Adults who stutter and metronome synchronization: evidence for a nonspeech timing deficit
Bibliographic record
Abstract
Speech timing deficits have been proposed as a causal factor in the disorder of stuttering. The question of whether individuals who stutter have deficits in nonspeech timing is one that has been revisited often, with conflicting results. Here, we uncover subtle differences in a manual metronome synchronization task that included tempo changes with adults who stutter and fluent speakers. We used sensitive circular statistics to examine both asynchrony and consistency in motor production. While both groups displayed a classic negative mean asynchrony (tapping before the beat), individuals who stutter anticipated the beat even more than their fluent peers, and their consistency was particularly affected at slow tempi. Surprisingly, individuals who stutter did not have problems with interval correction at tempo changes. We also examined the influence of music experience on synchronization behavior in both groups. While music perception and training were related to synchronization behavior in fluent participants, these correlations were not present for the stuttering group; however, one measure of stuttering severity (self-rated severity) was negatively correlated with music training. Overall, we found subtle differences in paced auditory-motor synchronization in individuals who stutter, consistent with a timing problem extending to nonspeech.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".