Computational Psychiatry (Aug 2022)
Enhancing the Psychometric Properties of the Iowa Gambling Task Using Full Generative Modeling
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' (β'f'), 'Perseveration Tendency' (β'p'), '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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