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. 2023 Oct 30;14:6922. doi: 10.1038/s41467-023-42540-8

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© The Author(s) 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 4 — For each measure in each dataset (A Luna; B NCANDA; C NKI; D PNC), model (GAM) comparisons were used to identify the percentage of age-related deviance attributed to a measure and domain-specific process (hashed bars) from that of a domain-general process (filled bars). Specific effects represent the incremental deviance attributed to that measure over a single, equally weighted composite metric of measures from the held-out domain (e.g., for inhibition and inhibition/switching measures in the Luna sample [Antisaccade, Fixation, Mixed Antisaccade] the composite metric was composed of working memory and planning measures; see Supplementary Fig. S8 for workflow visualization, Methods). Through model comparison, nonspecific effects are attributed to domain general effects. E All Measures effects are estimated via three-level meta-analysis nesting effects in measures and datasets.