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Complementary benefits of multivariate and hierarchical models for identifying individual differences in cognitive control.

Complementary benefits of multivariate and hierarchical models for identifying individual differences in cognitive control.

Authors :
Freund MC
Chen R
Chen G
Braver TS
Source :
BioRxiv : the preprint server for biology [bioRxiv] 2024 Nov 08. Date of Electronic Publication: 2024 Nov 08.
Publication Year :
2024

Abstract

Understanding individual differences in cognitive control is a central goal in psychology and neuroscience. Reliably measuring these differences, however, has proven extremely challenging, at least when using standard measures in cognitive neuroscience such as response times or task-based fMRI activity. While prior work has pinpointed the source of the issue - the vast amount of cross-trial variability within these measures - solutions remain elusive. Here, we propose one potential way forward: an analytic framework that combines hierarchical Bayesian modeling with multivariate decoding of trial-level fMRI data. Using this framework and longitudinal data from the Dual Mechanisms of Cognitive Control project, we estimated individuals' neural responses associated with cognitive control within a color-word Stroop task, then assessed the reliability of these individuals' responses across a time interval of several months. We show that in many prefrontal and parietal brain regions, test-retest reliability was near maximal, and that only hierarchical models were able to reveal this state of affairs. Further, when compared to traditional univariate contrasts, multivariate decoding enabled individual-level correlations to be estimated with significantly greater precision. We specifically link these improvements in precision to the optimized suppression of cross-trial variability in decoding. Together, these findings not only indicate that cognitive control-related neural responses individuate people in a highly stable manner across time, but also suggest that integrating hierarchical and multivariate models provides a powerful approach for investigating individual differences in cognitive control, one that can effectively address the issue of high-variability measures.<br />Competing Interests: Declaration of Competing Interests None declared.

Details

Language :
English
ISSN :
2692-8205
Database :
MEDLINE
Journal :
BioRxiv : the preprint server for biology
Publication Type :
Academic Journal
Accession number :
38712215
Full Text :
https://doi.org/10.1101/2024.04.24.591032