Lens vs. List Learning

Several years ago I wrote a piece called Algorithmic Learning, and then another one here.

This will be the third in the series, as evidently this is an idea I can’t get out of my mind.

The concept is this: There are two main ways we learn—passively and actively. Or as I put it before, via osmosis or via algorithm.

Here’s another way to look at it.

Imagine you’re moving through life and you have two things to help you—a pair of magical glasses, and a magical notebook.

Everything you hear, see, study, and otherwise learn from either modifies the notebook, or it updates your glasses to see the world in a different way.

So when you get ready to do a particular task—say, to create a new deck as part of a presentation—you have two ways of benefitting from your experience.

You can see or think about the world in a passive, indescribable, and fuzzy sort of way, orYou can turn your notebook to the page titled, “How to make a presentation” and look at your current best methodology

As an example for the first, lens-based approach, maybe read a book about how presentations are all about telling stories, and that slides should just be background imagery that support that story. In that scenario you’re not thinking of any specific part of a book; it’s just that you no longer see—for whatever reason—that slides will be the center of your talks from now on.

That’s one way for your talk to improve.

In the second example you open your notebook (or Github for me, actually), and look at your current optimal deck-making recipe. You can see that 3 years ago you started by creating a deck, deciding on fonts, and moving from slide to slide working on the ideas. And now, looking at the updates that have been made to the methodology, you see that step number one is to start with a bulleted outline. No slides. Just text that tells the story.

In both cases we’ve altered how we will make a deck, but in the case of the lens it’s a change in mindset and perspective about giving talks in general. In the case of the list it’s a tangible alteration to an algorithm.

I think it’s fascinating to think of learning in this way.

Practically what it means to me is that passive learning might help you as it accumulates from many years of reading many good books. But you often can’t do attribution on how your mindset has changed, or from what source. You’re left with something of a faith-based feeling of, “I guess I see this differently now based on a bunch of the reading I’ve done.”

Alternatively, algorithmic-based learning lets you look specifically at how your approach to solving a problem has changed over time. And if you do it right—which I’m doing with Github—you can actually annotate why you added or removed a step in your methodology. E.g.,

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OldEat oatmeal for breakfast. (How Not To Diet, Read in 2019) Brisk walk before lunch. (Some Book, Read in 2017) NewSkip Breakfast. (Lifespan, Read in 2021) Wake at 6:00am and do a walk outside in the sun. (Hubberman Podcast, Listened in 2021)

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The takeaway here is that you don’t have to stress as you read or otherwise consume content.

There are two different ways to learn. Maybe you have the time and focus to capture specific steps in an algorithm, and that’s great if you do. But even if you don’t, you can still learn via small bits of accrued knowledge accumulating over time.

Whether you’re upgrading your list or your lens, you’re still getting an upgrade.