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Nested sets theory, full stop: Explaining performance on bayesian inference tasks without dual-systems assumptions
Behavioral and Brain Sciences 30 (3):275-276 (2007)
Abstract
Consistent with Barbey & Sloman (B&S), it is proposed that performance on Bayesian inference tasks is well explained by nested sets theory (NST). However, contrary to those authors' view, it is proposed that NST does better by dispelling with dual-systems assumptions. This article examines why, and sketches out a series of NST's core principles, which were not previously definedMy notes
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Citations of this work
Instruction in information structuring improves Bayesian judgment in intelligence analysts.David R. Mandel - 2015 - Frontiers in Psychology 6:137593.
References found in this work
Individual differences in reasoning: Implications for the rationality debate?Keith E. Stanovich & Richard F. West - 2000 - Behavioral and Brain Sciences 23 (5):645-665.
How to improve Bayesian reasoning without instruction: Frequency formats.Gerd Gigerenzer & Ulrich Hoffrage - 1995 - Psychological Review 102 (4):684-704.
The base rate fallacy reconsidered: Descriptive, normative, and methodological challenges.Jonathan J. Koehler - 1996 - Behavioral and Brain Sciences 19 (1):1-17.
Naive probability: A mental model theory of extensional reasoning.Philip Johnson-Laird, Paolo Legrenzi, Vittorio Girotto, Maria Sonino Legrenzi & Jean-Paul Caverni - 1999 - Psychological Review 106 (1):62-88.
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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