Retention of Implicit Sequence Learning in Persons Who Stutter and Persons With Parkinson's Disease
Bibliographic record
Abstract
The authors investigated the retention of implicit sequence learning in 14 persons with Parkinson's disease (PPD), 14 persons who stutter (PWS), and 14 control participants. Participants completed a nonsense syllable serial reaction time task in a 120-min session. Participants named aloud 4 syllables in response to 4 visual stimuli. The syllables formed a repeating 8-item sequence not made known to participants. After 1 week, participants completed a 60-min retention session that included an explicit learning questionnaire and a sequence generation task. PPD showed retention of general learning equivalent to controls but PWS's reaction times were significantly slower on early trials of the retention test relative to other groups. Controls showed implicit learning during the initial session that was retained on the retention test. In contrast, PPD and PWS did not demonstrate significant implicit learning until the retention test suggesting intact, but delayed, learning and retention of implicit sequencing skills. All groups demonstrated similar limited explicit sequence knowledge. Performance differences between PWS and PPD relative to controls during the initial session and on early retention trials indicated possible dysfunction of the cortico-striato-thalamo-cortical loop. The etiological implications for stuttering, and clinical implications for both populations, of this dysfunction 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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.001 | 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".