Proceedings of the Annual Meeting of the Cognitive Science Society

Queries about singular causation face two problems: It needsto be decided whether the two observed events are instanti-ations of a generic cause-effect relation. Second, causationneeds to be distinguished from coincidence. We propose acomputational model that addresses both questions. It accessesgeneric causal knowledge either on the individual or the grouplevel. Moreover, the model considers the possibility of a co-incidence by adopting Cheng and Novick’s (2005) power PCmeasure of causal responsibility. This measure delivers theconditional probability that a cause is causally responsible foran effect given that both events have occurred. To take uncer-tainty about both the causal structure and the parameters intoaccount we embedded the causal responsibility measure withinthe structure induction (SI) model developed by Meder et al.(2014). We report the results of three experiments that showthat the SI model better captures the data than the power PCmodel.