Difference between revisions of "Narrow vs broad cognitive augmentation"

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Here is one axis I've been paying attention to when thinking about the question of "how to do we make people better at thinking?":
 
Here is one axis I've been paying attention to when thinking about the question of "how to do we make people better at thinking?":
  
* '''narrow/vertical/specialized''': This kind of augmentation looks at a concrete topic and tries to get at its "true nature", and then comes up with tools to help people think specifically about that topic. For instance, Arabic numerals look specifically at numbers, helping people manipulate and think about them. Sure, you might also learn some things about logarithms or Cauchy sequences by studying Arabic numerals, but mostly they only help you think about numbers, not about some completely unrelated topic like linear algebra. (I want to distinguish between how knowing the Arabic numerals helps you ''do'' linear algebra because of course you use numbers while doing linear algebra, which seems totally obvious to me, and Arabic numerals actually giving insight ''about'' linear algebra, which seems false to me.)
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* '''narrow/vertical/specialized''': This kind of augmentation looks at a concrete topic and tries to get at its "true nature", and then comes up with tools to help people think specifically about that topic. For instance, Arabic numerals look specifically at numbers, helping people manipulate and think about them. Sure, you might also learn some things about logarithms or sequences of real numbers by studying Arabic numerals, but mostly they only help you think about numbers, not about some completely unrelated topic like linear algebra. (I want to distinguish between how knowing the Arabic numerals helps you ''do'' linear algebra because of course you use numbers while doing linear algebra, which seems totally obvious to me, and Arabic numerals actually giving insight ''about'' linear algebra, which seems false to me.)
 
* '''broad/horizontal/universal''': This kind of augmentation looks at ''learning in general'', and asks how we can come up with tools that help across a diverse variety of fields. For instance, [[spaced repetition]] or the invention of writing are good examples here. Spaced repetition helps you not only learn languages, but also any subfield of math, and chemistry, and physics, and on and on. There is nothing about spaced repetition that is specifically about, say, linear algebra; it's more that spaced repetition is a fact about how the human brain learns.
 
* '''broad/horizontal/universal''': This kind of augmentation looks at ''learning in general'', and asks how we can come up with tools that help across a diverse variety of fields. For instance, [[spaced repetition]] or the invention of writing are good examples here. Spaced repetition helps you not only learn languages, but also any subfield of math, and chemistry, and physics, and on and on. There is nothing about spaced repetition that is specifically about, say, linear algebra; it's more that spaced repetition is a fact about how the human brain learns.
  
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A narrow augmentation is more satisfying to use, but inventing it still takes a lot of work and you must invent separate tools for every topic you want to think about.
 
A narrow augmentation is more satisfying to use, but inventing it still takes a lot of work and you must invent separate tools for every topic you want to think about.
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The kind of visualization work that [[3blue1brown]] does feels like kind of a mix. On one hand, visualization itself feels like a broad augmentation. But on the other hand, Grant has to create a separate video for each thing he wants to explain. The audience doesn't know how to animate math concepts; they are just there to consume. So it's narrow too in that sense.
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[[Andy Matuschak]] talks about a similar distinction in "We can develop the mnemonic medium by writing about topics with only moderate domain expertise".<ref>https://notes.andymatuschak.org/z6LEquSV2snbPHMcZ1ViRNVpVqXUP6uJRq9sB</ref> In particular see quotes like, "If you’re developing an interface that gets its power by reifying deep ideas about a medium (like Logo), you need deep expertise in that medium to reify the right ideas. Papert didn’t have that. By contrast, the mnemonic medium primarily reifies ideas about memory, theory of knowledge, habits, etc—not ideas about quantum computing."
  
 
==See also==
 
==See also==
  
 
* [[Explorable explanation]] -- explorable explanations mainly focus on the narrow kind of augmentation
 
* [[Explorable explanation]] -- explorable explanations mainly focus on the narrow kind of augmentation
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==What links here==
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{{Special:WhatLinksHere/{{FULLPAGENAME}} | hideredirects=1}}
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==References==
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<references/>
  
 
[[Category:Learning]]
 
[[Category:Learning]]

Latest revision as of 19:56, 5 November 2021

Here is one axis I've been paying attention to when thinking about the question of "how to do we make people better at thinking?":

  • narrow/vertical/specialized: This kind of augmentation looks at a concrete topic and tries to get at its "true nature", and then comes up with tools to help people think specifically about that topic. For instance, Arabic numerals look specifically at numbers, helping people manipulate and think about them. Sure, you might also learn some things about logarithms or sequences of real numbers by studying Arabic numerals, but mostly they only help you think about numbers, not about some completely unrelated topic like linear algebra. (I want to distinguish between how knowing the Arabic numerals helps you do linear algebra because of course you use numbers while doing linear algebra, which seems totally obvious to me, and Arabic numerals actually giving insight about linear algebra, which seems false to me.)
  • broad/horizontal/universal: This kind of augmentation looks at learning in general, and asks how we can come up with tools that help across a diverse variety of fields. For instance, spaced repetition or the invention of writing are good examples here. Spaced repetition helps you not only learn languages, but also any subfield of math, and chemistry, and physics, and on and on. There is nothing about spaced repetition that is specifically about, say, linear algebra; it's more that spaced repetition is a fact about how the human brain learns.

Finding a broad augmentation allows you to "unlock" a new ability that applies to everything you want to learn or think about, but it's also "shallow" in the sense that the augmentation itself does not "talk about" or "know about" any specific topic you want to learn about.

A narrow augmentation is more satisfying to use, but inventing it still takes a lot of work and you must invent separate tools for every topic you want to think about.

The kind of visualization work that 3blue1brown does feels like kind of a mix. On one hand, visualization itself feels like a broad augmentation. But on the other hand, Grant has to create a separate video for each thing he wants to explain. The audience doesn't know how to animate math concepts; they are just there to consume. So it's narrow too in that sense.

Andy Matuschak talks about a similar distinction in "We can develop the mnemonic medium by writing about topics with only moderate domain expertise".[1] In particular see quotes like, "If you’re developing an interface that gets its power by reifying deep ideas about a medium (like Logo), you need deep expertise in that medium to reify the right ideas. Papert didn’t have that. By contrast, the mnemonic medium primarily reifies ideas about memory, theory of knowledge, habits, etc—not ideas about quantum computing."

See also

What links here

References