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Characterizing information integration in reinforcement learning a neuro compu...

Characterizing information integration in reinforcement learning a neuro computational investigation Reinforcement learning (RL) characterizes how we adaptively learn, by trial and errors, to select actions that maximize the occurrence of rewards, and minimize the occurrence of punishments. While the behavioural, computational an... ver más

31/08/2026

PARIS SCHOOL OF EC...

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

ECOLE DECONOMIE DE PARIS No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 3 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2021-02-17

ERC-2020-STG: ERC STARTING GRANTS

I+D

Cerrada hace 5 años

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3 Participantes

PARIS SCHOOL OF ECONOMICS E...

1.27M€ | Lider

JEMA ENERGY

353.64K€ | Participante

UNIGE

227.80K€ | Participante

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Duración del proyecto: 66 meses Fecha Inicio: 2021-02-17
Fecha Fin: 2026-08-31

Líder del proyecto

ECOLE DECONOMIE DE PARIS No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 2M€

Descripción del proyecto Reinforcement learning (RL) characterizes how we adaptively learn, by trial and errors, to select actions that maximize the occurrence of rewards, and minimize the occurrence of punishments. While the behavioural, computational and neurobiological features of learning from singular experienced outcomes have been extensively studied, the mechanisms by which RL could leverage multiple, concurrent information samples – including abstract information about prospective outcomes – have been largely overlooked. As a consequence, little is known about how we prioritize, filter or value outcome information in RL, while these processes likely critically contribute to shaping learning behaviour. This project proposes to address this gap, and hypothesizes that humans can learn from multiple concurrent information samples, but that computational limitations and affective biases curb information integration. First, using a new experimental and computational framework, I will evidence and quantify these cognitive features. Using eye-tracking and complementary functional neuroimaging modalities (EEG, fMRI), I will build a neuro-computational model of information integration, by deciphering the interactions between attentional parieto-frontal network and the affective ventro-limbic networks during reinforcement learning. Then, I propose to investigate the strategic modulation of information integration, by investigating the effects of information quantity and quality on learning strategies and on the neural correlates of learning variables. Finally, I will assess several behavioural interventions to ameliorate information integration and improve learning performance. By investigating an overlooked aspect of reinforcement learning –the integration of available information–, this project could not only help refine computational and neurobiological models of the learning process, but also shed new lights on maladaptive traits of human behaviour in social and clinical contexts.

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