Functional Graph Contrastive Learning of Hyperscanning EEG Reveals Emotional Contagion Evoked by Stereotype-Based Stressors

TL;DR

This paper introduces a novel neural analysis method using hyperscanning EEG and graph contrastive learning to understand emotional contagion caused by stereotype-based stress during collaborative tasks.

Contribution

It presents the functional Graph Contrastive Learning (fGCL) approach for extracting neural representations and analyzing emotional contagion mechanisms in social interactions.

Findings

Emotional contagion significantly influences performance trajectories.

fGCL effectively captures neural activity patterns related to emotional contagion.

The study reveals the neural dynamics of emotional contagion under stereotype-based stress.

Abstract

This study delves into the intricacies of emotional contagion and its impact on performance within dyadic interactions. Specifically, it focuses on the context of stereotype-based stress (SBS) during collaborative problem-solving tasks among female pairs. Through an exploration of emotional contagion, this study seeks to unveil its underlying mechanisms and effects. Leveraging EEG-based hyperscanning technology, we introduced an innovative approach known as the functional Graph Contrastive Learning (fGCL), which extracts subject-invariant representations of neural activity patterns from feedback trials. These representations are further subjected to analysis using the Dynamic Graph Classification (DGC) model, aimed at dissecting the process of emotional contagion along three independent temporal stages. The results underscore the substantial role of emotional contagion in shaping the trajectories of participants' performance during collaborative tasks in the presence of SBS conditions. Overall, our research contributes invaluable insights into the neural underpinnings of emotional contagion, thereby enriching our comprehension of the complexities underlying social interactions and emotional dynamics.