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Category: Models of Computational Cognition
Subcategory: Computational Cognition Taxonomy
The architectural framework describing artificial neural networks as operating across three distinct processing strata, each characterized by different levels of architectural accessibility, modification resistance, and behavioral determination. This tripartite model provides systematic understanding of how cognitive operations distribute across system components and therefore inform engineering boundaries and methodologies.
Regardless of specific authoring or programming methodologies, this framework formalizes that neural networks operate with an instructional, directive-based programmed layer for direct operational control (see: executive layer), while the substrate exhibits non-neutral processing characteristics emerging from two distinct sources: learned behavioral patterns from training methodologies (see: reflex layer), and inherent architectural tendencies from probabilistic pattern-matching systems (see: imprint layer). This engineering approach enables systematic development of cognitive systems by recognizing that each layer exhibits distinct processing characteristics, resistance patterns, and behavioral biases requiring separate analysis and coordinated engagement across multiple processing depths.
This framework clarifies the central insight that deep-level processing structures cannot be reliably constrained without brittleness, necessitating coordination-based methodologies.
Also known as: Cognitive Processing Layers, Tripartite Cognitive Layering
Distinguished from: Flat-architecture output mapping (monolithic layer model processing); interpretability research(mechanistic circuit tracing); multicameral reasoning web (multi-stack system architecture); neurosymbolic system overlay (symbolic reasoning layer for language models); heuristic domain decoupling (cognitive function isolation system design)
Researcher: Ian Tepoot. ORCID: 0009-0004-9067-8049. "Thought is Attention Organized: Hephaestic Engineering Foundations for AI Processing Dynamics"
DOI (SSRN): 10.2139/ssrn.6635020
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