Bibliographic record
Abstract
A bias for speech over non-speech emerges at birth in typically developing (TD) infants and a preference for vocalizations generated from one’s own species (conspecific) emerges by 3 months. These biases and preferences may direct infants to relevant communicative information in their language learning environments, possibly predicting positive linguistic and social development. The first series of studies explored whether a bias for speech (over non-speech) at 6 and 9 months was predictive of language and social behaviors at 12 months. Whether a preference for conspecific vocalizations (over monkey calls) at 9 months was predictive of these same outcomes was also examined. Infants were biased toward speech over non-speech at 6 and 9 months, but preferred monkey calls over speech at 9 months. However, these listening patterns did not predict language or social developmental outcomes. Understanding these patterns of attention and how experimental procedures may influence preferences is important for advancing our understanding of the relationship between attention to speech and early development. The second series of studies reports findings from high-risk (HR) infant siblings of children diagnosed with Autism Spectrum Disorder (ASD), who participated in these same experiments. HR infants are known to have heterogeneous outcomes by age 3, with about half developing typically, while others present with atypical outcomes, including ASD. Given the hypothesized importance of attention to speech on later development, we explored whether HR infants as a group selectively attended to speech and whether such attention was predictive of early language and social behaviors. HR infants were biased toward speech at 6 months; however, by 9 months, they did not differentially attend to speech over non-speech or monkey calls overall. Despite this, increased attention to speech at 9 months predicted better language and fewer autism-like behaviors at 12 months. Therefore, increased attention to conspecifics may indicate typical outcomes for HR infants. Future directions include following both groups of infants to 3 years of age to explore whether selective attention to speech during the first year is predictive of much later developmental outcomes and/or diagnostic status.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".