@inproceedings{gheorghe-nisioi-2026-comparative,
    title = "A Comparative Study Between Mouse and Eye Tracking Signals for Long {R}omanian Texts",
    author = "Gheorghe, Bogdan Alexandru  and
      Nisioi, Sergiu",
    editor = {Acart{\"u}rk, Cengiz  and
      Can, Burcu  and
      Nasir, Jamal  and
      {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}},
    booktitle = "Proceedings fo the Second International Workshop on Eye-Tracking Resources and Evaluation for Human-Aligned {NLP}",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELDA",
    url = "https://preview.aclanthology.org/paragraph-normalization/2026.gaze4nlp-1.7/",
    doi = "10.63317/35h8hosqt746",
    pages = "41--49",
    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 {\ensuremath{\rho}} {\ensuremath{\approx}} 0.58 and a cross-modal correlation of {\ensuremath{\rho}} {\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."
}