Gesture imitation performance and visual exploration in young children with autism spectrum disorder
Bibliographic record
Abstract
Abstract Background Imitation behaviors develop very early and increase in frequency and complexity during childhood. Most studies in children with autism spectrum disorder (ASD) support a general decrement in imitation performance. To better understand this phenomenon in ASD, factors related to visual attention and motor execution have been proposed. However, these studies used various paradigms and explored different types of imitation in heterogeneous samples, leading to inconsistent findings. The present study examines imitation performance related to visual attention and motor execution. We focused on gesture imitation, consistently reported as more affected than imitation of actions with objects in ASD. We also investigated the influence of meaningful and meaningless gestures on imitation performance. Methods Our imitation eye-tracking task used a video of an actor who demonstrated gestures and prompted children to imitate them. The demonstrations comprised three types of gestures: meaningful (MF) and meaningless (ML) hand gestures, and meaningless facial gestures. We measured the total fixation duration to the actor’ face during child-directed speech and gesture demonstrations. During the eye-tracking task, we video-recorded children to later assess their imitation performance. Our sample comprised 100 participants, among which were 84 children with ASD (aged 3.55 ± 1.11 years). Results Our results showed that the ASD and typically developing (TD) groups globally displayed the same visual attention toward the face (during child-directed speech) and toward gesture demonstrations, although children with ASD spent less time fixating on the face during FAC stimuli. Visual exploration of actors’ faces and gestures did not influence imitation performance. Rather, imitation performance was positively correlated with chronological and developmental age. Moreover, imitation of MF gestures was associated with less severe autistic symptoms, whereas imitation of ML gestures was positively correlated with higher non-verbal cognitive skills and fine motor skills. Conclusions These findings contribute to a better understanding of the complexity of imitation. We delineated the distinct nature of imitation of MF and ML hand gestures in children with ASD. We discuss clinical implications in relation to assessment and intervention programs.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".