Cognitive Models¶
Advanced user modeling and personalization through cognitive fingerprinting.
Overview¶
Q-Memetic AI's cognitive modeling system creates detailed psychological and behavioral profiles of users to enable highly personalized memetic experiences. The system learns from user interactions to build comprehensive cognitive fingerprints.
Cognitive Fingerprinting¶
Core Concepts¶
A cognitive fingerprint captures:
- Learning preferences: Visual, auditory, kinesthetic preferences
- Cognitive load tolerance: Complexity handling capabilities
- Attention patterns: Focus duration and switching behaviors
- Emotional responses: Valence and arousal patterns
- Domain expertise: Knowledge levels across different fields
- Communication style: Formal, casual, technical preferences
Mathematical Foundation¶
The cognitive fingerprint is represented as a multi-dimensional vector:
F_user = [
learning_style, # [0,1]³ for VAK preferences
complexity_pref, # [0,1] complexity tolerance
attention_span, # R⁺ in minutes
emotional_profile, # [0,1]² for valence/arousal
domain_expertise, # [0,1]ⁿ for n domains
communication_style # categorical encoding
]
Implementation¶
Creating Cognitive Profiles¶
from qmemetic_ai.cognitive.models import CognitiveFingerprint
# Initialize with basic preferences
profile = CognitiveFingerprint(
user_id="researcher_001",
preferences={
"learning_style": {
"visual": 0.7,
"auditory": 0.2,
"kinesthetic": 0.1
},
"complexity_tolerance": 0.8,
"domain_expertise": {
"artificial_intelligence": 0.9,
"quantum_physics": 0.6,
"biology": 0.3,
"literature": 0.4
},
"communication_style": "technical",
"attention_span_minutes": 45,
"emotional_baseline": {
"valence": 0.6, # Positive emotional baseline
"arousal": 0.4 # Moderate activation level
}
}
)
Adaptive Learning¶
The system continuously updates cognitive profiles based on user interactions:
# Track user interaction
interaction_data = {
"content_consumed": "Advanced neural network architectures",
"engagement_time": 12.5, # minutes
"comprehension_score": 0.85,
"difficulty_rating": 0.7,
"emotional_response": {
"valence": 0.8,
"arousal": 0.6
},
"follow_up_actions": ["bookmark", "share", "deep_dive"]
}
# Update cognitive profile
profile.update_from_interaction(interaction_data)
print(f"Updated complexity tolerance: {profile.complexity_tolerance:.3f}")
Meme Personalization¶
# Create personalized meme recommendations
base_meme = engine.create_meme("Quantum machine learning applications")
# Adapt meme for specific user
personalized_meme = profile.personalize_meme(
meme=base_meme,
adaptation_strength=0.8
)
print(f"Original: {base_meme.content}")
print(f"Personalized: {personalized_meme.content}")
print(f"Estimated engagement: {personalized_meme.engagement_score:.3f}")
Cognitive Adaptation Algorithms¶
Learning Style Adaptation¶
def adapt_for_learning_style(content, learning_preferences):
"""Adapt content based on learning style preferences."""
adapted_content = {
"base_content": content,
"visual_elements": [],
"auditory_elements": [],
"kinesthetic_elements": []
}
if learning_preferences["visual"] > 0.5:
adapted_content["visual_elements"] = [
"Generate concept diagrams",
"Include flowcharts and infographics",
"Use color coding for key concepts"
]
if learning_preferences["auditory"] > 0.5:
adapted_content["auditory_elements"] = [
"Provide audio narration",
"Include musical mnemonics",
"Suggest discussion groups"
]
if learning_preferences["kinesthetic"] > 0.5:
adapted_content["kinesthetic_elements"] = [
"Interactive simulations",
"Hands-on exercises",
"Physical modeling activities"
]
return adapted_content
# Apply learning style adaptation
adapted = adapt_for_learning_style(
content="Neural network backpropagation",
learning_preferences=profile.learning_style
)
Complexity Calibration¶
def calibrate_complexity(content, user_profile):
"""Adjust content complexity for optimal comprehension."""
base_complexity = calculate_content_complexity(content)
user_tolerance = user_profile.complexity_tolerance
if base_complexity > user_tolerance + 0.2:
# Simplify content
adapted_content = simplify_content(
content=content,
target_complexity=user_tolerance,
preserve_accuracy=True
)
elif base_complexity < user_tolerance - 0.2:
# Add depth and nuance
adapted_content = enhance_content(
content=content,
target_complexity=user_tolerance,
add_examples=True,
include_edge_cases=True
)
else:
adapted_content = content
return adapted_content
# Example usage
original_content = "Machine learning uses algorithms to find patterns"
calibrated = calibrate_complexity(original_content, profile)
print(f"Calibrated content: {calibrated}")
Emotional Resonance Optimization¶
def optimize_emotional_resonance(content, emotional_profile):
"""Optimize content for emotional engagement."""
target_valence = emotional_profile["valence"]
target_arousal = emotional_profile["arousal"]
# Analyze current emotional tone
current_emotion = analyze_emotional_content(content)
# Adjust emotional framing
if target_valence > current_emotion["valence"]:
content = add_positive_framing(content)
elif target_valence < current_emotion["valence"]:
content = add_balanced_perspective(content)
if target_arousal > current_emotion["arousal"]:
content = increase_excitement(content)
elif target_arousal < current_emotion["arousal"]:
content = add_calming_elements(content)
return content
# Apply emotional optimization
emotionally_tuned = optimize_emotional_resonance(
content="Revolutionary AI breakthrough",
emotional_profile=profile.emotional_baseline
)
Advanced Cognitive Features¶
Multi-Modal Intelligence Assessment¶
class MultiModalIntelligence:
"""Assess and track multiple intelligence types."""
def __init__(self):
self.intelligence_types = {
"linguistic": 0.0,
"logical_mathematical": 0.0,
"spatial": 0.0,
"bodily_kinesthetic": 0.0,
"musical": 0.0,
"interpersonal": 0.0,
"intrapersonal": 0.0,
"naturalistic": 0.0
}
def assess_from_interactions(self, interaction_history):
"""Update intelligence assessments based on user interactions."""
for interaction in interaction_history:
content_type = interaction["content_type"]
performance = interaction["performance_score"]
if content_type == "text_analysis":
self.intelligence_types["linguistic"] += performance * 0.1
elif content_type == "mathematical_problem":
self.intelligence_types["logical_mathematical"] += performance * 0.1
elif content_type == "visual_pattern":
self.intelligence_types["spatial"] += performance * 0.1
# ... continue for other types
# Normalize scores
max_score = max(self.intelligence_types.values())
for intelligence in self.intelligence_types:
self.intelligence_types[intelligence] /= max_score
def get_dominant_intelligences(self, threshold=0.7):
"""Identify user's strongest intelligence types."""
return {
intelligence: score
for intelligence, score in self.intelligence_types.items()
if score >= threshold
}
# Integrate with cognitive profile
multi_modal = MultiModalIntelligence()
profile.intelligence_assessment = multi_modal
Cognitive Load Management¶
class CognitiveLoadManager:
"""Manage cognitive load to prevent overload."""
def __init__(self, baseline_capacity=1.0):
self.baseline_capacity = baseline_capacity
self.current_load = 0.0
self.load_history = []
def estimate_content_load(self, content):
"""Estimate cognitive load of content."""
factors = {
"complexity": calculate_complexity(content),
"novelty": calculate_novelty(content),
"length": len(content) / 1000, # Normalize by character count
"abstractness": calculate_abstractness(content)
}
# Weighted combination
load = (
0.3 * factors["complexity"] +
0.2 * factors["novelty"] +
0.2 * factors["length"] +
0.3 * factors["abstractness"]
)
return min(load, 1.0) # Cap at 1.0
def can_handle_content(self, content, safety_margin=0.2):
"""Check if user can handle additional cognitive load."""
content_load = self.estimate_content_load(content)
available_capacity = self.baseline_capacity - self.current_load
return content_load <= (available_capacity - safety_margin)
def suggest_break(self):
"""Suggest break when cognitive load is high."""
if self.current_load > 0.8:
return {
"suggestion": "Take a 5-10 minute break",
"reason": "High cognitive load detected",
"recovery_time": 10 # minutes
}
return None
# Integrate cognitive load management
load_manager = CognitiveLoadManager(baseline_capacity=profile.complexity_tolerance)
profile.load_manager = load_manager
Social Cognitive Modeling¶
class SocialCognitiveProfile:
"""Model social cognitive preferences and behaviors."""
def __init__(self):
self.social_preferences = {
"collaboration_preference": 0.5,
"competition_motivation": 0.5,
"social_learning_style": "observational", # observational, collaborative, independent
"feedback_sensitivity": 0.5,
"authority_acceptance": 0.5,
"peer_influence": 0.5
}
self.social_network = []
self.influence_weights = {}
def update_social_context(self, social_interaction):
"""Update profile based on social interactions."""
interaction_type = social_interaction["type"]
outcome = social_interaction["outcome"]
if interaction_type == "collaboration":
if outcome == "positive":
self.social_preferences["collaboration_preference"] += 0.05
else:
self.social_preferences["collaboration_preference"] -= 0.02
# Update influence weights based on interaction success
peer_id = social_interaction.get("peer_id")
if peer_id:
current_weight = self.influence_weights.get(peer_id, 0.5)
if outcome == "positive":
self.influence_weights[peer_id] = min(current_weight + 0.1, 1.0)
else:
self.influence_weights[peer_id] = max(current_weight - 0.05, 0.1)
def recommend_social_learning(self, content):
"""Recommend social learning approaches for content."""
recommendations = []
if self.social_preferences["collaboration_preference"] > 0.6:
recommendations.append("Group study session")
recommendations.append("Peer discussion")
if self.social_preferences["competition_motivation"] > 0.6:
recommendations.append("Competitive quiz")
recommendations.append("Leaderboard challenges")
return recommendations
# Add social cognitive modeling
social_profile = SocialCognitiveProfile()
profile.social_cognitive = social_profile
Cognitive Metrics and Analytics¶
Performance Tracking¶
def track_cognitive_performance(profile, content_interactions):
"""Track cognitive performance over time."""
metrics = {
"learning_efficiency": [],
"retention_rates": [],
"engagement_scores": [],
"complexity_progression": []
}
for interaction in content_interactions:
# Calculate learning efficiency
time_spent = interaction["time_minutes"]
comprehension = interaction["comprehension_score"]
efficiency = comprehension / time_spent if time_spent > 0 else 0
metrics["learning_efficiency"].append(efficiency)
# Track retention (requires follow-up assessment)
if "retention_test_score" in interaction:
metrics["retention_rates"].append(interaction["retention_test_score"])
# Track engagement
engagement = calculate_engagement_score(interaction)
metrics["engagement_scores"].append(engagement)
# Track complexity progression
content_complexity = calculate_content_complexity(interaction["content"])
metrics["complexity_progression"].append(content_complexity)
return metrics
# Generate performance report
performance_metrics = track_cognitive_performance(profile, user_interactions)
print(f"Average learning efficiency: {np.mean(performance_metrics['learning_efficiency']):.3f}")
print(f"Average retention rate: {np.mean(performance_metrics['retention_rates']):.3f}")
Cognitive State Prediction¶
class CognitiveStatePredictor:
"""Predict user's cognitive state for optimal content delivery."""
def __init__(self):
self.state_model = self.load_predictive_model()
self.state_history = []
def predict_optimal_timing(self, content, user_profile):
"""Predict optimal time to present content."""
features = self.extract_features(user_profile)
predicted_states = self.state_model.predict(features)
optimal_window = self.find_peak_receptivity(predicted_states)
return optimal_window
def predict_engagement(self, content, user_profile, delivery_time):
"""Predict user engagement for given content and timing."""
content_features = self.analyze_content(content)
user_features = self.extract_user_features(user_profile)
temporal_features = self.extract_temporal_features(delivery_time)
combined_features = {
**content_features,
**user_features,
**temporal_features
}
engagement_prediction = self.state_model.predict_engagement(combined_features)
return engagement_prediction
def recommend_content_sequence(self, content_list, user_profile):
"""Recommend optimal sequence for presenting content."""
scored_content = []
for content in content_list:
optimal_time = self.predict_optimal_timing(content, user_profile)
engagement_score = self.predict_engagement(content, user_profile, optimal_time)
scored_content.append({
"content": content,
"optimal_time": optimal_time,
"predicted_engagement": engagement_score
})
# Sort by optimal timing and engagement
return sorted(scored_content, key=lambda x: (x["optimal_time"], -x["predicted_engagement"]))
# Use cognitive state prediction
predictor = CognitiveStatePredictor()
content_sequence = predictor.recommend_content_sequence(content_list, profile)
Integration with Memetic Evolution¶
Cognitive-Driven Evolution¶
def cognitive_fitness_function(meme, cognitive_profiles):
"""Fitness function based on cognitive compatibility."""
total_fitness = 0.0
for profile in cognitive_profiles:
# Calculate fit score for this cognitive profile
fit_score = profile.calculate_fit_score(meme)
# Weight by profile importance (e.g., user activity level)
weight = profile.importance_weight
total_fitness += fit_score * weight
return total_fitness / len(cognitive_profiles)
# Use in evolution process
evolved_memes = engine.evolve(
initial_memes=base_memes,
fitness_function=cognitive_fitness_function,
generations=10,
cognitive_profiles=user_profiles
)
Personalized Mutation Operators¶
def cognitive_mutation(meme, cognitive_profile):
"""Mutate meme based on cognitive preferences."""
mutation_strategies = []
# Choose mutation based on learning style
if cognitive_profile.learning_style["visual"] > 0.6:
mutation_strategies.append("add_visual_metaphors")
if cognitive_profile.learning_style["auditory"] > 0.6:
mutation_strategies.append("add_rhythmic_structure")
if cognitive_profile.complexity_tolerance > 0.7:
mutation_strategies.append("increase_technical_depth")
else:
mutation_strategies.append("simplify_language")
# Apply selected mutations
mutated_meme = meme.copy()
for strategy in mutation_strategies:
mutated_meme = apply_mutation_strategy(mutated_meme, strategy)
return mutated_meme
Privacy and Ethics¶
Data Protection¶
class CognitivePrivacyManager:
"""Manage privacy for cognitive profile data."""
def __init__(self):
self.encryption_key = self.generate_encryption_key()
self.anonymization_level = "high"
def anonymize_profile(self, profile):
"""Remove personally identifiable information."""
anonymized = profile.copy()
anonymized.user_id = self.generate_anonymous_id()
anonymized.remove_pii()
return anonymized
def differential_privacy(self, profile_data, epsilon=1.0):
"""Apply differential privacy to profile data."""
# Add calibrated noise to protect individual privacy
noise_scale = 1.0 / epsilon
for key, value in profile_data.items():
if isinstance(value, (int, float)):
noise = np.random.laplace(0, noise_scale)
profile_data[key] = value + noise
return profile_data
def consent_management(self, user_id, data_types):
"""Manage user consent for different data types."""
consent_record = {
"user_id": user_id,
"timestamp": datetime.now(),
"cognitive_modeling": False,
"learning_analytics": False,
"personalization": False,
"research_participation": False
}
# Update based on user preferences
for data_type in data_types:
if data_type in consent_record:
consent_record[data_type] = True
return consent_record
# Implement privacy protection
privacy_manager = CognitivePrivacyManager()
anonymized_profile = privacy_manager.anonymize_profile(profile)
The cognitive modeling system in Q-Memetic AI provides sophisticated user personalization while maintaining privacy and ethical standards, enabling truly adaptive and personalized memetic computing experiences.