SUSTAIN: A Network Model of Category Learning.

Abstract

SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a model of how humans learn categories from examples. SUS- TAIN initially assumes a simple category structure. If simple solutions prove inadequate and SUSTAIN is confronted with a surprising event (e.g., it is told that a bat is a mammal instead of a bird), SUSTAIN recruits an additional cluster to represent the surprising event. Newly recruited clus- ters are available to explain future events and can themselves evolve into prototypes/attractors/rules. Importantly, SUSTAIN's discovery of category substructure is aected not only by the structure of the world, but by the nature of the learning task and the learner's goals. SUSTAIN successfully ex- tends category learning models to studies of inference learning, unsupervised learning, category construction, and contexts where identification learning is faster than classification learning.