Preference Conditioning in Healthy Individuals: Correlates With Hazardous Drinking
Bibliographic record
Abstract
BACKGROUND: Conditioned reward is a classic measure of drug-induced brain changes in animal models of addiction. The process can be examined in humans using the Conditioned Pattern Preference (CPP) task, in which participants associate nonverbal cues with reward but demonstrate low awareness of this conditioning. Previously, we reported that alcohol intoxication does not affect CPP acquisition in humans, but our data indicated that prior drug use may impact conditioning scores. METHODS: To test this possibility, the current study examined the relationship between self-reported alcohol use and preference conditioning in the CPP task. Working memory was assessed during conditioning by asking participants to count the cues that appeared at each location on a computer screen. Participants (69 female and 23 male undergraduate students) completed the Alcohol Use Disorders Identification Test (AUDIT) and the Rutgers Alcohol Problem Index (RAPI) as measures of hazardous drinking. RESULTS: Self-reported hazardous drinking was significantly correlated with preference conditioning in that individuals who scored higher on these scales exhibited an increased preference for the reward-paired cues. In contrast, hazardous drinking did not affect working memory errors on the CPP task. CONCLUSIONS: These findings support evidence that repeated drug use sensitizes neural pathways mediating conditioned reward and point to a neurocognitive disposition linking substance misuse and responses to reward-paired stimuli. The relationship between hazardous drinking and conditioned reward is independent of changes in cognitive function, such as working memory.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".