title: An associative framework for probability judgment: an application to biases.
abstract: Three experiments show that understanding of biases in probability judgment can be improved by extending the application of the associative-learning framework. In Experiment 1, the authors used M. A. Gluck and G. H. Bower's (1988a) diagnostic-learning task to replicate apparent base-rate neglect and to induce the conjunction fallacy in a later judgment phase as a by-product of the conversion bias. In Experiment 2, the authors found stronger evidence of the conversion bias with the same learning task. In Experiment 3, the authors changed the diagnostic-learning task to induce some conjunction fallacies that were not based on the conversion bias. The authors show that the conjunction fallacies obtained in Experiment 3 can be explained by adding an averaging component to M. A. Gluck and G. H. Bower's model.
authors.affiliation: Departamento de Psicología Básica, Facultad de Psicología, Universidad de Málaga, Spain. p_cobos@uma.es
authors.forename: Pedro L
authors.initials: PL
authors.lastname: Cobos
authors.forename: Julián
authors.initials: J
authors.lastname: Almaraz
authors.forename: Juan A
authors.initials: JA
authors.lastname: García-Madruga
mesh_terms: D001245:Association Learning; D006801:Humans; D007600:Judgment; D008960:Models, Psychological; D011336:Probability