Analysis Code and Data for Whom Do We Prefer to Learn From in Observational Reinforcement Learning?

This repository provides the full experimental code, preprocessing scripts, modeling analysis, statistical analysis, and figure generation for the study:Whom Do We Prefer to Learn From in Observational Reinforcement Learning?We explored whether people prefer to learn from decision-makers with high or low exploration tendencies in reinforcement learning tasks. Most participants preferred low-noise (less exploratory) partners, and computational modeling revealed that this preference relates to learning styles: imitators prefer low-noise partners, while reward-learners prefer high-noise partners. All experimental methods and analysis pipelines were preregistered: https://osf.io/g6etf