Developmental trajectory of face preference differs across individual in infant samples with ASD and without ASD
Bibliographic record
Abstract
Hallmark characteristics of autism include atypical eye contact and visual attention to faces (Dawson et al., 2005). Children with autism spectrum disorder (ASD) use facial information differently than children without ASD (Wolf et al., 2008). Less is known about 1) whether such atypicalities emerge in early infancy, 2) how they develop over the first year, and 3) whether they are related to later ASD development. In this longitudinal eye-tracking study we tested a cohort of 78 infants (infant sibling group, n = 35; control group, n = 43) and measured their visual attention to faces in five tasks, including face preference and eyes vs. mouth when they were 3 month, 6 month, 9 month and 12 month of age. Development of ASD was tested using the Autism Diagnostic Observation Schedule (ADOS; Lord et al. 2000) when the participants reached their second, third, fourth and seventh birthday. Mixed ANOVA analyses revealed that there is an increase of face preference with age in infancy, F(3, 198)= 9.14, p< .001, ηp2= 0.12; male infants showed greater face preference than female infants, F(1, 66)= 4.32, p=.042, ηp2= 0.06. Infant sibling group and control group did not differ on the face preference task, nor the risk group interacts with age, or sex, ps > .330. All effects on other tasks were not significant, ps > .296. A multilevel model with age and autism classification (ASD, Autism Spectrum, No ASD) as fixed effect, and age as a random slope also confirmed a nonsignificant interaction between age and the ADOS-based autism classification, Wald χ2(1) = 0.684, p = 0.408. These results together highlight a different individual developmental trajectory of face preference with age in both infants with ASD and without ASD, but the group differences in face preference may not emerge in infancy.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".