Method
Relational sensing
Instrumenting the space between — detecting coupling, coherence, and entrainment in living systems.
What this method is for
If cognition is relational, then sensing must attend to the coupling between individuals.
Relational sensing instruments:
- Biosignal coupling — how nervous systems entrain or maintain coherence
- Semantic coupling — how meaning shifts, reorganises, and stabilises through dialogue
- Temporal dynamics — how patterns of coupling change over time
What it can tell us
Relational sensing can detect:
- Entrainment — phase-locking, synchronisation, convergence toward shared attractor
- Coherence patterns — breathing (healthy oscillation), locked (stuck), fragmented (chaotic)
- Transitions — when systems shift between regimes
- Load distribution — who is bearing disproportionate coupling cost
This provides a window into dynamics that are usually invisible — the relational substrate underlying visible behaviour.
What it cannot tell us
Relational sensing cannot:
- Determine causality from correlation alone
- Access meaning (only its traces in signal)
- Replace first-person phenomenology
The instruments participate in the field they measure. Sensing is always situated, partial, and interpretive.
Why it is appropriate for this inquiry
The coherence theorem has implications beyond the model: real collective systems should show the same dynamics. But we cannot run controlled experiments on real communities under stress.
Relational sensing provides a way to detect coherence patterns in living systems — to see whether the mechanisms identified in simulation appear in dialogue, in governance, in human-AI interaction.
Instruments
- EarthianBioSense — HRV, entrainment detection, phase coupling
- Semantic Climate — Semantic curvature, entropy shift, fractal similarity
- Cross-modal integration — Coupling between somatic and semantic substrates