A quantitative network model for color categorization
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
Abstract To clarify the higher‐order mechanism of human color perception, we measured the color appearances of 78 colored lights by an elemental color‐scaling method and by a categorical color naming method. The colors covered nearly the entire CIE 1931 xy ‐chromaticity diagram with three different surrounds. The results showed that firm basic color zones derived by categorical color naming can be mapped with no overlap in an opponent‐color response space. We propose a network model with a threshold selector, maximum selectors, and multiplication units with gain factors to generate the categorical color responses quantitatively from the elemental color responses. The model can predict the categorical color naming results in different surround conditions with no change of parameters. This suggests that a nonlinear color vision mechanism for color categorization exists between the primary visual cortex (V1) and the inferior temporal cortex (IT) in the human brain. © 2002 Wiley Periodicals, Inc. Col Res Appl, 27, 225–232, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.10060
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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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 it