People have a really hard time with interventions often because they literally do not have a functioning causal model of the thing in question. People who apply deliberate practice to a working causal model often level up astonishingly quickly.
Could you give an example of a functioning causal model that quickly helps you learn a topic?
I’m not sure whether you’re thinking about something more meta-level, “what can I practice that will cause me to get better”, or something more object-level, “how does mechanics work”, and I think an example would help clarify. If it’s the latter, I’m curious about what the difference is between having a functioning causal model of the subject (the precondition for learning) and being good at the subject (the result of learning).
When I was trying to improve at touch typing I had to distinguish between causes of different kinds of errors. If my model was ‘speed is good’ and ‘tried to go faster’ I’d face constant frustration at the seeming interplay between speed and error rate. Instead, I built up a model of different errors like ‘left right finger confusion’, ‘moved wrong finger off home row’, ‘tendency to reverse key presses in certain sequences’, etc. Then I could find ways of practicing each error specifically, finding really cruxy examples that caused the worst traffic jams for me. This is a simple example because feedback loops are immediate. In many cases the added complexity is VoI calculations because gathering data on any given hypothesis costs some time or other resources.
Learning the causal model as you practice is a meta skill that levels up as you try to be careful when learning new domains.
Could you give an example of a functioning causal model that quickly helps you learn a topic?
I’m not sure whether you’re thinking about something more meta-level, “what can I practice that will cause me to get better”, or something more object-level, “how does mechanics work”, and I think an example would help clarify. If it’s the latter, I’m curious about what the difference is between having a functioning causal model of the subject (the precondition for learning) and being good at the subject (the result of learning).
When I was trying to improve at touch typing I had to distinguish between causes of different kinds of errors. If my model was ‘speed is good’ and ‘tried to go faster’ I’d face constant frustration at the seeming interplay between speed and error rate. Instead, I built up a model of different errors like ‘left right finger confusion’, ‘moved wrong finger off home row’, ‘tendency to reverse key presses in certain sequences’, etc. Then I could find ways of practicing each error specifically, finding really cruxy examples that caused the worst traffic jams for me. This is a simple example because feedback loops are immediate. In many cases the added complexity is VoI calculations because gathering data on any given hypothesis costs some time or other resources.
Learning the causal model as you practice is a meta skill that levels up as you try to be careful when learning new domains.