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Dynamic social learning in temporally and spatially variable environments

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Deffner,  Dominik       
Department of Human Behavior Ecology and Culture, Max Planck Institute for Evolutionary Anthropology, Max Planck Society;
The Leipzig School of Human Origins (IMPRS), Max Planck Institute for Evolutionary Anthropology, Max Planck Society;

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Kleinow,  Vivien
Department of Human Behavior Ecology and Culture, Max Planck Institute for Evolutionary Anthropology, Max Planck Society;

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McElreath,  Richard       
Department of Human Behavior Ecology and Culture, Max Planck Institute for Evolutionary Anthropology, Max Planck Society;

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Citation

Deffner, D., Kleinow, V., & McElreath, R. (2020). Dynamic social learning in temporally and spatially variable environments. Royal Society Open Science, 7. doi:10.1098/rsos.200734.


Cite as: https://hdl.handle.net/21.11116/0000-0006-83D4-6
Abstract
Cultural evolution is partly driven by the strategies individuals
use to learn behaviour from others. Previous experiments on
strategic learning let groups of participants engage in repeated
rounds of a learning task and analysed how choices are
affected by individual payoffs and the choices of group
members. While groups in such experiments are fixed, natural
populations are dynamic, characterized by overlapping
generations, frequent migrations and different levels of
experience. We present a preregistered laboratory experiment
with 237 mostly German participants including migration,
differences in expertise and both spatial and temporal
variation in optimal behaviour. We used simulation and
multi-level computational learning models including timevarying parameters to investigate adaptive time dynamics in
learning. Confirming theoretical predictions, individuals relied
more on (conformist) social learning after spatial compared
with temporal changes. After both types of change, they
biased decisions towards more experienced group members.
While rates of social learning rapidly declined in rounds
following migration, individuals remained conformist to
group-typical behaviour. These learning dynamics can be
explained as adaptive responses to different informational
environments. Summarizing, we provide empirical insights
and introduce modelling tools that hopefully can be applied
to dynamic social learning in other systems.