Dual language statistical word segmentation in infancy: Simulating a language‐mixing bilingual environment
Why this work is in the frame
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Bibliographic record
Abstract
Infants are sensitive to syllable co-occurrence probabilities when segmenting words from fluent speech. However, segmenting two languages overlapping at the syllabic level is challenging because the statistical cues across the languages are incongruent. Successful segmentation, thus, relies on infants' ability to separate language inputs and track the statistics of each language. Here, we report three experiments investigating how infants statistically segment words from two overlapping languages in a simulated language-mixing bilingual environment. In the first two experiments, we investigated whether 9.5-month-olds can use French and English phonetic markers to segment words from two overlapping artificial languages produced by one individual. After showing that infants could segment the languages when the languages were presented in isolation (Experiment 1), we presented infants with two interleaved languages differing in phonetic cues (Experiment 2). Both monolingual and bilingual infants successfully segmented words from one of the two languages-the language heard last during familiarization. In Experiment 3, a conceptual replication, we replicated the findings of Experiment 2 with a different population and with different cues. As before, when 12-month-old monolingual infants heard two interleaved languages differing in English and Finnish phonetic cues, they learned only the last language heard during familiarization. Together, our findings suggest that segmenting words in a language-mixing environment is challenging, but infants possess a nascent ability to recruit phonetic cues to segment words from one of two overlapping languages in a bilingual-like environment. A video abstract of this article can be viewed at https://www.youtube.com/watch?v=92pNcpxZguw.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it