Swiping colors in virtual reality: Color categories in action
Bibliographic record
Abstract
We adapted a paradigm from animal learning to investigate the stability of color category borders in humans using a VR videogame task. Observers held a colored saber in each hand and swiped approaching cubes which contained a colored stripe. Observers were instructed to use the saber whose color best matched the colored stripe. Saber colors were green and blue, or pink and purple, and the cube colors varied smoothly in fixed multiples of discrimination threshold. In a baseline block, observers were tested on a predetermined set of colors, where three of the in-between hues were ambiguous and close to the category border. We fit the saber choices with a psychometric function to determine the location and sharpness of the category border. Subsequent blocks shifted the tested color range toward one endpoint, and if observers’ color category borders were stable, there would be no difference between the baseline and shifted borders. Alternatively, observers could base their responses on the color difference between the cube and the saber only. In that case, the PSE would shift in the same direction as the shift in the colors tested. Our results show that observers exhibit a halfway shift of their category borders in the direction of the saber color shift. In follow-up studies, we found that this partial range effect persists even when equalizing the proportion of responses made with each saber color. We also found a comparable adaptation to the range when using green hues without a category border. This work suggests a very limited role of color categories for our task. We speculate that observers learn the task and quickly become adept at performing the match to sample task. This expertise may allow observers to respond automatically instead of focusing on the categorical distinction of the hues.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".