Speaker 1: Have you ever seen those pictures of blobs on a
Speaker 1: page and it doesn't look like anything to you until
Speaker 1: you're told what it is, and then you suddenly see it.
Speaker 1: Why does that give us a great clue about the
Speaker 1: wiring of the brain. And why are neuroscientists so magnetically
Speaker 1: attracted to those visual illusions that you scroll through on
Speaker 1: social media? What is the deep trick about the way
Speaker 1: your visual system works that you were never taught in school?
Speaker 1: And what does any of this have to do with
Speaker 1: catching a baseball or zooming down the road in New
Speaker 1: York City or the warp drive in Star Trek. Welcome
Speaker 1: to Inner Cosmos with me David Eagleman. I'm a neuroscientist
Speaker 1: and author at Stanford and in these episodes we sail
Speaker 1: deeply into our three pound universe to understand why and
Speaker 1: how our lives look the way they do.
Speaker 2: Today.
Speaker 1: I'm going to start with the notion of visual illusions.
Speaker 1: Elementary school students love these and they stare at them
Speaker 1: for about a minute and then they're on to the
Speaker 1: next thing, because why not. The illusion is just an
Speaker 1: interesting trick. There's nothing further to do about it. It's
Speaker 1: only later when you grow up to be a neuroscientist
Speaker 1: or a fan of a neuroscience podcast, that you might
Speaker 1: even return to one of these illusions to ask, wait,
Speaker 1: why exactly does that happen?
Speaker 2: Does that tell us.
Speaker 1: Something deep and fundamental about the way my consciousness constructs
Speaker 1: the world for me?
Speaker 2: What does it reveal? So?
Speaker 1: Have you ever seen the illusion where you're looking at
Speaker 1: lines like bicycle spokes, and then there's some straight lines
Speaker 1: drawn on top of that, and they don't look straight,
Speaker 1: they look bent. Why does that happen? Seems like it
Speaker 1: shouldn't be hard to answer, but it's actually taken well
Speaker 1: over a century to figure this out, and the answer
Speaker 1: is gonna blow your mind. I promise you that. But
Speaker 1: in order to get us there, I'm gonna start with
Speaker 1: something completely different. I'm gonna start with those pictures that
Speaker 1: look like just a bunch of blobs. Probably you've seen
Speaker 1: one of these before. There's just a bunch of random
Speaker 1: looking splotches of black and white on a page. If
Speaker 1: your brain doesn't have a prior expectation about what it's seeing,
Speaker 1: about what the blobs mean, then you simply see black
Speaker 1: and white blobs and there's no particular meaning to the picture.
Speaker 1: I'm gonna link an example of this on the show
Speaker 1: notes at eagleman dot com slash podcast, and I want
Speaker 1: you to stare at it for a few moments and
Speaker 1: then scroll down to the very bottom of the page
Speaker 1: for the hint. What you'll see is that you can't
Speaker 1: make heads or tails of these blobs. But then I
Speaker 1: only change one thing, and it has nothing to do
Speaker 1: with what's on the screen. I give you a hint,
Speaker 1: and as soon as you have a notion about how
Speaker 1: to interpret what is on your retinas, then you say,
Speaker 1: oh yeah, I see it now. Now the exact same
Speaker 1: blobs that confused you a moment ago make perfect sense.
Speaker 1: But again, nothing changed out there in the world. The
Speaker 1: only thing that changed is something in your neural networks.
Speaker 1: So what's the lesson from this? There has to be
Speaker 1: a match between incoming data and your expectations for you
Speaker 1: to see anything. But wait, what, That's not how vision
Speaker 1: is supposed to work, is it. I mean, after all,
Speaker 1: you look at any basic biology textbook and it will
Speaker 1: tell you that photons hit the retina and the information
Speaker 1: is carried on back to the visual cortex, and then
Speaker 1: you just see what's out there. The visual cortex is
Speaker 1: like a television screen, So what's going on?
Speaker 2: Why can't you see.
Speaker 1: The image in the blobs until you've got the right expectation.
Speaker 2: This ties into.
Speaker 1: A concept that you hear me refer to all the
Speaker 1: time on this podcast, and that is the concept of
Speaker 1: the internal model. Remember that your brain is isolated in
Speaker 1: soundless and lightless solitude inside your skull, and its single
Speaker 1: mission is to construct a loud, colorful mental model of
Speaker 1: the outside world. In other words, it builds an inner
Speaker 1: reality that tries to accurately reflect the outside. The key
Speaker 1: is that you don't see by capturing television pixels from
Speaker 1: the world. Instead, all you ever see is your internal model,
Speaker 1: and your internal model only perceives some thing when its
Speaker 1: expectations are sufficiently supported by the sensory data coming in. Now,
Speaker 1: this isn't really a widely known idea. I think you'll
Speaker 1: find if you ask people about it on airplanes, as
Speaker 1: I often do. This isn't really the way that most
Speaker 1: people are used to thinking about the brain. So it's
Speaker 1: a bit surprising that the basic conceptualization of this idea
Speaker 1: is almost seventy years old. One of the earliest examples
Speaker 1: of this framework that I know of came from the
Speaker 1: neuroscientist Donald McKay, who in nineteen fifty six said, Look,
Speaker 1: the job of the visual cortex is to construct an
Speaker 1: internal model, and it's always trying to anticipate the data
Speaker 1: coming up from the retina. Now, just as a reminder,
Speaker 1: the retina is the part of the back of your
Speaker 1: eye that captures light, and the visual cortex is all
Speaker 1: the way at the back of your head, on the
Speaker 1: far side of the brain. But here's the surprise. The
Speaker 1: information doesn't just shoot from the retina to the visual cortex. Instead,
Speaker 1: there's a train station in between, a structure called the thalamus.
Speaker 1: The thalamus sits right in the middle. So information doesn't
Speaker 1: go straight from the eye to the visual cortex, but
Speaker 1: instead it makes a stop and changes trains halfway at
Speaker 1: the thalamus.
Speaker 2: Okay, well that's weird. Why is there the setup?
Speaker 1: Well, to understand this, we need to understand that the
Speaker 1: model of vision in introductory textbooks isn't just misleading, it's
Speaker 1: dead wrong. The brain isn't built on straight lines, it's
Speaker 1: built with loops. So what McKay suggested is that the
Speaker 1: retina sends its data to the thalamus. In other words,
Speaker 1: what the eye is capturing about the world out there,
Speaker 1: and the cortex sends its predictions to the thalamus what
Speaker 1: the cortex is expecting to see next, and all that
Speaker 1: ever comes out of the thalamus back to the cortex
Speaker 1: is the difference, the difference between what you expected and
Speaker 1: what you got. In other words, the information that goes
Speaker 1: from the thalamis to the visual cortex is just that
Speaker 1: little bit which was unanticipated, the difference between what's out
Speaker 1: there and what was already expected. The thalamus sends to
Speaker 1: the cortex only that difference signal, because that's the only
Speaker 1: part that wasn't predicted away. And then, by the way,
Speaker 1: this unpredicted information adjusts the internal model so there will
Speaker 1: be less of a mismatch in the future. That way,
Speaker 1: the brain refines its model of the world by paying
Speaker 1: attention to its mistakes. Okay, so the idea here is
Speaker 1: that the brain is always trying to anticipate what it's
Speaker 1: seeing out there, and McKay pointed out that this is
Speaker 1: consistent with the anatomical fact that there are ten times
Speaker 1: as many fibers projecting from the visual cortex back to
Speaker 1: the thalamis as there are going from thalamis to visual cortex,
Speaker 1: which no one would have guessed. But that's just what
Speaker 1: you'd expect if detailed predictions are going from the cortex
Speaker 1: to the thalamis, and the little signal from thalamis back
Speaker 1: to cortex is just carrying the difference.
Speaker 2: Between what was expected and what was seen.
Speaker 1: Okay, So why am I telling you this level of
Speaker 1: detail because it exposes a giant idea. It means that
Speaker 1: what you perceive about the world emerges from an active
Speaker 1: comparison of sensory data with your internal predictions. Again, think
Speaker 1: about those blobs. If you don't have a prediction of
Speaker 1: what you're seeing out there, there's really nothing there. As
Speaker 1: soon as you have a close enough expectation because you've
Speaker 1: been given a hint, then that lights up a forest
Speaker 1: fire in your brain and you see the thing because
Speaker 1: there's a match.
Speaker 2: Now. So what this.
Speaker 1: Means is that the brain is always trying to predict
Speaker 1: everything that is coming or expected. And here's one way
Speaker 1: that the brain helps itself along. Whenever it sends out
Speaker 1: a signal to your body, like move your head or
Speaker 1: move your arm, it also sends copies of that command
Speaker 1: internally all around the brain. These are called efference copies.
Speaker 1: So now your movement isn't just happening in the outside
Speaker 1: world and then you react to it, but there's also
Speaker 1: a simulation of that movement happening inside your internal model,
Speaker 1: so that you can predict the outcome of that action.
Speaker 1: And this, by the way, is the reason you can't
Speaker 1: tickle yourself. Other people can tickle you because they're tickling
Speaker 1: maneuvers are not predictable to you. But you can't tickle
Speaker 1: you because your brain moves your fingers into the tickle
Speaker 1: position and it already expects the resulting sensations, that already
Speaker 1: knows what's come. Now, by the way, if you'd really
Speaker 1: like to tickle yourself, there is a way to do it,
Speaker 1: and this just involves taking predictability away from your own actions.
Speaker 1: So what you do is you control the position of
Speaker 1: a feather with a joystick that inserts a random time delay,
Speaker 1: so when you move the joystick, at least a second
Speaker 1: passes before the feather moves accordingly, so that takes away
Speaker 1: the predictability and now you can self tickle. By the way,
Speaker 1: related to this, I described in episode forty four how
Speaker 1: people with schizophrenia can tickle themselves, and this is because
Speaker 1: of a problem with their internal timing that doesn't allow
Speaker 1: their motor actions and resulting sensations to be correctly sequenced. Okay,
Speaker 1: so back to this issue about having a brain that's
Speaker 1: not just moving signals down a one way assembly line,
Speaker 1: but instead has all these internal loops so that it
Speaker 1: can always be feeding its internal model and guessing what's
Speaker 1: going to happen next. What is the advantage of this, Well,
Speaker 1: it allows us to transcend stimulus response behavior. In other words,
Speaker 1: we don't have to just observe the world and then
Speaker 1: react to it. Instead, a brain with an internal model
Speaker 1: gives us the ability to make predictions ahead of actual
Speaker 1: sensory input, like predicting what your fingers will feel like
Speaker 1: in your underarm. So our brains build these predictive internal
Speaker 1: models that tell us how things are likely to go
Speaker 1: in the world. And this way our brains don't work
Speaker 1: solely from the latest sensory data, but instead they're always
Speaker 1: guessing ahead to the next moment. Now, why do we
Speaker 1: need a complicated brain like this because our perception is
Speaker 1: massively delayed from reality. Why is it delayed because signals
Speaker 1: from the world, like something you see or a touch
Speaker 1: on your toe. These signals have to travel along nerve cells,
Speaker 1: and they move about a meter per second in the cortex,
Speaker 1: which is, by the way, about three hundred million times
Speaker 1: slower than electricity moving through your laptop. We are giant
Speaker 1: systems of cells, and it takes time for impulses and
Speaker 1: cells to travel around. Yes, they use electricity, but it's
Speaker 1: not like a signal propagating along a wire. Instead, with
Speaker 1: a cell, you've got these long extensions called axons, and
Speaker 1: the signals travel by causing little channels to open in
Speaker 1: the membrane, which allows little charged particles to flow through
Speaker 1: and change the voltage locally, and that propagates down the axon.
Speaker 1: So this is a very cool way that mother nature
Speaker 1: discovered how to run a signal down a cell. But
Speaker 1: it ain't fast, and the consequence is that it just
Speaker 1: takes a long time for signals to propagate through the
Speaker 1: system and eventually come together and settle into a coherent pattern.
Speaker 1: So by the time you become consciously aware of something
Speaker 1: in the outside world, the event has already happened a
Speaker 1: while ago. We live in the past. For example, clap
Speaker 1: your hands in front of you. By the time you
Speaker 1: see and feel and hear that it's already happened a
Speaker 1: tiny little while ago. Whatever conscious movie you're seeing right now, now,
Speaker 1: that world is already gone.
Speaker 2: Now.
Speaker 1: We don't often think about this, but this delay from reality,
Speaker 1: the fact that we're living in the past, is a
Speaker 1: major problem because you need to operate in the present,
Speaker 1: but your brain is always working with old news. All
Speaker 1: your sensory inputs like vision and hearing and touch, these
Speaker 1: take time to travel to the brain to get processed,
Speaker 1: and finally the brain croaks out a response. And even
Speaker 1: though this delay is less than a second, that's plenty
Speaker 1: of time to create issues. So just think about trying
Speaker 1: to catch a baseball that someone throws to you. If
Speaker 1: you were merely an assembly line device, you couldn't do it.
Speaker 1: Why because there would be a delay of hundreds of
Speaker 1: milliseconds from the time the light strikes your eyes until
Speaker 1: you could put up your glove in the right spot.
Speaker 1: And the problem is that by the time the image
Speaker 1: of the ball reaches your brain and gets processed, the
Speaker 1: ball has moved. Your hand would always be reaching for
Speaker 1: a place where the ball used to be.
Speaker 2: So how do you catch a baseball?
Speaker 1: It's because of these deeply hardwired internal models. Your internal
Speaker 1: model generates expectations about when and where the ball's going
Speaker 1: to hit, given momentum and gravity and so on. Your
Speaker 1: brain is not just passively processing information. It's predicting. It's
Speaker 1: not reactive, it's.
Speaker 2: Constantly guessing ahead.
Speaker 1: It predicts where the ball is going to be based
Speaker 1: on clues about its trajectory and speed, and that's what
Speaker 1: allows you to catch it. By the way, as a
Speaker 1: side note, these predictive internal models you have are trained
Speaker 1: up by lifelong exposure in your normal experience. If your
Speaker 1: great grandkids grow up on Mars, their internal models will
Speaker 1: get trained up with different parameters of physics, and they'll
Speaker 1: put up their glove at a different time the moment
Speaker 1: that's right for a Martian pop fly. Okay, But the
Speaker 1: critical point I want to make here is that we
Speaker 1: have these predictive internal models, and these things tell us
Speaker 1: from experience how things are likely to move in the world.
Speaker 1: And this way our brains don't work solely from the
Speaker 1: latest sensory information, but instead they construct predictions about where
Speaker 1: the ball is going to be. The same idea is
Speaker 1: in play when you're walking through a busy airport, when
Speaker 1: you have a flow of people moving in all directions
Speaker 1: around you. If you had access to only outdated information
Speaker 1: from photons a few hundred milliseconds ago, you'd be constantly
Speaker 1: crashing into people.
Speaker 2: But you don't. Your brain solves this.
Speaker 1: Your brain is constantly forecasting where the people are going
Speaker 1: to be based on their speed and direction, and that's
Speaker 1: what allows you to smoothly navigate without crashing despite the
Speaker 1: neural the and processing the visual information. So I want
Speaker 1: to summarize where we are so far. The foundation we're
Speaker 1: establishing here is that the brain is not just reacting
Speaker 1: to the world. Instead, it's a machine that continuously makes
Speaker 1: educated guesses. Prediction is how we compensate for our signal
Speaker 1: processing delays, and from an evolutionary standpoint, this ability to
Speaker 1: predict was absolutely critical for survival because animals who wanted
Speaker 1: any chance of living how to anticipate the movements of
Speaker 1: predators or prey to react quickly enough. You have to
Speaker 1: somehow operate in real time if you want to evade
Speaker 1: a thread or catch the running animal. So whenever you
Speaker 1: are next catching a ball or moving through the airport,
Speaker 1: think about how much you rely on your brain's predictive
Speaker 1: abilities to act without having to wait for all the
Speaker 1: signals to dribble their way in there. Okay, now we're
Speaker 1: finally ready to return to the issue that I started with.
Speaker 1: The illusions where you have some lines that are straight
Speaker 1: but they look bent. These fall into the category of
Speaker 1: geometric illusions. So what in the world do they have
Speaker 1: to do with what we've been talking about so far. Well,
Speaker 1: what I told you is that the visual system has
Speaker 1: developed these predictive mechanisms to deal with the signal delays
Speaker 1: so that it can see something at this moment in
Speaker 1: time and make a really good guess where that thing
Speaker 1: is going to be in say, one hundred milliseconds. So
Speaker 1: some of my colleagues proposed a framework called perceiving the present,
Speaker 1: and the idea is that your brain sees what is
Speaker 1: likely to be the case, rather than to perceive the
Speaker 1: recent past. So the first examples of this framework came
Speaker 1: out in the early nineteen nineties. So imagine you're looking
Speaker 1: at a small horizontal line on a computer screen and
Speaker 1: you're trying to judge its exact position, but there's a
Speaker 1: field of dots drifting continuously in the upward direction behind
Speaker 1: that line. In this case, you'll judge the line to
Speaker 1: be higher up on the screen. This is called motion capture.
Speaker 1: So by the beginning of the two thousands, my colleague
Speaker 1: Mark Chengizi started proposing that the explanation for this motion
Speaker 1: capture was the perceiving the present framework, which is that
Speaker 1: your brain sees the line, and it sees the motion
Speaker 1: and decides that in the next moment the line is
Speaker 1: probably going to be pushed up by the motion. So
Speaker 1: it's actually perceiving the line in a different place where
Speaker 1: it expects the line to be in the next moment.
Speaker 1: And besides that, he argued, he could explain the classical
Speaker 1: geometric illusion. What are these classical geometric illusions. Well, let's
Speaker 1: take what's known as the Herring illusion. You almost certainly
Speaker 1: saw this as a kid. There are a bunch of
Speaker 1: lines coming out of the center, like the spokes on
Speaker 1: a bicycle wheel. Okay, now you put two parallel lines
Speaker 1: on that bicycle wheel, let's say, a vertical line to
Speaker 1: the right of center and one to the left. You
Speaker 1: could do this by taping two pencils on the bicycle spokes.
Speaker 1: Now here's the illusion to two pencils. Although they are straight,
Speaker 1: they don't look that way anymore. Instead, it looks like
Speaker 1: the pencils are curving, they look slightly bent. Their middles
Speaker 1: are bowing outwards slightly. So this is an illusion that
Speaker 1: was first described by the physiologist Ewald Herring in eighteen
Speaker 1: sixty one.
Speaker 2: But why in the world does it happen?
Speaker 1: Well, Herring proposed that this has to do with the
Speaker 1: brain overestimating angles where the lines are meeting. And then
Speaker 1: other people proposed different things in the brain that might
Speaker 1: explain that angle overestimation. But Changhizi proposed a new explanation
Speaker 1: when which was quite stunning. He said, look, when you're
Speaker 1: looking at these radial lines, in other words, the lines
Speaker 1: like the bicycle spokes coming out from a central point,
Speaker 1: your brain might think that it's just looking at the
Speaker 1: convergence of lines to the vanishing point, like imagine you're
Speaker 1: looking straight ahead on a street in New York City
Speaker 1: and everything converges in the middle. But equally, he said,
Speaker 1: these lines are what a scene looks like to your
Speaker 1: visual system when you are moving forward. For example, imagine
Speaker 1: that you're driving down the road in New York City
Speaker 1: and up ahead on the left there's a hot dog
Speaker 1: stand and that zips by you on your left side,
Speaker 1: and at the same time, there's a street juggler on
Speaker 1: your right.
Speaker 2: Side, and he gets bigger and he zips by you
Speaker 2: on that.
Speaker 1: Side, and there's an overhead street sign that at a
Speaker 1: distance starts essentially in the middle in front of you,
Speaker 1: but as you get closer and closer, it moves over
Speaker 1: your head. So everything is streaking past you like radial lines,
Speaker 1: and this is known as optic flow. So one place
Speaker 1: you've seen this before is on Star Trek, where they
Speaker 1: yank down the lever and put the ship into warp
Speaker 1: drive and all the stars suddenly shoot past them, all
Speaker 1: moving away from the center, like the radial spokes of
Speaker 1: the bicycle wheel. So Tanghizi said, when you see radial
Speaker 1: lines like that, it's typically a visual signature of you
Speaker 1: moving forward towards the vanishing point. And certainly when you're
Speaker 1: moving fast, there's a radial smear, like the way that
Speaker 1: the stars and Star Trek smear into lines. And he said, look,
Speaker 1: Herring's radial lines essentially mimic this. It's like you are
Speaker 1: in the spaceship moving directly ahead. Now here's the key.
Speaker 1: Let's come back to the de lays in the visual
Speaker 1: system and how they can be accounted for by the
Speaker 1: brain making projections where things are about to be. Imagine
Speaker 1: that you're in New York City and driving and there
Speaker 1: are two skyscrapers up ahead of you, one on the
Speaker 1: left and one on the right. Now, as you race
Speaker 1: forward in your car, those two buildings will loom closer.
Speaker 1: But now something interesting is happening. The parts of the
Speaker 1: buildings closer to you will seem farther apart, because if
Speaker 1: you look up, the tips of the buildings are coming
Speaker 1: closer together, way up in the sky.
Speaker 2: So the point is that.
Speaker 1: Even though you see essentially straight skyscrapers when they're at
Speaker 1: a distance, as you approach, they are bending away from you.
Speaker 1: Their centers are bowing out. And Shannghizi's idea was that
Speaker 1: when you look at the radial lines the bicycle spokes,
Speaker 1: your brain thinks this might be a clue that I'm
Speaker 1: moving forward, and I don't want there to be delays
Speaker 1: in my perception, so I'm going to see the world
Speaker 1: as it will be a moment later. And so you
Speaker 1: see the two parallel lines bowed outward from the center.
Speaker 1: In other words, when you look at the radio lines
Speaker 1: on the piece of paper, even though nothing is moving.
Speaker 1: Your brain thinks this is what movement looks like, and
Speaker 1: so it predicts the next moment, and that's what you perceived.
Speaker 1: In other words, you perceive the lines just as they
Speaker 1: would project in the next moment if you were moving
Speaker 1: forward toward the vanishing point. So, as Changhesi wrote in
Speaker 1: this paper, evolution has seen to it that geometric drawings
Speaker 1: like this elicit in us premonitions of the near future. Okay,
Speaker 1: so the framework by Changizi and colleagues suggests that several
Speaker 1: geometric illusions are caused by temporal delays with which the
Speaker 1: visual system must cope. The idea is that the visual
Speaker 1: system extrapolates its current information to perceive the present. Instead
Speaker 1: of providing a conscious image of how the world was
Speaker 1: a few hundred milliseconds ago when the signals first struck
Speaker 1: the retina, the visual system estimates how the world is
Speaker 1: likely to look in the next moment. But how would
Speaker 1: we get at clues to the possible neural basis? In
Speaker 1: other words, how does the brain actually pull this off?
Speaker 1: So in my laboratory we wanted to figure this out.
Speaker 1: So my student Don Vaughan and I had people look
Speaker 1: at the herring illusion on a screen. You've got a
Speaker 1: background of radial lines like the bicycle spokes, and we
Speaker 1: flashed two vertical lines on top of this. And I'll
Speaker 1: just take a quick second to give you a sense
Speaker 1: of how we quantify illusions. In the laboratory, A person
Speaker 1: sits in front of the computer and they use let's
Speaker 1: say the right and left arrows on the keyboard to
Speaker 1: change the curvature of those two lines. So at one
Speaker 1: end of the range, they're actually physically bending the lines outward,
Speaker 1: and at the other extreme they're bending them inward, and
Speaker 1: somewhere in the middle they're physically straight. And what the
Speaker 1: person does is adjust the curvature of the line until
Speaker 1: it looks straight to them. But of course with the
Speaker 1: hairing illusion, you need to actually adjust the lines so
Speaker 1: the middles are curving inward in order for it to
Speaker 1: look straight. In other words, we see how much curvature
Speaker 1: it takes to cancel out the illusion, and that's the
Speaker 1: way we can quantify the size of the illusion. Okay,
Speaker 1: so we measure the heiring illusion, and no surprises there.
Speaker 1: But now what we do is we replaced the radio
Speaker 1: lines with an actual star field. We have dots moving
Speaker 1: in an expanding pattern like the stars and Star Trek,
Speaker 1: And now people are judging the size of the hering
Speaker 1: illusion against the background of expanding dots.
Speaker 2: And what we find is that.
Speaker 1: The illusion still happens. The lines still appear bent, which
Speaker 1: is just what you'd expect from the perceiving the present hypothesis. Okay,
Speaker 1: but here's the really wacky part, the part that uncovers
Speaker 1: an unexpected secret in the brain. We now measure the
Speaker 1: size of the hering illusion over a field of contracting dots.
Speaker 1: So picture that Star Trek footage running backwards. Now everything
Speaker 1: is moving from the outside to the inside.
Speaker 2: And here's the surprise.
Speaker 1: We find that the size of the herring illusion was
Speaker 1: exactly the same here. In other words, the lines still
Speaker 1: curve outward, just like in the other two cases.
Speaker 2: So what does that mean. We can get this.
Speaker 1: Illusion by having radial lines or dots expanding or dots contracting,
Speaker 1: and you find that the bars bend out in the
Speaker 1: same direction. Now, at first glance, the bending of the
Speaker 1: bars during contracting motion would seem to refute the perceiving
Speaker 1: the present framework. If your brain is doing an active
Speaker 1: of temporal extrapolation of the scene, it should make the
Speaker 1: bars bend in the other direction. But the key thing
Speaker 1: to note is that backward motion is ecologically quite rare.
Speaker 1: Unless you're a dog looking out the back window of
Speaker 1: a car. Most animals probably never see backward optic flow
Speaker 1: in their lives. Okay, so we did a lot of
Speaker 1: other experiments in this paper, but just this first result
Speaker 1: that the Herring illusion happens the same with expanding or
Speaker 1: contracting optic flow tells us some critical things. First, it
Speaker 1: tells us that this spatial warping we see isn't a
Speaker 1: sophisticated online computation. Instead, it's a basic mechanism that just
Speaker 1: acts to get it right in the most common scenario
Speaker 1: of forward motion. And because backward motion essentially never happens,
Speaker 1: it doesn't matter that the mechanism is so unrefined, and
Speaker 1: it gives us a big clue about the the underlying
Speaker 1: neural mechanisms. The most parsimonious explanation. In other words, the
Speaker 1: simplest idea would look for something in the brain that's
Speaker 1: equally sensitive to lines like the bicycle spoke and also
Speaker 1: to motion in either direction along that line. And it
Speaker 1: turns out there are very simple neurons in primary visual
Speaker 1: cortex that do exactly this. They're called orientation selective neurons,
Speaker 1: and they respond to lines, and they also respond to
Speaker 1: dots moving along the same trajectory as the line in
Speaker 1: either direction. So what this means is that our findings
Speaker 1: are consistent with the perceiving the present hypothesis, with the
Speaker 1: caveat that the spatial warping to counteract neural delays is
Speaker 1: not a smart active neural process, but instead it's just
Speaker 1: a simple mechanism that succeeds only under forward moving circumstances,
Speaker 1: which is most of the time. Now, as I said,
Speaker 1: we did a lot of other experiments, and I'm gonna
Speaker 1: skip the detail because they're not as important as this
Speaker 1: main point. But all the other experiments supported this hypothesis
Speaker 1: that the bars look bent because the brain is extrapolating
Speaker 1: ahead what things would look like in the next moment.
Speaker 1: And by the way, our findings weren't consistent with older
Speaker 1: theories like angle over estimation, as Herring had suggested. So
Speaker 1: if you want to dive deeper into the paper, I'm
Speaker 1: linking it to the show notes. So what we saw
Speaker 1: today is the issue that it always takes time for
Speaker 1: signals to move through the brain. And although people got
Speaker 1: excited when they discovered that the signals were carried by electricity,
Speaker 1: it's nothing like the way that electricity runs on wires,
Speaker 1: which is close to the speed of light. In the brain,
Speaker 1: we're talking hundreds of millions of times slower than that.
Speaker 1: So you have these signals limping along in the brain,
Speaker 1: and that means we are always living in the past.
Speaker 1: Our brains sometimes see fake news about the world out there,
Speaker 1: but we always see old news. Your brain is always
Speaker 1: getting information about an outdated version of the world, one
Speaker 1: that no longer exists. And so one of the many
Speaker 1: incredible things your brain does is make predictions so that
Speaker 1: you can perceive things as they probably are right now,
Speaker 1: rather than perceiving the stale version of the data, which
Speaker 1: is much less useful by the time you see it.
Speaker 1: This is the brain's very bold move to compensate for
Speaker 1: its own delays, and that wacky fact explains a lot
Speaker 1: about how we catch balls and walk through crowded airports,
Speaker 1: but it also seems to explain surprisingly why we see
Speaker 1: this basic geometric illusion of straight lines not looking straight,
Speaker 1: discovered by Heiring almost one hundred and sixty five years ago. Now,
Speaker 1: I wrote an article in Nature Reviews Neuroscience in which
Speaker 1: I covered all kinds of visual illusions, and that's in
Speaker 1: the show notes. And each one of these illusions opens
Speaker 1: a new mind shaft into the brain, teaching us why
Speaker 1: it's happening. And once we understand that, we can usually
Speaker 1: construct new illusions, which is why amazing new illusions are
Speaker 1: coming out each year.
Speaker 2: Now.
Speaker 1: Sometimes people see all these illusions on their social media
Speaker 1: feeds and they might become tempted to say that everything
Speaker 1: is an illusion. We don't accurately see what's really out there.
Speaker 1: But that's probably not the right conclusion, because often we
Speaker 1: do see what's out there accurately, as verified by our
Speaker 1: other senses and by our objective machines. For example, if
Speaker 1: I put a cup of steaming coffee on the table
Speaker 1: in front of you, you see that, and you heard
Speaker 1: the clunk when I set it down, and you can
Speaker 1: verify the heat with your fingers and smell the coffee
Speaker 1: and pick it up and taste it, and you can
Speaker 1: measure the presence of the cup with video, or the
Speaker 1: heat with an infrared camera, and the coffee with spectroscopy
Speaker 1: and so on. So it's not that everything you see
Speaker 1: is illusory. Instead, we see what is maximally useful to us.
Speaker 1: And every once in a while we can throw an
Speaker 1: unexpected wrench in the system by showing the brain something
Speaker 1: that happens to tickle the right receptors. And in those
Speaker 1: very special cases we can catch the system coming to
Speaker 1: the wrong conclusion. But it's precisely because the brain has
Speaker 1: evolved to do the optimal thing in all the other cases.
Speaker 1: And traditionally, when we find these special cases, we just
Speaker 1: laugh and enjoy and keep scrolling through our feed But
Speaker 1: the endeavor of figuring ourselves out encourages us to pause,
Speaker 1: to ask why, to dig deeper, and although these things
Speaker 1: sometimes take centuries to answer, they typically yield deep insights
Speaker 1: into who we are and what is actually going on.
Speaker 1: Go to Eagleman dot com slash podcast for more information
Speaker 1: and to find further reading. Send me an email at
Speaker 1: podcasts at eagleman dot com with questions or discussion, and
Speaker 1: check out and subscribe to Inner Cosmos on YouTube for
Speaker 1: videos of each episode and to leave comments until next time.
Speaker 1: I'm David Eagleman, and this is Inner Cosmos.