ERC Starting Grant · 2020
A Bayesian sense of probability in the human brain: Its characteristics, neural bases and functions
Bayesian inference optimally estimates probabilities from limited and noisy data by taking into account levels of uncertainty. I noticed that human probability estimates are accompanied by rational confidence levels denoting their precision; I thus propose here that the human sense of probability is Bayesian. This Bayesian nature constrains the estimation, neural representation and use of probabilities, which I aim to characterize by combining psychology, computational models and neuro-imaging. I will characterize the Bayesian sense of probability computationally and psychologically. Human confidence as Bayesian precision will be my starting point, I will test other formalizations and look…
From the public funding record at EU CORDIS. Describes the funded project, not the reviews below.
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