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.

自我障碍是精神分裂症出现的根源而非疾病症状的观点在认知科学中越来越受到关注。这与认为通过行动和与环境的互动构成的主动自我的哲学方法是一致的。因此，我们分析了自我的不同定义，并评估了支持这些想法的各种计算理论。贝叶斯和预测处理是对“主动自我”进行计算建模的有前途的方法。我们评估了他们在计算精神病学和认知发展机器人方面的实施和挑战。我们描述了具身机器人系统如何以及为什么在精神病学中提供一种有价值的工具来评估、验证和模拟自我障碍的机制。具体来说，涉及感觉运动学习、预测和自我其他区分的机制可以用人工代理进行评估。这种联系可以为自我的形成和治疗精神疾病的新途径提供重要的见解。