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Record W1998801990 · doi:10.1044/2014_jslhr-s-12-0207

Coupling Dynamics Interlip Coordination in Lower Lip Load Compensation

2014· article· en· W1998801990 on OpenAlexaff
Pascal van Lieshout, Chris Neufeld

Bibliographic record

VenueJournal of Speech Language and Hearing Research · 2014
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsToronto Rehabilitation InstituteUniversity of Toronto
FundersHealth Research Board
KeywordsKinematicsCoupling (piping)Lower lipDynamics (music)MathematicsPhase (matter)Control theory (sociology)AudiologyPsychologyCommunicationChemistryMaterials sciencePhysical medicine and rehabilitationPhysicsComputer scienceAcousticsMedicineControl (management)Surgery

Abstract

fetched live from OpenAlex

PURPOSE To study the effects of lower lip loading on lower and upper lip movements and their coordination to test predictions on coupling dynamics derived from studies in limb control. METHOD Movement data were acquired using electromagnetic midsagittal articulography under 4 conditions: (a) without restrictions, serving as a baseline; (b) with a small carrier device attached to the lower lip; (c) with a 50-g weight added to the device; and, at the end of the session (d) with the weight and device removed. For all conditions, 8 participants repeated nonwords at 2 speaking rates. Movement data were used to derive discrete kinematic measures, a cyclic index of spatiotemporal variability, phase deviations, and standard deviations of relative phase for interlip coupling. RESULTS Kinematic variables were not systematically affected by lower lip load. Phase deviations also showed no change, but in contrast, phase variability showed a significant increase for the lower lip load condition at fast rates. CONCLUSION Lower lip load effects are comparable to the reported impact of homologous limb loading, showing evidence for a tight coupling between both lips in line with predictions from coordination dynamics accounts in the literature.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.886
Threshold uncertainty score0.203

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
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 head0.058
GPT teacher head0.354
Teacher spread0.296 · 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 designSimulation or modeling
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

Citations12
Published2014
Admission routes1
Has abstractyes

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