Proceedings of the Annual Meeting of the Cognitive Science Society

Causal explanations for specific events (token causes) are widespread in daily life, from finding causes of a patient's symptoms to understanding why a policy was effective. Despite the importance of token causality in a wide-range of real-world scenarios, most of what is known about how people attribute causality is based on simple physics or highly constrained study set-ups. It is not yet known how people attribute responsibility when faced with both observed and unobserved factors. To address this, we conducted two experiments to examine how beliefs influence causal attribution and what information people infer indirectly. In both experiments, we found that people treated causality as a fixed resource to be split between observed and other factors, and that outcome valence influenced both causal and counterfactual judgments. On the other hand, prior beliefs about actions did not predict causal attributions.