A Comparative Study Between Mouse and Eye Tracking Signals for Long Romanian Texts
Proceedings fo the Second International Workshop on Eye-Tracking Resources and Evaluation for Human-Aligned NLP
Abstract
Understanding human language processing via eye-tracking (ET) is precise but limited by scalability. Mouse-Tracking (MoTR) offers a cost-effective alternative, yet its viability for long-form reading in languages like Romanian remains underexplored. The primary challenge lies in the motor-induced noise and biomechanical discrepancies between hand and eye movements. Here we show that combining targeted technical enhancements with a Hertz-based velocity transformation allows MoTR to serve as a robust proxy for ET. We evaluate this by training a BERT-enhanced Fusion Model that integrates semantic context to bridge the mechanical gap, achieving an internal consistency of ρ ≈ 0.58 and a cross-modal correlation of ρ ≈ 0.22 in the velocity domain. These results indicate that when properly normalized, manual tracking captures similar cognitive constraints as gaze, with predictive accuracy approaching the empirical bounds of human behavioral variance.