---
title: Research Guide
type: guide
status: draft
created: 2024-02-12
tags:
  - research
  - guide
  - methodology
semantic_relations:
  - type: implements
    links: [[documentation_standards]]
  - type: relates
    links:
      - [[machine_learning]]
      - [[ai_validation_framework]]
---

# Research Guide

## Overview

This guide outlines research methodologies, best practices, and workflows for conducting research in cognitive modeling.

## Research Areas

### Core Areas

1. Active Inference
   - Free energy principle
   - Belief updating
   - Action selection
   - See [[knowledge_base/cognitive/active_inference]]

2. Predictive Processing
   - Hierarchical prediction
   - Error minimization
   - Precision weighting
   - See [[knowledge_base/cognitive/predictive_processing]]

3. Cognitive Architecture
   - Memory systems
   - Learning mechanisms
   - Decision making
   - See [[knowledge_base/cognitive/cognitive_architecture]]

## Research Methodology

### Experimental Design

1. Hypothesis Formation
   ```python
   class ResearchHypothesis:
       def __init__(self):
           self.theory = Theory()
           self.predictions = Predictions()
           self.variables = Variables()
   ```

2. Experimental Setup
   ```python
   class Experiment:
       def __init__(self):
           self.conditions = Conditions()
           self.controls = Controls()
           self.measures = Measures()
   ```

3. Data Collection
   ```python
   class DataCollection:
       def __init__(self):
           self.sensors = Sensors()
           self.loggers = Loggers()
           self.storage = Storage()
   ```

### Analysis Methods

1. Statistical Analysis
   - Hypothesis testing
   - Effect size calculation
   - Power analysis
   - See [[knowledge_base/mathematics/statistical_analysis]]

2. Model Comparison
   - Parameter estimation
   - Model selection
   - Cross-validation
   - See [[knowledge_base/mathematics/model_comparison]]

3. Performance Metrics
   - Accuracy measures
   - Efficiency metrics
   - Robustness tests
   - See [[docs/concepts/quality_metrics]]

## Research Workflow

### Planning Phase

1. Literature Review
   - Search strategies
   - Paper organization
   - Citation management
   - See [[docs/guides/literature_review]]

2. Research Design
   - Hypothesis development
   - Method selection
   - Variable control
   - See [[docs/guides/research_design]]

3. Protocol Development
   - Experimental procedures
   - Data collection
   - Analysis plans
   - See [[docs/guides/research_protocol]]

### Execution Phase

1. Data Collection
   ```python
   def collect_data():
       """Collect experimental data."""
       experiment = Experiment()
       data = experiment.run()
       return data
   ```

2. Analysis
   ```python
   def analyze_data(data):
       """Analyze experimental data."""
       analysis = Analysis()
       results = analysis.process(data)
       return results
   ```

3. Validation
   ```python
   def validate_results(results):
       """Validate experimental results."""
       validation = Validation()
       metrics = validation.check(results)
       return metrics
   ```

### Documentation Phase

1. Results Documentation
   - Data organization
   - Analysis documentation
   - Figure generation
   - See [[docs/guides/results_documentation]]

2. Paper Writing
   - Structure
   - Style guide
   - Citation format
   - See [[docs/guides/paper_writing]]

3. Code Documentation
   - Implementation details
   - Usage examples
   - API documentation
   - See [[docs/guides/code_documentation]]

## Best Practices

### Research Standards
1. Reproducibility
2. Transparency
3. Rigor
4. Ethics

### Code Standards
1. Version control
2. Documentation
3. Testing
4. Sharing

### Documentation Standards
1. Clear writing
2. Complete methods
3. Accessible data
4. Open source

## Tools and Resources

### Research Tools
1. Literature Management
   - Reference managers
   - Paper organizers
   - Note-taking tools

2. Data Analysis
   - Statistical packages
   - Visualization tools
   - Analysis frameworks

3. Documentation
   - LaTeX templates
   - Figure tools
   - Documentation generators

### Computing Resources
1. Local Resources
   - Development environment
   - Testing setup
   - Data storage

2. Cloud Resources
   - Compute clusters
   - Storage systems
   - Collaboration tools

## Publication Process

### Paper Preparation
1. Writing guidelines
2. Figure preparation
3. Code packaging
4. Data organization

### Submission Process
1. Journal selection
2. Paper formatting
3. Code submission
4. Data sharing

### Review Process
1. Response strategies
2. Revision management
3. Rebuttal writing
4. Final submission

## Collaboration

### Team Coordination
1. Task management
2. Code sharing
3. Documentation
4. Communication

### External Collaboration
1. Data sharing
2. Code distribution
3. Knowledge transfer
4. Publication coordination

## Related Documentation
- [[docs/guides/machine_learning]]
- [[docs/guides/ai_validation_framework]]
- [[docs/guides/documentation_standards]]
- [[docs/guides/code_documentation]]