The growing methodological toolkit for identifying and studying social learning and culture in non-human animals

Andrew Whiten*, Christian Rutz

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

There is a growing consensus that animals’ socially transmitted knowledge should be recognized when planning conservation management, but demonstrating social learning or culture can present considerable challenges, especially in the wild. Fortunately, decades of research have spawned a rich methodological toolkit for exactly this purpose. Here, we review principal approaches, including: social learning experiments; analyses of natural or experimentally seeded diffusions of novel behaviours, sometimes using specialist statistical techniques; mapping of behavioural variation across neighbouring, sympatric or captive groups, or at larger scales; and assessment of aspects of cross-generational transmission, including teaching, learning during ontogenetic development and cumulative change. Some methods reviewed were developed for captive studies, but have subsequently been adapted for application in the wild, or are useful for exploring a species’ general propensity to learn and transmit information socially. We highlight several emerging ‘rapid assessment’ approaches—including camera trapping, passive acoustic monitoring, animal-borne tags, AI-assisted data mining and computer simulations—that should prove useful in addressing particularly urgent conservation needs. We conclude by considering how best to use this growing methodological toolkit in practice, to guide further research on animal social learning and cultures, and maximize conservation and policy impact.
Original languageEnglish
Article number20240140
JournalPhilosophical Transactions of the Royal Society B: Biological Sciences
Volume380
Issue number1925
DOIs
Publication statusPublished - 1 May 2025

Keywords

  • Animal culture
  • Bio-logging
  • Cultural transmission
  • Experience-weighted attraction
  • Network-based diffusion analysis
  • Social learning

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