Ten Mental Models for Learning - Scott H Young
Ten central ideas to keep in mind whenever you need to learn anything new.
A problem space is like a maze: you know where you are now, you’d know if you’ve reached the exit, but you don’t know how to get there. Along the way, you’re constrained in your movements by the maze’s walls.
Retrieving knowledge strengthens memory more than seeing something for a second time does. Testing knowledge isn’t just a way of measuring what you know—it actively improves your memory. In fact, testing is one of the best study techniques researchers have discovered.
How much you’re able to learn depends on what you already know. Research finds that the amount of knowledge retained from a text depends on prior knowledge of the topic. This effect can even outweigh general intelligence in some situations.
In an impressive review of significant inventions, Matt Ridley argues that innovation results from an evolutionary process. Rather than springing into the world fully-formed, new invention is essentially the random mutation of old ideas. When those ideas prove useful, they expand to fill a new niche.
Skills are specific.
Transfer refers to enhanced abilities in one task after practice or training in a different task. In research on transfer, a typical pattern shows up:
Practice at a task makes you better at it.
Practice at a task helps with similar tasks (usually ones that overlap in procedures or knowledge).
Practice at one task helps little with unrelated tasks, even if they seem to require the same broad abilities like “memory,” “critical thinking” or “intelligence.”
We can only keep a few things in mind at any one time. George Miller initially pegged the number at seven, plus or minus two items. But more recent work has suggested the number is closer to four things.
This incredibly narrow space is the bottleneck through which all learning, every idea, memory and experience must flow if it is going to become a part of our long-term experience. Subliminal learning doesn’t work. If you aren’t paying attention, you’re not learning.
The primary way we can be more efficient with learning is to ensure the things that flow through the bottleneck are useful. Devoting bandwidth to irrelevant elements may slow us down.
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We learn more from success than failure. The reason is that problem spaces are typically large, and most solutions are wrong. Knowing what works cuts down the possibilities dramatically, whereas experiencing failure only tells you one specific strategy doesn’t work.
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