One of the key methodologies we use is progressive overload. This means doing things slightly above the current capacity to prepare yourself for improvement. You may choose another iterative method base on your personal needs. More stuff you can read here, here, here, and here.
One of the things we do in speedreading, is asking a person to read 10% faster than his current comfortable speed. We hope that comprehension will catch up. In fact, it usually does.
When doing progressive overload it is important not to do it all the time. The risk is simple. If the comprehension does not catch up, the expectations get lower. As a result, instead of improved performance, we get satisfaction from lower standards.
While this method is taken from gyms, it is not used very often in gyms per se. The risk of physical injury is increased, and it is used as a possible method to overcome a plateau.
We use a very similar approach to creativity. Say we ask someone to come up with associations. At some point, he gets exhausted and stops. Then we ask for just one more association. That specific extra association tends to be different. From it, we can truly learn how one’s mind works and maybe open out-of-the-box possibilities.
Quite often this extra break-through opens access to a large resource previously unused, and A LOT of further associations. The brain can be forced to build new connections and learning to build new connections is more important than the specific connections we come up with.
In this sense, the associations are just a way to measure the creative products of the brain. After all, we need a single criterion to use with very different people and goals.
The harder the better
When doing computer training one way or another, many students ask if they are not cheating. Usually, I encourage “cheating” techniques but add new limitations, like particular timing.
The mountaineers often choose to climb in a certain way, introducing new and artificial obstacles. For me the reasons are simple. To get into the “flow” state the complexity of the task should be compatible with personal capabilities. There is a limited number of mountains, and getting to a new mountain can be hard and expensive. By adding other limitations, it is possible to vary the challenge level while staying on the same mountain and enjoy the “flow” state.
The harder ascent also gets more prestige and respect from other people working on similar tasks. When a programmer wants not just to write the code based on the specifications, but make this code “elegant” he adds limitations both to get a more interesting task and to get the respect of the peers.
There is an approach that is just the opposite of progressive overload. In our terms, we ask someone to read at 80% of the comfortable speed but invest at least 8 hours per day reading. Usually, we do this with people below 18 or above 70, simply to improve the endurance.
This low-intensity training approach is also common in sports. The idea is not just to build up the slow-release muscle tissue and bone density, but also to improve the technique. Everything that is not properly trained starts to ache after a while. The shape and the technique should be very accurate to avoid all sorts of unnecessary strain.
If someone needs to write 10000 words per day, this is not an issue of typing speed. After all, dictation is ~200 wpm, fast typing is ~60 wpm and slow typing is 20 wpm. At 20 wpm, the 10000 words will take 8 hours and we can work 12 hours per day with short breaks. The issue is being focused and coherent with moderately creative ideas for many hours. This is an endurance task.
As slow as you can
Occasionally we ask our students to read as slow as they want, but get 100% comprehension and retention. This means almost a word-by-word memorization.
In tai-chi, the practitioner also is asked to do very slowly the things that he should do very fast in challenging situations. The issue here is improving the balance and accuracy of the movement. Every small deviation can be monitored and corrected in slow motion.
Professional musicians often watch their videos and hear music in slow motion to notice the details everybody tends to miss. A very small aspect can be seen in slow motion and corrected there. These small aspects tend to accumulate.
Accumulating small changes
What is the main difference between video codecs? MPEG4 and H266 appear to be very similar conceptually. There were two huge intermediate milestones offered by H264 and H265 is incremental. When we get into the specific details, we see many small ideas. Each small idea adds maybe 20% performance in 20% of the use cases. No revolution. But when we add up these small changes we get a huge improvement.
Small changes tend to accumulate. When we build a startup we should offer something very different or x10 improvement, but not when we already established a routine. If we can improve something small without causing a degradation elsewhere, we can start to aggregate these small achievements. Eventually, there will be overwhelming progress, but we may not be able to attribute it to something specific.
People tend to ignore small changes. This is one of my personal sins. Meditation improves certain performances by 10%, I know it, yet I stopped meditating years ago. The 10% improvement is not very exciting, but it adds up to other things… 10% happier, less stressed, more focused… That’s actually a huge boost.
When we need to build a plan, we often start from a large vision. We add some resources and limitations. Then we break this vision into smaller milestones and allocate the resources accordingly. These milestones are broken into yet smaller milestones. Usually, we have enough information to refine the plan further only to the closest milestones, and this is OK. At some point, the refinement becomes useless, as there is uncertainty in our lives.
This is similar to the way a classical painter creates his image. First, there is a basic composition and outlines. Then there are some primer colors, adding lights and shadows. Above them, layers upon layers of semitransparent paint are added to create the gradients. Then with the finest brush, small details and decorations are finished and refined.
The memory structure that represents this approach is a mindmap, with increasingly more detailed branches refining previously visualized knowledge.
Mental palaces represent a very different and somewhat linear approach. We progress along fixed itineraries, adding further information as we move. This is similar to long-distance runner completing his distance each step at a time.
The focus, in this case, is both on the grit and on the pace. It is important to keep the eyes on the end goal even though the way may be tough. In fact, it is useful to visualize the achievement. At the same time, it is important to keep the proper technique each step of the way. Any unnecessary speedup or slow-down might ruin the end result.
While planning is often a mapping process, following the plan is an incremental completion task, demanding and tiresome.
Suppose we have a complex task. What is the best way to complete it? When we develop the best technique for a specific task, we have just a small toolbox to choose from. Typically people do not tend to invent new methods and learn new tools even for repeating a task.
When we have a small number of tools to apply, we still can use all kinds of combinations at each step of the task. Consider something simple, like learning a specific tax law. How many times and how fast should we read it? How do we refine our understanding? Do we keep the data in a mindmap or a mental palace? As we ask more questions, we have a huge number of possibilities.
In math, this is usually solved via some sort of gradient-descent optimization. In real life, we try different things, changing one pair of parameters at a time, until we get the task just right – for us.
What happens when we have an entirely new task? We probably learn the subject by asking new questions. Some things will be available on google, for other things we will ask experts or even plan and execute simulations and experiments. As we progress we ask further questions. We cannot predict in advance which questions we will ask. As we learn new things, so we learn new possible ways to progress.
This can be defined as “iterating around problem and solution while elaborating them concurrently”. We acquire new tools and learn more about the task simultaneously. The beauty and the tricky part of this approach: nothing apriori guarantees convergence or even validates the chosen direction.
Exploration is always an adventure.
Concretization is defined as “revisiting elements of the design while increasing their levels of definition, ensuring consistency”. Suppose we have used a mental palace to describe some articles. Then we reread the article and add details. Each reread we place further details on already well-described subjects. This is something we sometimes do when dealing with very complex patents and scientific articles.
In my work, the subject that usually requires concretization are software bugs. The QA team defines the test and failed behavior. The system team identifies the root cause: hardware, software, or algorithms. The team leader learns which subblock is responsible and which team member should fix it. Only then the relevant team member designs a working solution…
Know your limitations
Quite often tasks are treated iteratively as they bounce between the responsible team members. Usually, these iterations stop when there is an interdisciplinary team of 2 or 3 people who between them know the subject sufficiently well. For example, most startups are founded by two or three entrepreneurs. Possibly because one person just cannot know everything that is needed.
Who was the last man to know everything worth knowing? Most publications point fingers to Johann Goethe in 19th century, but some people go as early as Aristotle in ancient Greece. If we take a subject like math, we may point fingers to universal knowledge Henri Poincare at the beginning of 20th century. Possibly Max Weber was similarly positioned in social sciences. Currently, we double our shared knowledge on average every two years. Nobody can follow that trend.
I definitely know more than most people around me, but this only makes me more aware of the things I do not know. In fact, the more I learn the bigger the gap I can see. I guess this is just another kind of iterative process, this time diverging: learning more to ask more questions.