ABSTRACT
Belief updating—the process by which an agent alters an internal model of its environment—is a core function of the central nervous system. Recent theory has proposed broad principles by which belief updating might operate, but more precise details of its implementation in the human brain remain unclear. In order to address this question, we studied how two components of the human event-related potential encoded different aspects of belief updating. Participants completed a novel perceptual learning task while electroencephalography was recorded. Participants learned the mapping between the contrast of a dynamic visual stimulus and a monetary reward, and updated their beliefs about a target contrast on each trial. A Bayesian computational model was formulated to estimate belief states at each trial and used to quantify two variables: belief update size and belief uncertainty. Robust single-trial regression was used to assess how these model-derived variables were related to the amplitudes of the P3 and the stimulus-preceding negativity (SPN), respectively. Results showed a positive relationship between belief update size and P3 amplitude at one fronto-central electrode, and a negative relationship between SPN amplitude and belief uncertainty at a left central and a right parietal electrode. These results provide evidence that belief update size and belief uncertainty have distinct neural signatures that can be tracked in single trials in specific ERP components. This, in turn, provides evidence that the cognitive mechanisms underlying belief updating in humans can be described well within a Bayesian framework.
Significance Statement: Recent theories propose that a central function of the brain is belief updating, the process by which internal models of the environment are revised. However, despite strong implications for cognition, the neural correlates of belief updating remain poorly understood. This study combined computational modeling with analysis of the event-related potential (ERP) to investigate neural signals which systematically reflect belief updating in each trial. We found that two ERP components, the P3 and the SPN, respectively encoded belief update size and belief uncertainty. Our results shed light on the implementation of belief updating in the brain, and further demonstrate that computational modelling of cognition in ERP research can account for variability in neural signals which has often been dismissed as noise.
Footnotes
↵1 The authors declare no competing financial interests.
↵3 This work was funded by a Strategic Initiatives Fund grant from the Faculty of Business and Economics at The University of Melbourne to C.M. and S.B., and an Australian Research Council (ARC) Discovery Early Career Researcher Award (DE 140100350) to S.B.
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