Environment and Goals Jointly Direct Category Acquisition

Developing categorization schemes involves discovering structures in the world that support a learner's goals. Existing models of category learning, such as ex emplar and prototype models, neglect the role of goals in shaping conceptual organization. Here, a clustering ap proach is discussed that reflects the joint influences of the environment and goals in directing category acquisition. Clusters are a flexible representational medium that ex hibits properties of exemplar, prototype, and rule-based models. Clusters reflect the natural bundles of correlated features present in our environment. The clustering model Supervised and Unsupervised Stratified Incremental Adaptive Network (SUSTAIN) operates by assuming the world has a simple structure and adding complexity (i.e., clusters) when existing clusters fail to satisfy the learner's goals and thus elicit surprise. Although simple, this oper ation is sufficient to address findings from numerous lab oratory and cross-cultural categorization studies. KEYWORDS?-categories; clusters; goals; learning; stereo