Shifts in reported gaze position due to changes in pupil size
Bibliographic record
Abstract
Camera-based eye trackers are the mainstay of today's eye movement research and countless practical applications of eye tracking. Recently, a significant impact of changes in pupil size on the accuracy of camera-based eye trackers during fixation has been reported [Wyatt 2010]. We compared the pupil-size effect between a scleral search coil based eye tracker (DNI) and an up-to-date infrared camera-based eye tracker (SR Research Eyelink 1000) by simultaneously recording human eye movements with both techniques. Between pupil-constricted and pupil-relaxed conditions we find a subject-specific shift in reported gaze position exceeding 2 degrees only with the camera based eye tracker, while the scleral search coil system simultaneously reported steady fixation. This confirms that the actual point of fixation did not change during pupil constriction/relaxation, and the resulting shift in measured gaze position is solely an artifact of the camera-based eye tracking system. We demonstrate a method to partially compensate the pupil-based shift using separate calibrations in pupil-constricted and pupil-dilated conditions, with pupil size as an index to dynamically weight the two calibrations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".