Are two-year-olds sensitive to anticipatory coarticulation?
Bibliographic record
Abstract
Eyetracking studies have shown that adults are highly sensitive to subphonemic detail in speech [e.g., Shatzman and McQueen (2006)]. In some circumstances, adults use subphonemic coarticulatory information to anticipate which word will occur next in the speech stream [McDonough et al. (2009)]. In the current study, we ask whether two-year-old children use anticipatory coarticulation in a similar manner. Sixteen children were presented with pairs of images. In half of the trials, the names of the images presented on the screen had matching phonological onsets (e.g., doggy and ducky) that also matched in syllable length (e.g., monosyllabic or disyllabic). In the remaining trials, the names of the images had mismatching phonological onsets (e.g., cake and strawberry). In addition, a portion of each trial type was identity spliced (e.g., informative anticipatory coarticulation) and a portion was cross-spliced (e.g., misleading anticipatory coarticulation). We predicted that if two-year-olds are sensitive to anticipatory coarticulation, then they should be slowest to recognize named targets when the heard label was cross-spliced and the two objects on the screen had mismatching phonological onsets. However, all children looked to the named targets equally fast regardless of trial condition. Thus, no evidence of sensitivity to anticipatory coarticulation was observed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".