Learning Relational Concepts through Unitary versus Compositional Representations

Daniel Corral, Matt Jones

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

1 Scopus citations

Abstract

Current theories of relational learning on structure mapping emphasize the importance of compositional representations, based on the concept's components and the relations among them. We consider the possibility that relational concepts can also be represented unitarily, whereby the concept is a property of the stimulus as a whole. The distinction between compositional and unitary representations of relational concepts is a natural consequence of structure-mapping theory, but its psychological implications have not been explored. We report two experiments in which we examine how encouraging subjects to represent relational concepts compositionally versus unitarily affects learning on classification- and inference-based category learning tasks. Our findings show that unitary representations lead to better learning than compositional representations, especially for the inference task. We conclude that unitary representations incur less cognitive load than structural alignment of compositional representations, and thus may be the default for everyday relational reasoning.

Original languageEnglish (US)
Title of host publicationCogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society
Subtitle of host publicationComputational Foundations of Cognition
PublisherThe Cognitive Science Society
Pages1830-1835
Number of pages6
ISBN (Electronic)9780991196760
StatePublished - 2017
Externally publishedYes
Event39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition, CogSci 2017 - London, United Kingdom
Duration: Jul 26 2017Jul 29 2017

Publication series

NameCogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition

Conference

Conference39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition, CogSci 2017
Country/TerritoryUnited Kingdom
CityLondon
Period7/26/177/29/17

Keywords

  • Category Learning
  • Concept Representation
  • Inference
  • Relational Learning
  • Relational Structure

ASJC Scopus subject areas

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

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