Wu Qianying, Nakauchi Shigeki, Shehata Mohammad, Shimojo Shinsuke
Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, 91106.
Department of Computer Science and Engineering, Toyohashi University of Technology, Toyohashi, Japan, 441-8122.
ArXiv. 2024 Nov 19:arXiv:2411.12145v1.
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.
特质是随时间稳定但个体间存在差异的大脑信号和行为模式，而状态是随时间变化、受环境影响且围绕特质波动的阶段性模式。社交互动的质量取决于互动主体的特质和状态。然而，如何从同一组大脑信号中解读特质和状态仍不清楚。为了探索社交互动过程中与行为相关的隐藏神经特质和状态，我们开发了一种流程，用于从团队流动任务期间收集的脑电图（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：首先是非负矩阵分解（NMF），然后是线性判别分析（LDA）。这个流程产生了一个可解释的七维EEG潜在空间，揭示了一种特质 - 状态层次结构，其中宏观分离捕获神经特质，微观分离捕获神经状态。在这七个潜在维度中，我们发现有三个对个体间和任务状态的变化有显著贡献。使用表征相似性分析，我们将EEG潜在空间映射到技能 - 认知空间，建立了隐藏神经特征与社交互动行为之间的联系。我们的方法证明了在与社交行为变化相关的单一模型中表示特质和状态的可行性。
