Bibliographic record
Abstract
The speech signal is comprised of coarticulatory cues. Here, we explore whether adults’ access to coarticulatory information depends upon the particular language task. Specifically, we tested adults’ sensitivity to coarticulation when segmenting new sequences versus remembering presegmented items. Forty undergraduates were familiarized to either a continuous string of appropriately coarticulated nonsense CV syllables where every third syllable was stressed to facilitate segmentation, or to the same CV syllables presegmented into trisyllabic units without any stress contour. During the test phase, adults were presented with familiar and novel sequences appropriately coarticulated, and to both sequence types with inappropriate coarticulation. Subjects rated the familiarity of items using a seven point scale. Both groups rated familiar test sequences as more familiar than novel sequences. However, only the group presented with presegmented items demonstrated sensitivity to coarticulation. These subjects, compared to the segmentation group, significantly preferred the coarticulated items (p<0.05). These results suggest that access to information depends on the task. In the case of word segmentation, adults play attenation to the most useful properties (in this case, stress). Whereas adults in the presegmented group were able to pick up all the details of the sequences since their task only required word recognition.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".