Journal Article Ppublish
PubMed: 41538034 DOI: 10.1098/rspb.2025.1508
Dynamic strategic social learning in nest-building zebra finches and its generalizability.
Proc Biol Sci 292(2061)
Authors: Alexis J Breen, Tristan Eckersley, Andrés Camacho-Alpízar, Connor T Lambert, Gopika Balasubramanian, Susan D Healy, Richard McElreath, Lauren M Guillette
Affiliations
- Department of Human Behavior, Ecology and Culture, Max Planck Institute for Evolutionary Anthropology, Leipzig04103, Germany.
- School of Biology, University of St Andrew, St AndrewsKY16 9TH, UK.
- Department of Psychology, University of Alberta, Edmonton, AlbertaT6G 2R3, Canada.
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.
MeSH
Social Learning, Learning, Cognition