Investigating joint attention in a guided interaction between a child with ASD and therapists: A pilot eye-tracking study
Bibliographic record
Abstract
Introduction Deficits in joint attention are commonly seen in children with autism spectrum disorder. Research examining joint attention in autism spectrum disorder commonly uses two broad strategies to cue and measure joint attention: behavioral observation and eye tracking. These strategies trade off prioritizing ecological validity vs. gaze measurement accuracy, with a focus on one sacrificing the other. The purpose of this case study was to develop a method to accurately measure gaze position while maintaining an ecologically valid dyadic interaction. Methods A child with autism spectrum disorder completed two developmentally appropriate tabletop activities. Each activity was guided by a different occupational therapist who purposefully used a different interaction style with the child. Mobile eye trackers worn by both the child and the therapist recorded the dyadic interactions. Data collection included audio and video recording of interactive behaviors, eye movements and visual fixations in regions of shared interest. Results Differences were detected in gaze use and interactive joint attention behaviors between the therapists working with the child and within the child’s respective dyadic interactions. Conclusions The proof of concept method maintained both ecological validity and measurement accuracy of therapist–child joint attention. This method has promise to be scaled for larger studies.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".