Shifting Preferences for Primate Faces in Neurotypical Infants and Infants Later Diagnosed With ASD
Bibliographic record
Abstract
Infants look at others' faces to gather social information. Newborns look equally at human and monkey faces but prefer human faces by 1 month, helping them learn to communicate and interact with others. Infants later diagnosed with autism spectrum disorder (ASD) look at human faces less than neurotypical infants, which may underlie some deficits in social-communication later in life. Here, we asked whether infants later diagnosed with ASD differ in their preferences for both human and nonhuman primate faces compared to neurotypical infants over their first 2 years of life. We compare infants' relative looking times to human or monkey faces paired with nonface controls (Experiment 1) and infants' total looking times to pairs of human and monkey faces (Experiment 2). Across two experiments, we find that between 6 and 18 months, infants later diagnosed with ASD show a greater downturn (decrease after an initial increase) in looking at both primate faces than neurotypical infants. A decrease in attention to primate faces may partly underlie the social-communicative difficulties in children with ASD and could reveal how early perceptual experiences with faces affect development. Autism Res 2019, 12: 249-262 © 2018 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Looking at faces helps infants learn to interact with others. Infants look equally at human and monkey faces at birth but prefer human faces by 1 month. Infants later diagnosed with ASD who show deficits in social-communication look at human faces less than neurotypical infants. We find that a downturn (decline after an initial increase) in attention to both human and monkey faces between 6 and 18 months may partly underlie the social-communicative difficulties in children with ASD.
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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 | 0.001 | 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".