Proceedings of the Annual Meeting of the Cognitive Science Society

Can people learn causal relationships about the world from someone’s emotions? We present a computational model integrating observational causal learning with emotional information, which uses emotional displays to disambiguate the beliefs, desires, and knowledge of other agents, in turn allowing causal inferences about the world. We compared our model predictions to human causal judgements on two observational learning tasks involving multiple possible causes or multiple possible outcomes. Across three studies (N = 129,127,125), emotional displays (compared to actions alone) led people to interpret agents’ beliefs differently, which in some contexts resulted in different causal inferences. Our model closely reflected these patterns of belief and causal inference and revealed new insights on how people learn causal relationships from others’ emotions.