Proceedings of the Annual Meeting of the Cognitive Science Society

How do people use temporal information to make causal judg-ments? A number of studies have investigated the role of timein inferring generative causal structure, while few have exam-ined prevention. Here, we focus on a challenging task in whichparticipants learn the structure of several causal “devices” bywatching the devices’ patterns of activation over time. Eachdevice potentially includes both generative (producing an acti-vation of its effect) and preventative (blocking any effect acti-vations within a short time window) causal relationships. Weexamine judgment patterns through the lens of a normativemodel which incorporates actual causation with considerationsof prevention. We contrast this with a more computationallytractable feature-based approximation. Participants’ perfor-mance was substantially above chance in all conditions. Themajority of participants’ causal judgments were best fit by thefeature-based approximation based on delay and count heuris-tic cues.