An associative framework for probability judgement: 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.

Exploring the conjunction fallacy within a category learning framework

“…However, I would like to point out that the conclusions drawn above concern all tasks using probability estimates. That is, they are not only limited to the standard and mixed problems (for other designs covered by the conclusions drawn in the present paper see, Coboz et al, 2003;Lagnado & Shanks, 2002;Nilsson & Winman, 2007;Zizzo, 2003).…”

“…On this account, the present study is unique. A couple of studies have explored violations of the conjunction rule within a category learning frame-work (Coboz et al, 2003;Lagnado & Shanks, 2002) but the tasks have never been based on the standard problem and the mental representation of the environment have never been manipulated.…”

Walking with the Scarecrow: The Information‐Processing Approach to Decision Research

“…We believe that an important direction for broadening the information-processing framework is to examine how processes of judgment that evoke little or no attention demands (System 1 thinking) interact with "higher" level, attention-demanding, cognitive processes (System 2 thinking). One hypothesis is that an initial judgment involving little or no effort and no conscious awareness is arrived at quickly via System 1 thinking and that such an initial judgment may then either be expressed immediately or be confirmed or corrected by more effortful, conscious, System 2 processing (e.g., Cobos, Almaraz, & García-Madruga, 2003).…”

The Conjunction and Disjunction Fallacies: Explanations of the Linda Problem by the Equate-to-Differentiate Model

“…According to the CWA (Nilsson et al 2009 ), P ( A ∧ B ) = β P ( A ) + (1- β ) P ( B ) (where P ( A ) < P ( B ) and the coefficient β is the relative weight of the two components, .5 < β < 1). The CWA predicts that both P ( A ) and P ( B ) should influence P ( A ∧ B ), and that P ( A ) should attract more weight than P ( B ) if regression analyses are run with smaller and larger constituent probabilities as factors and conjunctive probabilities as dependent variables (also see Fisk 2002 and Cobos et al 2003 ).…”