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  1. A model of heuristic judgment.Daniel Kahneman & Shane Frederick - 2005 - In K. Holyoak & B. Morrison (eds.), The Cambridge handbook of thinking and reasoning. Cambridge, England: Cambridge University Press. pp. 267--293.
    The program of research now known as the heuristics and biases approach began with a study of the statistical intuitions of experts, who were found to be excessively confident in the replicability of results from small samples. The persistence of such systematic errors in the intuitions of experts implied that their intuitive judgments may be governed by fundamentally different processes than the slower, more deliberate computations they had been trained to execute. The ancient idea that cognitive processes can be partitioned (...)
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  • Probabilistic reasoning in clinical medicine: Problems and opportunities.David M. Eddy - 1982 - In Daniel Kahneman, Paul Slovic & Amos Tversky (eds.), Judgment Under Uncertainty: Heuristics and Biases. Cambridge University Press. pp. 249--267.
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  • Base-rate respect: From ecological rationality to dual processes.Aron K. Barbey & Steven A. Sloman - 2007 - Behavioral and Brain Sciences 30 (3):241-254.
    The phenomenon of base-rate neglect has elicited much debate. One arena of debate concerns how people make judgments under conditions of uncertainty. Another more controversial arena concerns human rationality. In this target article, we attempt to unpack the perspectives in the literature on both kinds of issues and evaluate their ability to explain existing data and their conceptual coherence. From this evaluation we conclude that the best account of the data should be framed in terms of a dual-process model of (...)
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  • Overcoming difficulties in Bayesian reasoning: A reply to Lewis and Keren (1999) and Mellers and McGraw (1999).Gerd Gigerenzer & Ulrich Hoffrage - 1999 - Psychological Review 106 (2):425-430.
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  • The empirical case for two systems of reasoning.Steven A. Sloman - 1996 - Psychological Bulletin 119 (1):3-22.
    Distinctions have been proposed between systems of reasoning for centuries. This article distills properties shared by many of these distinctions and characterizes the resulting systems in light of recent findings and theoretical developments. One system is associative because its computations reflect similarity structure and relations of temporal contiguity. The other is "rule based" because it operates on symbolic structures that have logical content and variables and because its computations have the properties that are normally assigned to rules. The systems serve (...)
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  • Representation facilitates reasoning: what natural frequencies are and what they are not.U. Hoffrage - 2002 - Cognition 84 (3):343-352.
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  • Evolutionary modules and Bayesian facilitation: The role of general cognitive resources.Elise Lesage, Gorka Navarrete & Wim De Neys - 2013 - Thinking and Reasoning 19 (1):27 - 53.
    (2013). Evolutionary modules and Bayesian facilitation: The role of general cognitive resources. Thinking & Reasoning: Vol. 19, No. 1, pp. 27-53. doi: 10.1080/13546783.2012.713177.
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  • How to improve Bayesian reasoning: Comment on Gigerenzer and Hoffrage (1995).Barbara A. Mellers & A. Peter McGraw - 1999 - Psychological Review 106 (2):417-424.
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  • Solving probabilistic and statistical problems: a matter of information structure and question form.Vittorio Girotto & Michel Gonzalez - 2001 - Cognition 78 (3):247-276.
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  • How to improve Bayesian reasoning without instruction: Frequency formats.Gerd Gigerenzer & Ulrich Hoffrage - 1995 - Psychological Review 102 (4):684-704.
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  • Visual aids improve diagnostic inferences and metacognitive judgment calibration.Rocio Garcia-Retamero, Edward T. Cokely & Ulrich Hoffrage - 2015 - Frontiers in Psychology 6:136977.
    Visual aids can improve comprehension of risks associated with medical treatments, screenings, and lifestyles. Do visual aids also help decision makers accurately assess their risk comprehension? That is, do visual aids help them become well calibrated? To address these questions, we investigated the benefits of visual aids displaying numerical information and measured accuracy of self-assessment of diagnostic inferences (i.e., metacognitive judgment calibration) controlling for individual differences in numeracy. Participants included 108 patients who made diagnostic inferences about three medical tests on (...)
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  • Are humans good intuitive statisticians after all? Rethinking some conclusions from the literature on judgment under uncertainty.L. Cosmides - 1996 - Cognition 58 (1):1-73.
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  • MINERVA-DM: A memory processes model for judgments of likelihood.Michael R. P. Dougherty, Charles F. Gettys & Eve E. Ogden - 1999 - Psychological Review 106 (1):180-209.
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  • On the difficulties underlying Bayesian reasoning: A comment on Gigerenzer and Hoffrage.Charles Lewis & Gideon Keren - 1999 - Psychological Review 106 (2):411-416.
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  • Instruction in information structuring improves Bayesian judgment in intelligence analysts.David R. Mandel - 2015 - Frontiers in Psychology 6:137593.
    An experiment was conducted to test the effectiveness of brief instruction in information structuring (i.e., representing and integrating information) for improving the coherence of probability judgments and binary choices among intelligence analysts. Forty-three analysts were presented with comparable sets of Bayesian judgment problems before and immediately after instruction. After instruction, analysts’ probability judgments were more coherent (i.e., more additive and compliant with Bayes theorem). Instruction also improved the coherence of binary choices regarding category membership: after instruction, subjects were more likely (...)
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  • Teaching Bayesian reasoning in less than two hours.Peter Sedlmeier & Gerd Gigerenzer - 2001 - Journal of Experimental Psychology: General 130 (3):380.
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  • Communicating risk in prenatal screening: the consequences of Bayesian misapprehension.Gorka Navarrete, Rut Correia & Dan Froimovitch - 2014 - Frontiers in Psychology 5.
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  • Chances and frequencies in probabilistic reasoning: rejoinder to Hoffrage, Gigerenzer, Krauss, and Martignon.V. Girotto - 2002 - Cognition 84 (3):353-359.
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