Assessing Perceptual Chromatic Equiluminance Using a Reflexive Pupillary Response
Bibliographic record
Abstract
Abstract Equiluminant stimuli help assess the integrity of colour perception and the relationship of colour to other visual features. As a result of individual variation, it is necessary to calibrate experimental visual stimuli to suit each individual’s unique equiluminant ratio. Most traditional methods rely on training observers to report their subjective equiluminance point. Such paradigms cannot easily be implemented on pre-verbal or non-verbal observers. Here, we present a novel Pupil Frequency-Tagging Method (PFTM) for detecting a participant’s unique equiluminance point without verbal instruction and with minimal training. PFTM analyses reflexive pupil oscillations induced by slow (< 2 Hz) temporal alternations between coloured stimuli. Two equiluminant stimuli will induce a similar pupil dilation response regardless of colour; therefore, an observer’s equiluminant point can be identified as the luminance ratio between two colours for which the oscillatory amplitude of the pupil at the tagged frequency is minimal. We compared pupillometry-based equiluminance ratios to those obtained with two established techniques in humans: minimum flicker and minimum motion. In addition, we estimated the equiluminance point in non-human primates, demonstrating that this new technique can be successfully employed in non-verbal subjects. Key Points Measuring equiluminance is challenging in non-verbal subjects. The pupil diameter varies as perceived brightness changes. Utilising oscillatory pupillometry, PFTM provides a way to measure equiluminance points without training or the need for a subjective report.
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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".