Alcohol Cues Increase Behavioral Economic Demand and Craving for Alcohol in Non-Treatment Seeking and Treatment-Seeking Heavy Drinkers
Bibliographic record
Abstract
Background. Behavioral economic research has revealed significant increases in alcohol demand following exposure to alcohol-related cues. Prior research has focused exclusively on non-treatment-seeking heavy drinkers, included only male participants, or used heterogeneous methods. The current studies sought to replicate and extend existing findings in treatment-seeking and non-treatment seeking heavy drinkers while also examining sex effects and moderation by AUD severity. Methods. Study 1 included 117 non-treatment seeking heavy drinkers (51.5% women; M age 34.69; 56.4% AUD+), and Study 2 included 89 treatment-seeking heavy drinkers with AUD (40.4% women; M age = 41.35). In both studies, alcohol demand was measured using a state-based alcohol purchase task (APT) and subjective alcohol craving was measured using visual analog scales. Measures were collected following exposure to neutral (water) cues in a standard room and alcohol cues in a bar lab.Results. Alcohol demand (intensity, Omax, breakpoint, and elasticity) and craving were significantly increased following alcohol cues compared to neutral cues (ps < .005) with effect sizes ranging from small to large (partial eta squared = .074-.480). Participants with AUD (Study 1) or with higher AUD severity (Study 2) reported higher craving and higher demand for most indices (i.e., main effects; ps < .032, partial eta squared = .043-.239), and a larger alcohol cue increase in Omax was found for AUD+ participants in Study 1 compared to non-AUD (p = .028, partial eta squared = .041). There were no significant sex effects. Conclusions. These findings replicate and extend prior research by offering additional insight into alcohol cue effects on the reinforcing value of alcohol and subjective motivation to drink. Results also suggest that the presence of an AUD may amplify cue effects on maximum alcohol expenditure but not for other indices of alcohol demand.
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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) | 0.000 | 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) | 0.004 | 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".