EXPLAINING BASIC CATEGORIES - FEATURE PREDICTABILITY AND INFORMATION

The category utility hypothesis holds that categories are useful because they can be used to predict the features of instances and that the categories that tend to survive and become preferred in a culture (basic-level categories) are those that best improve the category users' ability to perform this function. Starting from this hypothesis, a quantitative measure of the utility of a category is derived. Application to the special case of substitutive attributes is described. The measure is used successfully to predict the basic level in applications to data from hierarchies of natural categories and from hierarchies of artificial categories used in category-learning experiments. The relationship of the measure to previously proposed indicators of the basic level is discussed, as is its relation to certain concepts from information theory.