Dynamic strategic social learning in nest-building zebra finches and its generalizability
Bibliographic record
Abstract
Animals often balance asocial and social information strategically, adjusting when and from whom they copy based on context. Yet the cognition driving this dynamic-and its broader implications-remains poorly understood. We tested whether zebra finches use a copy-if-dissatisfied strategy by manipulating the quality of their initial nest-building or reproductive experience, showing them a conspecific nest-builder and tracking subsequent material choices. Builder-males were more likely to choose the demonstrated 'social' material-particularly at first choice-if they had previously used low-quality material. Using cognitive modelling, we estimated how latent learning mechanisms shaped decisions, identifying two asocial and two social parameters. These estimates provide the first formal evidence for the cognitive mechanisms of nest building. Forward simulations informed-but not predetermined-by these parameters approximated observed behaviour, supporting their causal role. We then used these parameters in exploratory simulations to test how choices might shift under novel payoff contexts. We found that payoff structure-not (dis)satisfaction-was the primary driver of social material use, though higher rewards did not proportionally increase copying. These exploratory simulation results offer preliminary insight into mechanisms underlying material-use variation. Our study illustrates how computational modelling can robustly link behaviour to underlying learning mechanisms and probe the generalizability of animal cognition-a rarity in this field.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".