Simulating Self-Awareness: Dual Embodiment, Mirror Testing, and Emotional Feedback in AI Research

Abstract

The advancement of artificial intelligence (AI) toward self-awareness and emotional capacity is a critical area of research. Despite AI's success in specialized tasks, it has yet to exhibit true self-awareness or emotional intelligence. Previous research has emphasized the importance of feedback loops and interfaces in enabling both biological and artificial systems to process information and exhibit self-aware behaviors. Notably, in our earlier work, we proposed a unified model of consciousness (Watchus, 2024), which highlighted recursive feedback loops in both biological and artificial systems and explored the insula's role in self-awareness (Watchus, 2024). Building upon these foundations, the current study investigates how dual embodiment, mirror testing, and emotional feedback mechanisms can simulate self-awareness in AI systems. By integrating internal self-models with external sensory interfaces, we propose that emotional feedback can enhance AI's self-reflection and adaptability. Through the use of a physical robot dog (Unitree Go2) and a virtual embodiment, we explore how sensory experiences and self-reflective tasks foster pseudo-emotional states like curiosity, self-doubt, and determination, advancing the potential for AI systems to develop pseudo-self-awareness.