Social behavior and related neural networks

Social behavior refers to interactions among individuals of the same species and plays a key role in survival and reproduction. It depends on each individual’s integration of perception, emotion, cognition, and motivation, enabling individuals to navigate complex social environments. Interactions among such individuals give rise to diverse behaviors, including cooperation, competition, empathy, and social hierarchy. Social behavior is coordinated by distributed neural networks. In mammals, and especially in primates, these behaviors are supported by distributed neural networks involving brain regions such as the medial prefrontal cortex, anterior cingulate cortex, insula, amygdala, hypothalamus, and striatum. These network systems are shaped by neuromodulators (e.g., oxytocin, dopamine), autonomic signals, and experience-dependent plasticity, making them essential to understanding a neural basis of social interactions. Advances in neurophysiology, neuroimaging, and computational modeling now allow for deeper insights into how these networks encode social processes.

Despite progress, key questions remain about how neural circuits support flexible social behavior, particularly across developmental, psychiatric, and technological contexts. Disruptions in circuits governing self–other distinction and social contingency are linked to social impairments observed in disorders such as autism, schizophrenia, borderline personality disorder, and social anxiety. Studying these dysfunctions offers insight into maladaptive social behavior and may support improved diagnosis and treatment strategies. In parallel, advances in neurophysiology, neuroimaging, artificial intelligence (AI), and computational neuroscience have expanded interest in the biological and theoretical foundations of social behavior. This interdisciplinary convergence reveals how social behavior is encoded and modulated not only in neuroscience-based, but also in AI-based systems, which favors a broader definition of social behavior, encompassing cross-species and human–AI interactions. Understanding how individuals represent and respond to the intentions and actions of both natural and artificial agents is increasingly relevant given social isolation, mental health concerns, and AI integration into human environments. Artificial agents, such as social robots and embodied AI, offer unique platforms for modeling and testing social cognition. This Research Topic brings together neuroscience, psychiatry, AI, and computational social science to explore the biological and artificial bases of social cognition. Simulating perspective-taking, emotion recognition, and strategic interaction helps isolate key social mechanisms that are difficult to study in humans. Comparing natural and synthetic agents may also uncover biologically inspired computational principles.

We invite contributions exploring the neural and computational mechanisms of social behavior across biological systems, from rodents to humans, and AI-based systems. We welcome approaches that are neuroscientific, computational, or integrative, spanning both health and disease contexts. Topics of interest include but are not limited to:

- Self–other processing circuits

- Social behavior in psychiatric and neurodevelopmental disorders

- Developmental plasticity of social and neural networks

- Circuits for cooperation, competition, and social emotion

- Social cohesion and collective decision-making

- Neural dynamics in real-time and cross-species interactions

- Artificial agents and social cognition mechanisms