Learning Language in Autism: Maternal Linguistic Input Contributes to Later Vocabulary
Bibliographic record
Abstract
It is well established that children with typical development (TYP) exposed to more maternal linguistic input develop larger vocabularies. We know relatively little about the linguistic environment available to children with autism spectrum disorders (ASD), and whether input contributes to their later vocabulary. Children with ASD or TYP and their mothers from English and French-speaking families engaged in a 10 min free-play interaction. To compare input, children were matched on language ability, sex, and maternal education (ASD n = 20, TYP n = 20). Input was transcribed, and the number of word tokens and types, lexical diversity (D), mean length of utterances (MLU), and number of utterances were calculated. We then examined the relationship between input and children's spoken vocabulary 6 months later in a larger sample (ASD: n = 19, 50-85 months; TYP: n = 44, 25-58 months). No significant group differences were found on the five input features. A hierarchical multiple regression model demonstrated input MLU significantly and positively contributed to spoken vocabulary 6 months later in both groups, over and above initial language levels. No significant difference was found between groups in the slope between input MLU and later vocabulary. Our findings reveal children with ASD and TYP of similar language levels are exposed to similar maternal linguistic environments regarding number of word tokens and types, D, MLU, and number of utterances. Importantly, linguistic input accounted for later vocabulary growth in children with ASD.
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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 | 0.004 |
| 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".