Neurobiological illness models of schizophrenia and stigma reduction: has that ship sailed?

Stigma substantially contributes to the burden of psychotic disorders such as schizophrenia. Previous attempts to reduce stigma based on neurobiological illness models were unsuccessful. In this Perspective article, we argue that these models may have failed to reduce stigma in schizophrenia because of their lack of explanatory power and their connotations of biological otherness and determinism. We suggest that recent developments in the realm of computational psychiatry may help to remedy this situation. In particular, we discuss the potential of predictive processing, a highly influential computational framework of brain function, to reduce the stigma associated with schizophrenia. We argue that predictive processing accounts may do so by bridging the explanatory gap between biology and psychotic experiences within a normative theory of brain function, thus offering a normalizing perspective on the neural underpinnings of schizophrenia. This may facilitate not only a better understanding of psychotic experiences but also counteract connotations of biological determinism and foster belief in change. Viewing schizophrenia through the lens of predictive processing may thus pave the way for the integration of neurobiologically grounded models into the communication with patients, their relatives, and the public, which have the potential to reduce stigma.