WEBVTT - Ep 87 "How do we operate in the present when we perceive the past?"

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<v Speaker 1>Have you ever seen those pictures of blobs on a

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<v Speaker 1>page and it doesn't look like anything to you until

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<v Speaker 1>you're told what it is, and then you suddenly see it.

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<v Speaker 1>Why does that give us a great clue about the

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<v Speaker 1>wiring of the brain. And why are neuroscientists so magnetically

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<v Speaker 1>attracted to those visual illusions that you scroll through on

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<v Speaker 1>social media? What is the deep trick about the way

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<v Speaker 1>your visual system works that you were never taught in school?

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<v Speaker 1>And what does any of this have to do with

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<v Speaker 1>catching a baseball or zooming down the road in New

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<v Speaker 1>York City or the warp drive in Star Trek. Welcome

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<v Speaker 1>to Inner Cosmos with me David Eagleman. I'm a neuroscientist

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<v Speaker 1>and author at Stanford and in these episodes we sail

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<v Speaker 1>deeply into our three pound universe to understand why and

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<v Speaker 1>how our lives look the way they do.

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<v Speaker 2>Today.

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<v Speaker 1>I'm going to start with the notion of visual illusions.

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<v Speaker 1>Elementary school students love these and they stare at them

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<v Speaker 1>for about a minute and then they're on to the

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<v Speaker 1>next thing, because why not. The illusion is just an

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<v Speaker 1>interesting trick. There's nothing further to do about it. It's

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<v Speaker 1>only later when you grow up to be a neuroscientist

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<v Speaker 1>or a fan of a neuroscience podcast, that you might

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<v Speaker 1>even return to one of these illusions to ask, wait,

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<v Speaker 1>why exactly does that happen?

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<v Speaker 2>Does that tell us.

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<v Speaker 1>Something deep and fundamental about the way my consciousness constructs

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<v Speaker 1>the world for me?

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<v Speaker 2>What does it reveal? So?

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<v Speaker 1>Have you ever seen the illusion where you're looking at

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<v Speaker 1>lines like bicycle spokes, and then there's some straight lines

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<v Speaker 1>drawn on top of that, and they don't look straight,

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<v Speaker 1>they look bent. Why does that happen? Seems like it

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<v Speaker 1>shouldn't be hard to answer, but it's actually taken well

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<v Speaker 1>over a century to figure this out, and the answer

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<v Speaker 1>is gonna blow your mind. I promise you that. But

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<v Speaker 1>in order to get us there, I'm gonna start with

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<v Speaker 1>something completely different. I'm gonna start with those pictures that

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<v Speaker 1>look like just a bunch of blobs. Probably you've seen

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<v Speaker 1>one of these before. There's just a bunch of random

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<v Speaker 1>looking splotches of black and white on a page. If

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<v Speaker 1>your brain doesn't have a prior expectation about what it's seeing,

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<v Speaker 1>about what the blobs mean, then you simply see black

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<v Speaker 1>and white blobs and there's no particular meaning to the picture.

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<v Speaker 1>I'm gonna link an example of this on the show

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<v Speaker 1>notes at eagleman dot com slash podcast, and I want

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<v Speaker 1>you to stare at it for a few moments and

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<v Speaker 1>then scroll down to the very bottom of the page

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<v Speaker 1>for the hint. What you'll see is that you can't

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<v Speaker 1>make heads or tails of these blobs. But then I

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<v Speaker 1>only change one thing, and it has nothing to do

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<v Speaker 1>with what's on the screen. I give you a hint,

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<v Speaker 1>and as soon as you have a notion about how

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<v Speaker 1>to interpret what is on your retinas, then you say,

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<v Speaker 1>oh yeah, I see it now. Now the exact same

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<v Speaker 1>blobs that confused you a moment ago make perfect sense.

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<v Speaker 1>But again, nothing changed out there in the world. The

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<v Speaker 1>only thing that changed is something in your neural networks.

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<v Speaker 1>So what's the lesson from this? There has to be

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<v Speaker 1>a match between incoming data and your expectations for you

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<v Speaker 1>to see anything. But wait, what, That's not how vision

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<v Speaker 1>is supposed to work, is it. I mean, after all,

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<v Speaker 1>you look at any basic biology textbook and it will

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<v Speaker 1>tell you that photons hit the retina and the information

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<v Speaker 1>is carried on back to the visual cortex, and then

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<v Speaker 1>you just see what's out there. The visual cortex is

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<v Speaker 1>like a television screen, So what's going on?

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<v Speaker 2>Why can't you see.

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<v Speaker 1>The image in the blobs until you've got the right expectation.

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<v Speaker 2>This ties into.

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<v Speaker 1>A concept that you hear me refer to all the

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<v Speaker 1>time on this podcast, and that is the concept of

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<v Speaker 1>the internal model. Remember that your brain is isolated in

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<v Speaker 1>soundless and lightless solitude inside your skull, and its single

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<v Speaker 1>mission is to construct a loud, colorful mental model of

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<v Speaker 1>the outside world. In other words, it builds an inner

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<v Speaker 1>reality that tries to accurately reflect the outside. The key

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<v Speaker 1>is that you don't see by capturing television pixels from

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<v Speaker 1>the world. Instead, all you ever see is your internal model,

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<v Speaker 1>and your internal model only perceives some thing when its

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<v Speaker 1>expectations are sufficiently supported by the sensory data coming in. Now,

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<v Speaker 1>this isn't really a widely known idea. I think you'll

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<v Speaker 1>find if you ask people about it on airplanes, as

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<v Speaker 1>I often do. This isn't really the way that most

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<v Speaker 1>people are used to thinking about the brain. So it's

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<v Speaker 1>a bit surprising that the basic conceptualization of this idea

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<v Speaker 1>is almost seventy years old. One of the earliest examples

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<v Speaker 1>of this framework that I know of came from the

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<v Speaker 1>neuroscientist Donald McKay, who in nineteen fifty six said, Look,

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<v Speaker 1>the job of the visual cortex is to construct an

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<v Speaker 1>internal model, and it's always trying to anticipate the data

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<v Speaker 1>coming up from the retina. Now, just as a reminder,

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<v Speaker 1>the retina is the part of the back of your

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<v Speaker 1>eye that captures light, and the visual cortex is all

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<v Speaker 1>the way at the back of your head, on the

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<v Speaker 1>far side of the brain. But here's the surprise. The

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<v Speaker 1>information doesn't just shoot from the retina to the visual cortex. Instead,

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<v Speaker 1>there's a train station in between, a structure called the thalamus.

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<v Speaker 1>The thalamus sits right in the middle. So information doesn't

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<v Speaker 1>go straight from the eye to the visual cortex, but

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<v Speaker 1>instead it makes a stop and changes trains halfway at

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<v Speaker 1>the thalamus.

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<v Speaker 2>Okay, well that's weird. Why is there the setup?

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<v Speaker 1>Well, to understand this, we need to understand that the

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<v Speaker 1>model of vision in introductory textbooks isn't just misleading, it's

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<v Speaker 1>dead wrong. The brain isn't built on straight lines, it's

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<v Speaker 1>built with loops. So what McKay suggested is that the

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<v Speaker 1>retina sends its data to the thalamus. In other words,

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<v Speaker 1>what the eye is capturing about the world out there,

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<v Speaker 1>and the cortex sends its predictions to the thalamus what

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<v Speaker 1>the cortex is expecting to see next, and all that

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<v Speaker 1>ever comes out of the thalamus back to the cortex

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<v Speaker 1>is the difference, the difference between what you expected and

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<v Speaker 1>what you got. In other words, the information that goes

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<v Speaker 1>from the thalamis to the visual cortex is just that

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<v Speaker 1>little bit which was unanticipated, the difference between what's out

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<v Speaker 1>there and what was already expected. The thalamus sends to

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<v Speaker 1>the cortex only that difference signal, because that's the only

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<v Speaker 1>part that wasn't predicted away. And then, by the way,

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<v Speaker 1>this unpredicted information adjusts the internal model so there will

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<v Speaker 1>be less of a mismatch in the future. That way,

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<v Speaker 1>the brain refines its model of the world by paying

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<v Speaker 1>attention to its mistakes. Okay, so the idea here is

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<v Speaker 1>that the brain is always trying to anticipate what it's

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<v Speaker 1>seeing out there, and McKay pointed out that this is

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<v Speaker 1>consistent with the anatomical fact that there are ten times

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<v Speaker 1>as many fibers projecting from the visual cortex back to

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<v Speaker 1>the thalamis as there are going from thalamis to visual cortex,

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<v Speaker 1>which no one would have guessed. But that's just what

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<v Speaker 1>you'd expect if detailed predictions are going from the cortex

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<v Speaker 1>to the thalamis, and the little signal from thalamis back

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<v Speaker 1>to cortex is just carrying the difference.

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<v Speaker 2>Between what was expected and what was seen.

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<v Speaker 1>Okay, So why am I telling you this level of

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<v Speaker 1>detail because it exposes a giant idea. It means that

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<v Speaker 1>what you perceive about the world emerges from an active

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<v Speaker 1>comparison of sensory data with your internal predictions. Again, think

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<v Speaker 1>about those blobs. If you don't have a prediction of

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<v Speaker 1>what you're seeing out there, there's really nothing there. As

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<v Speaker 1>soon as you have a close enough expectation because you've

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<v Speaker 1>been given a hint, then that lights up a forest

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<v Speaker 1>fire in your brain and you see the thing because

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<v Speaker 1>there's a match.

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<v Speaker 2>Now. So what this.

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<v Speaker 1>Means is that the brain is always trying to predict

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<v Speaker 1>everything that is coming or expected. And here's one way

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<v Speaker 1>that the brain helps itself along. Whenever it sends out

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<v Speaker 1>a signal to your body, like move your head or

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<v Speaker 1>move your arm, it also sends copies of that command

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<v Speaker 1>internally all around the brain. These are called efference copies.

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<v Speaker 1>So now your movement isn't just happening in the outside

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<v Speaker 1>world and then you react to it, but there's also

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<v Speaker 1>a simulation of that movement happening inside your internal model,

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<v Speaker 1>so that you can predict the outcome of that action.

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<v Speaker 1>And this, by the way, is the reason you can't

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<v Speaker 1>tickle yourself. Other people can tickle you because they're tickling

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<v Speaker 1>maneuvers are not predictable to you. But you can't tickle

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<v Speaker 1>you because your brain moves your fingers into the tickle

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<v Speaker 1>position and it already expects the resulting sensations, that already

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<v Speaker 1>knows what's come. Now, by the way, if you'd really

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<v Speaker 1>like to tickle yourself, there is a way to do it,

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<v Speaker 1>and this just involves taking predictability away from your own actions.

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<v Speaker 1>So what you do is you control the position of

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<v Speaker 1>a feather with a joystick that inserts a random time delay,

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<v Speaker 1>so when you move the joystick, at least a second

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<v Speaker 1>passes before the feather moves accordingly, so that takes away

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<v Speaker 1>the predictability and now you can self tickle. By the way,

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<v Speaker 1>related to this, I described in episode forty four how

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<v Speaker 1>people with schizophrenia can tickle themselves, and this is because

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<v Speaker 1>of a problem with their internal timing that doesn't allow

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<v Speaker 1>their motor actions and resulting sensations to be correctly sequenced. Okay,

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<v Speaker 1>so back to this issue about having a brain that's

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<v Speaker 1>not just moving signals down a one way assembly line,

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<v Speaker 1>but instead has all these internal loops so that it

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<v Speaker 1>can always be feeding its internal model and guessing what's

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<v Speaker 1>going to happen next. What is the advantage of this, Well,

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<v Speaker 1>it allows us to transcend stimulus response behavior. In other words,

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<v Speaker 1>we don't have to just observe the world and then

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<v Speaker 1>react to it. Instead, a brain with an internal model

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<v Speaker 1>gives us the ability to make predictions ahead of actual

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<v Speaker 1>sensory input, like predicting what your fingers will feel like

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<v Speaker 1>in your underarm. So our brains build these predictive internal

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<v Speaker 1>models that tell us how things are likely to go

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<v Speaker 1>in the world. And this way our brains don't work

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<v Speaker 1>solely from the latest sensory data, but instead they're always

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<v Speaker 1>guessing ahead to the next moment. Now, why do we

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<v Speaker 1>need a complicated brain like this because our perception is

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<v Speaker 1>massively delayed from reality. Why is it delayed because signals

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<v Speaker 1>from the world, like something you see or a touch

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<v Speaker 1>on your toe. These signals have to travel along nerve cells,

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<v Speaker 1>and they move about a meter per second in the cortex,

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<v Speaker 1>which is, by the way, about three hundred million times

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<v Speaker 1>slower than electricity moving through your laptop. We are giant

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<v Speaker 1>systems of cells, and it takes time for impulses and

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<v Speaker 1>cells to travel around. Yes, they use electricity, but it's

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<v Speaker 1>not like a signal propagating along a wire. Instead, with

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<v Speaker 1>a cell, you've got these long extensions called axons, and

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<v Speaker 1>the signals travel by causing little channels to open in

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<v Speaker 1>the membrane, which allows little charged particles to flow through

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<v Speaker 1>and change the voltage locally, and that propagates down the axon.

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<v Speaker 1>So this is a very cool way that mother nature

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<v Speaker 1>discovered how to run a signal down a cell. But

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<v Speaker 1>it ain't fast, and the consequence is that it just

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<v Speaker 1>takes a long time for signals to propagate through the

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<v Speaker 1>system and eventually come together and settle into a coherent pattern.

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<v Speaker 1>So by the time you become consciously aware of something

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<v Speaker 1>in the outside world, the event has already happened a

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<v Speaker 1>while ago. We live in the past. For example, clap

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<v Speaker 1>your hands in front of you. By the time you

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<v Speaker 1>see and feel and hear that it's already happened a

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<v Speaker 1>tiny little while ago. Whatever conscious movie you're seeing right now, now,

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<v Speaker 1>that world is already gone.

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<v Speaker 2>Now.

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<v Speaker 1>We don't often think about this, but this delay from reality,

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<v Speaker 1>the fact that we're living in the past, is a

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<v Speaker 1>major problem because you need to operate in the present,

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<v Speaker 1>but your brain is always working with old news. All

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<v Speaker 1>your sensory inputs like vision and hearing and touch, these

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<v Speaker 1>take time to travel to the brain to get processed,

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<v Speaker 1>and finally the brain croaks out a response. And even

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<v Speaker 1>though this delay is less than a second, that's plenty

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<v Speaker 1>of time to create issues. So just think about trying

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<v Speaker 1>to catch a baseball that someone throws to you. If

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<v Speaker 1>you were merely an assembly line device, you couldn't do it.

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<v Speaker 1>Why because there would be a delay of hundreds of

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<v Speaker 1>milliseconds from the time the light strikes your eyes until

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<v Speaker 1>you could put up your glove in the right spot.

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<v Speaker 1>And the problem is that by the time the image

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<v Speaker 1>of the ball reaches your brain and gets processed, the

0:15:01.280 --> 0:15:04.760
<v Speaker 1>ball has moved. Your hand would always be reaching for

0:15:04.840 --> 0:15:07.160
<v Speaker 1>a place where the ball used to be.

0:15:08.240 --> 0:15:09.560
<v Speaker 2>So how do you catch a baseball?

0:15:09.600 --> 0:15:15.360
<v Speaker 1>It's because of these deeply hardwired internal models. Your internal

0:15:15.400 --> 0:15:20.360
<v Speaker 1>model generates expectations about when and where the ball's going

0:15:20.440 --> 0:15:24.080
<v Speaker 1>to hit, given momentum and gravity and so on. Your

0:15:24.080 --> 0:15:29.560
<v Speaker 1>brain is not just passively processing information. It's predicting. It's

0:15:29.560 --> 0:15:31.320
<v Speaker 1>not reactive, it's.

0:15:31.200 --> 0:15:33.040
<v Speaker 2>Constantly guessing ahead.

0:15:33.600 --> 0:15:36.360
<v Speaker 1>It predicts where the ball is going to be based

0:15:36.400 --> 0:15:39.800
<v Speaker 1>on clues about its trajectory and speed, and that's what

0:15:39.920 --> 0:15:42.800
<v Speaker 1>allows you to catch it. By the way, as a

0:15:42.840 --> 0:15:46.480
<v Speaker 1>side note, these predictive internal models you have are trained

0:15:46.560 --> 0:15:52.640
<v Speaker 1>up by lifelong exposure in your normal experience. If your

0:15:52.920 --> 0:15:57.600
<v Speaker 1>great grandkids grow up on Mars, their internal models will

0:15:57.600 --> 0:16:00.680
<v Speaker 1>get trained up with different parameters of physics, and they'll

0:16:00.720 --> 0:16:04.000
<v Speaker 1>put up their glove at a different time the moment

0:16:04.040 --> 0:16:07.720
<v Speaker 1>that's right for a Martian pop fly. Okay, But the

0:16:07.760 --> 0:16:09.640
<v Speaker 1>critical point I want to make here is that we

0:16:09.840 --> 0:16:14.080
<v Speaker 1>have these predictive internal models, and these things tell us

0:16:14.120 --> 0:16:18.920
<v Speaker 1>from experience how things are likely to move in the world.

0:16:19.080 --> 0:16:21.960
<v Speaker 1>And this way our brains don't work solely from the

0:16:22.160 --> 0:16:26.440
<v Speaker 1>latest sensory information, but instead they construct predictions about where

0:16:26.520 --> 0:16:29.360
<v Speaker 1>the ball is going to be. The same idea is

0:16:29.360 --> 0:16:32.520
<v Speaker 1>in play when you're walking through a busy airport, when

0:16:32.560 --> 0:16:35.119
<v Speaker 1>you have a flow of people moving in all directions

0:16:35.160 --> 0:16:39.400
<v Speaker 1>around you. If you had access to only outdated information

0:16:39.920 --> 0:16:43.840
<v Speaker 1>from photons a few hundred milliseconds ago, you'd be constantly

0:16:43.880 --> 0:16:44.920
<v Speaker 1>crashing into people.

0:16:44.960 --> 0:16:47.360
<v Speaker 2>But you don't. Your brain solves this.

0:16:47.880 --> 0:16:51.480
<v Speaker 1>Your brain is constantly forecasting where the people are going

0:16:51.520 --> 0:16:54.680
<v Speaker 1>to be based on their speed and direction, and that's

0:16:54.720 --> 0:16:59.480
<v Speaker 1>what allows you to smoothly navigate without crashing despite the

0:16:59.520 --> 0:17:04.400
<v Speaker 1>neural the and processing the visual information. So I want

0:17:04.400 --> 0:17:07.400
<v Speaker 1>to summarize where we are so far. The foundation we're

0:17:07.520 --> 0:17:10.400
<v Speaker 1>establishing here is that the brain is not just reacting

0:17:10.400 --> 0:17:13.680
<v Speaker 1>to the world. Instead, it's a machine that continuously makes

0:17:13.880 --> 0:17:19.679
<v Speaker 1>educated guesses. Prediction is how we compensate for our signal

0:17:19.760 --> 0:17:23.919
<v Speaker 1>processing delays, and from an evolutionary standpoint, this ability to

0:17:24.000 --> 0:17:28.520
<v Speaker 1>predict was absolutely critical for survival because animals who wanted

0:17:28.600 --> 0:17:32.720
<v Speaker 1>any chance of living how to anticipate the movements of

0:17:32.800 --> 0:17:36.439
<v Speaker 1>predators or prey to react quickly enough. You have to

0:17:36.520 --> 0:17:40.200
<v Speaker 1>somehow operate in real time if you want to evade

0:17:40.280 --> 0:17:43.840
<v Speaker 1>a thread or catch the running animal. So whenever you

0:17:44.080 --> 0:17:46.480
<v Speaker 1>are next catching a ball or moving through the airport,

0:17:47.000 --> 0:17:50.920
<v Speaker 1>think about how much you rely on your brain's predictive

0:17:51.080 --> 0:17:55.399
<v Speaker 1>abilities to act without having to wait for all the

0:17:55.480 --> 0:17:59.480
<v Speaker 1>signals to dribble their way in there. Okay, now we're

0:17:59.560 --> 0:18:02.280
<v Speaker 1>finally ready to return to the issue that I started with.

0:18:02.480 --> 0:18:05.320
<v Speaker 1>The illusions where you have some lines that are straight

0:18:05.400 --> 0:18:08.560
<v Speaker 1>but they look bent. These fall into the category of

0:18:09.160 --> 0:18:12.399
<v Speaker 1>geometric illusions. So what in the world do they have

0:18:12.480 --> 0:18:17.000
<v Speaker 1>to do with what we've been talking about so far. Well,

0:18:17.359 --> 0:18:19.560
<v Speaker 1>what I told you is that the visual system has

0:18:19.600 --> 0:18:23.160
<v Speaker 1>developed these predictive mechanisms to deal with the signal delays

0:18:23.560 --> 0:18:25.879
<v Speaker 1>so that it can see something at this moment in

0:18:25.920 --> 0:18:28.560
<v Speaker 1>time and make a really good guess where that thing

0:18:28.640 --> 0:18:32.439
<v Speaker 1>is going to be in say, one hundred milliseconds. So

0:18:33.080 --> 0:18:38.160
<v Speaker 1>some of my colleagues proposed a framework called perceiving the present,

0:18:38.720 --> 0:18:42.119
<v Speaker 1>and the idea is that your brain sees what is

0:18:42.400 --> 0:18:46.199
<v Speaker 1>likely to be the case, rather than to perceive the

0:18:46.280 --> 0:18:49.679
<v Speaker 1>recent past. So the first examples of this framework came

0:18:49.720 --> 0:18:53.440
<v Speaker 1>out in the early nineteen nineties. So imagine you're looking

0:18:53.480 --> 0:18:56.399
<v Speaker 1>at a small horizontal line on a computer screen and

0:18:56.440 --> 0:18:59.439
<v Speaker 1>you're trying to judge its exact position, but there's a

0:18:59.480 --> 0:19:03.640
<v Speaker 1>field of dots drifting continuously in the upward direction behind

0:19:03.760 --> 0:19:06.720
<v Speaker 1>that line. In this case, you'll judge the line to

0:19:06.760 --> 0:19:10.480
<v Speaker 1>be higher up on the screen. This is called motion capture.

0:19:10.760 --> 0:19:13.400
<v Speaker 1>So by the beginning of the two thousands, my colleague

0:19:13.480 --> 0:19:18.120
<v Speaker 1>Mark Chengizi started proposing that the explanation for this motion

0:19:18.240 --> 0:19:23.080
<v Speaker 1>capture was the perceiving the present framework, which is that

0:19:23.119 --> 0:19:26.480
<v Speaker 1>your brain sees the line, and it sees the motion

0:19:27.040 --> 0:19:30.199
<v Speaker 1>and decides that in the next moment the line is

0:19:30.320 --> 0:19:33.320
<v Speaker 1>probably going to be pushed up by the motion. So

0:19:33.400 --> 0:19:37.679
<v Speaker 1>it's actually perceiving the line in a different place where

0:19:37.920 --> 0:19:41.840
<v Speaker 1>it expects the line to be in the next moment.

0:19:42.600 --> 0:19:46.880
<v Speaker 1>And besides that, he argued, he could explain the classical

0:19:47.000 --> 0:20:08.560
<v Speaker 1>geometric illusion. What are these classical geometric illusions. Well, let's

0:20:08.560 --> 0:20:12.560
<v Speaker 1>take what's known as the Herring illusion. You almost certainly

0:20:12.600 --> 0:20:14.919
<v Speaker 1>saw this as a kid. There are a bunch of

0:20:15.000 --> 0:20:17.440
<v Speaker 1>lines coming out of the center, like the spokes on

0:20:17.480 --> 0:20:21.920
<v Speaker 1>a bicycle wheel. Okay, now you put two parallel lines

0:20:22.359 --> 0:20:25.520
<v Speaker 1>on that bicycle wheel, let's say, a vertical line to

0:20:25.560 --> 0:20:28.320
<v Speaker 1>the right of center and one to the left. You

0:20:28.400 --> 0:20:32.240
<v Speaker 1>could do this by taping two pencils on the bicycle spokes.

0:20:32.720 --> 0:20:36.560
<v Speaker 1>Now here's the illusion to two pencils. Although they are straight,

0:20:36.960 --> 0:20:39.199
<v Speaker 1>they don't look that way anymore. Instead, it looks like

0:20:39.240 --> 0:20:43.560
<v Speaker 1>the pencils are curving, they look slightly bent. Their middles

0:20:43.680 --> 0:20:47.480
<v Speaker 1>are bowing outwards slightly. So this is an illusion that

0:20:47.560 --> 0:20:52.080
<v Speaker 1>was first described by the physiologist Ewald Herring in eighteen

0:20:52.160 --> 0:20:52.760
<v Speaker 1>sixty one.

0:20:53.119 --> 0:20:55.040
<v Speaker 2>But why in the world does it happen?

0:20:55.200 --> 0:20:57.320
<v Speaker 1>Well, Herring proposed that this has to do with the

0:20:57.359 --> 0:21:01.520
<v Speaker 1>brain overestimating angles where the lines are meeting. And then

0:21:01.600 --> 0:21:04.440
<v Speaker 1>other people proposed different things in the brain that might

0:21:04.800 --> 0:21:10.200
<v Speaker 1>explain that angle overestimation. But Changhizi proposed a new explanation

0:21:10.280 --> 0:21:14.520
<v Speaker 1>when which was quite stunning. He said, look, when you're

0:21:14.560 --> 0:21:17.520
<v Speaker 1>looking at these radial lines, in other words, the lines

0:21:17.640 --> 0:21:20.119
<v Speaker 1>like the bicycle spokes coming out from a central point,

0:21:20.960 --> 0:21:24.280
<v Speaker 1>your brain might think that it's just looking at the

0:21:24.400 --> 0:21:29.480
<v Speaker 1>convergence of lines to the vanishing point, like imagine you're

0:21:29.520 --> 0:21:32.040
<v Speaker 1>looking straight ahead on a street in New York City

0:21:32.280 --> 0:21:36.000
<v Speaker 1>and everything converges in the middle. But equally, he said,

0:21:36.080 --> 0:21:38.879
<v Speaker 1>these lines are what a scene looks like to your

0:21:38.960 --> 0:21:43.480
<v Speaker 1>visual system when you are moving forward. For example, imagine

0:21:43.480 --> 0:21:46.040
<v Speaker 1>that you're driving down the road in New York City

0:21:46.480 --> 0:21:48.680
<v Speaker 1>and up ahead on the left there's a hot dog

0:21:48.800 --> 0:21:51.119
<v Speaker 1>stand and that zips by you on your left side,

0:21:51.440 --> 0:21:53.840
<v Speaker 1>and at the same time, there's a street juggler on

0:21:53.880 --> 0:21:54.520
<v Speaker 1>your right.

0:21:54.320 --> 0:21:56.399
<v Speaker 2>Side, and he gets bigger and he zips by you

0:21:56.520 --> 0:21:56.840
<v Speaker 2>on that.

0:21:56.920 --> 0:21:59.840
<v Speaker 1>Side, and there's an overhead street sign that at a

0:22:00.040 --> 0:22:03.240
<v Speaker 1>distance starts essentially in the middle in front of you,

0:22:03.400 --> 0:22:05.919
<v Speaker 1>but as you get closer and closer, it moves over

0:22:05.960 --> 0:22:11.919
<v Speaker 1>your head. So everything is streaking past you like radial lines,

0:22:12.359 --> 0:22:15.399
<v Speaker 1>and this is known as optic flow. So one place

0:22:15.480 --> 0:22:18.680
<v Speaker 1>you've seen this before is on Star Trek, where they

0:22:19.080 --> 0:22:21.439
<v Speaker 1>yank down the lever and put the ship into warp

0:22:21.560 --> 0:22:25.440
<v Speaker 1>drive and all the stars suddenly shoot past them, all

0:22:25.600 --> 0:22:29.119
<v Speaker 1>moving away from the center, like the radial spokes of

0:22:29.160 --> 0:22:32.720
<v Speaker 1>the bicycle wheel. So Tanghizi said, when you see radial

0:22:32.840 --> 0:22:37.159
<v Speaker 1>lines like that, it's typically a visual signature of you

0:22:37.840 --> 0:22:41.520
<v Speaker 1>moving forward towards the vanishing point. And certainly when you're

0:22:41.520 --> 0:22:44.760
<v Speaker 1>moving fast, there's a radial smear, like the way that

0:22:44.800 --> 0:22:49.200
<v Speaker 1>the stars and Star Trek smear into lines. And he said, look,

0:22:49.600 --> 0:22:54.000
<v Speaker 1>Herring's radial lines essentially mimic this. It's like you are

0:22:54.040 --> 0:22:58.119
<v Speaker 1>in the spaceship moving directly ahead. Now here's the key.

0:22:58.880 --> 0:23:01.359
<v Speaker 1>Let's come back to the de lays in the visual

0:23:01.400 --> 0:23:04.560
<v Speaker 1>system and how they can be accounted for by the

0:23:04.720 --> 0:23:10.200
<v Speaker 1>brain making projections where things are about to be. Imagine

0:23:10.200 --> 0:23:12.840
<v Speaker 1>that you're in New York City and driving and there

0:23:12.840 --> 0:23:15.359
<v Speaker 1>are two skyscrapers up ahead of you, one on the

0:23:15.440 --> 0:23:17.680
<v Speaker 1>left and one on the right. Now, as you race

0:23:17.840 --> 0:23:22.160
<v Speaker 1>forward in your car, those two buildings will loom closer.

0:23:22.200 --> 0:23:25.280
<v Speaker 1>But now something interesting is happening. The parts of the

0:23:25.320 --> 0:23:30.000
<v Speaker 1>buildings closer to you will seem farther apart, because if

0:23:30.080 --> 0:23:32.800
<v Speaker 1>you look up, the tips of the buildings are coming

0:23:32.880 --> 0:23:34.639
<v Speaker 1>closer together, way up in the sky.

0:23:35.160 --> 0:23:36.320
<v Speaker 2>So the point is that.

0:23:36.520 --> 0:23:40.199
<v Speaker 1>Even though you see essentially straight skyscrapers when they're at

0:23:40.200 --> 0:23:44.640
<v Speaker 1>a distance, as you approach, they are bending away from you.

0:23:44.960 --> 0:23:50.720
<v Speaker 1>Their centers are bowing out. And Shannghizi's idea was that

0:23:50.760 --> 0:23:54.000
<v Speaker 1>when you look at the radial lines the bicycle spokes,

0:23:54.000 --> 0:23:56.240
<v Speaker 1>your brain thinks this might be a clue that I'm

0:23:56.280 --> 0:23:59.240
<v Speaker 1>moving forward, and I don't want there to be delays

0:23:59.280 --> 0:24:02.439
<v Speaker 1>in my perception, so I'm going to see the world

0:24:02.680 --> 0:24:06.919
<v Speaker 1>as it will be a moment later. And so you

0:24:07.000 --> 0:24:10.959
<v Speaker 1>see the two parallel lines bowed outward from the center.

0:24:11.359 --> 0:24:13.439
<v Speaker 1>In other words, when you look at the radio lines

0:24:13.440 --> 0:24:16.119
<v Speaker 1>on the piece of paper, even though nothing is moving.

0:24:16.920 --> 0:24:20.040
<v Speaker 1>Your brain thinks this is what movement looks like, and

0:24:20.119 --> 0:24:24.359
<v Speaker 1>so it predicts the next moment, and that's what you perceived.

0:24:24.520 --> 0:24:26.600
<v Speaker 1>In other words, you perceive the lines just as they

0:24:26.720 --> 0:24:29.320
<v Speaker 1>would project in the next moment if you were moving

0:24:29.400 --> 0:24:33.480
<v Speaker 1>forward toward the vanishing point. So, as Changhesi wrote in

0:24:33.520 --> 0:24:38.680
<v Speaker 1>this paper, evolution has seen to it that geometric drawings

0:24:38.840 --> 0:24:45.400
<v Speaker 1>like this elicit in us premonitions of the near future. Okay,

0:24:45.760 --> 0:24:50.000
<v Speaker 1>so the framework by Changizi and colleagues suggests that several

0:24:50.040 --> 0:24:54.199
<v Speaker 1>geometric illusions are caused by temporal delays with which the

0:24:54.280 --> 0:24:57.040
<v Speaker 1>visual system must cope. The idea is that the visual

0:24:57.040 --> 0:25:03.000
<v Speaker 1>system extrapolates its current information to perceive the present. Instead

0:25:03.000 --> 0:25:06.399
<v Speaker 1>of providing a conscious image of how the world was

0:25:06.440 --> 0:25:09.600
<v Speaker 1>a few hundred milliseconds ago when the signals first struck

0:25:09.640 --> 0:25:13.000
<v Speaker 1>the retina, the visual system estimates how the world is

0:25:13.240 --> 0:25:16.399
<v Speaker 1>likely to look in the next moment. But how would

0:25:16.400 --> 0:25:20.520
<v Speaker 1>we get at clues to the possible neural basis? In

0:25:20.520 --> 0:25:23.240
<v Speaker 1>other words, how does the brain actually pull this off?

0:25:23.960 --> 0:25:26.280
<v Speaker 1>So in my laboratory we wanted to figure this out.

0:25:26.520 --> 0:25:29.840
<v Speaker 1>So my student Don Vaughan and I had people look

0:25:29.920 --> 0:25:32.679
<v Speaker 1>at the herring illusion on a screen. You've got a

0:25:32.720 --> 0:25:35.960
<v Speaker 1>background of radial lines like the bicycle spokes, and we

0:25:36.040 --> 0:25:39.320
<v Speaker 1>flashed two vertical lines on top of this. And I'll

0:25:39.320 --> 0:25:40.960
<v Speaker 1>just take a quick second to give you a sense

0:25:41.000 --> 0:25:46.119
<v Speaker 1>of how we quantify illusions. In the laboratory, A person

0:25:46.160 --> 0:25:48.440
<v Speaker 1>sits in front of the computer and they use let's

0:25:48.440 --> 0:25:51.239
<v Speaker 1>say the right and left arrows on the keyboard to

0:25:51.440 --> 0:25:55.400
<v Speaker 1>change the curvature of those two lines. So at one

0:25:55.520 --> 0:25:58.920
<v Speaker 1>end of the range, they're actually physically bending the lines outward,

0:25:59.119 --> 0:26:01.760
<v Speaker 1>and at the other extreme they're bending them inward, and

0:26:01.840 --> 0:26:04.600
<v Speaker 1>somewhere in the middle they're physically straight. And what the

0:26:04.600 --> 0:26:08.560
<v Speaker 1>person does is adjust the curvature of the line until

0:26:08.600 --> 0:26:11.560
<v Speaker 1>it looks straight to them. But of course with the

0:26:11.600 --> 0:26:15.199
<v Speaker 1>hairing illusion, you need to actually adjust the lines so

0:26:15.240 --> 0:26:18.159
<v Speaker 1>the middles are curving inward in order for it to

0:26:18.240 --> 0:26:22.440
<v Speaker 1>look straight. In other words, we see how much curvature

0:26:22.520 --> 0:26:25.760
<v Speaker 1>it takes to cancel out the illusion, and that's the

0:26:25.800 --> 0:26:29.480
<v Speaker 1>way we can quantify the size of the illusion. Okay,

0:26:29.520 --> 0:26:32.240
<v Speaker 1>so we measure the heiring illusion, and no surprises there.

0:26:32.520 --> 0:26:36.080
<v Speaker 1>But now what we do is we replaced the radio

0:26:36.200 --> 0:26:40.800
<v Speaker 1>lines with an actual star field. We have dots moving

0:26:40.960 --> 0:26:44.639
<v Speaker 1>in an expanding pattern like the stars and Star Trek,

0:26:45.359 --> 0:26:48.240
<v Speaker 1>And now people are judging the size of the hering

0:26:48.280 --> 0:26:52.120
<v Speaker 1>illusion against the background of expanding dots.

0:26:52.800 --> 0:26:54.080
<v Speaker 2>And what we find is that.

0:26:54.000 --> 0:26:58.920
<v Speaker 1>The illusion still happens. The lines still appear bent, which

0:26:58.960 --> 0:27:04.040
<v Speaker 1>is just what you'd expect from the perceiving the present hypothesis. Okay,

0:27:04.040 --> 0:27:07.360
<v Speaker 1>but here's the really wacky part, the part that uncovers

0:27:07.480 --> 0:27:11.080
<v Speaker 1>an unexpected secret in the brain. We now measure the

0:27:11.080 --> 0:27:15.520
<v Speaker 1>size of the hering illusion over a field of contracting dots.

0:27:16.000 --> 0:27:19.520
<v Speaker 1>So picture that Star Trek footage running backwards. Now everything

0:27:19.600 --> 0:27:21.840
<v Speaker 1>is moving from the outside to the inside.

0:27:22.359 --> 0:27:23.440
<v Speaker 2>And here's the surprise.

0:27:23.560 --> 0:27:26.760
<v Speaker 1>We find that the size of the herring illusion was

0:27:26.840 --> 0:27:30.399
<v Speaker 1>exactly the same here. In other words, the lines still

0:27:30.520 --> 0:27:33.440
<v Speaker 1>curve outward, just like in the other two cases.

0:27:34.080 --> 0:27:35.920
<v Speaker 2>So what does that mean. We can get this.

0:27:35.920 --> 0:27:42.480
<v Speaker 1>Illusion by having radial lines or dots expanding or dots contracting,

0:27:43.000 --> 0:27:45.760
<v Speaker 1>and you find that the bars bend out in the

0:27:45.760 --> 0:27:51.119
<v Speaker 1>same direction. Now, at first glance, the bending of the

0:27:51.160 --> 0:27:56.560
<v Speaker 1>bars during contracting motion would seem to refute the perceiving

0:27:56.600 --> 0:27:59.959
<v Speaker 1>the present framework. If your brain is doing an active

0:28:00.119 --> 0:28:04.159
<v Speaker 1>of temporal extrapolation of the scene, it should make the

0:28:04.200 --> 0:28:08.200
<v Speaker 1>bars bend in the other direction. But the key thing

0:28:08.240 --> 0:28:12.800
<v Speaker 1>to note is that backward motion is ecologically quite rare.

0:28:13.000 --> 0:28:16.000
<v Speaker 1>Unless you're a dog looking out the back window of

0:28:16.040 --> 0:28:21.280
<v Speaker 1>a car. Most animals probably never see backward optic flow

0:28:21.320 --> 0:28:23.520
<v Speaker 1>in their lives. Okay, so we did a lot of

0:28:23.560 --> 0:28:27.080
<v Speaker 1>other experiments in this paper, but just this first result

0:28:27.119 --> 0:28:31.240
<v Speaker 1>that the Herring illusion happens the same with expanding or

0:28:31.400 --> 0:28:35.919
<v Speaker 1>contracting optic flow tells us some critical things. First, it

0:28:36.000 --> 0:28:39.760
<v Speaker 1>tells us that this spatial warping we see isn't a

0:28:40.120 --> 0:28:46.160
<v Speaker 1>sophisticated online computation. Instead, it's a basic mechanism that just

0:28:46.360 --> 0:28:49.400
<v Speaker 1>acts to get it right in the most common scenario

0:28:49.720 --> 0:28:53.760
<v Speaker 1>of forward motion. And because backward motion essentially never happens,

0:28:54.120 --> 0:28:58.120
<v Speaker 1>it doesn't matter that the mechanism is so unrefined, and

0:28:58.160 --> 0:29:00.960
<v Speaker 1>it gives us a big clue about the the underlying

0:29:01.120 --> 0:29:05.560
<v Speaker 1>neural mechanisms. The most parsimonious explanation. In other words, the

0:29:05.600 --> 0:29:09.160
<v Speaker 1>simplest idea would look for something in the brain that's

0:29:09.240 --> 0:29:13.640
<v Speaker 1>equally sensitive to lines like the bicycle spoke and also

0:29:13.760 --> 0:29:17.680
<v Speaker 1>to motion in either direction along that line. And it

0:29:17.720 --> 0:29:20.840
<v Speaker 1>turns out there are very simple neurons in primary visual

0:29:20.840 --> 0:29:25.640
<v Speaker 1>cortex that do exactly this. They're called orientation selective neurons,

0:29:25.920 --> 0:29:28.680
<v Speaker 1>and they respond to lines, and they also respond to

0:29:29.160 --> 0:29:32.479
<v Speaker 1>dots moving along the same trajectory as the line in

0:29:32.600 --> 0:29:35.960
<v Speaker 1>either direction. So what this means is that our findings

0:29:36.000 --> 0:29:40.360
<v Speaker 1>are consistent with the perceiving the present hypothesis, with the

0:29:40.400 --> 0:29:45.520
<v Speaker 1>caveat that the spatial warping to counteract neural delays is

0:29:45.560 --> 0:29:49.680
<v Speaker 1>not a smart active neural process, but instead it's just

0:29:49.720 --> 0:29:54.920
<v Speaker 1>a simple mechanism that succeeds only under forward moving circumstances,

0:29:54.920 --> 0:29:57.520
<v Speaker 1>which is most of the time. Now, as I said,

0:29:57.520 --> 0:29:59.320
<v Speaker 1>we did a lot of other experiments, and I'm gonna

0:29:59.320 --> 0:30:01.720
<v Speaker 1>skip the detail because they're not as important as this

0:30:01.960 --> 0:30:06.240
<v Speaker 1>main point. But all the other experiments supported this hypothesis

0:30:06.520 --> 0:30:10.680
<v Speaker 1>that the bars look bent because the brain is extrapolating

0:30:10.720 --> 0:30:14.440
<v Speaker 1>ahead what things would look like in the next moment.

0:30:14.920 --> 0:30:17.920
<v Speaker 1>And by the way, our findings weren't consistent with older

0:30:18.000 --> 0:30:22.320
<v Speaker 1>theories like angle over estimation, as Herring had suggested. So

0:30:22.640 --> 0:30:24.360
<v Speaker 1>if you want to dive deeper into the paper, I'm

0:30:24.400 --> 0:30:28.520
<v Speaker 1>linking it to the show notes. So what we saw

0:30:28.600 --> 0:30:31.800
<v Speaker 1>today is the issue that it always takes time for

0:30:31.960 --> 0:30:35.520
<v Speaker 1>signals to move through the brain. And although people got

0:30:35.520 --> 0:30:39.280
<v Speaker 1>excited when they discovered that the signals were carried by electricity,

0:30:39.320 --> 0:30:43.520
<v Speaker 1>it's nothing like the way that electricity runs on wires,

0:30:43.520 --> 0:30:46.400
<v Speaker 1>which is close to the speed of light. In the brain,

0:30:46.840 --> 0:30:50.480
<v Speaker 1>we're talking hundreds of millions of times slower than that.

0:30:51.040 --> 0:30:54.280
<v Speaker 1>So you have these signals limping along in the brain,

0:30:54.800 --> 0:30:58.120
<v Speaker 1>and that means we are always living in the past.

0:30:58.520 --> 0:31:01.920
<v Speaker 1>Our brains sometimes see fake news about the world out there,

0:31:01.920 --> 0:31:05.560
<v Speaker 1>but we always see old news. Your brain is always

0:31:05.600 --> 0:31:10.280
<v Speaker 1>getting information about an outdated version of the world, one

0:31:10.320 --> 0:31:13.600
<v Speaker 1>that no longer exists. And so one of the many

0:31:13.800 --> 0:31:18.200
<v Speaker 1>incredible things your brain does is make predictions so that

0:31:18.280 --> 0:31:21.880
<v Speaker 1>you can perceive things as they probably are right now,

0:31:22.440 --> 0:31:26.560
<v Speaker 1>rather than perceiving the stale version of the data, which

0:31:26.600 --> 0:31:28.560
<v Speaker 1>is much less useful by the time you see it.

0:31:29.120 --> 0:31:32.720
<v Speaker 1>This is the brain's very bold move to compensate for

0:31:32.800 --> 0:31:36.600
<v Speaker 1>its own delays, and that wacky fact explains a lot

0:31:36.680 --> 0:31:39.520
<v Speaker 1>about how we catch balls and walk through crowded airports,

0:31:39.800 --> 0:31:43.959
<v Speaker 1>but it also seems to explain surprisingly why we see

0:31:44.000 --> 0:31:49.080
<v Speaker 1>this basic geometric illusion of straight lines not looking straight,

0:31:49.640 --> 0:31:53.480
<v Speaker 1>discovered by Heiring almost one hundred and sixty five years ago. Now,

0:31:53.520 --> 0:31:56.480
<v Speaker 1>I wrote an article in Nature Reviews Neuroscience in which

0:31:56.520 --> 0:31:59.240
<v Speaker 1>I covered all kinds of visual illusions, and that's in

0:31:59.280 --> 0:32:02.960
<v Speaker 1>the show notes. And each one of these illusions opens

0:32:02.960 --> 0:32:07.760
<v Speaker 1>a new mind shaft into the brain, teaching us why

0:32:07.840 --> 0:32:11.240
<v Speaker 1>it's happening. And once we understand that, we can usually

0:32:11.560 --> 0:32:15.920
<v Speaker 1>construct new illusions, which is why amazing new illusions are

0:32:15.920 --> 0:32:16.920
<v Speaker 1>coming out each year.

0:32:17.600 --> 0:32:17.800
<v Speaker 2>Now.

0:32:17.840 --> 0:32:21.040
<v Speaker 1>Sometimes people see all these illusions on their social media

0:32:21.120 --> 0:32:24.720
<v Speaker 1>feeds and they might become tempted to say that everything

0:32:24.800 --> 0:32:28.040
<v Speaker 1>is an illusion. We don't accurately see what's really out there.

0:32:28.760 --> 0:32:31.680
<v Speaker 1>But that's probably not the right conclusion, because often we

0:32:31.840 --> 0:32:35.720
<v Speaker 1>do see what's out there accurately, as verified by our

0:32:35.840 --> 0:32:40.280
<v Speaker 1>other senses and by our objective machines. For example, if

0:32:40.320 --> 0:32:42.840
<v Speaker 1>I put a cup of steaming coffee on the table

0:32:42.880 --> 0:32:45.480
<v Speaker 1>in front of you, you see that, and you heard

0:32:45.560 --> 0:32:47.760
<v Speaker 1>the clunk when I set it down, and you can

0:32:47.880 --> 0:32:51.040
<v Speaker 1>verify the heat with your fingers and smell the coffee

0:32:51.080 --> 0:32:53.400
<v Speaker 1>and pick it up and taste it, and you can

0:32:53.480 --> 0:32:56.280
<v Speaker 1>measure the presence of the cup with video, or the

0:32:56.360 --> 0:32:59.960
<v Speaker 1>heat with an infrared camera, and the coffee with spectroscopy

0:33:00.080 --> 0:33:02.640
<v Speaker 1>and so on. So it's not that everything you see

0:33:02.760 --> 0:33:09.040
<v Speaker 1>is illusory. Instead, we see what is maximally useful to us.

0:33:09.720 --> 0:33:12.200
<v Speaker 1>And every once in a while we can throw an

0:33:12.320 --> 0:33:16.160
<v Speaker 1>unexpected wrench in the system by showing the brain something

0:33:16.520 --> 0:33:20.200
<v Speaker 1>that happens to tickle the right receptors. And in those

0:33:20.400 --> 0:33:24.760
<v Speaker 1>very special cases we can catch the system coming to

0:33:24.800 --> 0:33:28.880
<v Speaker 1>the wrong conclusion. But it's precisely because the brain has

0:33:28.920 --> 0:33:32.440
<v Speaker 1>evolved to do the optimal thing in all the other cases.

0:33:32.760 --> 0:33:37.280
<v Speaker 1>And traditionally, when we find these special cases, we just

0:33:37.520 --> 0:33:40.800
<v Speaker 1>laugh and enjoy and keep scrolling through our feed But

0:33:40.920 --> 0:33:46.760
<v Speaker 1>the endeavor of figuring ourselves out encourages us to pause,

0:33:47.400 --> 0:33:51.480
<v Speaker 1>to ask why, to dig deeper, and although these things

0:33:51.520 --> 0:33:57.160
<v Speaker 1>sometimes take centuries to answer, they typically yield deep insights

0:33:57.560 --> 0:34:01.560
<v Speaker 1>into who we are and what is actually going on.

0:34:07.280 --> 0:34:10.280
<v Speaker 1>Go to Eagleman dot com slash podcast for more information

0:34:10.440 --> 0:34:13.399
<v Speaker 1>and to find further reading. Send me an email at

0:34:13.440 --> 0:34:16.640
<v Speaker 1>podcasts at eagleman dot com with questions or discussion, and

0:34:16.760 --> 0:34:20.120
<v Speaker 1>check out and subscribe to Inner Cosmos on YouTube for

0:34:20.239 --> 0:34:24.479
<v Speaker 1>videos of each episode and to leave comments until next time.

0:34:24.600 --> 0:34:27.879
<v Speaker 1>I'm David Eagleman, and this is Inner Cosmos.