Visuospatial and Verbal Short-Term Memory Correlates of Vocabulary Ability in Preschool Children
Bibliographic record
Abstract
Background: Recent studies indicate that school-age children's patterns of performance on measures of verbal and visuospatial short-term memory (STM) and working memory (WM) differ across types of neurodevelopmental disorders. Because these disorders are often characterized by early language delay, administering STM and WM tests to toddlers could improve prediction of neurodevelopmental outcomes. Toddler-appropriate verbal, but not visuospatial, STM and WM tasks are available. A toddler-appropriate visuospatial STM test is introduced. Method: Tests of verbal STM, visuospatial STM, expressive vocabulary, and receptive vocabulary were administered to 92 English-speaking children aged 2-5 years. Results: Mean test scores did not differ for boys and girls. Visuospatial and verbal STM scores were not significantly correlated when age was partialed out. Age, visuospatial STM scores, and verbal STM scores accounted for unique variance in expressive (51%, 3%, and 4%, respectively) and receptive vocabulary scores (53%, 5%, and 2%, respectively) in multiple regression analyses. Conclusion: Replication studies, a fuller test battery comprising visuospatial and verbal STM and WM tests, and a general intelligence test are required before exploring the usefulness of these STM tests for predicting longitudinal outcomes. The lack of an association between the STM tests suggests that the instruments have face validity and test independent STM skills.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".