The impact of phonological biases on mispronunciation sensitivity and novel accent adaptation
Bibliographic record
Abstract
Accepted for publication in Language, Learning, and Development. Successful word recognition requires that listeners attend to differences that are phonemic in that language while also remaining flexible to the variation introduced by different voices and accents. Previous work has emonstrated that American-English-learning 19-month-olds are able to balance these demands: although one-off one-feature mispronunciations typically disrupt English-learning toddlers’ lexical access, they no longer do after toddlers are exposed to a novel accent in which these changes occur systematically (White & Aslin, 2011; White & Daub, 2021). The flexibility to deal with different types of variation may not be the same for toddlers learning different first languages, however, as language structure shapes early phonological biases. We examined French-learning 19-month-olds’ sensitivity and adaptation to a novel accent that shifted either the standard pronunciation of /a/ from [a] to [E] (Experiment 1) or the standard pronunciation of /p/ from [p] to [t] (Experiment 2). In Experiment 1, French-learning toddlers recognized words with /a/ produced as [E], regardless of whether they were previously exposed to an accent that contained this vowel shift or not. In Experiment 2, toddlers did not recognize words with /p/ pronounced as [t] at test unless they were first familiarized with an accent that contained this consonant shift. These findings are consistent with evidence that French-learning toddlers privilege consonants over vowels in lexical processing. Together with previous work, these results demonstrate both differences and similarities in how French- and English-learning children treat variation, in line with their language-specific phonological biases.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".