Phonetic imitation by individuals with Autism Spectrum Disorders: Investigating the role of procedural and declarative memory
Bibliographic record
Abstract
This study investigates the role of procedural and declarative memory in phonetic imitation, by examining the word- and phoneme-specificity of imitation produced by individuals with Autism Spectrum Disorders (ASD). Previous research has shown that individuals with ASD process language differently from the Neurotypical population (e.g., Ullman 2004; Walenski et al. 2006), with Autistic individuals relying more on declarative memory. Previous work with the general population has shown a robust effect of phonetic convergence (e.g., Pardo, 2006), as well as generalization and weak word- specificity effects (Nielsen, 2011). To test whether individuals with ASD exhibit increased specificity, we used Nielsen's (2011) experimental paradigm, which has been shown to elicit generalized phonetic imitation in the general population. A Linear Mixed Effects regression analysis revealed that increased VOT on the modeled phoneme /p/ was imitated by both ASD and control groups [p<0.05]. However, different patterns emerged in phoneme-level specificity: the control group exhibited sub-phonemic generalization (increasing VOT on /p/ and /k/), while the ASD group exhibited a phoneme-specific pattern (increasing VOT only on /p/) [p<0.05]. Furthermore, a stronger trend toward word-specificity was observed within the ASD group. Taken together, these results confirm the earlier finding that ASD individuals exhibit greater reliance on declarative memory.
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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 | 0.001 | 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".