How Do Infants Become Experts at Native-Speech Perception?
Why this work is in the frame
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Bibliographic record
Abstract
Infants begin life ready to learn any of the world’s languages, but they quickly become speech-perception experts in their native language. Although this phenomenon has been well described, the mechanisms leading to native-language-listening expertise have not. In this article, we provide an in-depth review of one learning mechanism: distributional learning (DL), which has been shown to be important in phonetic category learning. DL is a domain-general statistical learning mechanism that involves tracking the relative frequency of phonetic tokens in speech input. Although DL is powerful, recent research has identified limitations to it as well. We conclude with a discussion of possible supplementary phonetic-learning mechanisms, which focuses on the surrounding context in which infants hear phonetic tokens and how it can augment DL and highlight important linguistic differences between perceptually similar stimuli.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it