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A Bayesian sense of probability in the human brain Its characteristics neural...

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... ver más

31/01/2027

CEA

1M€

Presupuesto del proyecto: 1M€

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COMMISSARIAT A L ENERGIE ATOMIQUE ET AUX ENER... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

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Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2020-12-10

ERC-2020-STG: ERC STARTING GRANTS

I+D

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2 Participantes

CEA

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ABBA GAIA

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Duración del proyecto: 73 meses Fecha Inicio: 2020-12-10
Fecha Fin: 2027-01-31

Líder del proyecto

COMMISSARIAT A L ENERGIE ATOMIQUE ET AUX ENER... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 1M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto 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.

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