Use of Prosody and Information Structure in High Functioning Adults with Autism in Relation to Language Ability
Bibliographic record
Abstract
Abnormal prosody is a striking feature of the speech of those with Autism spectrum disorder (ASD), but previous reports suggest large variability among those with ASD. Here we show that part of this heterogeneity can be explained by level of language functioning. We recorded semi-spontaneous but controlled conversations in adults with and without ASD and measured features related to pitch and duration to determine (1) general use of prosodic features, (2) prosodic use in relation to marking information structure, specifically, the emphasis of new information in a sentence (focus) as opposed to information already given in the conversational context (topic), and (3) the relation between prosodic use and level of language functioning. We found that, compared to typical adults, those with ASD with high language functioning generally used a larger pitch range than controls but did not mark information structure, whereas those with moderate language functioning generally used a smaller pitch range than controls but marked information structure appropriately to a large extent. Both impaired general prosodic use and impaired marking of information structure would be expected to seriously impact social communication and thereby lead to increased difficulty in personal domains, such as making and keeping friendships, and in professional domains, such as competing for employment opportunities.
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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 | 0.003 |
| 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".