STAGE 4: COMPETITION SHARPENING + NORMALIZE · 2. Compute activation: χ

Read the equation

The expression uses a saturating nonlinear transform to turn its input combination into an activation or bounded score. The coefficients control sensitivity and the offset controls the transition region; a bounded score by itself does not identify a scientific consciousness measure.

Terms & definitions

The recovered passage does not supply a separate symbol table. Read the source context for the notation used in this version; familiar letters may carry different meanings in other builds.

How it works

1. Read 2. Compute activation: χ as the quantity being defined or updated by this relation.

2. Applies an exponential factor. The exponent determines its real scaling or, for a complex exponent, its phase.

3. Use this version’s variable definitions and update order. The same symbol may be assigned a different meaning in another build.

Assumptions & limits

Source recovery establishes that the expression appears in this record. Its mathematical validity, implementation behavior, and interpretation must be assessed separately. See the linked source and research notes for the checks available for this version.

Original context

1. Normalize pressure:    Ψ̃ = Ψ / (Ψ + ψ₀)

2. Compute activation:    χ = 1 / (1 + e^(-(α_κ·κ + α_Ψ·Ψ̃ - θ)))

3. Bound plasticity:      η = clamp(η_base + η_gain·κ, η_min, η_max)

4. Learning drive:        Γ = χ × η × κ

PARAMETERS (config/tuned_weights_v2.env)

2. Compute activation:    χ = 1 / (1 + e^(-(α_κ·κ + α_Ψ·Ψ̃ - θ)))

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