Modeling the dynamics of social exchange in groups with reinforcement learning and Theory of Mind
Accurately attributing belief to others is a milestone of social cognition and serves as an indicator of intact cognitive functioning. However, tracking all others beliefs in complex social interactions is often inefficient or even impossible. We propose that in such cases, attributing ones own belief to others (i.e., egocentric belief attribution) may serve as a functional approximation, enabling individuals to interact with others successfully. In this study, we employed computational modeling with behavioral measures to investigate this approach in social exchange in a novel group decision-making task across three experiments. We found that, beyond the well-documented reciprocity, participants exhibited consistent alternating behavior, characterized by the switching between potential recipients. This alternation was not driven by fairness concerns but reflected a strategic balance between maintaining stable partnerships and exploring alternatives. Crucially, a reinforcement learning model incorporating egocentric belief attribution (eToM) consistently outperformed all alternative models. These findings highlight that egocentric belief attribution is not a cognitive deficit, but a core mechanism supporting adaptive decision-making in complex social environments.