Warm versus cool colors and their relation to color perception
Bibliographic record
Abstract
The distinction between warm and cool colors is widely considered a fundamental aspect of human color experience, but whether it reflects properties of color perception or color associations remains unclear. We examined how the warm-cool division is related to perceptual landmarks of color coding and color appearance. Observers made warm-cool ratings for 36 hue angles at three luminance levels and also estimated the angles for their unique (e.g., yellow or red) and binary (e.g., orange) hues. The warm-cool dimension was reliably identified by most observers, was consistent across lightness levels, and varied along an orangish-red to greenish-blue dimension that is intermediate to both the principal chromatic dimensions of early cone-opponent (cardinal) or perceptual-opponent (red-green and blue-yellow) axes. When the stimuli were projected into a uniform color space (CIELAB), a close correspondence was found between the warm-cool dimension and the perceived strength (saturation) of different hues, based on the LAB chroma. Specifically, the peak warm and cool values were hue angles with the weakest saturation, and the boundaries between the two categories corresponded to hue angles with the highest saturation. This pattern could arise if vision is selectively adapted to the spectra of warm and cool colors and provides a potential basis for the strong but unexplained asymmetries in color coding built into perceptually uniform color spaces.
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 | 0.006 |
| 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 | 0.000 | 0.001 |
| 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".