Brood-parasitic female cowbirds have better numerical abilities than males on a task resembling nest prospecting behaviour
Bibliographic record
Abstract
Selection can act in a sex-specific manner on cognitive abilities, including numerosity, especially when ecological roles differ between sexes. However, few systems exist in which numerical abilities would be expected to differ between the sexes, and even fewer focus on systems in which females are predicted to outperform males. In obligate brood-parasitic brown-headed cowbirds ( Molothrus ater ), only females select and parasitize host nests, and would benefit from enhanced numerical abilities to distinguish suitable host nests in the process of egg laying from unsuitable nests that have begun incubation. To test this hypothesis, we trained cowbirds to use touchscreens and discriminate between sets of images differing in number. Cowbirds distinguished images based on number alone (i.e. without using non-numerical cues), and females outperformed males across combinations of objects ranging from one to six (range in host egg numbers), but this difference disappeared across higher numbered combinations. In addition, males spent less time deciding on the correct stimulus than females, but made less accurate decisions overall, suggesting they 'guessed' correct answers more than females. We add to the growing evidence for complex numerical abilities in diverse taxa, and show these abilities can be shaped by ecology in a sex-specific way.
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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) | 0.006 | 0.001 |
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".