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2/ The study of learning in real world environments is known as macrocognition. Fancy name, simple meaning: it just means that these theories aren't constructed by observation of humans in a lab, but are instead formed from real world observation. en.wikipedia.org/wiki/Macrocogn

3/ The questions these researchers had went something like this: how is it that some people become experts through trial and error, and others do not or cannot? Sure, it's great if you can take a course, or a coach. You will likely learn faster. But what if you can't?

4/ It turns out there's a theory about this. It's called Cognitive Transformation Theory, or CTT. It tells us how people build expertise in the real world. Think: less pianists and chess and more business and leadership and investing.

5/ The theory's central claim is that we learn by replacing flawed mental models with better ones. The key word is REPLACE. Here's the catch: the more advanced our mental models, the easier it is for us to ignore anomalous data, or to explain them away. This blocks progress.

6/ What do I mean by this? Well, let's say you're trying to get better in the real world. This means trial and error. If the learning environment is kind, you can improve quickly. You build mental models that help you achieve your goals. You become good at what you do. Yay!

Touisteur EmporteUneVache

7/ But most of the time, the learning environment is messy. Learning is hard. This should be obvious: you don't know what cues to look for in your experiences because you don't have good models. But you can't build good mental models because you don't notice the right cues!

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I always found (observing people learning) that interactive systems (including simple crud apps) with sandboxes and everything-undoable were the most easy to progress in. If the cost of a mistake is too high, people will fear replacing their inner model and learn far more slowly.

2:08 PM · Aug 12, 2021TweetDeck

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@touisteur

This is a great observation, thank you.

Touisteur EmporteUneVache

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