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.
@article{arxiv.1809.08031,
title = {A Discriminative Model for Identifying Readers and Assessing Text Comprehension from Eye Movements},
author = {Silvia Makowski and Lena Jäger and Ahmed Abdelwahab and Niels Landwehr and Tobias Scheffer},
journal= {arXiv preprint arXiv:1809.08031},
year = {2018}
}
Comments
Proceedings of the European Conference on Machine Learning, 2018