AI 中文总结
本文提出将意识通达建模为连续到离散转换的C/D框架，区分于现有意识理论，既为神经科学提供可检验预测，也为神经符号AI提供架构，无需解决意识难问题即可明确意识的功能作用。
AI 中文摘要
意识的科学研究常常因关于“难问题”的本体论争论而陷入停滞。本文提出一种务实的转向：我们不追问意识在形而上学层面是什么，而是研究将意识通达建模为一种特定计算转换，如何解决神经科学与人工智能领域现有的瓶颈。我们引入连续/离散（C/D）框架，该框架认为大脑执行两种不同的加工机制：系统C是在连续、高维流形上运行的分布式感觉运动网络，系统D是围绕离散、尺度不变符号构建的集中式引擎。我们认为意识通达需要在这两种机制间进行保持结构的转换，该转换将局域化的连续态映射为离散符号标记，并伴随将这些标记投射回感觉运动动力学的逆过程。通过将意识通达形式化为这种连续到离散的转换，我们推导出一组以表征几何为核心的可检验预测，具体而言，是从分级相似结构到低维类别等价类的可测量坍缩。这些预测明确区分了我们的理论与全局神经元工作空间理论、整合信息理论、预测加工理论及高阶理论，将焦点从本体论地位转向计算机制。除神经科学外，该框架还为神经符号人工智能提供了一种原则性架构。我们认为，将意识通达视为转换计算，提供了一条务实、可经验验证的前进路径，阐明了意识状态在功能上的作用，而无需解决现象学的难问题。
英文摘要
The scientific study of consciousness frequently stalls on ontological debates regarding the "Hard Problem." This paper proposes a pragmatic pivot. Rather than asking what consciousness is metaphysically, we ask how modeling conscious access as a specific computational transformation may address existing bottlenecks in neuroscience and artificial intelligence. We introduce the Continuous/Discrete (C/D) framework, which holds that the brain implements two distinct processing regimes: System C, a distributed sensory-motor network operating over continuous, high-dimensional manifolds, and System D, a centralized engine structured around discrete, scale-invariant symbols. We argue that conscious access requires a structure-preserving translation between these regimes, which maps localized continuous states onto discrete symbolic tokens, coupled with an inverse projection that grounds those tokens back into sensorimotor dynamics. By formalizing conscious access as this continuous-to-discrete conversion, we derive a unified set of testable predictions centered on representational geometry, specifically, on a measurable collapse from graded similarity structures to low-dimensional categorical equivalence classes. These predictions explicitly differentiate our account from Global Neuronal Workspace Theory, Integrated Information Theory, Predictive Processing, and Higher-Order Theories, shifting the focus from ontological status to computational mechanism. Beyond neuroscience, the framework provides a principled architecture for neuro-symbolic artificial intelligence. We argue that treating conscious access as translational computation offers a pragmatic, empirically tractable pathway forward, clarifying what conscious states functionally accomplish without requiring resolution of the hard problem of phenomenology.
