Learning Differences between Feral Pigeons and Zenaida Doves: The Role of Neophobia and Human Proximity
Bibliographic record
Abstract
Learning differences predicted from ecological variables can be confounded with differences in wariness of novel stimuli (neophobia). Previous work on feral pigeons (Columba livia), as well as on group‐feeding and territorial zenaida doves (Zenaida aurita), reported individual and social learning differences predicted from social foraging mode. In the present study, we show that speed of learning a foraging task covaries with neophobia and latency to feed from a familiar dish in the three types of columbids. Pigeons were much faster than either territorial or group‐feeding zenaida doves on all tests conducted in captivity, but showed unexpectedly strong neophobia in some urban flocks during field tests. Human proximity strongly affected performance in group‐feeding doves both in the field and in captivity. They were slightly faster at learning than their territorial conspecifics in cage tests. In multiple regressions, species identity, but not social foraging mode, significantly predicted individual variation in learning, as did individual variation in neophobia. Wariness of novel stimuli and species differences associated with artificial selection appear to be more important than foraging mode and wariness of humans in accounting for learning differences between these columbids.
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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 | 0.001 |
| 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 | 0.000 |
| 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".