Behavioral economic analysis of stress effects on acute motivation for alcohol
Bibliographic record
Abstract
Due to issues of definition and measurement, the heavy emphasis on subjective craving in the measurement of acute motivation for alcohol and other drugs remains controversial. Behavioral economic approaches have increasingly been applied to better understand acute drug motivation, particularly using demand curve modeling via purchase tasks to characterize the perceived reinforcing value of the drug. This approach has focused on using putatively more objective indices of motivation, such as units of consumption, monetary expenditure, and price sensitivity. To extend this line of research, the current study used an alcohol purchase task to determine if, compared to a neutral induction, a personalized stress induction would increase alcohol demand in a sample of heavy drinkers. The stress induction significantly increased multiple measures of the reinforcing value of alcohol to the individual, including consumption at zero price (intensity), the maximum total amount of money spent on alcohol (Omax), the first price where consumption was reduced to zero (breakpoint), and the general responsiveness of consumption to increases in price (elasticity). These measures correlated only modestly with craving and mood. Self-reported income was largely unrelated to demand but moderated the influence of stress on Omax. Moderation based on CRH-BP genotype (rs10055255) was present for Omax, with T allele homozygotes exhibiting more pronounced increases in response to stress. These results provide further support for a behavioral economic approach to measuring acute drug motivation. The findings also highlight the potential relevance of income and genetic factors in understanding state effects on the perceived reinforcing value of alcohol.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".