New behavioural trait adopted or rejected by observing heterospecific tutor fitness
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Animals can acquire behaviours from others, including heterospecifics, but should be discriminating in when and whom to copy. Successful individuals should be preferred as tutors, while adopting traits of poorly performing individuals should be actively avoided. Thus far it is unknown if such adaptive strategies are involved when individuals copy other species. Furthermore, rejection of traits based on tutor characteristics (negative bias) has not been shown in any non-human animal. Here we test whether a choice between two new, neutral behavioural alternatives-breeding-sites with alternative geometric symbols-is affected by observing the choice and fitness of a heterospecific tutor. A field experiment replicated in four different areas shows that the proportion of pied flycatcher females matching the choice of the tit tutor consistently increased with increasing number of offspring in the tit nest, to the extent of nearly complete prevalence in one of the areas when tit fitness was highest. Notably, all four replicates demonstrate rejection of the behaviour of lowest-fitness tutors. The results demonstrate both acquisition and avoidance of heterospecific behavioural traits, based on the perceived (lack of) tutor fitness. This has potential implications for understanding the origin, diversity and local adaptations of behavioural traits, and niche overlap/partitioning and species co-occurrence.
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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 | 0.001 | 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 | 0.001 | 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) | 0.001 | 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 it