In this chapter, we’ve looked at Bayesian inference and predictive processing as powerful frameworks for understanding perception. These approaches view the brain as a hypothesis-testing engine that continuously balances sensory evidence against prior expectations. In this interview, Professor Sterzer extends this framework to clinical contexts, showing how alterations in this balance may give rise to hallucinations, delusions, and other symptoms associated with mental disorders.

In our conversation, Prof. Dr. med. Philipp Sterzer, a leading researcher at the intersection of computational neuroscience and psychiatry, explores how this framework illuminates the mechanisms underlying perceptual and cognitive disturbances. He discusses how aberrant precision weighting, altered priors, and disruptions in hierarchical inference can lead to characteristic symptoms of psychosis. Through this perspective, clinical phenomena become understandable as deviations in the same inferential machinery that underlies normal perception.

Interview /podcast session to be scheduled with Philipp.

Topics to cover may include:

• What hallucinations are, and why they are particularly interesting from a Predictive Processing perspective.
• How prior knowledge confers perceptual advantages, including the Teufel et al. (2015) study.
• Weak vs strong prior accounts, how to reconcile conflicting findings?
• Concluding reflections on what understanding hallucinations can teach us about perception more generally, for example by highlighting how perception is always controlled by internal models, as illustrated by the blind spot.