Test-retest reliability of behavioral and computational measures of advice taking under volatility DOI Open Access
Povilas Karvelis, Daniel J. Hauke,

Michelle Wobmann

et al.

Published: Nov. 9, 2023

The development of computational models for studying mental disorders is on the rise. However, their psychometric properties remain understudied, posing a risk to undermine use in empirical research and clinical translation. Here we investigated test-retest reliability (with 2-week interval) assay probing advice-taking under volatility with Hierarchical Gaussian Filter (HGF) model. In sample 39 healthy participants, found measures have largely poor (intra-class correlation coefficient or ICC < 0.5), par behavioral task performance. Further analysis revealed that was substantially impacted by intrinsic measurement noise (indicated parameter recovery analysis) smaller extent practice effects. large portion within-subject variance remained unexplained may be attributable state-like fluctuations. Despite reliability, face validity at group level. Overall, our work highlights different sources affecting need studied greater detail. A better understanding these would facilitate design more psychometrically sound assays, which improve quality future increase probability

Language: Английский

Test-retest reliability of behavioral and computational measures of advice taking under volatility DOI Creative Commons
Povilas Karvelis, Daniel J. Hauke,

Michelle Wobmann

et al.

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(11), P. e0312255 - e0312255

Published: Nov. 18, 2024

The development of computational models for studying mental disorders is on the rise. However, their psychometric properties remain understudied, posing a risk undermining use in empirical research and clinical translation. Here we investigated test-retest reliability (with 2-week interval) assay probing advice-taking under volatility with Hierarchical Gaussian Filter (HGF) model. In sample 39 healthy participants, found measures to have largely poor (intra-class correlation coefficient or ICC < 0.5), par behavioral task performance. Further analysis revealed that was substantially impacted by intrinsic measurement noise (indicated parameter recovery analysis) smaller extent practice effects. large portion within-subject variance remained unexplained may be attributable state-like fluctuations. Despite reliability, face validity at group level. Overall, our work highlights different sources affecting need studied greater detail. A better understanding these would facilitate design more psychometrically sound assays, which improve quality future increase probability

Language: Английский

Citations

0
Test-retest reliability of behavioral and computational measures of advice taking under volatility DOI Open Access
Povilas Karvelis, Daniel J. Hauke,

Michelle Wobmann

et al.

Published: Nov. 9, 2023

The development of computational models for studying mental disorders is on the rise. However, their psychometric properties remain understudied, posing a risk to undermine use in empirical research and clinical translation. Here we investigated test-retest reliability (with 2-week interval) assay probing advice-taking under volatility with Hierarchical Gaussian Filter (HGF) model. In sample 39 healthy participants, found measures have largely poor (intra-class correlation coefficient or ICC &lt; 0.5), par behavioral task performance. Further analysis revealed that was substantially impacted by intrinsic measurement noise (indicated parameter recovery analysis) smaller extent practice effects. large portion within-subject variance remained unexplained may be attributable state-like fluctuations. Despite reliability, face validity at group level. Overall, our work highlights different sources affecting need studied greater detail. A better understanding these would facilitate design more psychometrically sound assays, which improve quality future increase probability

Language: Английский

Citations

1