% pubman genre = article @article{item_3236670, title = {{Dynamic social learning in temporally and spatially variable environments}}, author = {Deffner, Dominik and Kleinow, Vivien and McElreath, Richard}, language = {eng}, doi = {10.1098/rsos.200734}, publisher = {Royal Society}, year = {2020}, abstract = {{Cultural evolution is partly driven by the strategies individuals{\textless}br{\textgreater}use to learn behaviour from others. Previous experiments on{\textless}br{\textgreater}strategic learning let groups of participants engage in repeated{\textless}br{\textgreater}rounds of a learning task and analysed how choices are{\textless}br{\textgreater}affected by individual payoffs and the choices of group{\textless}br{\textgreater}members. While groups in such experiments are fixed, natural{\textless}br{\textgreater}populations are dynamic, characterized by overlapping{\textless}br{\textgreater}generations, frequent migrations and different levels of{\textless}br{\textgreater}experience. We present a preregistered laboratory experiment{\textless}br{\textgreater}with 237 mostly German participants including migration,{\textless}br{\textgreater}differences in expertise and both spatial and temporal{\textless}br{\textgreater}variation in optimal behaviour. We used simulation and{\textless}br{\textgreater}multi-level computational learning models including timevarying parameters to investigate adaptive time dynamics in{\textless}br{\textgreater}learning. Confirming theoretical predictions, individuals relied{\textless}br{\textgreater}more on (conformist) social learning after spatial compared{\textless}br{\textgreater}with temporal changes. After both types of change, they{\textless}br{\textgreater}biased decisions towards more experienced group members.{\textless}br{\textgreater}While rates of social learning rapidly declined in rounds{\textless}br{\textgreater}following migration, individuals remained conformist to{\textless}br{\textgreater}group-typical behaviour. These learning dynamics can be{\textless}br{\textgreater}explained as adaptive responses to different informational{\textless}br{\textgreater}environments. Summarizing, we provide empirical insights{\textless}br{\textgreater}and introduce modelling tools that hopefully can be applied{\textless}br{\textgreater}to dynamic social learning in other systems.}}, journal = {{Royal Society Open Science}}, volume = {7}, }