Project description
Is a human sense of probability Bayesian in nature?
Bayesian inference optimally estimates probabilities from limited and noisy data taking into account levels of uncertainty. The EU-funded NEURAL-PROB project proposes that the human sense of probability is Bayesian, based on the notion that human probability estimates are accompanied by rational confidence levels defining their precision. This Bayesian nature of the human sense of probability constrains the estimation, neural representation and use of probabilities. The researchers will build their theory by combining psychology, computational models and neuroimaging. Characterisation of the sense of probability will improve our understanding of how human brains represent our world with probabilistic internal models, the way they learn and make decisions.
Objective
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.
Fields of science (EuroSciVoc)
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CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
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Keywords
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Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
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Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC)
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(opens in new window) ERC-2020-STG
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75015 Paris
France
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