I am head of the Human Reinforcement Learning team, which is part of the Laboratoire de Neurosciences Cognitives et Computationelles (Institut National de la Santé et de la Recherche Médicale & École Normale Supérieure, Paris) . I am current a visiting scientist at the Centre for Cognition & Decision Making (Higher School of Economics, Moscow) and editorial board member of Communication Biology, PLoS Computational Biology and Computational Psychiatry.
My goal is understanding how humans learn to make decisions at the behavioral, computational and neural levels. I am mainly (but not only!) interested in situations when decisions are based on past experience (a.k.a., reinforcement learning) . My modus operandi consists in modifying reinforcement learning models, so that they can account for human behavior (in other terms, although my work involves building and testing formal models, I still define myself as an experimentalist). In the last few years I mainly investigated two research hypotheses concerning human reinforcement learning: 1. value is learned in a relative scale; 2. value is learned in a biased manner. In addition to extending the "relative value" and the "learning bias" frameworks, new lines of research in my team investigate social learning and the experience/description gap. In my spare time, I enjoy questioning the epistemological and methodological foundations of decision-making research and neuroeconomics.