Does Dichromatic Color Simulation Predict Color Identification Error Rates?
Bibliographic record
Abstract
PURPOSE: Several algorithms are available to transform colored digital images into simulated dichromatic color perception. These algorithms can be very illustrative of the problems dichromats experience in discriminating colors. The purpose of this study was to determine whether one type of transformation could provide a quantitative account of error rates in identifying colors displayed on a computer monitor. METHODS: The experimental task required observers to identify the color of small rectangles displayed on a computer monitor within a black background. There were eight colors. The number of errors for each color was recorded. Four deuteranopes and five protanopes participated. Color differences were calculated using normal trichromatic and dichromatic values. The dichromatic color differences were calculated using the procedure developed by Brettel et al. [J Opt Soc Am (A) 1997;14:2647-55]. RESULTS: The relationship between error rates and color differences calculated in either color space was fit by an exponential decay function. However, the fit provided by the dichromatic color differences was no better than that using color differences calculated in trichromatic color space and both regressions could only account for approximately 30% of the variance in the data. CONCLUSIONS: Correlations between the error rates in identifying colors for dichromats and color differences were low-to-moderate whether the color differences were based on normal trichromatic color space or a dichromatic transformation. This finding suggests that it may be sufficient to calculate the color difference only in color-normal space to determine whether the colors will be confused by a person with a congenital color vision defect. Although computer algorithms are useful in illustrating color discrimination problems experienced by dichromats, they may not offer any advantage over typical trichromatic color spaces in predicting performance in color identification. The lack of any advantage may be due to how dichromats use brightness information to identify colors.
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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 | 0.003 | 0.050 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".