Prosodic bootstrapping of syntax from cochlear implant-simulated speech
Bibliographic record
Abstract
It has been well-documented that prosodic boundaries often align with syntactic boundaries, and that both infants and adults capitalize on prosodic cues to bootstrap knowledge of syntax. However, it is less clear which prosodic cues—pre-boundary lengthening, pauses, and/or pitch resets across boundaries—are necessary for this bootstrapping to occur. It is also unknown how syntax acquisition is impacted for listeners who do not have access to the full spectrum of prosodic information. These questions were addressed using noise vocoded speech, which simulates speech perceived through a cochlear implant. While pre-boundary lengthening and pauses are well-transmitted through noise vocoded speech, pitch is not. In two experiments, adults listening to noise vocoded speech performed similarly to adults listening to unmanipulated speech in syntax acquisition tasks. This suggests that lengthening and pause cues alone are sufficient to facilitate acquisition of some syntactic structures, and that listeners with cochlear implants may be able to bootstrap syntax using prosody in a similar way as individuals with normal hearing.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".