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在时空多变环境中的动态社会学习

Dynamic social learning in temporally and spatially variable environments.

作者信息

Deffner Dominik, Kleinow Vivien, McElreath Richard

机构信息

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

出版信息

R Soc Open Sci. 2020 Dec 2;7(12):200734. doi: 10.1098/rsos.200734. eCollection 2020 Dec.

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 time-varying 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.

摘要

文化进化部分是由个体用于向他人学习行为的策略驱动的。先前关于策略学习的实验让参与者群体参与重复的学习任务轮次,并分析了选择如何受到个体收益和群体成员选择的影响。虽然此类实验中的群体是固定的,但自然种群是动态的,其特征是世代重叠、频繁迁移以及不同水平的经验。我们进行了一项预先注册的实验室实验,有237名大多为德国的参与者,其中包括迁移、专业知识差异以及最优行为的空间和时间变化。我们使用了包括时变参数的模拟和多层次计算学习模型来研究学习中的适应性时间动态变化。证实了理论预测,与时间变化相比,个体在空间变化后更多地依赖(从众的)社会学习。在两种类型的变化之后,他们将决策偏向更有经验的群体成员。虽然在迁移后的轮次中社会学习率迅速下降,但个体仍然从众于群体典型行为。这些学习动态变化可以解释为对不同信息环境的适应性反应。总之,我们提供了实证见解并引入了建模工具,希望这些工具能够应用于其他系统中的动态社会学习。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/649c/7813247/2168cf61124e/rsos200734-g1.jpg

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