ting, Chia-Chen Yu, Laurence T. Maloney, and Shih Wei Wu
- Subjects
Adult ,Male ,Decision theory ,Decision Making ,Posterior probability ,Bayesian probability ,Machine learning ,computer.software_genre ,Young Adult ,Bayes' theorem ,Decision Theory ,Prior probability ,Image Processing, Computer-Assisted ,Humans ,Statistic ,Brain Mapping ,Likelihood Functions ,Bayes estimator ,Models, Statistical ,business.industry ,General Neuroscience ,Brain ,Bayes Theorem ,Articles ,Magnetic Resonance Imaging ,Oxygen ,Knowledge ,Logistic Models ,Female ,Artificial intelligence ,Psychology ,business ,Likelihood function ,Social psychology ,computer - 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.
- Published
- 2015