The Effect of Mirror Neurons Stimulation on Syntax Development of Female Persian Autistic Children
Bibliographic record
Abstract
One common language disorder in autistic children is syntax disorder. The current research aims to examine the relationship between mirror neurons stimulation by intentional movements imitation and verbal imitation with syntax skill development in autistic children. This research was performed using an experimental applied design and convenience sampling method. First, the researcher designed a functional and easy model for autistic children rehabilitation based on intentional movement imitation and verbal imitation; Then, using TPR (Total Physical Response) method. A pilot study was conducted on a Persian-speaking autistic girl aged 7 in 12 sessions for four weeks in Iran University of Medical Sciences, School of Rehabilitation Sciences, and effective positive effects were observed. Then, 8 Persian-speaking autistic children were examined in terms of entrance criteria, and finally, 5 autistic girls aged 5-8 were selected and underwent training courses for 42 sessions over 14 weeks (3 20-30 minute sessions). In order to examine syntax skills of the subjects including grammatical understanding, sentence imitation and grammatical completion, before and after intervention, TOLD-P3 test was used. Each subject, as his/her Control was examined before and after intervention. Furthermore, two months after a 14-week stopping period, all the subjects were re-examined using TOLD-P3 test, and finally, results stability was examined. The research findings were analyzed using ANOVA test. The findings show that mirror neurons stimulation in autistic children through intentional movement imitation and verbal imitation has a positive effect on syntax skill improvement on these children thereby facilitating their verbal communication.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".