解码二元社会互动的分层特质-状态模型

ArXiv Pub Date : 2024-11-19
Qianying Wu, Shigeki Nakauchi, Mohammad Shehata, Shinsuke Shimojo
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引用次数: 0

摘要

特质是大脑信号和行为的模式,随时间而稳定,但因人而异;而状态则是阶段性模式,随时间而变化,受环境影响,但围绕特质摆动。社会互动的质量取决于互动者的特质和状态。然而,如何从同一组大脑信号中解读特质和状态仍是一个未知数。为了探索隐藏的神经特征和状态与社会互动过程中的行为特征和状态之间的关系,我们开发了一个管道,从团队流动任务中收集的脑电图(EEG)数据中提取大脑的潜在维度。我们的流程包括两个降维阶段:首先是非负矩阵因式分解(NMF),然后是线性判别分析(LDA)。这一方法产生了一个可解释的七维脑电潜在空间,它揭示了一种特质-状态分层结构,宏观分层捕捉神经特质,微观分层捕捉神经状态。在七个潜在维度中,我们发现有三个维度对不同个体和任务状态下的差异有显著影响。通过表征相似性分析,我们将脑电图潜空间映射到了技能认知空间,从而建立了隐藏神经特征与社会互动行为之间的联系。我们的方法证明了在一个模型中同时表示特质和状态的可行性,该模型与社会行为的变化相关联。
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Hierarchical Trait-State Model for Decoding Dyadic Social Interactions.

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: first, 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-state hierarchical structure, with macro-segregation capturing neural traits and micro-segregation capturing neural states. Out of the seven latent dimensions, we found that 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.

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