Comparing models of reinforcement learning in the laboratory and in real world settings

We will collect data in a reinforcement learning paradigm over varied temporal delivery programmes (hours to weeks) in the laboratory and in everyday life via stimulus delivery on participant's smartphones.

A number of common learning models will be tested to identify which model best fits temporally intensive versus temporally sparse stimulus delivery. Model outputs will be used to generate prediction of personality type (risk seeking vs. risk averse / high vs. low obsessive-compulsive (OC) tendencies).