Integrating novel dimensions to eliminate category exceptions: When more is less

Mark Blair, Donald Homa

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Category learning can be characterized as a process of discovering the dimensions that represent stimuli efficiently and effectively. Categories that are overlapping when represented in 1 dimensionality may be separate in a higher dimensional cue set. The authors report 2 experiments in which participants were shown an additional cue after learning to use 2 imperfect cues. The results revealed that participants can integrate new information into their categorization cue set. The authors discovered wide individual differences, however, with many participants favoring simpler, but less accurate, cue sets. Some participants demonstrated the ability to discard information previously used when new, more accurate information was introduced. The categorization model RASHNL (J. K. Kruschke & M. K. Johansen, 1999) gave qualitatively accurate fits of the data.

Original languageEnglish (US)
Pages (from-to)258-271
Number of pages14
JournalJournal of Experimental Psychology: Learning Memory and Cognition
Issue number2
StatePublished - Mar 2005

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Language and Linguistics
  • Linguistics and Language


Dive into the research topics of 'Integrating novel dimensions to eliminate category exceptions: When more is less'. Together they form a unique fingerprint.

Cite this