Distributional learning of bimodal and trimodal phoneme categories in monolingual and bilingual infants
Bibliographic record
Abstract
Distributional learning has been proposed as a mechanism for infants to learn the native phonemes of the language(s) to which they are exposed. When hearing two speech streams, bilingual infants may find other strategies more useful and rely on distributional learning less than monolingual infants. A series of studies examined how bilingual language experience affects the application of the distributional learning to novel phoneme distributions. Monolingual and bilingual infants between 6 and 8 months old performed a distributional learning task using palatal consonant stimuli grouped into one of three distributions based on voice onset time. Performance after exposure to a unimodal distribution was compared to performance after both a bimodal (Experiment 1) and trimodal distribution (Experiment 2) of the same voice onset time cue. Results indicated that monolingual and bilingual infants performed similarly on all tasks, and infants were able to learn both bimodal and trimodal phoneme distributions. The universality of the distributional learning mechanism is suggested by these results, but future research would need to test the two groups and distributions for equivalence of performance. • Distributional learning allows infants to acquire the phoneme categories of their native language. • Monolingual and bilingual infants performed similarly on distributional learning tasks. • All infants were able to learn a simpler bimodal and a more complicated trimodal distribution of phonemes. • Results suggest the broad applicability of distributional learning.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".