Empirical learning models have typically focused on statistical aspects of features (e.g., cue and category validity). In general, these models do not address the contact between people's prior knowledge that lies outside the category and their experiences of the category. A variety of extensions to these models are examined, which combine prior knowledge with empirical learning. Predictions of these models were compared in 4 experiments. These studies contrasted the cue and category validity of features with people's prior knowledge about the relevance of features to the functions of novel artifacts. The findings suggest that the influences of knowledge and experience are more tightly integrated than some models would predict. Furthermore, relatively straightforward ways of incorporating knowledge into an empirical learning algorithm appear insufficient (e.g., use of knowledge to weight features by general relevance or to individually weight features). Other extensions to these models are suggested that focus on the importance of intermediary features, coherence, and conceptual roles.
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