Adolescent rats extend help to outgroup members, highlighting a neural network for group identity categorization
Bibliographic record
Abstract
Summary Prosocial behavior, in particular helping others in need, occurs preferentially in response to the perceived distress of one’s own group members, or ingroup. The development of neural mechanisms underlying social selectivity towards ingroup members are not well established. Here, we used a rat helping behavior test to explore the development and neural basis of ingroup bias for prosocial behavior in adolescent rats. We previously found that adult rats selectively help others from their own social group, and that this selectivity is associated with activation in reward and motivation circuits. Surprisingly, we found that adolescent rats helped both ingroup and outgroup members, evidence suggesting that ingroup bias emerges in adulthood. Analysis of brain-wide neural activity, indexed by expression of the early-immediate gene c-Fos, revealed increased activity for ingroup members across a broad set of regions, which was congruent for adults and adolescents. However, adolescents showed reduced hippocampal and insular activity, and increased orbitofrontal cortex activity compared to adults. Adolescent rats who did not help trapped others also demonstrated increased amygdala connectivity. Together, these findings demonstrate that biases for group-dependent prosocial behavior develop with age in rats and suggest that specific brain regions contribute to this prosocial selectivity, overall pointing to possible targets for the functional modulation of ingroup bias. One Sentence Summary Prosocial selectivity increases with age in parallel with hippocampal and insular activation, providing insight into the neural classification of group membership.
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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) | 0.003 | 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".