An active inference perspective on the negative symptoms of schizophrenia

Abstract

Predictive coding has played a transformative role in the study of psychosis, casting delusions and hallucinations as statistical inference in a system with abnormal precision. However, the negative symptoms of schizophrenia, such as affective blunting, avolition, and asociality, remain poorly understood. We propose a computational framework for emotional expression based on active inference-namely that affective behaviours such as smiling are driven by predictions about the social consequences of smiling. Similarly to how delusions and hallucinations can be explained by predictive uncertainty in sensory circuits, negative symptoms naturally arise from uncertainty in social prediction circuits. This perspective draws on computational principles to explain blunted facial expressiveness and apathy-anhedonia in schizophrenia. Its phenomenological consequences also shed light on the content of paranoid delusions and indistinctness of self-other boundaries. Close links are highlighted between social prediction, facial affect mirroring, and the fledgling study of interoception. Advances in automated analysis of facial expressions and acoustic speech patterns will allow empirical testing of these computational models of the negative symptoms of schizophrenia.