理論THY-2026-0103v0.1
跨層證據三角化與測量等級
神經科學沒有「1 個想法 = N 個 spike」的換算，有的是跨層代理量測：行為 → 潛在認知模型 → 神經編碼 → 細胞事件 → 物理實現，每層各有單位。IPM 借用五個原則（層級分離、潛變量推斷、群體優先於原子、編碼—解碼對偶、因果擾動），把 Marr 三層擴成五層（任務、語意、演算法、物理事件、熱力學），並為 AI 定義跨層三角化 E =（輸出、語意、內部軌跡、消融、硬體）證據，μI 的可信度分級從 D（行為）到 A+（物理—語意對齊）。
定義
definitions- Five layers L4 task achievement, L3 semantic/cognitive operation, L2 algorithmic realization, L1 physical events, L0 thermodynamic realization; L4 ≠ L3 ≠ L2 ≠ L1 ≠ L0.
- Cross-level triangulation X_L = (E_B behavioral, E_C cognitive-model, E_N neural, E_P perturbational); for AI X_AI = (E_O, E_S, E_I, E_A, E_H).
- Measurement grades D behavioral / C structured semantic / B internal correlation / A causal internal / A+ physical-semantic alignment.
- Eight borrowing principles: level separation, proxy discipline, model-mediated inference, distributed realization, causal perturbation, scale declaration, trial separation, grounding downward.
前提
assumptions- Latent quantities are scientific when they make observable predictions, have competitors, are falsifiable and accept intervention (the diffusion-decision-model template).
主張
claims- CognitiveUnit ≠ NeuralEvent ≠ InformationBit ≠ PhysicalOperation; bits/spike ≠ cognitive bits; Decodable ≠ CausallyUsed.
- OutputRate ≠ InternalComputationRate (the ~10 bits/s behavioral throughput is not the brain's computation rate; 1000 output tokens are not 1000 intelligent events).
- EnsemblePerformance ≠ SingleEpisodePerformance; Proxy ≠ Ontology; BiologicalNeuron ≠ ANNNeuron.
形式化
formalization- Conf(μI) = F(E_O, E_S, E_I, E_A, E_H) ∈ [0, 1]; report N̂_μ ± uncertainty with Grade_μ, never a falsely precise count.
- ValueOfComputation = ExpectedImprovement − Cost (resource-rational template for μI → Cost → Value).
預測
predictions- Confidence that μI^obs ≈ μI^int rises only when behavioral, semantic, internal, causal and physical evidence converge.
證偽／失敗條件
falsification_conditions- A μI account that survives behavioral evidence but is contradicted by ablation or hardware evidence must lose its grade, not be kept by verbal interpretation.
已知限制
known_limitations- Methodological borrowing only; no biological unit is equated with an AI unit and no measurement was performed.
關係
| 來源 | 關係 | 目標 | 狀態 | ID |
|---|---|---|---|---|
THY-2026-0103 跨層證據三角化與測量等級 | extends | THY-2026-0102 μI——最小智能語意執行單位（候選理論） | ACTIVE | REL-2026-0369 |
RES-2026-0101 執行與物理線——一個答案在回合、語意工作、能量與計算時空上的代價 | develops | THY-2026-0103 跨層證據三角化與測量等級 | ACTIVE | REL-2026-0360 |
THY-2026-0104 能量帳本層級與熱力學型別安全 | extends | THY-2026-0103 跨層證據三角化與測量等級 | ACTIVE | REL-2026-0370 |
PAP-2026-0103 從認知到神經元：人腦如何跨層測量智能計算 | formalizes | THY-2026-0103 跨層證據三角化與測量等級 | ACTIVE | REL-2026-0400 |
歷史與來源歷程
- Canonical URL
- https://evemisslab.com/ai/theory/THY-2026-0103/
- 快照
AI-SNAPSHOT-v0.1-fe85b9694a45- 來源歷程
source- EveMissLab research collection: Intelligence Physical Metrology (真本體論13)
extracted_by- Splice (Claude Code), reading the canonical UTF-8 sources and each package's own reports
extracted_at- 2026-09-11
generator- tools/extract_all.py
claim_boundary- status, evidence level and result type follow the source artifact's own stated claim boundary; nothing is upgraded beyond what the report supports