Neural correlates of extinction in a rat model of appetitive Pavlovian conditioning
Bibliographic record
Abstract
Abstract Extinction is a fundamental form of inhibitory learning that is important for adapting to changing environmental contingencies. While numerous studies have investigated the neural correlates of extinction using Pavlovian fear conditioning and appetitive operant reward-seeking procedures, less is known about the neural circuitry mediating the extinction of appetitive Pavlovian conditioned responding. Here, we aimed to generate an extensive brain activation map of extinction learning in a rat model of appetitive Pavlovian conditioning. Male Long-Evans rats were trained to associate a conditioned stimulus (CS; 20 s white noise) with the delivery of a 10% sucrose unconditioned stimulus (US; 0.3 ml/CS) to a fluid port. Control groups also received CS presentations, but sucrose was delivered either during the inter-trial interval or in the home-cage. After conditioning, 1 or 6 extinction sessions were conducted in which the CS was presented but sucrose was withheld. We performed Fos immunohistochemistry and network connectivity analyses on a set of cortical, striatal, thalamic, and amygdalar brain regions. Neural activity in the prelimbic cortex, ventral orbitofrontal cortex, nucleus accumbens core, and paraventricular nucleus of the thalamus was greater during recall relative to extinction. Conversely, prolonged extinction following 6 sessions induced increased neural activity in the infralimbic cortex, medial orbitofrontal cortex, and nucleus accumbens shell compared to home-cage controls. All these structures were similarly recruited during recall on the first extinction session. These findings provide novel evidence for the contribution of brain areas and neural networks that are differentially involved in the recall versus extinction of appetitive Pavlovian conditioned responding.
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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) | 0.001 | 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".