Language proficiency and metacognition as predictors of spontaneous rehearsal in children.
Bibliographic record
Abstract
Despite decades of research on fundamental memory strategies such as verbal rehearsal, the potential underlying skills associated with the emergence of rehearsal are still not fully understood. Two studies examined the relative roles of language proficiency and metamemory in predicting rehearsal use, as well as the prediction of metamemory performance by language proficiency. In Study 1, 59 children, 5 to 8 years old, were administered a serial recall task, 2 language measures, a nonverbal cognitive measure, and a rapid automatized naming (RAN) task. Language proficiency, RAN, and age were significant individual predictors of rehearsal use. In hierarchical regression analyses, language proficiency mediated almost completely the age → rehearsal use relation. In addition, automatized naming was a strong but partial mediator of the contribution of language proficiency to rehearsal use. In Study 2, 54 children were administered a metamemory test, a language measure, and a serial recall task. Metamemory skills and, again, language proficiency significantly predicted rehearsal use in the task. The predictive strength of metamemory skills was mediated by the children's language proficiency. The mutually supportive roles of automatized naming, language, and metamemory in the emergence of spontaneous cumulative verbal rehearsal are discussed in the context of the resulting model, along with the minimal roles of age and aspects of intelligence.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".