You are here : Home > Funding & TTO > Europe > NEURAL-PROB

Europe | Brain

A Bayesian sense of probability in the human brain: its characteristics, neural bases and functions


With the Neural-PROB project, Florent Meyniel seeks to understand how the human brain estimates and uses uncertainty and the impact of these mechanisms on our ability to adapt to change in an uncertain world. Part of the project aims to determine the factors that influence our adaptability, particularly in learning situations.

European funding: European Research Council (ERC) Starting Grant

Call ERC-2020-STG

Published on 8 September 2020

Presentation (ERC abstract)

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 for the human algorithms that approximate Bayesian inference. I will test whether confidence depends on explicit reasoning (with implicit electrophysiological measures), develop ways of measuring its accuracy in a learning context, test whether it is trainable and domain-general.

I will then look for the neural codes of Bayesian probabilities, leveraging encoding models for functional magnetic resonance imaging (fMRI) and goal-driven artificial neural networks to propose new codes. I will ask whether the confidence information is embedded in the neural representation of the probability estimate itself, or separable.

Last, I will investigate a key function of confidence: the regulation of learning. I will test the implication of neuromodulators such as noradrenaline in this process, using both within and between-subject variability in the activity of key neuromodulatory nuclei (with advanced fMRI), the cortical release of noradrenaline during learning and its receptor density (with positron-emission tomography) and test for causality with pharmacological intervention.

Characterizing the sense of probability has broad implications: it should improve our understanding of the way we represent our world with probabilistic internal models, the way we learn and make decisions.


5 years
EU contribution

€ 1 499 963 millions

Start date​

01 February 2021

Researcher: Florent Meyniel


Grant agreement ID: 948105


The Starting Grants and Consolidator Grants aim to support talented, leading or emerging researchers who wish to establish their own research team and conduct independent research in Europe. This grant targets promising researchers who have demonstrated their potential to become independent research leaders. It supports the creation of new research teams of excellence.

These grants are intended for researchers of any nationality with 2-7 years (Starting Grants) or 7-12 years (Consolidator Grants) of experience since obtaining their PhD (or equivalent degree) with a very promising scientific track record.