Finding the features that represent stimuli

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 unimportant, and that a small subset of important features is adequate to describe the similarities. We also show that the features inferred to be more important are intuitively reasonable, and present analyses showing that important features tend to focus on narrow sets of stimuli, providing information about the category structures that organize the stimuli into groups.