Tracking the Progression of Reading Using Eye-Gaze Point Measurements and Hidden Markov Models
Why this work is in the frame
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Bibliographic record
Abstract
In this article, we consider the problem of tracking the eye gaze of individuals while they engage in reading. In particular, we develop the ways to accurately track the line being read by an individual using commercially available eye-tracking devices. Such an approach will enable futuristic functionalities, such as comprehension evaluation, interest level detection, and user-assisting applications such as hand-free navigation and automatic scrolling. Furthermore, the proposed approach will pave the way to develop technology that may generate valuable feedback to content makers, such as web designers, authors, educators, and social media users. The existing commercial eye trackers provide an estimated location of the eye-gaze points every few milliseconds. However, these estimated gaze points are not sufficient to quantify reading progression—a specific eye-gaze activity. In this article, we propose algorithms to bridge the commercial gaze tracker outputs and informative eye-gaze patterns while reading. The proposed system consists of Kalman filters and hidden Markov models to parameterize these statistical models and to accurately detect the line being read. The proposed approach is shown to yield an improvement of 27.1% in line detection accuracy over line tracking using estimated eye-gaze points alone by the eye tracker.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it