Impulsivity moderates the effects of dopamine D2 and mixed D1–D2 antagonists in individuals with gambling disorder
Bibliographic record
Abstract
BACKGROUND: The functional role of dopamine D1 and D2 receptors in gambling disorder (GD) remains unclear. AIMS: This study aimed to investigate the role of D1 activation and the moderating effects of impulsivity, a trait linked with weaker D2-mediated inhibition of dopamine release, in GD subjects. METHODS: Thirty (nine female) non-comorbid GD subjects with low (LI), moderate (MI), or high impulsivity (HI) received the preferential D2 antagonist haloperidol (HAL; 3 mg) or the mixed D1-D2 antagonist fluphenazine (FLU; 3 mg), on separate sessions before a 15-minute slot machine game or amphetamine (AMPH; 20 mg), in a placebo-controlled, double-blind, counterbalanced design. RESULTS: On their own, HAL and FLU led to linear increases and decreases, respectively, in desire to gamble across increasing levels of impulsivity. The slot machine and AMPH each evoked an inverted-U pattern of desire to gamble across increasing impulsivity. HAL reversed this effect of the game, whereas FLU did not alter post-game desire. HAL and FLU decreased and increased psychostimulant-like effects of the game, respectively, in LI and MI subjects, but consistently reduced these effects in HI subjects. HAL also altered the salience of negative affective words on a reading task, such that greater salience of negative words coincided with lower post-game desire to gamble. CONCLUSIONS: D1 receptors appear to gauge the incentive value of gambling in GD subjects. D1 activation has negative reinforcing effects in HI gamblers and positive reinforcing effects in LI gamblers. Medications that activate D1 could curtail chasing in HI gamblers. D1 blockade could benefit HI gamblers whose main concern is craving.
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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) | 0.001 | 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".