Computational transmission layer · Phase 1 (didactic)
Interactive Model: Predicted Afferent Transmission
Current status
- Research architecture established.
- Measurement protocols under development.
- Computational transmission layer currently illustrative (not empirical tissue data).
- Empirical validation pending.
This page is a simplified, exploratory representation of the computational transmission layer under investigation at Rex Autistikōn Labs. The working hypothesis is that measurable differences in local tissue mechanics at eight sensor-rich interfaces may alter the statistics of ascending afferent signals. “Mechanical restriction” names that hypothesized latent state; it is not treated here as a directly observed scalar.
Adjust the didactic parameters below to see how stiffness- and viscosity-like terms change predicted transmission quantities. Nothing here is a measurement; it is a way of making the transmission hypotheses legible and criticisable.
Model parameters
Global tissue properties
Scales the selected illustrative profile. Moving this resets any per-zone adjustments. Not a measured restriction index.
Rate of the mechanical event being sensed (0.05–300 Hz, logarithmic).
Elastic term. Higher stiffness reduces tissue deformation and receptor drive.
Damping term. Higher viscosity attenuates rapid events more than slow ones.
Model visualisation and readouts
Systemic network
Select a zone to inspect it
- Retained fidelity
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- Transmitted amplitude |H|
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- Effective precision πeff
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- Prediction error ε
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- Weighted error πε
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- Zone free energy F
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- Gradient ∂F/∂r
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Systemic summary
Observational MPA-style map from didactic latent state (0–8 each · composite 0–64). Not a validated mechanical gold standard.
- Mean fidelity
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- Mean πeff
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- ΣF
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What this means
View underlying values for all eight zones
| Zone | fc (Hz) | r (didactic) | MPA (obs.) | |H| | Fidelity | πeff | ε | F |
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How the model is computed (Phase 1 didactic)
Place in the hierarchy. Empirical work at Rex Autistikōn Labs separates (1) measurement of mechanical quantities, (2) mechanical representation (e.g. moduli, strain, relaxation), and (3) computational transmission predictions. This interactive page is layer (3) only. It does not acquire data. Full policy: Research Framework· Model documentation.
Each zone is represented as a Kelvin–Voigt viscoelastic element. Driving the element at angular frequency ω yields transmitted strain amplitude |H(ω)| = G₀ / √(G² + (ωη)²), normalised so an unrestricted didactic baseline responds fully to very slow events. The control labelled as a latent-state / restriction-like parameter r is a hypothesis-level didactic variable: it raises both stiffness- and viscosity-like terms in the demo so predicted attenuation can be inspected. It is not an instrument reading and must not be used to define “restriction” retrospectively from model outputs.
Effective sensory precision is modelled as πeff = (|H|² + c) / (1 + c). Prediction error ε is the difference between the unrestricted didactic expectation and the current transmitted signal. Variational free energy follows as F = ½πε² − ½lnπ. The gradient ∂F/∂r is a software sensitivity readout—not a scientific falsification test of the restriction construct (see Research Framework criteria: reliability, construct coherence, and preregistered physiological prediction).
MPA–Zone integers map each zone’s didactic latent state onto a 0–8 scale (composite 0–64). In the research program, MPA-style scores are treated as independent observational variables until preregistered analyses establish correspondence with objective mechanical measurements. They are not the primary definition of computational mechanical state.
Intended use: Research communication, educational exploration, and generation of testable predictions for future experimental work. Zone anatomy differs; one illustrative control law does not imply one physical instrument for all eight interfaces.