We favor formal models of heuristics rather than lists of loose dichotomies: A reply to Evans and Over

Détails

ID Serval
serval:BIB_B3B7CDEAA5FD
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
We favor formal models of heuristics rather than lists of loose dichotomies: A reply to Evans and Over
Périodique
Cognitive Processing
Auteur⸱e⸱s
Marewski J. N., Gaissmaier W., Gigerenzer G.
ISSN
1612-4782
Statut éditorial
Publié
Date de publication
05/2010
Peer-reviewed
Oui
Volume
11
Numéro
2
Pages
177-179
Langue
anglais
Résumé
In their comment on Marewski et al. (good judgments do not require complex cognition, 2009) Evans and Over (heuristic thinking and human intelligence: a commentary on Marewski, Gaissmaier and Gigerenzer, 2009) conjectured that heuristics can often lead to biases and are not error free. This is a most surprising critique. The computational models of heuristics we have tested allow for quantitative predictions of how many errors a given heuristic will make, and we and others have measured the amount of error by analysis, computer simulation, and experiment. This is clear progress over simply giving heuristics labels, such as availability, that do not allow for quantitative comparisons of errors. Evans and Over argue that the reason people rely on heuristics is the accuracy-effort trade-off. However, the comparison between heuristics and more effortful strategies, such as multiple regression, has shown that there are many situations in which a heuristic is more accurate with less effort. Finally, we do not see how the fast and frugal heuristics program could benefit from a dual-process framework unless the dual-process framework is made more precise. Instead, the dual-process framework could benefit if its two "black boxes" (Type 1 and Type 2 processes) were substituted by computational models of both heuristics and other processes.
Web of science
Open Access
Oui
Création de la notice
14/10/2011 13:03
Dernière modification de la notice
20/08/2019 16:22
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