The status of primary and secondary colours in colour term acquisition
Bibliographic record
Abstract
Berlin & Kay (1969) proposed children would acquire basic colour terminology in an order analogous to that by which colour terms are added to languages, as both reflected the physiological structure underpinning perceptual-conceptual colour space. Accordingly, children should acquire the six primary colour terms (red, green, blue, yellow, black & white) before the five secondary colour terms (orange, pink, purple, brown & grey). However, in an extensive developmental study we found little support for an advantage in the acquisition of primary over secondary colours. Instead, our data suggested a dichotomous developmental order, marked only by the late acquisition of brown and grey, relative to the other nine basic colours (Pitchford & Mullen, 2002). In this study we investigate factors that may constrain the acquisition of brown and grey by comparing (i) the performance of a group of 159 preschool children on three tasks of perceptual colour processing, and (ii) two objective counts of colour term usage in preschool directed-speech. Results showed the tardy conceptualisation of brown and grey is not limited by (1) perception, as children can discriminate and will group these two colours, even when they cannot comprehend and name them, or (2) linguistic input, as primary colour terms appear more frequently in preschool texts and mothers' speech. Interestingly, our data show brown and grey are colours children least prefer, suggesting colour preference and colour conceptualisation are linked in early childhood: an association which may be mediated by a third factor relating to the perceptual organisation of colour space.
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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 | 0.002 | 0.009 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".