Neural quantification of emotion influencing learning based on dynamic brain network analyses.

Emotions have a significant impact on learning processes. Different aspects of emotion and learning, including attention and motivation, have been widely explored. However, the underlying emotional factors that explicitly affect the learning process remain unclear. To this end, we designed an emotion-learning paradigm that incorporates videos to elicit emotions and reading materials as the learning content, complemented by a series of behavioral tests. Initially, we employed multiscale entropy (MSE) to quantify the brain complexity of EEG signals across various emotional states during the learning process. Subsequently, we extracted specific neural rhythms from EEG signals using masking empirical mode decomposition (MEMD) and constructed the dynamic brain network of the learning process by sliding window and phase locking value (PLV). Brain networks, including the prefrontal-temporal reading and the frontal-parietal cognitive control networks, in the learning stage were examined with their global efficiency, clustering coefficient, and local efficiency calculated. The results indicated that the group experiencing negative emotions exhibited a lower rate of information integration, despite higher brain complexity. This may be attributed to the additional cognitive resource consumption triggered by negative emotions. Conversely, subjects experiencing positive emotions demonstrated not only lower brain complexity during the learning process but also higher rates of information integration and improved learning performance.