The role of dopamine in risk taking: a specific look at Parkinson’s disease and gambling
Bibliographic record
Abstract
An influential model suggests that dopamine signals the difference between predicted and experienced reward. In this way, dopamine can act as a learning signal that can shape behaviors to maximize rewards and avoid punishments. Dopamine is also thought to invigorate reward seeking behavior. Loss of dopamine signaling is the major abnormality in Parkinson's disease. Dopamine agonists have been implicated in the occurrence of impulse control disorders in Parkinson's disease patients, the most common being pathological gambling, compulsive sexual behavior, and compulsive buying. Recently, a number of functional imaging studies investigating impulse control disorders in Parkinson's disease have been published. Here we review this literature, and attempt to place it within a decision-making framework in which potential gains and losses are evaluated to arrive at optimum choices. We also provide a hypothetical but still incomplete model on the effect of dopamine agonist treatment on these value and risk assessments. Two of the main brain structures thought to be involved in computing aspects of reward and loss are the ventral striatum (VStr) and the insula, both dopamine projection sites. Both structures are consistently implicated in functional brain imaging studies of pathological gambling 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".