Summary

Numerous proposals have been put forward concerning the nature of human category representations, ranging from rules to exemplars to prototypes. However, it is unlikely that a single, fixed form of representation is sufficient to account for the flexibility of human categories. In this chapter, we describe an alternative to these fixed-representation accounts based on the principle of adaptive clustering. The specific model we consider, SUSTAIN, represents categories in terms of feature bundles called clusters which are adaptively recruited in response to task demands. In some cases, SUSTAIN acts like an exemplar model, storing each category instance as a separate memory trace, while in others it appears more like a prototype model, extracting only the central tendency of a number of items. In addition, selective attention in the model allows it to mimic many of the behaviours associated with rule-based systems. We review a variety of evidence in support of the clustering principle, including studies of the relationship between categorization and recognition memory, changes in unsupervised category learning abilities across development, and the influence of category learning on perceptual discrimination. In each case, we show how the nature of human category representations is best accounted for using an adaptive clustering scheme. SUSTAIN is just one example of a system that casts category learning in terms of adaptive clustering, and future directions for the approach are discussed.