Analyzing implicit intervention of rumination during web browsing

Abstract

In the current highly developed information society, having a habit of rumination can be dangerous for mental health. We built a system integrating the ACT-R cognitive model and nudge to prevent rumination during web browsing. Participants were divided based on ACT-R models that they used into two groups: the control group (as Normal: NOR Model) and the test group (as inverted: INV Model). For each group, the task was divided into the mood-induction task (MI) and the main task (MT). Our aim is to detect and analyze the emotional responses of participants to determine how each model affects the participants in the MT. While the participants engaged in the two tasks, we measured and collected the different emotional response data, including physiological arousal (Heart Rate data) and facial expression (eye gaze data) separately, to make a dataset. Using the dataset, the support vector machine (SVM) successfully classified NOR and INV models in the MT, while the SVM model exhibits comparatively less accuracy in classifying the participants engaging the MI task in the two groups. The results simultaneously indicate the success of the INV model to prevent rumination and the effectiveness of using heart rate and eye movement to detect rumination during web browsing.