My Journey Toward Genius: Understanding the Brain, Thinking, and Creativity (Part II)

 

My Journey Toward Genius: Understanding the Brain, Thinking, and Creativity

Part II: Finding the Answers

Albert Einstein once remarked, "Imagination is more important than knowledge." For many years, I admired the quotation without fully understanding its depth. At the time, I did not appreciate the central role that imagination, visualization, or what I now like to call "imagineering"—engineering entirely within the mind before building anything in reality—could play in scientific discovery and invention. It was only in 2013 that I began learning a collection of ideas that finally allowed me to understand why Einstein placed such extraordinary value on imagination.

How It All Began

From 2003 onward, I became deeply fascinated by metacognition—the study of thinking about thinking—and by techniques for improving the human brain. Rather than merely acquiring knowledge, I wanted to understand how knowledge itself is created. I constantly asked myself questions such as: How do great inventors think? How do geniuses solve problems? How can the human mind become more creative?

In 2007, while traveling to the United States with my family, we stopped at Muscat International Airport in Oman. While browsing a bookstore, I noticed a newly published book titled The Emotion Machine by Marvin Minsky, one of the pioneers of artificial intelligence. The book immediately caught my attention because it explored both AI and the workings of the human mind.

Although many of its ideas would take years to mature in my thinking, that encounter planted an important seed. Later, after arriving in the United States, I ordered The Emotion Machine along with another influential book, Cracking Creativity by Michael Michalko. Together, these works introduced me to ways of thinking that would eventually become cornerstones of my own intellectual framework.

From Minsky's work, I gradually came to appreciate ideas such as levels of abstraction, higher-order thinking, and reflective thinking. At the time, I understood them only partially, but they would later fit together into a much larger picture.

Meanwhile, as a student of Computer Science and Engineering, I was learning concepts that initially appeared purely technical but would later become universal principles of thinking. Among these were abstraction—the ability to ignore unnecessary details and focus on essential structure—and computational thinking, the systematic approach to solving complex problems by decomposition, pattern recognition, abstraction, and algorithmic design.

In 2013, another influential book entered my life: Mindstorms by Seymour Papert, a pioneer in educational computing. From this book, I encountered ideas such as mental models and affective thinking—concepts that profoundly influenced how I approached learning, reasoning, and creativity.

A mental model is an internal representation of how a system, object, or process works. Rather than memorizing isolated facts, you construct a coherent picture of the relationships, interactions, and underlying principles. This enables you to simulate situations in your mind, predict outcomes, troubleshoot problems, and transfer knowledge from one domain to another. In Mindstorms, Seymour Papert emphasized learning by constructing and manipulating such internal models, allowing learners to "think with" ideas instead of merely recalling them. Closely related is affective thinking—the idea that effective learning is influenced not only by logic but also by personal engagement, curiosity, emotion, and intuition. When you genuinely understand a concept and develop an emotional or personal connection to it, the knowledge becomes natural and intuitive rather than mechanical. Instead of consciously recalling formulas or rules, you begin to feel how a system behaves. Together, mental models and affective thinking transform knowledge from a collection of disconnected facts into a living, intuitive understanding. They make it easier to reason about complex systems, recognize patterns, generate creative solutions, and learn across multiple disciplines, because ideas become interconnected, meaningful, and readily accessible during problem solving.

Around the same time, I also began creating mind maps. Their branching visual structure allowed me to organize complex subjects into interconnected ideas instead of isolated facts. I found that this made learning both faster and more intuitive, while revealing relationships that traditional note-taking often concealed.

Then came the breakthrough.

One evening in 2013, while browsing the recommended reading list known as The Personal MBA, I noticed several books on systems thinking. The title itself immediately intrigued me. I remember thinking, This might be exactly what I have been searching for.

I downloaded one of the recommended books and began reading.

Within an hour, I felt an extraordinary sense of discovery.

This was it.

For years, I had been collecting individual pieces of a puzzle without knowing how they fit together. Systems thinking provided the missing framework. It taught me to see every object, organism, machine, organization, and scientific phenomenon as a collection of interacting components forming an integrated whole.

Suddenly, many previously unrelated ideas began to converge.

I realized that when we construct rich mental models of engineering systems, biological organisms, or physical phenomena, and then explore those models through imagination, supported by affective thinking, levels of abstraction, and systems thinking, our understanding deepens dramatically. Instead of memorizing isolated facts, we begin to simulate reality inside our minds.

This realization transformed the way I approached learning and invention.

I gradually came to believe that many of the cognitive tools used by scientists, engineers, inventors, and polymaths are not isolated techniques but complementary parts of a unified framework. Imagination generates possibilities. Mental models represent reality. Systems thinking reveals interactions. Abstraction reduces unnecessary complexity. Computational thinking structures solutions. Reflection refines understanding.

Later, I expanded this framework further by adopting the concept of a knowledge ontology—a structured network of concepts and relationships that I regarded as a more powerful extension of traditional mind maps. Rather than simply connecting ideas visually, a knowledge ontology organizes entire domains of knowledge into interconnected conceptual structures, making it easier to learn, retrieve, and integrate information across multiple disciplines.

Looking back, I now see that these discoveries answered many of the questions I had been asking since my teenage years. They did not merely teach me new techniques; they fundamentally changed the way I thought about thinking itself.

A practical way to invent faster is to combine the concepts from my framework into a repeatable thinking process rather than waiting for inspiration. Consider the challenge of designing a smart wearable device that monitors stress and recommends interventions.

You begin with abstraction, reducing the complex problem into a few essential questions: How can stress be detected? How can it be predicted? How can it be reduced? Instead of thinking about thousands of details, you focus only on the core functions.

Next, you use systems thinking to model the wearable as an interacting system. You identify its major subsystems: sensors (heart rate, skin conductivity, temperature), data processing, machine learning, user interface, battery management, and intervention mechanisms. You also consider the interactions between the wearable, the user, the smartphone, and cloud services.

You then build mental models of each subsystem. Rather than memorizing facts about heart-rate variability or machine learning algorithms, you construct internal models of how they work and mentally simulate their behavior. This allows you to ask "What happens if..." questions without immediately building prototypes.

Using imagination or imagineering, you perform engineering experiments entirely in your mind. You visualize a user becoming stressed during an exam or a meeting. You mentally observe how the sensors respond, how the AI classifies the data, and how different interventions—breathing exercises, music, or lighting changes—might influence the user's physiological state. Many weak ideas are discarded before any physical prototype is built, saving considerable time.

Affective thinking enriches this process by making the system intuitive rather than mechanical. Instead of viewing the wearable merely as electronics and software, you empathize with the user. You imagine their frustration, anxiety, comfort, and relief. This emotional understanding often reveals design improvements that purely analytical reasoning might overlook, such as minimizing intrusive notifications or making feedback calming rather than alarming.

Throughout the process, computational thinking helps decompose the problem into manageable components, recognize recurring patterns, create abstractions, and design algorithms for sensing, prediction, and decision-making.

You organize all related knowledge using mind maps or, for larger projects, a knowledge ontology. Physics connects to sensor design, biology to stress physiology, psychology to emotional regulation, computer science to machine learning, and electrical engineering to hardware architecture. Seeing these relationships visually makes it easier to transfer ideas across disciplines.

Finally, you apply metacognition by continually evaluating your own thinking. You ask yourself: Am I making assumptions without evidence? Have I explored enough alternatives? Can I simplify the system further? Is there a better abstraction? This continuous reflection improves both the quality and speed of invention.

The result is not a single flash of genius but a systematic invention process. Instead of relying solely on inspiration, you repeatedly cycle through abstraction, systems thinking, mental models, imagination, affective thinking, computational thinking, knowledge organization, and metacognitive reflection. Each cycle generates, tests, refines, and combines ideas more efficiently. Over time, this integrated approach enables you to tackle increasingly complex problems, connect knowledge from multiple disciplines, and produce innovative solutions at a much faster rate than relying on trial and error alone.

A far more powerful approach is to integrate all of these concepts into a single, unified mental model rather than treating them as isolated techniques. Instead of applying abstraction, systems thinking, imagination, computational thinking, metacognition, mind mapping, and affective thinking independently, I mentally weave them together into one coherent framework for reasoning and problem solving. This integrated mental model allows each method to reinforce the others: abstraction reduces complexity, systems thinking reveals relationships, imagination enables mental experimentation, computational thinking structures the solution process, affective thinking makes knowledge intuitive, and metacognition continuously evaluates and refines my thinking. By combining these methods into a single way of thinking, I can approach complex problems more holistically, learn more efficiently, and generate creative ideas and inventions at a significantly faster pace than by using any one technique alone.

In my view, many of these concepts and methods were either unavailable or had not yet been developed during the lifetimes of Einstein and Newton. I sometimes wonder that, had they possessed this broader toolkit for understanding thinking, learning, and creativity, they might have been even more productive than they already were. Of course, this remains a matter of personal speculation, but it reinforces my belief that advances in our understanding of cognition can significantly amplify human creativity and scientific discovery.

I still remember that, only a few days after discovering systems thinking in 2013 and integrating diverse concepts into a unified mental model, I experienced a profound realization. It occurred to me that even if every body of human knowledge, every institution, and every engineering device, machine, gadget, and structure were wiped out by a human-made catastrophe or a natural disaster, I could reconstruct them all within a few years using the cognitive toolkit I had developed. My conviction was that a deep understanding of the underlying principles, patterns, and relationships that govern knowledge would make it possible to rediscover and rebuild civilization from first principles rather than merely recover what had been lost.