New Word Acquisition in Children: Examining the Contribution of Verbal Short‐term Memory to Lexical and Semantic Levels of Learning
Bibliographic record
Abstract
Summary A variety of evidence suggests that human vocabulary acquisition and verbal short‐term ability are related. The aim of this study was to investigate the learning of new lexical and semantic representation in 7 to 12 years old children selected on the basis of their poor working memory capacity. A deep characterization of the short‐term memory (STM) capacities has been carried out through a series of tasks derived from recent STM models tapping STM, language and attentional processes. Participants experienced a three conditions word learning task designed to reflect lexical learning, semantic learning and lexical–semantic learning capacities. Other aspects of the learning such as the learning rate and the word length effect were evaluated. The experimental participants scored more poorly than controls on lexical learning, and this deficit was associated with the serial order STM and the attentional capacities. The current study also highlighted that neither the experimental group nor the control group took advantage in lexical learning of semantic information supplement. Our results suggest that children with verbal STM problems learn a smaller number of new words but present a similar way of learning than children without verbal STM problems. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".