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Causal Networks or Causal Islands? The Representation of Mechanisms and the Transitivity of Causal Judgment
Author(s) -
Johnson Samuel G. B.,
Ahn Wookyoung
Publication year - 2015
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1111/cogs.12213
Subject(s) - transitive relation , causal reasoning , causal chain , normative , causal model , causality (physics) , set (abstract data type) , representation (politics) , psychology , mechanism (biology) , cognitive psychology , causal inference , cognitive science , computer science , epistemology , cognition , mathematics , philosophy , econometrics , statistics , physics , combinatorics , quantum mechanics , neuroscience , politics , political science , law , programming language
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).