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As online mental health support groups become increasingly popular, they require more support from volunteers and trained moderators who help their users through "interventions" (i.e., responding to questions and providing support). We present a system that supports such human interventions using Natural Language Generation (NLG) techniques. The system generates draft responses aimed at reducing moderators' workload, and improving their efficacy. NLG and human interventions were compared through the ratings of 35 psychology interns. The NLG-based system was capable of generating messages that are grammatically correct with clear language. The system needs improvement, however, moderators can already use it as draft responses. [ABSTRACT FROM AUTHOR]

Cognitive load (CL) 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 CL from nonverbal information challenging. This paper investigates automatic CL detection from facial features, physiology and task performance under affective interference. Data were collected from participants (n=20) solving mental arithmetic tasks with emotional stimuli in the background, and a combined classifier was used for detecting CL levels. Results indicate that the face modality for CL 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 an increasing intensity of emotional arousal. [ABSTRACT FROM AUTHOR]

This paper presents the evaluation of the synchronization of three emotional measurement methods (automatic facial expression recognition, self-report, electrodermal activity) and their agreement regarding learners’ emotions. Data were collected from 67 undergraduates enrolled at a North American University whom learned about a complex science topic while interacting with MetaTutor, a multi-agent computerized learning environment. Videos of learners’ facial expressions captured with a webcam were analyzed using automatic facial recognition software (FaceReader 5.0). Learners’ physiological arousal was recorded using Affectiva’s Q-Sensor 2.0 electrodermal activity measurement bracelet. Learners’ self-reported their experience of 19 different emotional states on five different occasions during the learning session, which were used as markers to synchronize data from FaceReader and Q-Sensor. We found a high agreement between the facial and self-report data (75.6%), but low levels of agreement between them and the Q-Sensor data, suggesting that a tightly coupled relationship does not always exist between emotional response components. [ABSTRACT FROM AUTHOR]