Cross-Linguistic Analysis of Eye Movement Patterns: Insights from the First Arabic Eye-Tracking Corpus for NLP
Year: 2026
Type: conference-paper
Abstract: 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 identifies distinct patterns in fixation and regression durations, reflecting the unique orthographic characteristics of Arabic: cursive script, diacritization, bidirectional reading (text right-to-left, numbers left-to-right), and morphological complexity.The findings indicate that Arabic readers exhibit relatively longer mean fixation and regression durations than 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.
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166524102