Repeated Cocaine Exposure Attenuates the Desire to Actively Avoid: A Novel Active Avoidance Runway Task
Bibliographic record
Abstract
Drug addiction is a disorder in which drug seeking persists despite aversive consequences. While it is well documented in animal models of drug sensitization that repeated drug exposure enhances positive incentive motivation for drug and natural reinforcers, its effect on negative incentive motivation, defined here as the motivation to avoid a cued aversive outcome, remains an open question. In the present study, we designed a novel active avoidance runway paradigm to assess the effects of repeated cocaine pre-exposure on the motivation to avoid an aversive outcome. Cocaine and saline pre-exposed rats were first trained to perform a conditioned active avoidance lever press response to prevent the occurrence of foot shock administrations. The rats were subsequently tested in a runway apparatus, wherein they were required to traverse the length of a straight alley maze to reach the lever and emit a conditioned active avoidance response. Run times were measured as an indication of negative incentive motivation. Cocaine pre-exposed rats demonstrated longer latencies to emit the conditioned active avoidance response, but showed no differences in latency to initiate runway behavior, nor in their acquisition of the active avoidance response compared to the saline pre-exposed controls. Subsequent testing in an elevated plus maze revealed no differences in the expression of anxiety in cocaine pre-exposed rats compared to saline pre-exposed controls. Our results indicate that prior repeated cocaine exposure attenuated cued negative incentive motivation, which suggests that drug addiction may be attributable to a decrease in motivation to avoid aversive consequences associated with drug use.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".