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Memory Systems

Memory is the foundation of intelligence, enabling agents to learn from experience, maintain context, and make informed decisions. Cognito Simulation Engine implements a sophisticated multi-store memory architecture based on cutting-edge cognitive science research.

Memory Architecture Overview

Our memory system implements a biologically-inspired, multi-component architecture:

graph TB
    A[Sensory Input] --> B[Sensory Memory]
    B --> C[Working Memory]
    C --> D[Long-Term Memory]

    C --> E[Central Executive]
    C --> F[Phonological Loop]
    C --> G[Visuospatial Sketchpad]
    C --> H[Episodic Buffer]

    D --> I[Episodic Memory]
    D --> J[Semantic Memory]
    D --> K[Procedural Memory]

    I --> C
    J --> C
    K --> L[Skill Execution]

Working Memory System

Core Architecture

Working memory serves as the cognitive workspace where active information processing occurs. Our implementation follows Baddeley's multi-component model:

from cognito_sim_engine import MemoryManager, MemoryItem, MemoryType

# Create memory manager with cognitive constraints
memory = MemoryManager(
    working_capacity=7,  # Miller's 7±2 rule
    decay_rate=0.1,      # Information decay per second
    refresh_rate=2.0     # Attention refresh frequency
)

# Add item to working memory
item = MemoryItem(
    content="Current goal: solve puzzle",
    memory_type=MemoryType.WORKING,
    importance=0.8,
    activation_level=1.0
)

memory.store_memory(item)

Capacity Limitations

Working memory capacity is strictly limited to simulate human cognitive constraints:

  • Span Limit: 7±2 items (configurable)
  • Decay Function: Exponential decay over time
  • Interference: New items can displace existing ones
  • Attention Refresh: Active maintenance through attention

Components

Central Executive

The supervisory system that:

  • Controls attention allocation
  • Manages information flow between components
  • Coordinates cognitive strategies
  • Monitors and regulates processing

Phonological Loop

Specialized for verbal and acoustic information:

  • Phonological Store: Temporary storage of speech-based information
  • Articulatory Rehearsal: Subvocal repetition to maintain information
  • Capacity: Approximately 2-second duration limit

Visuospatial Sketchpad

Handles visual and spatial information:

  • Visual Cache: Storage of visual form and color information
  • Inner Scribe: Processing of spatial and movement information
  • Capacity: 3-4 visual objects or spatial locations

Episodic Buffer

Integrates information from multiple sources:

  • Multimodal Integration: Combines visual, auditory, and semantic information
  • Conscious Access: Interface to conscious awareness
  • Temporal Binding: Links information across time

Long-Term Memory Systems

Episodic Memory

Stores personally experienced events with rich contextual detail:

from cognito_sim_engine import EpisodicMemory, MemoryItem

episodic = EpisodicMemory()

# Store an episode
episode = MemoryItem(
    content="Solved the tower puzzle using recursive strategy",
    memory_type=MemoryType.EPISODIC,
    importance=0.7,
    context={
        "timestamp": "2025-07-13T10:30:00",
        "location": "laboratory",
        "emotional_state": "satisfaction",
        "strategy_used": "recursive_decomposition",
        "outcome": "success"
    }
)

episode_id = episodic.store_episode(episode)

Key Features

  • Temporal Context: When events occurred
  • Spatial Context: Where events took place
  • Emotional Context: Affective state during encoding
  • Causal Context: Relationships between events
  • Autobiographical Nature: Self-referential memories

Retrieval Mechanisms

  • Temporal Retrieval: Accessing memories by time
  • Contextual Cuing: Using environmental cues
  • Associative Retrieval: Following memory links
  • Reconstructive Process: Rebuilding memories from fragments

Semantic Memory

Contains factual knowledge and conceptual understanding:

from cognito_sim_engine import SemanticMemory, ConceptNode

semantic = SemanticMemory()

# Add conceptual knowledge
concepts = [
    ConceptNode("puzzle", properties=["challenging", "solvable", "logical"]),
    ConceptNode("strategy", properties=["systematic", "goal-directed"]),
    ConceptNode("recursion", properties=["self-similar", "divide-and-conquer"])
]

for concept in concepts:
    semantic.add_concept(concept)

# Create semantic relationships
semantic.add_relationship("recursion", "is_a", "strategy")
semantic.add_relationship("puzzle", "requires", "strategy")

Knowledge Representation

  • Concepts: Abstract categories and their properties
  • Relations: Connections between concepts
  • Hierarchies: Taxonomic and part-whole structures
  • Schemas: Structured knowledge frameworks

Organization Principles

  • Categorical Structure: Superordinate, basic, and subordinate levels
  • Semantic Networks: Interconnected concept nodes
  • Feature-Based: Concepts defined by properties
  • Prototype Theory: Central tendencies and typicality effects

Procedural Memory

Stores skills, habits, and automated behaviors:

from cognito_sim_engine import ProceduralMemory, Skill

procedural = ProceduralMemory()

# Define a cognitive skill
problem_solving = Skill(
    name="recursive_problem_solving",
    steps=[
        "identify_base_case",
        "decompose_problem", 
        "apply_recursion",
        "combine_solutions"
    ],
    proficiency=0.7,
    automaticity=0.5
)

procedural.learn_skill(problem_solving)

Characteristics

  • Implicit Nature: Often unconscious and automatic
  • Gradual Acquisition: Learning through practice and repetition
  • Resistance to Forgetting: Highly durable once established
  • Transfer Effects: Skills can generalize across contexts

Memory Processes

Encoding

The process of transforming information into storable format:

Encoding Strategies

  • Elaborative Encoding: Connecting new information to existing knowledge
  • Visual Encoding: Creating mental images and spatial representations
  • Organizational Encoding: Structuring information into meaningful patterns
  • Self-Referential Encoding: Relating information to personal experience

Factors Affecting Encoding

  • Attention Level: Higher attention improves encoding quality
  • Processing Depth: Deeper semantic processing enhances retention
  • Emotional Significance: Emotional content receives encoding priority
  • Repetition Effects: Multiple exposures strengthen memory traces

Storage

Maintaining information in memory over time:

Consolidation Process

# Memory consolidation simulation
def consolidate_memories(memory_manager, time_elapsed):
    """Simulate memory consolidation over time."""
    for memory_id, memory in memory_manager.episodic_memory.memories.items():
        if time_elapsed > memory.consolidation_threshold:
            # Transfer to long-term storage
            memory.consolidation_level += 0.1
            memory.interference_resistance += 0.05

            # Semantic extraction
            if memory.consolidation_level > 0.8:
                semantic_content = extract_semantic_content(memory)
                memory_manager.semantic_memory.integrate(semantic_content)

Storage Mechanisms

  • Synaptic Consolidation: Protein synthesis-dependent stabilization
  • Systems Consolidation: Gradual cortical storage integration
  • Interference Resistance: Protection against competing memories
  • Capacity Management: Forgetting mechanisms to prevent overload

Retrieval

Accessing stored information when needed:

Retrieval Types

# Different retrieval modes
class MemoryRetrieval:
    def recall(self, cue):
        """Generate information from memory without external cues."""
        pass

    def recognition(self, stimulus):
        """Identify previously encountered information."""
        pass

    def cued_recall(self, cue):
        """Use external cues to trigger memory retrieval."""
        pass

    def free_recall(self):
        """Retrieve information without specific cues."""
        pass

Retrieval Processes

  • Activation Spreading: Spreading activation through associative networks
  • Context Reinstatement: Recreating encoding context to aid retrieval
  • Competitive Retrieval: Competition between similar memories
  • Retrieval Inhibition: Suppressing irrelevant memories

Memory Dynamics

Forgetting Mechanisms

Forgetting is not just passive decay but serves important cognitive functions:

Forgetting Functions

  • Interference Reduction: Reducing competition between memories
  • Generalization: Extracting general principles from specific experiences
  • Adaptive Updating: Prioritizing current over outdated information
  • Cognitive Efficiency: Focusing on relevant information

Forgetting Curves

import numpy as np

def ebbinghaus_forgetting_curve(t, initial_strength=1.0, decay_rate=0.1):
    """Ebbinghaus forgetting curve implementation."""
    return initial_strength * np.exp(-decay_rate * t)

def power_law_forgetting(t, initial_strength=1.0, decay_rate=0.5):
    """Power law forgetting for semantic memory."""
    return initial_strength * (t + 1) ** (-decay_rate)

Memory Interactions

Cross-System Interactions

  • Working-Episodic: Episodes provide context for current processing
  • Working-Semantic: Semantic knowledge guides interpretation
  • Episodic-Semantic: Episodes contribute to semantic knowledge
  • Procedural-Declarative: Skills incorporate factual knowledge

Memory Binding

  • Feature Binding: Combining features into coherent objects
  • Temporal Binding: Linking events across time
  • Cross-Modal Binding: Integrating information across sensory modalities
  • Contextual Binding: Associating content with environmental context

Advanced Memory Features

Metamemory

Knowledge and monitoring of memory processes:

class MetamemoryMonitor:
    def __init__(self):
        self.confidence_ratings = {}
        self.strategy_effectiveness = {}
        self.memory_load_assessment = {}

    def assess_memory_confidence(self, memory_id):
        """Assess confidence in memory accuracy."""
        memory = self.get_memory(memory_id)
        factors = [
            memory.vividness,
            memory.consistency,
            memory.source_reliability,
            memory.retrieval_fluency
        ]
        return np.mean(factors)

    def select_retrieval_strategy(self, task_demands):
        """Choose optimal retrieval strategy for current task."""
        if task_demands.speed_priority:
            return "direct_access"
        elif task_demands.accuracy_priority:
            return "generate_and_test"
        else:
            return "elaborative_search"

Memory Reconstruction

Memories are not static recordings but dynamic reconstructions:

Reconstruction Processes

  • Schema-Based Reconstruction: Using knowledge frameworks to fill gaps
  • Inference Integration: Adding plausible inferences to memory traces
  • Source Monitoring: Distinguishing between different memory sources
  • Reality Monitoring: Discriminating between perceived and imagined events

Adaptive Memory Systems

Memory systems that adapt to task demands and experience:

class AdaptiveMemorySystem:
    def __init__(self):
        self.allocation_strategy = "balanced"
        self.priority_weights = {
            "recency": 0.3,
            "frequency": 0.3, 
            "importance": 0.4
        }

    def adapt_to_task(self, task_type):
        """Adapt memory allocation to task requirements."""
        if task_type == "learning":
            self.priority_weights["importance"] = 0.6
        elif task_type == "planning":
            self.priority_weights["recency"] = 0.5
        elif task_type == "skill_execution":
            self.activate_procedural_dominance()

Research Applications

Memory Research

  • Capacity Studies: Investigating working memory limitations
  • Forgetting Research: Understanding forgetting mechanisms and functions
  • Learning Studies: Examining memory formation and consolidation
  • Individual Differences: Modeling memory variations across individuals

Clinical Applications

  • Memory Disorders: Simulating amnesia, dementia, and other conditions
  • Rehabilitation: Developing memory training and support systems
  • Assessment: Creating memory evaluation tools
  • Intervention: Testing memory enhancement strategies

AI Development

  • Learning Systems: Implementing adaptive learning algorithms
  • Knowledge Management: Organizing and retrieving large knowledge bases
  • Context Awareness: Maintaining situational awareness over time
  • Transfer Learning: Applying knowledge across different domains

Performance Optimization

Memory Efficiency

  • Compression: Reducing memory storage requirements
  • Indexing: Fast access to relevant memories
  • Garbage Collection: Removing obsolete or low-value memories
  • Load Balancing: Distributing memory load across systems

Scalability Considerations

  • Hierarchical Organization: Multi-level memory structures
  • Parallel Processing: Concurrent memory operations
  • Incremental Learning: Adding knowledge without catastrophic forgetting
  • Memory Consolidation: Efficient long-term storage management

Next: Explore the Reasoning Engine that operates on these memory systems, or learn about Agent Design principles.