Two causal theories of counterfactual conditionals

Lance J. Rips*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

Bayes nets are formal representations of causal systems that many psychologists have claimed as plausible mental representations. One purported advantage of Bayes nets is that they may provide a theory of counterfactual conditionals, such as If Calvin had been at the party, Miriam would have left early. This article compares two proposed Bayes net theories as models of people's understanding of counterfactuals. Experiments 1-3 show that neither theory makes correct predictions about back-tracking counterfactuals (in which the event of the if-clause occurs after the event of the then-clause), and Experiment 4 shows the same is true of forward counterfactuals. An amended version of one of the approaches, however, can provide a more accurate account of these data.

Original languageEnglish (US)
Pages (from-to)175-221
Number of pages47
JournalCognitive Science
Volume34
Issue number2
DOIs
StatePublished - Mar 2010

Keywords

  • Bayes nets
  • Causal reasoning
  • Conditionals
  • Counterfactuals

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Cognitive Neuroscience
  • Artificial Intelligence

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