UCTORI-EFCV-P7: The Neurophysiological Signature of Informational Rebalancing — A Positive7 Multirecord Test of Perturbation, Closure and Return

This work tests whether MCAP events in seven neurophysiological records show a measurable signature of perturbation, closure, and return compared to matched OFF periods. Using the Positive7 package, it analyzes whether the initial discrepancy DeltaC0 distinguishes MCAP events from controls, interpreted as a dynamic process of informational rebalancing. Results show a positive multirecord signal: all seven records present a direction compatible with DeltaC0(MCAP) > DeltaC0(control), based on 1882 analyzed MCAP events. The UCTORI-EFCV framework explains more dimensions of the data than IIT-like and GNWT-like approaches in this specific task, ranking ahead in six out of seven cases. The work opens a reproducible empirical path for testing the ORI hypothesis, inviting the scientific community to apply the code to larger cohorts.

Study at a glance

Abstract

UCTORI-EFCV-P7 presents an empirical-formal continuation of UCTORI-EFCV: Empirical-Formal Extension with Neurophysiological Cross-Validation, applied to seven Positive7 neurophysiological records: BRUX1, N8, N9, INS3, INS7, N1, and N4. The central aim is not to proclaim a definitive validation of UCTORI or of the Informational Rebalancing Operator, but to submit a concrete hypothesis to empirical testing: whether MCAP events show a measurable signature of perturbation, closure and return when compared with matched OFF periods. The document preserves the operational core of ORI: DeltaC, DeltaC0, T_c, return R, persistence π, the MEI window, and null tests NT1–NT5. Using the Positive7 package, it analyzes whether the initial discrepancy DeltaC0 distinguishes MCAP events from OFF controls, and whether this difference can be interpreted as a dynamic process of informational rebalancing. The results show a positive multirecord signal: all seven records present a direction compatible with DeltaC0(MCAP) > DeltaC0(control), with 1882 analyzed MCAP events, high operational closure, and a documentary comparison against IIT-like and GNWT-like frameworks. Against the other two theoretical families compared, UCTORI-EFCV is better positioned in this package when the criterion is not merely the detection of activation, but the explanation of a complete dynamic sequence of perturbation, closure and return. IIT-like can describe dimensions of integration, differentiation or state change; GNWT-like can capture activation, arousal, ignition or broadcast; but ORI/UCTORI-EFCV adds a broader and more falsifiable requirement: that the system must show an observable trajectory from initial perturbation to operational closure and dynamic return. In this sense, the superiority of UCTORI-EFCV should not be understood as a definitive refutation of IIT or GNWT, but as a specific operational advantage within the Positive7 dataset: it explains more dimensions of the data using the available variables of the protocol itself. In the documentary proxy index applied to the seven records, ORI/UCTORI-EFCV ranks ahead in six out of seven cases, and is practically tied with GNWT-like in INS7, where the activation/arousal reading is especially strong. This comparison does not demonstrate a definitive ontological victory, but it does suggest that, when MCAP > OFF contrast, finite closure, post-event trajectory, null tests and rebalancing dynamics are required simultaneously, UCTORI-EFCV provides a more complete explanatory coverage than IIT-like and GNWT-like approaches in this specific task. The main contribution of this work is not to claim that ORI has been proven, but to open a reproducible empirical path for testing it. IIT-like can describe dimensions of integration or state change; GNWT-like can capture activation, arousal or broadcast; ORI/UCTORI-EFCV adds a third requirement: that the system must display an observable trajectory of perturbation → closure → return, submitted to controls, metrics and real falsifiability. This PDF integrates the Positive7 package material, derived tables, figures, scripts, links, manifest, TXT annotations and reproducible results. Its purpose is to allow other researchers to review, execute, audit, extend, confirm or refute the analysis. The strength of ORI should not depend on a claim, but on its capacity to survive external data, independent laboratories, larger cohorts and adversarial testing. For this reason, this work is also presented as an open invitation to the scientific community: researchers in sleep neurophysiology, neurology, anesthesia, coma, epilepsy, consciousness studies, data science, artificial intelligence, systems theory and philosophy of mind are invited to apply the ORI/UCTORI-EFCV code to the largest possible number of records, subjects or patients. The immediate goal should be to exceed 1,000 cases and move toward 5,000 records or more, with preregistered analyses, strict OFF controls, predefined channels, transparent criteria, and publication of both positive and negative results. If ORI fails, we will know where its scope ends.If ORI survives, we may be facing a new way of measuring how a neurophysiological system becomes perturbed, corrects itself, closes and returns. The ambition of UCTORI-EFCV is not to demand acceptance, but to demand testing. It does not seek to replace existing theories, but to offer a complementary, falsifiable and reproducible dynamic metric. This document leaves a simple and profound question on the table: Does a neurophysiological signature of informational rebalancing exist, capable of recurring across thousands of independent records? The answer should not come from one voice alone. It must come from the scientific community.