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

arXiv: https://arxiv.org/abs/1809.08031 · HTML: https://mlsanity.com/paper/1809.08031/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2018.

## Built on by (most cited first, 8 of 8)

- [Relative Importance in Sentence Processing](https://mlsanity.com/paper/2106.03471/) (2021)
- [Eye Know You: Metric Learning for End-to-end Biometric Authentication Using Eye Movements from a Longitudinal Dataset](https://mlsanity.com/paper/2104.10489/) (2021)
- [Fine-Grained Prediction of Reading Comprehension from Eye Movements](https://mlsanity.com/paper/2410.04484/) (2024)
- [PoTeC: A German Naturalistic Eye-tracking-while-reading Corpus](https://mlsanity.com/paper/2403.00506/) (2024)
- [Reporting Eye-Tracking Data Quality: Towards a New Standard](https://mlsanity.com/paper/2404.00620/) (2024)
- [Revising with a Backward Glance: Regressions and Skips during Reading as Cognitive Signals for Revision Policies in Incremental Processing](https://mlsanity.com/paper/2310.18229/) (2023)
- [Eye-Tracking-while-Reading: A Living Survey of Datasets with Open Library Support](https://mlsanity.com/paper/2602.19598/) (2026)
- [LEXIC: Lightweight On-Device Decoding of Reading Comprehension from Eye Movements](https://mlsanity.com/paper/2607.08152/) (2026)

## Abstract (arXiv)

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.