Alcohol cues increase behavioral economic demand and craving for alcohol in nontreatment‐seeking and treatment‐seeking heavy drinkers
Bibliographic record
Abstract
Abstract Background Behavioral economic research has revealed significant increases in alcohol demand following exposure to alcohol‐related cues. Prior research has focused exclusively on nontreatment‐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 nontreatment‐seeking heavy drinkers while also examining sex effects and moderation by alcohol use disorder (AUD) severity. Methods Study 1 included 117 nontreatment‐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 hypothetical 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, O max , breakpoint, and elasticity) and craving were significantly increased following alcohol cues compared to neutral cues ( p s < 0.005) with effect sizes ranging from small to large ( η p 2 = 0.074–0.480). 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; p s < 0.032, η p 2 = 0.043–0.239). A larger alcohol cue increase in O max was found for AUD+ participants in Study 1 compared to non‐AUD participants ( p = 0.028, η p 2 = 0.041) but not for any other indices in Study 1 or Study 2. 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. The results also suggest that sex and AUD severity do not meaningfully impact cue effects across most indices of demand.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".