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Record W2131470021 · doi:10.1177/0956797614529978

Brief Periods of Auditory Perceptual Training Can Determine the Sensory Targets of Speech Motor Learning

2014· article· en· W2131470021 on OpenAlexaff
Daniel R. Lametti, Sonia A. Krol, Douglas M. Shiller, David J. Ostry

Bibliographic record

VenuePsychological Science · 2014
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-JustineCentre for Research on Brain Language and MusicMcGill University
FundersNational Institute on Deafness and Other Communication Disorders
KeywordsPsychologyPerceptionSensory systemTraining (meteorology)Auditory perceptionCognitive psychologyPerceptual learningAudiologyCommunicationNeuroscience

Abstract

fetched live from OpenAlex

The perception of speech is notably malleable in adults, yet alterations in perception seem to have little impact on speech production. However, we hypothesized that speech perceptual training might immediately influence speech motor learning. To test this, we paired a speech perceptual-training task with a speech motor-learning task. Subjects performed a series of perceptual tests designed to measure and then manipulate the perceptual distinction between the words head and had. Subjects then produced head with the sound of the vowel altered in real time so that they heard themselves through headphones producing a word that sounded more like had. In support of our hypothesis, the amount of motor learning in response to the voice alterations depended on the perceptual boundary acquired through perceptual training. The studies show that plasticity in adults' speech perception can have immediate consequences for speech production in the context of speech learning.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.001
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 head0.091
GPT teacher head0.382
Teacher spread0.291 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations39
Published2014
Admission routes1
Has abstractyes

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