that people are often unaware of in 'false effect'

that we have two completely different ways

of seeing the world, two different neural networks we access

when we're perceiving things.

So what this means is

when we first sit down to learn something,

for example, we're going to study math,

you sit down and you focus on it.

So you focus, and you're activating task-positive networks,

and then what happens is you're working away,

and then you start to get frustrated.

You can't figure out what's going on,

and what's happening is you're focusing

and you're using one small area of your brain

to analyze the material, but it isn't the right circuit

to actually understand and comprehend the material.

So you get frustrated, you finally give up,

and then when you give up and get your attention off it,

it turns out that you activate a completely different set

of neural circuits that's the default mode network

and the related neurocircuits.

So what happens is you stop thinking about it,

you relax, you go off for a walk, you take a shower,

you're doing something different

and in the background, this default mode network

is doing some sort of neural processing on the side,

and then what happens is you come back

and, 'voila!'

suddenly the information makes sense.

And in fact, it can suddenly seem so easy

that you can't figure out

why you didn't understand it before.

So learning often involves going back and forth

between these two different neural modes,

'focus mode' and what I often call 'diffuse mode,'

which involves loose, neural resting-states.

You can only be in one mode at the same time,

so you might wonder, 'Is there a certain task

that is more appropriate for focus mode or diffuse mode?'

The reality is that learning involves going back and forth

between these two modes.

You often have to focus at first

in order to sorta load that information into your brain,

and then you do something different,

get your attention off it,

and that's when that background-processing occurs.

This happens no matter what you're learning,

whether you're learning something in math and science,

you're learning a new language, early music, a dance,

even learning to back up a car.

And think about it this way,

here's a very important related idea-

here's a very important related idea-

and that is that when you're learning something new,

you want to create a well-practiced neural pattern

that you can easily draw to mind when you need it.

So this is called a 'neural chunk,'

and chunking theory is incredibly important in learning.

So, for example, if you are trying to

learn to back up a car,

when you first begin, it's crazy, right?

You're looking all around-

do you look in this mirror or this mirror,

or do you look behind you?

What do you do?

It's this crazy set of information.

But after you've practiced a while,

you develop this very nice sort of pattern

that's well-practiced, so all you have to do is think,

"I'm gonna back up a car."

Instantly, that pattern comes to mind

and you're able to back up a car.

Not only are you doing that,

but you're maybe talking to friends, listening to the radio.

It's that well-practiced neural chunk

that makes it seem easy.

So it's important in any kind of learning

to create these well-practiced patterns.

And the bigger the library of these patterns,

the more well-practiced,

sort of deeper and broader they are

as neural patterns in your mind,

the more expertise you have in that topic.

And chunking was first sort of thought of or explored

by Nobel Prize-winner Simon,

who found that if you're a chess master,

that the higher your ranking in chess,

the more patterns of chess you had memorized,

so you could access more and more patterns of chess.

So research began developing,

and what they found was that

the better your expertise at anything,

the more solid neural patterns,

what I called neural chunks, you have.

So, for example,

if you might know how to do mathematics very well;

well, you've got certain patterns related to multiplication,

and you've practiced quite a bit with them,

and so you can pull them instantly to mind,

and, likewise, division.

And then you go higher, so you've got calculus,

you've got the concept of the limit,

you've got integrals, derivatives.

And you've practiced with each one of those enough

so that it is almost like backing up a car.

All you have to do is think,

"Oh, I've got to take this derivative,"

and, boom, off you go.

You're taking the derivative, and it seems very easy to you.

So a challenge that we've had is,

for a long time, particularly in mathematics education,

it was felt that if you practiced too much,

that it would kill your creativity,

and that's actually not true.

You want to do the right kind of practice,

where you're interleaving and doing one technique,

and then trying that with another technique.

You don't want to just be doing the same thing

over and over again.

But practicing by-

here's a good way to practice developing a chunk:

Let's say that you've got a homework assignment,

that you've got this homework problem,

and it's a really difficult homework problem.

So, what do you tend to do?

Well, you do it and you turn it in.

That is the equivalent of you have just sung a song one time

and thinking that you know how to sing that song beautifully

in front of an audience.

Well, it doesn't work that way.

A good thing to do

when you're learning something that's difficult

is find, in math, key problems

and then see if you can work it cold.

If you can't, take a peek at whatever hints you need

to be able to finish working it.

Then maybe a little later, try working it again cold,

without looking at the answer.

And maybe you go further.

The next day, try it again.

Go a little further and practice it.

What you're trying to do is to develop the same patterns

that you would develop

if you practiced singing a song a number of times.

And if you do this with key problems in math,

or if you're learning a language,

key conjugation patterns, for example,

then those patterns become automatic.

So, for example with your problem,

after several days of practice,

you find you've worked it out enough times by pencil

that when you just look at the problem,

you can step through all the solution steps in your mind.

You've created a valuable chunk,

and so then when it comes test time,

and you've got maybe five, ten of these key problems,

so you can just look at 'em

and know what you're supposed to be doing.

Suddenly when you're taking that test,

you can pull this chunk up and connect it with this chunk,

and solve new problems you haven't seen before.

And it's really, really powerful technique is to realize

that all learning involves

getting these neural chunks together.