Pavlovian Conditioning With Ethanol: Sign‐Tracking (Autoshaping), Conditioned Incentive, and Ethanol Self‐Administration
Bibliographic record
Abstract
BACKGROUND: Conditioned incentive theories of addictive behavior propose that cues signaling a drug's reinforcing effects activate a central motivational state. Incentive motivation enhances drug-taking and drug-seeking behavior. We investigated the behavioral response to cues associated with ethanol and their interaction with operant self-administration of ethanol. METHODS: In two experiments, rats received operant training to press a lever for a sweetened ethanol solution. After operant training, the animals were given Pavlovian pairings of a brief and localized cue light with the sweetened ethanol solution (no lever present). Lever pressing for ethanol was then re-established, and the behavioral effects of the cue light were tested during an ethanol self-administration session. RESULTS: The conditioned responses resulting from pairing cue lights with the opportunity to ingest ethanol had three main effects: (1) induction of operant behavior reinforced by ethanol, (2) stimulation of ethanol-seeking behavior (magazine entries), and (3) signal-directed behavior (i.e., autoshaping, or sign-tracking). Signal-directed behavior interacted with the other two effects in a manner predicted by the location of the cue light. These conditioned responses interact with operant responding for ethanol reinforcement. CONCLUSIONS: These findings demonstrate the importance of Pavlovian conditioning effects on ethanol self-administration and are consistent with conditioned incentive theories of addictive 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) | 0.000 | 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 | 0.001 |
| 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".