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Article

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

1
Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104, USA
2
Center for Research in Animal Behaviour, University of Exeter, Exeter EX4 4QG, UK
3
Department of Statistical Science, Duke University, Durham, NC 27708, USA
4
Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104,USA
5
Marketing Department, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104, USA
*
Author to whom correspondence should be addressed.
Brain Sci. 2017, 7(7), 91; https://doi.org/10.3390/brainsci7070091
Received: 29 March 2017 / Revised: 11 July 2017 / Accepted: 16 July 2017 / Published: 21 July 2017
(This article belongs to the Special Issue Best Practices in Social Neuroscience)
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. View Full-Text
Keywords: machine learning; behavioral genetics; social neuroscience; animal models machine learning; behavioral genetics; social neuroscience; animal models
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MDPI and ACS Style

Madlon-Kay, S.; Brent, L.; Montague, M.; Heller, K.; Platt, M. Using Machine Learning to Discover Latent Social Phenotypes in Free-Ranging Macaques. Brain Sci. 2017, 7, 91. https://doi.org/10.3390/brainsci7070091

AMA Style

Madlon-Kay S, Brent L, Montague M, Heller K, Platt M. Using Machine Learning to Discover Latent Social Phenotypes in Free-Ranging Macaques. Brain Sciences. 2017; 7(7):91. https://doi.org/10.3390/brainsci7070091

Chicago/Turabian Style

Madlon-Kay, Seth, Lauren Brent, Michael Montague, Katherine Heller, and Michael Platt. 2017. "Using Machine Learning to Discover Latent Social Phenotypes in Free-Ranging Macaques" Brain Sciences 7, no. 7: 91. https://doi.org/10.3390/brainsci7070091

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