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Record W4383228278 · doi:10.31234/osf.io/guefc

Memories of Hand Movements are Tied to Speech Through Learning

2023· preprint· en· W4383228278 on OpenAlexafffund
Daniel R. Lametti, Gina L. Vaillancourt, Maura A. Whitman, Jeremy I Skipper

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsAcadia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGesturePsychologyMovement (music)Context (archaeology)Cognitive psychologyCoarticulationAdaptation (eye)Motor theory of speech perceptionCommunicationSpeech perceptionSpeech recognitionComputer sciencePerceptionArtificial intelligenceNeuroscience

Abstract

fetched live from OpenAlex

Hand movements frequently occur with speech. The extent to which the memories that guide co-speech hand movements are tied to the speech they occur with is unclear. Here, we paired the acquisition of a new hand movement with speech. Thirty participants adapted a ballistic hand movement of a joystick to a visuomotor rotation either in isolation or while producing a word in time with their movements. Within participants, the after-effect of adaptation (i.e., the motor memory) was examined with or without co-incident speech. After-effects were greater for hand movements produced in the context in which adaptation occurred—i.e., with or without speech. In a second experiment, thirty new participants adapted a hand movement while saying the words “tap” or “hit”. After-effects were greater when hand movements occurred with the specific word produced during adaptation. The results demonstrate that memories of co-speech hand movements are partially tied to the speech they are learned with. The findings have implications for theories of sensorimotor control and our understanding of the relationship between gestures, speech, and meaning.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.267
Threshold uncertainty score1.000

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.149
GPT teacher head0.405
Teacher spread0.256 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2023
Admission routes2
Has abstractyes

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