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Cross-Linguistic Analysis of Eye Movement Patterns: Insights from the First Arabic Eye-Tracking Corpus for NLP

Eye-tracking corpora have become valuable resources for understanding human reading behavior and developing cognitively-informed NLP models. However, existing resources predominantly focus on left-to-right Latin script languages, leaving a significant gap for morphologically rich, right-to-left languages like Arabic. This paper presents a cross-linguistic analysis of eye movement patterns using the AraEyebility corpus, the first Arabic eye-tracking corpus comprising 57,617 words read by 15 native speakers. We systematically compare gaze metrics across Arabic and established English corpora. Our analysis reveals distinct patterns in fixation duration, saccade length, and regression frequency that reflect Arabic’s unique orthographic properties: cursive script, diacritization, bidirectional reading (text right-to-left, numbers left-to-right), and morphological complexity. The findings demonstrate that Arabic readers exhibit longer mean fixation durations and more frequent regressions compared to English readers, suggesting higher cognitive processing demands. We discuss implications for developing cognitively-aligned NLP models and provide recommendations for future multilingual eye-tracking research. The AraEyebility corpus is publicly available to support Arabic NLP research.