Stefano PALMINTERI

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.