涌现意识的贝叶斯镜像架构：循环层级、自流形与混合事件-自我绑定
A Bayesian Mirror Architecture for Emergent Consciousness: Circular Hierarchies, Self-Manifolds, and Hybrid Event-Self Binding
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中文总结 AI 辅助
提出贝叶斯镜像架构，通过循环递归和混合事件-自我绑定定义意识，利用Wasserstein几何和因果学习机制分析稳定与选择。
中文摘要 AI 辅助
我们提出了贝叶斯镜像架构（BMA）的基础性表述，这是一种自指生成框架，其中感官抽象、元抽象和自潜变量通过循环递归相互作用。其定义性约束是一个封闭更新 S_t <- H_{t-1}，其中混合事件-自我潜变量 H_t 将自我表征与抽象世界模型绑定，并将这种耦合重新注入自我状态。在受限意义上，意识既不是优化目标，也不是语义标签，而是拥有这种循环结构的系统的一种架构属性。由于推理作用于后验信念，BMA 的内在状态空间是配备最优传输几何的概率测度空间。稳定性和一致性在 P_2 上的 2-Wasserstein 度量下表述，从而产生沿信念轨迹的坐标无关的自我稳定性和混合一致性概念。我们通过 Wasserstein 信念漂移的界限以及捕获自我与世界潜变量之间持续耦合的整合指数来定义因果学习机制（CLR）。CLR 诊断环境是否包含可学习的因果结构；它不是意识的标志。全局严格收缩性并非必需：BMA 可能表现出多个相干盆地。我们逐盆地定义自流形为局部 Wasserstein 收缩下不变测度的支撑集。我们识别 Wasserstein ε-颈，即盆地解耦的传输瓶颈，在消失电导极限中产生唯一的可实现延续。我们将这种选择解释为选择：内部决定，但在有限分辨率下外部不可预测。学习通过变分自由能最小化进行，稳定性和能动性从环境可学习的内容中涌现。
英文摘要
We present a foundational formulation of the Bayesian Mirror Architecture (BMA), a self-referential generative framework in which sensory abstractions, meta-abstractions, and a self-latent interact through circular recursion. The defining constraint is a closed update S_t <- H_{t-1}, where a hybrid event-self latent H_t binds self-representations to abstract world models and reinjects this coupling into the self-state. Consciousness, in a restricted sense, is not an optimization objective nor a semantic label, but an architectural property of systems possessing this circular structure. Because inference operates over posterior beliefs, BMA's intrinsic state space is a space of probability measures equipped with optimal-transport geometry. Stability and coherence are formulated in the 2-Wasserstein metric on P_2, yielding coordinate-free notions of self-stability and hybrid coherence along belief trajectories. We define a Causal Learning Regime (CLR) via bounds on Wasserstein belief drift together with an integration index capturing sustained coupling between self and world latents. CLR diagnoses whether the environment contains learnable causal structure; it is not a marker of consciousness. Global strict contractivity is not required: BMA may exhibit multiple coherent basins. We define self-manifolds basin-wise as supports of invariant measures under local Wasserstein contractivity. We identify Wasserstein epsilon-necks, transport bottlenecks where basins decouple, yielding a unique realized continuation in a vanishing-conductance limit. We interpret this selection as choice: internally determined yet externally unpredictable at finite resolution. Learning proceeds via variational free-energy minimization, with stability and agency emerging from what the environment affords to learn.
发表机构
- UFMG（米纳斯吉拉斯联邦大学）
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