Transitive Self-Reflection–A Fundamental Criterion for Detecting Intelligence

This survey investigates the concept of transitive self-reflection as a fundamental criterion for detecting and measuring intelligence. We explore the manifestation of this ability in humans, consider its potential presence in other animals, and discuss the challenges and possibilities of replicating it in artificial intelligence systems. Transitive self-reflection is characterized by an awareness of oneself through complex cognitive abilities rooted in evolutionary mechanisms that are innate in humans. Although transitive self-reflection cannot be fully replicated in AI as an origin, its behavioral characteristics can be analyzed and, to some extent, imitated. The study delves into various forms of transitive self-reflection, including self-recognition, object-mediated self-reflection, and reflective social cognition, highlighting their philosophical roots and recent advancements in cognitive science. We also examine the multifaceted nature of intelligence, encompassing cognitive, emotional, and social dimensions. Despite significant progress, current AI systems lack true transitive self-reflection. Developing AI with this capability requires advances in knowledge representation, reasoning algorithms, and machine learning. Incorporating transitive self-reflection into AI systems holds transformative potential for creating socially adept and more human-like intelligence in machines. This research underscores the importance of transitive self-reflection in advancing our understanding of and the development of intelligent systems.