| Abstract: | 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. |
