Magnitude of phonetic distinction predicts success at early word learning in native and non-native accents
Bibliographic record
Abstract
Although infants perceptually attune to native vowels and consonants well before 12 months, at 13-15 months, they have difficulty learning to associate novel words that differ by their initial consonant (e.g., BIN and DIN) to their visual referents. However, this difficulty may not apply to all minimal pair novel words. While Canadian English (CE) 15-month-olds failed to respond to a switch from the newly learned word DEET to the novel non-word DOOT, they did notice a switch from DEET to DIT (Curtin et al., 2009). Those authors argued that early word learners capitalize on large phonetic differences, seen in CE DEET-DIT, but not on smaller phonetic differences, as in CE DEET-DOOT. To assess this hypothesis, we tested Australian English (AusE) 15-month-olds, as AusE has a smaller magnitude of phonetic difference in both novel word pairs. Two groups of infants were trained on the novel word DEET and tested on the vowel switches in DIT and DOOT, produced by an AusE female speaker or the same CE female speaker as in Curtin et al. (2009). If the size of the phonetic distinction plays a more central role than native accent experience in early word learning, AusE children should more easily recognize both of the unfamiliar but larger CE vowel switches than the more familiar but smaller AusE ones. The results support our phonetic-magnitude hypothesis: AusE children taught and tested with the CE-accented novel words looked longer to both of the switch test trials (DIT, DOOT) than same test trials (DEET), while those who heard the AusE-accented tokens did not notice either switch. Implications of our findings for models of early word learning are discussed.
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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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".