An ERP study of vocal emotion processing in asymmetric Parkinson’s disease
Bibliographic record
Abstract
Parkinson's disease (PD) has been related to impaired processing of emotional speech intonation (emotional prosody). One distinctive feature of idiopathic PD is motor symptom asymmetry, with striatal dysfunction being strongest in the hemisphere contralateral to the most affected body side. It is still unclear whether this asymmetry may affect vocal emotion perception. Here, we tested 22 PD patients (10 with predominantly left-sided [LPD] and 12 with predominantly right-sided motor symptoms) and 22 healthy controls in an event-related potential study. Sentences conveying different emotional intonations were presented in lexical and pseudo-speech versions. Task varied between an explicit and an implicit instruction. Of specific interest was emotional salience detection from prosody, reflected in the P200 component. We predicted that patients with predominantly right-striatal dysfunction (LPD) would exhibit P200 alterations. Our results support this assumption. LPD patients showed enhanced P200 amplitudes, and specific deficits were observed for disgust prosody, explicit anger processing and implicit processing of happy prosody. Lexical speech was predominantly affected while the processing of pseudo-speech was largely intact. P200 amplitude in patients correlated significantly with left motor scores and asymmetry indices. The data suggest that emotional salience detection from prosody is affected by asymmetric neuronal degeneration in 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 | 0.001 |
| 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".