title: Comparing supervised and unsupervised category learning.
abstract: Two unsupervised learning modes (incidental and intentional unsupervised learning) and their relation to supervised classification learning are examined. The approach allows for direct comparisons of unsupervised learning data with the Shepard, Hovland, and Jenkins (1961) seminal studies in supervised classification learning. Unlike supervised classification learning, unsupervised learning (especially under incidental conditions) favors linear category structures over compact nonlinear category structures. Unsupervised learning is shown to be multifaceted in that performance varies with task conditions. In comparison with incidental unsupervised learning, intentional unsupervised learning is more rule like, but is no more accurate. The acquisition and application of knowledge is also more laborious under intentional unsupervised learning.
authors.affiliation: Department of Psychology, University of Texas, Austin, Texas 78712, USA. love@psy.utexas.edu
authors.forename: Bradley C
authors.initials: BC
authors.lastname: Love
mesh_terms: D000328:Adult; D001288:Attention; D003118:Color Perception / Q000145:classification*; D003657:Decision Making; D004193:Discrimination Learning / Q000145:classification; D006801:Humans; D010364:Pattern Recognition, Visual / Q000145:classification*; D011214:Practice, Psychological; D011340:Problem Solving; D011597:Psychomotor Performance; D011601:Psychophysics; D011930:Reaction Time; D012858:Size Perception / Q000145:classification*