Acuity to Changes in Self-Generated Vocal Pitch in Parkinson's Disease
Bibliographic record
Abstract
Purpose Given the role of auditory perception in voice production, studies have investigated whether impairments in auditory perception may underlie the noted disruptions in speech in Parkinson's disease (PD). Studies of loudness perception in PD show impairments in the perception of self-generated speech, but not external tones. Studies of pitch perception in PD have only examined external tones, but these studies differed in terms of the interstimulus intervals (ISIs) that were used, did not examine the impact of cognition, and report conflicting results. To clarify pitch perception in PD, this work investigated perception of self-generated vocal pitch, controlling for cognition and ISI. Method A total of 30 individuals with and without PD completed (a) hearing threshold testing, (b) the Montreal Cognitive Assessment, and (c) an adaptive just-noticeable-difference paradigm under two separate ISIs (100 ms and 1,000 ms) to assess acuity to self-generated vocal pitch. Results There was no significant difference in acuity between individuals with and without PD. Both groups demonstrated significantly worse acuity for longer compared to shorter ISIs. Montreal Cognitive Assessment scores were not a significant predictor of acuity. Conclusions The results suggest that acuity to self-generated vocal pitch does not differ between individuals with and without PD.
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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 | 0.000 | 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".