Dopaminergic medication increases motivation to exert cognitive control by reducing subjective effort costs in Parkinson’s patients
Bibliographic record
Abstract
Abstract Engaging in demanding mental activities requires the allocation of cognitive control, which can be effortful and aversive. Individuals thus tend to avoid exerting cognitive effort if less demanding behavioral options are available. Recent accounts propose a key role for dopamine in motivating behavior by increasing the sensitivity to rewards associated with effort exertion. Whether dopamine additionally plays a specific role in modulating the sensitivity to the costs of cognitive effort, even in the absence of any incentives, is much less clear. To address this question, we assessed cognitive effort avoidance in patients (n = 38) with Parkinson’s disease, a condition characterized by loss of midbrain dopaminergic neurons, both ON and OFF dopaminergic medication and compared them to healthy controls (n = 24). Effort avoidance was assessed using the Demand Selection Task (DST), in which participants could freely choose between performing a high-demand or a low-demand version of a task-switching paradigm. Critically, participants were not offered any incentives to choose the more effortful option, nor for good performance. Healthy controls and patients OFF their dopaminergic medications preferred the low-demand option, in keeping with the tendency to avoid effort on this task previously demonstrated in young adults. In contrast, patients ON dopaminergic medications displayed significantly less effort avoidance than when they were OFF medications. This change in preference could not be explained by differences in task-switching performance or the patients’ ability to detect the different levels of cognitive demand in the DST. Our findings provide evidence that dopamine replacement in Parkinson’s patients increases the willingness to engage in cognitively demanding behavior, even in the absence of any clear benefits. These results suggest that dopamine plays a role in reducing the sensitivity to effort costs that is independent of its role in enhancing the sensitivity to the benefits of effort exertion.
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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".