“Mummy, keep it steady”: phonetic variation shapes word learning at 15 and 17 months
Bibliographic record
Abstract
Fifteen-month-olds have difficulty detecting differences between novel words differing in a single vowel. Previous work showed that Australian English (AusE) infants habituated to the word-object pair DEET detected an auditory switch to DIT and DOOT in Canadian English (CanE) but not in their native AusE (Escudero et al., ). The authors speculated that this may be because the vowel inherent spectral change variation (VISC) in AusE DEET is larger than in CanE DEET. We investigated whether VISC leads to difficulty in encoding phonetic detail during early word learning, and whether this difficulty dissipates with age. In Experiment 1, we familiarized AusE-learning 15-month-olds to AusE DIT, which contains smaller VISC than AusE DEET. Unlike infants familiarized with AusE DEET (Escudero et al., ), infants detected a switch to DEET and DOOT. In Experiment 2, we familiarized AusE-learning 17-month-olds to AusE DEET. This time, infants detected a switch to DOOT, and marginally detected a switch to DIT. Our acoustic analysis showed that AusE DEET and DOOT are differentiated by the second vowel formant, while DEET and DIT can only be distinguished by their changing dynamic properties throughout the vowel trajectory. Thus, by 17 months, AusE infants can encode highly dynamic acoustic properties, enabling them to learn the novel vowel minimal pairs that are difficult at 15 months. These findings suggest that the development of word learning is shaped by the phonetic properties of the specific word minimal pair.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".