Transitive reasoning distorts induction in causal chains.
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...
| Published in: | Memory & Cognition Vol. 44; no. 3; pp. 469 - 488 |
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| Main Authors: | , , |
| Format: | Article |
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Springer Nature
Apr2016
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=114120347&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 114120347 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 0090502X MEG jtl: Memory & Cognition issn: 0090502X maglogo: N pubinfo: dt: Apr2016 vid: 44 iid: 3 pid: 237 pub: Springer Nature artinfo: ui: 114120347 10.3758/s13421-015-0568-5 ppf: 469 ppct: 19 formats: fmt: @attributes: type: P size: 1.2MB tig: atl: Transitive reasoning distorts induction in causal chains. aug: au: von Sydow, Momme Hagmayer, York Meder, Björn affil: Department of Psychology, University of Göttingen, Göttingen Germany Center for Adaptive Behavior and Cognition, Max Planck Institute for Human Development, Berlin Germany keyword: Categorization Causal coherence Causal induction Causal learning Causality Knowledge-based induction Markov condition Mixing of causal relationships Probabilistic reasoning Transitive distortion effects Transitivity Categorization Causal coherence Causal induction Causal learning Causality Knowledge-based induction Markov condition Mixing of causal relationships Probabilistic reasoning Transitive distortion effects Transitivity ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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