A key challenge to understanding self-awareness has been a principled way of quantifying whether an intelligent system has a concept of a "self," and if so how to differentiate the "self" from other cognitive structures. We propose that the "self" can be isolated by seeking the invariant portion of cognitive process that changes relatively little compared to more rapidly acquired cognitive knowledge and skills, because our self is the most persistent aspect of our experiences. We used this principle to analyze the cognitive structure of robots under two conditions: One robot learns a constant task, while a second robot is subjected to continual learning under variable tasks. We find that robots subjected to continual learning develop an invariant subnetwork that is significantly more stable (p < 0.001) compared to the control. We suggest that this principle can offer a window into exploring selfhood in other cognitive AI systems.

翻译：理解自我意识的一个关键挑战在于，如何通过一种原则性的方式量化智能系统是否具有“自我”概念，以及如何区分“自我”与其他认知结构。我们提出，可以通过寻找认知过程中变化相对较少的不变部分来隔离“自我”，与快速习得的认知知识和技能相比，自我是我们经验中最持久的方面。我们利用这一原则分析了两种条件下机器人的认知结构：一个机器人学习固定任务，而另一个机器人在可变任务下进行持续学习。我们发现，经历持续学习的机器人形成的不变子网络其稳定性显著高于对照组（p < 0.001）。我们提出，这一原则可为探索其他认知AI系统中的自我性提供窗口。