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 se

SUBMITTER: Sullivan-Toole H 

PROVIDER: S-EPMC10275579 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature