Cognitive Regimes and Asymmetric Recognition Interfaces, Null Spaces, and Access Constraints

This technical note presents a structural analysis of cognitive recognition failures through an interface-based framework. Cognitive regimes are treated not as psychological categories or hierarchical levels, but as architectures of access that determine how information becomes available for stabilization, articulation, and evaluation. The analysis shows that when global, cross-domain integrative processes are assessed exclusively through local, domain-specific evaluative interfaces, structurally relevant content may fail to produce interpretable signal. This phenomenon is characterized as a cognitive null space, arising from projection mismatch rather than from lack of content, correctness, or ability. Artificial intelligence systems are examined as asymmetric mediators within this framework. While AI can function as a high-resolution local interface that amplifies access to domain-specific operations for global cognitive regimes, it does not generate global access when applied to local regimes. This asymmetry is shown to be invariant under optimization, translation, and metric-driven evaluation. The contribution is purely diagnostic and introduces no cognitive taxonomy, hierarchy of value, or ontological claims. Its purpose is to clarify structural limits of recognition and to reduce systemic attribution errors arising from interface incompatibility across evaluative, organizational, and academic contexts.