A Discriminative Model for Identifying Readers and Assessing Text Comprehension from Eye Movements

  • Silvia MakowskiEmail author
  • Lena A. Jäger
  • Ahmed Abdelwahab
  • Niels Landwehr
  • Tobias Scheffer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11051)


We study the problem of inferring readers’ identities and estimating their level of text comprehension from observations of their eye movements during reading. We develop a generative model of individual gaze patterns (scanpaths) that makes use of lexical features of the fixated words. Using this generative model, we derive a Fisher-score representation of eye-movement sequences. We study whether a Fisher-SVM with this Fisher kernel and several reference methods are able to identify readers and estimate their level of text comprehension based on eye-tracking data. While none of the methods are able to estimate text comprehension accurately, we find that the SVM with Fisher kernel excels at identifying readers.



This work was partially funded by the German Science Foundation under grants SFB1294, SFB1287, and LA3270/1-1, and by the German Federal Ministry of Research and Education under grant 16DII116-DII.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Silvia Makowski
    • 1
    Email author
  • Lena A. Jäger
    • 1
    • 2
    • 3
  • Ahmed Abdelwahab
    • 1
    • 4
  • Niels Landwehr
    • 1
    • 4
  • Tobias Scheffer
    • 1
  1. 1.Department of Computer ScienceUniversity of PotsdamPotsdamGermany
  2. 2.Department of LinguisticsUniversity of PotsdamPotsdamGermany
  3. 3.Weizenbaum Institute for the Networked SocietyBerlinGermany
  4. 4.Leibniz Institute for Agricultural Engineering and BioeconomyPotsdamGermany

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