A hierarchical trait and state model for decoding dyadic social interactions.

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

SUBMITTER: Wu Q 

PROVIDER: S-EPMC11968930 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature