An Unsupervised Method for Quantifying the Behavior of Interacting Individuals

TL;DR

This paper presents an unsupervised, high-throughput approach to quantify complex social behaviors in fruit flies, capturing subtle interactions and contextual effects that are difficult to analyze with traditional methods.

Contribution

It introduces a novel pipeline combining video tracking and unsupervised learning to analyze social interactions in animals, specifically addressing challenges like occlusion and subtle behaviors.

Findings

Behavioral differences between paired and solitary flies identified

Specific behaviors affected by social and spatial context detected

Pipeline enables comprehensive analysis of multi-individual interactions

Abstract

Social 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 quantify. 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 courting fruit flies 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 in paired and solitary flies of both sexes, identifying specific behaviors that are affected by social and spatial context. Our pipeline allows for a comprehensive description of the interaction between multiple individuals using unsupervised machine learning methods, and will be used to answer questions about the depth of complexity and variance in fruit fly courtship.