Are You Better Than Me? Social Comparisons in Carrion Crows (Corvus corone)
Bibliographic record
Abstract
Abstract Comparing oneself to others is a key process in humans that allows individuals to gauge their performances and abilities and thus develop and calibrate their self-image. Very little is known about its evolutionary foundations. A key feature of social comparison is the sensitivity to other individuals’ performance. Recent studies on primates produced equivocal results, leading us to distinguish a ‘strong’ variant of the social comparison hypothesis formulated for humans from a ‘weak’ variant found in non-human primates. Here, we focus on animals that are distantly related to primates but renowned for their socio-cognitive skills, birds from the family Corvidae. We were interested in whether crows’ task performances were influenced i) by the presence of a conspecific co-actor performing the same discrimination task and ii) by the simulated acoustic cues of a putative co-actor performing better or worse than themselves. Crows reached a learning criterion quicker when tested simultaneously as compared to when tested alone, indicating a facilitating effect of social context. The performance of a putative co-actor influenced their performance: crows were better at discriminating familiar images when their co-actor was better than they were. Standard extremity, i.e., how pronounced the difference was between the performance of the subject and that of the co-actor, and category membership (i.e., affiliation status and sex), of the putative co-actors had no effect on their performance. Our findings are in line with the ‘weak’ variant of social comparison and indicate that elements of human social comparison can be found outside of primates.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".