AI 中文总结
本文在理想化世界Gradland中提出并验证假说：物理交互的梯度/雅可比结构决定现象体验，通过有效秩与内聚性指标解释了体验时长、清晰度、质感、新生儿混乱、观念区分、学习感受及丰富体验的功能。
AI 中文摘要
本文探讨了一个假说：物理交互的一阶结构，即梯度或雅可比矩阵，刻画了现象体验的结构。该研究在一个由神经网络栖居的理想化世界——梯度之地（Gradland）中进行，那里的物理规律已知，函数（大部分）可微。文章引入了两个衡量雅可比矩阵结构的指标：有效秩（effective rank）和基于基尔霍夫复杂度（Kirchhoff complexity）的内聚性（cohesion）。将这些指标应用于一系列实例分析，结果表明该假说能够解释：（1）体验的持续时间，即体验可延续数百毫秒；（2）生动体验与模糊体验之间的差异；（3）质感的体验；（4）新生儿可能经历的“纷繁喧嚣的混乱”（blooming buzzing confusion）；（5）清晰铭记于心的观念与混淆不清的观念之间的区别；（6）学习的主观感受；最后，（7）文章解释了丰富而稠密的体验的功能。
英文摘要
This paper investigates the hypothesis that the first-order structure of physical interactions, i.e. gradients or Jacobians, characterizes the structure of phenomenal experience. It does so in an idealized world inhabited by neural networks, Gradland, where the physics are known and the functions are (mostly) differentiable. The paper introduces two measures of Jacobian structure: effective rank and cohesion, based on Kirchhoff complexity. Applying the measures to a series of worked examples shows the hypothesis accounts for: (1) the duration of experience, that it can prolong over hundreds of milliseconds; (2) the difference between what is experienced vividly and obscurely; (3) the experience of texture; (4) the blooming buzzing confusion presumably experienced by newborns; (5) the difference between ideas that are held distinctly in mind and ideas that are confused; (6) what learning is like; and finally (7) the paper explains the function of rich, dense experience.
CommentsCode: https://github.com/dbalduzzi/gradland/
