January 13, 2025 • 34 mins
Because visual signals take time to process, we live slightly in the past. So how do we ever catch a baseball? And what does this have to do with certain visual illusions, or the view in New York City, or the things you were never taught in school, or the warp drive in Star Trek? Join Eagleman this week for a mind blowing look at the strange relationship between vision and time.
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Episode Transcript
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Speaker 1 (00:05):
Have you ever seen those pictures of blobs on a
page and it doesn't look like anything to you until
you're told what it is, and then you suddenly see it.
Why does that give us a great clue about the
wiring of the brain. And why are neuroscientists so magnetically
attracted to those visual illusions that you scroll through on
(00:27):
social media? What is the deep trick about the way
your visual system works that you were never taught in school?
And what does any of this have to do with
catching a baseball or zooming down the road in New
York City or the warp drive in Star Trek. Welcome
to Inner Cosmos with me David Eagleman. I'm a neuroscientist
(00:50):
and author at Stanford and in these episodes we sail
deeply into our three pound universe to understand why and
how our lives look the way they do.
Speaker 2 (01:09):
Today.
Speaker 1 (01:09):
I'm going to start with the notion of visual illusions.
Elementary school students love these and they stare at them
for about a minute and then they're on to the
next thing, because why not. The illusion is just an
interesting trick. There's nothing further to do about it. It's
only later when you grow up to be a neuroscientist
(01:30):
or a fan of a neuroscience podcast, that you might
even return to one of these illusions to ask, wait,
why exactly does that happen?
Speaker 2 (01:41):
Does that tell us.
Speaker 1 (01:42):
Something deep and fundamental about the way my consciousness constructs
the world for me?
Speaker 2 (01:50):
What does it reveal? So?
Speaker 1 (01:53):
Have you ever seen the illusion where you're looking at
lines like bicycle spokes, and then there's some straight lines
drawn on top of that, and they don't look straight,
they look bent. Why does that happen? Seems like it
shouldn't be hard to answer, but it's actually taken well
over a century to figure this out, and the answer
is gonna blow your mind. I promise you that. But
(02:17):
in order to get us there, I'm gonna start with
something completely different. I'm gonna start with those pictures that
look like just a bunch of blobs. Probably you've seen
one of these before. There's just a bunch of random
looking splotches of black and white on a page. If
your brain doesn't have a prior expectation about what it's seeing,
(02:40):
about what the blobs mean, then you simply see black
and white blobs and there's no particular meaning to the picture.
I'm gonna link an example of this on the show
notes at eagleman dot com slash podcast, and I want
you to stare at it for a few moments and
then scroll down to the very bottom of the page
for the hint. What you'll see is that you can't
(03:01):
make heads or tails of these blobs. But then I
only change one thing, and it has nothing to do
with what's on the screen. I give you a hint,
and as soon as you have a notion about how
to interpret what is on your retinas, then you say,
oh yeah, I see it now. Now the exact same
(03:21):
blobs that confused you a moment ago make perfect sense.
But again, nothing changed out there in the world. The
only thing that changed is something in your neural networks.
So what's the lesson from this? There has to be
a match between incoming data and your expectations for you
(03:43):
to see anything. But wait, what, That's not how vision
is supposed to work, is it. I mean, after all,
you look at any basic biology textbook and it will
tell you that photons hit the retina and the information
is carried on back to the visual cortex, and then
you just see what's out there. The visual cortex is
(04:04):
like a television screen, So what's going on?
Speaker 2 (04:08):
Why can't you see.
Speaker 1 (04:09):
The image in the blobs until you've got the right expectation.
Speaker 2 (04:14):
This ties into.
Speaker 1 (04:15):
A concept that you hear me refer to all the
time on this podcast, and that is the concept of
the internal model. Remember that your brain is isolated in
soundless and lightless solitude inside your skull, and its single
mission is to construct a loud, colorful mental model of
(04:37):
the outside world. In other words, it builds an inner
reality that tries to accurately reflect the outside. The key
is that you don't see by capturing television pixels from
the world. Instead, all you ever see is your internal model,
(04:57):
and your internal model only perceives some thing when its
expectations are sufficiently supported by the sensory data coming in. Now,
this isn't really a widely known idea. I think you'll
find if you ask people about it on airplanes, as
I often do. This isn't really the way that most
(05:18):
people are used to thinking about the brain. So it's
a bit surprising that the basic conceptualization of this idea
is almost seventy years old. One of the earliest examples
of this framework that I know of came from the
neuroscientist Donald McKay, who in nineteen fifty six said, Look,
the job of the visual cortex is to construct an
(05:39):
internal model, and it's always trying to anticipate the data
coming up from the retina. Now, just as a reminder,
the retina is the part of the back of your
eye that captures light, and the visual cortex is all
the way at the back of your head, on the
far side of the brain. But here's the surprise. The
information doesn't just shoot from the retina to the visual cortex. Instead,
(06:01):
there's a train station in between, a structure called the thalamus.
The thalamus sits right in the middle. So information doesn't
go straight from the eye to the visual cortex, but
instead it makes a stop and changes trains halfway at
the thalamus.
Speaker 2 (06:16):
Okay, well that's weird. Why is there the setup?
Speaker 1 (06:19):
Well, to understand this, we need to understand that the
model of vision in introductory textbooks isn't just misleading, it's
dead wrong. The brain isn't built on straight lines, it's
built with loops. So what McKay suggested is that the
retina sends its data to the thalamus. In other words,
(06:40):
what the eye is capturing about the world out there,
and the cortex sends its predictions to the thalamus what
the cortex is expecting to see next, and all that
ever comes out of the thalamus back to the cortex
is the difference, the difference between what you expected and
what you got. In other words, the information that goes
(07:02):
from the thalamis to the visual cortex is just that
little bit which was unanticipated, the difference between what's out
there and what was already expected. The thalamus sends to
the cortex only that difference signal, because that's the only
part that wasn't predicted away. And then, by the way,
(07:23):
this unpredicted information adjusts the internal model so there will
be less of a mismatch in the future. That way,
the brain refines its model of the world by paying
attention to its mistakes. Okay, so the idea here is
that the brain is always trying to anticipate what it's
seeing out there, and McKay pointed out that this is
(07:45):
consistent with the anatomical fact that there are ten times
as many fibers projecting from the visual cortex back to
the thalamis as there are going from thalamis to visual cortex,
which no one would have guessed. But that's just what
you'd expect if detailed predictions are going from the cortex
(08:07):
to the thalamis, and the little signal from thalamis back
to cortex is just carrying the difference.
Speaker 2 (08:15):
Between what was expected and what was seen.
Speaker 1 (08:18):
Okay, So why am I telling you this level of
detail because it exposes a giant idea. It means that
what you perceive about the world emerges from an active
comparison of sensory data with your internal predictions. Again, think
about those blobs. If you don't have a prediction of
(08:38):
what you're seeing out there, there's really nothing there. As
soon as you have a close enough expectation because you've
been given a hint, then that lights up a forest
fire in your brain and you see the thing because
there's a match.
Speaker 2 (08:54):
Now. So what this.
Speaker 1 (08:56):
Means is that the brain is always trying to predict
everything that is coming or expected. And here's one way
that the brain helps itself along. Whenever it sends out
a signal to your body, like move your head or
move your arm, it also sends copies of that command
internally all around the brain. These are called efference copies.
(09:20):
So now your movement isn't just happening in the outside
world and then you react to it, but there's also
a simulation of that movement happening inside your internal model,
so that you can predict the outcome of that action.
And this, by the way, is the reason you can't
tickle yourself. Other people can tickle you because they're tickling
(09:45):
maneuvers are not predictable to you. But you can't tickle
you because your brain moves your fingers into the tickle
position and it already expects the resulting sensations, that already
knows what's come. Now, by the way, if you'd really
like to tickle yourself, there is a way to do it,
(10:05):
and this just involves taking predictability away from your own actions.
So what you do is you control the position of
a feather with a joystick that inserts a random time delay,
so when you move the joystick, at least a second
passes before the feather moves accordingly, so that takes away
(10:29):
the predictability and now you can self tickle. By the way,
related to this, I described in episode forty four how
people with schizophrenia can tickle themselves, and this is because
of a problem with their internal timing that doesn't allow
their motor actions and resulting sensations to be correctly sequenced. Okay,
(11:08):
so back to this issue about having a brain that's
not just moving signals down a one way assembly line,
but instead has all these internal loops so that it
can always be feeding its internal model and guessing what's
going to happen next. What is the advantage of this, Well,
(11:29):
it allows us to transcend stimulus response behavior. In other words,
we don't have to just observe the world and then
react to it. Instead, a brain with an internal model
gives us the ability to make predictions ahead of actual
sensory input, like predicting what your fingers will feel like
(11:52):
in your underarm. So our brains build these predictive internal
models that tell us how things are likely to go
in the world. And this way our brains don't work
solely from the latest sensory data, but instead they're always
guessing ahead to the next moment. Now, why do we
(12:13):
need a complicated brain like this because our perception is
massively delayed from reality. Why is it delayed because signals
from the world, like something you see or a touch
on your toe. These signals have to travel along nerve cells,
and they move about a meter per second in the cortex,
(12:36):
which is, by the way, about three hundred million times
slower than electricity moving through your laptop. We are giant
systems of cells, and it takes time for impulses and
cells to travel around. Yes, they use electricity, but it's
not like a signal propagating along a wire. Instead, with
(12:59):
a cell, you've got these long extensions called axons, and
the signals travel by causing little channels to open in
the membrane, which allows little charged particles to flow through
and change the voltage locally, and that propagates down the axon.
So this is a very cool way that mother nature
discovered how to run a signal down a cell. But
(13:22):
it ain't fast, and the consequence is that it just
takes a long time for signals to propagate through the
system and eventually come together and settle into a coherent pattern.
So by the time you become consciously aware of something
in the outside world, the event has already happened a
(13:44):
while ago. We live in the past. For example, clap
your hands in front of you. By the time you
see and feel and hear that it's already happened a
tiny little while ago. Whatever conscious movie you're seeing right now, now,
that world is already gone.
Speaker 2 (14:03):
Now.
Speaker 1 (14:03):
We don't often think about this, but this delay from reality,
the fact that we're living in the past, is a
major problem because you need to operate in the present,
but your brain is always working with old news. All
your sensory inputs like vision and hearing and touch, these
take time to travel to the brain to get processed,
(14:27):
and finally the brain croaks out a response. And even
though this delay is less than a second, that's plenty
of time to create issues. So just think about trying
to catch a baseball that someone throws to you. If
you were merely an assembly line device, you couldn't do it.
Why because there would be a delay of hundreds of
(14:48):
milliseconds from the time the light strikes your eyes until
you could put up your glove in the right spot.
And the problem is that by the time the image
of the ball reaches your brain and gets processed, the
ball has moved. Your hand would always be reaching for
a place where the ball used to be.
Speaker 2 (15:08):
So how do you catch a baseball?
Speaker 1 (15:09):
It's because of these deeply hardwired internal models. Your internal
model generates expectations about when and where the ball's going
to hit, given momentum and gravity and so on. Your
brain is not just passively processing information. It's predicting. It's
not reactive, it's.
Speaker 2 (15:31):
Constantly guessing ahead.
Speaker 1 (15:33):
It predicts where the ball is going to be based
on clues about its trajectory and speed, and that's what
allows you to catch it. By the way, as a
side note, these predictive internal models you have are trained
up by lifelong exposure in your normal experience. If your
great grandkids grow up on Mars, their internal models will
(15:57):
get trained up with different parameters of physics, and they'll
put up their glove at a different time the moment
that's right for a Martian pop fly. Okay, But the
critical point I want to make here is that we
have these predictive internal models, and these things tell us
from experience how things are likely to move in the world.
(16:19):
And this way our brains don't work solely from the
latest sensory information, but instead they construct predictions about where
the ball is going to be. The same idea is
in play when you're walking through a busy airport, when
you have a flow of people moving in all directions
around you. If you had access to only outdated information
(16:39):
from photons a few hundred milliseconds ago, you'd be constantly
crashing into people.
Speaker 2 (16:44):
But you don't. Your brain solves this.
Speaker 1 (16:47):
Your brain is constantly forecasting where the people are going
to be based on their speed and direction, and that's
what allows you to smoothly navigate without crashing despite the
neural the and processing the visual information. So I want
to summarize where we are so far. The foundation we're
establishing here is that the brain is not just reacting
(17:10):
to the world. Instead, it's a machine that continuously makes
educated guesses. Prediction is how we compensate for our signal
processing delays, and from an evolutionary standpoint, this ability to
predict was absolutely critical for survival because animals who wanted
any chance of living how to anticipate the movements of
(17:32):
predators or prey to react quickly enough. You have to
somehow operate in real time if you want to evade
a thread or catch the running animal. So whenever you
are next catching a ball or moving through the airport,
think about how much you rely on your brain's predictive
abilities to act without having to wait for all the
(17:55):
signals to dribble their way in there. Okay, now we're
finally ready to return to the issue that I started with.
The illusions where you have some lines that are straight
but they look bent. These fall into the category of
geometric illusions. So what in the world do they have
to do with what we've been talking about so far. Well,
(18:17):
what I told you is that the visual system has
developed these predictive mechanisms to deal with the signal delays
so that it can see something at this moment in
time and make a really good guess where that thing
is going to be in say, one hundred milliseconds. So
some of my colleagues proposed a framework called perceiving the present,
(18:38):
and the idea is that your brain sees what is
likely to be the case, rather than to perceive the
recent past. So the first examples of this framework came
out in the early nineteen nineties. So imagine you're looking
at a small horizontal line on a computer screen and
you're trying to judge its exact position, but there's a
(18:59):
field of dots drifting continuously in the upward direction behind
that line. In this case, you'll judge the line to
be higher up on the screen. This is called motion capture.
So by the beginning of the two thousands, my colleague
Mark Chengizi started proposing that the explanation for this motion
capture was the perceiving the present framework, which is that
(19:23):
your brain sees the line, and it sees the motion
and decides that in the next moment the line is
probably going to be pushed up by the motion. So
it's actually perceiving the line in a different place where
it expects the line to be in the next moment.
And besides that, he argued, he could explain the classical
(19:47):
geometric illusion. What are these classical geometric illusions. Well, let's
(20:08):
take what's known as the Herring illusion. You almost certainly
saw this as a kid. There are a bunch of
lines coming out of the center, like the spokes on
a bicycle wheel. Okay, now you put two parallel lines
on that bicycle wheel, let's say, a vertical line to
the right of center and one to the left. You
could do this by taping two pencils on the bicycle spokes.
(20:32):
Now here's the illusion to two pencils. Although they are straight,
they don't look that way anymore. Instead, it looks like
the pencils are curving, they look slightly bent. Their middles
are bowing outwards slightly. So this is an illusion that
was first described by the physiologist Ewald Herring in eighteen
sixty one.
Speaker 2 (20:53):
But why in the world does it happen?
Speaker 1 (20:55):
Well, Herring proposed that this has to do with the
brain overestimating angles where the lines are meeting. And then
other people proposed different things in the brain that might
explain that angle overestimation. But Changhizi proposed a new explanation
when which was quite stunning. He said, look, when you're
looking at these radial lines, in other words, the lines
(21:17):
like the bicycle spokes coming out from a central point,
your brain might think that it's just looking at the
convergence of lines to the vanishing point, like imagine you're
looking straight ahead on a street in New York City
and everything converges in the middle. But equally, he said,
these lines are what a scene looks like to your
(21:38):
visual system when you are moving forward. For example, imagine
that you're driving down the road in New York City
and up ahead on the left there's a hot dog
stand and that zips by you on your left side,
and at the same time, there's a street juggler on
your right.
Speaker 2 (21:54):
Side, and he gets bigger and he zips by you
on that.
Speaker 1 (21:56):
Side, and there's an overhead street sign that at a
distance starts essentially in the middle in front of you,
but as you get closer and closer, it moves over
your head. So everything is streaking past you like radial lines,
and this is known as optic flow. So one place
you've seen this before is on Star Trek, where they
(22:19):
yank down the lever and put the ship into warp
drive and all the stars suddenly shoot past them, all
moving away from the center, like the radial spokes of
the bicycle wheel. So Tanghizi said, when you see radial
lines like that, it's typically a visual signature of you
moving forward towards the vanishing point. And certainly when you're
(22:41):
moving fast, there's a radial smear, like the way that
the stars and Star Trek smear into lines. And he said, look,
Herring's radial lines essentially mimic this. It's like you are
in the spaceship moving directly ahead. Now here's the key.
Let's come back to the de lays in the visual
system and how they can be accounted for by the
(23:04):
brain making projections where things are about to be. Imagine
that you're in New York City and driving and there
are two skyscrapers up ahead of you, one on the
left and one on the right. Now, as you race
forward in your car, those two buildings will loom closer.
But now something interesting is happening. The parts of the
(23:25):
buildings closer to you will seem farther apart, because if
you look up, the tips of the buildings are coming
closer together, way up in the sky.
Speaker 2 (23:35):
So the point is that.
Speaker 1 (23:36):
Even though you see essentially straight skyscrapers when they're at
a distance, as you approach, they are bending away from you.
Their centers are bowing out. And Shannghizi's idea was that
when you look at the radial lines the bicycle spokes,
your brain thinks this might be a clue that I'm
moving forward, and I don't want there to be delays
(23:59):
in my perception, so I'm going to see the world
as it will be a moment later. And so you
see the two parallel lines bowed outward from the center.
In other words, when you look at the radio lines
on the piece of paper, even though nothing is moving.
Your brain thinks this is what movement looks like, and
(24:20):
so it predicts the next moment, and that's what you perceived.
In other words, you perceive the lines just as they
would project in the next moment if you were moving
forward toward the vanishing point. So, as Changhesi wrote in
this paper, evolution has seen to it that geometric drawings
like this elicit in us premonitions of the near future. Okay,
(24:45):
so the framework by Changizi and colleagues suggests that several
geometric illusions are caused by temporal delays with which the
visual system must cope. The idea is that the visual
system extrapolates its current information to perceive the present. Instead
of providing a conscious image of how the world was
(25:06):
a few hundred milliseconds ago when the signals first struck
the retina, the visual system estimates how the world is
likely to look in the next moment. But how would
we get at clues to the possible neural basis? In
other words, how does the brain actually pull this off?
So in my laboratory we wanted to figure this out.
(25:26):
So my student Don Vaughan and I had people look
at the herring illusion on a screen. You've got a
background of radial lines like the bicycle spokes, and we
flashed two vertical lines on top of this. And I'll
just take a quick second to give you a sense
of how we quantify illusions. In the laboratory, A person
sits in front of the computer and they use let's
(25:48):
say the right and left arrows on the keyboard to
change the curvature of those two lines. So at one
end of the range, they're actually physically bending the lines outward,
and at the other extreme they're bending them inward, and
somewhere in the middle they're physically straight. And what the
person does is adjust the curvature of the line until
(26:08):
it looks straight to them. But of course with the
hairing illusion, you need to actually adjust the lines so
the middles are curving inward in order for it to
look straight. In other words, we see how much curvature
it takes to cancel out the illusion, and that's the
way we can quantify the size of the illusion. Okay,
(26:29):
so we measure the heiring illusion, and no surprises there.
But now what we do is we replaced the radio
lines with an actual star field. We have dots moving
in an expanding pattern like the stars and Star Trek,
And now people are judging the size of the hering
illusion against the background of expanding dots.
Speaker 2 (26:52):
And what we find is that.
Speaker 1 (26:54):
The illusion still happens. The lines still appear bent, which
is just what you'd expect from the perceiving the present hypothesis. Okay,
but here's the really wacky part, the part that uncovers
an unexpected secret in the brain. We now measure the
size of the hering illusion over a field of contracting dots.
(27:16):
So picture that Star Trek footage running backwards. Now everything
is moving from the outside to the inside.
Speaker 2 (27:22):
And here's the surprise.
Speaker 1 (27:23):
We find that the size of the herring illusion was
exactly the same here. In other words, the lines still
curve outward, just like in the other two cases.
Speaker 2 (27:34):
So what does that mean. We can get this.
Speaker 1 (27:35):
Illusion by having radial lines or dots expanding or dots contracting,
and you find that the bars bend out in the
same direction. Now, at first glance, the bending of the
bars during contracting motion would seem to refute the perceiving
(27:56):
the present framework. If your brain is doing an active
of temporal extrapolation of the scene, it should make the
bars bend in the other direction. But the key thing
to note is that backward motion is ecologically quite rare.
Unless you're a dog looking out the back window of
a car. Most animals probably never see backward optic flow
(28:21):
in their lives. Okay, so we did a lot of
other experiments in this paper, but just this first result
that the Herring illusion happens the same with expanding or
contracting optic flow tells us some critical things. First, it
tells us that this spatial warping we see isn't a
sophisticated online computation. Instead, it's a basic mechanism that just
(28:46):
acts to get it right in the most common scenario
of forward motion. And because backward motion essentially never happens,
it doesn't matter that the mechanism is so unrefined, and
it gives us a big clue about the the underlying
neural mechanisms. The most parsimonious explanation. In other words, the
simplest idea would look for something in the brain that's
(29:09):
equally sensitive to lines like the bicycle spoke and also
to motion in either direction along that line. And it
turns out there are very simple neurons in primary visual
cortex that do exactly this. They're called orientation selective neurons,
and they respond to lines, and they also respond to
dots moving along the same trajectory as the line in
(29:32):
either direction. So what this means is that our findings
are consistent with the perceiving the present hypothesis, with the
caveat that the spatial warping to counteract neural delays is
not a smart active neural process, but instead it's just
a simple mechanism that succeeds only under forward moving circumstances,
(29:54):
which is most of the time. Now, as I said,
we did a lot of other experiments, and I'm gonna
skip the detail because they're not as important as this
main point. But all the other experiments supported this hypothesis
that the bars look bent because the brain is extrapolating
ahead what things would look like in the next moment.
And by the way, our findings weren't consistent with older
(30:18):
theories like angle over estimation, as Herring had suggested. So
if you want to dive deeper into the paper, I'm
linking it to the show notes. So what we saw
today is the issue that it always takes time for
signals to move through the brain. And although people got
excited when they discovered that the signals were carried by electricity,
(30:39):
it's nothing like the way that electricity runs on wires,
which is close to the speed of light. In the brain,
we're talking hundreds of millions of times slower than that.
So you have these signals limping along in the brain,
and that means we are always living in the past.
Our brains sometimes see fake news about the world out there,
(31:01):
but we always see old news. Your brain is always
getting information about an outdated version of the world, one
that no longer exists. And so one of the many
incredible things your brain does is make predictions so that
you can perceive things as they probably are right now,
(31:22):
rather than perceiving the stale version of the data, which
is much less useful by the time you see it.
This is the brain's very bold move to compensate for
its own delays, and that wacky fact explains a lot
about how we catch balls and walk through crowded airports,
but it also seems to explain surprisingly why we see
(31:44):
this basic geometric illusion of straight lines not looking straight,
discovered by Heiring almost one hundred and sixty five years ago. Now,
I wrote an article in Nature Reviews Neuroscience in which
I covered all kinds of visual illusions, and that's in
the show notes. And each one of these illusions opens
a new mind shaft into the brain, teaching us why
(32:07):
it's happening. And once we understand that, we can usually
construct new illusions, which is why amazing new illusions are
coming out each year.
Speaker 2 (32:17):
Now.
Speaker 1 (32:17):
Sometimes people see all these illusions on their social media
feeds and they might become tempted to say that everything
is an illusion. We don't accurately see what's really out there.
But that's probably not the right conclusion, because often we
do see what's out there accurately, as verified by our
other senses and by our objective machines. For example, if
(32:40):
I put a cup of steaming coffee on the table
in front of you, you see that, and you heard
the clunk when I set it down, and you can
verify the heat with your fingers and smell the coffee
and pick it up and taste it, and you can
measure the presence of the cup with video, or the
heat with an infrared camera, and the coffee with spectroscopy
and so on. So it's not that everything you see
(33:02):
is illusory. Instead, we see what is maximally useful to us.
And every once in a while we can throw an
unexpected wrench in the system by showing the brain something
that happens to tickle the right receptors. And in those
very special cases we can catch the system coming to
(33:24):
the wrong conclusion. But it's precisely because the brain has
evolved to do the optimal thing in all the other cases.
And traditionally, when we find these special cases, we just
laugh and enjoy and keep scrolling through our feed But
the endeavor of figuring ourselves out encourages us to pause,
(33:47):
to ask why, to dig deeper, and although these things
sometimes take centuries to answer, they typically yield deep insights
into who we are and what is actually going on.
Go to Eagleman dot com slash podcast for more information
(34:10):
and to find further reading. Send me an email at
podcasts at eagleman dot com with questions or discussion, and
check out and subscribe to Inner Cosmos on YouTube for
videos of each episode and to leave comments until next time.
I'm David Eagleman, and this is Inner Cosmos.
Have you ever seen those pictures of blobs on a
page and it doesn't look like anything to you until
you're told what it is, and then you suddenly see it.
Why does that give us a great clue about the
wiring of the brain. And why are neuroscientists so magnetically
attracted to those visual illusions that you scroll through on
(00:27):
social media? What is the deep trick about the way
your visual system works that you were never taught in school?
And what does any of this have to do with
catching a baseball or zooming down the road in New
York City or the warp drive in Star Trek. Welcome
to Inner Cosmos with me David Eagleman. I'm a neuroscientist
(00:50):
and author at Stanford and in these episodes we sail
deeply into our three pound universe to understand why and
how our lives look the way they do.
Speaker 2 (01:09):
Today.
Speaker 1 (01:09):
I'm going to start with the notion of visual illusions.
Elementary school students love these and they stare at them
for about a minute and then they're on to the
next thing, because why not. The illusion is just an
interesting trick. There's nothing further to do about it. It's
only later when you grow up to be a neuroscientist
(01:30):
or a fan of a neuroscience podcast, that you might
even return to one of these illusions to ask, wait,
why exactly does that happen?
Speaker 2 (01:41):
Does that tell us.
Speaker 1 (01:42):
Something deep and fundamental about the way my consciousness constructs
the world for me?
Speaker 2 (01:50):
What does it reveal? So?
Speaker 1 (01:53):
Have you ever seen the illusion where you're looking at
lines like bicycle spokes, and then there's some straight lines
drawn on top of that, and they don't look straight,
they look bent. Why does that happen? Seems like it
shouldn't be hard to answer, but it's actually taken well
over a century to figure this out, and the answer
is gonna blow your mind. I promise you that. But
(02:17):
in order to get us there, I'm gonna start with
something completely different. I'm gonna start with those pictures that
look like just a bunch of blobs. Probably you've seen
one of these before. There's just a bunch of random
looking splotches of black and white on a page. If
your brain doesn't have a prior expectation about what it's seeing,
(02:40):
about what the blobs mean, then you simply see black
and white blobs and there's no particular meaning to the picture.
I'm gonna link an example of this on the show
notes at eagleman dot com slash podcast, and I want
you to stare at it for a few moments and
then scroll down to the very bottom of the page
for the hint. What you'll see is that you can't
(03:01):
make heads or tails of these blobs. But then I
only change one thing, and it has nothing to do
with what's on the screen. I give you a hint,
and as soon as you have a notion about how
to interpret what is on your retinas, then you say,
oh yeah, I see it now. Now the exact same
(03:21):
blobs that confused you a moment ago make perfect sense.
But again, nothing changed out there in the world. The
only thing that changed is something in your neural networks.
So what's the lesson from this? There has to be
a match between incoming data and your expectations for you
(03:43):
to see anything. But wait, what, That's not how vision
is supposed to work, is it. I mean, after all,
you look at any basic biology textbook and it will
tell you that photons hit the retina and the information
is carried on back to the visual cortex, and then
you just see what's out there. The visual cortex is
(04:04):
like a television screen, So what's going on?
Speaker 2 (04:08):
Why can't you see.
Speaker 1 (04:09):
The image in the blobs until you've got the right expectation.
Speaker 2 (04:14):
This ties into.
Speaker 1 (04:15):
A concept that you hear me refer to all the
time on this podcast, and that is the concept of
the internal model. Remember that your brain is isolated in
soundless and lightless solitude inside your skull, and its single
mission is to construct a loud, colorful mental model of
(04:37):
the outside world. In other words, it builds an inner
reality that tries to accurately reflect the outside. The key
is that you don't see by capturing television pixels from
the world. Instead, all you ever see is your internal model,
(04:57):
and your internal model only perceives some thing when its
expectations are sufficiently supported by the sensory data coming in. Now,
this isn't really a widely known idea. I think you'll
find if you ask people about it on airplanes, as
I often do. This isn't really the way that most
(05:18):
people are used to thinking about the brain. So it's
a bit surprising that the basic conceptualization of this idea
is almost seventy years old. One of the earliest examples
of this framework that I know of came from the
neuroscientist Donald McKay, who in nineteen fifty six said, Look,
the job of the visual cortex is to construct an
(05:39):
internal model, and it's always trying to anticipate the data
coming up from the retina. Now, just as a reminder,
the retina is the part of the back of your
eye that captures light, and the visual cortex is all
the way at the back of your head, on the
far side of the brain. But here's the surprise. The
information doesn't just shoot from the retina to the visual cortex. Instead,
(06:01):
there's a train station in between, a structure called the thalamus.
The thalamus sits right in the middle. So information doesn't
go straight from the eye to the visual cortex, but
instead it makes a stop and changes trains halfway at
the thalamus.
Speaker 2 (06:16):
Okay, well that's weird. Why is there the setup?
Speaker 1 (06:19):
Well, to understand this, we need to understand that the
model of vision in introductory textbooks isn't just misleading, it's
dead wrong. The brain isn't built on straight lines, it's
built with loops. So what McKay suggested is that the
retina sends its data to the thalamus. In other words,
(06:40):
what the eye is capturing about the world out there,
and the cortex sends its predictions to the thalamus what
the cortex is expecting to see next, and all that
ever comes out of the thalamus back to the cortex
is the difference, the difference between what you expected and
what you got. In other words, the information that goes
(07:02):
from the thalamis to the visual cortex is just that
little bit which was unanticipated, the difference between what's out
there and what was already expected. The thalamus sends to
the cortex only that difference signal, because that's the only
part that wasn't predicted away. And then, by the way,
(07:23):
this unpredicted information adjusts the internal model so there will
be less of a mismatch in the future. That way,
the brain refines its model of the world by paying
attention to its mistakes. Okay, so the idea here is
that the brain is always trying to anticipate what it's
seeing out there, and McKay pointed out that this is
(07:45):
consistent with the anatomical fact that there are ten times
as many fibers projecting from the visual cortex back to
the thalamis as there are going from thalamis to visual cortex,
which no one would have guessed. But that's just what
you'd expect if detailed predictions are going from the cortex
(08:07):
to the thalamis, and the little signal from thalamis back
to cortex is just carrying the difference.
Speaker 2 (08:15):
Between what was expected and what was seen.
Speaker 1 (08:18):
Okay, So why am I telling you this level of
detail because it exposes a giant idea. It means that
what you perceive about the world emerges from an active
comparison of sensory data with your internal predictions. Again, think
about those blobs. If you don't have a prediction of
(08:38):
what you're seeing out there, there's really nothing there. As
soon as you have a close enough expectation because you've
been given a hint, then that lights up a forest
fire in your brain and you see the thing because
there's a match.
Speaker 2 (08:54):
Now. So what this.
Speaker 1 (08:56):
Means is that the brain is always trying to predict
everything that is coming or expected. And here's one way
that the brain helps itself along. Whenever it sends out
a signal to your body, like move your head or
move your arm, it also sends copies of that command
internally all around the brain. These are called efference copies.
(09:20):
So now your movement isn't just happening in the outside
world and then you react to it, but there's also
a simulation of that movement happening inside your internal model,
so that you can predict the outcome of that action.
And this, by the way, is the reason you can't
tickle yourself. Other people can tickle you because they're tickling
(09:45):
maneuvers are not predictable to you. But you can't tickle
you because your brain moves your fingers into the tickle
position and it already expects the resulting sensations, that already
knows what's come. Now, by the way, if you'd really
like to tickle yourself, there is a way to do it,
(10:05):
and this just involves taking predictability away from your own actions.
So what you do is you control the position of
a feather with a joystick that inserts a random time delay,
so when you move the joystick, at least a second
passes before the feather moves accordingly, so that takes away
(10:29):
the predictability and now you can self tickle. By the way,
related to this, I described in episode forty four how
people with schizophrenia can tickle themselves, and this is because
of a problem with their internal timing that doesn't allow
their motor actions and resulting sensations to be correctly sequenced. Okay,
(11:08):
so back to this issue about having a brain that's
not just moving signals down a one way assembly line,
but instead has all these internal loops so that it
can always be feeding its internal model and guessing what's
going to happen next. What is the advantage of this, Well,
(11:29):
it allows us to transcend stimulus response behavior. In other words,
we don't have to just observe the world and then
react to it. Instead, a brain with an internal model
gives us the ability to make predictions ahead of actual
sensory input, like predicting what your fingers will feel like
(11:52):
in your underarm. So our brains build these predictive internal
models that tell us how things are likely to go
in the world. And this way our brains don't work
solely from the latest sensory data, but instead they're always
guessing ahead to the next moment. Now, why do we
(12:13):
need a complicated brain like this because our perception is
massively delayed from reality. Why is it delayed because signals
from the world, like something you see or a touch
on your toe. These signals have to travel along nerve cells,
and they move about a meter per second in the cortex,
(12:36):
which is, by the way, about three hundred million times
slower than electricity moving through your laptop. We are giant
systems of cells, and it takes time for impulses and
cells to travel around. Yes, they use electricity, but it's
not like a signal propagating along a wire. Instead, with
(12:59):
a cell, you've got these long extensions called axons, and
the signals travel by causing little channels to open in
the membrane, which allows little charged particles to flow through
and change the voltage locally, and that propagates down the axon.
So this is a very cool way that mother nature
discovered how to run a signal down a cell. But
(13:22):
it ain't fast, and the consequence is that it just
takes a long time for signals to propagate through the
system and eventually come together and settle into a coherent pattern.
So by the time you become consciously aware of something
in the outside world, the event has already happened a
(13:44):
while ago. We live in the past. For example, clap
your hands in front of you. By the time you
see and feel and hear that it's already happened a
tiny little while ago. Whatever conscious movie you're seeing right now, now,
that world is already gone.
Speaker 2 (14:03):
Now.
Speaker 1 (14:03):
We don't often think about this, but this delay from reality,
the fact that we're living in the past, is a
major problem because you need to operate in the present,
but your brain is always working with old news. All
your sensory inputs like vision and hearing and touch, these
take time to travel to the brain to get processed,
(14:27):
and finally the brain croaks out a response. And even
though this delay is less than a second, that's plenty
of time to create issues. So just think about trying
to catch a baseball that someone throws to you. If
you were merely an assembly line device, you couldn't do it.
Why because there would be a delay of hundreds of
(14:48):
milliseconds from the time the light strikes your eyes until
you could put up your glove in the right spot.
And the problem is that by the time the image
of the ball reaches your brain and gets processed, the
ball has moved. Your hand would always be reaching for
a place where the ball used to be.
Speaker 2 (15:08):
So how do you catch a baseball?
Speaker 1 (15:09):
It's because of these deeply hardwired internal models. Your internal
model generates expectations about when and where the ball's going
to hit, given momentum and gravity and so on. Your
brain is not just passively processing information. It's predicting. It's
not reactive, it's.
Speaker 2 (15:31):
Constantly guessing ahead.
Speaker 1 (15:33):
It predicts where the ball is going to be based
on clues about its trajectory and speed, and that's what
allows you to catch it. By the way, as a
side note, these predictive internal models you have are trained
up by lifelong exposure in your normal experience. If your
great grandkids grow up on Mars, their internal models will
(15:57):
get trained up with different parameters of physics, and they'll
put up their glove at a different time the moment
that's right for a Martian pop fly. Okay, But the
critical point I want to make here is that we
have these predictive internal models, and these things tell us
from experience how things are likely to move in the world.
(16:19):
And this way our brains don't work solely from the
latest sensory information, but instead they construct predictions about where
the ball is going to be. The same idea is
in play when you're walking through a busy airport, when
you have a flow of people moving in all directions
around you. If you had access to only outdated information
(16:39):
from photons a few hundred milliseconds ago, you'd be constantly
crashing into people.
Speaker 2 (16:44):
But you don't. Your brain solves this.
Speaker 1 (16:47):
Your brain is constantly forecasting where the people are going
to be based on their speed and direction, and that's
what allows you to smoothly navigate without crashing despite the
neural the and processing the visual information. So I want
to summarize where we are so far. The foundation we're
establishing here is that the brain is not just reacting
(17:10):
to the world. Instead, it's a machine that continuously makes
educated guesses. Prediction is how we compensate for our signal
processing delays, and from an evolutionary standpoint, this ability to
predict was absolutely critical for survival because animals who wanted
any chance of living how to anticipate the movements of
(17:32):
predators or prey to react quickly enough. You have to
somehow operate in real time if you want to evade
a thread or catch the running animal. So whenever you
are next catching a ball or moving through the airport,
think about how much you rely on your brain's predictive
abilities to act without having to wait for all the
(17:55):
signals to dribble their way in there. Okay, now we're
finally ready to return to the issue that I started with.
The illusions where you have some lines that are straight
but they look bent. These fall into the category of
geometric illusions. So what in the world do they have
to do with what we've been talking about so far. Well,
(18:17):
what I told you is that the visual system has
developed these predictive mechanisms to deal with the signal delays
so that it can see something at this moment in
time and make a really good guess where that thing
is going to be in say, one hundred milliseconds. So
some of my colleagues proposed a framework called perceiving the present,
(18:38):
and the idea is that your brain sees what is
likely to be the case, rather than to perceive the
recent past. So the first examples of this framework came
out in the early nineteen nineties. So imagine you're looking
at a small horizontal line on a computer screen and
you're trying to judge its exact position, but there's a
(18:59):
field of dots drifting continuously in the upward direction behind
that line. In this case, you'll judge the line to
be higher up on the screen. This is called motion capture.
So by the beginning of the two thousands, my colleague
Mark Chengizi started proposing that the explanation for this motion
capture was the perceiving the present framework, which is that
(19:23):
your brain sees the line, and it sees the motion
and decides that in the next moment the line is
probably going to be pushed up by the motion. So
it's actually perceiving the line in a different place where
it expects the line to be in the next moment.
And besides that, he argued, he could explain the classical
(19:47):
geometric illusion. What are these classical geometric illusions. Well, let's
(20:08):
take what's known as the Herring illusion. You almost certainly
saw this as a kid. There are a bunch of
lines coming out of the center, like the spokes on
a bicycle wheel. Okay, now you put two parallel lines
on that bicycle wheel, let's say, a vertical line to
the right of center and one to the left. You
could do this by taping two pencils on the bicycle spokes.
(20:32):
Now here's the illusion to two pencils. Although they are straight,
they don't look that way anymore. Instead, it looks like
the pencils are curving, they look slightly bent. Their middles
are bowing outwards slightly. So this is an illusion that
was first described by the physiologist Ewald Herring in eighteen
sixty one.
Speaker 2 (20:53):
But why in the world does it happen?
Speaker 1 (20:55):
Well, Herring proposed that this has to do with the
brain overestimating angles where the lines are meeting. And then
other people proposed different things in the brain that might
explain that angle overestimation. But Changhizi proposed a new explanation
when which was quite stunning. He said, look, when you're
looking at these radial lines, in other words, the lines
(21:17):
like the bicycle spokes coming out from a central point,
your brain might think that it's just looking at the
convergence of lines to the vanishing point, like imagine you're
looking straight ahead on a street in New York City
and everything converges in the middle. But equally, he said,
these lines are what a scene looks like to your
(21:38):
visual system when you are moving forward. For example, imagine
that you're driving down the road in New York City
and up ahead on the left there's a hot dog
stand and that zips by you on your left side,
and at the same time, there's a street juggler on
your right.
Speaker 2 (21:54):
Side, and he gets bigger and he zips by you
on that.
Speaker 1 (21:56):
Side, and there's an overhead street sign that at a
distance starts essentially in the middle in front of you,
but as you get closer and closer, it moves over
your head. So everything is streaking past you like radial lines,
and this is known as optic flow. So one place
you've seen this before is on Star Trek, where they
(22:19):
yank down the lever and put the ship into warp
drive and all the stars suddenly shoot past them, all
moving away from the center, like the radial spokes of
the bicycle wheel. So Tanghizi said, when you see radial
lines like that, it's typically a visual signature of you
moving forward towards the vanishing point. And certainly when you're
(22:41):
moving fast, there's a radial smear, like the way that
the stars and Star Trek smear into lines. And he said, look,
Herring's radial lines essentially mimic this. It's like you are
in the spaceship moving directly ahead. Now here's the key.
Let's come back to the de lays in the visual
system and how they can be accounted for by the
(23:04):
brain making projections where things are about to be. Imagine
that you're in New York City and driving and there
are two skyscrapers up ahead of you, one on the
left and one on the right. Now, as you race
forward in your car, those two buildings will loom closer.
But now something interesting is happening. The parts of the
(23:25):
buildings closer to you will seem farther apart, because if
you look up, the tips of the buildings are coming
closer together, way up in the sky.
Speaker 2 (23:35):
So the point is that.
Speaker 1 (23:36):
Even though you see essentially straight skyscrapers when they're at
a distance, as you approach, they are bending away from you.
Their centers are bowing out. And Shannghizi's idea was that
when you look at the radial lines the bicycle spokes,
your brain thinks this might be a clue that I'm
moving forward, and I don't want there to be delays
(23:59):
in my perception, so I'm going to see the world
as it will be a moment later. And so you
see the two parallel lines bowed outward from the center.
In other words, when you look at the radio lines
on the piece of paper, even though nothing is moving.
Your brain thinks this is what movement looks like, and
(24:20):
so it predicts the next moment, and that's what you perceived.
In other words, you perceive the lines just as they
would project in the next moment if you were moving
forward toward the vanishing point. So, as Changhesi wrote in
this paper, evolution has seen to it that geometric drawings
like this elicit in us premonitions of the near future. Okay,
(24:45):
so the framework by Changizi and colleagues suggests that several
geometric illusions are caused by temporal delays with which the
visual system must cope. The idea is that the visual
system extrapolates its current information to perceive the present. Instead
of providing a conscious image of how the world was
(25:06):
a few hundred milliseconds ago when the signals first struck
the retina, the visual system estimates how the world is
likely to look in the next moment. But how would
we get at clues to the possible neural basis? In
other words, how does the brain actually pull this off?
So in my laboratory we wanted to figure this out.
(25:26):
So my student Don Vaughan and I had people look
at the herring illusion on a screen. You've got a
background of radial lines like the bicycle spokes, and we
flashed two vertical lines on top of this. And I'll
just take a quick second to give you a sense
of how we quantify illusions. In the laboratory, A person
sits in front of the computer and they use let's
(25:48):
say the right and left arrows on the keyboard to
change the curvature of those two lines. So at one
end of the range, they're actually physically bending the lines outward,
and at the other extreme they're bending them inward, and
somewhere in the middle they're physically straight. And what the
person does is adjust the curvature of the line until
(26:08):
it looks straight to them. But of course with the
hairing illusion, you need to actually adjust the lines so
the middles are curving inward in order for it to
look straight. In other words, we see how much curvature
it takes to cancel out the illusion, and that's the
way we can quantify the size of the illusion. Okay,
(26:29):
so we measure the heiring illusion, and no surprises there.
But now what we do is we replaced the radio
lines with an actual star field. We have dots moving
in an expanding pattern like the stars and Star Trek,
And now people are judging the size of the hering
illusion against the background of expanding dots.
Speaker 2 (26:52):
And what we find is that.
Speaker 1 (26:54):
The illusion still happens. The lines still appear bent, which
is just what you'd expect from the perceiving the present hypothesis. Okay,
but here's the really wacky part, the part that uncovers
an unexpected secret in the brain. We now measure the
size of the hering illusion over a field of contracting dots.
(27:16):
So picture that Star Trek footage running backwards. Now everything
is moving from the outside to the inside.
Speaker 2 (27:22):
And here's the surprise.
Speaker 1 (27:23):
We find that the size of the herring illusion was
exactly the same here. In other words, the lines still
curve outward, just like in the other two cases.
Speaker 2 (27:34):
So what does that mean. We can get this.
Speaker 1 (27:35):
Illusion by having radial lines or dots expanding or dots contracting,
and you find that the bars bend out in the
same direction. Now, at first glance, the bending of the
bars during contracting motion would seem to refute the perceiving
(27:56):
the present framework. If your brain is doing an active
of temporal extrapolation of the scene, it should make the
bars bend in the other direction. But the key thing
to note is that backward motion is ecologically quite rare.
Unless you're a dog looking out the back window of
a car. Most animals probably never see backward optic flow
(28:21):
in their lives. Okay, so we did a lot of
other experiments in this paper, but just this first result
that the Herring illusion happens the same with expanding or
contracting optic flow tells us some critical things. First, it
tells us that this spatial warping we see isn't a
sophisticated online computation. Instead, it's a basic mechanism that just
(28:46):
acts to get it right in the most common scenario
of forward motion. And because backward motion essentially never happens,
it doesn't matter that the mechanism is so unrefined, and
it gives us a big clue about the the underlying
neural mechanisms. The most parsimonious explanation. In other words, the
simplest idea would look for something in the brain that's
(29:09):
equally sensitive to lines like the bicycle spoke and also
to motion in either direction along that line. And it
turns out there are very simple neurons in primary visual
cortex that do exactly this. They're called orientation selective neurons,
and they respond to lines, and they also respond to
dots moving along the same trajectory as the line in
(29:32):
either direction. So what this means is that our findings
are consistent with the perceiving the present hypothesis, with the
caveat that the spatial warping to counteract neural delays is
not a smart active neural process, but instead it's just
a simple mechanism that succeeds only under forward moving circumstances,
(29:54):
which is most of the time. Now, as I said,
we did a lot of other experiments, and I'm gonna
skip the detail because they're not as important as this
main point. But all the other experiments supported this hypothesis
that the bars look bent because the brain is extrapolating
ahead what things would look like in the next moment.
And by the way, our findings weren't consistent with older
(30:18):
theories like angle over estimation, as Herring had suggested. So
if you want to dive deeper into the paper, I'm
linking it to the show notes. So what we saw
today is the issue that it always takes time for
signals to move through the brain. And although people got
excited when they discovered that the signals were carried by electricity,
(30:39):
it's nothing like the way that electricity runs on wires,
which is close to the speed of light. In the brain,
we're talking hundreds of millions of times slower than that.
So you have these signals limping along in the brain,
and that means we are always living in the past.
Our brains sometimes see fake news about the world out there,
(31:01):
but we always see old news. Your brain is always
getting information about an outdated version of the world, one
that no longer exists. And so one of the many
incredible things your brain does is make predictions so that
you can perceive things as they probably are right now,
(31:22):
rather than perceiving the stale version of the data, which
is much less useful by the time you see it.
This is the brain's very bold move to compensate for
its own delays, and that wacky fact explains a lot
about how we catch balls and walk through crowded airports,
but it also seems to explain surprisingly why we see
(31:44):
this basic geometric illusion of straight lines not looking straight,
discovered by Heiring almost one hundred and sixty five years ago. Now,
I wrote an article in Nature Reviews Neuroscience in which
I covered all kinds of visual illusions, and that's in
the show notes. And each one of these illusions opens
a new mind shaft into the brain, teaching us why
(32:07):
it's happening. And once we understand that, we can usually
construct new illusions, which is why amazing new illusions are
coming out each year.
Speaker 2 (32:17):
Now.
Speaker 1 (32:17):
Sometimes people see all these illusions on their social media
feeds and they might become tempted to say that everything
is an illusion. We don't accurately see what's really out there.
But that's probably not the right conclusion, because often we
do see what's out there accurately, as verified by our
other senses and by our objective machines. For example, if
(32:40):
I put a cup of steaming coffee on the table
in front of you, you see that, and you heard
the clunk when I set it down, and you can
verify the heat with your fingers and smell the coffee
and pick it up and taste it, and you can
measure the presence of the cup with video, or the
heat with an infrared camera, and the coffee with spectroscopy
and so on. So it's not that everything you see
(33:02):
is illusory. Instead, we see what is maximally useful to us.
And every once in a while we can throw an
unexpected wrench in the system by showing the brain something
that happens to tickle the right receptors. And in those
very special cases we can catch the system coming to
(33:24):
the wrong conclusion. But it's precisely because the brain has
evolved to do the optimal thing in all the other cases.
And traditionally, when we find these special cases, we just
laugh and enjoy and keep scrolling through our feed But
the endeavor of figuring ourselves out encourages us to pause,
(33:47):
to ask why, to dig deeper, and although these things
sometimes take centuries to answer, they typically yield deep insights
into who we are and what is actually going on.
Go to Eagleman dot com slash podcast for more information
(34:10):
and to find further reading. Send me an email at
podcasts at eagleman dot com with questions or discussion, and
check out and subscribe to Inner Cosmos on YouTube for
videos of each episode and to leave comments until next time.
I'm David Eagleman, and this is Inner Cosmos.
Host
David Eagleman
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