Computational models of the “active self” and its disturbances in schizophrenia

•Growing evidence supports the notion of schizophrenia as a self-disorder stemming from a system-level disturbance of the bodily active self.•Computational psychiatry and cognitive developmental robotics share common promising computational approaches in modeling the self, and linking them might reveal mechanisms underlying self disorders.•Predictive processing-based approaches seem promising for modeling the self and its disorders.•Robotic systems allow for a valuable embodied testing ground for theories about the self and psychiatric disorders.•Simulating symptoms of schizophrenia in embodied artificial agents allows researchers to better understand phenomenological anomalies. The notion that self-disorders are at the root of the emergence of schizophrenia rather than a symptom of the disease, is getting more traction in the cognitive sciences. This is in line with philosophical approaches that consider an enactive self, constituted through action and interaction with the environment. We thereby analyze different definitions of the self and evaluate various computational theories lending to these ideas. Bayesian and predictive processing are promising approaches for computational modeling of the “active self”. We evaluate their implementation and challenges in computational psychiatry and cognitive developmental robotics. We describe how and why embodied robotic systems provide a valuable tool in psychiatry to assess, validate, and simulate mechanisms of self-disorders. Specifically, mechanisms involving sensorimotor learning, prediction, and self-other distinction, can be assessed with artificial agents. This link can provide essential insights to the formation of the self and new avenues in the treatment of psychiatric disorders.