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Record W4362512676 · doi:10.21203/rs.3.rs-2742517/v1

Gesture imitation performance and visual exploration in young children with autism spectrum disorder

2023· preprint· en· W4362512676 on OpenAlexaff
Kenza Latrèche, Nada Kojovic, Irène Pittet, Shreyasvi Natraj, Martina Franchini, Isabel M. Smith, Marie Schaer

Bibliographic record

VenueResearch Square · 2023
Typepreprint
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsDalhousie University
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsImitationGestureAutism spectrum disorderPsychologyAutismCognitive psychologyDevelopmental psychologyComputer scienceArtificial intelligenceNeuroscience

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.069
GPT teacher head0.377
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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