The generalized context model: an exemplar model of classification

According to the generalized context model (GCM) (Nosofsky, 1986 ), people represent categories by storing individual exemplars (or examples) in memory, and classify objects based on their similarity to these stored exemplars. For example, the model assumes that people represent the category of ‘birds’ by storing in memory the vast collection of different sparrows, robins, eagles, ostriches (and so forth) that they have experienced. If an object is sufficiently similar to some of these bird exemplars, then the person would tend to classify the object as a ‘bird’. This exemplar view of categorization contrasts dramatically with major alternative approaches that assume that people form abstract summary representations of categories, such as rules or idealized prototypes. The standard version of the GCM adopts a multidimensional scaling (MDS) approach to modelling similarity relations among exemplars (Shepard, 1958 , 1987 ). In this approach, exemplars are represented as points in a multidimensional psychological space . Similarity between exemplars is a decreasing function of their distance in the space. In many applications, a first step in the modelling is to conduct similarity-scaling studies to derive MDS solutions for the exemplars and to discover their locations in the multidimensional similarity space (Nosofsky, 1992b ). A crucial assumption in the modelling, however, is that similarity is not an invariant relation, but a highly context-dependent one. To take an example from Medin and Schaffer ( 1978 ), humans and mannequins may be judged as highly similar in a context that emphasizes structural appearance, but would be judged as highly dissimilar in a context that emphasizes vitality. In the GCM, the context-dependent nature of 2 The generalized context model: an exemplar model of classification