A concept bridging neuroscience and psychology into an architecture
Epistemic status: Hypothesis inviting falsification. Individual findings are established science; proposed connections are new and unvalidated.
A framework connecting neuroscience, psychology, and evolutionary biology into an architecture — mapping how the brain’s core systems produce behavior, from opioid-dopamine signaling through body-level evaluation of threat, novelty, social status, and connection — to collective behavior. 200+ source files with explicit dependencies, open-source, CC0.
Core premise: the body evaluates first, the prefrontal cortex observes second. Most behavior runs on compiled body-level patterns — the conscious mind is the observer, not the executor.
When you’re thirsty, the conscious mind sets one goal: get water. Everything after — walking, reaching for the cup, pouring, drinking — executes automatically.
You speak your native language fluently — grammar, intonation, coordination of throat and tongue, all running automatically with high precision. Yet your conscious mind cannot describe the grammatical rules you’re using.
Applying this premise consistently reframes several commonly misunderstood mechanisms:
- Dopamine Signals Salience, Not Reward — a 7-step mechanism + five preconditions for when pleasure actually fires
- Cortisol Is Not Your Stress Hormone — the Source > Level principle + the Inverted-U
- ADHD Is Not Attention Deficit — one threshold, six paradoxes resolved
- Logic Is Not the Opposite of Intuition — one process, two observer labels, and why neither can verify itself
Full framework with explicit dependencies (200+ source files, CC0): GitHub — Human Predictive Drive
Stress-test it with AI
Clone the repository. Drop the entire folder into a large-context AI — the framework is 200+ files and benefits from full-context reasoning. The README contains a starter prompt and reading order.
The framework was built through personal observation cross-referenced against published research, with AI-assisted synthesis — a method that can surface cross-disciplinary connections, but also carries risk of individual bias. AI can verify logical consistency and citation accuracy. It cannot verify replication status or methodology quality — that requires domain expertise.
Find where it breaks. The most valuable contribution is a well-documented case where the framework’s prediction doesn’t match observation — or where a cited paper doesn’t actually support the claimed mechanism.
Counter-evidence is more valuable than confirmation. The framework itself predicts that reading it will bias you toward confirming evidence. If something doesn’t fit, that’s the most useful thing you can share.