Rethinking the connection between working memory and language impairment
Bibliographic record
Abstract
BACKGROUND: Working memory deficits have been found for children with specific language impairment (SLI) on tasks imposing increasing short-term memory load with or without additional, consistent (and simple) processing load. AIMS: To examine the processing function of working memory in children with low language (LL) by employing tasks imposing increasing processing loads with constant storage demands individually adjusted based on each participant's short-term memory capacity. METHODS & PROCEDURES: School-age groups with LL (n = 17) and typical language with either average (n = 28) or above-average nonverbal intelligence (n = 15) completed complex working memory-span tasks varying processing load while keeping storage demands constant, varying storage demands while keeping processing load constant, simple storage-span tasks, and measures of language and nonverbal intelligence. Teachers completed questionnaires about cognition and learning. OUTCOMES & RESULTS: Significantly lower scores were found for the LL than either matched group on storage-based tasks, but no group differences were found on the tasks varying processing load. Teachers' ratings of oral expression and mathematics abilities discriminated those who did or did not complete the most challenging cognitive tasks. CONCLUSIONS & IMPLICATIONS: The results implicate a deficit in the phonological storage but not in the central executive component of working memory for children with LL. Teacher ratings may reveal personality traits related to perseverance of effort in cognitive research.
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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 | 0.008 |
| 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 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".