Bilingual children with autism spectrum disorders: The impact of amount of language exposure on vocabulary and morphological skills at school age
Bibliographic record
Abstract
Studies of bilingual children with Autism Spectrum Disorders (ASD) have focused on early language development using parent report measures. However, the effect of bilingual exposure on more complex linguistic abilities is unknown. In the current study, we examined the impact of amount of language exposure on vocabulary and morphological skills in school-aged children with ASD who did not have intellectual disability. Forty-seven typically developing children and 30 children with ASD with varying exposure to French participated in the study. We investigated the impact of amount of language exposure, nonverbal IQ, age, and working memory on language abilities via regression analyses. Current amount of language exposure was the strongest predictor of both vocabulary skills (accounting for 62% of the variance) and morphological skills (accounting for 49% of the variance), for both typically-developing children and children with ASD. These findings highlight the central role amount of language exposure plays in vocabulary and morphological development for children with ASD, as it does for typically-developing children. In addition, they provide further evidence that, when provided with adequate language exposure, many children with ASD are capable of acquiring two languages. Autism Research 2018, 11: 1667-1678. © 2018 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: We studied typically developing children and children with ASD living in a bilingual society who had varying exposure to French (ranging from bilinguals to monolinguals). We investigated the impact of amount of language exposure, nonverbal IQ, age, and working memory on their vocabulary and morphological skills. Current amount of language exposure was the strongest predictor of language skills in both groups of children. Findings indicate that when provided with adequate language exposure, many children with ASD are capable of acquiring two languages.
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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 | 0.001 |
| 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".