Behavioral and Neural Correlates of Speech Motor Sequence Learning in Stuttering and Neurotypical Speakers: An fMRI Investigation
Bibliographic record
Abstract
Stuttering is a neurodevelopmental disorder characterized by impaired production of coordinated articulatory movements needed for fluent speech. It is currently unknown whether these abnormal production characteristics reflect disruptions to brain mechanisms underlying the acquisition and/or execution of speech motor sequences. To dissociate learning and control processes, we used a motor sequence learning paradigm to examine the behavioral and neural correlates of learning to produce novel phoneme sequences in adults who stutter (AWS) and neurotypical controls. Participants intensively practiced producing pseudowords containing non-native consonant clusters (e.g., "gvasf") over two days. The behavioral results indicated that although the two experimental groups showed comparable learning trajectories, AWS performed significantly worse on the task prior to and after speech motor practice. Using functional magnetic resonance imaging (fMRI), the authors compared brain activity during articulation of the practiced words and a set of novel pseudowords (matched in phonetic complexity). FMRI analyses revealed no differences between AWS and controls in cortical or subcortical regions; both groups showed comparable increases in activation in left-lateralized brain areas implicated in phonological working memory and speech motor planning during production of the novel sequences compared to the practiced sequences. Moreover, activation in left-lateralized basal ganglia sites was negatively correlated with in-scanner mean disfluency in AWS. Collectively, these findings demonstrate that AWS exhibit no deficit in constructing new speech motor sequences but do show impaired execution of these sequences before and after they have been acquired and consolidated.
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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 | 0.001 |
| 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 | 0.000 |
| 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".