Causal networks or causal islands? The representation of mechanisms and the transitivity of causal judgment

Samuel G.B. Johnson, Woo kyoung Ahn

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

Knowledge of mechanisms is critical for causal reasoning. We contrasted two possible organizations of causal knowledge-an interconnected causal network, where events are causally connected without any boundaries delineating discrete mechanisms; or a set of disparate mechanisms-causal islands-such that events in different mechanisms are not thought to be related even when they belong to the same causal chain. To distinguish these possibilities, we tested whether people make transitive judgments about causal chains by inferring, given A causes B and B causes C, that A causes C. Specifically, causal chains schematized as one chunk or mechanism in semantic memory (e.g., exercising, becoming thirsty, drinking water) led to transitive causal judgments. On the other hand, chains schematized as multiple chunks (e.g., having sex, becoming pregnant, becoming nauseous) led to intransitive judgments despite strong intermediate links ((Experiments 1-3). Normative accounts of causal intransitivity could not explain these intransitive judgments (Experiments 4 and 5).

Original languageEnglish
Pages (from-to)1468-1503
Number of pages36
JournalCognitive Science
Volume39
Issue number7
DOIs
Publication statusPublished - 1 Sep 2015

Keywords

  • Causal mechanisms
  • Causal reasoning
  • Knowledge representation
  • Transitive inference

ASJC Scopus subject areas

  • Language and Linguistics
  • Experimental and Cognitive Psychology
  • Cognitive Neuroscience
  • Artificial Intelligence

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