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 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 undergoes 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 validate this pattern across three different robots spanning locomotion and manipulation.

翻译：理解自我意识的一个关键挑战在于，能否以原则性的方式量化一个智能系统是否具备"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对于快速习得的认知技能变化较慢的不变部分来分离"自我"——因为自我是我们经验中最持久的方面。我们利用这一原理分析了机器人在两种条件下的认知结构：一个机器人学习恒定任务，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。我们通过三个不同的机器人（涵盖移动与操控任务）验证了这一模式。