Pheromone-Induced Odor Associative Fear Learning in Rats
Bibliographic record
Abstract
Alarm pheromones alert conspecifics to the presence of danger. Can pheromone communication aid in learning specific cues? Such facilitation has an evident evolutionary advantage. We use two associative learning paradigms to test this hypothesis. The first is stressed cage mate-induced conditioning. One pair-housed adult rat received 4 pairings of terpinene + shock over 30 min. Ten minutes after return to the home cage, its companion rat was removed and exposed to terpinene. Single-housed controls were exposed to either terpinene or shock only. Companion rats showed terpinene-specific freezing, which was prevented by β-adrenoceptor blockade. Using Arc to index neuronal activation in response to terpinene re-exposure, stressed cage-mate induced associative learning was measured. Companion rats showed increased neuronal activity in the accessory olfactory bulb, while terpinene + shock-conditioned rats showed increased activity in the main olfactory bulb. Both groups had enhanced activity in the anterior basolateral amygdala and central amygdala. To test involvement of pheromone mediation, in the 2nd paradigm, we paired terpinene with soiled bedding from odor + shock rats or a rat alarm pheromone. Both conditioning increased rats' freezing to terpinene. Blocking NMDA receptors in the basolateral amygdala prevented odor-specific learning suggesting shock and pheromone-paired pathways converge in the amygdala. An alarm pheromone thus enables cue-specific learning as well as signalling danger.
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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) | 0.001 | 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".