Mixed emotion recognition and priming effect on cognitive control

Advanced emotion monitoring and intervention systems are crucial for human–machine collaboration to accomplish specific tasks. One key challenge is accurately identifying mixed emotions and strategically implementing measures to enhance individual cognitive control abilities through facilitating emotional state regulation. To achieve these objectives, this research consists of two parts: the development of mixed emotion recognition methods based on EEG signals and the study of the neural mechanisms underlying the impact of mixed emotions on cognitive control. The results reveal that the proposed DBN-GBDT (Deep Belief Network-Gradient Boosting Decision Tree) method outperformed the other two methods, attaining a high classification accuracy of 96.7 %. Furthermore, mixed emotions of different valences exert differential effects on cognitive control, which can be captured by a two-stage model involving the P200 (early attention engagement) and N300 (late conflict resolution) components. The preparatory mechanisms induced by positive mixed emotions and the adaptive mechanisms induced by heterogeneously mixed emotions contribute to conflict resolution and enhance cognitive control abilities. The primary contributions of this study lie in providing feasible solutions for machine understanding of mixed emotions and enhancing individual cognitive control abilities, which holds immense potential for applications in the field of human–machine collaboration.