Franz Mathias, Nunn Charles L
Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany.
Learn Behav. 2010 Aug;38(3):235-42. doi: 10.3758/LB.38.3.235.
Experiments in captivity have provided evidence for social learning, but it remains challenging to demonstrate social learning in the wild. Recently, we developed network-based diffusion analysis (NBDA; 2009) as a new approach to inferring social learning from observational data. NBDA fits alternative models of asocial and social learning to the diffusion of a behavior through time, where the potential for social learning is related to a social network. Here, we investigate the performance of NBDA in relation to variation in group size, network heterogeneity, observer sampling errors, and duration of trait diffusion. We find that observation errors, when severe enough, can lead to increased Type I error rates in detecting social learning. However, elevated Type I error rates can be prevented by coding the observed times of trait acquisition into larger time units. Collectively, our results provide further guidance to applying NBDA and demonstrate that the method is more robust to sampling error than initially expected. Supplemental materials for this article may be downloaded from http://lb.psychonomic-journals.org/content/supplemental.
圈养实验为社会学习提供了证据，但在野外证明社会学习仍然具有挑战性。最近，我们开发了基于网络的扩散分析（NBDA；2009）作为一种从观察数据中推断社会学习的新方法。NBDA将非社会学习和社会学习的替代模型应用于行为随时间的扩散，其中社会学习的可能性与社会网络相关。在这里，我们研究了NBDA在群体规模变化、网络异质性、观察者抽样误差和特征扩散持续时间方面的表现。我们发现，当观察误差足够严重时，会导致检测社会学习时I型错误率增加。然而，通过将观察到的特征习得时间编码为更大的时间单位，可以防止I型错误率升高。总的来说，我们的结果为应用NBDA提供了进一步的指导，并证明该方法对抽样误差的耐受性比最初预期的更强。本文的补充材料可从http://lb.psychonomic-journals.org/content/supplemental下载。
