Modeling individual differences in the Iowa Gambling Task

2006 ◽  
Author(s):  
Jason L. Harman ◽  
Robert M. Roe
PLoS ONE ◽  
2013 ◽  
Vol 8 (11) ◽  
pp. e81498 ◽  
Author(s):  
Virginie Bagneux ◽  
Noémylle Thomassin ◽  
Corentin Gonthier ◽  
Jean-Luc Roulin

Assessment ◽  
2018 ◽  
Vol 27 (2) ◽  
pp. 232-245 ◽  
Author(s):  
Florian Schmitz ◽  
Olga Kunina-Habenicht ◽  
Andrea Hildebrandt ◽  
Klaus Oberauer ◽  
Oliver Wilhelm

The Iowa Gambling Task (IGT) is one of the most prominent paradigms employed for the assessment of risk taking in the laboratory, and it was shown to distinguish between various patient groups and controls. The present study was conducted to test the psychometric characteristics of the original IGT and of a new gambling task variant for assessing individual differences. Two studies were conducted with adults of the general population ( n = 220) and with adolescents ( n = 389). Participants were also tested on multiple measures of working memory capacity, fluid intelligence, personality traits associated with risk-taking behavior, and self-reported risk taking in various domains. Both gambling tasks had only moderate retest reliability within the same session. Moderate relations were obtained with cognitive ability. However, card selections in the gambling tasks were not correlated with personality or risk taking. These findings point to limitations of IGT type gambling tasks for the assessment of individual differences in risky decision making.


2019 ◽  
Author(s):  
Cristina Bañuelos ◽  
Timothy Verstynen

Value-based decision-making relies on effective communication across disparate brain networks. Given the scale of the networks involved in adaptive decision-making, variability in how they communicate should impact behavior; however, precisely how the topological pattern of structural connectivity of individual brain networks influences individual differences in value-based decision-making remains unclear. Using diffusion MRI, we measured structural connectivity networks in a sample of community dwelling adults (N=124). We used standard graph theoretic measures to characterize the topology of the networks in each individual and correlated individual differences in these topology measures with differences in the Iowa Gambling Task. A principal components regression approach revealed that individual differences in brain network topology associate with differences in optimal decision-making, as well as associate with differences in each participant’s sensitivity to high frequency rewards. These findings show that aspects of structural brain network organization can constrain how information is used in value-based decision-making.AbbreviationsMRI - Magnetic Resonance Imaging; IGT – Iowa Gambling Task; DWI – Diffusion Weighted Imaging; QSDR – Q-Space Diffeomorphic Reconstruction; PCA – Principal Components Analysis; GLM – Generalized Linear Models


Decision ◽  
2016 ◽  
Vol 3 (2) ◽  
pp. 115-131 ◽  
Author(s):  
Helen Steingroever ◽  
Ruud Wetzels ◽  
Eric-Jan Wagenmakers

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