The instantiation principle in natural categories

Abstract

According to the instantiation principle, the representation of a category includes detailed information about its diverse range of instances. Many accounts of categorisation, including classical and standard prototype theories, do not follow the instantiation principle, because they assume that detailed, exemplar-level information is filtered out of category representations. Nevertheless, the instantiation principle can be implemented in a wide class of models, including both exemplar and abstraction models. To assess the instantiation principle empirically, a parameter-free exemplar-based model of instantiation was applied to typicality judgments for 16 simple categories (e.g. mammal, beverage) and 14 complex categories (e.g. dangerous mammal) in four superordinates (animal, food, small animal, dangerous animal). Across three studies, the model did an excellent job of predicting mean typicality judgments (correlations generally above 0.9) and a good job of predicting standard deviations (fits generally from 0.6 to 0.9). In Study 3, predicting the skew of typicality distributions was successful as well (a fit of 0.87), and dropping atypical exemplars from the simulations degraded prediction. All of these results support the instantiation principle, indicating that subjects incorporate detailed information about category instances into their representations of categories.