Sparse category labels obstruct generalization of category membership

John V. McDonnell, Carol A. Jew, Todd M. Gureckis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Studies of human category learning typically focus on situations where explicit category labels accompany each example (supervised learning) or on situations were people must infer category structure entirely from the distribution of unlabeled examples (unsupervised learning). However, real-world category learning likely involves a mixture of both types of learning (semi-supervised learning). Surprisingly, a number of recent findings suggest that people have difficulty learning in semi-supervised tasks. To further explore this issue, we devised a category learning task in which the distribution of labeled and unlabeled items suggested alternative organizations of a category. This design allowed us to determine whether learners combined information from both types of episodes via their patterns of generalization at test. In contrast with the prediction of many models, we find little evidence that unlabeled items influenced categorization behavior when labeled items were also present.

Original languageEnglish (US)
Title of host publicationBuilding Bridges Across Cognitive Sciences Around the World - Proceedings of the 34th Annual Meeting of the Cognitive Science Society, CogSci 2012
EditorsNaomi Miyake, David Peebles, Richard P. Cooper
PublisherThe Cognitive Science Society
Pages749-754
Number of pages6
ISBN (Electronic)9780976831884
StatePublished - 2012
Event34th Annual Meeting of the Cognitive Science Society: Building Bridges Across Cognitive Sciences Around the World, CogSci 2012 - Sapporo, Japan
Duration: Aug 1 2012Aug 4 2012

Publication series

NameBuilding Bridges Across Cognitive Sciences Around the World - Proceedings of the 34th Annual Meeting of the Cognitive Science Society, CogSci 2012

Conference

Conference34th Annual Meeting of the Cognitive Science Society: Building Bridges Across Cognitive Sciences Around the World, CogSci 2012
Country/TerritoryJapan
CitySapporo
Period8/1/128/4/12

Keywords

  • rule induction
  • Semi-supervised category learning
  • unsupervised learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Cognitive Neuroscience

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