  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.
