Into the dark room: a predictive processing account of major depressive disorder

Abstract

Major depression is a prevalent mental disorder that leads to persistent negative mood and tremendous suffering in affected individuals. However, the biological realization of this disorder and associated symptom clusters remain poorly understood. Recently, phenomenological accounts of major depressive disorder and contributions to the emerging predictive processing account have provided valuable insights into the phenomenological and neuro-functional components that lead to manifestations of major depressive episodes. The purpose of this paper is to weave together these different strands of research to develop a predictive processing account of major depressive disorder. In doing so, I will relate personal-level descriptions of associated phenomenal experiences to a sub-personal-level predictive processing account of the functional realization of major depression. I will argue that pervasive symptoms of the disorder, which include a diminished sense of agency, fatigue, social withdrawal, and rumination, are integrated by existential feelings of loss and impossibility. These phenomenal experiences, I will argue, are associated with dysfunctional processes of prediction error minimization, which are characterized by an overall decrease of the causal contributions of active inference and by distorted precision estimates. The emerging account promises to contribute to a better understanding of the complex processes that give rise to depressive experiences.