Finding similarity in a model of relational reasoning

Eric G. Taylor, John E. Hummel

Research output: Contribution to journalArticlepeer-review


Similarity plays a central role in the study of perception and cognition. Previous attempts to model similarity have captured effects of either featural or structural similarity, but typically not both. We simulated both by fitting similarity data with the LISA model of relational reasoning [Hummel, J. E., & Holyoak, K. J. (1997). Distributed representations of structure: A theory of analogical access and mapping. Psychological Review, 104, 427-466, Hummel, J. E., & Holyoak, K. J. (2003a). A symbolic-connectionist theory of relational inference and generalization. Psychological Review, 110, 220-264]. The same mechanisms LISA uses to simulate analogy also provide a natural account of feature-based similarity effects (e.g., violations of symmetry), structural effects (e.g., the advantage of alignable over non-alignable differences), and the combined effects of featural and structured information (i.e., MIPs and MOPs; "Matches In/Out of Place") on similarity judgments. Our approach differs from most models of similarity in that LISA was not originally designed to simulate similarity judgments, but rather analogical reasoning. LISA's incidental ability to simulate diverse similarity effects speaks to the plausibility of the model's account of human knowledge representation.

Original languageEnglish (US)
Pages (from-to)229-239
Number of pages11
JournalCognitive Systems Research
Issue number3
StatePublished - Sep 2009


  • Analogy
  • Knowledge representation
  • Neural networks
  • Reasoning
  • Similarity
  • Working memory

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Cognitive Neuroscience
  • Artificial Intelligence


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