%0 Journal Article
%A Deffner, Dominik
%A Kleinow, Vivien
%A McElreath, Richard
%+ 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
Department of Human Behavior Ecology and Culture, Max Planck Institute for Evolutionary Anthropology, Max Planck Society
Department of Human Behavior Ecology and Culture, Max Planck Institute for Evolutionary Anthropology, Max Planck Society
%T Dynamic social learning in temporally and spatially variable environments : 
%G eng
%U https://hdl.handle.net/21.11116/0000-0006-83D4-6
%R 10.1098/rsos.200734
%7 2020-12-02
%D 2020
%8 02.12.2020
%* Review method: peer-reviewed
%X Cultural evolution is partly driven by the strategies individuals<br>use to learn behaviour from others. Previous experiments on<br>strategic learning let groups of participants engage in repeated<br>rounds of a learning task and analysed how choices are<br>affected by individual payoffs and the choices of group<br>members. While groups in such experiments are fixed, natural<br>populations are dynamic, characterized by overlapping<br>generations, frequent migrations and different levels of<br>experience. We present a preregistered laboratory experiment<br>with 237 mostly German participants including migration,<br>differences in expertise and both spatial and temporal<br>variation in optimal behaviour. We used simulation and<br>multi-level computational learning models including timevarying parameters to investigate adaptive time dynamics in<br>learning. Confirming theoretical predictions, individuals relied<br>more on (conformist) social learning after spatial compared<br>with temporal changes. After both types of change, they<br>biased decisions towards more experienced group members.<br>While rates of social learning rapidly declined in rounds<br>following migration, individuals remained conformist to<br>group-typical behaviour. These learning dynamics can be<br>explained as adaptive responses to different informational<br>environments. Summarizing, we provide empirical insights<br>and introduce modelling tools that hopefully can be applied<br>to dynamic social learning in other systems.
%J Royal Society Open Science
%V 7
%I Royal Society
%U https://rs.figshare.com/collections/Supplementary_material_from_Dynamic_social_learning_in_temporally_and_spatially_variable_environments_/5219333https://github.com/DominikDeffner/Dynamic-Social-Learninghttps://zenodo.org/record/4034787#.X9MwjedCeUk