Bibliographic record
Abstract
During locomotion, humans switch gaits from walking to running, and horses from walking to trotting to cantering to galloping, as they increase their movement rate. It is unknown whether gait change leading to a wider movement rate range is limited to locomotive-type behaviours, or instead is a general property of any rate-varying motor system. The tongue during speech provides a motor system that can address this gap. In controlled speech experiments, using phrases containing complex tongue-movement sequences, we demonstrate distinct gaits in tongue movement at different speech rates. As speakers widen their tongue-front displacement range, they gain access to wider speech-rate ranges. At the widest displacement ranges, speakers also produce categorically different patterns for their slowest and fastest speech. Speakers with the narrowest tongue-front displacement ranges show one stable speech-gait pattern, and speakers with widest ranges show two. Critical fluctuation analysis of tongue motion over the time-course of speech revealed these speakers used greater effort at the beginning of phrases-such end-state-comfort effects indicate speech planning. Based on these findings, we expect that categorical motion solutions may emerge in any motor system, providing that system with access to wider movement-rate ranges.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".