<scp>P</scp>avlovian‐conditioned alcohol‐seeking behavior in rats is invigorated by the interaction between discrete and contextual alcohol cues: implications for relapse
Bibliographic record
Abstract
INTRODUCTION: Drug craving can be independently stimulated by cues that are directly associated with drug intake (discrete drug cues), as well as by environmental contexts in which drug use occurs (contextual drug cues). We tested the hypothesis that the context in which a discrete alcohol-predictive cue is experienced can influence how robustly that cue stimulates alcohol-seeking behavior. METHODS: Male, Long-Evans rats received Pavlovian discrimination training (PDT) sessions in which one conditioned stimulus (CS+; 16 trials/session) was paired with ethanol (0.2 mL/CS+) and a second stimulus (CS-; 16 trials/session) was not. PDT occurred in a specific context, and entries into a fluid port where ethanol was delivered were measured during each CS. Next, rats were acclimated to an alternate (nonalcohol) context where cues and ethanol were withheld. Responses to the nonextinguished CS+ and CS- were then tested without ethanol in the alcohol-associated PDT context, the nonalcohol context or a third, novel context. RESULTS: Across PDT the CS+ elicited more port entries than the CS-, indicative of Pavlovian discrimination learning. At test, the CS+ elicited more port entries than the CS- in all three contexts: however, alcohol seeking driven by the CS+ was more robust in the alcohol-associated context. In a separate experiment, extinguishing the context-alcohol association did not influence subsequent CS+ responding but reduced alcohol seeking during non-CS+ intervals during a spontaneous recovery test. CONCLUSION: These results indicate that alcohol-seeking behavior driven by a discrete Pavlovian alcohol cue is strongly invigorated by an alcohol context, and suggest that contexts may function as excitatory Pavlovian conditioned stimuli that directly trigger alcohol-seeking behavior.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.002 |
| 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".