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Computational study demonstrates latent state reconstruction across synthetic neural models, establishing a rigorous mathematical framework for prospective biological testing.
The Qualia Tomogram develops a falsifiable projection-slice framework for studying how partial, receiver-conditioned neural measurements could support a shared, body-relative reconstruction. It connects the Self Aware Networks concepts of tonic context, Phase-Wave Differentials, NAPOT, action-dependent sampling, and returned sensory evidence to a staged experimental and mathematical program. The paper includes two deterministic synthetic reference applications, explicit baseline contests, perturbation and rescue studies, carrier-drift and targeted-write tests, executable mathematics, and a machine-checked Lean kernel containing fifteen bounded theorems. The primary synthetic model reconstructs its latent state and predicts a withheld receiver, while a raw-signal ridge model remains a near-tied alternative across smaller replications and an adverse generator shows that phase-relational features are not universally dominant. The biological study remains prospective. The manuscript reports no neural or consciousness result and does not claim to have measured qualia. Instead, it defines the controls, failure conditions, and evidence ladder required to move from synthetic executability toward neural prediction, causal specificity, and eventual phenomenal discrimination. This is a preprint and has not undergone peer review.
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Micah Blumberg (2026) studied this question.
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