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Record W2896542738 · doi:10.1037/pag0000306

Aging of speech production, from articulatory accuracy to motor timing.

2018· article· en· W2896542738 on OpenAlexafffund
Pascale Tremblay, Isabelle Deschamps, Pascale Bédard, Marie‐Hélène Tessier, Micaël Carrier, Mélanie Thibeault

Bibliographic record

VenuePsychology and Aging · 2018
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversité Laval
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsSpeech productionPsychologySyllableAudiologyDuration (music)Language productionCodaCognitionSpeech recognitionMedicineComputer science

Abstract

fetched live from OpenAlex

Despite the huge importance of spoken language production in everyday life, little is known about the manner and extent to which the motor aspects of speech production evolve with advancing age, as well as the nature of the underlying senescence mechanisms. In this cross-sectional group study, we examined the relationship between age and speech production performance using a nonlexical speech production task in which spoken syllable frequency and phonological complexity were systematically varied to test hypotheses about underlying mechanisms. A nonprobabilistic sample of 60 cognitively healthy adults (18-83 years) produced meaningless nonwords aloud as quickly and accurately as possible. Error rate, vocal reaction time (RT), vocal RT variability, vocal response duration, and vocal response duration variability were used as dependent variables to characterize speech production performance. The results showed an overall increase in error rate, which occurred mainly in the final syllable position (coda). There was also an increase in vocal response duration and in duration variability with age, which was moderated by phonological complexity and syllable frequency. Finally, we also found an age-related change in the relationship between vocal RT and vocal response duration. Together, these findings were interpreted as reflecting an age-related decline in the planning and execution of speech movements in cognitively healthy adults. (PsycINFO Database Record (c) 2018 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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.016
Threshold uncertainty score0.340

Codex and Gemma teacher scores by category

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 integrity0.0000.000
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 head0.050
GPT teacher head0.360
Teacher spread0.310 · 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 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

Citations29
Published2018
Admission routes2
Has abstractyes

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