Aging of speech production, from articulatory accuracy to motor timing.
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".