Effects of Parkinson Disease on Two Putative Nondeclarative Learning Tasks
Bibliographic record
Abstract
OBJECTIVE: To assess performance on two nondeclarative (implicit) memory tasks of Parkinson disease (PD) patients without dementia in the earlier or later stages of the disease (Hoehn and Yahr Scale scores of 1-2.5 or 3-4, respectively). BACKGROUND: Different subtypes of nondeclarative memory appear to depend on different components of frontostriatal circuitry. Performance on a probabilistic classification learning (PCL) task was impaired by striatal damage (eg, in PD or Huntington disease) but not by circumscribed frontal lobe damage. On the other hand, performance on the Iowa Gambling Task (IGT) was impaired by damage to the prefrontal cortex. METHOD AND RESULTS: On the PCL, the learning of the control (age- and education-matched) group (n = 19) and the early PD group (n = 16) was comparable with each other, and both groups showed better performance than the later PD group (n = 16). On the IGT, the control group learned better than both of the PD groups. The control and early PD groups were similar on measures from the Wisconsin Card Sorting Test, Stroop Test, Mini-Mental State Examination, and Beck Depression Inventory II. CONCLUSIONS: The PCL and IGT tasks appear to rely on different parts of the frontostriatal circuitry in patients with early PD. The current finding that IGT performance was impaired in early PD implies ventromedial prefrontal cortical dysfunction early in the disease.
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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) | 0.001 | 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".