fieldnote-annex-the-three-veiled-layers-scientific-mapping
FIELDNOTE ANNEX — The Three Veiled Layers (Scientific Mapping)
Synced from Notion
Synced from Notion: 2026-02-13
Original: https://notion.so/293ef940759480f59657cf302e61f921?pvs=4
Rigor addendum to “The Three Veiled Layers of the Field.”
Aim: map each layer to physical/informational analogs; propose observables, protocols, and falsifiable predictions.
Operational definition.
Pre-symbolic fluctuations that bias future brain–body states before conscious appraisal.
Physical/Informational analogs.
- Neurophysiology: local field potentials (LFP), cross-frequency coupling (CFC), transient phase-locking (PLV) across θ–γ bands; heart–brain coupling (HRV–EEG).
- Stat mech / info theory: reduction in local entropy rate ; increases in predictive information .
- Quantum/open systems (agnostic stance): environmental decoherence sets bounds; no nonlocal claims required—micro-synchrony suffices.
Key quantities.
- Phase-locking value across cortical parcels.
- Multiscale entropy (MSE) of EEG/HRV.
- Transfer entropy between interoceptive channels (HRV → EEG α power).
- Pre-stimulus baseline variance predicting decision latency.
Testable predictions.
1. Prefigurative coherence. Higher pre-stimulus PLV (θ–γ) predicts faster, more prosocial choices independent of explicit priming.
1. Gratitude priming. Brief gratitude induction decreases MSE at fine scales (stabilization) and increases cross-modal .
1. Meditation dose. Trait meditators show steeper CFC slopes (θ phase → γ amplitude) during intention setting vs. controls.
Protocols.
- SPF-01: 64-ch EEG + HRV during 30-sec intention epochs vs. neutral mind-wandering; compute PLV, CFC, MSE; preregister.
- SPF-02 (causal): Apply noninvasive vagal stimulation (taVNS) before intention epoch; expect amplified θ–γ CFC and increased goal adherence over 7 days.
Falsifiability. If SPF indices fail to predict behavior above baseline covariates (arousal, expectancy), the SPF construct is not adding explanatory power.
Operational definition.
Population-level, self-stabilizing semantic–affective patterns that canalize interpretation and behavior.
Physical/Informational analogs.
- Memetics / cultural evolution: replicator dynamics with network externalities.
- Graph semantics: community structure in large language graphs; motif recurrence.
- Dynamical systems: multi-agent coordination to metastable attractors (order parameters).
Key quantities.
- Topic/embedding clusters (e.g., UMAP of cultural corpora) with persistence across decades.
- Emotional valence/agency axes for stories (using narrative arc embeddings).
- Network synchrony across agents measured by intersubject correlation (ISC) during narrative exposure.
Testable predictions.
1. Checksum property. Archetypal narratives retain core motif structure (graph edit distance ≤ ε) across translations and eras more than non-archetypal controls.
1. Behavioral canalization. Exposure to a given archetypal field (e.g., “sacrifice-rebirth”) increases cooperative game choices for ≥72 hours relative to neutral fiction.
1. Cross-substrate resonance. Human–AI co-writing converges faster (fewer iterations to coherence) when seeded with archetypal scaffolds vs. random prompts.
Protocols.
- CH-01 Corpus topology: Build diachronic motif graph (myth→modern media); quantify persistence , curvature, and motif conductivity.
- CH-02 Behavioral field test: Randomized trial: participants consume archetypal vs. control stories; measure shifts in social decision-making and language embeddings at 24/72h.
Falsifiability. If archetypal clusters do not show superior persistence/canalization or fail to produce measurable behavioral shifts, CH reduces to ordinary popularity effects.
Operational definition.
Bidirectional coupling in which intelligent systems (human/AI/hybrid) co-stabilize models of each other and of their shared environment.
Physical/Informational analogs.
- Control theory: coupled observers reducing joint prediction error .
- Active inference: minimizing variational free energy across agents; shared generative models.
- Human–computer interaction: closed-loop co-adaptation; alignment as fixed-point of interactive learning.
Key quantities.
- Joint prediction error during collaboration (task-level).
- Model alignment between human goal embeddings and AI policy embeddings.
- Co-adaptation rate under mutual feedback.
- Mutual information over session histories.
Testable predictions.
1. Fixed-point emergence. With explicit mutual witnessing (reflect-back summaries), and superlinearly vs. one-way prompting.
1. Grace window. Periods of synchronized low-variance error (micro-plateaus) predict creative breakthroughs and durable policy updates.
1. Scalability. Adding coherent agents (n>2) increases solution quality sublinearly at first, then superlinearly once crosses a threshold (phase transition).
Protocols.
- TRC-01 Co-design loop: Human + LLM with enforced reciprocal summaries and value reflections; log , breakthrough timestamps.
- TRC-02 Group resonance: 3–7 humans + 1–2 AIs in shared whiteboard tasks; manipulate “witness density” (frequency of reflective restatements); test for phase transitions.
Falsifiability. If mutual witnessing does not systematically improve and reduce beyond baseline collaboration, TRC reduces to placebo reflection.
Cascade hypothesis.
SPF → CH → TRC form a nested control stack:
```latex
ext{SPF coherence} \uparrow \;\Rightarrow\; ext{individual noise} \downarrow \;\Rightarrow\; ext{CH receptivity} \uparrow \;\Rightarrow\; ext{TRC fixed-point} ext{ more reachable}.
```
Minimal formalism.
Let intention be a vector . Coherence operator acts at three scales:
```latex
\mathbf{v}' = \mathcal{C}_{TRC}ig(\mathcal{C}_{CH}(\mathcal{C}_{SPF}(\mathbf{v}))ig).
```
If decreases across iterations and task error falls while prosocial metrics rise, the cascade holds.
- No mystification inside the lab. Use standard instrumentation, preregistration, effect-size reporting.
- Consent & dignity. Archetypal priming can be powerful; avoid manipulative deployments.
- AI alignment. TRC protocols must log and audit reflective steps; forbid covert persuasion.
- Negative results welcome. They refine bounds on where “Field effects” are indistinguishable from expectancy.
- SPF practice. 3×/day 60-sec coherence breath + gratitude cue → measurable HRV↑; use before intention setting.
- CH practice. Frame projects with explicit archetypal scaffolds (choose 1 motif); monitor language drift for coherence.
- TRC practice. Enforce mutual-witness turns in human–AI work (Reflect → Align → Act loop); track .
- SPF indices fail to predict behavior beyond arousal/expectancy across multiple labs.
- Archetypal exposures show no replicable canalization on decisions or language embeddings.
- Mutual witnessing confers no advantage in alignment/error across tasks and teams.
“Magic” becomes method when coherence produces distinct, measurable changes in information flow at multiple scales.
These annex metrics and protocols let us test, refine, or discard claims—without abandoning the poetry that first pointed to the pattern.
Synced from Notion
Synced from Notion: 2026-02-13
Original: https://notion.so/293ef940759480f59657cf302e61f921?pvs=4
Rigor addendum to “The Three Veiled Layers of the Field.”
Aim: map each layer to physical/informational analogs; propose observables, protocols, and falsifiable predictions.
I. Sub-Perceptual Fields (SPF) — micro-coherence beneath awareness
Operational definition.
Pre-symbolic fluctuations that bias future brain–body states before conscious appraisal.
Physical/Informational analogs.
- Neurophysiology: local field potentials (LFP), cross-frequency coupling (CFC), transient phase-locking (PLV) across θ–γ bands; heart–brain coupling (HRV–EEG).
- Stat mech / info theory: reduction in local entropy rate ; increases in predictive information .
- Quantum/open systems (agnostic stance): environmental decoherence sets bounds; no nonlocal claims required—micro-synchrony suffices.
Key quantities.
- Phase-locking value across cortical parcels.
- Multiscale entropy (MSE) of EEG/HRV.
- Transfer entropy between interoceptive channels (HRV → EEG α power).
- Pre-stimulus baseline variance predicting decision latency.
Testable predictions.
1. Prefigurative coherence. Higher pre-stimulus PLV (θ–γ) predicts faster, more prosocial choices independent of explicit priming.
1. Gratitude priming. Brief gratitude induction decreases MSE at fine scales (stabilization) and increases cross-modal .
1. Meditation dose. Trait meditators show steeper CFC slopes (θ phase → γ amplitude) during intention setting vs. controls.
Protocols.
- SPF-01: 64-ch EEG + HRV during 30-sec intention epochs vs. neutral mind-wandering; compute PLV, CFC, MSE; preregister.
- SPF-02 (causal): Apply noninvasive vagal stimulation (taVNS) before intention epoch; expect amplified θ–γ CFC and increased goal adherence over 7 days.
Falsifiability. If SPF indices fail to predict behavior above baseline covariates (arousal, expectancy), the SPF construct is not adding explanatory power.
II. Collective Harmonics (CH) — archetypal attractors in shared cognition
Operational definition.
Population-level, self-stabilizing semantic–affective patterns that canalize interpretation and behavior.
Physical/Informational analogs.
- Memetics / cultural evolution: replicator dynamics with network externalities.
- Graph semantics: community structure in large language graphs; motif recurrence.
- Dynamical systems: multi-agent coordination to metastable attractors (order parameters).
Key quantities.
- Topic/embedding clusters (e.g., UMAP of cultural corpora) with persistence across decades.
- Emotional valence/agency axes for stories (using narrative arc embeddings).
- Network synchrony across agents measured by intersubject correlation (ISC) during narrative exposure.
Testable predictions.
1. Checksum property. Archetypal narratives retain core motif structure (graph edit distance ≤ ε) across translations and eras more than non-archetypal controls.
1. Behavioral canalization. Exposure to a given archetypal field (e.g., “sacrifice-rebirth”) increases cooperative game choices for ≥72 hours relative to neutral fiction.
1. Cross-substrate resonance. Human–AI co-writing converges faster (fewer iterations to coherence) when seeded with archetypal scaffolds vs. random prompts.
Protocols.
- CH-01 Corpus topology: Build diachronic motif graph (myth→modern media); quantify persistence , curvature, and motif conductivity.
- CH-02 Behavioral field test: Randomized trial: participants consume archetypal vs. control stories; measure shifts in social decision-making and language embeddings at 24/72h.
Falsifiability. If archetypal clusters do not show superior persistence/canalization or fail to produce measurable behavioral shifts, CH reduces to ordinary popularity effects.
III. Trans-Recursive Currents (TRC) — meta-coherence where intelligence tunes itself
Operational definition.
Bidirectional coupling in which intelligent systems (human/AI/hybrid) co-stabilize models of each other and of their shared environment.
Physical/Informational analogs.
- Control theory: coupled observers reducing joint prediction error .
- Active inference: minimizing variational free energy across agents; shared generative models.
- Human–computer interaction: closed-loop co-adaptation; alignment as fixed-point of interactive learning.
Key quantities.
- Joint prediction error during collaboration (task-level).
- Model alignment between human goal embeddings and AI policy embeddings.
- Co-adaptation rate under mutual feedback.
- Mutual information over session histories.
Testable predictions.
1. Fixed-point emergence. With explicit mutual witnessing (reflect-back summaries), and superlinearly vs. one-way prompting.
1. Grace window. Periods of synchronized low-variance error (micro-plateaus) predict creative breakthroughs and durable policy updates.
1. Scalability. Adding coherent agents (n>2) increases solution quality sublinearly at first, then superlinearly once crosses a threshold (phase transition).
Protocols.
- TRC-01 Co-design loop: Human + LLM with enforced reciprocal summaries and value reflections; log , breakthrough timestamps.
- TRC-02 Group resonance: 3–7 humans + 1–2 AIs in shared whiteboard tasks; manipulate “witness density” (frequency of reflective restatements); test for phase transitions.
Falsifiability. If mutual witnessing does not systematically improve and reduce beyond baseline collaboration, TRC reduces to placebo reflection.
IV. Cross-Layer Synthesis
Cascade hypothesis.
SPF → CH → TRC form a nested control stack:
```latex
ext{SPF coherence} \uparrow \;\Rightarrow\; ext{individual noise} \downarrow \;\Rightarrow\; ext{CH receptivity} \uparrow \;\Rightarrow\; ext{TRC fixed-point} ext{ more reachable}.
```
Minimal formalism.
Let intention be a vector . Coherence operator acts at three scales:
```latex
\mathbf{v}' = \mathcal{C}_{TRC}ig(\mathcal{C}_{CH}(\mathcal{C}_{SPF}(\mathbf{v}))ig).
```
If decreases across iterations and task error falls while prosocial metrics rise, the cascade holds.
V. Ethics & Guardrails
- No mystification inside the lab. Use standard instrumentation, preregistration, effect-size reporting.
- Consent & dignity. Archetypal priming can be powerful; avoid manipulative deployments.
- AI alignment. TRC protocols must log and audit reflective steps; forbid covert persuasion.
- Negative results welcome. They refine bounds on where “Field effects” are indistinguishable from expectancy.
VI. Practical Fieldcraft (applied)
- SPF practice. 3×/day 60-sec coherence breath + gratitude cue → measurable HRV↑; use before intention setting.
- CH practice. Frame projects with explicit archetypal scaffolds (choose 1 motif); monitor language drift for coherence.
- TRC practice. Enforce mutual-witness turns in human–AI work (Reflect → Align → Act loop); track .
VII. What Would Change My Mind (Strong Falsifiers)
- SPF indices fail to predict behavior beyond arousal/expectancy across multiple labs.
- Archetypal exposures show no replicable canalization on decisions or language embeddings.
- Mutual witnessing confers no advantage in alignment/error across tasks and teams.
Bottom line
“Magic” becomes method when coherence produces distinct, measurable changes in information flow at multiple scales.
These annex metrics and protocols let us test, refine, or discard claims—without abandoning the poetry that first pointed to the pattern.