Brain activation during non-habitual speech production: Revisiting the effects of simulated disfluencies in fluent speakers
Bibliographic record
Abstract
Over the past decades, brain imaging studies in fluently speaking participants have greatly advanced our knowledge of the brain areas involved in speech production. In addition, complementary information has been provided by investigations of brain activation patterns associated with disordered speech. In the present study we specifically aimed to revisit and expand an earlier study by De Nil and colleagues, by investigating the effects of simulating disfluencies on the brain activation patterns of fluent speakers during overt and covert speech production. In contrast to the De Nil et al. study, the current findings show that the production of voluntary, self-generated disfluencies by fluent speakers resulted in increased recruitment and activation of brain areas involved in speech production. These areas show substantial overlap with the neural networks involved in motor sequence learning in general, and learning of speech production, in particular. The implications of these findings for the interpretation of brain imaging studies on disordered and non-habitual speech production are discussed.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".