This article is very important as a way to describe my approach to memory and speedreading. Read all of it carefully, please. For more information here, here, here, here, here, and here.
World as chaos
If we address all the details of what we perceive we will drown in information.
Fortunately, our perceptual mechanisms reduce most of the information they get. We see perfectly only a very small part of our visual field and hear perfectly only very specific sounds. Everything else drowns in noise: we literally do not perceive weak signals in presence of noise. Our brain filters more based on how focused we are, so that very little information is actually registered. Still, less information is remembered.
While the world is immensely complex, with fractal features, multiple reflections and all sorts of noises, our perception of it is relatively very complex.
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Registering patterns
The brain can encode information efficiently since it can recognize and register patterns. Humans recognize patterns in a way very different from animals, as we use language. We do not recognize wavelengths and textures, instead, we recognize colors and patterns which have names in our language. The use of dictionaries is very convenient for compressing the information and revealing patterns hidden in the noise. At the same time, if we do not have a word for what we see, we might perceive something different.
So while our language and pattern recognition help us encode the information, they introduce new kinds of errors.
Common perception error
Without further training we have certain perception errors:
- Over-representation of what is already known. We like to detect common familiar patterns, and yet we might use the most informative and innovative information. Some “innovation” training like “find the differences” game may offset this tendency.
- Priming. We are likely to perceive better the patterns we apriori expect to see. This makes our perception highly contextual. If we do not define the context correctly, the perception will be lowered. For example, we teach our students to scan the article with their eyes before actually reading it.
- Confirmation bias. Most people will notice the things that correspond to the way they already perceive reality. Unless we try “critical thinking” of analyzing the author’s hidden motives and actively looking for lapses of judgement we may miss crucial messages.
- Emotional bias. The way we feel makes us look for patterns that correspond to our feelings. It is so bad, that there are systems like “6 thinking hats” for reviewing the same content multiple times from different perspectives.
- Conspiration theories. We are likely to see known patterns even in complete noise, like clouds in the sky. This may cause us to add meaning that was never present and suggest things that do not belong.
- Spaced repetition. If we see something multiple times, we remember it much better.
There are several hundreds of related biases. We try to leverage them for our purposes or at least counter them with effective techniques. Since we have limited resources and need to focus, only some of them are addressed.
Speedreading as effective compression
The basis of our methodology is compressing the information in a way that makes our brain process it more efficiently.
- Visual information is processed faster than sequential data
- Adding several details to visualization is more effective than considering several separate visualizations.
- Visualizations that are related to each other, are easier to represent than standalone visualization, as we can use a common context.
- Since we already know a lot of the common context, it is sufficient to encode only the new information and only the parts of the information we actually need.
And so on. We reduce the motion of our eyes, the amount of information the perceive, the number of representations that express this information. We reuse or visual dictionary. Eventually, we focus only on innovation, removing all contextual data to and repetitions to facilitate the processing. This goes directly against what authors try to do.
Writing as a story
Authors are trained to build stories. The context is more important for a good author than the innovation. A good author will
- add repetitions so we notice the important details,
- expose characters so we understand which patterns look for,
- make every sentence different so we will not get the feeling the sentence can be ignored,
- add emotional background so we can relate.
The author will want you to spend more time on his pieces, as you may want to move on to the next story.
At the same time, a good author will introduce things that help us
- simple text structure so we can follow,
- metaphors to facilitate visualization,
- unique and rare words so we will not miss the innovative parts,
- different script to focus our attention.
Learning as communication
While we learn to encode information better, we get more time to focus on other aspects of communication:
- address the subject from multiple perspectives,
- see how we can apply what we learned in our lives,
- write diaries that make the main concepts available long after the initial read,
- research the subject deeper based on clues provided by the author,
- communicate with other authors as a part of a community.
All in all, we will probably spend the same amount of time per piece as a less skilled reader. However, we are likely to get much more from this sort of communication. It will be less mechanical repetition for retention, and more active and interesting communication.
Perceptive advantage
As a side-effect, we will need to work on our perception.
- We learn to group items in a way that enables common context. This is called “semantic clustering”.
- There are long and complex patterns we see that others miss, as we encounter them in other places. Possibly this is because we learn to access associations better.
- It is easy to recognize patterns from our domain of expertise. This was discovered by Dutch psychologist Adriaan de Groot in the 1940s. As we gain expertise in different subjects we can recognize more patterns.
- We learn to ask a lot of diverse questions and become more creative, as we look for various perspectives to address each piece.
- Our statistical perception improves as we are exposed to further data. Quite often we do not truly understand how: like we do not always understand how the specific weights of artificial intelligence neural network work.
Unfortunately, we often need to relearn these skills for each expertise we gain and each language we know. Since a lot of the processing is common, relearning is significantly faster than acquiring the skills for the initial expertise.
The more we know the easier it gets
Some people have a strange idea that the slower we learn the better we remember, or that our brain capacity is limited so we should learn only what is required to function properly. As far as I know, we have an optimal rate of processing information, which usually increases as we gain further knowledge. If we process more than the optimal rate, we will get stressed and start losing details. When we process less, we get bored and add details that make processing harder.
As we learn more, we get more patterns to encode the new information. Most of the stuff we learn becomes very similar to the stuff we already know and the amount of innovation is reduced. Since we need to encode mostly the innovation, we become extremely efficient. The quality of the way we understand the information also increases, as we notice finer details of innovation. Possibly this effect is limited, but I do not know anyone who reached this limit.
