Logic as Reactance: Truth-Judgment as Substrate Mechanics, and Why 'Merely Statistical' Fails to Distinguish Minds from Models

Paper 14 in the Friction Theory paper-series — version 3. A philosophy-of-AI position paper on truth-judgment as substrate mechanics. Target venue: Minds and Machines (Springer). Abstract. A recurring objection to treating large language models as cognitive systems holds that their output is "merely statistical" — distinguished from genuine reasoning by the probabilistic nature of its substrate. This paper argues the objection is self-undermining. Biological cognition is also a probabilistic, substrate-bound process: forty-five years of sequential-sampling and accumulator modelling in mathematical psychology (Ratcliff 1978; Usher Brown it cannot be the mark that makes one "real" reasoning and the other not. The objection, consistently applied, dissolves its own asymmetry. The positive account — logic as reactance. On a race-architecture substrate (Pødenphant Lund 2026b, 2026e), truth-value judgment is the substrate's reactance signature: the differential race-pressure that arises when an input violates the substrate's gradient-encoded distribution. The substrate registers "true" when no reactance is triggered, "false" when it triggers strongly, and "irrelevant" when it is below detection threshold. Binary truth is the discrete-symbolic readout of a continuous substrate process — in biological tissue and in silicon alike. Empirical illustration. The paper reports a measurable cliff-event signature in fine-tuned LLMs at the first content-token: a discontinuous log-probability collapse when input violates the encoded distribution, replicated across two architectures (Qwen2. 5-7B, Mistral-7B-v0. 3, p < 1e-17 on both) and eleven encoding-depth checkpoints (ep₀–ep₃00). Whether this signature is best described as "reactance" or as calibration geometry, it is structured substrate-internal dynamics — which is all the argument needs. Theoretical positioning. The paper situates the account against five prior frameworks at substrate-mechanism level — predictive processing (Friston 2010; Clark 2013), quantum cognition (Pothos beim Graben v1 (2026-05-16) is archived. The concept DOI auto-resolves to the latest version.