Comparing generalization by humans and adaptive networks

M. Pavel, Mark A. Gluck, Van Henkle

Research output: Contribution to journalConference articlepeer-review


One goal of study reported was to examine how people generalize in a simple deterministic categorization task in which each pattern is characterized in terms of known binary features. While we expected certain similarities to emerge across human learners we anticipated that the particular generalizations might be subject to considerable individual differences. To test this idea, we used an experimental paradigm that would permit us to to observe individual subjects during the learning of a categorization task on a set of training patterns and then allow us examine the types of categorizations they made on a set of novel test patterns. Later we compared human generalizations to those of a small adaptive network. We have demonstrated that subjects who learn the same pattern categorization may abstract different principles and therefore show large individual differences in their generalization behavior. Adaptive networks with the minimum number of hidden units exhibit a similar behavior but generalize differently.

Original languageEnglish (US)
Pages (from-to)208
Number of pages1
JournalNeural Networks
Issue number1 SUPPL
StatePublished - 1988
EventInternational Neural Network Society 1988 First Annual Meeting - Boston, MA, USA
Duration: Sep 6 1988Sep 10 1988

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence


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