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, and that this subnetwork is also functionally important: preserving it aids adaptation while damaging it impairs performance. We suggest that this principle can offer a window into exploring selfhood in other cognitive AI systems

翻译：理解自我意识的关键挑战一直在于缺乏一种原则性方法来量化智能系统是否具有"自我"概念，以及如何将"自我"与其他认知结构区分开来。我们提出，可以通过寻找认知过程中相较于快速获取的认知知识和技能变化较小的不变部分来分离"自我"，因为自我是我们经验中最持久的部分。我们利用这一原则分析了两种条件下机器人的认知结构：一个机器人学习恒定任务，而另一个机器人则在可变任务中进行持续学习。我们发现，与对照组相比，接受持续学习的机器人会发展出一个显著更稳定的不变子网络（p < 0.001），且该子网络在功能上也至关重要：保留它有助于适应，而破坏它则会损害性能。我们提出，这一原则可为探索其他认知人工智能系统中的自我性提供窗口。