Category learning from equivalence constraints

Rubi Hammer*, Tomer Hertz, Shaul Hochstein, Daphna Weinshall

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


Information for category learning may be provided as positive or negative equivalence constraints (PEC/NEC)-indicating that some exemplars belong to the same or different categories. To investigate categorization strategies, we studied category learning from each type of constraint separately, using a simple rule-based task. We found that participants use PECs differently than NECs, even when these provide the same amount of information. With informative PECs, categorization was rapid, reasonably accurate and uniform across participants. With informative NECs, performance was rapid and highly accurate for only some participants. When given directions, all participants reached high-performance levels with NECs, but the use of PECs remained unchanged. These results suggest that people may use PECs intuitively, but not perfectly. In contrast, using informative NECs enables a potentially more accurate categorization strategy, but a less natural, one which many participants initially fail to implement-even in this simplified setting.

Original languageAmerican English
Pages (from-to)211-232
Number of pages22
JournalCognitive Processing
Issue number3
StatePublished - Aug 2009

Bibliographical note

Funding Information:
Acknowledgments This study was supported by a ‘‘Center of Excellence’’ grant from the Israel Science Foundation, a grant from the US-Israel Binational Science Foundation, and a grant by the EU under the DIRAC integrated project IST-027787. Preliminary results of this study were presented in the annual meeting of the Cognitive Science Society, Stresa, Italy, July 2005. We would like to thank Lee Brooks and Gil Diesendruck for their comments. We also thank Michael Ziessler and an anonymous reviewer for their useful comments.


  • Categorization
  • Category learning
  • Concept acquisition
  • Dimension weighting
  • Learning to learn
  • Perceived similarity
  • Rule-based


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