An unsupervised method for quantifying the behavior of paired animals. Author Ugne Klibaite, Gordon Berman, Jessica Cande, David Stern, Joshua Shaevitz Publication Year 2017 Type Journal Article Abstract Behaviors involving the interaction of multiple individuals are complex and frequently crucial for an animal's survival. These interactions, ranging across sensory modalities, length scales, and time scales, are often subtle and difficult to characterize. Contextual effects on the frequency of behaviors become even more difficult to quantify when physical interaction between animals interferes with conventional data analysis, e.g. due to visual occlusion. We introduce a method for quantifying behavior in fruit fly interaction that combines high-throughput video acquisition and tracking of individuals with recent unsupervised methods for capturing an animal's entire behavioral repertoire. We find behavioral differences between solitary flies and those paired with an individual of the opposite sex, identifying specific behaviors that are affected by social and spatial context. Our pipeline allows for a comprehensive description of the interaction between two individuals using unsupervised machine learning methods, and will be used to answer questions about the depth of complexity and variance in fruit fly courtship. Keywords Animals, Female, Male, Drosophila melanogaster, Behavior, Animal, Video Recording, Sexual Behavior, Animal, Machine Learning, Pair Bond Journal Phys Biol Volume 14 Issue 1 Pages 015006 Date Published 02/2017 ISSN Number 1478-3975 DOI 10.1088/1478-3975/aa5c50 Alternate Journal Phys Biol PMCID PMC5414632 PMID 28140374 PubMedPubMed CentralGoogle ScholarBibTeXEndNote X3 XML