Abstract # 31:

Scheduled for Thursday, June 18, 2015 02:10 PM-02:30 PM: (Cascade AJBCD) Oral Presentation


E. Feczko1,2,3, T. A. Mitchell1,2,3, H. Walum1,2,3, J. M. Brooks1,2,3, T. R. Heitz1,2,3, L. J. Young1,2,3 and L. A. Parr1,2,3
1Emory University School of Medicine, 100 Woodruff Circle, Atlanta, GA 30322, USA, 2Yerkes National Primate Research Center, 3Silvio O. Conte Center for Oxytocin and Social Cognition, Center for Translational Social Neuroscience
     Understanding the properties of a social environment is important for understanding the dynamics of social relationships. To quantify social environment properties, recent studies have incorporated social network analysis (SNA). SNA quantifies both the global and local properties of a social environment, such as social network efficiency and the roles played by specific individuals, respectively. Determining the amount of data necessary for a reliable network is critical for measuring changes in the social environment, for example following an experimental manipulation, and therefore may be critical for using SNA to statistically assess social behavior. We extend methods for measuring error in acquired data and determining the amount of data necessary to generate reliable social networks. We derived social networks from a group of 10 male Macaca mulatta (rhesus macaques) for three behaviors: spatial proximity, grooming, and mounting. Behaviors were coded using a video observation technique, where video cameras recorded the compound where the macaques resided. 10 hours of video data were collected, coded and used to construct these networks. Using the methods described here, we found in our data that one hour of spatial proximity observations produced reliable social networks. However, this may not be true for other studies due to differences in data acquisition. Our results have broad implications for measuring and predicting the amount of error in any social network, regardless of species.