利用多模态测量探索认知负荷与情绪唤醒的动态相互作用：情绪投入任务中瞳孔直径与情绪唤醒的相关性
Exploring the dynamic interplay of cognitive load and emotional arousal by using multimodal measurements: Correlation of pupil diameter and emotional arousal in emotionally engaging tasks
AI总结：
本研究通过同步采集眼动追踪与FACTs深度学习情绪识别数据，分析道德困境任务中瞳孔直径与情绪唤醒的相关性及时间滞后，发现仅高唤醒时期呈显著负相关，平均滞后2.8 ms。
摘要：
基于来自最先进传感器技术的数据流进行的多模态数据分析与验证，例如眼动追踪，或利用面部动作编码系统（Facial Action Coding System，FACTs）结合深度学习进行情绪识别，使教育研究人员能够研究多层面的学习与问题解决过程，并改善教育体验。本研究旨在考察两个连续传感器数据流之间的相关性，即作为认知工作负荷指标的瞳孔直径与作为情绪唤醒指标的结合深度学习的FACTs之间的相关性（研究问题1a），并专门针对高、中、低唤醒时期进行分析（研究问题1b）。此外，还将分析情绪唤醒与瞳孔直径数据之间的时间滞后（研究问题2）。28名参与者处理了三项具有认知要求且能引发情绪投入的日常道德困境，同时收集眼动追踪和情绪识别数据。数据在Phyton中进行预处理（同步、眨眼控制、降采样），并使用相关分析和Granger因果检验进行分析。结果显示，情绪唤醒与瞳孔直径的数据流之间存在负相关且具有统计显著性的相关关系。然而，这种负相关且显著的关系仅存在于高唤醒时期，而在中唤醒或低唤醒时期发现的关系为正相关但不显著。唤醒与瞳孔直径之间关系的平均时间滞后为2.8 ms。与以往未采用多模态方法、表明这些构念之间存在正相关的研究结果不同，本研究结果通过强调多模态数据验证和收敛变异性研究的重要性，为研究现状作出了贡献。未来研究应考虑情绪调节策略和情绪效价。
英文摘要：
Multimodal data analysis and validation based on streams from state-of-the-art sensor technology such as eye-tracking or emotion recognition using the Facial Action Coding System (FACTs) with deep learning allows educational researchers to study multifaceted learning and problem-solving processes and to improve educational experiences. This study aims to investigate the correlation between two continuous sensor streams, pupil diameter as an indicator of cognitive workload and FACTs with deep learning as an indicator of emotional arousal (RQ 1a), specifically for epochs of high, medium, and low arousal (RQ 1b). Furthermore, the time lag between emotional arousal and pupil diameter data will be analyzed (RQ 2). 28 participants worked on three cognitively demanding and emotionally engaging everyday moral dilemmas while eye-tracking and emotion recognition data were collected. The data were pre-processed in Phyton (synchronization, blink control, downsampling) and analyzed using correlation analysis and Granger causality tests. The results show negative and statistically significant correlations between the data streams for emotional arousal and pupil diameter. However, the correlation is negative and significant only for epochs of high arousal, while positive but non-significant relationships were found for epochs of medium or low arousal. The average time lag for the relationship between arousal and pupil diameter was 2.8 ms. In contrast to previous findings without a multimodal approach suggesting a positive correlation between the constructs, the results contribute to the state of research by highlighting the importance of multimodal data validation and research on convergent vagility. Future research should consider emotional regulation strategies and emotional valence.
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