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🧮
Working Memory Patterns(WMP)
Short-term context management for active cognitive processing
Complexity: mediumMemory Management
🎯 30-Second Overview
Pattern: Capacity-limited active memory for cognitive processing with attention control and interference suppression
Why: Enables complex reasoning, multi-step tasks, real-time cognitive processing within bounded resource constraints
Key Insight: Central Executive + Limited Capacity (4±1 chunks) + Attention Control + Temporal Maintenance → Human-like cognitive processing
⚡ Quick Implementation
1Set Capacity:Define working memory limits (4±1 chunks for complex tasks)
2Control Attention:Implement central executive for selective information filtering
3Manage Content:Load, maintain, update, clear information dynamically
4Suppress Interference:Block irrelevant information and manage conflicts
5Coordinate Agents:Synchronize working memory across multi-agent system
Example: capacity_limits → attention_control → content_management → interference_suppression → agent_coordination
📋 Do's & Don'ts
✅Use 4±1 item capacity for complex tasks (Cowan, 2001) vs 7±2 for simple items
✅Implement central executive for attention control and task switching
✅Use chunking strategies to group related information effectively
✅Apply rehearsal mechanisms to prevent 15-30 second decay
✅Monitor cognitive load and implement offloading when approaching limits
❌Ignore individual differences in working memory capacity across agents
❌Let irrelevant information consume working memory without filtering
❌Assume unlimited capacity - working memory constraints are crucial
❌Forget to implement interference suppression for competing information
❌Overlook the temporal dynamics - information decays without maintenance
🚦 When to Use
Use When
- • Complex multi-step reasoning requiring active information maintenance
- • Interactive systems with dynamic information updates
- • Multi-agent coordination needing shared cognitive state
- • Planning tasks with multiple constraints and goals
- • Learning systems integrating new with existing knowledge
Avoid When
- • Simple lookup operations without manipulation
- • Batch processing without real-time interaction
- • Unlimited computational resource scenarios
- • Stateless operations without temporal persistence
- • Single-step tasks without cognitive complexity
📊 Key Metrics
Capacity Utilization
Effective chunks maintained vs theoretical limit
Information Persistence
% information retained during task processing
Interference Resistance
Success rate suppressing irrelevant information
Chunking Efficiency
Information compression through organization
Attention Control Quality
Precision of selective filtering mechanisms
Multi-Agent Synchronization
Consistency of shared working memory states
💡 Top Use Cases
Multi-Step Problem Solving: Mathematical reasoning, code debugging, scientific analysis (maintain intermediate results across reasoning steps)
Interactive Dialogue Systems: Conversation context management, user intent tracking (dynamic information updates within cognitive limits)
Multi-Agent Coordination: Shared task state, distributed problem solving (synchronized working memory across agent network)
Planning & Decision Making: Goal management, constraint satisfaction, resource allocation (juggle multiple factors within capacity limits)
Learning & Adaptation: Knowledge integration, skill acquisition, concept formation (active manipulation of new information with existing knowledge)
References & Further Reading
Deepen your understanding with these curated resources
Foundational Cognitive Science
Contribute to this collection
Know a great resource? Submit a pull request to add it.
🧮
Working Memory Patterns(WMP)
Short-term context management for active cognitive processing
Complexity: mediumMemory Management
🎯 30-Second Overview
Pattern: Capacity-limited active memory for cognitive processing with attention control and interference suppression
Why: Enables complex reasoning, multi-step tasks, real-time cognitive processing within bounded resource constraints
Key Insight: Central Executive + Limited Capacity (4±1 chunks) + Attention Control + Temporal Maintenance → Human-like cognitive processing
⚡ Quick Implementation
1Set Capacity:Define working memory limits (4±1 chunks for complex tasks)
2Control Attention:Implement central executive for selective information filtering
3Manage Content:Load, maintain, update, clear information dynamically
4Suppress Interference:Block irrelevant information and manage conflicts
5Coordinate Agents:Synchronize working memory across multi-agent system
Example: capacity_limits → attention_control → content_management → interference_suppression → agent_coordination
📋 Do's & Don'ts
✅Use 4±1 item capacity for complex tasks (Cowan, 2001) vs 7±2 for simple items
✅Implement central executive for attention control and task switching
✅Use chunking strategies to group related information effectively
✅Apply rehearsal mechanisms to prevent 15-30 second decay
✅Monitor cognitive load and implement offloading when approaching limits
❌Ignore individual differences in working memory capacity across agents
❌Let irrelevant information consume working memory without filtering
❌Assume unlimited capacity - working memory constraints are crucial
❌Forget to implement interference suppression for competing information
❌Overlook the temporal dynamics - information decays without maintenance
🚦 When to Use
Use When
- • Complex multi-step reasoning requiring active information maintenance
- • Interactive systems with dynamic information updates
- • Multi-agent coordination needing shared cognitive state
- • Planning tasks with multiple constraints and goals
- • Learning systems integrating new with existing knowledge
Avoid When
- • Simple lookup operations without manipulation
- • Batch processing without real-time interaction
- • Unlimited computational resource scenarios
- • Stateless operations without temporal persistence
- • Single-step tasks without cognitive complexity
📊 Key Metrics
Capacity Utilization
Effective chunks maintained vs theoretical limit
Information Persistence
% information retained during task processing
Interference Resistance
Success rate suppressing irrelevant information
Chunking Efficiency
Information compression through organization
Attention Control Quality
Precision of selective filtering mechanisms
Multi-Agent Synchronization
Consistency of shared working memory states
💡 Top Use Cases
Multi-Step Problem Solving: Mathematical reasoning, code debugging, scientific analysis (maintain intermediate results across reasoning steps)
Interactive Dialogue Systems: Conversation context management, user intent tracking (dynamic information updates within cognitive limits)
Multi-Agent Coordination: Shared task state, distributed problem solving (synchronized working memory across agent network)
Planning & Decision Making: Goal management, constraint satisfaction, resource allocation (juggle multiple factors within capacity limits)
Learning & Adaptation: Knowledge integration, skill acquisition, concept formation (active manipulation of new information with existing knowledge)
References & Further Reading
Deepen your understanding with these curated resources
Foundational Cognitive Science
Contribute to this collection
Know a great resource? Submit a pull request to add it.