# Learning takes both instruction and hands-on practice

Markdown copy of https://realalpha.app/s/learning-takes-both-instruction-and-hands-on-practice/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy.

- Speaker: [Terry Sejnowski](https://realalpha.app/people/terry-sejnowski/) · Terry Sejnowski is a professor of computational neurobiology at the Salk Institute for Biological Studies.
- Host: [Andrew Huberman](https://realalpha.app/people/andrew-huberman/) · Host, Huberman Lab
- Show: Huberman Lab
- Episode: [How to Improve at Learning Using Neuroscience & AI | Dr. Terry Sejnowski](https://realalpha.app/v/etbfLTHD_VU/), Nov 18, 2024
- Moment: 18:59 to 26:41 of the episode (8 min), playing on https://realalpha.app/s/learning-takes-both-instruction-and-hands-on-practice/; the episode from there: https://realalpha.app/v/etbfLTHD_VU/?t=1139

## Quote

> “when you're trying to solve a problem on your own, right, this is where you're really **learning by trial and error**”
> — [Terry Sejnowski](https://realalpha.app/people/terry-sejnowski/) at [24:45](https://realalpha.app/v/etbfLTHD_VU/?t=1485) · Huberman Lab

## Takeaways

1. Practice helps make skills usable without step-by-step thought.
2. Procedural practice and explicit knowledge reinforce each other.
3. Learning How to Learn is free and includes quizzes and forums.

## About this segment

Terry Sejnowski contrasts explicit instruction with procedural practice, using tennis, scuba diving and physics as examples. He says the two learning systems reinforce each other and points to a free course on learning how to learn.

From “How to Improve at Learning Using Neuroscience & AI | Dr. Terry Sejnowski”, Nov 18, 2024. [Watch the full episode](https://realalpha.app/v/etbfLTHD_VU/)

## Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

[19:04](https://realalpha.app/v/etbfLTHD_VU/?t=1144) thinking is much less efficient because you have to go step by step. With procedural learning, it's automatic. Can you give me an example of procedural learning in the context of a comparison to cognitive learning? Like, is there an example of perhaps like how to make a decent cup of coffee using, you know, purely knowledge-based learning versus procedural learning where procedural learning wins? And I can imagine one, but you're the true expert here.

[19:36](https://realalpha.app/v/etbfLTHD_VU/?t=1176) Well, you know a lot of examples, but since we've been talking about tennis, can you imagine learning how to play tennis through a book, reading a book? That's so funny. On the plane back from Nashville yesterday, the guy sitting across the aisle from me was reading a book about maybe he was working on his pilot's license or something. And I looked over and couldn't help but notice these diagrams.

[20:00](https://realalpha.app/v/etbfLTHD_VU/?t=1200) of the plane flying. And I thought, I'm just so glad that this guy is a passenger and not a pilot. And then I thought about how the pilots learned. And presumably it was a combination of practical learning and textbook learning. I mean, when you scuba dive, this is true. I'm scuba dive certified. And when you get your certification, you learn your dive tables and you learn why you have to wait between dives, et cetera, and gas exchange and a number of things. But there's really no way to simulate what it is to take your mask off underwater, put it back on, and then blow the water out of your mask. You just have to do that in a pool. And you actually have to do it when you need to for it to really get drilled in. Yes. It's really essential for things that have to be executed quickly and expertly to get that really down pat so you don't have to think.

[20:55](https://realalpha.app/v/etbfLTHD_VU/?t=1255) and this happens in school right in other words you you you have classroom lessons where you're given explicit instruction but then you go do homework that's procedural learning you do problems you solve problems and and you know i'm a phd physicist so i i went through all of the classes you know in theoretical physics and um it was really the problems that really were the core of becoming a good physicist.

[21:23](https://realalpha.app/v/etbfLTHD_VU/?t=1283) You know, you can memorize the equations, but that doesn't mean you understand how to use the equations. I think it's worth highlighting something. A lot of times on this podcast, we talk about what I call protocols. It would be, you know, like get some morning sunlight in your eyes to stimulate your suprachiasmatic nucleus by way of your retinal ganglion cells. Audiences of this podcast will recognize those terms.

[21:41](https://realalpha.app/v/etbfLTHD_VU/?t=1301) It's basically get sunlight in your eyes in the morning and set your circadian clock. That's right. And you can hear that a trillion times. But I do believe that there's some value to both knowing what the protocol is, the underlying mechanisms, there are these things in your eye that encode the sunrise qualities of light, et cetera, and then send them to your brain, et cetera, et cetera. But then once we link knowledge, pure knowledge to a practice, I do believe that the two things merge someplace in a way that, let's say, reinforces both the knowledge and the practice. So these things are not necessarily separate, they bridge. In other words, doing your theoretical physics problem sets reinforces the examples that you learned in lecture and in your textbooks and vice versa. So this is a battle that's going on right now

[22:29](https://realalpha.app/v/etbfLTHD_VU/?t=1349) in schools. What you've just said is absolutely right. You need both. We have two major learning systems. We have a cognitive learning system, which is cortical. We have a procedural learning system, which is subcortical, basal ganglia. And the two go hand in hand. If you want to become good at anything, the two are going to help each other. And what's going on right now in schools, in California at least, is that they're trying to get rid of the procedural.

[23:01](https://realalpha.app/v/etbfLTHD_VU/?t=1381) That's ridiculous. They don't want students to practice because it's going to be, you know, you're stressing them. You don't want them to feel that, you know, that they're having difficulty. But we can do everything. For those listening, I'm covering my eyes because, I mean, this would be like saying, goodness, there's so many examples. Like here's a textbook on swimming and then you're going to go out to the ocean someday and you will have never actually swum.

[23:27](https://realalpha.app/v/etbfLTHD_VU/?t=1407) Right. And now you're expected to be able to survive, let alone swim well. It's crazy. It's crazy. And I'll tell you, Barbara Oakley and I have a MOOC, Massive Open Online Course, on learning how to learn. And it helps students. We aimed it at students, but it actually has been taken by 4 million people in 200 countries ages 10 to 90. What is this called? Learning how to learn. Is there a paywall?

[23:57](https://realalpha.app/v/etbfLTHD_VU/?t=1437) No, it's free, completely free. Amazing. And, you know, I get incredible feedback, you know, fan letters almost every day. Well, you're about to get a few more. Okay. I did an episode on learning how to learn. And my understanding of the research is that we need to test ourselves on the material. The testing is not just a form of evaluation. It is a form of identifying the errors that help us then compensate for the errors and learn.

[24:24](https://realalpha.app/v/etbfLTHD_VU/?t=1464) But it's very procedural. It's not about just listening and regurgitating. You know, you put your finger on it, which is that, and this is what we teach the students, is that you have to, the way the brain works, right, is not, it doesn't memorize things like a computer, but it has to be active learning. You have to actively engage. In fact, when you're trying to solve a problem on your own, right, this is where you're really learning by trial and error, and that's the procedural system.

[24:57](https://realalpha.app/v/etbfLTHD_VU/?t=1497) But if someone tells you what the right answer is, you know, that's just something that is a fact that it gets stored away somewhere, but it's not going to automatically come up if you actually are faced with something that's not exactly the same problem, but similar. And by the way, this is the key to AI, completely essential for the recent success of these large language models, you know, that the public now is beginning to use, is that they're not parrots.

[25:28](https://realalpha.app/v/etbfLTHD_VU/?t=1528) They just don't memorize what the data that they've taken in. They have to generalize. That means to be able to do well on new things that come in that are similar to the old things that you've seen, but allow you to solve new problems. That's the key to the brain. The brain is really, really good at generalizing. In fact, in many cases, you only need one example to generalize. Like going to a restaurant for the first time, there are a number of new interactions, right? There might be a host or a hostess. You sit down at these tables you've never sat at, Somebody asks you questions, you read it. Okay. Maybe it's a QR code these days, but forever after you understand the process of going into a restaurant, it doesn't matter what the genre of food happens to be or what city sitting inside or outside, you can pretty much work it out. Sit

[26:22](https://realalpha.app/v/etbfLTHD_VU/?t=1582) at the counter, sit outside, sit at the table. There are a number of key action steps that I think pretty much translate to everywhere. Unless you go to some super high end thing or some super low-end thing where it's a buffet or whatever. You know, you can start to fill in the blanks