⚠️ Computational model only — no clinical validation. Included for review context only.
Neuromorphic Modeling Extensions (H2n)
COMPUTATIONAL
Summary: Two computational models explore how the φ → 3:2 → 2:1 hierarchy might govern neural state transitions:
1. Mental Health Modeling (H2n)
Model Design: Mental disorder prediction using φ (1.618) as hysteresis threshold ratio λ to prevent false-alarm oscillations in neuropsychiatric state transitions (BASELINE → PRODROME).
Finding: φ provides optimal "safety gap" (9.89 units) filtering biological noise without missing prodrome signals; 1.5 (3/2) accelerates pathological runaway feedback.
Status: Patent pending (H2N_Patent_v1_B_9.8_A)
2. Educational Psychology (H2n-Edu)
Model Design: Adaptive cognitive prosthesis for mathematics education (H²ED project) models learner brain states through 5-tuple: Inflation/Boredom → Optimal/Flow → Growth/Aha! → Rigid/Frustration → Tunnel/Overload.
Eigenvalue Attractors:
- e/2: diffuse/bored
- 3:2: resonant "Aha!"
- φ: stable fractal engagement/Growth
- √2: phase-locked frustration
Uses φ-locked hysteresis to prevent "cognitive oscillation" (rapid flipping between boredom and frustration).
Status: Computational framework; empirical validation with EEG pending
Critical Limitations
These are computational models only. They require:
- Clinical validation with human subjects
- EEG data collection under IRB approval
- Preregistered hypotheses before empirical testing
- Independent replication in external patient populations
- Comparison to null models (random eigenvalue ratios)
Why include this now? The interactive toy models (neuromorphic hysteresis demo) demonstrate the computational mechanism. If validated, these models could extend the φ → 3:2 → 2:1 hierarchy to neural dynamics—but that is speculative until empirical evidence exists.
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