Haloperidol modifies instrumental aspects of slot machine gambling in pathological gamblers and healthy controls
Bibliographic record
Abstract
Instrumental conditioning has been implicated in persistence at slot machine gambling, but its specific role remains unclear. Dopamine (DA) mediates aspects of instrumental responding, and D2 antagonists reliably alter this process. This study investigated the effects of the preferential D2 antagonist, haloperidol (3 mg) on reward-related betting behavior in 20 subjects with pathological gambling (PG) and 18 healthy controls. Hierarchical regression assessed the prospective relationship between Payoff and Bet Size on consecutive trials, along with potential moderating effects of Cumulative Winnings and Phase of game (early/late) under drug and placebo. Payoff predicted Bet Size on the next trial regardless of other factors, consistent with an instrumental view of slot machine gambling. Under placebo, this correlation varied as a function of Winnings and Phase in PG subjects but was strong and invariant in Controls. Under haloperidol, the Payoff-Bet Size correlation in PG subjects resembled the invariant pattern of Controls under placebo. In contrast, the Payoff-Bet Size correlation rose then fell sharply over trials under haloperidol in controls. The correlation of Payoff with Bet Size is remarkable given that there is no actual contingency between winning and betting, and suggests that reward expectancies largely drive slot machine gambling. By blocking inhibitory D2 receptors, haloperidol may have reversed 'tolerance' to monetary reward mediated by increased tonic DA in PG subjects. Disturbance of the Payoff-Bet Size correlation in controls may reflect indiscriminate reward signaling under haloperidol in subjects with normal DA function. Indirect enhancement of DA transmission may reduce undue reward-related responding in PG 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".