Effects of Emotional Facial Expression on Time Perception in Patients with Parkinson’s Disease
Bibliographic record
Abstract
Previous studies have demonstrated that emotional facial expressions alter temporal judgments. Moreover, while some studies conducted with Parkinson's disease (PD) patients suggest dysfunction in the recognition of emotional facial expression, others have shown a dysfunction in time perception. In the present study, we investigate the magnitude of temporal distortions caused by the presentation of emotional facial expressions (anger, shame, and neutral) in PD patients and controls. Twenty-five older adults with PD and 17 healthy older adults took part in the present study. PD patients were divided into two sub-groups, with and without mild cognitive impairment (MCI), based on their neuropsychological performance. Participants were tested with a time bisection task with standard intervals lasting 400 ms and 1600 ms. The effect of facial emotional stimuli on time perception was evident in all participants, yet the effect was greater for PD-MCI patients. Furthermore, PD-MCI patients were more likely to underestimate long and overestimate short temporal intervals than PD-non-MCI patients and controls. Temporal impairment in PD-MCI patients seem to be mainly caused by a memory dysfunction. (JINS, 2016, 22, 890-899).
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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 | 0.002 |
| 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".