A hierarchical trait and state model for decoding dyadic social interactions
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1.
California Institute of Technology
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2.
Toyohashi University of Technology
Abstract
Traits are patterns of brain signals and behaviors that are stable over time but differ across individuals, whereas states are phasic patterns that vary over time, are influenced by the environment, yet oscillate around the traits. The quality of a social interaction depends on the traits and states of the interacting agents. However, it remains unclear how to decipher both traits and states from the same set of brain signals. To explore the hidden neural traits and states in relation to the behavioral ones during social interactions, we developed a pipeline to extract latent dimensions of the brain from electroencephalogram (EEG) data collected during a team flow task. Our pipeline involved two stages of dimensionality reduction: non-negative matrix factorization (NMF), followed by linear discriminant analysis (LDA). This pipeline resulted in an interpretable, seven-dimensional EEG latent space that revealed a trait to state (trait-state) hierarchical structure, with macro-segregation capturing neural traits and micro-segregation capturing neural states. Out of the seven latent dimensions, we found three that significantly contributed to variations across individuals and task states. Using representational similarity analysis, we mapped the EEG latent space to a skill-cognition space, establishing a connection between hidden neural signatures and social interaction behaviors. Our method demonstrates the feasibility of representing both traits and states within a single model that correlates with changes in social behavior.
Copyright and License
© The Author(s) 2025. This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Acknowledgement
This work has been supported by Japan Society for Promotion of Science (JSPS), Grants-in-Aid for Scientific Research (Fostering Joint International Research(B), Grant Number 18KK0280 for MS and SN. Japan Science and Technology Agency (JST) - Moonshot Research and Development, Grant Number JPMJMS2295-03 for SS and MS. QW was supported by the Caltech NIMH Conte Center (P50MH094258) and Tianqiao and Chrissy Chen Graduate Fellowship, and a grant from the Simons Foundation Autism Initiative to R. Adolphs. We thank Ralph Adolphs and Yue Xu for a helpful discussion and feedback.
Funding
This work has been supported by Japan Society for Promotion of Science (JSPS), Grants-in-Aid for Scientific Research (Fostering Joint International Research(B), Grant Number 18KK0280 for MS and SN. Japan Science and Technology Agency (JST) - Moonshot Research and Development, Grant Number JPMJMS2295-03 for SS and MS. QW was supported by the Caltech NIMH Conte Center (P50MH094258) and Tianqiao and Chrissy Chen Graduate Fellowship, and a grant from the Simons Foundation Autism Initiative to R. Adolphs.
Data Availability
The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. The analysis code used in this study is publicly available at https://github.com/wuqy052/team_flow_latent_trait_state.
Code Availability
The analysis code used in this study is publicly available at https://github.com/wuqy052/team_flow_latent_trait_state.
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Additional details
Related works
- Describes
- Journal Article: https://rdcu.be/eV4Q8 (URL)
Funding
- Tianqiao and Chrissy Chen Graduate Fellowship
- Japan Society for the Promotion of Science
- 18KK0280
- Japan Science and Technology Agency
- JPMJMS2295-03
- Simons Foundation
- National Institute of Mental Health
- P50MH094258