Selective attention is insensitive to reward and to dopamine in Parkinson’s disease
Bibliographic record
Abstract
Abstract Patients with Parkinson’s disease exhibit reduced reward sensitivity in addition to early cognitive deficits, among which attention impairments are common. Attention allocation is controlled at multiple levels and recent work has shown that reward, in addition to its role in the top-down goal-directed control of attention, also guides the automatic allocation of attention resources, a process thought to rely on striatal dopamine. Whether Parkinson’s patients, due to their striatal dopamine loss, suffer from an inability to use reward information to guide the allocation of their attention is unknown. To address this question, we tested Parkinson’s patients (n=43) ON and OFF their dopaminergic medication, and compared them to a group of older controls (n=31). We used a standard two-phase attention capture task in which subjects were first implicitly trained to make colour-reward associations. In the second phase, the previously reward-associated colours were used as distractors in a visual search task. We found that patients did not use reward information to modulate their attention; they were similarly distracted by the presence of low and high-reward distractors. However, contrary to our predictions, we did not find evidence that dopamine modulated this inability to use reward to guide attention allocation. Additionally, we found slightly increased overall distractibility in Parkinson’s patients compared to older controls, but interestingly, the degree of distractibility was not influenced by dopamine replacement. Our results suggest that loss of reward-guided attention allocation may contribute to early attention deficits and raise the possibility that this inability to prioritize cognitive resource allocation could contribute to executive deficits more broadly in Parkinson’s 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".