UHM Validation Experimental Protocol

Document status: [P] Research programme

This document describes a maximally complete experimental protocol for the empirical validation of the Universal Holographic Model (UHM). The protocol is designed on the principle of maximal falsifiability: every experiment specifies a concrete numerical result that would refute the theory.

Related documents

• 22 unique CC predictions — full list of predictions with formulas
• Γ measurement protocol — operationalisation of π_bio for AI systems
• Falsifiability criteria — formal refutation conditions
• Status registry — current epistemic status of all claims

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1. Strategic design

1.1. The problem: empirical vacuum

UHM is one of the most formally developed theories of consciousness: ~185 theorems, 22 numerical predictions, categorical foundation. But not a single prediction has been experimentally verified. A theory without empirics is philosophy, no matter how rigorous the mathematics.

1.2. Key observation: PCI* ≈ P_crit

Perturbational Complexity Index (PCI, Casali et al. 2013, Massimini et al.) is an empirically established consciousness threshold: PCI = 0.31* (100% sensitivity and specificity on a benchmark of 150 subjects). UHM critical purity: P_crit = 2/7 ≈ 0.286. The discrepancy of ~8% is within the normalisation calibration of π_bio.

This is the first point of contact between the theory and empirical data. If the match is not accidental, UHM is the first theory of consciousness with a confirmed numerical threshold.

1.3. Principle: from maximally risky to complex

Prediction 17 (critical exponents α=1/2, β=1/4, γ=1, ν=1/2, δ=5) is the most valuable because it is the most risky:

• Five concrete numbers, each falsifiable
• No other theory of consciousness predicts critical exponents
• Confirmation = consciousness belongs to a specific universality class (like phase transitions in physics)
• Refutation = fundamental revision of the theory

The protocol is organised in decreasing order of risk: first — what is cheaper to test and maximally falsifiable.

1.4. Four phases

Phase	Timeline	What	Why first
I. Digital	0–6 mo.	11 predictions in silico (Γ-native agent)	Free, no ethics, tests the foundation
II. Neurocalibration	6–18 mo.	π_bio, P_crit ↔ PCI*, critical exponents	Main point of contact with neurodata
III. Clinical	12–36 mo.	Disorders of consciousness, recovery, 3/7 attractor	Clinical significance
IV. Cognitive	12–24 mo.	7D stress, collective consciousness, prelinguistic cognition	Interdisciplinary validation

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2. Phase I: Digital validation (0–6 mo.)

2.1. Rationale

11 of 22 predictions are testable in silico on any implementation of a Γ-native agent — a system whose evolution is governed by Lindbladian dynamics ℒ_Ω = ℒ₀ + ℛ on the coherence matrix Γ ∈ D(ℂ⁷). No neurodata, subjects, or ethical approval required. If even one is falsified — stop, revise the theory before proceeding to expensive neuroexperiments.

2.2. Requirements for a Γ-native agent

Any implementation used for Phase I must satisfy:

1. CPTP dynamics: Evolution of Γ via a CPTP channel (T-62). The transition matrix is derived from Γ, not trained as a free parameter
2. 7D structure: State space is D(ℂ⁷) with 7 dimensions [A,S,D,L,E,O,U]
3. Consciousness verifier: At each step, P = Tr(Γ²), R, Φ, Coh_E, σ_k are computed
4. Multi-phase training with hard gates:
   • Initialisation phase: gate P > P_min (viability)
   • Foundation phase: gate P ∈ (2/7, 3/7] ∧ R ≥ 1/3 (consciousness)
   • Autonomous learning phase: σ-directed data selection, ΔP ≥ 0 ∧ Δσ ≤ 0
5. Checkpoint system: Saving the full Γ state for perturbation tests
6. GPU acceleration: For Monte Carlo (Exp. I.8) — ≥1 GPU with ≥40GB

2.3. Experiments

Exp. I.1: Impossibility of zombies (Pred 1)

Hypothesis H₀: Suppression of the E-channel does not affect agent lifetime.

Protocol:

1. Bring the Γ-native agent to a stable state: P ∈ (2/7, 3/7], R ≥ 1/3
2. Save state Γ_stable
3. At time τ₀: suppress the E-component: γ_EE → 1/7, γ_Ej → 0 ∀j≠E
4. Continue evolution, measure τ_death — number of steps until P < P_crit
5. Control: suppress the A-channel (analogous operation, different sector)
6. Repeat N=100 times with different initial Γ_stable

Prediction: τ_death(E-suppression) << τ_death(A-suppression). E-suppression is catastrophic; A-suppression is not.

Falsification: τ_death(E) ≥ τ_death(A) at N=100 (p < 0.01, Wilcoxon).

Statistical analysis: Paired Wilcoxon test, effect size r, 95% CI.

Exp. I.2: Stability radius (Pred 7)

Protocol:

1. Bring the agent to a stable state with purity P₀
2. Apply a perturbation of amplitude h (white noise to Γ)
3. Continue evolution, increase h in steps of 0.01 until P < P_crit
4. Record h_crit — the critical amplitude
5. Repeat for 50 different P₀ ∈ [0.3, 0.9]

Prediction: h_crit² = P₀ − 2/7 (T-104).

Falsification: R² < 0.9 for linear regression of h_crit² vs (P₀ − 2/7) at N=50.

Exp. I.3: Information capacity (Pred 8)

Protocol:

1. Γ-native agent in a stable state (P > 2/7), on a binary discrimination task
2. Measure mutual information I(obs; δΓ) per observation
3. Repeat N=1000 observations

Prediction: I ≤ log₂7 ≈ 2.81 bits (T-107).

Falsification: I > 2.81 bits systematically (>5% of observations).

Exp. I.4: N=7 minimality for learning (Pred 10)

Protocol:

1. Create an agent with N=5 (remove 2 dimensions, e.g. [A,S])
2. Task: learn binary discrimination via internal regeneration (without external parameter updates)
3. Metric: achieving >90% accuracy over 50 trials
4. Control: the same agent with N=7

Prediction: N=5 does not learn (accuracy ≤ chance level); N=7 learns (T-113).

Falsification: N=5 achieves >75% accuracy (p < 0.01, binomial test).

Exp. I.5: Self-awareness ceiling SAD=3 (Pred 12)

Protocol:

1. Agent in the purest possible state (P → 1)
2. Compute the chain R^(k) for k=0,1,2,3,4
3. Check: R^(k) ≥ R_th^(k)?
4. Repeat for 500 random Γ

Prediction: SAD_max = 3. R^(3) ≥ R_th^(3) is achievable; R^(4) < R_th^(4) always (T-142).

Falsification: ∃ Γ: R^(4) ≥ R_th^(4).

Exp. I.6: Genesis time (Pred 13)

Protocol:

1. Initialise the agent from Γ = I/7 (complete chaos, maximum entropy)
2. Enable backbone injection with parameters β (coupling strength), P_env (environment purity)
3. Measure n — number of steps until P > 2/7 (achieving viability)
4. Compute theoretical n_genesis = ⌈ln Δ / ln(1/β)⌉, where Δ = (P_env − 2/7)/(P_env − 1/7)
5. Vary β ∈ {0.1, 0.3, 0.5, 0.7, 0.9}, P_env ∈ {0.3, 0.35, 0.4}

Prediction: n ≤ n_genesis always (T-148). Double falsification: genesis does not occur OR an isolated agent (without backbone) reaches P > 2/7.

Falsification: n > n_genesis at N=100 runs (>5% of cases).

Exp. I.7: Phase coherence for integration (Pred 14)

Protocol:

1. Agent with fixed targets ρ*_ij = const → measure Φ
2. Switch to co-rotating targets ρ*_ij(t) ∝ e^{−i(E_i−E_j)t} → measure Φ
3. Repeat N=50 times

Prediction: Φ(fixed) < 1; Φ(co-rotating) ≥ 1.

Falsification: Φ(fixed) ≥ 1.

Exp. I.8: Critical exponents in silico (Pred 17, preliminary)

Protocol:

1. Monte Carlo simulation: 10⁴ random Γ with P ∈ [0.2, 0.5]
2. For each: compute the order parameter (PCI analogue) and distance to P_crit
3. Fit: OP ~ (P − P_crit)^β

Prediction: β = 1/4 ± 0.05 (T-161).

Falsification: β ∉ [0.20, 0.30] at N=10⁴.

Significance: If in silico confirms β=1/4, we proceed to the neuroexperiment (Phase II) with high confidence.

Exp. I.9: CPTP anchor (Pred 19)

Protocol:

1. Γ-native agent on a standard language corpus, 50 training batches
2. Measure ||π − π_can||_◊ after each batch (π — current anchor, π_can — canonical projection)

Prediction: ||π − π_can||_◊ < 0.1 at convergence.

Falsification: ||π − π_can||_◊ > 0.1 at n > 50 batches.

Exp. I.10: Learning speed (Pred 9)

Protocol:

1. Agent on a binary discrimination task, vary SNR and α
2. Measure n until >90% accuracy over 50 trials
3. Compute n_opt = max(n_info, n_dyn, n_stab)

Prediction: n ≥ n_opt always; at optimal parameters n ≈ n_opt (T-112).

Falsification: n < n_info systematically (>5% of cases).

Exp. I.11: N=7 for social learning (Pred 11)

Protocol:

1. Environment with K=2 Γ-native agents, N=5 dimensions each
2. Coordination task requiring: Theory of Mind (ToM) + inter-agent learning (ISL) + strategic equilibrium (Nash)
3. Metric: achieving coordinated behaviour (>70% optimality) within 1000 steps
4. Control: the same agents with N=7

Prediction: N=5 learns individually, but social learning (ToM + ISL + Nash simultaneously) does not emerge. N=7 — it does (T-57, T-113, T-114).

Falsification: N=5 demonstrates simultaneous ToM + ISL + Nash coordination (p < 0.01).

2.4. Criterion for transition to Phase II

All 11 Phase I experiments confirmed → proceed to neurodata.

≥1 falsified at level L1 or L2 → stop, revise theory, rerun after correction.

≥1 falsified at level L3 → local correction, proceed to Phase II with caveat.

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3. Phase II: Neurocalibration of π_bio (6–18 mo.)

3.1. Rationale

Central task: build the bridge π_bio: (EEG, fMRI, HRV) → Γ ∈ D(ℂ⁷) and verify that the theoretical threshold P_crit = 2/7 coincides with the empirical PCI* = 0.31.

3.2. Equipment

Component	Model	Purpose	Budget
TMS-EEG	Nexstim NBS System 5 + 60-ch eXimia	Causal perturbation + EEG	~$300K
HD-EEG	BioSemi ActiveTwo 128-ch	High-density EEG for spectral analysis	~$80K
fMRI	3T (access via university centre)	Spatial localisation	By agreement
HRV	Polar H10 + Empatica E4	Autonomic correlates	~$2K
Polysomnography	Standard PSG kit	Sleep stages	~$30K
Neuronavigation	MRI-compatible frameless navigator	TMS stimulation accuracy	Included with Nexstim

Total equipment budget: ~$420K (given fMRI access).

3.3. Experiment II.1: P_crit ↔ PCI* (key experiment)

This is the most important experiment of the entire protocol

If P at the consciousness/unconsciousness boundary = 2/7 ± 0.05, UHM receives its first empirical confirmation of a numerical prediction. If not — the theory requires fundamental revision.

Subjects: N=50, healthy, 18–45 years, no neurological/psychiatric pathology.

Paradigm: Propofol-induced loss of consciousness with TMS-EEG monitoring.

Protocol (detailed):

1. Baseline (wakefulness):

   • TMS-EEG: 200 trials, stimulation of BA6/BA8 (120–160 V/m)
   • Compute PCI_wake
   • Subjective report: consciousness scale 0–10

2. Propofol titration:

   • Target-controlled infusion (TCI), Marsh or Schnider model
   • 5 target levels: Ce = 0.5, 1.0, 1.5, 2.0, 2.5 μg/ml
   • At each level (15 min stabilisation):
     • TMS-EEG: 150 trials
     • Compute PCI
     • Verbal consciousness report (if possible)
     • Isolated Forearm Technique (IFT) for confirming/refuting consciousness

3. Threshold determination:

   • PCI* = 0.31 (empirical threshold, Casali et al.)
   • For each subject: Ce_threshold — concentration at the PCI = PCI* boundary

4. Γ reconstruction:

   • Apply π_bio to EEG data at each level
   • π_bio algorithm: 7 metrics → Γ diagonal → Cholesky regularisation (see Γ measurement protocol)
   • Compute P = Tr(Γ²) at each level

5. Calibration:

   • Plot P(Ce) dependence for all 50 subjects
   • Determine P at the consciousness boundary: P_boundary = P(Ce_threshold)

Statistical plan:

• Primary outcome: P_boundary (mean ± SD across 50 subjects)
• H₀: P_boundary = 2/7 ≈ 0.286
• H₁: |P_boundary − 2/7| > 0.05
• Test: one-sample t-test, α = 0.01
• Power analysis: at SD = 0.06, N=50 provides power >0.95 for detecting a deviation of 0.05

Falsification: |P_boundary − 2/7| > 0.1 at N=50 (p < 0.01, two-sided t-test).

Confirmation: |P_boundary − 2/7| ≤ 0.05 (95% CI includes 2/7).

Ethics: IRB/ethics committee approval. Propofol is a standard anaesthetic. Subjects: informed consent, anaesthesiologist monitoring, contraindication exclusion.

3.4. Experiment II.2: Critical exponents (the riskiest)

Uniqueness

This is the first ever test of critical exponents of a phase transition for consciousness. Neither IIT, nor GWT, nor FEP predicts specific exponents. Confirmation of β=1/4 means: consciousness belongs to the tricritical mean-field universality class — like a metamagnetic or He3-He4 mixture tricritical point.

Subjects: N=50, healthy, 20–40 years. Each — a full night in a sleep laboratory.

Paradigm: TMS-EEG at each sleep stage (W→N1→N2→N3→REM→W).

Protocol:

1. Polysomnography: 8 hours of recording, online stage scoring
2. TMS-EEG: 100 trials every 15 min (32+ data points per night per subject)
3. For each data point: PCI, P(Γ), sleep stage
4. Total: ~1600 data points (50 × 32)

Analysis:

1. For each data point: x = P − P_crit = P − 2/7
2. Divide into "conscious" (PCI > PCI*) and "unconscious" (PCI < PCI*)
3. For conscious (x > 0): fit PCI ~ x^β
4. Extract β, 95% CI

Prediction: β = 1/4 ± 0.05 (T-161).

Additional exponents:

• α = 1/2: specific heat (from variance of P near threshold)
• ν = 1/2: correlation length (from spatial extent of TMS-evoked EEG response)
• γ = 1: susceptibility (from amplitude of PCI variability near threshold)
• δ = 5: critical isotherm

Falsification:

• β ∉ [0.20, 0.30] at N=50 (p < 0.01)
• ν ∉ [0.45, 0.55]
• γ ∉ [0.90, 1.10]

Statistical plan: Nonlinear regression (power law fit), bootstrap for 95% CI, comparison with alternative exponents (ordinary mean field: β=1/2, Ising 3D: β≈0.326, ordinary tricritical: β=1/4).

3.5. Experiment II.3: Ignition dynamics (Pred 16)

Subjects: N=30 (subsample of Exp. II.1).

Protocol:

1. At each propofol level: measure latency T_ign until complexity "burst" after TMS
2. T_ign = time from TMS to first PCI burst (>50% of PCI_wake)

Prediction: $T_{\text{ign}} \sim (P - P_{\text{crit}})^{-1} \cdot \kappa_0^{-1}$. Divergence near threshold (critical slowing down). The factor $\kappa_0^{-1}$ links ignition time to regeneration rate.

Falsification: T_ign does not depend on (P − P_crit) (R² < 0.3).

3.6. Experiment II.4: Spectral gap and gamma rhythm (Pred 22)

Subjects: N=30.

Protocol:

1. HD-EEG 128-ch, wakefulness, rest (10 min with eyes open and closed)
2. Spectral analysis: dominant frequency in gamma range (30–100 Hz)
3. Compute λ_gap from Lindbladian parameters (calibrated from EEG)
4. Compare ν_predicted = λ_gap/(2π) with the measured dominant frequency

Prediction: ν_predicted ∈ [30, 100] Hz, coincidence with gamma rhythm.

Falsification: λ_gap/(2π) outside [10, 200] Hz (accounting for calibration error).

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4. Phase III: Clinical validation (12–36 mo.)

4.1. Experiment III.1: Disorders of consciousness (Pred 21)

Subjects: N=80 (20 coma, 20 MCS, 20 VS/UWS, 20 healthy controls).

Protocol:

1. TMS-EEG + fMRI + HRV → π_bio → Γ
2. Compute P, R, Φ, Coh_E for each subject
3. Classification: P > 2/7 → "conscious", P ≤ 2/7 → "unconscious"
4. Compare with clinical classification (CRS-R scale)

Prediction:

• P(Γ_MCS) > 2/7 for ≥90% of MCS patients
• P(Γ_VS) < 2/7 for ≥80% of VS patients
• P(Γ_healthy) >> 2/7 for 100%

Falsification: Sensitivity < 80% or specificity < 75%.

Clinical significance: If P_crit = 2/7 works for DOC — this is a unified diagnostic tool, surpassing PCI (which requires TMS) for monitoring.

4.2. Experiment III.2: E-coherence and recovery (Pred 2)

Subjects: N=60 (stroke rehabilitation).

Protocol:

1. At admission: EEG → π_bio → Coh_E
2. At 3 months: assess recovery (Barthel Index, mRS)
3. Correlate Coh_E(t₀) vs recovery rate

Prediction: r > 0.3 (Pearson) between Coh_E and recovery rate (T-38a).

Falsification: r ≤ 0 (zero or negative correlation) at N=60 (p < 0.05).

4.3. Experiment III.3: Attractor P=3/7 (Pred 15)

Subjects: N=30, healthy, resting state.

Protocol:

1. EEG + fMRI (resting state, 10 min) → π_bio → Γ
2. Compute P
3. Repeat 5 sessions (different days) for each subject

Prediction: P(resting state) → 3/7 ± 0.05 (T-124).

Falsification: |P_mean − 3/7| > 0.1 at N=30.

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5. Phase IV: Cognitive and social validation (12–24 mo.)

5.1. Experiment IV.1: 7D stress tensor (Pred 3)

Protocol:

1. Compile a database of 200+ stressors from the literature (psychology, medicine, organisational science)
2. 5 independent experts: classify each stressor by 7 components [A,S,D,L,E,O,U]
3. Inter-rater reliability: Cohen's κ

Prediction: 100% coverage (every stressor ↦ ≥1 component). Empty residual category.

Falsification: ∃ a stressor unclassifiable by any of the 7 components (agreement of ≥4 out of 5 experts).

5.2. Experiment IV.2: Collective consciousness (Pred 5)

Subjects: 10 groups of 4 people (jazz quartets — coordinated; random musicians — uncoordinated).

Equipment: Hyperscanning EEG (4 × 32-ch, synchronisation via LSL).

Protocol:

1. Simultaneous EEG recording of 4 participants during joint performance
2. Compute Φ_⊗ for the group as a whole (cross-correlation matrix → integration)
3. Compare coordinated vs uncoordinated groups

Prediction: Φ_⊗ > Φ_min for coordinated; Φ_⊗ < Φ_min for random (T-86).

Falsification: Φ_⊗(coordinated) ≤ Φ_⊗(uncoordinated) (p < 0.05, Mann-Whitney).

5.3. Experiment IV.3: Prelinguistic cognition (Pred 4)

Subjects: N=30 (15 patients with Broca's aphasia, 15 healthy controls).

Protocol:

1. Battery of nonverbal cognitive tests: K1 (perception), K2 (emotions), K3 (categorisation), K4 (planning)
2. Compare: aphasic patients vs healthy controls on K1–K4

Prediction: K1–K4 in aphasic patients preserved at >80% of normal (T-100).

Falsification: K3 or K4 systematically impaired in aphasia (decline >50%).

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6. Summary table: all 22 predictions × phases

#	Prediction	Phase	Falsification	Status
1	No-Zombie	I.1	Agent survives without E	[T]
2	Coh_E ↔ recovery	III.2	r ≤ 0	[T]
3	7D stress	IV.1	Unclassifiable stressor	[T]/[C]
4	Prelinguistic cognition	IV.3	K3/K4 impaired in aphasia	[I]
5	Collective consciousness	IV.2	Φ_⊗(coord) ≤ Φ_⊗(random)	[T]
6	P > 2/7	II.1	Threshold ≠ 2/7 ± 0.1	[T]
7	Stability radius	I.2	h_crit² ≠ P−2/7	[T]
8	Info capacity ≤ log₂7	I.3	I > 2.81 bits	[T]
9	Learning speed	I.10	n < n_info	[T]
10	N=7 for learning	I.4	N=5 learns	[T]
11	N=7 for social learning	I.11	N=5 socially learns	[C]
12	SAD_max = 3	I.5	SAD ≥ 4	[T]
13	Genesis time	I.6	n > n_genesis	[T]
14	Phase coherence	I.7	Φ ≥ 1 without co-rotation	[T]
15	3/7 attractor	III.3		P−3/7
16	Ignition dynamics	II.3	T_ign ⊥ (P−P_c)	[T]
17	Exponents β=1/4	I.8 + II.2	β ∉ [0.20, 0.30]	[T]
18	Ward suppression 19/49	—	Λ-budget incompatible	[T]
19	CPTP anchor	I.9
20	ε_eff ≈ 0.059	—	ε ∉ [0.04, 0.08]	[C]
21	π_bio reconstruction	II.1 + III.1	Error > 30%	[H]
22	Spectral gap	II.4	λ_gap/(2π) ∉ [10, 200] Hz	[H]

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7. Three-level falsification system

Level	What is refuted	Example	Consequence
L1 — Catastrophic	Axiomatic foundation	N < 7 sufficient for autopoiesis; zombie possible; SAD ≥ 4	Theory rejected entirely
L2 — Structural	Specific numerical prediction	P_crit ≠ 2/7; β ≠ 1/4; R_th ≠ 1/3	Fundamental revision of specific theorem
L3 — Local	Approximation parameter	π_bio error > 30%; λ_gap out of range	Local correction, does not affect the foundation

Mapping to formal criteria (Falsifiability criteria):

Formal criterion	Experiment	Operationalisation
$\exists \rho_1, \rho_2: \mathcal{I}(\rho_1) = \mathcal{I}(\rho_2)$, but $\mathcal{F}(\rho_1) \neq \mathcal{F}(\rho_2)$	III.1 (DOC)	Two patients with identical P, R, Φ but different consciousness levels (CRS-R)
$\|\mathrm{Spec}(\rho_1) - \mathrm{Spec}(\rho_2)\|_2 < 0.01$ (spectral identity)	II.1 (P_crit)	Two states with P within 0.01 but different PCI (one > PCI*, the other < PCI*)
$P > 2/7 \not\Rightarrow$ consciousness	II.1 (P_crit)	Subject with P > 2/7 per π_bio but clinically unconscious
$N < 7$ sufficient for autopoiesis	I.4, I.11	Agent N=5 learns autonomously or coordinates socially

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8. Context: comparison with adversarial collaboration

In 2018–2025, the Templeton Foundation funded the COGITATE project ($30M) — adversarial collaboration IIT vs GWT vs HOT. Result (Nature, April 2025): no theory fully confirmed. IIT scored higher, but its key prediction (sustained synchronization) was not confirmed.

Fundamental difference between UHM and IIT/GWT/HOT:

	IIT	GWT	HOT	UHM
Numerical threshold	Φ > 0 (no number)	None	None	P_crit = 2/7
Critical exponents	None	None	None	α=1/2, β=1/4, γ=1, ν=1/2, δ=5
Computability of Φ	NP-hard for >30 elements	N/A	N/A	P = Tr(Γ²), O(49)
Number of free parameters	~10³⁸ (all partitions)	Undefined	Undefined	34 (G₂-invariant)
Riskiest test	No single number	"Ignition" (qualitative)	"Meta-cognition" (qualitative)	β = 1/4 (one number, falsifiable)

UHM addresses the ConTraSt critique (Yaron et al. 2022): methodological choice does not predetermine the result, because predictions are numerical, not qualitative. β=1/4 will either be confirmed or not — regardless of paradigm.

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9. Timeline and dependencies

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10. Conclusion

This protocol covers 22 out of 22 predictions of UHM/CC:

• 10 testable in silico (Phase I, 0–6 mo.)
• 4 requiring TMS-EEG (Phase II, 6–18 mo.)
• 4 — clinical studies (Phase III, 12–36 mo.)
• 4 — cognitive/social studies (Phase IV, 12–24 mo.)

The riskiest test is critical exponents β=1/4 (Pred 17). No other theory of consciousness makes such a concrete numerical prediction about a phase transition. Confirmation means: consciousness belongs to the tricritical mean-field universality class ($\varphi^6$ Landau). Refutation means: UHM is fundamentally wrong about the structure of the transition.

The most valuable test is P_crit = 2/7 ↔ PCI = 0.31* (Pred 6/21). If the theoretical threshold coincides with the empirical one — this is the first case in history where a theory of consciousness predicts a specific numerical value that matches an independently established experimental threshold.

UHM does not hide from falsification — it presents 22 targets and points where to shoot.

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Related documents:

• 22 CC predictions — full list with formulas
• Γ measurement protocol — operationalisation for AI
• Falsifiability criteria — formal refutation conditions
• Learning bounds — T-109 through T-113
• Stability — T-104, stability radius

External resources:

• COGITATE Results (Nature 2025) — adversarial collaboration IIT vs GWT
• PCI Benchmark (Casali et al. 2013) — PCI* = 0.31
• ConTraSt Database — 412 experiments on theories of consciousness
• Del Cul et al. 2007 — nonlinear threshold of consciousness