Individual models of color differentiation to improve interpretability of information visualization
Bibliographic record
Abstract
Color is commonly used to represent categories and values in many computer applications, but differentiating these colors can be difficult in many situations (e.g., for users with color vision deficiency (CVD), or in bright light). Current solutions to this problem can adapt colors based on standard simulations of CVD, but these models cover only a fraction of the ways in which color perception can vary. To improve the specificity and accuracy of these approaches, we have developed the first ever individualized model of color differentiation (ICD). The model is based on a short calibration performed by a particular user for a particular display, and so automatically covers all aspects of the user's ability to see and differentiate colors in an environment. In this paper we introduce the new model and the manner in which differentiability limits are predicted. We gathered empirical data from 16 users to assess the model's accuracy and robustness. We found that the model is highly effective at capturing individual differentiation abilities, works for users with and without CVD, can be tuned to balance accuracy and color availability, and can serve as the basis for improved color adaptation schemes.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".