Dopaminergic therapy affects learning and impulsivity in Parkinson's disease
Bibliographic record
Abstract
OBJECTIVE: The aim was to examine the effect of dopaminergic medication on stimulus-response learning versus performing decisions based on learning. METHOD: To see the effect of dopaminergic therapy on stimulus-response learning and response selection, participants with Parkinson's disease (PD) were either tested on and/or off their prescribed dose of dopaminergic therapy during different testing days. Forty participants with PD and 34 healthy controls completed the experiment on consecutive days. On Day 1, participants learned to associate abstract images with spoken, "right" or "left" responses via feedback (Session 1). On Day 2, participants recalled these responses (Session 2) and indicated the location (i.e., right or left of center) of previously studied images intermixed with new images (Session 3). RESULTS: Participants with PD off medication learned stimulus-response associations equally well compared to healthy controls. Learning was impaired by dopaminergic medication. Regardless of medication status, patients recalled the stimulus-response associations from Day 1 as well as controls. In Session 3 off medication, patients demonstrated enhanced facilitation relative to controls and patients on medication, when the stimulus location was congruent with the spoken response that was learned for the stimulus in Session 1. INTERPRETATION: Learning in PD was comparable to that of healthy controls off medication. Learning was worsened by dopaminergic therapy in PD. We interpret greater facilitation in participants with PD off medication for congruent responses as evidence of greater impulsivity. This motor or reflexive impulsivity was normalized by medication in PD. These findings shed light on the cognitive profile of PD and have implications for dopaminergic treatment.
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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".