Eye-tracking based classification of Mandarin Chinese readers with and without dyslexia using neural sequence models
This work addresses dyslexia diagnosis for children in a logographic script context, though it is incremental as it builds on prior SVM-based methods by using sequence models.
The authors tackled the problem of classifying Mandarin Chinese readers with and without dyslexia using eye-tracking data, achieving state-of-the-art performance with sequence models that process eye movements without feature aggregation, but found that incorporating linguistic stimulus did not improve results.
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.