Structure Recognition Research Program · Program Hub
Research Portfolio
A long-term investigation into how humans discover structure and generate research
How do humans come to see something new?
Abstract
This portfolio documents a long-term research program investigating how humans discover structure, assign meaning to it, and generate research questions. Beginning with specific observations of Karnaugh map visual patterns, the program developed through three empirical case studies into a theoretical framework — Structure Recognition Theory (SRT) — addressing the cognitive mechanisms of research generation and human-AI collaborative discovery.
The program follows Architecture B (Empirical Foundation Model): three empirical papers ground the theoretical integration in Paper 4, with Paper 5 addressing human-AI collaboration as a methodological meta-layer.
Keywords
structure recognition
Karnaugh map
Boolean function
visual pattern
human-AI collaboration
research generation
concept evolution
symmetric Boolean function
variable rearrangement
structure discovery
explanatory significance
SRT
Program Architecture
Empirical Foundation Layer:
Paper 1: KMap Structure Invariance
Paper 2: Symmetric Boolean Function Patterns
Paper 3: Variable Rearrangement Invariance
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Theoretical Integration Layer:
Paper 4: Structure Recognition Theory (SRT) — H1–H10, v0.3
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Methodological Layer:
Paper 5: Human-AI Research Collaboration
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Application / Extension Domains:
Branch 8: Boolean Function Space Theory