Can qualia be geometric? A geometro-diffusion framework for the relational structure of color experience
Cognitive Neurodynamics December 1, 2026 DOI: 10.1007/s11571-026-10577-2 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper Qualitative Peer reviewed |
|---|---|
| Measures | Spearman correlation, CIEDE2000, CIELAB |
| Key points | Argues that a diffusion-based geometric scaffold learned on a 76-region human connectome can encode color relations, transferring to 256 new colors with a Spearman correspondence of 0.919 to the CIEDE2000 reference (versus 0.923 for direct CIELAB distances) and 0.924 under nonlinear Izhikevich encoding. The authors propose that intrinsic access and recursive use link this relational organization to qualitative color experience. |
Abstract
Abstract: Within the Geometro-Diffusion Theory (GDT) of consciousness, we formulate a candidate geometry for the relational organization of color experience. We distinguish a diffusion-induced scaffold, the color content encoded on it, and its neuronal realization. Elementary network examples establish these distinctions and show how consensus-directed nonlinear activity preserves the diffusion scaffold. On a 76-region human structural connectome, a distributed encoding is learned from 16 controlled colors and then frozen. Evaluation on 256 new colors gives a Spearman correspondence of 0.919 with the CIEDE2000 perceptual reference, compared with 0.923 for direct CIELAB distances. Nonlinear Izhikevich encoding followed by the same anatomical propagation gives analytic–neuronal correspondence of 0.924. The learned construction therefore transfers color relations to new stimuli and expresses much of their geometric organization through nonlinear neuronal activity. Connections with predictive coding and cortical gradients identify possible roles for the geometry of anticipated and stimulus-evoked responses. Within GDT, intrinsic access and recursive use link this relational organization to qualitative experience. The results provide a mathematical and computational candidate for that organization, together with explicit empirical comparisons for evaluating its experiential interpretation.