Swiping colors in virtual reality: Color categories in action
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it