Dark Matter as a Bayesian Bias: A Proposed Gravitational Influence on Predictive Coding in Neural Systems
Year: 2025
Type: article
Abstract: Description: This paper presents a novel hypothesis proposing that dark matter (DM) density fluctuations exert subtle gravitational effects that influence neural predictive coding processes. Building on the Orch-OR theory and gravitational decoherence models, the hypothesis suggests that DM biasing Bayesian priors results in measurable perceptual errors. The proposed mechanism connects dark matter and brain function through gravitational interactions, challenging current models of perception and consciousness. A potential experimental design is outlined, focusing on correlating perceptual errors with mapped local dark matter density gradients. This hypothesis bridges neuroscience and cosmology, offering a testable framework that could significantly impact both fields. It is highly speculative but grounded in falsifiable claims, with implications for understanding the nature of perception and consciousness. Keywords: Dark Matter, Predictive Coding, Bayesian Inference, Quantum Biology, Orch-OR, Gravitational Decoherence, Neuroscience, Consciousness, Perception, Experimental Design
Author: WILCOX, Clyde Jermaine
Language: English
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W6930582199
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