The Categorisation of Non-Categorical Colours: A Novel Paradigm in Colour Perception
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
In this paper, we investigate a new paradigm for studying the development of the colour 'signal' by having observers discriminate and categorize the same set of controlled and calibrated cardinal coloured stimuli. Notably, in both tasks, each observer was free to decide whether two pairs of colors were the same or belonged to the same category. The use of the same stimulus set for both tasks provides, we argue, an incremental behavioural measure of colour processing from detection through discrimination to categorisation. The measured data spaces are different for the two tasks, and furthermore the categorisation data is unique to each observer. In addition, we develop a model which assumes that the principal difference between the tasks is the degree of similarity between the stimuli which has different constraints for the categorisation task compared to the discrimination task. This approach not only makes sense of the current (and associated) data but links the processes of discrimination and categorisation in a novel way and, by implication, expands upon the previous research linking categorisation to other tasks not limited to colour perception.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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