The interplay between covariation, temporal, and mechanism information in singular causation judgments

Abstract

Most psychological studies on causal cognition have focused on how people make predictions from causes to effects or how they assess causal strength for general causal relationships (e.g., “smoking causes cancer”). In the past years, there has been a surge of interest in other types of causal judgments, such as diagnostic inferences or causal selection. Our focus here is on how people assess singular causation relations between cause and effect events that occurred at a particular spatiotemporal location (e.g., “Mary’s having taking this pill caused her sickness.”). The analysis of singular causation has received much attention in philosophy, but relatively few psychological studies have investigated how lay people assess these relations. Based on the power PC model of causal attribution proposed by Cheng and Novick (2005), we have developed and tested a new computational model of singular causation judgments integrating covariation, temporal, and mechanism information. We provide an overview of this research and outline important questions for future research.