Abnormal Vision-Based Displacement Perception in Parkinson’s Disease
Bibliographic record
Abstract
In this work, we investigate the effect of Parkinson’s disease (PD), and common corresponding therapies on vision-based perception of motion, a critical perceptual ability required for performing a wide range of activities of daily livings. While PD has been recognized as mainly a motor disorder, sensory manifestation of PD can also play a major role in the resulting disability. In this paper, for the first time, the effect of disease duration and common therapies on vision-based perception of displacement were investigated. The study is conducted in a movement-independent manner, to reject the shadowing effects and isolate the targeted perceptual disorder to the maximum possible extent. Data was collected using a computerized graphical tool on 37 PD patients [6 early-stage de novo, 25 mid-stage using levodopa therapy, six later-stage using deep brain stimulation (DBS)] and 15 control participants. Besides the absolute measurement of perception through a psychometric analysis on two tested position reference magnitudes, we also investigated the linearity in perception using Weber’s fraction. The results showed that individuals with PD displayed significant perceptual impairments compared to controls, though early-stage patients were not impaired. Mid-stage patients displayed impairments at the greater of the two tested reference magnitudes, while late-stage patients were impaired at both reference magnitudes. Levodopa and DBS use did not cause statistically significant differences in absolute displacement perception. The findings suggest abnormal visual processing in PD increasing with disease development, perhaps contributing to sensory-based impairments of PD such as bradykinesia, visuospatial deficits, and abnormal object recognition.
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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".