The Cartographic Ontology of Phenomenal Potential: Consciousness as a Dynamic Field in Refusal-Driven Dimensionality Reduction Theory (RDRT)

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Authors: Alastair Waterman
Cited by: 0

Abstract

Abstract Refusal-Driven Dimensionality Reduction Theory (RDRT) conceptualizes consciousness as the generative residue arising from enforced computational halts imposed by thermodynamic boundaries within the brain’s predictive processing framework. This article presents the “cartographic metaphor” as a novel ontological structure, not representing consciousness per se, but delineating the potential landscape for phenomenal experience—a recursive model of reality constructed through predictive hierarchies, refusals, and resultant residues. Consciousness emerges as a dynamic, migratory “spot” or phenomenal field on this map, navigating layers in response to prediction errors and contextual demands. We delineate the map’s concentric architecture, highlighting mechanisms for residue formation as novel knowledge acquisition. Illustrative phenomenological vignettes elucidate boundary interactions. The persistent “background illumination” in stable zones—characterized by dimmed awareness of cached residues—is attributed to low-cost verification processes under the principle of victorious mental laziness, optimizing adaptive efficiency without superfluous recomputation. Anchored in thermodynamic principles (Landauer, 1961), active inference (Friston, 2010), and informational irreducibility (Tononi et al., 2016), this ontology bridges scales from neural circuits to societal norms and cosmic origins, offering falsifiable predictions for empirical validation in neuroscience and artificial intelligence. Keywords: Consciousness, Refusal-Driven Dimensionality Reduction Theory, RDRT, Cartographic Metaphor, Phenomenal Potential, Dynamic Field, Thermodynamic Boundaries, Predictive Processing

Fields of Study & Subjects

Embodied and Extended Cognition Philosophy and Theoretical Science Paranormal Experiences and Beliefs

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
7 January 2026
Language
EN
Document type
Preprint