MEASURING REFRACTIVE ERROR USING CONTINUOUS PSYCHOPHYSICS AND EYE TRACKING
Bibliographic record
Abstract
For animal subjects, or human patients who have difficulty with conventional measurement methods, finding the best optical correction for refractive errors can be challenging. Refractive corrections are of growing concern in vision research due to the high prevalence of myopia in humans, but also with some colony-reared animals used in research. Traditional methods like psychophysical paradigms require extensive training or retinoscopy, which in animals requires anesthesia. However, tracking a moving target on a blank background is a natural task that is relatively easy for subjects to learn and execute. Here we used continuous psychophysics and eye tracking to measure contrast thresholds and assess refractive errors. Using custom MATLAB software with PsychToolbox-3 and an EyeLink 1000 eye tracker, we evaluated refractive errors by monitoring contrast sensitivity during dynamic visual stimulus tracking with gradually decreasing contrast. Applying this approach to the task of tracking a Gabor stimulus, we evaluated seven spherical lens powers, from -3 to +3 diopters, in successive runs on two human subjects . Each contrast sensitivity measurement necessitated less than a minute of eye tracking data. Contrast thresholds were derived from the positional errors between the target stimulus and the subjects' gaze positions. A plot of contrast threshold vs. lens power showed a clear dependence on positive diopter values and shallow dependence on negative ones, likely due to partial compensation from accommodation. We found that each subject’s optimal lens power coincided with their previously measured corrected-to-normal vision. Our findings demonstrate the utility of continuous psychophysics integrated with eye tracking for more ecologically valid measurements of contrast sensitivity and refractive errors. This method could be used for clinically challenging human populations, and might be adapted for non-human primates such as marmosets or macaques, extensively used in vision research, thereby eliminating the need for anesthesia in retinoscopy or prolonged behavioral training.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".