Parallel Roles for Dopamine in Pathological Gambling and Psychostimulant Addiction
Bibliographic record
Abstract
A variety of evidence suggests important commonalities in the neurochemical basis of reinforcement in pathological gambling (PG) and psychostimulant addiction. This article focuses on the parallel and specific roles that dopamine (DA) activation plays in these two disorders, beyond its generic role in reinforcement. A psychostimulant-mimetic model for PG is proposed based on evidence from the following domains: Acute subjective-behavioral effects of gambling and psychostimulants; Effects of anticipated rewards and uncertainty of reward delivery (key elements of gambling) on DA release; Relationship between DA release and positive arousal; Cross-priming of motivation for gambling by amphetamine; Effects of DA D2 antagonists on gambling and amphetamine reward; Effects of mixed D1-D2 antagonists on clinical symptoms of PG; Effects of DA D2 agonists on experimental measures of risk-taking, gambling, and induction of PG in patients with Parkinson's disease; Electrophysiological and cognitive disturbances associated with chronic exposure to gambling and psychostimulants, and the possible role of sensitization in these effects. Limitations of the model regarding the exclusive role of DA are discussed with particular reference to genetic risk, co-morbidity, and sub-types of PG. Suggestions for future research include isolating the roles of DA receptor subtypes in PG, and parallel within-subject assessment of DA manipulations on gambling and psychostimulant reinforcement in PG subjects and controls.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".