Affect and gaze responses during an Emotion-Evoking Task in infants at an increased likelihood for autism spectrum disorder
Bibliographic record
Abstract
BACKGROUND: The majority of research examining emotional difficulties in autism spectrum disorder (ASD) prior to age 2 relies on parent report. METHODS: We examined behavioral responses (affect and gaze) during emotionally salient tasks designed to elicit mildly positive and negative emotional states in infants. At 12 and 18 months, infants at an increased likelihood for an ASD diagnosis (IL; have an older sibling with ASD; n = 60) and low likelihood (LL; no family history of ASD; n = 21) completed the Emotion-Evoking (EE) Task and parents completed the Infant Behavior Questionnaire-Revised (IBQ-R). All children received an Autism Diagnostic Observation Scale-second Edition assessment for ASD symptomatology at 24 months. RESULTS: The main findings were (1) the IL group displayed higher rates of negative affect and spent less time looking at the task objects compared to the LL group, and (2) affect and gaze scores at 12 and 18 months, but not scores on the IBQ-R, predicted ASD symptoms at 24 months. LIMITATIONS: The data were drawn from an IL sample and may not be generalizable to the general ASD population, and the children were not followed to determine a diagnosis of ASD. CONCLUSION: These results suggest that behavioral responses can provide important information that complements parent reports of emotional regulation in IL infants as early as 12 months of age.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".