Test-retest reliability of behavioral and computational measures of advice taking under volatility.

ABSTRACT: The development of computational models for studying mental disorders is on the rise. However, their psychometric properties remain understudied, posing a risk of undermining their use in empirical research and clinical translation. Here we investigated test-retest reliability (with a 2-week interval) of a computational assay probing advice-taking under volatility with a Hierarchical Gaussian Filter (HGF) model. In a sample of 39 healthy participants, we found the computational measures to have largely poor reliability (intra-class correlation coefficient or ICC < 0.5), on par with the behavioral measures of task performance. Further analysis revealed that reliability was substantially impacted by intrinsic measurement noise (indicated by parameter recovery analysis) and to a smaller exten

SUBMITTER: Karvelis P 

PROVIDER: S-EPMC11573178 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature