How People Learn to Decide: Individual Differences in Reinforcement Learning in Large Community Samples

People differ in how they integrate new information, update expectations, and translate experience into choices. This project will use a reinforcement-learning paradigm and computational modeling to study these individual differences in large community samples.

We will estimate participant-level characteristics of learning and decision formation, potentially including sensitivity to positive and negative outcomes, learning rates, exploration versus exploitation, and consistency of choice. We will then examine how these information-processing characteristics relate to demographic, socioeconomic, cognitive, and mental-health measures, as well as real life decisions. The broader goal is to identify interpretable dimensions of learning that explain meaningful differences in decision making across individuals—differences that may be obscured when analyses focus only on average behavior.

The research assistants will participate in the full empirical workflow: reviewing relevant behavioral-economics and computational-learning literature; documenting the experimental paradigm and measures; cleaning and quality-checking behavioral data; implementing and validating reinforcement-learning models; conducting individual-difference analyses; and producing reproducible tables, figures, and written summaries for a manuscript.

The project is designed for two research assistants with complementary responsibilities. One student will focus primarily on computational modeling and model validation, while the other will focus on organizing the individual-difference measures and analyzing their relationships with learning parameters. Both students will be cross-trained and will contribute to shared, publication-oriented deliverables.