Cognitive Architecture¶

Understanding the theoretical foundation of Cognito Simulation Engine is crucial for effective use and research applications.

Overview¶

Cognito Simulation Engine implements a comprehensive cognitive architecture inspired by decades of research in cognitive science, neuroscience, and artificial intelligence. Our architecture goes beyond traditional neural network approaches to provide a structured, interpretable framework for AGI research.

Core Architectural Principles¶

1. Multi-System Integration¶

The engine integrates multiple cognitive systems that work together to produce intelligent behavior:

graph TD
    A[Perception System] --> B[Working Memory]
    B --> C[Reasoning Engine]
    C --> D[Action Selection]
    D --> E[Motor Output]

    B --> F[Episodic Memory]
    B --> G[Semantic Memory]
    F --> B
    G --> B

    H[Metacognitive Monitor] --> B
    H --> C
    H --> I[Strategy Selection]
    I --> C

2. Symbolic-Subsymbolic Hybrid¶

Our architecture combines:

Symbolic Processing: Rule-based reasoning, logical inference, explicit knowledge representation
Subsymbolic Processing: Activation spreading, constraint satisfaction, emergent patterns

3. Biologically-Inspired Constraints¶

The architecture respects known cognitive limitations:

Working Memory Capacity: Miller's 7±2 limit with realistic capacity constraints
Attention Bottleneck: Selective attention mechanisms with resource allocation
Memory Decay: Realistic forgetting curves and consolidation processes
Processing Speed: Bounded rationality with time constraints

Architectural Components¶

Perception-Action Loop¶

The fundamental cycle of cognitive processing:

Perception: Environmental input processing and feature extraction
Attention: Selective focus on relevant information
Memory Access: Retrieval of relevant stored knowledge
Reasoning: Inference and problem-solving processes
Planning: Goal-directed action sequence generation
Action: Motor output and environmental interaction
Monitoring: Performance evaluation and strategy adjustment

Memory Systems Architecture¶

Based on Baddeley's Working Memory Model and Tulving's Memory Systems:

Working Memory¶

class WorkingMemory:
    """
    Central executive with phonological loop, visuospatial sketchpad,
    and episodic buffer components.
    """
    capacity: int = 7  # Miller's magical number
    decay_rate: float = 0.1  # Information decay over time
    refresh_rate: float = 0.5  # Attention-based refreshing

Long-Term Memory¶

Episodic Memory: Personal experiences with temporal context
Semantic Memory: Factual knowledge and conceptual understanding
Procedural Memory: Skills and automated behaviors

Reasoning Architecture¶

Multi-strategy inference system:

Forward Chaining¶

Data-driven reasoning from facts to conclusions
Suitable for exploration and discovery tasks

Backward Chaining¶

Goal-driven reasoning from desired outcomes to required facts
Optimal for planning and problem-solving

Abductive Reasoning¶

Hypothesis generation for explaining observations
Essential for scientific thinking and creativity

Attention Mechanisms¶

Selective attention system with:

Endogenous Control: Top-down, goal-directed attention
Exogenous Control: Bottom-up, stimulus-driven attention
Resource Management: Computational resource allocation

Cognitive Control¶

Executive Functions¶

The cognitive control system manages:

Inhibition: Suppressing irrelevant or inappropriate responses
Updating: Maintaining and manipulating working memory contents
Shifting: Flexible switching between mental sets or tasks

Metacognition¶

Higher-order cognition about cognition:

Metacognitive Knowledge: Understanding of cognitive processes
Metacognitive Regulation: Control and monitoring of cognition
Strategy Selection: Adaptive choice of cognitive strategies

Agent Architecture Types¶

CognitiveAgent: Full Architecture¶

Implements complete cognitive architecture with all systems integrated:

class CognitiveAgent:
    def __init__(self):
        self.memory_manager = MemoryManager()
        self.inference_engine = InferenceEngine()
        self.attention_system = AttentionSystem()
        self.metacognitive_monitor = MetacognitiveMonitor()
        self.action_controller = ActionController()

ReasoningAgent: Logic-Focused¶

Specialized for symbolic reasoning with enhanced inference capabilities:

Expanded rule base capacity
Multiple reasoning strategies
Formal logic integration
Proof generation capabilities

LearningAgent: Adaptive Architecture¶

Optimized for learning and skill acquisition:

Experience-based learning mechanisms
Skill level tracking and progression
Adaptive strategy development
Transfer learning capabilities

MetaCognitiveAgent: Self-Reflective¶

Advanced self-awareness and cognitive monitoring:

Real-time cognitive load assessment
Strategy effectiveness evaluation
Self-model maintenance and updating
Cognitive bias detection and correction

Theoretical Foundations¶

ACT-R Integration¶

Adaptive Control of Thought-Rational principles:

Production Rules: Condition-action pairs for procedural knowledge
Declarative Memory: Chunk-based factual knowledge representation
Activation Spreading: Memory retrieval through associative networks
Learning Mechanisms: Strengthening through practice and reinforcement

Global Workspace Theory¶

Consciousness and information integration:

Global Broadcasting: Making information globally available
Competition: Multiple processes competing for conscious access
Coalition Formation: Temporary alliances of cognitive processes

Dual Process Theory¶

System 1 (Fast) and System 2 (Slow) processing:

System 1: Automatic, intuitive, low-effort processing
System 2: Controlled, analytical, high-effort processing
Conflict Resolution: Managing competition between systems

Implementation Philosophy¶

Modularity and Extensibility¶

Plug-and-Play Components: Easy substitution of cognitive modules
Interface Standardization: Consistent APIs across components
Hierarchical Organization: Clear separation of concerns

Research Orientation¶

Transparency: All processes are inspectable and interpretable
Configurability: Extensive parameter control for experimentation
Metrics Collection: Comprehensive performance measurement
Reproducibility: Deterministic simulation with random seed control

AGI Readiness¶

The architecture is designed with AGI development in mind:

Scalability: Efficient handling of complex cognitive tasks
Generality: Domain-independent cognitive processes
Self-Improvement: Metacognitive optimization capabilities
Safety Mechanisms: Built-in monitoring and control systems

Research Applications¶

Cognitive Science Research¶

Hypothesis Testing: Formal modeling of cognitive theories
Phenomenon Replication: Simulating known cognitive effects
Parameter Exploration: Investigating cognitive constraints
Individual Differences: Modeling cognitive variation

AI Development¶

Architecture Prototyping: Testing new cognitive designs
Component Evaluation: Comparing alternative implementations
Emergent Behavior Study: Observing unexpected system behaviors
Safety Research: Testing containment and alignment strategies

Educational Applications¶

Cognitive Training: Developing cognitive skill training programs
Learning Analytics: Understanding learning processes
Adaptive Tutoring: Personalized educational systems
Cognitive Assessment: Measuring cognitive capabilities

Future Directions¶

Enhanced Biological Fidelity¶

Neural Implementation: Connecting to neural network substrates
Developmental Models: Implementing cognitive development
Emotional Integration: Adding affective processing systems

Advanced Capabilities¶

Creativity Systems: Implementing creative problem-solving
Social Cognition: Multi-agent interaction and theory of mind
Language Processing: Natural language understanding and generation
Embodied Cognition: Integration with robotic systems

Next: Learn about Memory Systems in detail, or explore Agent Design principles.