COGNITIVE PSYCHOLOGY 7, 573-605 (1975)
'Family Resemblances: Studies in the Internal Structure of Categories'
ELEANOR ROSCH AND CAROLYN B. MERVIS
University of California, Berkeley
Six experiments explored the hypothesis that the members of categories which are considered most prototypical are those with most attributes in common with other members of the category and least attributes in common with other categories.
In probabilistic terms, the hypothesis is that prototypicality is a function of the total cue validity of the attributes of items.
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As speakers of our language and members of our culture, we know that a chair is a more reasonable exemplar of the category furniture than a radio, and that some chairs fit our idea or image of a chair better than others.
However, when describing categories analytically, most traditions of thought have treated category membership as a digital, all-or-none phenomenon.
That is, much work in philosophy, psychology, linguistics, and anthropology assumes that categories are logical bounded entities, membership in which is defined by an item’s posses-
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sion of a simple set of criterial features, in which all instances possessing the criterial attributes have a full and equal degree of membership.
In contrast to such a view, it has been recently argued that some natural categories are analog and must be represented logically in a manner which reflects their analog structure.
Rosch (1973, 1975b) has further characterized some natural analog categories as internally structured into a prototype (clearest cases, best examples of the category) and nonprototype members, with nonprototype members tending toward an order from better to poorer examples.
While the domain for which such a claim has been demonstrated most unequivocally is that of color there is also considerable evidence that natural superordinate semantic categories have a prototype structure. Subjects can reliably rate the extent to which a member of a category fits their idea or image of the meaning of the category name , and such ratings predict performance in a number of tasks.
