cognitive/docs/guides/research.md

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

   class ResearchHypothesis:
       def __init__(self):
           self.theory = Theory()
           self.predictions = Predictions()
           self.variables = Variables()
   

2. Experimental Setup

   class Experiment:
       def __init__(self):
           self.conditions = Conditions()
           self.controls = Controls()
           self.measures = Measures()
   

3. Data Collection

   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

   def collect_data():
       """Collect experimental data."""
       experiment = Experiment()
       data = experiment.run()
       return data
   

2. Analysis

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

3. Validation

   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