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To Discover Novice Expert Paradigm: Sequence-Based Time-Domain and Graph-Based Frequency-Domain Analysis Method of Eye Movement

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HCI International 2022 - Late Breaking Papers. Design, User Experience and Interaction (HCII 2022)

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Abstract

Eye movement data can show the cognitive process in performing tasks to a certain extent. The existing researches on eye movement analysis are usually based on statistics, and it is difficult to show the correlation between the information associated with the scene. Other probabilistic algorithms usually focus on user feature recognition based on eye movement representation. In this paper, the concept of time-domain and frequency-domain analysis of eye movement area of interest is proposed, within which, the frequent pattern mining method and visual cognitive graph model are constructed to mine the relationship between the areas of interest. Finally, some application examples of this model in the novice expert paradigm are presented.

Supported by the Key Research and Development Program of Shaanxi 2020SF-152, and Educational reform research project 2022JGY10.

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References

  1. Blascheck, T., Kurzhals, K., Raschke, M., Burch, M., Weiskopf, D., Ertl, T.: Visualization of eye tracking data: a taxonomy and survey. In: Computer Graphics Forum, vol. 36, pp. 260–284. Wiley Online Library (2017)

    Google Scholar 

  2. Blascheck, T., Kurzhals, K., Raschke, M., Strohmaier, S., Weiskopf, D., Ertl, T.: Aoi hierarchies for visual exploration of fixation sequences. In: Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research and Applications. ETRA 2016, pp. 111–118. Association for Computing Machinery, New York (2016). https://doi.org/10.1145/2857491.2857524

  3. Blascheck, T., Schweizer, M., Beck, F., Ertl, T.: Visual comparison of eye movement patterns. In: Computer Graphics Forum, vol. 36, pp. 87–97. Wiley Online Library (2017)

    Google Scholar 

  4. Brandes, U.: A faster algorithm for betweenness centrality. J. Math. Sociol. 25(2), 163–177 (2001)

    Article  Google Scholar 

  5. Burch, M., Kumar, A., Timmermans, N.: An interactive web-based visual analytics tool for detecting strategic eye movement patterns. In: Proceedings of the 11th ACM Symposium on Eye Tracking Research and Applications. ETRA 2019. Association for Computing Machinery, New York (2019). https://doi.org/10.1145/3317960.3321615

  6. Burch, M., Veneri, A., Sun, B.: Eyeclouds: a visualization and analysis tool for exploring eye movement data. In: Proceedings of the 12th International Symposium on Visual Information Communication and Interaction. VINCI’2019. Association for Computing Machinery, New York (2019). https://doi.org/10.1145/3356422.3356423

  7. Chew, J.Y., Ohtomi, K., Suzuki, H.: Monitoring attention of crane operators during load oscillations using gaze entropy measures. In: Stephanidis, C., et al. (eds.) HCII 2021. LNCS, vol. 13096, pp. 44–61. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-90328-2_3

    Chapter  Google Scholar 

  8. Chuk, T., Chan, A.B., Shimojo, S., Hsiao, J.H.: Eye movement analysis with switching hidden Markov models. Behav. Res. Methods 52(3), 1026–1043 (2020)

    Article  Google Scholar 

  9. Chuk, T., Crookes, K., Hayward, W.G., Chan, A.B., Hsiao, J.H.: Hidden Markov model analysis reveals the advantage of analytic eye movement patterns in face recognition across cultures. Cognition 169, 102–117 (2017)

    Google Scholar 

  10. Dong, W., Jiang, Y., Zheng, L., Liu, B., Meng, L.: Assessing map-reading skills using eye tracking and Bayesian structural equation modelling. Sustainability 10(9) (2018). https://doi.org/10.3390/su10093050

  11. Guo, J.J., Zhou, R., Zhao, L.M., Lu, B.L.: Multimodal emotion recognition from eye image, eye movement and EEG using deep neural networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3071–3074. IEEE (2019)

    Google Scholar 

  12. Katona, J.: A review of human-computer interaction and virtual reality research fields in cognitive infocommunications. Appl. Sci. 11(6) (2021). https://doi.org/10.3390/app11062646

  13. Kovari, A., Katona, J., Costescu, C.: Evaluation of eye-movement metrics in a software debbuging task using gp3 eye tracker. Acta Polytechnica Hungarica 17(2), 57–76 (2020)

    Article  Google Scholar 

  14. Kurzhals, K., Hlawatsch, M., Seeger, C., Weiskopf, D.: Visual analytics for mobile eye tracking. IEEE Trans. Visual Comput. Graphics 23(1), 301–310 (2017). https://doi.org/10.1109/TVCG.2016.2598695

    Article  Google Scholar 

  15. Liversedge, S.P., Findlay, J.M.: Saccadic eye movements and cognition. Trends Cogn. Sci. 4(1), 6–14 (2000)

    Google Scholar 

  16. Seelig, S.A., Rabe, M.M., Malem-Shinitski, N., Risse, S., Reich, S., Engbert, R.: Bayesian parameter estimation for the swift model of eye-movement control during reading. J. Math. Psychol. 95, 102313 (2020)

    Google Scholar 

  17. Török, Á., Török, Z.G., Tölgyesi, B.: Cluttered centres: interaction between eccentricity and clutter in attracting visual attention of readers of a 16th century map. In: 2017 8th IEEE International Conference on Cognitive Infocommunications (CogInfoCom), pp. 000433–000438. IEEE (2017)

    Google Scholar 

  18. Ujbanyi, T., Katona, J., Sziladi, G., Kovari, A.: Eye-tracking analysis of computer networks exam question besides different skilled groups. In: 2016 7th IEEE International Conference on Cognitive Infocommunications (CogInfoCom), pp. 000277–000282. IEEE (2016)

    Google Scholar 

  19. Xiong, W., Wang, Yu., Zhou, Q., Liu, Z., Zhang, X.: The research of eye movement behavior of expert and novice in flight simulation of landing. In: Harris, D. (ed.) EPCE 2016. LNCS (LNAI), vol. 9736, pp. 485–493. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-40030-3_47

    Chapter  Google Scholar 

  20. Yang, C.K., Wacharamanotham, C.: AlpScarf: augmenting scarf plots for exploring temporal gaze patterns. In: Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1–6 (2018)

    Google Scholar 

  21. Zhang, L., Ma, G., Zhou, J., Jia, F.: Human-computer interface design of intelligent spinning factory monitoring system based on eye tracking technology. In: Ahram, T.Z., Falcão, C.S. (eds.) AHFE 2021. LNNS, vol. 275, pp. 579–586. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-80091-8_69

    Chapter  Google Scholar 

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Authors and Affiliations

  1. School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, 710072, China

    Dian Jin & Weiwei Yu

  2. Avionics Research Institute, Chinese Flight Test Establishment, Xi’an, 710089, China

    Xinliang Yang, Haiyang Wang & Ran Peng

Authors
  1. Dian Jin
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  2. Weiwei Yu
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  3. Xinliang Yang
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  4. Haiyang Wang
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  5. Ran Peng
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Corresponding author

Correspondence to Weiwei Yu .

Editor information

Editors and Affiliations

  1. The Open University of Japan, Chiba, Japan

    Masaaki Kurosu

  2. Tokyo University of Science, Tokyo, Saitama, Japan

    Sakae Yamamoto

  3. Tokyo City University, Tokyo, Japan

    Hirohiko Mori

  4. Southern University of Science and Technology, Shenzhen, China

    Marcelo M. Soares

  5. World Usability Day and Bubble Mountain Consulting, Newton Center, MA, USA

    Elizabeth Rosenzweig

  6. Aaron Marcus and Associates, Berkeley, CA, USA

    Aaron Marcus

  7. Tsinghua University, Beijing, China

    Pei-Luen Patrick Rau

  8. Coventry University, Coventry, UK

    Don Harris

  9. Cranfield University, Cranfield, UK

    Wen-Chin Li

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Jin, D., Yu, W., Yang, X., Wang, H., Peng, R. (2022). To Discover Novice Expert Paradigm: Sequence-Based Time-Domain and Graph-Based Frequency-Domain Analysis Method of Eye Movement. In: Kurosu, M., et al. HCI International 2022 - Late Breaking Papers. Design, User Experience and Interaction. HCII 2022. Lecture Notes in Computer Science, vol 13516. Springer, Cham. https://doi.org/10.1007/978-3-031-17615-9_17

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  • DOI: https://doi.org/10.1007/978-3-031-17615-9_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-17614-2

  • Online ISBN: 978-3-031-17615-9

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