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Taming the Brute Fact: A Falsifiable Resolution to the Hard Problem and the Explanatory Gap

Tyler, Steven

Zenodo (CERN European Organization for Nuclear Research) June 20, 2026 DOI: 10.5281/zenodo.20775548 via OpenAlex

Summary

AI-generated from the abstract

Consciousness, or the subjective experience of 'what it is like,' may arise from the brain's metabolic registration of thermodynamic state transitions. A proposed framework suggests that the sense of self is a late-arriving, post-hoc narrator, created by a 300–500 millisecond delay in neural processing. This process generates measurable 'friction signatures' as the brain recursively audits a constructed self. A theoretical index quantifies this at approximately 36.2 bits, based on the brain's energy budget and Landauer's principle. The Venus flytrap's simple threshold integration shows similar scaling from minimal energy levels to human cognition. Anesthetic agents that block this integration across species support the idea that awareness requires a specific mechanical, dissipative process, offering a pathway to measure aspects of consciousness.

Study at a glance

Characteristics Theoretical or philosophical paper Peer reviewed
Keywords Falsifiability Phenomenology philosophy Observable Dissipative system Bounded function
Key finding The inside view of phenomenology can be lawfully reduced to the organism's bounded, first-person metabolic registration of friction signatures, providing a falsifiable framework for consciousness.

Abstract

The Hard Problem of consciousness—the question of why physical processes give rise to subjective experience—remains a central challenge in neuroscience and philosophy of mind. Traditionally, the "what it is like" of experience has been treated as an emergent brute fact unamenable to physicalist explanation. This paper presents a falsifiable mechanistic framework that identifies the inside view of phenomenology as the bounded metabolic registration of thermodynamic state transitions generated when accumulated metabolic work crosses an informational resolution threshold. Readiness potential data integrated with Default Mode Network (DMN) research reveal the "Me" as a late-arriving narrator subject to a non-zero System Latency (t_s). This 300–500 ms transmission delay from the Primary Integration Layer reveals the conditioned self as a post-hoc virtual byproduct. The inside view of phenomenology manifests as the metabolic friction generated by the persistent recursive audit of a constructed "Self" operating from psychological time. This dissipative process appears as measurable Friction Signatures. A quantitative benchmark is derived: a first-order theoretical Phenomenal Compression Index (PCI) of approximately 36.2 bits, calibrated to the ~20 W human cortical budget (consuming ~80% of cortical metabolism) and Landauer’s bound (k_B T ln 2 per bit resolution). A Mapping Theorem is developed showing that the inside view of phenomenology is lawfully reducible to the organism’s bounded, first-person metabolic registration of Friction Signatures. This mapping theorem provides a formal, quantitative bridge between physical processes and phenomenology, offering a potential pathway to measure aspects of consciousness through observable Friction Signatures. The Venus flytrap provides a decisive forensic bridge: its binary recursive threshold integration (n ≈ 2 bits) demonstrates invariant scaling from minimal voltage-gated accumulation (10^{-9} J) through the octopus intermediate (10^{-4} J) to human-level recursion (0.56 J). Three falsifiable tests (Energy, Temporal, and Magnitude) probe whether the inside view properties are independent of the underlying dissipative process. Supported by anesthetic "kill-switches" (ether/lidocaine block integration in flytrap, octopus, and human DMN), the framework establishes the mechanical requirement for threshold awareness using the same predictive anesthesia recovery equations across Dionaea muscipula, Octopus vulgaris, and Homo sapiens, grounded in Landauer’s Principle and the Fluctuation-Dissipation Theorem.

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