Control parameters for coordinative structures in speech production
Bibliographic record
Abstract
In skilled speech production, sets of articulators work cooperatively to achieve task-specific movement goals, despite rampant contextual variation. Efforts to understand these functional units, termed coordinative structures, have focused on identifying the essential control parameters responsible for allowing articulators to achieve these goals, with some research focusing on temporal parameters (relative timing of movements) and other research focusing on spatiotemporal parameters (phase angle of movement onset for one articulator, relative to another). Here, we compared findings across three recent studies where both types of control parameters were investigated, using electromagnetic articulography recordings. In each study, talkers produced VCV utterances, with alternative V (/ɑ/−/ɛ/) and C (/t/−/d/ or /p/−/ b/), across variation in rate (fast–slow) and stress (first syllable stressed–unstressed). Two measures were obtained: (i) the timing of tongue-tip or lower-lip raising onset for intervocalic C, relative to jaw opening–closing cycles, and (ii) the angle of tongue-tip or lower-lip raising onset, relative to the jaw phase plane. All three studies showed that the correlations of tongue-tip/lower-lip movement onset latencies and jaw opening-closing cycle durations were stronger and more reliable than the correlations of tongue-tip/lower-lip phase angles and jaw opening-closing cycle durations, demonstrating that timing is the critical control parameter.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".