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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.00033 |
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| _version_ | 1866909006610563072 |
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| author | Kaltenberger, Franziska Chen, Wei-Ling Thaqi, Enkeleda Kasneci, Enkelejda |
| author_facet | Kaltenberger, Franziska Chen, Wei-Ling Thaqi, Enkeleda Kasneci, Enkelejda |
| contents | Remote and webcam-based eye tracking in multi-line reading suffers from various noise factors and layout ambiguity, precisely where real-time reading support needs reliable, per-fixation line assignment. Prior work largely addresses this challenge post hoc or by restricting behavior (e.g., disallowing re-reading), undermining interactive use. We propose CONF-LA (Confidence-score-based Online Fixation-to-Line Assignment), a principled, low-latency approach that integrates knowledge about reading behavior and Gaussian line likelihoods over fixations to compute a posterior-line-score and defers assignments when uncertainty is high. Evaluated on existing open-source data, CONF-LA demonstrates stable performance in post hoc analysis and closes the online-offline gap (1-2 %) with a mean per-fixation latency of 0.348 ms. Our approach exhibits particular invariance toward regressions, yielding significant improvement in ad hoc median accuracies on children data (approx. 95 %) over all tested algorithms. We encourage further research in this direction and discuss possibilities for future development. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_00033 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Sure About That Line? Approaching Confidence-Based, Real-Time Line Assignment in Reading Gaze Data Kaltenberger, Franziska Chen, Wei-Ling Thaqi, Enkeleda Kasneci, Enkelejda Neurons and Cognition Artificial Intelligence Human-Computer Interaction Machine Learning Image and Video Processing Remote and webcam-based eye tracking in multi-line reading suffers from various noise factors and layout ambiguity, precisely where real-time reading support needs reliable, per-fixation line assignment. Prior work largely addresses this challenge post hoc or by restricting behavior (e.g., disallowing re-reading), undermining interactive use. We propose CONF-LA (Confidence-score-based Online Fixation-to-Line Assignment), a principled, low-latency approach that integrates knowledge about reading behavior and Gaussian line likelihoods over fixations to compute a posterior-line-score and defers assignments when uncertainty is high. Evaluated on existing open-source data, CONF-LA demonstrates stable performance in post hoc analysis and closes the online-offline gap (1-2 %) with a mean per-fixation latency of 0.348 ms. Our approach exhibits particular invariance toward regressions, yielding significant improvement in ad hoc median accuracies on children data (approx. 95 %) over all tested algorithms. We encourage further research in this direction and discuss possibilities for future development. |
| title | Sure About That Line? Approaching Confidence-Based, Real-Time Line Assignment in Reading Gaze Data |
| topic | Neurons and Cognition Artificial Intelligence Human-Computer Interaction Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2605.00033 |