Abstract
Existing research on category-based induction has primarily focused on reasoning aboutblank properties, or predicates that are designed to elicit little prior knowledge. Here, we address reasoning about nonblank properties. We introduce a model of conditional probability that assumes that the conclusion prior probability is revised to the extent warranted by the evidence in the premise. The degree of revision is a function of the relevance of the premise category to the conclusion and the informativeness of the premise statement. An algebraic formulation with no free parameters accurately predicted conditional probabilities for single- and two-premise conditionals (Experiments 1 and 3), as well as problems involving negative evidence (Experiment 2).
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This article is based on a Ph.D. dissertation submitted to Northwestern University by the first author. The work was supported by NIH Grant MH68889 to S.V.B., NSF DLS 0132469 to D.L.M., and NSF IIS-9978135 to D.N.O.
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Blok, S.V., Medin, D.L. & Osherson, D.N. Induction as conditional probability judgment. Memory & Cognition 35, 1353–1364 (2007). https://doi.org/10.3758/BF03193607
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DOI: https://doi.org/10.3758/BF03193607