Social behavior, defined as the process by which individuals act and react in response to others, is crucial for the function of societies and holds profound implications for mental health. To fully grasp the intricacies of social behavior and identify potential therapeutic targets for addressing social deficits, it is essential to understand its core principles. Although machine learning algorithms have made it easier to study specific aspects of complex behavior, current methodologies tend to focus primarily on single-animal behavior. In this study, we introduce LISBET (seLf-supervIsed Social BEhavioral Transformer), a model designed to detect and segment social interactions. Our model eliminates the need for feature selection and extensive human annotation by using self-supervised learning to detect and quantify social behaviors from dynamic body parts tracking data. LISBET can be used in hypothesis-driven mode to automate behavior classification using supervised finetuning, and in discovery-driven mode to segment social behavior motifs using unsupervised learning. We found that motifs recognized using the discovery-driven approach not only closely match the human annotations but also correlate with the electrophysiological activity of dopaminergic neurons in the Ventral Tegmental Area (VTA). We hope LISBET will help the community improve our understanding of social behaviors and their neural underpinnings.

翻译：社会行为被定义为个体在回应他人时行动和反应的互动过程，它对社会的正常运作至关重要，并对心理健康具有深远影响。要完全理解社会行为的复杂性并识别潜在的治疗靶点以解决社交障碍，必须把握其核心原理。尽管机器学习算法使研究复杂行为的特定方面变得更加容易，但当前方法主要聚焦于单动物行为。在本研究中，我们提出LISBET（seLf-supervIsed Social BEhavioral Transformer），一种旨在检测和分割社会互动的模型。该模型通过自我监督学习从动态身体部位追踪数据中检测和量化社会行为，无需特征选择和大规模人工标注。LISBET可在假设驱动模式下通过监督微调实现行为分类自动化，也可在发现驱动模式下利用无监督学习分割社会行为基序。我们发现，采用发现驱动方法识别的基序不仅与人工标注高度吻合，还与腹侧被盖区（VTA）多巴胺能神经元的电生理活动相关。我们希望LISBET能帮助学界加深对社会行为及其神经基础的理解。