Emotional Activity Is Negatively Associated With Cognitive Load in Multimedia Learning: A Case Study With EEG Signals - Guo, Xiang; Zhu, Tianshui; Wu, Chennan; Bao, Zongliang; Liu, Yang (Frontiers in Psychology, 2022)
We aimed to investigate the relationship between emotional activity and cognitive load during multimedia learning from an emotion dynamics perspective using EEG) signals. Using a between-subjects design, 42 university students were randomly assigned to two video lecture conditions (color-coded versus grayscale). While the participants watched the assigned video, their EEG signals were recorded. After processing the EEG signals, we employed the correlation-based feature selector (CFS) method to identify emotion-related subject-independent features. We then put these features into the Isomap model to obtain a one-dimensional trajectory of emotional changes. Next, we used the zero-crossing rate (ZCR) as the quantitative characterization of emotional changes ZCR_EC. Meanwhile, we extracted cognitive load-related features to analyze the degree of cognitive load (CLI). We employed a linear regression fitting method to study the relationship between ZCR_EC and CLI. We conducted this study from two perspectives. One is the frequency domain method (wavelet feature), and the other is the nonlinear dynamic method (approximate entropy). The results indicate that emotional activity is negatively associated with cognitive load. These findings have practical implications for designing video lectures for multimedia learning. Learning material should reduce learners’ cognitive load to keep their emotional experience at optimal levels to enhance learning.