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Brain Sci. 2017 Jul 21;7(7). pii: E91. doi: 10.3390/brainsci7070091.

Using Machine Learning to Discover Latent Social Phenotypes in Free-Ranging Macaques.

Author information

1
Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104, USA. madseth@mail.med.upenn.edu.
2
Center for Research in Animal Behaviour, University of Exeter, Exeter EX4 4QG, UK. L.J.N.Brent@exeter.ac.uk.
3
Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104, USA. montag@mail.med.upenn.edu.
4
Department of Statistical Science, Duke University, Durham, NC 27708, USA. kheller@stat.duke.edu.
5
Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104, USA. mplatt@mail.med.upenn.edu.
6
Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104,USA. mplatt@mail.med.upenn.edu.
7
Marketing Department, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104, USA. mplatt@mail.med.upenn.edu.

Abstract

Investigating the biological bases of social phenotypes is challenging because social behavior is both high-dimensional and richly structured, and biological factors are more likely to influence complex patterns of behavior rather than any single behavior in isolation. The space of all possible patterns of interactions among behaviors is too large to investigate using conventional statistical methods. In order to quantitatively define social phenotypes from natural behavior, we developed a machine learning model to identify and measure patterns of behavior in naturalistic observational data, as well as their relationships to biological, environmental, and demographic sources of variation. We applied this model to extensive observations of natural behavior in free-ranging rhesus macaques, and identified behavioral states that appeared to capture periods of social isolation, competition over food, conflicts among groups, and affiliative coexistence. Phenotypes, represented as the rate of being in each state for a particular animal, were strongly and broadly influenced by dominance rank, sex, and social group membership. We also identified two states for which variation in rates had a substantial genetic component. We discuss how this model can be extended to identify the contributions to social phenotypes of particular genetic pathways.

KEYWORDS:

animal models; behavioral genetics; machine learning; social neuroscience

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