# Eye-tracking based classification of Mandarin Chinese readers with and without dyslexia using neural sequence models

arXiv: https://arxiv.org/abs/2210.09819 · HTML: https://mlsanity.com/paper/2210.09819/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 35 references that are in the map.
- First posted on arXiv 18 Oct 2022.

## Builds on (most cited first, 5 of 5)

- [Human Sentence Processing: Recurrence or Attention?](https://mlsanity.com/paper/2005.09471/) (2020)
- [Deep Eyedentification: Biometric Identification using Micro-Movements of the Eye](https://mlsanity.com/paper/1906.11889/) (2019)
- [Measuring the Impact of (Psycho-)Linguistic and Readability Features and Their Spill Over Effects on the Prediction of Eye Movement Patterns](https://mlsanity.com/paper/2203.08085/) (2022)
- [Reading Task Classification Using EEG and Eye-Tracking Data](https://mlsanity.com/paper/2112.06310/) (2021)
- [Detection of ADHD based on Eye Movements during Natural Viewing](https://mlsanity.com/paper/2207.01377/) (2022)

## Built on by (most cited first, 10 of 10)

- [Eyettention: An Attention-based Dual-Sequence Model for Predicting Human Scanpaths during Reading](https://mlsanity.com/paper/2304.10784/) (2023)
- [ScanDL: A Diffusion Model for Generating Synthetic Scanpaths on Texts](https://mlsanity.com/paper/2310.15587/) (2023)
- [Decoding Reading Goals from Eye Movements](https://mlsanity.com/paper/2410.20779/) (2024)
- [EMTeC: A Corpus of Eye Movements on Machine-Generated Texts](https://mlsanity.com/paper/2408.04289/) (2024)
- [Fixation Sequences as Time Series: A Topological Approach to Dyslexia Detection](https://mlsanity.com/paper/2604.21698/) (2026)
- [Reading ability detection using eye-tracking data with LSTM-based few-shot learning](https://mlsanity.com/paper/2409.08798/) (2024)
- [Decoding Open-Ended Information Seeking Goals from Eye Movements in Reading](https://mlsanity.com/paper/2505.02872/) (2025)
- [Eye-Tracking-while-Reading: A Living Survey of Datasets with Open Library Support](https://mlsanity.com/paper/2602.19598/) (2026)
- [Eyettention II: A Dual-Sequence Architecture for Modeling Fixation Location, Within-Word Landing Position, and Fixation Duration in Reading](https://mlsanity.com/paper/2606.01964/) (2026)
- [Language Proficiency Assessment from Eye Movements in Naturalistic Passage Reading](https://mlsanity.com/paper/2608.30583/) (2026)

## Abstract (arXiv)

Eye movements are known to reflect cognitive processes in reading, and psychological reading research has shown that eye gaze patterns differ between readers with and without dyslexia. In recent years, researchers have attempted to classify readers with dyslexia based on their eye movements using Support Vector Machines (SVMs). However, these approaches (i) are based on highly aggregated features averaged over all words read by a participant, thus disregarding the sequential nature of the eye movements, and (ii) do not consider the linguistic stimulus and its interaction with the reader's eye movements. In the present work, we propose two simple sequence models that process eye movements on the entire stimulus without the need of aggregating features across the sentence. Additionally, we incorporate the linguistic stimulus into the model in two ways -- contextualized word embeddings and manually extracted linguistic features. The models are evaluated on a Mandarin Chinese dataset containing eye movements from children with and without dyslexia. Our results show that (i) even for a logographic script such as Chinese, sequence models are able to classify dyslexia on eye gaze sequences, reaching state-of-the-art performance, and (ii) incorporating the linguistic stimulus does not help to improve classification performance.