Maël P. Lebreton

Dissociating the functional role of the para-hippocampal and the parietal cortex in human multi-step reinforcement learning

This study identifies distinct roles of the parahippocampal and parietal cortex in multi-step reinforcement learning using fMRI data from 28 participants.

Metacognition and the effect of incentive motivation in two compulsive disorders: gambling disorder and obsessive-compulsive disorder

This study investigates metacognition and incentive motivation in gambling disorder and obsessive-compulsive disorder using fMRI, finding increased confidence in GD during gain contexts but no group differences in VMPFC activity.

A shared brain system forming confidence judgment across cognitive domains

The paper investigates whether confidence judgments are processed similarly across different cognitive domains using behavioral and neural data from two tasks.

How motivational signals disrupt metacognitive signals in the human VMPFC.

This study investigates how motivational signals affect metacognitive signals in the VMPFC during decision-making, finding that confidence signals are disrupted in trials with negative monetary prospects.

Investigating the origin and consequences of endogenous default options in repeated economic choices.

This study investigates endogenous default options in economic choices, revealing two distinct biases: natural preference for certain options and learned defaults from past choices.

When are inter-individual brain-behavior correlations informative?

The paper warns about spurious results in inter-individual brain-behavior correlations due to scaling issues in neuroimaging analyses.

Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences

GWAS in over one million individuals identify 611 loci across risk tolerance and risky behaviors, revealing shared genetic architecture and implicating glutamatergic and GABAergic neurotransmission.

How the level of reward awareness changes the computational and electrophysiological signatures of reinforcement learning

This study investigates how reward awareness affects reinforcement learning by combining computational modeling and EEG, showing that reduced reward visibility impairs learning but increases repetition of previous choices.

Optimistic reinforcement learning: computational and neural bases

The paper shows that people update beliefs more strongly with positive outcomes than negative ones in a learning task, linking this to enhanced prediction error signaling in reward circuits.