I am going to talk about a dichotomy that has been a useful mental model for me—Recognizing versus Generating. Recognizing tasks rely on your brain's ability to quickly notice patterns. Generating tasks rely on your brain's ability to put pieces together to form new ones.
Here’s an example of the two types of tasks:
A student is reading their textbook, studying for their math exam. They go over the proof in their book. "Does this step make sense?" they ask themselves. They make sure they can follow what's going on before moving to the next step.
This is a Recognizing task.
Now, consider a second student who has the same exam. After reading the textbook, the student closes the textbook and thinks back to the proof. "How did that proof go, again?" they ask themselves. Starting with a blank page, they derive each step. At each step, rather than just following along, they're asking themselves what they need to show next to get to the end of the proof.
This is a Generating task.
When people think about "studying for math", I think both the Recognizing and the Generating task can jump to mind as correct things to do. Yet, there is a specific type of effort lacking from the Recognizing approach. In particular, the Generating approach is going to help much more when the test asks for proofs from scratch.
Here, as in many other Recognizing vs Generating dichotomies, it can be easy to trick ourselves into thinking we're putting in more effort than we really are. "After all," the tempting thought goes, "both reading the textbook and writing proofs feel like they fit the definition 'studying', so why can't the easier one work?"
The issue is that we're conflating the two definitions.
Overall, this substitution is a clear-cut example of mixing up concept space and action space. We think that just because the tasks are conceptually similar, they must also have similar effects. Or, maybe we do know the difference but some part of us believes that we can do the easier Recognizing task, yet still reap the greater benefits of the Generating task. Either way, this intuition of being able to carry over similarities from other categorical domains is flawed.
This is why math can end up being a difficult subject for some people. It’s all too easy to fool yourself that you understand what's going on when you’re just following along with the textbook. Cover up the steps, though, and things get a lot harder if you try to solve it yourself.
In a similar vein, when you’re reading a book and the author says, “Now, obviously, we can conclude X,” this is a sign to pause. Stop. You should take a step back and examine what you actually think. If the author hadn’t presented you with the conclusion, could you have generated it with the pieces you already had?
See also group dynamics, where everyone is all too happy to offer their opinion when someone has an idea, but reluctant to submit their own. It's easier to critcize something that's already there, then to come up with something novel to present to others.
And, of course, this dichotomy is of utmost importance in the realm of self-help. I think that there is a tendency for advice to get cached as declarative knowledge, rather than procedural knowledge, in a way which parallels the Recognizing vs Generating dichotomy.
(If you haven’t seen either of these terms before: Briefly, declarative knowledge refers to things you know how to relate, like Paris being the capital of France. Procedural knowledge refers to things you know how to do, like tying a knot.)
The big issue here is that most models in self-help appear to be declarative, but their efficacy lies in their being procedural.
For example: When you first learn about the planning fallacy, you learn that the planning fallacy is a cognitive bias, it revolves around underestimation, and that reference class forecasting is a way to help. You learn that reference class forecasting is a method of creating estimations, that it's been successful in public works projects, and that it requires choosing your reference class carefully.
This is fine if your goal is to do well on a pop quiz. You'll be able to answer questions like:
- "Does the planning fallacy say we systematically overestimate or underestimate?"
- "What does reference class forecasting do?"
- "Which researcher discovered the premortem technique?"
You've built up detailed internal representation of the concept and its related background information.
But this may not be what you want! From a practical perspective, what you may want is to be able to put all those concepts into practice, so they actually become useful. This requires finding examples of using the concept and thinking of ways to integrate the concept into your existing schedule. These are questions like:
- What is my average underestimation rate?
- How do I use past data to inform future decisions?
- Am I surprised when I don't meet up expected deadlines?
If building a mental map is the Recognizing task, then finding ways to actually have the concepts cash out into actions is the Generating task.
When giving advice, for example, most advice looks like telling people what to do. From a Recognizing perspective, people can fall into the trap of asking themselves, "Have I heard this category of advice before?" This can cause people to dismiss advice rather easily. Presumably, you actually want them to take a Generative perspective, asking themselves something "How do I expect this advice to change future actions I take in the future?"
One possibly counterintuitive corollary of this for giving advice is that you want to focus on giving people information that would lead them to generate the advice you want them to have, not necessarily just giving them the advice outright. This explains why anecdotes and insights can be so powerful because they draw the audience's attention to trails of thought that can cause them to take a more Generative stance.
Importantly, as a general pattern, Generating tasks are harder than their Recognizing counterparts, even if they appear to be doing similar things:
- Generating a proof from scratch vs reviewing the textbook
- Drawing conclusions from data vs remembering what other people wrote
- Practicing relevant skills vs understanding how concepts are related
- Imagining how you'd change your actions given advice vs just checking if you've heard it before
In conclusion, it's not that Recognizing isn't useful. Sometimes, it's a necessary heuristic for cutting through the cruft, and it's not always necessary. But keep in mind that Recognition does not necessarily imply understanding, and pushing for something you can Generate can help expose what you actually know.
Thus, next time you have a learning goal (e.g. new language, programming framework, mathematics, etc.), remember to find ways to sharpen your skills Generatively, as that's where most of the useful work lies.