Acoustical cues versus top-down bias in infants’ parsing
Bibliographic record
Abstract
French liaison involves the surfacing of an underlying consonant as the onset of the following vowel-initial word (e.g., les amis - /le/ /zami /), creating misalignment. However, acoustic cues that support vowel-initial parsing may exist. We tested French-learning 30-month-olds using a preferential looking procedure. Familiarization stimuli in experiment 1 were sentences each containing a determiner preceding a vowel-initial non-word (e.g., ces onches). Infants’ parsing of the non-word was assessed. The vowel-initial condition presented the vowel-initial non-word versus another non-target (onches - èque). The syllabic condition tested the consonant-initial parse (zonches - zèque). Infants in the vowel-initial, but not the syllabic condition, showed discrimination (p = 0.008), i.e., they correctly parsed the vowel-initial target, possibly using acoustic cues. However, knowledge of underlying liaison consonants can also explain these results. In experiment 2, we removed acoustic cues to vowel-initial parsing by using a consonant-initial non-word following a determiner as the familiarization stimuli (e.g., un zonches). Infants were tested with the same two conditions as in experiment 1. Infants yielded the same results as in experiment 1, showing discrimination only in the vowel-initial condition (p = 0.047). Taken together, 30-month-olds perceived /z/ as an underlying floating element; they used this liaison knowledge, rather than possible acoustical cues, for parsing.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".