Gradland: On Phenomenal Experience, Differentiated Across Many Dimensions

A paper posted to arXiv (2609.09306v1) introduces "Gradland," an idealized world of neural networks whose physics are known and functions mostly differentiable, to test whether the first-order structure of physical interactions — gradients or Jacobians — characterizes the structure of phenomenal experience. The paper proposes two measures of Jacobian structure, effective rank and cohesion, based on Kirchhoff complexity, and applies them to worked examples it says account for seven phenomena, including why experience can last hundreds of milliseconds, the vivid-versus-obscure distinction, texture, the "blooming buzzing confusion" of newborns, distinct versus confused ideas, what learning is like, and the function of rich, dense experience.

arXiv:2609.09306v1 Announce Type: new Abstract: 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.