Abstract
Mind wandering is a ubiquitous phenomenon where attention involuntary shifts from task-related processing to task-unrelated thoughts. Mind wandering has negative effects on performance, hence, intelligent interfaces that detect mind wandering can intervene to restore attention to the current task. We investigated the use of eye gaze and contextual cues to automatically detect mind wandering during reading with a computer interface. Participants were pseudo-randomly probed to report mind wandering instances while an eye tracker recorded their gaze during a computerized reading task. Supervised machine learning techniques detected positive responses to mind wandering probes from gaze and context features in a user-independent fashion. Mind wandering was predicted with an accuracy of 72% (expected accuracy by chance was 62%) when probed at the end of a page and an accuracy of 59% (chance was 50%) when probed in the midst of reading a page. Possible improvements to the detectors and applications are discussed.
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References
Baayen, R.H., et al.: The CELEX Lexical Database, Release 2 [CD-ROM]. Linguistic Data Consortium, University of Pennsylvania, Philadelphia (1995)
Bednarik, R., et al.: What Do You Want to Do Next: A Novel Approach for Intent Prediction in Gaze-Based Interaction. In: Proceedings of the Symposium on Eye Tracking Research and Applications, pp. 83–90. ACM, New York (2012)
Biedert, R., et al.: A Robust Realtime Reading-Skimming Classifier. In: Proceedings of the Symposium on Eye Tracking Research and Applications, pp. 123–130. ACM, New York (2012)
Calvo, R.A., D’Mello, S.: Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications. IEEE Transactions on Affective Computing 1(1), 18–37 (2010)
Cohen, J.: A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement 20(1), 37–46 (1960)
D’Mello, S., et al.: Automatic Gaze-Based Detection of Mind Wandering During Reading. Educational Data Mining (2013)
Domingos, P.: A Few Useful Things to Know About Machine Learning. Communications of the ACM 55(10), 78–87 (2012)
Drummond, J., Litman, D.: In the Zone: Towards Detecting Student Zoning Out Using Supervised Machine Learning. In: Aleven, V., Kay, J., Mostow, J. (eds.) ITS 2010, Part II. LNCS, vol. 6095, pp. 306–308. Springer, Heidelberg (2010)
Feng, S., et al.: Mind Wandering While Reading Easy and Difficult Texts. Psychonomic Bulletin & Review, 1–7 (2013)
Hall, M., et al.: The WEKA Data Mining Software: an Update. ACM SIGKDD Explorations Newsletter 11(1), 10–18 (2009)
Hall, M.A.: Correlation-Based Feature Selection for Discrete and Numeric Class Machine Learning. In: Proceedings of the Seventeenth International Conference on Machine Learning, pp. 359–366 (2000)
Killingsworth, M.A., Gilbert, D.T.: A Wandering Mind is an Unhappy Mind. Science 330(6006), 932–932 (2010)
Mooneyham, B.W., Schooler, J.W.: The Costs and Benefits of Mind-Wandering: A Review. Canadian Journal of Experimental Psychology/Revue Canadienne de Psychologie Expérimentale 67(1), 11 (2013)
Muir, M., Conati, C.: An Analysis of Attention to Student – Adaptive Hints in an Educational Game. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K., et al. (eds.) ITS 2012. LNCS, vol. 7315, pp. 112–122. Springer, Heidelberg (2012)
Navalpakkam, V., Kumar, R., Li, L., Sivakumar, D.: Attention and Selection in Online Choice Tasks. In: Masthoff, J., Mobasher, B., Desmarais, M.C., Nkambou, R., et al. (eds.) UMAP 2012. LNCS, vol. 7379, pp. 200–211. Springer, Heidelberg (2012)
Rayner, K.: Eye Movements in Reading and Information Processing: 20 Years of Research. Psychological Bulletin 124(3), 372 (1998)
Reichle, E.D., et al.: Eye Movements During Mindless Reading. Psychological Science 21(9), 1300–1310 (2010)
Schooler, J.W., et al.: Meta-Awareness, Perceptual Decoupling and the Wandering Mind. Trends in Cognitive Sciences 15(7), 319–326 (2011)
Schooler, J.W., et al.: Zoning Out While Reading: Evidence for Dissociations Between Experience and Metaconsciousness. In: Thinking and Seeing: Visual Metacognition in Adults and Children, pp. 203–226 (2004)
Seibert, P.S., Ellis, H.C.: Irrelevant Thoughts, Emotional Mood States, and Cognitive Task Performance. Memory & Cognition 19(5), 507–513 (1991)
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. 3729–3744. ACM, Atlanta (2010)
Smallwood, J., et al.: Subjective Experience and the Attentional Lapse: Task Engagement and Disengagement During Sustained Attention. Consciousness and Cognition 13(4), 657–690 (2004)
Smallwood, J., et al.: When Attention Matters: The Curious Incident of the Wandering Mind. Memory & Cognition 36(6), 1144–1150 (2008)
Smilek, D., et al.: Out of Mind, Out of Sight Eye Blinking as Indicator and Embodiment of Mind Wandering. Psychological Science 21(6), 786–789 (2010)
Voßkühler, A., et al.: OGAMA (Open Gaze and Mouse Analyzer): Open-Source Software Designed to Analyze Eye and Mouse Movements in Slideshow Study Designs. Behavior Research Methods 40(4), 1150–1162 (2008)
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)
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Bixler, R., D’Mello, S. (2014). Toward Fully Automated Person-Independent Detection of Mind Wandering. In: Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., Houben, GJ. (eds) User Modeling, Adaptation, and Personalization. UMAP 2014. Lecture Notes in Computer Science, vol 8538. Springer, Cham. https://doi.org/10.1007/978-3-319-08786-3_4
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DOI: https://doi.org/10.1007/978-3-319-08786-3_4
Publisher Name: Springer, Cham
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