Categorical speech perception deficits distinguish language and reading impairments in children
Bibliographic record
Abstract
We examined categorical speech perception in school-age children with developmental dyslexia or Specific Language Impairment (SLI), compared to age-matched and younger controls. Stimuli consisted of synthetic speech tokens in which place of articulation varied from 'b' to 'd'. Children were tested on categorization, categorization in noise, and discrimination. Phonological awareness skills were also assessed to examine whether these correlated with speech perception measures. We observed similarly good baseline categorization rates across all groups; however, when noise was added, the SLI group showed impaired categorization relative to controls, whereas dyslexic children showed an intact profile. The SLI group showed poorer than expected between-category discrimination rates, whereas this pattern was only marginal in the dyslexic group. Impaired phonological awareness profiles were observed in both the SLI and dyslexic groups; however, correlations between phonological awareness and speech perception scores were not significant. The results of the study suggest that in children with language and reading impairments, there is a significant relationship between receptive language and speech perception, there is at best a weak relationship between reading and speech perception, and indeed the relationship between phonological and speech perception deficits is highly complex.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".