[Submitted on 8 Sep 2026]
Title:Gradland: On Phenomenal Experience, Differentiated Across Many Dimensions
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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.
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Subjects:
Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
Cite as: arXiv:2609.09306 [cs.AI]
(or arXiv:2609.09306v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.09306
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: David Balduzzi [view email] [v1] Tue, 8 Sep 2026 18:01:23 UTC (159 KB)
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