Parallel role for the dopamine D1 receptor in gambling and amphetamine reinforcement in healthy volunteers
Bibliographic record
Abstract
This study investigated the role of dopamine, and specifically the D1 receptor (D1R), in the reinforcing effects of a slot-machine game in healthy volunteers ( n=30). To compare gambling and drug effects, subjects received the prototypic psychostimulant drug d-amphetamine (AMPH; 20 mg) in a multi-session, placebo-controlled design. To isolate D1R, half the subjects were pretreated with the preferential D2 receptor antagonist haloperidol (HAL; 3 mg), and the other half with the mixed D1-D2 antagonist fluphenazine (FLU; 3 mg) before the game (Phase I) and AMPH (Phase II). HAL decreased and FLU increased the post-game desire to gamble and post-AMPH desire to take AMPH again, as well as amphetamine scale ratings on the Addiction Research Center Inventory after gambling and AMPH. The effects of the antagonists on desire to gamble and to take AMPH again were significantly intercorrelated. HAL increased and FLU decreased the salience of negative affective words on a rapid reading task after both reinforcers. HAL also decreased the salience of gambling words after AMPH. Both reinforcers increased diastolic blood pressure equally under antagonists and placebo. Results indicate that D1R plays a parallel role in the psychostimulant-like, incentive-motivational, and salience-enhancing effects of gambling and AMPH. Moderate D1R activation appears to optimize these effects in healthy subjects.
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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) | 0.000 | 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) | 0.002 | 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".