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Automatic Cognitive Load Detection from Face, Physiology, Task Performance and Fusion During Affective Interference

Cognitive load is experienced during critical tasks and also while engaged emotional states are induced either by the task itself or by extraneous experiences. Emotions irrelevant to the working memory representation may interfere with the processing of relevant tasks and can influence task performance and behavior, making the accurate detection of cognitive load from nonverbal information challenging. This paper investigates automatic cognitive load detection from facial features, physiology and task performance under affective interference. Data was collected from participants (n=20) solving mental arithmetic tasks with emotional stimuli in the background and a combined classifier was used for detecting cognitive load levels. Results indicate that the face modality for cognitive load detection was more accurate under affective interference, whereas physiology and task performance were more accurate without the affective interference. Multimodal fusion improved detection accuracies, but it was less accurate under affective interferences. More specifically, the accuracy decreased with increasing intensity of emotional arousal.