CORDIS Project
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This project explores the Bayesian nature of human probability estimation, investigating how confidence levels influence decision-making. By integrating psychology, computational modeling, and neuroimaging, it aims to uncover the neural codes behind probabilistic reasoning.
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…
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