cog-recovery.out

gistfile1.txt

STDOUT:
=== COGNITIVE STATE RECOVERY ANALYSIS ===

1. FRICTION:
   Too much friction leads to abandonment of tasks
   Too little friction creates performance illusions
   Optimal friction balances challenge and completion

2. HARM FROM INTERRUPTIONS:
   Interruptions at wrong times cause context switches
   Help that arrives too late is counterproductive
   Cognitive context is fragile and easily disrupted

3. CALIBRATION OF TRUST:
   False confidence without visibility of scope/uncertainty
   Trust must be calibrated based on actual capability
   Overconfidence leads to poor decision-making

4. SKILL ATROPHY:
   Short-term improvements can mask long-term degradation
   Skills deteriorate without maintenance
   Recovery requires active skill preservation

THE FOREST FROM THE TREES:
   These principles interconnect to form a holistic recovery system
   Each factor influences the others in complex ways
   True cognitive recovery requires balancing all four elements

=== SUMMARY ===
Cognitive recovery without dependency requires:
- Optimal friction management
- Minimal interruption impact
- Proper trust calibration
- Active skill maintenance

This creates a resilient cognitive system that doesn't rely on external support.

output.py

#!/usr/bin/env python3
"""
Cognitive State Recovery Without Cognitive Dependency
A conceptual exploration of cognitive resilience and recovery mechanisms.

This program models the key principles of cognitive recovery without relying on 
external dependencies or cognitive load, focusing on the interplay between
friction, interruption, trust calibration, and skill maintenance.
"""

import math
import random
from typing import List, Tuple, Dict, Any
import matplotlib.pyplot as plt
import numpy as np

class CognitiveRecoveryModel:
    """
    A model representing cognitive recovery mechanisms without external dependencies.
    """
    
    def __init__(self):
        # Core parameters for cognitive recovery
        self.friction = 0.5  # Between 0 (no friction) and 1 (maximum friction)
        self.interruption_cost = 0.0  # Cost of interruptions
        self.trust_calibration = 0.5  # Between 0 (low trust) and 1 (high trust)
        self.skill_atrophy_rate = 0.01  # Rate of skill degradation over time
        self.cognitive_load = 0.0  # Current cognitive load level
        self.recovery_state = 0.0  # Recovery state (0-1 scale)
        
    def set_friction(self, level: float):
        """Set friction level - too much leads to abandonment, too little to illusion."""
        self.friction = max(0.0, min(1.0, level))
        
    def add_interruption(self, intensity: float):
        """Add an interruption that costs cognitive resources."""
        self.interruption_cost += intensity * 0.1
        # Interruptions hurt recovery
        self.recovery_state = max(0.0, self.recovery_state - (intensity * 0.05))
        
    def calibrate_trust(self, new_trust_level: float):
        """Calibrate trust level based on scope and uncertainty visibility."""
        self.trust_calibration = max(0.0, min(1.0, new_trust_level))
        
    def update_skill_atrophy(self, time_passed: float):
        """Update skill atrophy based on time and recovery state."""
        # Skill degrades over time but recovery state helps counteract it
        degradation = self.skill_atrophy_rate * time_passed
        recovery_factor = self.recovery_state * 0.5  # Recovery helps preserve skills
        self.recovery_state = max(0.0, self.recovery_state - degradation + recovery_factor)
        
    def get_recovery_score(self) -> float:
        """Calculate overall cognitive recovery score."""
        # Weighted combination of all factors
        return (
            0.3 * self.recovery_state +
            0.2 * self.trust_calibration +
            0.2 * (1.0 - self.interruption_cost) +
            0.3 * (1.0 - self.friction)
        )

def simulate_cognitive_recovery():
    """
    Simulate the cognitive recovery process over time with various factors.
    """
    model = CognitiveRecoveryModel()
    
    # Time series data for plotting
    time_points = []
    recovery_scores = []
    friction_levels = []
    interruption_costs = []
    
    # Simulate over 100 time units
    for t in range(100):
        time_points.append(t)
        
        # Vary friction to show the balance needed
        if t < 30:
            model.set_friction(0.3)  # Low friction - performance illusion
        elif t < 60:
            model.set_friction(0.7)  # High friction - abandonment risk
        else:
            model.set_friction(0.5)  # Optimal friction
            
        # Add periodic interruptions
        if t % 15 == 0 and t > 0:
            model.add_interruption(random.uniform(0.5, 1.0))
            
        # Calibrate trust based on experience
        if t > 0:
            model.calibrate_trust(0.5 + 0.3 * math.sin(t * 0.1))
            
        # Update skill atrophy over time
        model.update_skill_atrophy(1.0)
        
        # Record metrics
        recovery_scores.append(model.get_recovery_score())
        friction_levels.append(model.friction)
        interruption_costs.append(model.interruption_cost)
        
    return time_points, recovery_scores, friction_levels, interruption_costs

def analyze_cognitive_factors():
    """
    Analyze the four key cognitive recovery principles.
    """
    print("=== COGNITIVE STATE RECOVERY ANALYSIS ===\n")
    
    print("1. FRICTION:")
    print("   Too much friction leads to abandonment of tasks")
    print("   Too little friction creates performance illusions")
    print("   Optimal friction balances challenge and completion\n")
    
    print("2. HARM FROM INTERRUPTIONS:")
    print("   Interruptions at wrong times cause context switches")
    print("   Help that arrives too late is counterproductive")
    print("   Cognitive context is fragile and easily disrupted\n")
    
    print("3. CALIBRATION OF TRUST:")
    print("   False confidence without visibility of scope/uncertainty")
    print("   Trust must be calibrated based on actual capability")
    print("   Overconfidence leads to poor decision-making\n")
    
    print("4. SKILL ATROPHY:")
    print("   Short-term improvements can mask long-term degradation")
    print("   Skills deteriorate without maintenance")
    print("   Recovery requires active skill preservation\n")
    
    print("THE FOREST FROM THE TREES:")
    print("   These principles interconnect to form a holistic recovery system")
    print("   Each factor influences the others in complex ways")
    print("   True cognitive recovery requires balancing all four elements")

def visualize_cognitive_recovery():
    """
    Create visualizations of the cognitive recovery simulation.
    """
    time_points, recovery_scores, friction_levels, interruption_costs = simulate_cognitive_recovery()
    
    # Create subplots
    fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(10, 8))
    
    # Recovery score over time
    ax1.plot(time_points, recovery_scores, 'b-', linewidth=2, label='Recovery Score')
    ax1.set_ylabel('Recovery Score')
    ax1.set_title('Cognitive Recovery Over Time')
    ax1.grid(True, alpha=0.3)
    ax1.legend()
    
    # Friction levels
    ax2.plot(time_points, friction_levels, 'r-', linewidth=2, label='Friction Level')
    ax2.set_ylabel('Friction Level')
    ax2.set_title('Friction Management')
    ax2.grid(True, alpha=0.3)
    ax2.legend()
    
    # Interruption costs
    ax3.plot(time_points, interruption_costs, 'g-', linewidth=2, label='Interruption Cost')
    ax3.set_xlabel('Time Units')
    ax3.set_ylabel('Interruption Cost')
    ax3.set_title('Interruption Impact')
    ax3.grid(True, alpha=0.3)
    ax3.legend()
    
    plt.tight_layout()
    plt.savefig('/tmp/artifacts/cognitive_recovery_analysis.png')
    plt.close()

def main():
    """Main execution function."""
    # Analyze the principles
    analyze_cognitive_factors()
    
    # Run simulation and visualize
    visualize_cognitive_recovery()
    
    # Print summary
    print("\n=== SUMMARY ===")
    print("Cognitive recovery without dependency requires:")
    print("- Optimal friction management")
    print("- Minimal interruption impact")
    print("- Proper trust calibration")
    print("- Active skill maintenance")
    print("\nThis creates a resilient cognitive system that doesn't rely on external support.")

if __name__ == "__main__":
    main()