Biomechanical Simulation of Lateral Asymmetry in Tongue Bracing
Bibliographic record
Abstract
Tongue bracing occurs when the lateral edges of the tongue maintain contact with the palate or upper molars. Bracing is pervasive in speech: previous EPG data analysis shows lateral contact to be maintained for 97.5% of the total observation duration [Gick et al. (2017) JSLHR 60, 494]. This research has also indicated the presence of lateral bias or asymmetry in the tongue: a consistent tendency to release contact more on one side than the other, both in unilateral release and in sequential loss of contact during a bilateral release. However, in these cases of asymmetry, it remains unclear which side of the tongue should be considered dominant. The present study explores the question of dominance in lateral tongue bracing through biomechanical simulation of tongue muscle activation. Certain muscles have been identified as bracing agonists (GGP, GGM, MH, VERT, SL) and antagonists (GGA, STY, HG, TRANS, IL) [Liu et al. (2022) Phonetica 79, 523]. Agonists increase the likelihood of bilateral bracing and tend to raise or widen the tongue while antagonists decrease the likelihood of bilateral bracing and tend to lower or narrow the tongue. The current simulations explore whether a unilateral bracing outcome can be produced by asymmetric activation of agonists, antagonists or a combination thereof. Preliminary results from our simulation study show that reducing agonist activation or increasing antagonist activation on one side while keeping activations constant on the other side results in lower tongue-palate contact on the opposite side, due to the hydrostatic properties of the tongue, though the reduction is much greater in the former case. These results suggest that unilateral bracing may be instantiated primarily by contra-lateral muscle activation/deactivation from the side on which bracing contact is maintained, providing a starting point for further exploration of lateral tongue dominance.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".