Advertisement

Document Classification on Relevance: A Study on Eye Gaze Patterns for Reading

  • Daniel Fahey
  • Tom Gedeon
  • Dingyun Zhu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7063)

Abstract

This paper presents a study that investigates the connection between the way that people read and the way that they understand content. The experiment consisted of having participants read some information on selected documents while an eye-tracking system recorded their eye movements. They were then asked to answer some questions and complete some tasks, on the information they had read. With the intention of investigating effective analysis approaches, both statistical methods and Artificial Neural Networks (ANN) were applied to analyse the collected gaze data in terms of several defined measures regarding the relevance of the text. The results from the statistical analysis do not show any significant correlations between those measures and the relevance of the text. However, good classification results were obtained by using an Artificial Neural Network. This suggests that using advanced learning approaches may provide more insightful differentiations than simple statistical methods particularly in analysing eye gaze reading patterns.

Keywords

Document Classification Relevance Gaze Pattern Reading Behavior Statistical Analysis Artificial Neural Networks 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Compbell, C.S., Maglio, P.P.: A Rbust Algorithm for Reading Detection. In: 2001 Workshop on Perceptive User Interfaces, vol. 15, pp. 1–7. ACM (2001)Google Scholar
  2. 2.
    Jacob, R.J.K.: The Use of Eye Movements in Human-computer Interaction Techniques: What You Look at is What You Get. ACM Transactions on Information Systems 9(2), 152–169 (1991)CrossRefGoogle Scholar
  3. 3.
    Koczy, L.T., Vamos, T., Biro, G.: Fuzzy Signatures. In: Proceedings of the 4th Meeting of the Euro Working Group on Fuzzy Sets and the 2nd International Conference on Soft and Intelligent Computing (EUROPUSE-SIC 1999), Budapest, Hungary, pp. 210–217 (1999)Google Scholar
  4. 4.
    Rayner, K.: Eye Movements in Reading and Information Processing: 20 Years of Research. Psychological Bulletin 124(3), 372–422 (1998)CrossRefGoogle Scholar
  5. 5.
    Seeingmachines, Inc: FaceLAB (2011), http://www.seeingmachines.com/faceLAB.html
  6. 6.
    Vo, T., Mendis, B.S.U., Gedeon, T.: Gaze Pattern and Reading Comprehension. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds.) ICONIP 2010 Part II. LNCS, vol. 6444, pp. 124–131. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    Zhu, D., Gedeon, T., Taylor, K.: Keyboard before Head Tracking Depresses User Success in Remote Camera Control. In: Gross, T., Gulliksen, J., Kotzé, P., Oestreicher, L., Palanque, P., Prates, R.O., Winckler, M. (eds.) INTERACT 2009. LNCS, vol. 5727, pp. 319–331. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  8. 8.
    Zhu, D., Mendis, B.S.U., Gedeon, T., Asthana, A., Goecke, R.: A Hybrid Fuzzy Approach for Human Eye Gaze Pattern Recognition. In: Köppen, M., Kasabov, N., Coghill, G. (eds.) ICONIP 2008. LNCS, vol. 5507, pp. 655–662. Springer, Heidelberg (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Daniel Fahey
    • 1
  • Tom Gedeon
    • 1
  • Dingyun Zhu
    • 1
  1. 1.Research School of Computer Science, College of Engineering and Computer ScienceThe Australian National UniversityCanberraAustralia

Personalised recommendations