Linking Infant-Directed Speech and Face Preferences to Language Outcomes in Infants at Risk for Autism Spectrum Disorder
Bibliographic record
Abstract
PURPOSE: In this study, the authors aimed to examine whether biases for infant-directed (ID) speech and faces differ between infant siblings of children with autism spectrum disorder (ASD) (SIBS-A) and infant siblings of typically developing children (SIBS-TD), and whether speech and face biases predict language outcomes and risk group membership. METHOD: Thirty-six infants were tested at ages 6, 8, 12, and 18 months. Infants heard 2 ID and 2 adult-directed (AD) speech passages paired with either a checkerboard or a face. The authors assessed expressive language at 12 and 18 months and general functioning at 12 months using the Mullen Scales of Early Learning (Mullen, 1995). RESULTS: Both infant groups preferred ID to AD speech and preferred faces to checkerboards. SIBS-TD demonstrated higher expressive language at 18 months than did SIBS-A, a finding that correlated with preferences for ID speech at 12 months. Although both groups looked longer to face stimuli than to the checkerboard, the magnitude of the preference was smaller in SIBS-A and predicted expressive vocabulary at 18 months in this group. Infants' preference for faces contributed to risk-group membership in a logistic regression analysis. CONCLUSION: Infants at heightened risk of ASD differ from typically developing infants in their preferences for ID speech and faces, which may underlie deficits in later language development and social communication.
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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 | 0.000 | 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".