It’s not all about choices: the influence of response times on inferring other people’s social preferences
Humans are known to be capable of inferring hidden preferences and beliefs of their conspecifics when observing their decisions. While observational learning based on choices has been explored extensively, the question of how response times (RT) impact our learning of others’ social preferences has received little attention. Yet, there is only limited potential for inferring the strength of preference (i.e., the confidence with which the person has made their choice or how likely they are to make the same choice again) from choices alone, and RT can provide critical information in this regard. Here, we propose an orthogonal design to investigate the role of both choices and RT in learning and inferring others’ social preferences. In our lab study, participants (n = 46) observed other people’s decision process in a Dictator Game, where the dictators were asked to choose between different monetary allocations. Choice and RT information was either hidden or revealed to participants in a 2-by-2 within-subject design. Behavioral analyses confirmed our hypothesis: trial by trial, observers were able to learn the dictators' social preferences when they could observe their choices, but also when they could only observe their RT. To gain mechanistic insights into these observational learning processes, we developed a reinforcement learning model that takes both choices and RT into account to infer the dictator’s social preference. This model closely captured the performance and learning curves of observers in the different conditions. By comparing this model to a Bayes-optimal model, we show that while our participants’ learning is close-to optimal when they can observe choices, they substantially deviate from optimality when they can only observe RT, suggesting that the underlying mechanisms are better captured by our approximate reinforcement learning model. Overall, our study proposes an innovative approach to investigate the role of RT in learning and inferring preferences and highlights the importance of considering decision processes when investigating observational learning.