Associative processes in addiction relapse models: A review oftheir Pavlovian and instrumental mechanisms, history, andterminology
Bibliographic record
Abstract
Animal models of relapse to drug-seeking have borrowed heavily from associative learning approaches.In studies of relapse-like behaviour, animals learn to self-administer drugs then receive a period of extinction during which they learn to inhibit the operant response.Several triggers can produce a recovery of responding which form the basis of a variety of models.These include the passage of time (spontaneous recovery), drug availability (rapid reacquisition), extinction of an alternative response (resurgence), context change (renewal), drug priming, stress, and cues (reinstatement).In most cases, the behavioural processes driving extinction and recovery in operant drug self-administration studies are similar to those in the Pavlovian and behavioural literature, such as context effects.However, reinstatement in addiction studies have several differences with Pavlovian reinstatement, which have emerged over several decades, in experimental procedures, associative mechanisms, and terminology.Interestingly, in cue-induced reinstatement, drug-paired cues that are present during acquisition are omitted during lever extinction.The unextinguished drug-paired cue may limit the model's translational relevance to cue exposure therapy and renders its underlying associative mechanisms ambiguous.We review major behavioural theories that explain recovery phenomena, with a particular focus on cue-induced reinstatement because it is a widely used model in addiction.We argue that cue-induced reinstatement may be explained by a combination of behavioural processes, including reacquisition of conditioned reinforcement and Pavlovian to Instrumental Transfer.While there are important differences between addiction studies and the behavioural literature in terminology and procedures, it is clear that understanding associative learning processes is essential for studying relapse.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".