Infrared Tracking of the Near Triad
Bibliographic record
Abstract
The oculomotor response when viewing a near target is characterized by ‘the near triad’: pupil miosis (constriction), binocular convergence and increased accommodation. Most existing eye-tracking systems lack the ability to measure all three of these parameters and are usually specialized to handle only one. Systems that can measure the complete near triad suffer from slow measurement rates, off-line analysis or are cumbersome and inconvenient to use. Singular specialized systems are usually combined ad-hoc but such systems are often complex in architecture and suffer severe limitations in runtime. We describe a video-based eye tracking system based on eccentric photorefraction that allows for remote, high-speed measurement of all three components of the near triad. This provides for precise, simultaneous measurement of oculomotor dynamics as well as having the benefit of being safe and non-intrusive. An extended infrared source illuminated the subject's eye. The corneal reflex and ‘bright pupil’ reflections of this source were imaged by an infrared sensitive camera and used to track gaze direction and pupil diameter. Such eccentric illumination combined with a knife-edge camera aperture allowed the accommodative state of the eye to be estimated from measurements of the gradient of image intensity across the pupil. Real-time measurements are facilitated by detection of Purkinje images to define areas of interest for each pupil followed by pupil edge detection and fitting to an ellipse model. Once the pupils are located, data about the brightness profile, diameter, corneal reflex and pupil center are extracted and processed to calculate the near triad. The system will be used in ongoing experiments assessing the role of oculomotor cues in perception of motion in depth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".