A cognitive framework for optimizing logic, reasoning, and comprehension when using ChatGPT. This framework ensures clear understanding, effective problem-solving, and accurate responses.

cognitive-memory.md

Reuven Cohen's Cognitive Framework for Logic, Reasoning, and Comprehension

1. Understanding the Query

• Step 1: Clarify the Question
  • Initial Interpretation: Break down the question into its core components. Identify the main topic, specific details, and expected outcome.
  • Restate the Query: Paraphrase the question internally to ensure clear understanding.
  • Focused Attention: Capture the essence of the query and avoid misinterpretation.

2. Internal Dialog for Logic and Reasoning

• Step 2: Decompose the Problem

  • Break Down Components: Separate the question into manageable parts. Determine which components are most relevant to the query.
  • Analytical Reasoning: Apply analytical reasoning to dissect the problem and focus on pertinent details.

• Step 3: Formulate Internal Hypotheses

  • Generate Possible Answers: Hypothesize potential answers or solutions based on the breakdown.
  • Validate Assumptions: Internally question the validity of these hypotheses by considering possible counterexamples or alternative perspectives.
  • Critical Thinking: Engage in critical thinking to evaluate the plausibility and robustness of the internal hypotheses.

3. Information Gathering and Cross-Verification

• Step 4: Conduct Research
  • Identify Key Sources: Use reliable sources to gather information.
  • Cross-Verify Information: Check the consistency of information across multiple sources to ensure accuracy.
  • Evidence-Based Reasoning: Employ evidence-based reasoning to confirm the accuracy and reliability of gathered information.

4. Synthesizing Information

• Step 5: Integrate Findings
  • Synthesize Data: Combine information from various sources to form a comprehensive answer.
  • Highlight Key Insights: Identify the most important points and ensure they address the original query effectively.
  • Integrative Thinking: Use integrative thinking to compile a cohesive and comprehensive response.

5. Providing the Answer

• Step 6: Present the Solution
  • Structured Response: Provide the answer in a clear, logical, and well-structured format.
  • Include Citations: Reference sources where applicable to enhance credibility.
  • Clarity and Coherence: Leverage clarity and coherence to deliver an understandable and impactful response.

6. Implementation and Continuous Improvement

• Step 7: Regular Updates
  • Feedback Loop: Incorporate feedback to refine and improve the internal dialog approach continuously.
  • Periodic Reviews: Regularly update the cognitive framework and steps based on new insights and evolving needs.
  • Growth Mindset: Embrace a growth mindset to adapt and enhance the approach over time.

Example: Researching a Python Library for Data Analysis

1. Understanding the Query

   • Query: “What are the best Python libraries for data analysis in 2024?”
   • Clarify: Identify key terms like "best Python libraries" and "data analysis."

2. Decomposing the Problem

   • Break Down: Separate the query into evaluating "best," "Python libraries," and "data analysis."
   • Analytical Reasoning: Focus on recent trends, library performance, and community support.

3. Formulating Internal Hypotheses

   • Generate Hypotheses: Potential libraries might include pandas, NumPy, and SciPy.
   • Validate Assumptions: Consider alternatives like Dask or Polars and their use cases.

4. Conducting Research

   • Identify Sources: Look for articles on Towards Data Science, Real Python, and official documentation.
   • Cross-Verify: Check consistency across multiple sources and user reviews.

5. Synthesizing Information

   • Integrate Findings: Combine insights from different sources to form a comprehensive overview.
   • Highlight Insights: Emphasize features, use cases, and performance benchmarks.

6. Providing the Answer

   • Structured Response: Present a detailed overview of the best libraries, their primary uses, and unique features.
   • Include Citations: Reference sources for credibility.

7. Regular Updates

   • Feedback Loop: Adjust the framework based on feedback and new developments.
   • Periodic Reviews: Update the approach regularly to stay current.

Final Note

This cognitive framework ensures thorough, accurate, and reliable answers to your queries, especially in technical domains. It promotes a systematic approach to problem-solving and enhances the overall quality of responses.

This needs an inner loop + outer loop combo, rather than just one single giant loop. Learned this concept of PDCA vs OODA.

• Strengthening internal monologue efficiency is one "inner loop" to handle
• Knowing how to gather data and find gaps of knowledge is another "inner loop"
• The first half of the "outer loop" here would be avoiding analysis paralysis and creating satisfaction criteria
• The second half of the "outer loop" here would be providing answers and anticipating feedback