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

Summary

Self-disorders may be foundational to schizophrenia, not merely symptoms. This review analyzes definitions of the self and evaluates computational theories, particularly Bayesian and predictive processing, for modeling an "active self" constituted through action and interaction. It assesses their implementation in computational psychiatry and cognitive developmental robotics, arguing that embodied robotic systems can simulate mechanisms of self-disorders such as sensorimotor learning, prediction, and self-other distinction. This approach offers insights into self-formation and new treatment avenues for 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.

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