Bayesian techniques for analyzing group differences in the Iowa Gambling Task : A case study of intuitive and deliberate decision-makers

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14230%2F18%3A00102037" target="_blank" >RIV/00216224:14230/18:00102037 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.3758/s13423-017-1331-7" target="_blank" >https://doi.org/10.3758/s13423-017-1331-7</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3758/s13423-017-1331-7" target="_blank" >10.3758/s13423-017-1331-7</a>

Result language

angličtina

Original language name

Bayesian techniques for analyzing group differences in the Iowa Gambling Task : A case study of intuitive and deliberate decision-makers

Original language description

The Iowa Gambling Task (IGT) is one of the most popular experimental paradigms for comparing complex decision-making across groups. Most commonly, IGT behavior is analyzed using frequentist tests to compare performance across groups, and to compare inferred parameters of cognitive models developed for the IGT. Here, we present a Bayesian alternative based on Bayesian repeated-measures ANOVA for comparing performance, and a suite of three complementary model-based methods for assessing the cognitive processes underlying IGT performance. The three model-based methods involve Bayesian hierarchical parameter estimation, Bayes factor model comparison, and Bayesian latent-mixture modeling. We illustrate these Bayesian methods by applying them to test the extent to which differences in intuitive versus deliberate decision style are associated with differences in IGT performance. The results show that intuitive and deliberate decision-makers behave similarly on the IGT, and the modeling analyses consistently suggest that both groups of decision-makers rely on similar cognitive processes. Our results challenge the notion that individual differences in intuitive and deliberate decision styles have a broad impact on decision-making. They also highlight the advantages of Bayesian methods, especially their ability to quantify evidence in favor of the null hypothesis, and that they allow model-based analyses to incorporate hierarchical and latent-mixture structures.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

50100 - Psychology and cognitive sciences

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Psychonomic Bulletin & Review

ISSN

1069-9384

e-ISSN

1531-5320

Volume of the periodical

25

Issue of the periodical within the volume

3

Country of publishing house

US - UNITED STATES

Number of pages

20

Pages from-to

951-970

UT code for WoS article

000434642400006

EID of the result in the Scopus database

2-s2.0-85021973840