SUSTAIN: A Network Model of Category Learning.

TLDR

SUSTAIN is a model of human category learning that begins with a simple category structure. When a surprising event occurs, SUSTAIN adds a new cluster to represent it, and these clusters can evolve into prototypes, attractors, or rules. SUSTAIN’s ability to uncover category substructure depends on world structure, task type, and learner goals, and it successfully extends category learning to inference, unsupervised learning, category construction, and situations where identification outpaces classification.

Abstract

SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a model of how humans learn categories from examples. SUSTAIN 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 clusters are available to explain future events and can themselves evolve into prototypes-attractors-rules. SUSTAIN's discovery of category substructure is affected not only by the structure of the world but by the nature of the learning task and the learner's goals. SUSTAIN successfully extends category learning models to studies of inference learning, unsupervised learning, category construction, and contexts in which identification learning is faster than classification learning.