A New Methodology for Determining Point-of-Gaze in Head-Mounted Eye Tracking Systems
Bibliographic record
Abstract
The ability to determine point-of-gaze with respect to an observed scene provides significant insight into human cognitive processes, since shifts in gaze position are generally guided by shifts in attentional focus. Using a head-mounted eye tracking system, a new methodology based on four or more point correspondences in two views was developed to reconstruct the subject's point-of-gaze. For exact point correspondences, 95% of the reconstruction errors are less than 0.32 degrees when the homography algorithm with distortion compensation is used to determine gaze position. In a typical visual scanning experiment, 95% of the reconstruction errors are less than 0.90 degrees. Analysis of normalization techniques that reduce the sensitivity of the homography algorithm to input errors suggests that the point correspondences should be arranged in a radially symmetric distribution around the area to be scanned. The new methodology was used in a clinical study on visual selective attention and mood disorders; this study showed that depressed subjects spent significantly more time looking at images with dysphoric themes than normal control subjects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".