Pareidolia Without Error: A Structural Account

This paper proposes a non-reductive structural alignment between pareidolic experience and contemporary models of human perception, particularly within predictive processing frameworks. Rather than treating pareidolia as a perceptual error or cognitive malfunction, the paper argues that pareidolic phenomena emerge naturally from high-sensitivity perceptual systems operating under uncertainty. By outlining a sequence of experiential states and mapping them onto established neuroscientific mechanisms, the paper demonstrates that pareidolia can be understood as a lawful outcome of perceptual prediction and attribution, without reducing lived experience to neural causation.