Resolving the Delusion Paradox

The neurocomputational framework of predictive processing (PP) provides a promising approach to explaining delusions, a key symptom of psychotic disorders. According to PP, the brain makes inferences about the world by weighing prior beliefs against the available sensory data. Mismatches between prior beliefs and sensory data result in prediction errors that may update the brain's model of the world. Psychosis has been associated with reduced weighting of priors relative to the sensory data. However, delusional beliefs are highly resistant to change, suggesting increased rather than decreased weighting of priors. We propose that this "delusion paradox" can be resolved within a hierarchical PP model: Reduced weighting of prior beliefs at low hierarchical levels may be compensated by an increased influence of higher-order beliefs represented at high hierarchical levels, including delusional beliefs. This may sculpt perceptual processing into conformity with delusions and foster their resistance to contradictory evidence.

We review several lines of experimental evidence on low- and high-level processes, and their neurocognitive underpinnings in delusion-related phenotypes and link them to predicted processing.

The reviewed evidence supports the notion of decreased weighting of low-level priors and increased weighting of high-level priors, in both delusional and delusion-prone individuals. Moreover, we highlight the role of prefrontal cortex as a neural basis for the increased weighting of high-level prior beliefs and discuss possible clinical implications of the proposed hierarchical predictive-processing model.

Our review suggests the delusion paradox can be resolved within a hierarchical PP model.