Prelimbic and infralimbic cortical inactivations attenuate contextually driven discriminative responding for reward
Bibliographic record
Abstract
The infralimbic (IL) and prelimbic (PL) cortices of the medial prefrontal cortex (mPFC) have been shown to differentially control context-dependent behavior, with the PL implicated in the expression of contextually conditioned fear and drug-seeking, and the IL in the suppression of these behaviors. However, the roles of these subregions in contextually driven natural reward-seeking remain relatively underexplored. The present study further examined the functional dichotomy within the mPFC in the contextual control over cued reward-seeking, using a contextual biconditional discrimination (CBD) task. Rats were first trained to emit a nose poke response to the presentation of an auditory stimulus (e.g., X) for the delivery of sucrose reward, and to withhold a nose poke response to the presentation of another auditory stimulus (e.g., Y) in a context-specific manner (e.g. Context A: X+, Y-; Context B: X-, Y+). Following acquisition, rats received bilateral microinjections of GABA receptor agonists (muscimol and baclofen), or saline into the IL or PL, prior to a CBD training session and a probe test (under extinction conditions). Both IL and PL inactivation resulted in robust impairment in CBD performance, indicating that both subregions are involved in the processing of appetitively motivated contextual memories in reward-seeking.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".