Linearizing Screen Gamma for Precise Psychophysical Online Studies in Less Than 5 Minutes
Bibliographic record
Abstract
Online data collection has multiple advantages, including access to larger, more diverse samples as well as fast data collection. However, a particular challenge for vision science studies is that they require visual stimuli to be standardized. We propose measuring perceived brightness at different luminance values to estimate screen Gamma, thus creating a luminance lookup table so researchers can correct their stimuli when running online experiments. Participants adjust the luminance of a uniform square to match the perceived brightness of flanker stimuli, lines alternating between two luminance levels on every pixel across stimulus width. We validated this task, based on an adaptation of the code included in the PsyCalibrator package (Lin et al., 2023), in an online sample of 19 participants recruited through Prolific and tested using VPixx Pack & Go (VPixx Technologies, 2021). Participants completed five 1-parameter Gamma curve measurements (nPoints= 1, 3, 7, 15 and 31 equally spaced luminance levels between 0 and 1, each measured once), twice to assess test/retest reliability. As 0 and 1 necessarily correspond to minimal and maximal luminance respectively, the estimated Gamma curve is fit using nPoints + 2 data points. The best speed/accuracy tradeoff is found using 31 luminance measurements, allowing for a precise estimate in around 3 minutes . Comparing luminance lookup tables, extrapolated to 256 luminance levels, across measurements reveals the absolute value error between measurements is on average .008 (sd = .012). Corrections applied on the 31 measured luminance levels fit a linear model with an average R2 of .999. We find this short and simple task to be a very robust measure of screen Gamma for online participants. Using it will allow vision scientists to account for varying gray level rendering of participant display configurations in their online studies, increasing their control on the presented visual stimuli.
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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 | 0.005 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.076 | 0.024 |
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".