Automatic Gaze-Based Detection of Mind Wandering with Metacognitive Awareness
- 2.4k Downloads
Mind wandering (MW) is a ubiquitous phenomenon where attention involuntarily shifts from task-related processing to task-unrelated thoughts. There is a need for adaptive systems that can reorient attention when MW is detected due to its detrimental effects on performance and productivity. This paper proposes an automated gaze-based detector of self-caught MW (i.e., when users become consciously aware that they are MW). Eye gaze data and self-reports of MW were collected as 178 users read four instructional texts from a computer interface. Supervised machine learning models trained on features extracted from users’ gaze fixations were used to detect pages where users caught themselves MW. The best performing model achieved a user-independent kappa of .45 (accuracy of 74% compared to a chance accuracy of 52%); the first ever demonstration of a self-caught MW detector. An analysis of the features revealed that during MW, users made more regression fixations, had longer saccades that crossed lines more often, and had more uniform fixation durations, indicating a violation from normal reading patterns. Applications of the MW detector are discussed.
KeywordsGaze tracking Mind wandering Affect detection User modeling
Unable to display preview. Download preview PDF.
- 1.Bixler, R., D’Mello, S.: Toward Fully Automated Person-Independent Detection of Mind Wandering. In: Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., Houben, G.-J. (eds.) UMAP 2014. LNCS, vol. 8538, pp. 37–48. Springer, Heidelberg (2014)Google Scholar
- 5.D’Mello, S., et al.: Automatic Gaze-Based Detection of Mind Wandering during Reading. Educational Data Mining (2013)Google Scholar
- 17.Randall, J.G., et al.: Mind-Wandering, Cognition, and Performance: A Theory-Driven Meta-Analysis of Attention Regulation. Psychological Bulletin (2014)Google Scholar
- 18.Schooler, J.W., et al.: Zoning Out While Reading: Evidence for Dissociations Between Experience and Metaconsciousness. In: Levin, D.T. (ed.) Thinking and Seeing: Visual Metacognition in Adults and Children, pp. 203–226. MIT Press, Cambridge (2004)Google Scholar
- 19.Sewell, W., Komogortsev, O.: Real-Time Eye Gaze Tracking with an Unmodified Commodity Webcam Employing a Neural Network. In: CHI 2010 Extended Abstracts on Human Factors in Computing Systems, pp. 3739–3744 ACM (2010)Google Scholar
- 25.Yonetani, R. et al.: Multi-Mode Saliency Dynamics Model for Analyzing Gaze and Attention. In: Proceedings of the Symposium on Eye Tracking Research and Applications, pp. 115–122. ACM, New York (2012)Google Scholar