Adaptive Learning Across Task Environments
Abstract
The major accomplishment of this project was increasing the range of situations in which experimental results and models are informative for making predictions about how to best train personnel. In concert with a series of empirical investigations, the SUSTAIN (Supervised and Unsupervised Stratified Adaptive Incremental Network) model of how humans learn categories from examples was developed. 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. SUSTAIN brings the field closer to making a priori predictions about learning and performance under a variety of conditions.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 29, 2004
- Accession Number
- ADA425764
Entities
People
- Bradley C. Love
Organizations
- University of Texas at Austin