Home Environment, But Not Socioeconomic Status, is Linked to Differences in Early Phonetic Perception Ability
Bibliographic record
Abstract
Infants perceptually tune to the phonemes of their native languages in the first year of life, thereby losing the ability to discriminate non-native phonemes. Infants who perceptually tune earlier have been shown to develop stronger language skills later in childhood. We hypothesized that socioeconomic disparities, which have been associated with differences in the quality and quantity of language in the home, would contribute to individual differences in phonetic discrimination. Seventy-five infants were assessed on measures of phonetic discrimination at 9 months, on the quality of the home environment at 15 months, and on language abilities at both ages. Phonetic discrimination did not vary according to socioeconomic status (SES), but was significantly associated with the quality of the home environment. This association persisted when controlling for 9-month expressive language abilities, rendering it less likely that infants with better expressive language skills were simply engendering higher quality home interactions. This suggests that infants from linguistically richer home environments may be more tuned to their native language and therefore less able to discriminate non-native contrasts at 9 months relative to infants whose home environments are less responsive. These findings indicate that home language environments may be more critical than SES in contributing to early language perception, with possible implications for language development more broadly.
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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 | 0.001 | 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".