时序整合、双向临界与意识层级涌现 压缩V60 Temporal Integration, Bidirectional Criticality and Emergence of Consciousness Hierarchy V60
Zenodo (CERN European Organization for Nuclear Research) June 18, 2026 DOI: 10.5281/zenodo.21238911 via OpenAlex
Summary
AI-generated from the abstractA new theoretical model proposes that consciousness arises from a self-referential closed loop in the brain, constrained by its 20-watt power budget. The model starts with three axioms: limited resources, survival goals, and discrete inputs. It quantifies consciousness using temporal span, reconstruction activity, and closed-loop integrity. Two energy allocation patterns are identified: a low-power baseline configuration and a self-referential overlay. The model reframes the hard problem of consciousness as a parameter-interval problem, defines qualia as calibration signals, and proposes a five-level consciousness spectrum. It also offers a diagnostic framework for Alzheimer's disease and specifies thresholds for artificial general intelligence.
Study at a glance
| Characteristics | Theoretical or philosophical paper Peer reviewed |
|---|---|
| Keywords | Consciousness Metric unit Hierarchy Constraint computer-aided design Qualia |
| Key finding | Consciousness can be modeled as a self-referential closed loop quantified by temporal span, reconstruction activity, and closed-loop integrity, operating within the brain's 20-watt power constraint. |
Abstract
现有意识理论偏重静态因果结构,忽视人脑20瓦功耗约束与时序动力学核心地位。本文以资源有限、存续目标、离散输入三条公理为起点,建构由时序跨度T、重构活跃度A、闭环完整度C量化描述的自指闭环层级意识模型。阐明人脑在20瓦功耗约束下将有限能量动态分配给不同神经回路,两种最常见的分配模式被命名为归档固化组态(B组态)与自指闭环组态(S组态):B组态为全天候低功基线,S组态以B组态为基础叠加运行。推导“对折成圈+锚定成面”两步涌现几何,揭示遗忘双重机制与记忆的叙事建构本质。将感受质定义为自指闭环的内生校准信号,将意识难问题转化为参数区间问题。依托双向临界效应划分五级意识谱系,提出语言是T/A/C天然测量标尺,建立阿尔茨海默病分型诊断框架。模型可统一解释多类认知与临床现象,推导AGI生成门槛,具备统一公理、跨域解释力、可量化标尺三大原创优势。 Existing theories of consciousness focus on static causal structures while overlooking the 20-watt power constraint of the human brain and the central role of temporal dynamics. Starting from three axioms—limited resources, survival-maintenance goals, and discrete external inputs—this paper constructs a hierarchical self-referential closed-loop model quantified by temporal span T, reconstruction activity A, and closed-loop integrity C. It elucidates how the human brain dynamically allocates limited energy across neural circuits under the 20-watt budget, identifying two most common allocation patterns: the baseline archival configuration (B-config) operating as a low-power default, and the self-referential closed-loop configuration (S-config) running as an overlay upon the B-config baseline. The two-step emergence geometry of the self-referential loop—“folding into a circle” followed by “anchoring into a surface”—is derived, along with dual forgetting mechanisms and the narrative construction nature of memory. Qualia are defined as endogenous calibration signals of the self-referential closed loop, reframing the hard problem of consciousness as a parameter-interval problem. A five-level consciousness spectrum is established via bidirectional criticality; language is identified as a natural metric for T, A, and C, enabling a differential diagnostic framework for Alzheimer’s disease. The model unifies diverse cognitive and clinical phenomena and specifies the generation thresholds for AGI, offering three original advantages: unified axioms, cross-domain explanatory power, and quantifiable variables. This Version 60 is the latest compressed official manuscript; all prior full uncompressed drafts (V1–V59) are obsolete and superseded.