Experiment 0: Substrate Engineering

Status: Complete. V13 content-based coupling Lenia with lethal resource dynamics. Foundation for all subsequent measurement experiments.

This is not a result about affect by itself; it is the substrate-control record. The question is whether the system produces long-running, perturbable trajectories with enough internal structure to justify measuring world models, self-models, and social integration later. The answer is yes, but with an important limit: V13 improves on earlier convolution-only substrates without breaking the agency wall.

Experiment 1: Emergent Existence

Status: Complete. Patterns persist, maintain boundaries, and respond to perturbation. Established by V11-V12, confirmed in V13.

This is the first gate against over-interpreting the archive. Before asking whether a pattern has a world model, the program checks whether there is a persisting pattern to measure at all. V11 and V12 establish persistence and perturbation response; V13 provides the content-coupled substrate used for the later Exp2-Exp12 analyses.

Experiment 2: Emergent World Model

Question: When does a pattern's internal state carry predictive information about the environment beyond current observations?

Method: Prediction gap $\mathcal{W}(\tau) = \text{MSE}[f_{\text{env}}] - \text{MSE}[f_{\text{full}}]$ using Ridge regression with 5-fold CV.

Finding: World model signal present but weak. Seed 123 at bottleneck shows 100x amplification. World models are amplified by bottleneck selection, not gradual evolution. To be clear about magnitude: $\mathcal{C}_{\text{wm}} \approx 0.0002$ for most seeds means the internal state predicts the environment barely better than the environment alone. Only seed 123 at maximum bottleneck pressure reaches 0.028 — detectable but still small. These patterns are not building substantial world models; they carry a faint trace of environmental predictive information, amplified briefly under extreme selection.

Experiment 3: Internal Representation Structure

Question: When do patterns develop low-dimensional, compositional representations?

Finding: Compression is cheap — $d_{\text{eff}} \approx 7$/68 from cycle 0. But quality only improves under bottleneck selection. Note the asymmetry: abstraction ($\mathcal{A} \approx 0.89$) is high and stable from the start — the system compresses efficiently without effort. But disentanglement ($\mathcal{D} \approx 0.25$) remains low — the compressed representations are tangled, not cleanly factored. Disentanglement requires active information-seeking that this substrate lacks.

Experiment 4: Emergent Language and Multi-Agent Culture

Question: When do patterns develop structured, compositional communication?

Finding: Chemical commons, not proto-language. MI above baseline in 15/20 snapshots but $\rho_{\text{topo}} \approx 0$ everywhere. Unstructured broadcast, not language.

Experiment 5: Counterfactual Detachment

Question: When do patterns decouple from external driving and run offline world model rollouts?

Result: Null. $\rho_{\text{sync}} \approx 0$ from cycle 0. Patterns are inherently internally driven. The FFT convolution kernel integrates over the full grid — there is no reactive-to-autonomous transition because the starting point is already autonomous.

Experiment 6: Self-Model Emergence

Question: When does a pattern predict itself better than an external observer can?

Result: Weak signal at bottleneck only. $\rho_{\text{self}} \approx 0$ everywhere. $\text{SM}_{\text{sal}} > 1$ appears once: seed 123, cycle 20, one pattern at $\text{SM}_{\text{sal}} = 2.28$.

Experiment 7: Affect Geometry Verification

Question: Does the geometric affect structure predicted by the thesis actually appear? RSA between structural affect (Space A) and behavioral affect (Space C).

Finding: Structure-behavior alignment in 8/19 snapshots. Seed 7 shows evolutionary trend. A-B alignment null (structure maps to behavior but not communication).

Experiment 8: Perceptual Mode and Computational Animism

Question: Do patterns develop modulable perceptual coupling? The earlier scalar “inhibition coefficient” is retired here in favor of the entity-indexed ascription field $\alpha(x)$ — how much agency a perceiver attributes to a target — with coupling $\kappa$ and gain $\gamma$ as the further, separately-measurable quantities. This experiment measures $\alpha$ via the teleology bias in how patterns represent each target.

Experiment 9: Proto-Normativity

Question: Does the viability gradient generate structural normativity?

Result: Null. No $\Delta V$ asymmetry between cooperative and competitive contexts. But $\intinfo_{\text{social}} \gg \intinfo_{\text{isolated}}$ (~4.9 vs ~3.1). Social context increases integration regardless of interaction type. Normativity requires agency — the capacity to act otherwise.

Experiment 10: Social-Scale Integration

Question: Does $\intinfo_G > \sum_i \intinfo_i$?

Finding: No superorganism. Ratio 1-12% but increasing. Seed 7: 6.1% to 12.3% over evolution. Moving toward threshold but not reaching it.

Experiment 11: Entanglement Analysis

Question: Are world models, abstraction, communication, detachment, and self-modeling separable or entangled?

Finding: Four clusters — but not the ones predicted. Most measures cluster into one large group driven by population-mediated selection. Overall entanglement increases (mean |r| from 0.68 to 0.91). Everything becomes more correlated, just not in the clusters the theory expected.

Experiment 12: Identity Thesis Capstone

Question: Does the full program hold in a system with zero human contamination?

Verdict: All seven criteria met, most at moderate/weak strength. Geometry confirmed; dynamics undertested, blocked by the coupling wall.