Initiation of joint attention and related visual attention processes in infants with autism spectrum disorder: Literature review
Bibliographic record
Abstract
Autism spectrum disorder (ASD) represents a group of neurodevelopmental disabilities that can be difficult to identify before the age of 2 or 3 years, the age when the full range of behavioral symptoms has emerged in most cases. Initiation of joint attention is an important developmental function in which impairments are already observable before the second birthday and can predict children's ASD symptomatology. In the first part of this review, we summarize results pertaining to retrospective studies of initiation of joint attention in children with ASD and prospective studies of infants at high risk for ASD during the first 2 years, when this behavior is becoming more complex in terms of frequency, quality, and variety. We will also discuss the implications of impairments in dyadic engagement, a precursor of joint attention behavior, for the early development of joint attention. Finally, the early development of initiation of joint attention has been related to specific visual attention mechanisms such as social orienting and visual disengagement. In the second part of this review, we provide an overview of the relationship between those visual attention mechanisms and subsequent social-communication impairments. Clinical and research implications of these findings for both early detection and early intervention will be discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".