Quantifying the role of rhythm in infants' language discrimination abilities: A meta-analysis
Bibliographic record
Abstract
More than 30 years have passed since Mehler et al. (1988) proposed that newborns can discriminate between languages that belong to different rhythm classes: stress-, syllable- or mora-timed. Thereupon they developed the hypothesis that infants are sensitive to differences in vowel and consonant interval durations as acoustic correlates of rhythm classes. It remains unknown exactly which durational computations infants use when perceiving speech for the purposes of distinguishing languages. Here, a meta-analysis of studies on infants' language discrimination skills over the first year of life was conducted, aiming to quantify how language discrimination skills change with age and are modulated by rhythm classes or durational metrics. A systematic literature search identified 42 studies that tested infants' (birth to 12 months) discrimination or preference of two language varieties, by presenting infants with auditory or audio-visual continuous speech. Quantitative data synthesis was conducted using multivariate random effects meta-analytic models with the factors rhythm class difference, age, stimulus manipulation, method, and metrics operationalising proportions of and variability in vowel and consonant interval durations, to explore which factors best account for language discrimination or preference. Results revealed that smaller differences in vowel interval variability (△V) and larger differences in successive consonantal interval variability (rPVI-C) were associated with more successful language discrimination, and better accounted for discrimination results than the factor rhythm class. There were no effects of age for discrimination but results on preference studies were affected by age: the older infants get, the more they prefer non-native languages that are rhythmically similar to their native language, but not non-native languages that are rhythmically distinct. These findings can inform theories on language discrimination that have previously focussed on rhythm class, by providing a novel way to operationalise rhythm in language in the extent to which it accounts for infants' language discrimination abilities.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.014 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".