Proceedings of the Annual Meeting of the Cognitive Science Society

A number of researchers have recently claimed that higher-order human learning, such as categorization and causal induction, can be explained by the same principles as govern lower order learning, such as classical conditioning in animals. An alternative view is that people often impose abstract causal models on observations, rather than simply associating inputs with outputs. W e report three experiments using a multiple-cue learning paradigm in which models based on associative learning versus abstract causal models make opposing predictions. We show that different causal models can yield radically different learning from identical observations. In particular, we compared people's abilities to learn when the positive cases were defined by a linear cue-combination rule versus a rule involving a within-category correlation between cues. The linear structure was more readily learned when the cues were interpreted as possible causes of an effect to be predicted, whereas the correlated structure was more readily learned when the cues were interpreted as the effects of a cause to be diagnosed. The results disconfirm all associative models of causal induction in which inputs are associated with outputs without regard for causal directionality.