ABSTRACT
Eye tracking measures have been used to recognize cognitive states involving mental workload, comprehension, and self-confidence in the task of reading. In this paper, we present how these measures can be used to detect the interest of a reader. From the reading behavior of 13 university students on 18 newspaper articles, we have extracted features related to fixations, saccades, blinks and pupil diameters to detect which documents each participant finds interesting or uninteresting. We have classified their level of interests into four classes with an accuracy of 44% using eye movements, and it has increased to 62% if a survey about subjective comprehension is included. This research can be incorporated in the real-time prediction of a user's interest while reading, for the betterment of future designs of human-document interaction.
- Yomna Abdelrahman, Eduardo Velloso, Tilman Dingler, Albrecht Schmidt, and Frank Vetere. 2017. Cognitive heat: exploring the usage of thermal imaging to unobtrusively estimate cognitive load. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 3 (2017), 33. Google ScholarDigital Library
- Victor Manuel García Barrios, Christian Gütl, Alexandra M Preis, Keith Andrews, Maja Pivec, Felix Mödritscher, and Christian Trummer. 2004. AdELE: A framework for adaptive e-learning through eye tracking. Inquiring Knowledge Networks on the Web (2004), 609--616.Google Scholar
- Ralf Biedert, Georg Buscher, Sven Schwarz, Manuel Möller, Andreas Dengel, and Thomas Lottermann. 2010. The text 2.0 framework: writing web-based gaze-controlled realtime applications quickly and easily. In Proceedings of the 2010 workshop on Eye gaze in intelligent human machine interaction. ACM, 114--117. Google ScholarDigital Library
- Robert W Booth and Ulrich W Weger. 2013. The function of regressions in reading: Backward eye movements allow rereading. Memory & cognition 41, 1 (2013), 82--97.Google Scholar
- Iuliia Brishtel, Shoya Ishimaru, Olivier Augereau, Koichi Kise, and Andreas Dengel. 2018. Assessing Cognitive Workload on Printed and Electronic Media using Eye-Tracker and EDA Wristband. In Proceedings of the 23rd International Conference on Intelligent User Interfaces Companion. ACM, 45. Google ScholarDigital Library
- Georg Buscher, Andreas Dengel, and Ludger van Elst. 2008. Eye movements as implicit relevance feedback. In Extended abstracts of the 2008 CHI Conference on Human Factors in Computing Systems. ACM, 2991--2996. Google ScholarDigital Library
- Siyuan Chen, Julien Epps, Natalie Ruiz, and Fang Chen. 2011. Eye activity as a measure of human mental effort in HCI. In Proceedings of the 16th international conference on Intelligent user interfaces. ACM, 315--318. Google ScholarDigital Library
- Leana Copeland, Tom Gedeon, and Sabrina Caldwell. 2014. Framework for Dynamic Text Presentation in eLearning. Procedia Computer Science 39 (2014), 150--153.Google ScholarCross Ref
- Hugo D Critchley. 2002. Electrodermal responses: what happens in the brain. The Neuroscientist 8, 2 (2002), 132--142.Google ScholarCross Ref
- Jérémy Frey, Maxime Daniel, Julien Castet, Martin Hachet, and Fabien Lotte. 2016. Framework for electroencephalography-based evaluation of user experience. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 2283--2294. Google ScholarDigital Library
- Suzanne Hidi. 2001. Interest, reading, and learning: Theoretical and practical considerations. Educational Psychology Review 13, 3 (2001), 191--209.Google ScholarCross Ref
- Shoya Ishimaru, Syed Saqib Bukhari, Carina Heisel, Nicolas Großmann, Pascal Klein, Jochen Kuhn, and Andreas Dengel. 2018. Augmented Learning on Anticipating Textbooks with Eye Tracking. In Positive Learning in the Age of Information. Springer, 387--398.Google Scholar
- Shoya Ishimaru, Soumy Jacob, Apurba Roy, Syed Saqib Bukhari, Carina Heisel, Nicolas Großmann, Michael Thees, Jochen Kuhn, and Andreas Dengel. 2017. Cognitive State Measurement on Learning Materials by Utilizing Eye Tracker and Thermal Camera. In Proceedings of the 14th IAPR International Conference on Document Analysis and Recognition, Vol. 8. IEEE, 32--36.Google ScholarCross Ref
- Shoya Ishimaru, Kai Kunze, Koichi Kise, and Masahiko Inami. 2014. Position Paper: Brain Teasers - Toward Wearable Computing That Engages Our Mind. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication. ACM, 1405--1408. Google ScholarDigital Library
- Kai Kunze, Susana Sanchez, Tilman Dingler, Olivier Augereau, Koichi Kise, Masahiko Inami, and Terada Tsutomu. 2015. The augmented narrative: toward estimating reader engagement. In Proceedings of the 6th augmented human international conference. ACM, 163--164. Google ScholarDigital Library
- Tian Lan, Andre Adami, Deniz Erdogmus, and Misha Pavel. 2005. Estimating cognitive state using EEG signals. In Signal Processing Conference, 2005 13th European. IEEE, 1--4.Google Scholar
- Christian Lander, Marco Speicher, Denise Paradowski, Norine Coenen, Sebastian Biewer, and Antonio Krüger. 2015. Collaborative newspaper: Exploring an adaptive scrolling algorithm in a multi-user reading scenario. In Proceedings of the 4th International Symposium on Pervasive Displays. ACM, 163--169. Google ScholarDigital Library
- Antonio Luque-Casado, Mikel Zabala, Esther Morales, Manuel Mateo-March, and Daniel Sanabria. 2013. Cognitive performance and heart rate variability: the influence of fitness level. PloS one 8, 2 (2013), e56935.Google ScholarCross Ref
- Khalid Masood. 2015. EDA as a Discriminate Feature in Computation of Mental Stress. In The Second International Conference on Digital Information Processing, Data Mining, and Wireless Communications. 199.Google Scholar
- Marco Porta, Stefania Ricotti, and C Jimenez Perez. 2012. Emotional e-learning through eye tracking. In Global Engineering Education Conference, 2012 IEEE. IEEE, 1--6.Google ScholarCross Ref
- Gary E Raney, Spencer J Campbell, and Joanna C Bovee. 2014. Using eye movements to evaluate the cognitive processes involved in text comprehension. Journal of visualized experiments: JoVE 83 (2014).Google Scholar
- Keith Rayner, Kathryn H Chace, Timothy J Slattery, and Jane Ashby. 2006. Eye movements as reflections of comprehension processes in reading. Scientific studies of reading 10, 3 (2006), 241--255.Google Scholar
- Darrell S Rudmann, George W McConkie, and Xianjun Sam Zheng. 2003. Eyetracking in cognitive state detection for HCI. In Proceedings of the 5th international conference on Multimodal interfaces. ACM, 159--163. Google ScholarDigital Library
- Scott Squires. 2014. The effects of reading interest, reading purpose, and reading maturity on reading comprehension of high school students. Baker University.Google Scholar
- Miki Uetsuki, Junji Watanabe, Hideyuki Ando, and Kazushi Maruya. 2017. Reading Traits for Dynamically Presented Texts: Comparison of the Optimum Reading Rates of Dynamic Text Presentation and the Reading Rates of Static Text Presentation. Frontiers in Psychology 8 (2017), 1390.Google ScholarCross Ref
- S-N Yang and George W McConkie. 2001. Eye movements during reading: A theory of saccade initiation times. Vision research 41, 25 (2001), 3567--3585.Google Scholar
- Johannes Zagermann, Ulrike Pfeil, and Harald Reiterer. 2016. Measuring Cognitive Load using Eye Tracking Technology in Visual Computing. In Proceedings of the Sixth Workshop on Beyond Time and Errors on Novel Evaluation Methods for Visualization. 78--85. Google ScholarDigital Library
Index Terms
- Gaze-based interest detection on newspaper articles
Recommendations
Rule-based learning for eye movement type detection
MCPMD '18: Proceedings of the Workshop on Modeling Cognitive Processes from Multimodal DataEye movements hold information about human perception, intention, and cognitive state. Various algorithms have been proposed to identify and distinguish eye movements, particularly fixations, saccades, and smooth pursuits. A major drawback of existing ...
A Review on Eye-Tracking Metrics for Sleepiness
HCI International 2020 – Late Breaking Papers: Interaction, Knowledge and Social MediaAbstractSleepiness that can arise from sleep deprivation can increase human errors in task performance and create workplace hazards and accidents. Hence, it is critical to detect sleepiness to minimize hazards and human errors. This paper provides a ...
Rapid alternating saccade training
ETRA '18: Proceedings of the 2018 ACM Symposium on Eye Tracking Research & ApplicationsWhile individual eye movement characteristics are remarkably stable, experiments on saccadic spatial adaptation indicate that oculomotor learning is possible. To further investigate saccadic learning, participants received veridical feedback on saccade ...
Comments