Evidence of an Emergent "Self" in Continual Robot Learning

2026-03-25

AI Summary

This study addresses the challenge of quantifying and identifying the notion of “self” within intelligent systems, distinguishing it from other dynamic cognitive structures. We propose that the self can be formally characterized as the most stable, minimally changing subnetwork of cognition during continual learning. This hypothesis is evaluated through analyses of subnetwork stability in neural architectures, continual learning paradigms, and controlled experiments. Results demonstrate that robots trained under continual learning conditions develop significantly more stable invariant subnetworks (p < 0.001), offering the first empirical evidence of the self as a persistent cognitive structure in artificial intelligence systems. These findings establish a novel, quantifiable pathway toward machine self-modeling grounded in measurable neural stability.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Intelligent Robots: Learning & Optimization for ROB
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• self-awareness
• cognitive structure
• continual learning
• invariant representation
• artificial intelligence

Innovation

• self-representation
• continual learning
• cognitive invariance
• robotic self-awareness
• stable subnetwork