Getting oriented: Redefining attention deficits in Parkinson’s disease.
Bibliographic record
Abstract
OBJECTIVE: Parkinson's disease (PD) may affect not only motor functions, but also cognitive processes such as attention. While past research has examined PD impact on spatial attention, it has not addressed how the key functions of attentional orienting and alerting in PD are mediated by cueing format, an ecologically relevant parameter. We assessed how exogenous and endogenous orienting cue modes affect PD patients' visuospatial attention expressed as dorsal attention network orienting benefits, ventral attention network reorienting costs, and alerting abilities. METHOD: Ninety PD patients and 72 healthy comparison participants performed a spatial attention task in an engaging game format which required selection of a target location without prior cueing, or with temporal, valid spatial, or invalid spatial exogenous or endogenous cueing. RESULTS: PD patients differed from healthy participants only in response time benefits in orienting under endogenous probabilistically predictive cue processing. They did not exhibit greater reorienting costs, differences in inhibition of return, or alerting deficits, irrespective of modes of cueing. CONCLUSION: These results suggest that fundamental orienting and alerting functions might be intact in PD, with challenges emerging only if additional cognitive processes, including those related to motor preparation, are required to utilize cue information. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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) | 0.002 | 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".