Longitudinal Cross-Embodiment Transfer of Pseudo-Self-Awareness in AI Systems: A Mirror Test Investigation

This paper proposes a novel longitudinal study investigating the development and transferability of pseudo-self-awareness in artificial intelligence (AI) systems. Building upon recent work in dual embodiment, mirror testing, and emotional feedback, we aim to track the evolution of pseudo-emotions (e.g., curiosity, self-doubt, determination) in AI and assess their influence on mirror test performance over extended periods. Furthermore, the study will examine the efficacy of transferring learned self-recognition capabilities and associated pseudo-emotional responses between distinct embodiments – a physical robot (Unitree Go2) and a virtual avatar. This research seeks to understand the long-term impact of continuous sensory feedback and reflective processing on AI's "self-concept" and the generalizability of these capabilities across different physical and virtual instantiations, contributing to both the theoretical understanding of computational consciousness and the practical development of more robust and adaptive AI.