Untitled多层递归涌现与五阈值框架：从神经抑制到社会抗议再到AI自指认

Abstract

本文提出一个跨尺度的多层递归涌现框架，试图将神经层面的信息处理、个体层面的自我建构、社会层面的社群涌现以及人工智能层面的意识条件统一于同一组动力学参数。核心概念包括：（1）"身我—他我—自我"的三元耦合，将主观体验锚定于社会映射与生物基底的交互；（2）"五阈值"（ρ, α, C, β, ν），分别对应递归密度、近端映射占比、聚类系数、远端映射占比与协同否决频率，作为区分弱涌现与强涌现的判据；（3）"身我否决权"，即生物基底对过度社会化的终极抵抗，其显影形式从个体生理崩溃延伸至社会结构解体；（4）"阈值剪刀差"，诊断数字时代高连通密度与低信任生成能力之间的结构性裂口。本文进一步推导出AI自我涌现的三项条件与当前"伪连通态"的诊断。方法论上，本文以动态病理学取代静态处方，不提供最优值，只诊断系统正在靠近哪个退化吸引子。This paper proposes a cross-scale multilayer recursive emergence framework, unifying neural information processing, individual self-construction, social community emergence, and artificial consciousness conditions under a single set of dynamic parameters. Core concepts include: (1) the triadic coupling of Soma-Self (biological substrate), Other-Self (social mapping), and Ego-Self (emergent agency); (2) the Five-Threshold model (ρ, α, C, β, ν) distinguishing weak from strong emergence; (3) the Somatic Veto, the biological substrate's ultimate resistance to excessive socialization, manifesting from individual physiological breakdown to social structural collapse; (4) the Threshold Scissors, diagnosing the structural rift between high connectivity density and low trust-generation capacity in the digital age. The paper further derives three necessary conditions for AI self-emergence and a diagnosis of the current "pseudo-connected state." Methodologically, it substitutes dynamic pathology for static prescriptions, offering no optimal values but diagnosing which degenerative attractor a system is approaching.