Stefano Palminteri et son équipe étudient les biais de l’apprentissage humain et de la prise de décision. Plus précisément, ils déchiffrent les mécanismes cognitifs et les bases neuronales de ces biais cognitifs en combinant psychologie expérimentale, modélisation mathématique et imagerie cérébrale.
My goal is to understand how humans learn to make decisions at the behavioral, computational and neural levels. I am mainly (but not only) interested in situations where decisions are based on past experience (i.e., reinforcement learning). My modus operandi is to modify models of reinforcement learning so that they can account for human behavior (in other words, although my job is to build and test formal models, I still define myself as an experimenter).
Over the past few years, I have primarily investigated two research hypotheses regarding human reinforcement learning:
1. value is learned on a relative scale
2. value is learned in a biased manner
In addition to extending the “relative value” and “learning bias” frameworks, new lines of research in my team are studying social learning and the experience-description gap.
In my spare time, I enjoy questioning the epistemological and methodological foundations of decision research and neuroeconomics.
2012: PhD, Pierre and Marie Curie University, Paris, France
2012-2013: Post-doctorate, Ecole Normale Supérieure, Paris, France, Giorgio Coricelli's team
2014-2015 : Post-doctoral fellow, University College London, Great Britain, Sarah-Jayne Blakemore's team
2016 : Post-doctoral fellow, Ecole Normale Supérieure, Paris, France, Etienne Koechlin's team
2017: appointed Team Leader, Ecole Normale Supérieure, Paris, France
2018 : appointed Research Officer, Ecole Normale Supérieure, Paris, France
2017: winner of the Emergence(s) program of the City of Paris
2017: Winner of the ATIP-Avenir program
2018: winner of the Fyssen Foundation
2019, Nat Hum Behav