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Dynamic social learning in temporally and spatially variable environments.

Dominik Deffner1, Vivien Kleinow1, Richard McElreath1

  • 1Max Planck Institute for Evolutionary Anthropology, Department of Human Behavior, Ecology and Culture, Leipzig, Germany.

Royal Society Open Science
|January 25, 2021
PubMed
Summary
This summary is machine-generated.

Individuals adapt their social learning strategies based on environmental changes, favoring conformity after spatial shifts and seeking experienced members after both spatial and temporal changes. Migration temporarily reduces social learning but not conformity.

Keywords:
collective behaviourcomputational modellingcultural evolutiondecision-makingsocial learning

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Area of Science:

  • Behavioral Economics
  • Social Psychology
  • Evolutionary Biology

Background:

  • Cultural evolution relies on social learning strategies.
  • Previous research used fixed groups, lacking dynamic population features like migration and varying expertise.
  • Natural populations exhibit dynamic social learning environments.

Purpose of the Study:

  • Investigate adaptive time dynamics in social learning within a dynamic environment.
  • Model individual learning strategies in response to environmental changes.
  • Examine the impact of migration, expertise, and environmental variation on learning.

Main Methods:

  • Conducted a preregistered laboratory experiment with 237 participants.
  • Simulated learning dynamics using multi-level computational models with time-varying parameters.
  • Introduced migration, spatial, and temporal variations in optimal behavior.

Main Results:

  • Individuals increased conformist social learning after spatial compared to temporal environmental changes.
  • Participants biased decisions towards more experienced group members following both spatial and temporal changes.
  • Migration temporarily reduced social learning rates, but conformity to group behavior persisted.

Conclusions:

  • Social learning dynamics are adaptive responses to varying informational environments.
  • Computational models can capture complex learning behaviors in dynamic populations.
  • Findings offer insights into cultural evolution and dynamic social learning systems.