Out-Group Threat Responses, In-Group Bias, and Nonapeptide Involvement Are Conserved across Vertebrates: (A Comment on Bruintjes et al., “Out-Group Threat Promotes Within-Group Affiliation in a Cooperative Fish”)
Bibliographic record
Abstract
The challenges and threats posed by out-groups have major effects on human social behavior and how individuals interact with one another. We briefly review evidence here that out-group threat similarly affects nonhuman animal behavior. Actual and potential threats posed by out-group individuals (unfamiliar and genetically nonrelated individuals of the same species) affect social behavior promoting "out-group" avoidance and "in-group" bias and enhancing in-group (familiar and/or genetically related individuals) affiliation and interactions. Individuals from out-groups present risks of pathogen exposure as well as being threats to resources, territory, and offspring. All of these threats function to promote in-group bias in humans and nonhumans. There are also striking similarities in the underlying neurobiological mechanisms mediating the responses to out-group threat and the expression of in-group bias. In particular, the evolutionarily conserved, hormone-regulated nonapeptide systems (oxytocin, arginine-vasopressin, and homologous neuropeptides and their receptors) are involved in the mediation of the detection and avoidance of out-groups and response to in-groups and facilitation of in-group responses across multiple vertebrate species. Consequently, comparative investigations of both the behavioral expression of and the mechanism underlying out-group avoidance and in-group bias are necessary for a full understanding of the evolution of social behavior and responses to in- and out-groups.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.042 | 0.039 |
| Insufficient payload (model declined to judge) | 0.004 | 0.007 |
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".