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In this paper we consider the “size principle” for featural similarity, which states that rare features should be weighted more heavily than common features in people’s evaluations of the similarity between two entities. Specifically, it predicts that if a feature is possessed by n objects, the expected weight scales according to a 1/n law. One justification of the size principle emerges from a Bayesian...
We develop a model for finding the features that represent a set of stimuli, and apply it to the Leuven Concept Database. The model combines the feature generation and similarity judgment task data, inferring whether each of the generated features is important for explaining the patterns of similarity between stimuli. Across four datasets, we show that features range from being very important to very...
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