Perception as Predictive Construction

Why This Is an Axiom

This represents a theoretical commitment to understanding perception within the predictive processing framework. It is axiomatic because it provides the foundational architecture for explaining how perception works—not a description of isolated phenomena but a unified framework. The claim that perception is prediction-based rather than stimulus-determined is a theoretical stance that organizes empirical findings into a coherent model.

Theoretical Framework

Predictive processing (also called predictive coding or the Bayesian brain hypothesis) proposes that the brain is fundamentally a prediction machine that generates internal models of the world and constantly compares predictions with incoming sensory data. Perception emerges from this prediction-error minimization process. This framework unifies diverse phenomena: why context affects perception, how expectations shape experience, why confirmation bias exists at the perceptual level, and how learning occurs through model updating. The "controlled hallucination" phrasing emphasizes that what we perceive is primarily generated internally, with sensory input serving to constrain and correct these predictions.

Curricular Connection

This theoretical framework grounds understanding of cognitive biases (as prediction-driven processing), contemplative practices (as training in noticing predictions vs. sensory data), and learning (as model updating). It explains why changing perception requires changing predictions, why direct experience often contradicts expectations, and why phenomenological investigation reveals the constructed nature of experience. This framework is essential for understanding how cognitive training actually changes perceptual experience.