The Reasoning Type Analyzer: Unlock the Hidden Patterns in How We Think

For Critical Thinking & Education:

• See beyond the surface arguments to understand the underlying reasoning patterns

• Identify when beliefs are based on emotion, authority, or systematic analysis

• Learn to recognize the difference between defending existing beliefs (abduction) and building understanding from evidence (induction)

• Develop sharper analytical skills by studying real examples

For Content Analysis:

• Evaluate the quality of reasoning in articles, reports, and proposals

• Understand how different sources construct their arguments

• Identify reliance on citation vs. independent analysis

• Spot patterns of reactive thinking vs. deliberate reasoning

For Self-Reflection:

• Analyze your own writing to understand your reasoning habits

• Identify whether you're working from evidence or from beliefs

• Discover blind spots in how you make decisions

• Strengthen your argumentation by diversifying your reasoning types

Most arguments, decisions, and beliefs aren't based on careful reasoning—they're built on habit, emotion, what we've heard from others, and quick associations. The Reasoning Type Analyzer reveals exactly how thinking works in any document, article, transcript, or webpage by identifying 11 distinct types of reasoning. Understanding how someone reasons is often more important than what they conclude.

The 11 Types of Reason:

The analyzer categorizes reasoning into a spectrum from reactive to systematic:

Lower-Order Reasoning:

• Reaction - Habit, emotion, belief, immediate response

• Citation - Authority, experts, rules, what others say

• Association - Connections, analogies, relationships

• Definition - Labels, descriptions, identity, meaning

Mid-Level Reasoning:

• Deduction - Logical arguments from premises to conclusions

• Abduction - Working backwards to defend existing beliefs

• Succession - Process, prioritization, evaluation

Higher-Order Reasoning:

• Inspection - Systematic analysis, causality, factors

• Induction - Building hypotheses from observations

• Motivation - Goals, purpose, constraints

• Narration - Projects, transformation, innovation

How to Use the Analyzer:

1. Upload your content - PDF, website URL, or text file (including video transcript or Zoom/Teams meeting transcript)

2. Run the analysis - AI identifies assertions, matching reasons, and orphaned claims with no reasons

3. Study the results - Each example includes an explanation of why it's classified that way

4. Download your report - Save both detailed text reports and visual charts

Best practices for meaningful analysis:

• Analyze complete arguments or documents, not fragments

• Compare multiple sources on the same topic to see reasoning differences

• Look for patterns: Do the assertions rely heavily on reaction and citation, or indicate inspection and induction, or provide no reason at all?

• Use the bracketed explanations to learn how to recognize reasoning types yourself

• Analyze your own writing to identify habits you want to change

What to look for in results:

• Heavy Reaction & Citation? The reasoning may be based more on emotion and authority than analysis

• High Abduction? The author may be defending pre-existing beliefs rather than exploring evidence

• Strong Inspection & Induction? Indicates systematic, evidence-based reasoning

• Diverse reasoning types? Suggests more sophisticated, multi-dimensional thinking

Try It Now:

Humans are wired for stories, and our reasons operate in specific locations of our story. Making this connection will improve your critical thinking, decision making, and communication skills.

Click below to access the Reason Analyzer, and provide Text, PDF, or URL for the analysis. (Requires Free Claude Account)

Business Insights

Methods from the last age were linear and prescriptive: Do-These-Steps-In-This-Order. ADIIEA is a method that is cyclic and descriptive. It provides the phases of thinking through a project which begins and ends in our most natural state: automation.

Oliver Wendell Holmes once said, “I wouldn’t give a fig for the simplicity on this side of complexity, but I would give my right arm for the simplicity on the far side of complexity.” He was describing the difference between maintaining the status quo with reactive training and memorization, and the elegance found only after knowing the options and choices considered throughout the entire story structure which leads to a state of proficient automation, with understanding.

Understanding this story cycle allows us to evaluate each individual phase, and holistically manage an organization’s ability to prepare, innovate, change, and perform. And it allows us to connect the now-separate functions of Idea Management, Risk Management, and Operations Management:

• Automation is where we manage proficiency through Operations Management, and distinguish between simplicity and elegance.

• Disruption is where we manage preparedness through Risk Management, for problems as well as opportunities.

• Investigation is where we manage impartial analysis through Investigation Management, for inspections and explorations.

• Ideation is where we manage constructive ideas through Idea Management, and seek both derivations and innovations.

• Expectation is where we manage realistic goals through Change Management, and act on what we put our hope and faith in.

• Affirmation is where we manage certainty through Quality and Assurance Management, and distinguish between knowing and trusting.

Education Reform Insights

Our education systems are currently driven by evaluation models, not learning models. And these models are based on the behavioral psychology developed in a rat lab by B.F. Skinner, giving us operant conditioning, but not understanding. While these models have served us in the Industrial Age, where many students would learn to perform repetitive tasks, the AI Age demands that humans operate from their full potential.

Educators today are typically trained to evaluate student progress using Bloom's Taxonomy of educational objectives. In the introduction to his 1956 Taxonomy of Educational Objectives, Handbook 1, Bloom acknowledges that the idea for a taxonomy of learning objectives was formed by college examiners (not teachers), and that there was concern the taxonomy “might lead to fragmentation and atomization of educational purposes such that the parts and pieces finally placed into the classification might be very different from the more complete objective with which one started” (Bloom 1956). We now know through neuroscience that the underlying model being fragmented is the story.

Learning Objectives Framework Progression

Following the implementation of Bloom's 1956 taxonomy, the fears of mental fragmentation were realized with a drop in student critical thinking skills. The U.S. government published "A Nation At Risk" in 1983 in response to the growing concerns and Bloom's taxonomy was revised in 2001 with a rearrangement of categories but still not a realignment with story patterns. Following the findings within neuroscience that our minds are wired for stories, Lewis published Story Thinking in 2019 with sections on Change, Learning, and Leadership.

Bloom 1956

Bloom 2001

Lewis 2019

• Designed from Evaluators for the Industrial Age

• Taxonomy starts at bottom with Remembering

• Creates Fragmentation in Critical Thinking Skills

• Designed from Neuroscience for the AI Age

• Learning starts at top with Muse Questions

• Critical Thinking from Narrative Intelligence

Learning Objectives Aligned with Story Thinking

“When you can teach a course on Critical Thinking, but can’t test the students for understanding, pedagogy is the problem, not the curriculum.” — John Lewis, Ed.D.

U.S. 1983