Immediate gain is long-term loss: Are there foresighted decision makers in the Iowa Gambling Task?

Chiu, Yao Chu; Lin, Ching-Hung; Huang, Jong Tsun; Lin, Saulwood; Lee, Po Lei; Hsieh, Jen Chuen

doi:10.1186/1744-9081-4-13

Cited by 92 publications

(155 citation statements)

References 50 publications

Supporting

Mentioning

141

Contrasting

Order By: Relevance

“…The crucial difference with crossvalidation is that in the generalization criterion method, the training set and the test set do not overlap in terms of experimental design. For instance, Ahn, Busemeyer, Wagenmakers, and Stout (2008) compared several models of reinforcement learning by fitting them to one experiment (e.g., the Iowa gambling task; Bechara, Damasio, Damasio, & Anderson, 1994) and evaluating them on a different experiment (e.g., the Soochow gambling task; Chiu et al, 2005).…”

Section: The Generalization Criterion Methodsmentioning

confidence: 99%

A Survey of Model Evaluation Approaches With a Tutorial on Hierarchical Bayesian Methods

et al. 2008

View full text Add to dashboard Cite

This article reviews current methods for evaluating models in the cognitive sciences, including theoretically based approaches, such as Bayes factors and minimum description length measures; simulation approaches, including model mimicry evaluations; and practical approaches, such as validation and generalization measures. This article argues that, although often useful in specific settings, most of these approaches are limited in their ability to give a general assessment of models. This article argues that hierarchical methods, generally, and hierarchical Bayesian methods, specifically, can provide a more thorough evaluation of models in the cognitive sciences. This article presents two worked examples of hierarchical Bayesian analyses to demonstrate how the approach addresses key questions of descriptive adequacy, parameter interference, prediction, and generalization in principled and coherent ways.

show abstract

Section: The Generalization Criterion Methodsmentioning

confidence: 99%

A Survey of Model Evaluation Approaches With a Tutorial on Hierarchical Bayesian Methods

et al. 2008

View full text Add to dashboard Cite

show abstract

“…In contrast, in the SGT, although advantageous decks have the same average expected values as those in the IGT, normal decision makers (young healthy adults) were unable to learn which decks are good decks and, therefore, lost money (Chiu et al, 2008). In this study, the same data patterns were replicated using a within-subjects design, and the data sets were used to further evaluate decision learning models.…”

Section: Experiments and Basic Findingsmentioning

confidence: 98%

Comparison of Decision Learning Models Using the Generalization Criterion Method

Ahn¹,

Busemeyer²,

Wagenmakers³

et al. 2008

Cognitive Science

213

366

View full text Add to dashboard Cite

It is a hallmark of a good model to make accurate a priori predictions to new conditions (Busemeyer & Wang, 2000). This study compared 8 decision learning models with respect to their generalizability. Participants performed 2 tasks (the Iowa Gambling Task and the Soochow Gambling Task), and each model made a priori predictions by estimating the parameters for each participant from 1 task and using those same parameters to predict on the other task. Three methods were used to evaluate the models at the individual level of analysis. The first method used a post hoc fit criterion, the second method used a generalization criterion for short-term predictions, and the third method again used a generalization criterion for long-term predictions. The results suggest that the models with the prospect utility function can make generalizable predictions to new conditions, and different learning models are needed for making short-versus long-term predictions on simple gambling tasks.

show abstract

“…Therefore, a growing number of studies recommend considering gain-loss frequency score (GLF score; e.g. Chiu et al, 2008), calculated by subtracting the number of choices from the low-loss frequency decks (B + D) and the high-loss frequency decks (A + C) (e.g. Lin, Song, Chen, Lee, & Chiu, 2013).…”

Section: Introductionmentioning

confidence: 99%

Decision-making of Prefrontal Patients with the Iowa Gambling Task: Unexpected Spared Performances and Preliminary Evidence for the Need of Alternative Measures

Besnard

Allain

Aubin

et al. 2015

The Clinical Neuropsychologist

View full text Add to dashboard Cite

show abstract

Immediate gain is long-term loss: Are there foresighted decision makers in the Iowa Gambling Task?

Cited by 92 publications

References 50 publications

A Survey of Model Evaluation Approaches With a Tutorial on Hierarchical Bayesian Methods

A Survey of Model Evaluation Approaches With a Tutorial on Hierarchical Bayesian Methods

Comparison of Decision Learning Models Using the Generalization Criterion Method

Decision-making of Prefrontal Patients with the Iowa Gambling Task: Unexpected Spared Performances and Preliminary Evidence for the Need of Alternative Measures

Contact Info

Product

Resources

About