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首页> 外文期刊>International Journal of Machine Consciousness >A MODEL OF PRIMITIVE CONSCIOUSNESS BASED ON SYSTEM-LEVEL LEARNING ACTIVITY IN AUTONOMOUS ADAPTATION
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A MODEL OF PRIMITIVE CONSCIOUSNESS BASED ON SYSTEM-LEVEL LEARNING ACTIVITY IN AUTONOMOUS ADAPTATION

机译：基于模型的原始意识在自治系统级的学习活动适应

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摘要

Although many models of consciousness have been proposed from various viewpoints, they have not been based on learning activities in a whole system with the capabilities of autonomous adaptation. We have been investigating a simplified system using artificial neural nodes to clarify the functions and configuration needed for learning in a system that autonomously adapts to the environment. We demonstrated that phenomenal consciousness is explained using a method of "virtualization" in the information system and that learning activities in a whole system adaptation are related to consciousness. However, we have not sufficiently clarified the learning activities of such a system. Consciousness is basically modeled as a system-level learning activity to modify both its own configuration and states in autonomous adaptation through investigating learning activities as a whole system. The model not only explains the time delay in Libet's experiment, but is also positioned as an improved model of Global Workspace Theory (GWT).
机译：尽管许多模型的意识提出不同观点,他们没有是基于在整个学习活动系统与自主的能力适应。使用人工神经节点简化系统澄清所需的功能和配置学习系统,自动适应环境。非凡的意识是使用一个解释“虚拟化”信息的方法在整个系统和学习活动系统适应性相关的意识。然而,我们尚未充分阐明这样一个系统的学习活动。意识是基本建模为系统级的学习活动来修改它自己的配置和州自治通过调查学习适应活动作为一个整体系统。利贝特的实验解释了时间延迟,但也定位的改进模型全球工作空间理论(GWT)。

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