A nonvisual eye tracker calibration method for video-based tracking
Bibliographic record
Abstract
Video-based eye trackers have enabled major advancements in our understanding of eye movements through their ease of use and their non-invasiveness. One necessity to obtain accurate eye recordings using video-based trackers is calibration. The aim of the current study was to determine the feasibility and reliability of alternative calibration methods for scenarios in which the standard visual-calibration is not possible. Fourteen participants were tested using the EyeLink 1000 Plus video-based eye tracker, and each completed the following 5-point calibration methods: 1) standard visual-target calibration, 2) described calibration where participants were provided with verbal instructions about where to direct their eyes (without vision of the screen), 3) proprioceptive calibration where participants were asked to look at their hidden finger, 4) replacement calibration, where the visual calibration was performed by 3 different people; the calibrators were temporary substitutes for the participants. Following calibration, participants performed a simple visually-guided saccade task to 16 randomly presented targets on a grid. We found that precision errors were comparable across the alternative calibration methods. In terms of accuracy, compared to the standard calibration, non-visual calibration methods (described and proprioception) led to significantly larger errors, whilst the replacement calibration method had much smaller errors. In conditions where calibration is not possible, for example when testing blind or visually impaired people who are unable to foveate the calibration targets, we suggest that using a single stand-in to perform the calibration is a simple and easy alternative calibration method, which should only cause a minimal decrease in accuracy.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".