We are very happy to announce that Mael Lebreton has been awarded European Research Council starting grants (2021-2026) for his project “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 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.

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.