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Cited by 35Neural and Behavioral Psychology StudiesPsychometric Methodologies and TestingAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes

Enhancing the psychometric properties of the Iowa gambling task using full generative modeling

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Sullivan-Toole, Holly, Haines, Nathaniel, Dale, Kristina, Olino, Thomas M.·2022·Computational Psychiatry
DOI 10.5334/cpsy.89PMC PMC10275579
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Abstract

Poor psychometrics, particularly low test-retest reliability, pose a major challenge for using behavioral tasks in individual differences research. Here, we show that full generative modeling of the Iowa Gambling Task (IGT) substantially improved test-retest reliability and enhanced the IGT’s validity for use in characterizing internalizing pathology, compared to the traditional analytic approach. IGT data (n =50) was collected across two sessions, one month apart. Our full generative model incorporated (1) the Outcome Representation Learning (ORL) computational model at the person-level and (2) a group-level model that explicitly modeled test-retest reliability, along with other group-level effects. Compared to the traditional ‘summary score’ (proportion good decks selected), the ORL model provides a theoretically rich set of performance metrics (Reward Learning Rate (A+), Punishment Learning Rate (A-), Win Frequency Sensitivity (βf), Perseveration Tendency (βp), Memory Decay (K)), capturing distinct psychological processes. While test-retest reliability for the traditional summary score was only moderate (r = .37, BCa 95% CI [.04, .63]), test-retest reliabilities for ORL performance metrics produced by the full generative were substantially improved, with test-retest correlations ranging between r = .64–.82 for the five ORL parameters. Further, while summary scores showed no substantial associations with internalizing symptoms, ORL parameters were significantly associated with internalizing symptoms. Specifically, Punishment Learning Rate was associated with higher self-reported depression and Perseveration Tendency was associated with lower self-reported anhedonia. Generative modeling offers promise for advancing individual differences research using the IGT, and behavioral tasks more generally, through enhancing task psychometrics.

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Cited by

Sorted by citations

Authors
Sullivan-Toole, H., Haines, N., Dale, K., & Olino, T. M.
Affiliations
  • The Ohio State University (US)
  • Temple University (US)
ORCID
Venue
Computational Psychiatry
Publisher
Ubiquity Press, Ltd.
Published
2022
Volume
6
Issue
1
Pages
189-212
ISSN
2379-6227 (ISSN-L)
DOI
10.5334/cpsy.89
PMC
PMC10275579
OpenAlex
W4293108872
Cited by
35
References
7
Citations by year
2026: 12 · 2025: 8 · 2024: 9 · 2023: 4 · 2022: 2
FWCI
2.91
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Computational Psychiatry (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • TUScholarShare (Temple University) (submittedVersion)
  • TUScholarShare (Temple University) (submittedVersion, cc-by)
  • PsyArXiv (OSF Preprints) (submittedVersion)
Funders
National Institute of Mental Health (F32 MH127948-01A1 (Sullivan-Toole))
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Keywords
Iowa Gambling Task, test-retest reliability, generative models, internalizing problems, depression, anhedonia, perseveration, individual differences

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