title: Transitive reasoning distorts induction in causal chains.
abstract: A probabilistic causal chain A→B→C may intuitively appear to be transitive: If A probabilistically causes B, and B probabilistically causes C, A probabilistically causes C. However, probabilistic causal relations can only guaranteed to be transitive if the so-called Markov condition holds. In two experiments, we examined how people make probabilistic judgments about indirect relationships A→C in causal chains A→B→C that violate the Markov condition. We hypothesized that participants would make transitive inferences in accordance with the Markov condition although they were presented with counterevidence showing intransitive data. For instance, participants were successively presented with data entailing positive dependencies A→B and B→C. At the same time, the data entailed that A and C were statistically independent. The results of two experiments show that transitive reasoning via a mediating event B influenced and distorted the induction of the indirect relation between A and C. Participants' judgments were affected by an interaction of transitive, causal-model-based inferences and the observed data. Our findings support the idea that people tend to chain individual causal relations into mental causal chains that obey the Markov condition and thus allow for transitive reasoning, even if the observed data entail that such inferences are not warranted.
authors.affiliation: Department of Psychology, Ruprecht-Karls-Universität Heidelberg, Hauptstr. 47, 69117, Heidelberg, Germany. Momme.von-Sydow@uni-heidelberg.de.|Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg, Heidelberg, Germany. Momme.von-Sydow@uni-heidelberg.de.|Munich Center for Mathematical Philosophy (MCMP), University of Munich, Munich, Germany. Momme.von-Sydow@uni-heidelberg.de.
authors.forename: Momme
authors.initials: M
authors.lastname: von Sydow
authors.affiliation: Department of Psychology, University of Göttingen, Göttingen, Germany.
authors.forename: York
authors.initials: Y
authors.lastname: Hagmayer
authors.affiliation: Center for Adaptive Behavior and Cognition, Max Planck Institute for Human Development, Berlin, Germany.
authors.forename: Björn
authors.initials: B
authors.lastname: Meder
mesh_terms: D000328:Adult; D005260:Female; D006801:Humans; D007600:Judgment / Q000502:physiology; D008128:Logic; D008297:Male; D008390:Markov Chains; D013850:Thinking / Q000502:physiology*; D055815:Young Adult
keywords: Categorization; Causal coherence; Causal induction; Causal learning; Causality; Knowledge-based induction; Markov condition; Mixing of causal relationships; Probabilistic reasoning; Transitive distortion effects; Transitivity