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Record W3128753683 · doi:10.3389/fcomm.2020.612973

Repetition Preferences in Two-Handed Balanced Signs: Vestigial Locomotor Central Pattern Generators Shape Sign Language Phonetics and Phonology

2021· article· en· W3128753683 on OpenAlexafffund
Oksana Tkachman, Gracellia Purnomo, Bryan Gick

Bibliographic record

VenueFrontiers in Communication · 2021
Typearticle
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaNational Institutes of Health
KeywordsSign languageQuadrupedalismSign (mathematics)PhonologyPsychologyCommunicationIconicityRepetition (rhetorical device)LinguisticsPhilosophyMathematicsBiologyAnatomy

Abstract

fetched live from OpenAlex

Language is produced by bodies that evolved to fulfill a variety of functions, most of them non-communicative. Vestigial influences of adaptation for quadrupedal locomotion are still affecting bimanual actions, and have consequences on manual communication systems such as sign languages of the deaf. We discuss how central pattern generators (CPGs), networks of nerve cells in the spinal cord that drive locomotion, influence bimanual actions with alternating movements to be produced with repeated motion. We demonstrate this influence with data from three unrelated sign languages, American Sign Language, British Sign Language, and Hong Kong Sign Language: in all three sign languages two-handed balanced signs produced with alternating movements have a tendency to be repeated, whereas other types of two-handed balanced signs show the opposite tendency for single movements. These tendencies cannot be fully explained by factors such as iconicity. We propose a motoric account for these results: as alternating bimanual movements are influenced by locomotor patterns, they favor repeated movements.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.662

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.021
GPT teacher head0.303
Teacher spread0.282 · 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

Citations2
Published2021
Admission routes2
Has abstractyes

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