Bilingualism and Processing Speed in Typically Developing Children and Children With Developmental Language Disorder
Bibliographic record
Abstract
Purpose The aim of the current study was to investigate whether dual language experience modulates processing speed in typically developing (TD) children and in children with developmental language disorder (DLD). We also examined whether processing speed predicted vocabulary and sentence-level abilities in receptive and expressive modalities. Method We examined processing speed in monolingual and bilingual school-age children (ages 8-12 years) with and without DLD. TD children (35 monolinguals, 24 bilinguals) and children with DLD (17 monolinguals, 10 bilinguals) completed a visual choice reaction time task. The Clinical Evaluation of Language Fundamentals, the Peabody Picture Vocabulary Test, and the Expressive Vocabulary Test were used as language measures. Results The children with DLD exhibited slower response times relative to TD children. Response time was not modified by bilingual experience, neither in children with typical development nor children with DLD. Also, we found that faster processing speed was related to higher language abilities, but this relationship was not significant when socioeconomic status was controlled for. The magnitude of the association did not differ between the monolingual and bilingual groups across the language measures. Conclusions Slower processing speed is related to lower language abilities in children. Processing speed is minimally influenced by dual language experience, at least within this age range. Supplemental Material https://doi.org/10.23641/asha.12210311.
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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 | 0.000 | 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 | 0.000 |
| 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".