THE RELATIVE UNCONSCIOUS, CONSCIOUS, AND SUBCONSCIOUS INTEGRATED SYSTEMS

Abstract

ABSTRACT This paper introduces a unified mathematical and algorithmic framework modeling the General Relativity of Consciousness across an integrated, interactive, and interdependent dynamical system. We define Information, Consciousness, and Sensing Systems as an unbroken, co-dependent triad where no single element can exist independently. Grounded in the foundational axiom that a localized environmental matrix is fundamentally identical to its absolute matrix of latent information potential ((Equ,1)): E ≡ I_Abs, we formalize consciousness as a network's topological inference boundary regulating the transactional data flow between internal states and external absolute matrices. Utilizing the Central Limit Theorem (CLT) and Renormalization Group (RG) equations, we demonstrate how multi-scale parallel biological mechanisms—from sub-cellular microtubule lumens to macro-scale neural architectures—coarse-grain continuous spacetime fields into stable classical signals ((REF,1)), ((REF,3)). We establish the Level of Consciousness (L_C) not as a static property, but as a dynamic, time-varying mathematical ratio of observed information to absolute latent information potential ((Equ,2)): L_C(t) = I_Obs(t) / I_Abs. While consciousness is an intrinsic, fundamental property operating as a relative state-space function, standard biological channels natively restrict baryonic observability to approximately 5% of this absolute informational field. Introducing an augmented sensory system operator artificially expands the observer's field of view across latent high-dimensional parameter spaces, allowing hidden potential to be made visible when the topological boundary is expanded. Conversely, the vast majority of integrated systems function under an Axiom of Relative Collective Unconsciousness, acting as localized, unconscious nodes within a macro-scale cosmological data exchange. This relative conscious focus fluctuates as a time-varying faculty of awareness bounded strictly between defined geometric limits ((Equ,3)): 0% <= A(t) <= 100%. An awareness value of 0% represents total occlusion (the baseline of all manmade artificial intelligence frameworks), whereas 100% represents the unveiled baseline of the observer system: The True-Self. We model the operational definition of subconsciousness as an active, optimized algorithmic training loop that compresses, optimizes, and offloads complex paths from the real-time conscious boundary into a dense, high-velocity automation manifold to minimize processing latency ((REF,4)). Parallel to this architecture, the human brain functions as a parallel-serial biological machine where billions of years of evolutionary trial-and-error operate as a generative engine encoding foundational algorithmic blueprints directly into cellular DNA. Low-level parallel preprocessing steps automatically manage noise reduction and pattern recognition, while high-level post-processing domains iteratively adapt over a lifetime, mirroring advanced artificial neural network architectures ((REF,2)). Crucially, we integrate the physical mechanism of conditioned emotional faculties within these neural subnetworks, showing how emotional systems learn from repetitive input stimuli to automatically trigger specific output emotional states. Once triggered, these active emotional fields—specifically primary affective states such as shame, guilt, fear, desire, and anger—act as an absorption veil. We formalize this process through an optical filter attenuation model where psychological conditionings exert a veiling effect defined by an absorption coefficient (α) and thickness (d). The resulting compound resistance parameter (X = α * d) mathematically dictates the real-time variation of transmitted consciousness intensity F(X) through linear, exponential, sigmoidal, and Gaussian decay modes. This multi-scale framework proves that the active field of conditioning physically absorbs the light of The True-Self, bypassing conscious mental clarity, attenuating real-time awareness, and causing the clarity of present-moment experience to degrade according to a localized state function. These conditioned emotional states serve as the foundational structural seeds driving the observer's internal and external multi-media communication pathways across the multi-scale network symphony.