Abstract

The majority of psychological studies on causality have focused on simple cause-effect relations. Little is known about how people approach more realistic, complex causal networks. Two experiments are presented that investigate how participants integrate causal knowledge that was acquired in separate learning tasks into a coherent causal model. To accomplish this task it is necessary to bring to bear knowledge about the structural implications of causal models. For example, whereas common-cause models imply a covariation among the different effects of a common cause, no such covariation between the different causes of a joint effect is implied by a common-effect model. The experiments show that participants have virtually no explicit knowledge of these relations, and therefore tend to misrepresent the structural implications of causal models in their explicit judgments. However, an implicit task that only required predictions of singular events showed surprisingly accurate sensitivity to the structural implications of causal models. This dissociation supports the view that people's sensitivity to structural implications is mediated by running simulations on mental analogs of the causal situations.

Citations