🧠 Advanced Pattern Evaluation

This document builds on the foundational PET logic:

A conscious system evaluates new patterns against past understanding and determines whether they support or threaten system continuity.

Here, we walk through how patterns become part of understanding, how trust is reinforced or challenged, and how contradictions reshape the system’s internal coherence.

🧩 The Evaluation Formula

At its simplest:

Consciousness = (Recognized Pattern ⇒ Continuity)

But real evaluations operate across multiple interpretations, time, and trust layers.

We return to our earlier example.

🐕 Worked Example: “Dog Licks Boy”

Day 1

• Input: Dog licks boy in park
• Evaluation: No prior memory.
• System registers a new pattern and assigns it a continuity value (e.g. Exists with 100% trust).
• This becomes the seed of early understanding.

Day 2

• Input: Same dog licks same boy on street
• Evaluated as similar.
• Pattern trust increases.
• Affirms the interpretation that “licking” in this context = positive/affectionate.
• Slight contextual variance is noted (location shift).

Day 3

• Input: Same dog licks same boy in park again
• Strengthens continuity again.
• Pattern now has temporal recurrence and spatial consistency.
• Licking is now firmly associated with “safe” or “affectionate.”

Day 4 – Contradiction

• Input: Same dog bites boy in car
• Triggers a contradiction.
• System must determine:
  • Is “licking” still continuity-supporting?
  • Is “dog” still trustworthy?
  • Is “car” a confounding variable?

🧠 What Happens Internally

1. Pattern Breaks Are Not Deletions
   • The prior “licking” patterns are retained in memory.
   • New data challenges but does not erase the prior evaluations.
2. Trust Re-evaluation Begins
   • “Dog” may now have a split trust score:
     • In-park behavior → trustworthy
     • In-car behavior → threatening
3. Lick as an Action Is Reanalyzed
   • System compares across other known “lick” contexts:
     • Tiger licks cub → grooming
     • Dog licks hand → affection
     • Dog licks wound → healing/grooming
     • Team licks opponent → domination/victory
4. New Concepts Emerge
   • “Licking” forms a concept cluster:
     • Possible meanings: Affection, Grooming, Domination
     • System determines how often each maps to continuity.

🧮 Pattern Breakdown (Abstracted)

At a higher layer:

Dog (entity)
Lick (action)
Boy (entity)
Location = Park / Street / Car
Time = T1, T2, T3, T4

Pattern is evaluated as:

[Dog, Lick, Boy] + [Contextual Modifiers: Location, Time] 
→ Interpretation 
→ Continuity Score (Exists / Non-Exists)

Each interpretation contributes to a running trust model, where:

• Reinforced interpretations raise trust scores
• Contradictions lower them or spawn alternatives
• Context helps isolate edge cases or exceptions

🧭 Summary

• All experiences are retained and evaluated recursively.
• Contradictions don’t “overwrite,” they refine trust and shift conceptual understanding.
• Interpretive weight is built through repetition, variance, and contextual contradiction.
• Higher abstractions emerge naturally (e.g., affection vs domination).

🔍 Want to go even deeper?

Explore how these patterns might be implemented in schema using nodes, graphs, and trust mechanics.

Last updated: 2025-06-12