Female Advantage in Speeded Colour Naming: A Special Naming Factor or Superior Motor Sequencing?
Bibliographic record
Abstract
Women name colours more quickly than men do, and our recent research suggests that the female advantage for colour naming extends to speeded naming of shapes. The female advantage could reflect a superiority in producing and execuring the motor sequences underlying the required vocal response. Or, women could have faster access to or retrieval of colour labels. The present study tested these two possibilities by administering 3 speeded colour-naming tasks. In the first task, participants named a patch of colour as quickly as possible after it was presented. In the second task, participants made manual (instead of vocal) responses. In the third task, vocal responses were required but a randomly varying delay period was introduced between the presentation of the colour patch and the required response. Females reponded more quickly on the first task but there was no such advantage in the manual or delayed conditions. Taken together, these results suggest that the female advantage for speeded naming tasks reflects an advantage for sequencing movements rather than a special naming ability.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.026 | 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 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".