DRAFT - Generalized Pareidolia: A Unified Operator for Structural Inference in Uncertain Systems

We propose a domain-general rendering operator by which structured percepts are actively constructed from incoming signal under the influence of learned prior structure. All perception follows the same operator - Z = R_θ,α(S) - where raw signal S is rendered into experienced percept Z through priors θ under a precision or gain parameter α. Under this framework, phenomena traditionally labeled as illusion, hallucination, belief persistence, and perceptual insight are not distinct processes but parameter regimes of the same rendering function. Classical visual pareidolia—seeing faces in clouds—is a special case of this operator. The framework originated in Plasma Pareidolia, which formalized the perceptual mechanics underlying anomalous aerial phenomena reports. Here we demonstrate that the rendering operator is substrate-independent and scale-invariant, appearing across: Neural Perception: Where S (sensory input) is rendered through θ (priors) into Z (conscious experience). Financial Markets: Where S (fundamental value) is rendered through θ (collective belief) into Z (narrative price). Geopolitics: Where S (ambiguous military signaling) is rendered through θ (strategic doctrine) into Z (threat perception). Medicine: Where S (inert input) is rendered through θ (expectancy) into Z (somatic healing). Artificial Intelligence: Where S (prompt) is rendered through θ (training weights) into Z (output response)—"hallucinations" arising when θ dominates sparse S. We further introduce a control-theoretic interpretation in which perception operates as a feedback system: priors function as setpoints, attention as gain allocation across internal and external signal channels, and characteristic failure modes arise from feedback disconnection, gain saturation, or open-loop drift on threat-dominated priors. We conclude that agents do not experience signal S directly, but its rendering Z, shaped by prior structure and attentional configuration. Generalized Pareidolia thus describes a fundamental operator of structural inference rather than a localized cognitive error, with implications for neuroscience, psychiatry, artificial intelligence, and epistemic risk across complex systems.Active working documents in an open research program. This work forms part of a broader research program examining how rendering, constraint, and convergence emerge in uncertain physical, cognitive, and social systems. Research Program Invitation - https://grodriguez6.github.io/amo-collab