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Record W4386116086 · doi:10.1037/xge0001459

Speech motor adaptation during synchronous and metronome-timed speech.

2023· article· en· W4386116086 on OpenAlexaff
Abigail Bradshaw, Daniel R. Lametti, Douglas M. Shiller, Kyle Jasmin, Ruiling Huang, Carolyn McGettigan

Bibliographic record

VenueJournal of Experimental Psychology General · 2023
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsUniversité de MontréalAcadia University
FundersLeverhulme Trust
KeywordsMetronomeFormantSpeech productionSpeech recognitionAdaptation (eye)Auditory feedbackComputer scienceSpeech processingAudiologyPsychologyRhythmVowelAcoustics

Abstract

fetched live from OpenAlex

= 15) and found a similar pattern of increased between-participant variability in formant changes. These findings demonstrate that speech motor adaptation can be measured robustly at the group level during performance of more complex speaking tasks; however, further work is needed to resolve whether self-voice adaptation and other-voice convergence reflect additive or interactive effects during sensorimotor control of speech. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.881
Threshold uncertainty score0.999

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)0.0020.000

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 head0.048
GPT teacher head0.365
Teacher spread0.317 · 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.

Study designBench or experimental
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
Published2023
Admission routes1
Has abstractyes

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