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Enhancing the Psychometric Properties of the Iowa Gambling Task Using Full Generative Modeling
Haines, Nathaniel Dale, Kristina
Haines, Nathaniel
Dale, Kristina
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Journal article
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2022-08-26
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Psychology and Neuroscience
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https://doi.org/10.5334/cpsy.89
Abstract
Poor psychometrics, particularly low test-retest reliability, pose a major challenge for using behavioral tasks in individual differences research. Here, we demonstrate that full generative modeling of the Iowa Gambling Task (IGT) substantially improves test-retest reliability and may also enhance 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 model were substantially improved, with test-retest correlations ranging between r = .64–.82. 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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Sullivan-Toole, H., Haines, N., Dale, K., & Olino, T. M. (2022). Enhancing the Psychometric Properties of the Iowa Gambling Task Using Full Generative Modeling. Computational Psychiatry, 6(1), 189–212. DOI: https://doi.org/10.5334/cpsy.89
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Ubiquity Press
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Computational Psychiatry, Vol. 6, Iss. 1
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