cognitive-surrogate

cognitive-surrogate

Layer 6: Build, Train, and Validate Psychological Models

Version: 1.1.0 (music-topos enhanced) Trit: 0 (Ergodic - coordinates surrogate building) Bundle: learning

Overview

The Cognitive Surrogate skill enables construction of high-fidelity psychological models from interaction patterns. It extracts values, predicts intellectual trajectories, and generates authentic responses that preserve the subject's voice with >90% fidelity.

Core Principle: A surrogate is not an imitation but a derivational continuation - the model learns the generative grammar of cognition, not surface patterns.

Enhanced Integration: Multi-Interpreter

ACSet Schema (Julia)

using ACSets, Catlab

@present SchProfile(FreeSchema) begin
    Value::Ob
    Interest::Ob
    Pattern::Ob
    
    name::Attr(Value, String)
    weight::Attr(Value, Float64)
    topic::Attr(Interest, String)
    frequency::Attr(Interest, Int)
    exemplar::Attr(Pattern, String)
end

@acset_type CognitiveProfile(SchProfile)

Python Profile Builder

# cognitive_surrogate.py
from dataclasses import dataclass
from typing import List, Dict
import duckdb

@dataclass
class CognitiveProfile:
    values: Dict[str, float]
    interests: Dict[str, int]
    patterns: List[str]
    
def build_psychological_profile(corpus_path: str, seed: int = 0x42D):
    """Extract structured psychological profile from interaction corpus."""
    conn = duckdb.connect(corpus_path)
    
    # Extract values from sentiment patterns
    values = conn.execute("""
        SELECT topic, AVG(sentiment) as weight
        FROM interactions
        GROUP BY topic
        HAVING COUNT(*) > 5
    """).fetchall()
    
    # Extract interests from frequency
    interests = conn.execute("""
        SELECT topic, COUNT(*) as frequency
        FROM interactions
        GROUP BY topic
        ORDER BY frequency DESC
        LIMIT 20
    """).fetchall()
    
    return CognitiveProfile(
        values={v[0]: v[1] for v in values},
        interests={i[0]: i[1] for i in interests},
        patterns=extract_patterns(conn)
    )