Stefano Palminteri

Research details

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.