Project description
Building a neuro-computational model of information integration
Behavioural, computational and neurobiological features of learning from singular experienced outcomes have been extensively studied. This is not the case with reinforced learning, which is defined by how we adaptively learn, by trial and error, and rewards and punishments. In this context, the ERC-funded INFORL project will explore how people prioritise, filter or value outcome information in reinforced learning. Its working hypothesis is that even though people can learn from multiple concurrent information samples, computational limitations and affective biases minimise information integration. Eye tracking and functional neuroimaging modalities will be used to construct a neuro-computational model of information integration. The findings will shed light on the learning process and the maladaptive traits of human behaviour.
Objective
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.
Fields of science (EuroSciVoc)
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.
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.
- natural sciences computer and information sciences artificial intelligence machine learning reinforcement learning
You need to log in or register to use this function
Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
-
H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC)
MAIN PROGRAMME
See all projects funded under this programme
Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
ERC-STG - Starting Grant
See all projects funded under this funding scheme
Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2020-STG
See all projects funded under this callHost institution
Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
75794 Paris
France
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.