Neural and Behavioral Correlates of Individual Variability in Rat Helping Behavior: A Role for Social Affiliation and Oxytocin Receptors
Bibliographic record
Abstract
A prosocial response to others in distress is increasingly recognized as a natural behavior for many social species. While prosocial behavior is more frequently observed toward familiar conspecifics, even within the same social context, some individuals are more prone to help than others. In a rat helping behavior test where animals can release a distressed conspecific trapped inside a restrainer, most rats are motivated and consistently release the trapped rat ("openers"), yet ∼30% do not open the restrainer ("nonopeners"). To characterize the difference between these populations, behavioral and neural markers were compared between opener and nonopener rats in males and females. Openers showed significantly more social affiliative behavior both before and after door opening compared with nonopeners. Oxytocin receptor mRNA levels were higher in the nucleus accumbens (NAc), but not the anterior insula, of openers. Several transcription control pathways were significantly upregulated in openers' NAc. Chemogenetically inhibiting paraventricular oxytocin neurons did not significantly impair helping but reduced sociality measures, indicating that helping does not rely solely on oxytocin signaling. Analysis of brain-wide neural activity based on the immediate-early gene c-Fos in males revealed increased activity in openers in prosocial brain regions compared with nonopeners. These include regions associated with empathy in humans (insula, somatosensory, cingulate, and frontal cortices) and motivation and reward regions such as the NAc. These findings indicate that prosocial behavior may be predicted by affiliative behavior and activity in the prosocial neural network and provide targets for the investigation of causal mechanisms underlying prosocial behavior.
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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 | 0.001 | 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 | 0.000 |
| 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".