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Record W1993307712 · doi:10.1152/jn.00286.2002

Relationship Between Jaw Stiffness and Kinematic Variability in Speech

2002· article· en· W1993307712 on OpenAlexaff
Douglas M. Shiller, Rafael Laboissière, David J. Ostry

Bibliographic record

VenueJournal of Neurophysiology · 2002
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsMcGill University
FundersNational Institute on Deafness and Other Communication Disorders
KeywordsKinematicsStiffnessSagittal planeEllipseBiomechanicsMathematicsAnatomyGeometryStructural engineeringPhysicsEngineeringBiology

Abstract

fetched live from OpenAlex

Humans produce speech by controlling a complex biomechanical apparatus to achieve desired speech sounds. We show here that kinematic variability in speech may be influenced by patterns of jaw stiffness. A robotic device was used to deliver mechanical perturbations to the jaw to quantify its stiffness in the mid-sagittal plane. Measured jaw stiffness was anisotropic. Stiffness was greatest along a protrusion-retraction axis and least in the direction of jaw raising and lowering. Consistent with the idea that speech movements reflect directional asymmetries in jaw stiffness, kinematic variability during speech production was found to be high in directions in which stiffness is low and vice versa. In addition, for higher jaw elevations, stiffness was greater and kinematic variability was less. The observed patterns of kinematic variability were not specific to speech-similar patterns appeared in speech and nonspeech movements. The empirical patterns of stiffness were replicated by using a physiologically based model of the jaw. The simulation studies support the idea that the pattern of jaw stiffness is affected by musculo-skeletal geometry and muscle-force-generating abilities with jaw geometry being the primary determinant of the orientation of the stiffness ellipse.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.973
Threshold uncertainty score0.410

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.075
GPT teacher head0.279
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations56
Published2002
Admission routes1
Has abstractyes

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