DIAZEPAM DOSE-DEPENDENTLY INCREASES OR DECREASES IMPLICIT PRIMING OF ALCOHOL ASSOCIATIONS IN PROBLEM DRINKERS
Bibliographic record
Abstract
AIMS: Words denoting negative affect (NEG) have been found to prime alcohol-related words (ALC) on semantic priming tasks, and this effect is tied to severity of addiction. Previous research suggested that high doses of benzodiazepines may dampen NEG-ALC priming. The present study tested this possibility and the role of motivation for alcohol in this process. METHODS: A placebo-controlled, double blind, between-within, counterbalanced design was employed. Two groups of male problem drinkers (n = 6/group) received a high (15-mg) or low (5-mg) dose of diazepam versus placebo on two identical test sessions. A lexical decision task assessed priming. RESULTS: Under placebo, significant NEG-->ALC priming emerged in each group. High-dose diazepam selectively reversed this effect, while low-dose selectively enhanced it. Correlations between NEG-->ALC priming and desire for alcohol provided further support that semantic priming of ALC concepts reflects a motivational process. The bi-directional effects found here parallel the effects of high- versus low-dose benzodiazepines on alcohol self-administration in animals. CONCLUSIONS: High-dose diazepam reduces prime-induced activation of ALC concepts in problem drinkers. Low-dose diazepam facilitates this process, and cross-priming of motivation for alcohol appears to explain this effect. Neurochemical modulation of the alcohol memory network may contribute to the motivational effects of benzodiazepines in problem drinkers.
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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) | 0.002 | 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".