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Record W2981093628 · doi:10.1037/xge0000706

Sensorimotor control of vocal production in early childhood.

2019· article· en· W2981093628 on OpenAlexafffund
Nichole E. Scheerer, Danielle S. Jacobson, Jeffery A. Jones

Bibliographic record

VenueJournal of Experimental Psychology General · 2019
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsWilfrid Laurier University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAuditory feedbackFormantSpeech productionPsychologyAudiologyNeurocomputational speech processingMotor controlControl (management)VowelCognitive psychologyPerceptionSpeech perceptionSpeech recognitionNeuroscienceComputer scienceMedicine

Abstract

fetched live from OpenAlex

Children maintain fluent speech despite dramatic changes to their articulators during development. Auditory feedback aids in the acquisition and maintenance of the sensorimotor mechanisms that underlie vocal motor control. MacDonald, Johnson, Forsythe, Plante, and Munhall (2012) reported that toddlers' speech motor control systems may "suppress" the influence of auditory feedback, since exposure to altered auditory feedback regarding their formant frequencies did not lead to modifications of their speech. This finding is not parsimonious with most theories of motor control. Here, we exposed toddlers to perturbations to the pitch of their auditory feedback as they vocalized. Toddlers compensated for the manipulations, producing significantly different responses to upward and downward perturbations. These data represent the first empirical demonstration that toddlers use auditory feedback for vocal motor control. Furthermore, our findings suggest toddlers are more sensitive to changes to the postural properties of their auditory feedback, such as fundamental frequency, relative to the phonemic properties, such as formant frequencies. (PsycInfo Database Record (c) 2020 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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.516
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.320
Teacher spread0.309 · 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 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

Citations7
Published2019
Admission routes2
Has abstractyes

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