Strategy development and learning differences in supervised and unsupervised categorization

Erin Colreavy*, Stephan Lewandowsky

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)

12 Citations (Scopus)

Abstract

The processes that determine unsupervised categorization, the task of classifying stimuli without guidance or feedback, are poorly understood. Two experiments examined the emergence and plasticity of unsupervised strategies using perceptual stimuli that varied along two separable dimensions. In the first experiment, participants either classified stimuli into any two categories of their choice or learned identical classifications by supervised categorization. Irrespective of the complexity of classification, supervised and unsupervised learning rates differed little when both modes of learning were maximally comparable. The second experiment examined the plasticity of unsupervised classifications by introducing novel stimuli halfway through training. Whether or not people altered their strategies, they responded to novel stimuli in a gradual manner. The gradual and continuous evolution and adaptation of strategies suggests that unsupervised categorization involves true learning which shares many properties of supervised category learning. We also show that the choice of unsupervised strategy cannot be predicted from the properties of early learning trials, but is best understood as a function of the initial distribution of dimensional attention.

Original languageEnglish
Pages (from-to)762-775
Number of pages14
JournalMemory and Cognition
Volume36
Issue number4
DOIs
Publication statusPublished - Jun 2008

Structured keywords

  • Cognitive Science

Keywords

  • NETWORK MODEL
  • CATEGORY
  • KNOWLEDGE
  • CLASSIFICATION
  • PSYCHOPHYSICS
  • ACQUISITION
  • STIMULI

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