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
C. Kosel, S. Michel, T. Seidel, M. Foerster

TL;DR
This study investigates the relationship between cognitive load and emotional arousal using multimodal sensor data, revealing significant correlations during high arousal states and emphasizing the importance of multimodal validation in educational research.
Contribution
It introduces a multimodal approach combining eye-tracking and emotion recognition to analyze cognitive and emotional interactions during problem-solving tasks, highlighting the significance of data validation.
Findings
Negative correlation between arousal and pupil diameter during high arousal epochs
Average time lag between arousal and pupil diameter was 2.8 ms
Significant correlation only during high arousal states
Abstract
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…
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Taxonomy
TopicsHuman-Automation Interaction and Safety · Color perception and design
