Neuropsychological effects of second language exposure in Down syndrome
Bibliographic record
Abstract
BACKGROUND: While it has been common practice to discourage second language learning in neurodevelopmental disorders involving language impairment, little is known about the effects of second language exposure (SLE) on broader cognitive function in these children. Past studies have not found differences on language tasks in children with Down syndrome (DS) and SLE. We expand on this work to determine the effects on the broader cognitive profile, including tests tapping deficits on neuropsychological measures of prefrontal and hippocampal function. METHOD: This study examined the specific cognitive effects of SLE in children with DS (aged 7-18 years). Children with SLE (n = 13: SLE predominantly Spanish) and children from monolingual homes (n = 28) were assessed on a standardised battery of neuropsychological tests developed for DS, the Arizona Cognitive Test Battery. The current exposure level to a language other than English in the SLE group was greater than 4 h per day on average. RESULTS: No group differences were observed for any outcome, and level of exposure was also not linearly related to neuropsychological outcomes, several of which have been shown to be impaired in past work. CONCLUSION: There were no measurable effects of SLE on neuropsychological function in this sample of children with DS. Potential clinical implications of these findings are discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".