ABSTRACT

One central function of categories is to allow people to infer the presence of features that cannot be directly observed. Although the effect of observing past category members on such inferences has been considered, the effect of theoretical or causal knowledge about the category has not. We compared the effects of causal laws on feature prediction with the effects of the inter-feature correlations that are produced by those laws, and with the effect of exemplar typicality or similarity. Feature predictions were strongly influenced by causal knowledge. However, they were also influenced by similarity, in violation of normative behavior as defined by a Bayesian network view of causal reasoning. Finally, feature predictions were not influenced by the presence of correlations among features in observed category members, indicating that causal relations versus correlations lead to different inferences regarding the presence of unobserved features.