Learning & Behavior

, Volume 38, Issue 3, pp 235–242 | Cite as

Investigating the impact of observation errors on the statistical performance of network-based diffusion analysis

  • Mathias FranzEmail author
  • Charles L. Nunn


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


Social Learning Network Hetero Geneity Observation Error Japanese Macaque Observation Probability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Psychonomic Society, Inc. 2010

Authors and Affiliations

  1. 1.Max Planck Institute for Evolutionary AnthropologyLeipzigGermany
  2. 2.Courant Research Centre “Evolution of Social Behaviour,”University of GöttingenGöttingenGermany
  3. 3.Harvard UniversityCambridge

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