Bibliographic record
Abstract
Recent studies have reported individual differences in the capacity to learn new categories; the differences had electrophysiological correlates. The objective of the present study was to test whether these differences reflected individual differences in cognitive traits. 15 participants (aged 20 to 30) who had participated in the prior category-learning studies agreed to take some tests of cognitive ability, including (1) the perceptual reasoning subtests of the Weschler Adult Intelligence Scale (WAIS-IV) and (2) the Doors & People test of visual memory. Prior category learning performance was found to be positively correlated with perceptual reasoning and with visual memory in partial correlations. These findings confirm that the differences in category learning may be linked at least in part to differences in cognitive abilities. Des études récentes ont rapporté des différences individuelles dans la capacité à apprendre de nouvelles catégories. L’apprentissage des catégories possédait des corrélats électrophysiologiques. L’objectif de l’étude actuel était de tester si ces différences reflétaient des différences individuelles dans des traits. Quinze participants (entre 20 et 30 ans) qui avaient participé à des études précédentes d’apprentissage catégoriel ont accepté de participer aux sous-tests du raisonnement perceptuel du WAIS-IV et le Doors test du Doors & People test pour tester la mémoire visuelle. La performance dans la tkche d’apprentissage des catégories était corrélée positivement au raisonnement perceptuel et à la mémoire visuelle suite à des 3 corrélations partielles. Ces résultats confirment que les différences dans l’apprentissage des catégories seraient liées, au moins en partie, à des différences cognitives.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".