Perceptual narrowing in the context of increased variation: Insights from bilingual infants
Bibliographic record
Abstract
Human infants become native-language listeners through a process of perceptual narrowing. Monolingual infants are initially sensitive to a wide range of language-relevant contrasts. However, as they mature and gain native-language experience, their sensitivity to nonnative contrasts declines. Here, we consider the case of infants growing up bilingual as a window into how increased variation affects early perceptual development. These infants encounter different meaningful contrasts in each of their languages, and must also attend to contrasts that occur between their languages. Bilingual infants share many classic developmental patterns with monolinguals. However, they also show unique developmental patterns in the perception of native distinctions such as U-shaped trajectories and dose-response relationships, and show some enhanced sensitivity to nonnative distinctions. Analogous developmental patterns can be observed in individuals exposed to two nonlinguistic systems in domains such as music and face perception. Some preliminary evidence suggests that bilingual individuals might retain more sensitivity to nonnative contrasts, reaching a less narrow end state than monolinguals. Nevertheless, bilingual infants do become perceptually specialized native listeners to both of their languages, despite increased variation and differing patterns of perceptual development in comparison to monolinguals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".