Ventral hippocampus inactivation enhances the extinction of active avoidance responses in the presence of safety signals but leaves discrete trial operant active avoidance performance intact
Bibliographic record
Abstract
The acquisition of active avoidance (AA) behavior is typically aided by the presence of two signals-the warning signal, which predicts the future occurrence of an aversive event (e.g., shocks), and the safety signal, which is presented upon successful avoidance of oncoming shocks. While the warning signal could be conceived to act as a Pavlovian fear cue, and is likely mediated by brain areas that underlie Pavlovian fear cue conditioning, the neural substrates underlying safety signaling are less clear, largely due to the unavailability of AA tasks that are devoid of an explicit warning signal. The present study sought to investigate the role of the ventral hippocampus (VH) in safety signaled AA performance acquired without an explicit warning signal, using a novel discrete trial paradigm. Adult male Long Evans rats were divided into two groups and trained to acquire AA responses with, or without a safety signal. Analysis of the acquisition and stable state performance data revealed that the availability of a safety signal alone did not improve the acquisition or performance of AA responses. Furthermore, post-training, reversible VH inactivation did not impact stable state avoidance behavior. However, extinction of avoidance responses was facilitated in the group trained with a safety signal, and this effect was further potentiated by VH inactivation. Additional elevated plus maze (EPM), light-dark box, and locomotor tests demonstrated that VH inactivation reduced anxiety without affecting locomotor activity. Taken together, these results demonstrate the importance of VH in the extinction of persistent pathological avoidance behavior when safety is signaled.
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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) | 0.002 | 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".