算法介导下的情感趋同:生成式人工智能情感传染机制OA

This study addresses the emerging phenomenon of emotion contagion in Artificial Intelligence Generated Content(AIGC),systematically examining its fundamental distinctions from both traditional interpersonal emotion contagion and digital emotion contagion.It proposes the"enactment-modulation"mechanism as a theoretical framework.The research identifies four key characteristics of AIGC-driven emotion contagion—intersubjectivity,knowledge dependency,non-threatening and de-identified nature,and moral relevance—which collectively underpin the"enactment-modulation"mechanism.Specifically,AIGC enacts human-like emotional expression patterns through algorithmic simulation("enactment"),while dynamically refining interaction strategies based on real-time user feedback("modulation"),thereby forming a continuously evolving human−machine emotional feedback loop.The"enactment-modulation"mechanism transcends anthropocentric paradigms,contributes to cross-subjective emotion theory,and reveals the novel role of algorithms as proactive emotion modulators.It has already found applications in domains such as mental health intervention,communication studies,and educational motivation.Research on AIGC emotion contagion extends the theoretical scope of emotion contagion studies and offers a fresh perspective for understanding human−AI emotional interaction.However,it also confronts several challenges,including difficulties in multidisciplinary integration,complexities in multimodal emotion measurement,barriers to cross-cultural adaptation,and the risk of emotional misdirection due to algorithmic bias.