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<p>Humans are inherently social species. Our behavior is heavily influenced by the social environment and context. While the traditional social neuroscience approach has made significant progress in mapping isolated social cognitive processes, it often fails to capture the complexity of real-world social environment—where perception, decision-making, and interaction unfold simultaneously and contextually. In this review, we introduce naturalistic social neuroscience as a paradigm shift that bridges this gap through incorporating multidisciplinary naturalistic measurements, which integrates lab-based simulation, such as movie watching and virtual reality, real-world embedded measures, such as digital phenotyping and wearables devices, as well as the strategic integration of multiple approaches. The framework also includes an artificial intelligence (AI)-powered multilevel data analysis to synthesize behavioral, computational, and neurobiological data to reveal the mechanism underlying real-world behaviors. Finally, we propose opportunities, critical considerations, and future directions for pushing forward naturalistic social neuroscience. This review broadens the view and equips researchers with an extended toolkit for understanding the richness of social behavior.</p>

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Towards naturalistic social neuroscience: A multi-level framework integrating real-world phenotyping, neurobiology, and computational mechanisms

  • 期刊论文

  • 发表日期：

    2026年5月11日

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  • Ruien Wang,
  • Yi Luo,
  • Yin Wang,
  • Lei Zhang,
  • Haiyan Wu

摘要

Humans are inherently social species. Our behavior is heavily influenced by the social environment and context. While the traditional social neuroscience approach has made significant progress in mapping isolated social cognitive processes, it often fails to capture the complexity of real-world social environment—where perception, decision-making, and interaction unfold simultaneously and contextually. In this review, we introduce naturalistic social neuroscience as a paradigm shift that bridges this gap through incorporating multidisciplinary naturalistic measurements, which integrates lab-based simulation, such as movie watching and virtual reality, real-world embedded measures, such as digital phenotyping and wearables devices, as well as the strategic integration of multiple approaches. The framework also includes an artificial intelligence (AI)-powered multilevel data analysis to synthesize behavioral, computational, and neurobiological data to reveal the mechanism underlying real-world behaviors. Finally, we propose opportunities, critical considerations, and future directions for pushing forward naturalistic social neuroscience. This review broadens the view and equips researchers with an extended toolkit for understanding the richness of social behavior.