Prosocial behavior in competitive fish: the case of the archerfish
Bibliographic record
Abstract
Humans are social creatures, demonstrate prosocial behaviors, and are sensitive to the actions and consequent payoff of others. This social sensitivity has also been found in many other species, though not in all. Research has suggested that prosocial tendencies are more pronounced in naturally cooperative species whose social structure requires a high level of interdependence and allomaternal care. The present study challenges this assumption by demonstrating, in a laboratory setting, that archerfish, competitive by nature, preferred targets rewarding both themselves and their tankmates, but only when the payoff was equal. With no tankmate on the other side of the partition, they exhibited no obvious preference. Finding evidence for prosocial behavior and negative responses to unequal distribution of reward to the advantage of the other fish suggests that in a competitive social environment, being prosocial may be the most adaptive strategy for personal survival, even if it benefits others as well.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".