An algebraic theory to discriminate qualia in the brain
arXiv Preprint Archive May 31, 2023 Yoshiyuki Ohmura, Wataru Shimaya, Yasuo Kuniyoshi
The mind-brain problem asks how mental events relate to neural events. Mathematical models have tried to explain how the brain represents the discriminative structure of qualia (subjective experiences), but lack validation. In unsupervised learning, independence between axes in a latent space cannot distinguish between different qualia types (e.g., vision vs. touch) and different instances within the same type (e.g., green vs. red). The authors hypothesize that weakening inter-axis independence is necessary to discriminate qualia types. They formulate an algebraic independence linked to other-qualia-type invariant transformations, where the transformation value is a vector space. A brain model learning this algebraic independence separates the latent space into multiple metric spaces corresponding to qualia types, suggesting a contribution to the mathematical theory of consciousness.