Proceedings of the Annual Meeting of the Cognitive Science Society

We propose a theoretical framework for interpreting the roles of covariation and the idea of in psychological causal induction. According to this framework, the computation of inference is purely covariation-based, but covariation is computed only on a set of selected dimensions in a set of selected events. Whether or not a dimension has power or efficacy exerts an influence on whether or not that dimension is selected. W e present an experiment testing two predictions based on this framework. Our experiment showed a strong bias towards inferring a movement by a human agent (compared to a state) to be the cause of an event. In support of our hypothesis, this bias was found only when the state was not salient and the inference was made within a relatively short time, suggesting that the bias occurred at the selection stage.