Bibliographic record
Abstract
English-learning 7.5-month-olds are heavily biased to perceive stressed syllables as word onsets. By 11 months, however, infants begin segmenting non-initially stressed words from speech. Using the same artificial language methodology as Johnson and Jusczyk (2001), we explored the possibility that the emergence of this ability is linked to a decreased reliance on prosodic cues to word boundaries accompanied by an increased reliance on syllable distribution cues. In a baseline study, where only statistical cues to word boundaries were present, infants exhibited a familiarity preference for statistical words. When conflicting stress cues were added to the speech stream, infants exhibited a familiarity preference for stress as opposed to statistical words. This was interpreted as evidence that 11-month-olds weight stress cues to word boundaries more heavily than statistical cues. Experiment 2 further investigated these results with a language containing convergent cues to word boundaries. The results of Experiment 2 were not conclusive. A third experiment using new stimuli and a different experimental design supported the conclusion that 11-month-olds rely more heavily on prosodic than statistical cues to word boundaries. We conclude that the emergence of the ability to segment non-initially stressed words from speech is not likely to be tied to an increased reliance on syllable distribution cues relative to stress cues, but instead may emerge due to an increased reliance on and integration of a broad array of segmentation cues.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".