When emotion and expression diverge: The social costs of Parkinson’s disease
Bibliographic record
Abstract
INTRODUCTION: Patients with Parkinson's disease (PD) are perceived more negatively than their healthy peers, yet it remains unclear what factors contribute to this negative social perception. METHOD: Based on a cohort of 17 PD patients and 20 healthy controls, we assessed how naïve raters judge the emotion and emotional intensity displayed in dynamic facial expressions as adults with and without PD watched emotionally evocative films (Experiment 1), and how age-matched peers naïve to patients' disease status judge their social desirability along various dimensions from audiovisual stimuli (interview excerpts) recorded after certain films (Experiment 2). RESULTS: In Experiment 1, participants with PD were rated as significantly more facially expressive than healthy controls; moreover, ratings demonstrated that PD patients were routinely mistaken for experiencing a negative emotion, whereas controls were rated as displaying a more positive emotion than they reported feeling. In Experiment 2, results showed that age-peers rated PD patients as significantly less socially desirable than control participants. Specifically, PD patients were rated as less involved, interested, friendly, intelligent, optimistic, attentive, and physically attractive than healthy controls. CONCLUSIONS: Taken together, our results point to a disconnect between how PD patients report feeling and attributions that others make about their emotions and social characteristics, underlining significant social challenges of the disease. In particular, changes in the ability to modulate the expression of negative emotions may contribute to the negative social impressions that many PD patients face.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".