Similarity, frequency, and category representations.

TLDR: In this article, the joint roles of similarity and frequency in determining graded category structure were studied, and a frequency sensitive similarity-to-exemplars model provided a good quantitative account of the classification learning and typicality data.

Abstract: This article studies the joint roles of similarity and frequency in determining graded category structure. Perceptual classification learning experiments were conducted in which presentation frequencies of individual exemplars were manipulated. The exemplars had varying degrees of similarity to members of the target and contrast categories. Classification accuracy and typicality ratings increased for exemplars presented with high frequency and for members of the target category that were similar to the high-frequency exemplars. Typicality decreased for members of the contrast category that were similar to the high-frequency exemplars. A frequency-sensitive similarity-to-exemplars model provided a good quantitative account of the classification learning and typicality data. The interactive relations among similarity, frequency, and categorization are considered in the General Discussion. Among the most well-established findings in the categorization literature is that categories have "graded structures" (Rips, Schoben, & Smith, 1973; Rosch, 1973, 1978; Rosch & Mervis, 1975; Smith & Medin, 1981). Rather than all instances of a category being "equal," it appears that certain instances are better examples than others. For example, people reliably rate a robin as a better example of the category birds