Computational models of the "active self" and its disturbances in schizophrenia.

Self-disorders are increasingly seen as the root cause of schizophrenia, not merely a symptom. This aligns with philosophical views of an enactive self, formed through action and interaction. The authors analyze definitions of the self and evaluate computational theories, particularly Bayesian and predictive processing approaches, for modeling the active self. They assess the implementation and challenges of these models in computational psychiatry and cognitive developmental robotics. Embodied robotic systems are described as valuable tools for assessing, validating, and simulating mechanisms of self-disorders, especially those involving sensorimotor learning, prediction, and self-other distinction. This link offers insights into self-formation and new avenues for treating psychiatric disorders.

Study at a glance

Abstract

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.