Proceedings of the Annual Meeting of the Cognitive Science Society

Associationist theories of causal induction model learning as the acquisition of associative weights between cues and outcomes. An important deficit of this class of models is its insensitivity to the causal role of cues. A number of recent experimental findings have shown that human learners differentiate between cues that represent causes and cues that represent effects. Our Bayesian network model overcomes this restriction. The model starts learning with initial stractural assumptions about the causal model underlying the learning domain. This causal model guides the estimation of causal strength, and suggests integration schemas for multiple cues. In this way, causal models effectively reduce the potential computational complexity inherent in even relatively simple learning tasks. The Bayesian model is applied to a number of experimental findings, including studies on estimation of causal strength, cue competition, base rate use, and learning linearly and nonlinearly separable categories.