The Robles Quiroz Equation: An Integration-Boundary (I-B) Model of Self-Model Collapse

A paradox in neuroimaging of altered states is that psilocybin and ketamine produce opposite brain network dynamics but similar experiences like ego dissolution and entity encounters. Current models using single measures like network entropy cannot fully explain how brain network structure relates to stability of the self-model. A new falsifiable framework separates network Integration from self-model Boundary Stability, showing that psilocybin and ketamine move in opposite directions on the integration axis but both lead to the same collapse of boundary stability, resolving the paradox.

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Abstract

Contemporary neuroimaging of altered states reveals a persistent paradox: pharmacologically distinct agents—specifically 5-HT2A agonists (e.g., psilocybin) and NMDA antagonists (e.g., ketamine)—produce reliably opposing network dynamics yet yield strikingly convergent phenomenological reports, notably ego dissolution and entity encounters. Current models relying on monolithic scalar metrics like "network entropy" do not fully specify the mapping between network topology and self-model stability. We propose a falsifiable framework that resolves this paradox by orthogonalizing network Integration (I) from self-model Boundary Stability (B). We introduce a two-dimensional heuristic demonstrating that psilocybin and ketamine represent opposing trajectories on the I axis while converging at a shared failure regime of B collapse.