A few years ago I wrote a short series of posts on my old blog about what I had learned in this direction. Glancing over it I don't think it's 100% what you're looking for, but might point you in some useful, interesting directions. The posts, in order:
I wrote these when I was at a different stage of cognitive development than I'm in now, so they don't totally match the way I would address these topics today, but hopefully they will be of some use nonetheless.
Any system that takes a huge amount of input data and reduces it to some sort of representation will have input cases it doesn't handle well. The reduction throws away data of a certain, supposedly unimportant, variety. Input cases are bound to exist where the data thrown away by the reduction algorithm are, in fact, important. Visual illusions are such cases for the human visual system. Those that work on autonomous vehicles have to deal with such cases. Humans that understand how such recognition systems work can purposefully construct such cases in order to "hack" them. It's a jungle out there.
Tangentially related: following the Mazur link he discusses these four:
I noticed that an Electrical Engineering education hits all four of these pretty well, with the caveat that they are specific and applied stabs into each area. I feel like other branches of engineering are prone to dropping computation completely in favor of more time on physical and geometric problems. Things like systems and circuit diagrams are also heavily emphasized, which are in line with ways of thinking which provide speedups.
I think it would be possible to maximize the gains if a student were to be aware of this going in.
[x-post from r/slatestarcodex]
Is there a list/textbook of immediately usable mental representations like memory palaces?
I'm not talking about theoretical models of cognitive processes here (although they might suggest entire classes of the sort of thing I'm looking for). I'm talking specific techniques and 'ways of thinking' about certain things that immediately give huge speedups.
In case I'm still not making sense, lemme provide another example. Around five years ago, Brienne Yudkowsky, then Brienne Strohl, made a video about how to remember things.
TL;DW: If you want to remember duck -> apple -> Overton window -> ischemia, encapsulate each concept in some sensually vivid scenario in your head and link these scenes like a story.
After learning what she calls 'binding' [1], I've never had problems remembering lists again. I've never lost a string of thoughts and ideas in the shower, nor forgotten what I needed to once I'm out of the house. This is an especially drastic improvement for me, because I have severe combined-type ADHD.
I'm not talking about chunked models per se (e.g., Meditations on Moloch gives you a lens through which to view the vast majority of social structures, but it's not a model that I'd call native to our wetware) but more of stuff a couple of levels down. Data structures that we as humans can immediately use, algorithms that by being "close to the metal" dramatically obviates lots of brain cycles that would have been wasted [2].
Some things in the cluster of what I'm getting at in conceptspace: heuristics, knowledge representation, perceptual differences, Tufte's body of work, neurolinguistics and related fields, Math vs Verbal distinction, Mazur's four kinds of mathematical intuitions
Why I think this is a real thing rather than some made-up attempt to crystallise nonsense: everyone knows about optical illusions. But why do we have them? Well, from Wikipedia:
The hypothesis claims that visual illusions occur because the neural circuitry in our visual system evolves, by neural learning, to a system that makes very efficient interpretations of usual 3D scenes based in the emergence of simplified models in our brain that speed up the interpretation process but give rise to optical illusions in unusual situations. In this sense, the cognitive processes hypothesis can be considered a framework for an understanding of optical illusions as the signature of the empirical statistical way vision has evolved to solve the inverse problem.
If you know something about neural networks, our visual system seems to have learned how to see in layers, where each layer corresponds to some structural aspect of seeing that the brain then mashes up into something whole and perceptually understandable.
So in a sense, what I wanna know is: what are the layers we use for everything else?
[1]: In the literature this falls under the umbrella of associative memory.
[2]: I'm reminded of the apocryphal story about Gauss' childhood where he and his classmates were tasked to sum the numbers 1 to 100. He figured it'd be way faster to pair 1 and 100, 2 and 99, and so on because they all have the same sum of 101. There are 50 such pairs, so the sum should be 101 x 50 = 5050, which is the answer he arrived at after a minute or so of thinking.
This act of pairing is such a fundamental mental shortcut that it took until Lobachevsky and Dirichlet to cast it as a mathematical concept separate from all its uses.