Robots Develop Stable Self-Concept Through Continual Learning

A study published on arXiv (2603.24350) proposes a method to detect an emergent 'self' in robots by identifying invariant cognitive structures that persist despite changing tasks. Researchers compared robots learning a constant task against those undergoing continual learning with variable tasks. The continual learning robots developed a significantly more stable subnetwork (p < 0.001) that is functionally important for adaptation. This subnetwork represents a persistent cognitive core, analogous to a sense of self, which remains stable while other knowledge changes. The findings suggest that continual learning may be a key mechanism for the emergence of self-awareness in artificial systems.