Abstract
Social hierarchy refers to the set of social ranks in a group of animals where individuals can gain priority access to resources through repeated social interactions. Key mechanisms involved in this process include conflict, social negotiation, prior experience, and physical advantages. The establishment and maintenance of social hierarchies not only promote group stability and well-being but also shape individual social behaviors by fostering cooperation and reducing conflict. Existing research indicates that social hierarchy is closely associated with immune responses, neural regulation, metabolic processes, and endocrine functions. These physiological systems collectively modulate an individual's sensitivity to stress and influence adaptive responses, thereby playing a critical role in the development of psychiatric disorders such as depression and anxiety. This review summarizes the primary behavioral methods used to assess social dominance in mice, evaluates their applicability and limitations, and discusses potential improvements. Additionally, it explores the underlying neural mechanisms associated with these methods to deepen our understanding of their biological basis. By critically assessing existing methodologies and proposing refinements, this study aims to provide a systematic reference framework and methodological guidance for future research, facilitating a more comprehensive exploration of the neural mechanisms underlying social behavior. The role of sex differences in social hierarchy formation remains underexplored. Most studies focus predominantly on males, while the distinct social strategies and physiological mechanisms of females are currently overlooked. Future studies should place greater emphasis on evaluating social hierarchy in female mice to achieve a more comprehensive understanding of sex-specific social behaviors and their impact on group structure and individual health. Advances in automated tracking technologies may help address this gap by improving behavioral assessments in female mice. Future research may also benefit from integrating physiological data (e.g., hormone levels) to gain deeper insights into the relationships between social status, stress regulation, and mental health. Additionally, developments in artificial intelligence and deep learning could enhance individual recognition and behavioral analysis, potentially reducing reliance on chemical markers or implanted devices.