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. 2024 Feb 3;14:76. doi: 10.1038/s41398-024-02802-5

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© The Author(s) 2024

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. 2 — Cross-validation ratio of feature weights (A) and sign-based consistency (B) for the FACEsync model. The features depicted correspond to the person-specific adaptation of intensity of a participant to their dyadic counterpart in the respective facial action units (AU) for either hobbies or mealplanning task (min minimum, sd standard deviation, max maximum).