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

• Published in: Machine Learning and Knowledge Discovery in Databases Vol. 11051; pp. 209 - 225
• Main Authors: Makowski, Silvia, Jäger, Lena A., Abdelwahab, Ahmed, Landwehr, Niels, Scheffer, Tobias
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2019; Springer International Publishing
• Series: Lecture Notes in Computer Science
• ISBN: 9783030109240; 3030109240
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-10925-7_13

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.