Immertreu Mathis, Schilling Achim, Maier Andreas, Krauss Patrick
CCN Group, Pattern Recognition Lab, Erlangen, Germany.
University Erlangen-Nürnberg, Erlangen, Germany.
Front Artif Intell. 2025 Aug 20;8:1610225. doi: 10.3389/frai.2025.1610225. eCollection 2025.
This study explores the potential for artificial agents to develop core consciousness, as proposed by Antonio Damasio's theory of consciousness. According to Damasio, the emergence of core consciousness relies on the integration of a self model, informed by representations of emotions and feelings, and a world model. We hypothesize that an artificial agent, trained via reinforcement learning (RL) in a virtual environment, can develop preliminary forms of these models as a byproduct of its primary task. The agent's main objective is to learn to play a video game and explore the environment. To evaluate the emergence of world and self models, we employ probes-feedforward classifiers that use the activations of the trained agent's neural networks to predict the spatial positions of the agent itself. Our results demonstrate that the agent can form rudimentary world and self models, suggesting a pathway toward developing machine consciousness. This research provides foundational insights into the capabilities of artificial agents in mirroring aspects of human consciousness, with implications for future advancements in artificial intelligence.
本研究探讨了如安东尼奥·达马西奥的意识理论所提出的，人工智能体发展核心意识的潜力。根据达马西奥的观点，核心意识的出现依赖于一个由情感和感觉表征所告知的自我模型与一个世界模型的整合。我们假设，在虚拟环境中通过强化学习（RL）进行训练的人工智能体，可以作为其主要任务的副产品，发展出这些模型的初步形式。该智能体的主要目标是学习玩电子游戏并探索环境。为了评估世界模型和自我模型的出现，我们采用了探测前馈分类器，其利用经过训练的智能体神经网络的激活来预测智能体自身的空间位置。我们的结果表明，该智能体能够形成初步的世界模型和自我模型，这表明了一条发展机器意识的途径。这项研究为人工智能体在反映人类意识方面的能力提供了基础性见解，对人工智能的未来发展具有启示意义。
