Proceedings of the Annual Meeting of the Cognitive Science Society

Everyday reasoning about others involves accounting for why they act the way they do. With many explanations for someone's behavior, how do observers choose the best one? A large body of work in social psychology suggests that people's explanations rely heavily on traits rather than external factors. Recent results have called this into question, arguing that people balance traits, mental states, and situation to make sense of others' actions. How might they achieve this? In the current work, we hypothesize that people rely on counterfactual simulation to weigh different explanations for others' behavior. We propose a computational model of this process that makes concrete predictions about when people will prefer to explain events based on the actor's traits or their situation. We test the predictions of this model in an experimental paradigm in which trait and situation each guide behavior to varying degrees. Our model predicts people's causal judgments well overall but is less accurate for trait explanations than situational explanations. In a comparison with simpler causal heuristics, a majority of participants were better predicted by the counterfactual model. These results point the way toward a more comprehensive understanding of how social reasoning is performed within the context of domain-general causal inference.