An integrative study on adaptation of human perception based on Bayesian models

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The human brain faces many kinds of uncertainty in the outer world and the nervous systems. It has been known that we can deal with such uncertainty using information from directly observable quantities (called likelihood) and information that is known prior to the observation. How can we learn those information? In this study, we theoretically and experimentally investigate how the learning of those two factors relate to each other, and especially how the likelihood is learned, which is the problem that few studies has put focus on so far. We showed how the learning of the two factors differed, and showed that the likelihood is learned very effectively and flexibly.