Systematicity and surface similarity in the development of analogy

Dedre Gentner*, Cecile Toupin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

426 Scopus citations

Abstract

This research investigates the development of analogy: In particular, we wish to study the development of systematicity in analogy. Systematicity refers to the mapping of systems of mutually constraining relations, such as causal chains or chains of implication. A preference for systematic mappings is a central aspect of analogical processing in adults (Gentner, 1980, 1983). This research asks two questions: Does systematicity make analogical mapping easier? And, if so, when, developmentally, do children become able to utilize systematicity? Children aged 5-7 and 8-10 acted out stories with toy characters. Then they were asked to act out the same stories with new characters. Two variables were manipulated: systematicity, or the degree of explicit causal structure in the original stories, and the transparency of the object-mappings. Transparency was manipulated by varying the similarity between the original characters and the corresponding new characters: it was included in order to vary the difficulty of the transfer task. If children can utilize systematicity, then their transfer accuracy should be greater for systematic stories. The results show: (1) As expected, transparency strongly influenced transfer accuracy (for both age groups, transfer accuracy dropped sharply as the object correspondences became less transparent); and (2) for the older group, there was also a strong effect of systematicity and an interaction between the two variables. Given a systematic story, 9-year-olds could transfer it accurately regardless of the transparency of the object correspondence.

Original languageEnglish (US)
Pages (from-to)277-300
Number of pages24
JournalCognitive Science
Volume10
Issue number3
DOIs
StatePublished - 1986

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Cognitive Neuroscience
  • Artificial Intelligence

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