Learning and the Subconscious Bullet Points

Summary: Your subconscious has most of your brainpower. To become a great thinker, focus on teaching your subconscious and delegating work to it. Don’t focus on conscious analysis. The subconscious isn’t the irrational part of your mind; it’s a necessary part of rational thinking. I explain the importance of the subconscious, discuss how to improve your subconscious with practice, and discuss managing your error rate.

This is a companion discussion topic for the original entry at https://criticalfallibilism.com/learning-and-the-subconscious-bullet-points/

CONTEXT: Just quickly thought up some questions while reading the article.

How does the idea of circling, from Objectivism, relate to automatization? How much does building up context around ideas contribute to automatization? What is the role of alternating between studying the big picture and the details in building subconscious understanding? What else is in the subconscious besides automatizated ideas? What role does the organization or structure of the subconscious play? Can automatizations fail for reasons of psycho-espistemology, or integration of ideas? How does automatization address problems of integrating ideas?

What’s a good ratio of reading to practicing?

What are your own attempts to answer those questions?

BTW re circling I think you might mean the “spiral theory of knowledge” from Understanding Objectivism.

In the article you say:

Over 99% of your computing power is controlled/used by your subconscious

Why did you pick that %?

I looked into possible sources, when I searched for 99% I didn’t get any results that agreed. I couldn’t find many articles which cite any research; most make assertions at 95% or 90%. Those figures seem to be conventional knowledge which you disagree with.

The only cite I found is Emma Young’s New Scientist article which seems to be where 95% comes from.

It’s paywalled and I haven’t read it. I’m not convinced by the introductory text (before the paywall) that it contains any basis for the 95% claim though so I don’t want to pay to read the rest.

I couldn’t find anything that claims to be the origin of the 90% claim.

It’s just a conservative under-estimate. It’s not measured by science.

The issue is similar to comparing what I see on my computer screen to what goes on in the computer that I don’t see. What I see on screen is way under 1%.

I forgot the term. I might have only heard it once, possibly in the Understanding Objectivism lectures. If I had thought about it for another second, then I think I might have recognized that I didn’t remember very well what idea I was talking about. I think it would have been better if I labeled that I didn’t remember well what idea I was trying to ask about. Or, it would have been better if I had looked up more about the idea and gotten the right term.

Below is my first attempt at writing out something in the way of an answer to one these questions. I have pondered some aspects of this stuff for a while without writing about it.

I’m not too sure what the relationship is between spiraling (rather than circling) and automatization. They both involve the idea of layers of knowledge. I think automatization is like building really solid foundations for a knowledge skyscraper. Spiraling is like building scaffolding and framing for a knowledge skyscraper. Or, spiraling is kind of like the crane that’s lifting heavy beams into place.

I think spiraling involves stuff like reviewing concepts, previewing concepts, building context, making connections, trying to see the big picture, zooming in and zooming out, and filling in details. Part of the reason for spiraling is that learning abstract ideas requires a lot of analyzing (or chewing) and concretization. That means using lots of examples to relate the abstract ideas to tangible, common sense situations.

Also, I guess that spiraling involves recognizing that learning isn’t always, or isn’t often, a straight path. Though, maybe that’s a stretch as to what spiraling is about.

I think one way in which spiraling helps automatize ideas is by looking at the same concept from a variety of angles. Spiraling might involve questioning an argument in a bunch of different ways and seeing how well it responds each time. That allows you to see where the gaps are in your knowledge.

I think spiraling helps avoid the potential for practicing poorly. Sometimes practice can become rote or sort of like memorization. It seems like it is valuable to memorize stuff too though. Memorization is bad if done without criticism, critical thinking, or evaluation. Spiraling combines getting ideas stored correctly with getting ideas stored in the correct mental filing cabinets so that the ideas can be triggered for effective use in the correct context.

I was reading Goals and Status Update, from 2017, on Curiosity Blog and it had a link to a Quora answer about learning from Jordan Peterson.

Peterson mentions studying things from low resolution to high resolution. That sounds close to the way that I have experienced learning things. I think automatization is something you do once you already have a high resolution understanding of something. In overreaching and powering up [Fallible Ideas podcast], ET talks about how you first learn something to a really high standard (low error rate) then make the application of that knowledge really efficient. I think my questions about automatization and building up context came from thinking about learning things to lower standards and higher resource cost than what ET normally talks about. Learning in bits and pieces and returning to things might have more to do with getting to a low error rate but with high resource cost than fully automatizing skills. Just getting to the initial step of having a low error rate is significantly more than what people are used to with a lot skills. So, it’s counter-intuitive to start learn things to a higher standard than anything you’ve done since early childhood and then realize you still have significant work to do.

Learning things to a very high standard should be quick and mostly easy. So then you get to the automatization stage frequently. Automatization is never something to just plan to do later, months in the future, after a bunch of learning.

How can you quickly learn with high standards? By learning very small chunks.

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