The Role of Causal Models in Analogical Inference

Hee Seung Lee, Keith J. Holyoak

Research output: Contribution to journalArticlepeer-review

37 Citations (Scopus)


Computational models of analogy have assumed that the strength of an inductive inference about the target is based directly on similarity of the analogs and in particular on shared higher order relations. In contrast, work in philosophy of science suggests that analogical inference is also guided by causal models of the source and target. In 3 experiments, the authors explored the possibility that people may use causal models to assess the strength of analogical inferences. Experiments 1-2 showed that reducing analogical overlap by eliminating a shared causal relation (a preventive cause present in the source) from the target increased inductive strength even though it decreased similarity of the analogs. These findings were extended in Experiment 3 to cross-domain analogical inferences based on correspondences between higher order causal relations. Analogical inference appears to be mediated by building and then running a causal model. The implications of the present findings for theories of both analogy and causal inference are discussed.

Original languageEnglish
Pages (from-to)1111-1122
Number of pages12
JournalJournal of Experimental Psychology: Learning Memory and Cognition
Issue number5
Publication statusPublished - 2008 Sept

All Science Journal Classification (ASJC) codes

  • Experimental and Cognitive Psychology
  • Language and Linguistics
  • Linguistics and Language


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