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        <title>A Comparative Study Between Mouse and Eye Tracking Signals for Long Romanian Texts</title>
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    <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 \ensuremathρ \ensuremath\approx 0.58 and a cross-modal correlation of \ensuremathρ \ensuremath\approx 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.</abstract>
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%0 Conference Proceedings
%T A Comparative Study Between Mouse and Eye Tracking Signals for Long Romanian Texts
%A Gheorghe, Bogdan Alexandru
%A Nisioi, Sergiu
%Y Acartürk, Cengiz
%Y Can, Burcu
%Y Nasir, Jamal
%Y Çöltekin, Çağrı
%S Proceedings fo the Second International Workshop on Eye-Tracking Resources and Evaluation for Human-Aligned NLP
%D 2026
%8 May
%I ELDA
%C Palma de Mallorca, Spain
%F gheorghe-nisioi-2026-comparative
%X 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 \ensuremathρ \ensuremath\approx 0.58 and a cross-modal correlation of \ensuremathρ \ensuremath\approx 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.
%R 10.63317/35h8hosqt746
%U https://aclanthology.org/2026.gaze4nlp-1.7/
%U https://doi.org/10.63317/35h8hosqt746
%P 41-49

Markdown (Informal)

[A Comparative Study Between Mouse and Eye Tracking Signals for Long Romanian Texts](https://aclanthology.org/2026.gaze4nlp-1.7/) (Gheorghe & Nisioi, Gaze4NLP 2026)

• A Comparative Study Between Mouse and Eye Tracking Signals for Long Romanian Texts (Gheorghe & Nisioi, Gaze4NLP 2026)

ACL

• Bogdan Alexandru Gheorghe and Sergiu Nisioi. 2026. A Comparative Study Between Mouse and Eye Tracking Signals for Long Romanian Texts. In Proceedings fo the Second International Workshop on Eye-Tracking Resources and Evaluation for Human-Aligned NLP, pages 41–49, Palma de Mallorca, Spain. ELDA.