What is a Mental Blueprint and why is it an important concept to adopt?
Since science has discovered the way our brains record information, skills, beliefs and habits, we can now set about to develop techniques and self-hacks to accelerate how we learn, develop skills, cultivate creativity and even develop our intuition.
Imagine a system of education designed to cut grooves in the brain, turbo charging the rate and depth of learning. Imagine experiencing 50-90% faster learning, comprehension and creativity for life. Would it be possible to churn out a generation of PhD graduates ate ages 20-22 on average? How about graduate study programs for “blueprinted” students to complete a degree in months rather than years?
Based on several modern discoveries this article contemplates and proposes just such a possibility. The approach builds upon these modern discoveries in the fields of education, neuroscience, computer science and particularly in sub-specialty fields of AI called machine learning and transfer learning.
Imagine learning musical theory and composition to plant the right seeds for accelerated absorption of advanced mathematics, physics, data science, and economics. Immersion in the reading and composing of music (especially multi-instrumental and orchestral pieces) develops patterns of thought and comprehension that readily adapt to fields of study such as wave theory, business strategy, data science, mass psychology, political science, just to name a few.
What would be involved in such a process of systematic brain development to plant a lattice work of blueprints designed for prodigal learning? Is it possible to deliberately design coursework in eclectic subjects that yield genius-like results down the road? Before diving in, let us take a brief look at the advances in cognitive science that lead to the possibility of mental blueprints.
- AI Machine Learning
- Neural Circuits
- Transfer Learning
A Quick Tour: AI Machine Learning
Within the domain of artificial intelligence, lies the ever-popular technique called Machine Learning. It aims to use large data samples to mathematically “train” a data blob (called an ML model) in one skill. It can then repeat the trained conclusions about broader data in that domain. For example, one could train an ML model using 1,000 images and a back feed of right and wrong answers to spot people wearing sweaters. Then given any image, the model could tell us whether or not it sees a person wearing a sweater, within a certain margin of error.
Once an ML model has been trained to produce a type of output based on data and its trained skill, that model can behave as if it were intelligent about that one trained subject. Within the model itself (a data blob representing its “knowledge”), one could observe a complex set of mathematically represented patterns. The model is “modeling” the reality it was trained to understand, using stored math and data. In some ways this is similar to what the biological brain does. In others, it is vastly different.
A Quick Tour: Neural Circuits
Let us start with a metaphor to understand the brain’s ability to store seemingly unlimited information and skill.
Let us say someone tells five people in a small town something about recent political news. Some or all of them tell their friends and family about it, based on how important that topic is to them, and which people in their social circle has the kind of relationship. If those same five people likewise hear about a new Italian restaurant, they might discuss that with different people in their social circle. Information flows through the town differently by topic and the intensity of the reaction from each participant in the chain. Zooming out, one might observe that the town appears to hold certain consistent opinions about each topic.
These topic-specific relationships form patterns in the community which resemble river beds or pathways through which different information flows. Some communities support rapid and broad participation in chatter about certain topics, while not caring as much about other topics.
Similarly, the brain can process, store and hold opinions about information using topic-oriented relationships between neurons and neuron communities. This demonstrates one of the key concepts for this article: neural circuits. The paths through which certain topics flow through an individual brain make up a virtual groove. Once established, a neural circuit will remain for an unknown duration (possibly for life). However, the more a circuit gets used, the more prominent, refined and connected to other neural circuits it becomes.
According to Wikipedia: “A Neural Circuit is a population of neurons interconnected by synapses to carry out a specific function when activated.[1] Neural circuits interconnect to one another to form large scale brain networks.”
So, the result of an AI ML model learning is a blob of math and data representing the knowledge. The result of learning in the brain is one or more patterns of neural circuits.
A Quick Tour: AI Transfer Learning
In the field of AI, the concept of “transfer learning” has helped Data Scientists accelerate how they train new machine learning models to develop added skills with a fraction of time and resources.
According to Wikipedia: “Transfer learning (TL) is a research problem in machine learning (ML) that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. For example, knowledge gained while learning to recognize cars could be applied when trying to recognize trucks.”
The research that has come out around Transfer Learning in the field of Machine Learning has caused an offshoot of scientific research into this phenomenon in the field of human learning and neuroscience.
In humans, transfer learning is the phenomenon of learning about a topic by transferring knowledge (or leveraging neural circuits) from a different topic. Loosely speaking, transfer learning occurs when the shape or pattern of information partially resembles the shape or pattern of something already known, and can therefor use the same neural circuits already forged.
Think of the pattern of a 6-point star made from 2 overlapping triangles. A 6-sided hexagon matches the interior pattern of the star. If you had a neural circuit already well ingrained into your brain like this star, then when encountering new information like the hexagon, it would fit nicely with the existing neural circuit and make the hexagonal information easier to learn.
Some transfer learning applies to bodies of knowledge in the same category, like spoken languages. While Swedish and Mandarin may benefit from a 5-10% overlap, Spanish and Italian will benefit from a 60%+ overlap in similarity. So if a fluent Spanish speaker sets out to learn Italian, the journey will be far smoother than picking up Swedish or Mandarin.
A computer science student who learns programming languages like C++ and JavaScript will learn Java at least 30% faster than Java as a first language. An experienced Java Enterprise Engineer will learn C# and the .NET framework at least 40-50% faster than if they didn’t have experience with the vast Java enterprise library.
An avid golfer who spends time practicing and perfecting their posture and swing will find archery much easier to pick up, since posture and form neural circuits are already ingrained.
Other transfer learning can leverage neural circuiuts to accelerate learning in entirely different categories. For example, an avid golfer may find business process engineering resonates in some ways with the benefits of the investment in the form of their golf swing. A karate student may find it easier to design a training course in diplomacy, applying the kata-based training techniques to building layers of conversational tools for the budding diplomat.
Mental Blueprints
So what? Knowing that our brains are riddled with groves and complex networks of neural circuits that can grant boons of accelerated learning and insights, what can we do with this knowledge? We can lean into it, creating new self-hacks that reinforce and leverage our skills and knowledge to amass new capabilities at prodigal speed and depth.
Enter Mental Blueprints. A Mental Blueprint is a highly reusable neural circuit that offers patterns of thought, skill and/or knowledge to provide fertile ground for us to take in new information rapidly and transform it into gains.
More succinctly put:
A Mental Blueprint is a cognitive asset applied to multiple disciplines of skill, providing accelerated cognitive learning, processing and connectivity.
We can intentionally lean into neural circuits and transfer learning phenomena to reap the benefits. The practice of identifying and developing Mental Blueprints is to curate and make use of specific thought patterns that accelerate learning, insight and creativity.
The steps to cash in on Mental Blueprints are simple.
Step 1: Inventory
First, take stock of your own skills and specialties. Start with your top 3 but no more than your top 10. What hobbies, skills, study topics, talents or capabilities do you most enjoy? With which of those do you have deep experience or natural expression? Does anything come easily to you? Are there any that relate to or fit in similar categories with any others?
List and then group them.
Step 2: Rate Them
For each topic or skill, rate them from 1-3. A rating of 1 is for Good skill level or depth of understanding, 2 for Strong, and 3 for Exceptional.
Our intuitive sense of strength in these areas can provide a clue to how deeply and broadly entrenched the neural circuits are for that skill or topic. Stronger neural circuits make for even better Mental Blueprints.
Step 3: Make a Wish List
Create a list of skills or knowledge topics you would like to acquire or master next. Match them up with one or more Mental Blueprints you identified in step 1.
The new topics you wish to learn that already match up to Mental Blueprints with the deepest and most utilized Mental Blueprints (from step 2) will yield the fastest results.
The more skills you acquire and practice that match up to the same blueprint, the more skill you will observe across those related skills.
Personal Anecdotal Evidence – Programming Languages
I first noticed the experience of accelerated learning in subtler form, applying transfer learning in the process of learning multiple programming languages. At the time I did not know about the concept of transfer learning. I realized after learning Apple BASIC, followed by Business BASIC, followed by Visual BASIC, once the first language was learned, it was like 2% effort to learn the differences and acquire the new language.
Then, as I put in 80% effort to learn a language vastly different like C, I was still able to apply foundational concepts like variables, conditionals, operators, and flow structures to accelerate my learning. From there, all of the C-like languages only required 5-20% effort to learn the differences.
When Object Oriented concepts emerged, and the SmallTalk language presented itself, I had another 80% investment to acquire all new concepts of classes, objects, member/friend scopes, methods, accessors, and the like.
Then with both C and SmallTalk under my belt, learning C++, Java and all other downstream Object Oriented languages only took another 5-10% effort to learn. Within the first 5 years of my programming career (age 17-22), I taught myself around 15 programming languages. At the time, I just assumed there was something strange about my particular brain that was purpose built for being a programmer. I didn’t know that I had simply stumbled on a pattern of learning that efficiently applied a fundamental reality about neural circuits and transfer learning.
Personal Anecdotal Evidence – Martial Arts & Golf
The next leap in recognition and understanding of this type of phenomenon was when I decided to lead my 10 year old son into solo sports that he found interesting. He as not enthused about team sports, and did not like competitive sports at all. But, he and I both enjoyed the fantasy novels. Anything related to medieval weapons or martial arts were exciting and fun for both of us.
We both spent a few years in a dojo learning karate. We then tried our hand for a couple years at archery (bare bow recurve). To help him I went through a program to become a certified archery coach.
We then moved on to fencing and enjoyed both foil fencing and saber fencing. At the time, I still did not see any particular gains in efficient learning from these separate, but seemingly related martial practices. However, about 10 years later, I decided to pick up a golf club and try to learn that skill. A friend took me out to learn the basics and showed me the foundational postures, grips, swings and clubs. Within the first hour of swinging clubs, he commented on what a natural I was at putting, pitching and chipping. He asked if I had golfed before and commented that I looked like I had been chipping and pitching for years. While these skills were mysteriously easy for me to immediately apply after being instructed on posture, grip and form, the skill of driving eluded me at first.
As I reflected later that week, I realized that I was applying various skills acquired over the prior 2 decades in karate, archery and fencing to newly acquired information about how to golf. I felt excited that maybe I could do the same thing with acquiring athletic skills that I had done with programming skills. This is when I began intentionally experimenting with the idea of Mental Blueprints.
What Now?
I encourage you to do the same. Pay attention to the things you know and how you learn new things related to what you already know. Experiment with applying Mental Blueprints to accelerate your ability to master new skills in a fraction of the time you would expect. Watch how your mind opens up to recognizing similarities between seemingly unrelated ideas to produce new insights and creativity.
