Classifying the Differences in Gaze Patterns of Alphabetic and Logographic L1 Readers – A Neural Network Approach

  • André Frank Krause
  • Kai Essig
  • Li-Ying Essig-Shih
  • Thomas Schack
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 363)


Using plain, but large multi-layer perceptrons, temporal eye-tracking gaze patterns of alphabetic and logographic L1 readers were successfully classified. The Eye-tracking data was fed directly into the networks, with no need for pre-processing. Classification rates up to 92% were achieved using MLPs with 4 hidden units. By classifying the gaze patterns of interaction partners, artificial systems are able to act adaptively in a broad variety of application fields.


Hide Layer Reading Comprehension Hide Unit Perceptual Span Handwritten Digit Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Essig, K.: Vision-Based Image Retrieval (VBIR) - A New Approach for Natural and Intuitive Image Retrieval. VDM Verlag, Saarbrücken (2008)Google Scholar
  2. 2.
    Duchowski, A.: Eye Tracking Methodology: Theory and Practice. Springer, London (2003)zbMATHGoogle Scholar
  3. 3.
    Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall PTR, Upper Saddle River (1994)zbMATHGoogle Scholar
  4. 4.
    Essig, K., Pomplun, M., Ritter, H.: A neural network for 3d gaze recording with binocular eye trackers. International Journal of Parallel, Emergent and Distributed Systems 21(2), 79–95 (2006)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Macaš, M., Lhotská, L., Novák, D.: Bio-inspired methods for analysis and classification of reading eye movements of dyslexic children. Technical report, University in Prague, Algarve, Portugal (October 3-5, 2005)Google Scholar
  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.
    Ciresan, D.C., Meier, U., Gambardella, L.M., Schmidhuber, J.: Deep big simple neural nets excel on handwritten digit recognition. CoRR (2010)Google Scholar
  8. 8.
    Just, M.A., Carpenter, P.A.: The psychology of reading and language comprehension. Allyn and Bacon, Boston (1987)Google Scholar
  9. 9.
    Essig-Shih, L.Y.: Effekte simultanen Hörens und Lesens auf das L2-Lesen: Eine Eyetracking-Studie im BereichDeutsch als Fremdsprache. PhD thesis, University of Bielefeld (2008)Google Scholar
  10. 10.
    Irwin, J.W.: Teaching reading comprehension process. Allyn & Bacon, Boston (1991)Google Scholar
  11. 11.
    Rötting, M.: Parametersystematik der Augen- und Blickbewegungen für arbeitswissenschaftliche Untersuchungen. Shaker, Aachen (2001)Google Scholar
  12. 12.
    Inhoff, A.W., Liu, W.: The perceptual span and oculomotor activity during the reading of chinese sentences. Journal of Experimental Psychology: Human Perception and Performance 24, 20–34 (1998)CrossRefGoogle Scholar
  13. 13.
    Rayner, K., Sereno, S.C.: Eye movements in reading. Psycholinguistic studies. In: Handbook of Psycholinguistics, pp. 57–81. Academic Press, San Diego (1994)Google Scholar
  14. 14.
    Nerius, D. (ed.): Deutsche Orthographie. 3. Aufl. Dudenverlag, Mannheim (2000)Google Scholar
  15. 15.
    Rayner, K., Carroll, P.J.: Issues and problems in the use of eye movement data in study reading comprehension. In: New Methods in Reading Comprehension Research, pp. 129–150. Erlbaum, Hillsdale (1984)Google Scholar

Copyright information

© International Federation for Information Processing 2011

Authors and Affiliations

  • André Frank Krause
    • 1
    • 3
  • Kai Essig
    • 1
    • 3
  • Li-Ying Essig-Shih
    • 2
  • Thomas Schack
    • 1
    • 3
  1. 1.Faculty of Sport Science, Dept. Neurocognition & ActionBielefeld UniversityBielefeldGermany
  2. 2.cultureblend IIT GmbHBielefeld UniversityBielefeldGermany
  3. 3.Cognitive Interaction Technology, Center of ExcellenceBielefeld UniversityBielefeldGermany

Personalised recommendations