Whom do we prefer to learn from in observational reinforcement learning?

Learning by observing others' experiences is a hallmark of human intelligence. While the neurocomputational mechanisms underlying observational learning are well understood, less is known about whom people prefer to learn from in the context of observational learning. One hypothesis posits that learners prefer individuals who exhibit a high degree of decision noise, 'free riding' on the costly exploration of others. An alternative hypothesis is that learners prefer individuals with low decision noise, as lower decision noise is often associated with better performance. In a preregistered experiment, we found that most participants preferred to learn from low-noise (high-performing) individuals. Furthermore, exploratory analyses revealed that participants who preferred low-noise individuals tended to rely on imitation of others' actions. These findings offer a potential computational account of how learning styles are related to partner selection in social learning.