Food for Thought: Cross-Classification and Category Organization in a Complex Real-World Domain

Brian H. Ross, Gregory L. Murphy

Research output: Contribution to journalArticlepeer-review


Seven studies examined how people represent, access, and make inferences about a rich real-world category domain, foods. The representation of the category was assessed by category generation, category ratings, and item sortings. The first results indicated that the high-level category of foods was organized simultaneously by taxonomic categories for the kind of food (e.g., vegetables, meats) and script categories for the situations in which foods are eaten (e.g., breakfast foods, snacks). Sortings were dominated by the taxonomic categories, but the script categories also had an influence. The access of the categories was examined both by a similarity rating task, with and without the category labels, and by a speeded priming experiment. In both studies, the script categories showed less access than the taxonomic catego-ries, but more than novel ad hoc categories, suggesting some intermediate level of access. Two studies on induction found that both types of categories could be used to make a wide range of inferences about food properties, but that they were differ-entially useful for different kinds of inferences. The results give a detailed picture of the use of cross-classification in a complex domain, demonstrating that multiple categories and ways of categorizing can be used in a single domain at one time.

Original languageEnglish (US)
Pages (from-to)495-553
Number of pages59
JournalCognitive Psychology
Issue number4
StatePublished - Jun 1999

ASJC Scopus subject areas

  • Neuropsychology and Physiological Psychology
  • Experimental and Cognitive Psychology
  • Developmental and Educational Psychology
  • Linguistics and Language
  • Artificial Intelligence


Dive into the research topics of 'Food for Thought: Cross-Classification and Category Organization in a Complex Real-World Domain'. Together they form a unique fingerprint.

Cite this