Preference for Eyes Decreases in the First Year of Life in Infants with a Familial History of Autism
Bibliographic record
Abstract
Autistic individuals have difficulty processing information in the eye region, and autistic traits are more common in their family members. In this longitudinal study, we examined looking to eyes and faces in infants with and without relatives with autism, using faces with open or closed eyes. Infants were recruited at 3, 6, 9, and 12 months, and later assessed for autism. A linear mixed model was used to predict preferential looking toward the eyes. There were main effects for open eyes at both 9 (t(335) = -2.70, p = 0.007; Std.beta = -0.57) and 12 months (t(335) = -2.03, p = 0.043; Std.beta = -0.43), and relatedness to autism (t(335) = -2.99, p = 0.003; Std.beta = -0.89). The interactions between age and relatedness to autism were significant at all age points: 6 months (t(335) = 2.02, p = 0.044; Std.beta = 0.73), 9 months (t(335) = 2.82, p = 0.005; Std.beta = 0.99), and 12 months (t(335) = 2.31, p = 0.021; Std.beta = 0.81). Infants preferred looked to the eyes rather than the mouth at 6 months (t(239) = -2.00, p = 0.047; Std.beta = -0.51) and 12 months (t(239) = -3.18, p = 0.002; Std.beta = -0.84). Relatedness to autism was marginally predicted this preference (p = 0.063). The interactions were significant at 6 (t(239) = 2.04, p = 0.042; Std.beta = 0.74) and 12 months (t(239) = 2.48, p = 0.014; Std.beta = 0.95). The comparison group initially preferred the eyes less but developed a stronger preference over time, surpassing the high-risk group. At 9 months, they preferred open eyes more, though this wasn't seen in the eyes vs. mouth task, possibly reflecting typical language-related attention to the mouth.
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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 | 0.000 |
| 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".