An associative framework for probability judgment: An application to biases

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. A great deal of research in decision making has focused on people’s biases in probability judgment. The main reason for this is that deviations from normative theories are more informative than correct estimations in order to infer the cognitive processes underlying probability judgment, which, in turn, is a basic com-ponent of decision making. Some of these biases are remarkable because they seem to involve the violation of some basic and simple principles of the probability theory. Our main purpose is to show that the associative-learning frame-work can improve our understanding of biases in probability judgment. We focus on three well-known errors: the base-rate neglect, the conjunction fallacy, and the conversion of conditional probabilities (thereafter referred to as the conversion bias). First, let us briefly outline these biases