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Plain Text Benefits in Cognitive Modeling
title: Plain Text Benefits type: concept status: stable created: 2024-02-06 tags:
- methodology
- tools
- best-practices related:
- git_workflow
- obsidian_usage
- knowledge_organization
- model_generalization
Overview
Plain text formats serve as the foundation for our cognitive modeling ecosystem, enabling seamless integration between version control, knowledge management, and model development. This document explores the comprehensive benefits of this approach.
Core Benefits
1. Version Control Integration
- Git-Friendly Format
- Line-by-line diff_tracking
- Meaningful change history
- Conflict resolution
- Branch-based development
- See git_workflow for details
2. Knowledge Management
- Obsidian Integration
- bidirectional_linking
- graph_visualization
- Dynamic knowledge networks
- Emergent relationships
- See obsidian_linking for usage
3. Machine Readability
- Automated Processing
4. Research Benefits
- Reproducibility
- Collaboration
Applications
1. Model Development
# Model specification in plain text
model = {
"architecture": "active_inference",
"components": ["perception", "action"],
"parameters": {
"learning_rate": 0.01
}
}
See model_specification for details.
2. Knowledge Representation
## Cognitive Architecture
- [[belief_updating]]
- [[prediction_error]]
- [[precision_weighting]]
See knowledge_representation for more.
3. Educational Resources
- Interactive tutorials
- Self-documenting code
- Linked learning paths See educational_resources for examples.
Intelligence Augmentation
1. Machine Learning Integration
- Training Data Generation
- Structured text for dataset_creation
- Annotated examples for model_training
- Version-controlled datasets
2. Knowledge Discovery
- Pattern Recognition
3. Model Generalization
- Transfer Learning
Research Workflow
1. Literature Integration
- Citation Management
2. Experiment Documentation
---
experiment:
id: exp001
hypothesis: "[[active_inference_hypothesis]]"
methods: "[[experimental_design]]"
results: "[[data_analysis]]"
---
3. Result Analysis
- Data Processing
Educational Applications
1. Learning Pathways
- Structured Learning
2. Interactive Examples
# Interactive code with documentation
def belief_update(observation):
"""See [[belief_update_theory]] for mathematical details"""
pass
3. Knowledge Assessment
- Progress Tracking
Model Generalization
1. Abstract Representations
- Formal Specifications
2. Cross-Domain Transfer
- Knowledge Transfer
3. Scalability
- System Growth
Best Practices
1. File Organization
project/
├── models/ # Implementation
├── docs/ # Documentation
├── tests/ # Validation
└── experiments/ # Research
See project_structure for details.
2. Documentation Standards
- Consistent Formatting
3. Version Control
- Meaningful History
Integration Examples
1. Research Pipeline
graph TD
A[Literature Review] --> B[Model Design]
B --> C[Implementation]
C --> D[Experimentation]
D --> E[Analysis]
E --> F[Documentation]
2. Knowledge Flow
graph LR
A[Raw Data] --> B[Processing]
B --> C[Analysis]
C --> D[Knowledge]
D --> E[Models]