Constraint-Based Human Causal Learning

Much of human cognition and activity depends on causal beliefs and reasoning. In psychological research on human causal learning and inference, we usually suppose that we have a set of binary potential causes, C1, ..., Cn, and a known binary effect, E, all typically present-absent values of a property or event. The differentiation into potential causes and effect is made on the basis of external factors, including prior knowledge or temporal information. Given these variables, people are then asked to infer the existence and strength of causal relationships between the Ci’s and E from observed data in one of several formats (serially, as a list, or in a summary). The standard measure of people’s causal beliefs is a rating of some proxy for causal influence, where a zero rating indicates no causal relationship. The exact probe question varies between experiments, and has been found to significantly impact participants’ ratings (e.g., Collins & Shanks, under review). A variety of theories have been proposed to explain people’s causal inferences in this type of highly limited scenario (see Danks, forthcoming, for a theoretical overview and synthesis). One general view for which there is a growing body of evidence is that people’s causal beliefs and learning are well-modeled as though they are learning a causal Bayesian network (CBN, henceforth).