R2R (Reaction to Reflection) Model

Intelligence is traditionally framed as information processing or computation, but these approaches fail to capture its recursive nature. This paper presents the Reaction to Reflection (R2R) model, which defines intelligence as the observable depth of recursion and awareness as the capacity to engage recursive processes. Using comparative analysis of biological systems, we demonstrate how recursion serves as the fundamental structuring principle enabling intelligence to scale from simple to complex forms.The R2R model traces intelligence evolution through key transitions: chemical recursion, anticipatory prediction (Temporogenesis), cooperative integration (Symbiogenesis), and self-referential modeling (Cognogenesis). These transitions involve both smooth incremental developments and fundamental symmetry-breaking reorganizations. Three critical mechanisms drive these transitions: symmetry breaking, which destabilizes existing recursive patterns; feedback loops, which stabilize recursive processes; and temporal structuring, which enables integration across multiple timescales.Cognogenesis—where recursion becomes an explicit construct within cognition—represents the highest stage of recursive depth currently observable. We distinguish between implicit recursion (where recursion operates as an underlying process) and explicit recursion (where recursion becomes a manipulable construct), explaining the emergence of metacognition and self-awareness as natural consequences of sufficient recursive depth. The model provides a unified explanation for how consciousness emerges when recursion becomes an explicit construct within cognition.We support the model through biological case studies ranging from predictive processing mechanisms to comparative cognition and evidence from severe cognitive deprivation. We also distinguish recursive depth from mere computational complexity, explaining why current AI systems may demonstrate sophisticated processing without achieving true recursive intelligence. This mechanistic account addresses energy constraints in intelligence and offers implications for both biological and artificial systems.