LEARNING FUNCTION · π¹_s

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 π¹_s as the quantity being defined or updated by this relation.

2. Substitute the source-defined variables or recorded values and follow the displayed arithmetic and indexing exactly.

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

STAGE 1: MARGIN EMPHASIS
m_s = r_s - max_{u≠s}(r_u)           // How much better than second-best?
π¹_s = π⁰_s × σ(α_m × m_s)           // High margin → high confidence

Prevents going all-in on ambiguous cases

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STAGE 2: TRUTH MODULATION + NOVELTY SUPPRESSION

π¹_s = π⁰_s × σ(α_m × m_s)           // High margin → high confidence

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