Variations in phonological working memory: Linking early language experiences and language learning outcomes
Bibliographic record
Abstract
ABSTRACT In order to build complex language from perceptual input, children must have access to a powerful information processing system that can analyze, store, and use regularities in the signal to which the child is exposed. In this article, we propose that one of the most important parts of this underlying machinery is the linked set of cognitive and language processing components that comprise the child's developing working memory (WM). To examine this hypothesis, we explore how variations in the timing, quality, and quantity of language input during the earliest stages of development are related to variations in WM, especially phonological WM (PWM), and in turn language learning outcomes. In order to tease apart the relationships between early language experience, WM, and language development, we review research findings from studies of groups of language learners who clearly differ with respect to these aspects of input. Specifically, we consider the development of PWM in children with delayed exposure to language, that is, children born profoundly deaf and exposed to oral language following cochlear implantation and internationally adopted children who have delayed exposed to the adoption language; children who experience impoverished language input, that is, children who experience early bouts of otitis media and signing deaf children born to nonsigning hearing parents; and children with enriched early language input, that is, simultaneous bilinguals and second language learners.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".