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Measures of Bayesian Reasoning Performance on ‘Normal’ and ‘Natural’ Frequency Tasks

Bibliographic Data

ID10123343
AuthorsRosemary Stock (0000-0003-0405-1019, University of West London, corresponding author), John E Fisk (0000-0002-2981-0870), Catharine Montgomery (0000-0003-2805-5807, Liverpool John Moores University)
Year2016
Volume143
Issue3
Pages185-214
Publication date2016-07-02
Open AccessNo
TypeARTICLE
VenueThe Journal of General Psychology (JOURNAL)
Journal identifiersISSN: 0022-1309 • E-ISSN: 1940-0888
PublisherTaylor & Francis (PUBLISHER • GB)
DOI10.1080/00221309.2016.1200531
PMID27410053
OpenAlexW2473764414
LanguageEN
References cited50

While the majority of similar studies examining Bayesian reasoning investigate how participants avoid common errors such as base-rate neglect, the current research also examines whether different formats (frequency and probability) lead to a difference in levels of absolute accuracy. In Study One, older (≥60 years) and younger (18 to 29 years) participants completed tasks in probability and normalized frequency formats. In Study 2, participants completed tasks in probability and natural frequency formats. Findings are that frequencies lead to less over-estimation, particularly in natural frequency tasks, which also reveal an interaction between age and task format whereby older adults seem unaffected by format. There was no association found between format and the avoidance of errors such as base-rate neglect. Findings are discussed in the light of dual and multi-process theories of reasoning, having failed to support the theory that frequency formats elicit System 2 reasoning processes

Association (psychology · Bayes' theorem · Bayesian inference · Bayesian probability · Cognition · Cognitive psychology · Dual (grammatical number · Dual process theory (moral psychology · Frequency · Frequentist probability · Natural (archaeology · Neglect · Process (computing · Statistics · Task (project management · Artificial Intelligence · Clinical Reasoning and Diagnostic Skills · Computer Science · Decision-Making and Behavioral Economics · Mathematics · Psychology

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