Impaired disengagement of attention in young children with autism
Bibliographic record
Abstract
BACKGROUND: The present study examined the disengage and shift operations of visual attention in young children with autism. METHODS: For this purpose, we used a simple visual orienting task that is thought to engage attention automatically. Once attention was first engaged on a central fixation stimulus, a second stimulus was presented on either side, either simultaneously or successively. Latency to begin an eye movement to the peripheral stimulus served as the main dependent measure. The two stimulus conditions (simultaneous and successive) provided independent measures of disengaging and shifting attention, respectively. Performance of children with autism was compared to that of children with Down syndrome and a normal group. RESULTS: The main finding was that relative to both comparison groups, children with autism had marked difficulty in disengaging attention. Indeed, on 20% of trials they remained fixated on the first of two competing stimuli for the entire 8-second trial duration. Evidence is also provided for a more subtle problem in executing rapid shifts of attention. CONCLUSIONS: Our findings on disengagement in autism parallel those reported in normal 2-month-olds, in whom attention has been described as 'obligatory'. Discussion focuses on the potential role of general versus domain-specific processes in producing some of the core features of autism.
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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) | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".