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Two Causal Theories of Counterfactual Conditionals
Author(s) -
Rips Lance J.
Publication year - 2010
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/j.1551-6709.2009.01080.x
Subject(s) - counterfactual conditional , counterfactual thinking , bayes' theorem , event (particle physics) , causal decision theory , causal model , computer science , mathematical economics , cognitive psychology , mathematics , econometrics , artificial intelligence , psychology , bayesian probability , social psychology , statistics , business decision mapping , physics , quantum mechanics , decision engineering , decision analysis
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 backtracking 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.