Probing the Mental Representation of Relation-Defined Categories

Ava Y. Du, John E. Hummel, Alexander A. Petrov

Research output: Contribution to conferencePaperpeer-review

Abstract

The mental representation of relation-based concepts is different from that of feature-based concepts. In the present experiment, participants learned to categorize two fictional diseases that were defined either by a feature (e.g., short cells) or an ordinal relation (e.g., diseased cells being shorter than healthy cells). After the participants learned the categorization task to criterion, their strategies were probed in transfer task in which features and relations were pitted against one another. Finally, participants engaged in a stimulus reconstruction task. The results supported the prediction that participants who had adopted a feature-based strategy on a stimulus dimension, as identified by transfer data, tended to reconstruct values close to the means presented during training. By contrast, participants who had adopted a relation-based strategy tended to exaggerate that dimension away from the mean of the training examples and in the direction of the category-defining comparative relation. These data add to the growing literature suggesting that, unlike featural categories, relational categories are not represented in terms of the category’s central tendency.

Original languageEnglish (US)
Pages2024-2030
Number of pages7
StatePublished - 2021
Event43rd Annual Meeting of the Cognitive Science Society: Comparative Cognition: Animal Minds, CogSci 2021 - Virtual, Online, Austria
Duration: Jul 26 2021Jul 29 2021

Conference

Conference43rd Annual Meeting of the Cognitive Science Society: Comparative Cognition: Animal Minds, CogSci 2021
Country/TerritoryAustria
CityVirtual, Online
Period7/26/217/29/21

Keywords

  • Category learning
  • extreme-value hypothesis
  • probing the category representation
  • relations
  • typicality

ASJC Scopus subject areas

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

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