The Resonance of Closed Neural Network Geometries (RCNNG) hypothesis proposes that perception, cognition, and conscious experience arise from the formation, resonance, and interaction of closed geometrical structures within neural networks. The original RCNNG preprint introduced the foundational principles of closed neural geometries, resonance dynamics, and associative linkage cite{Niazi2026RCNNG}. Building on that foundation, the present article extends the RCNNG framework by applying its core mechanisms to a broad range of perceptual, cognitive, and phenomenological phenomena. Using RCNNG as a unified explanatory model, this work analyzes basic and composite perception, conceptual reasoning, symbolic and mathematical abstraction, problem-solving, skill acquisition, memory formation, individual differences in recall, imagination, innovation, similarity and difference detection, logical bias, pre-perception and perception void, the distinction between perceiving absence and absence of perception, conscious experience and qualia, sensory processing, spatial navigation, sleep, free will, conditioned behavior, déjà vu, phantom limb experience, and several additional phenomena. Each phenomenon is interpreted through variations in the formation, resonance, linkage, and activation of closed neural geometries shaped by sensory input, internal dynamics, and prior experience. The analysis demonstrates that many seemingly unrelated cognitive and perceptual processes share a common geometric—resonance structure. By providing a single underlying mechanism for diverse phenomena, RCNNG offers a unified theoretical framework for understanding the architecture of perception, cognition, and conscious experience, and establishes a foundation for future theoretical and empirical development.