The psychology of inferring conditionals from disjunctions: A probabilistic study

By Angelo Gilio, David E. Over. 2012

64 citations recorded by the source corpus.

Abstract There is a new probabilistic paradigm in the psychology of reasoning that is, in part, based on results showing that people judge the probability of the natural language conditional, if A then B , P ( i f A t h e n B ) , to be the conditional probability, P ( B ∣ A ) . We apply this new approach to the study of a very common inference form in ordinary reasoning: inferring the conditional if not - A then B from the disjunction A or B . We show how this inference can be strong, with P ( if not - A then B ) “close to” P ( A o r B ) , when A or B is non-constructively justified. When A or B is constructively justified, the inference can be very weak. We also define suitable measures of “closeness” and “constructivity”, by providing a probabilistic analysis of these notions.

Subfields: Artificial Intelligence, Developmental and Educational Psychology, General Decision Sciences

View Paper: https://www.semanticscholar.org/paper/5aa5ec518272edfa781dcb3e61bc4a72d8e9919d

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In Artificial Intelligence

In Developmental and Educational Psychology

In General Decision Sciences

Citations in the corpus

Dual process theory 2.0 (2020, methodology): “But it is an invalid inference in most other analyses of the conditional, including a probabilistic one (Gilio & Over, 2012), although the disjunctive syllogism is valid in these other analyses.”
Reasoning to and from belief: Deduction and induction are still distinct (2013, background): “Intervals are also derivable for inferences from and to the probability conditional (Gilio & Over, 2012; Pfeifer & Kleiter, 2010).”
Uncertainty and the de Finetti tables (2013, background): “Even though P(W) ¼ 0, we can still say that the probability of this counterfactual is the conditional probability, P(LjW), with this value being high or low depending on a Ramsey test for the counterfactual (Gilio & Over, 2012; Over et al., 2007).”
Dynamic inference and everyday conditional reasoning in the new paradigm (2013, methodology): “Gilio and Over (2012) further investigate the conditions under which this static inference is rational or strong.”
Bayesian reasoning with ifs and ands and ors (2015, methodology): “Johnson-Laird and Byrne (2002, p. 650) claimed that people always endorse 1.5, but Gilio and Over (2012) have an analysis of when 1.5 and 1.6 are, and are not, reasonable inferences to make, and Over et al. (2010) have supporting results.”