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♪ ♪

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♪ ♪

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ANIL SETH:
The brain is one of
the most complex objects

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that we know of in the universe.

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BOBBY KASTHURI:
There are more connections
in your brain

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than there are stars
in the Milky Way galaxy.

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So, we literally walk around
with about 10,000 galaxies'

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worth of neuronal connections

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in one of our brains.

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HEATHER BERLIN:
That vast web of
connections creates you.

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But how?

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NANCY KANWISHER:
Figuring out how the brain
implements the mind

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is a massive challenge.

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♪ ♪

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SETH:
It seems as though

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the world just pours itself
into the mind

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through the transparent windows
of the eyes

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and the ears and,
and all our other senses.

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♪ ♪

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BERLIN:
But is what we see,

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hear,

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and feel real?

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You might think that the reality

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outside is actually
what you're perceiving.

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And the answer is no,
it really isn't.

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Almost at the very first moment,

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we are transforming reality.

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It feels so real

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because we don't know better.

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Think about illusions.

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ROSA LAFER-SOUSA:
Do you remember the dress?

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Of course-- it's like a
celebrity, the dress.

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A polarizing debate

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that took over the internet.

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White and gold.
Blue and black.

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SETH:
Illusions are fascinating;

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they're like
fractures in the matrix.

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BERLIN:
Ow!

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Isn't that interesting?
Whoa!

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They reveal to us that the way

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we perceive things isn't
necessarily the way they are.

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BERLIN:
Could you be
the biggest illusion of all?

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SUSANA MARTINEZ-CONDE:
Your sense of who you are
is an illusion

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as everything else;
you're no exception.

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BERLIN:
"Your Brain:
Perception Deception."

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Right now, on "NOVA."

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♪ ♪

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MAN:
Okay, rolling.

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♪ ♪

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Take three.

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BERLIN:
Have you ever thought
about what's real?

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(echoing)

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Somehow the whole world out
there gets inside my head.

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How do I know
what I see, what I hear,

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what I feel is right?

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It's a question
that's fascinated me

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ever since I was a little girl.

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I couldn't sleep one night,

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and I had this
thought for the first time:

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And then I thought, well,
even if I don't have a body,

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can I at least keep
my own inner thoughts?

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So I asked my dad the next day,

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"Dad, where do my thoughts come
from?"

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And he said,
"They come from your brain."

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(explosion echoes)

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♪ ♪

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"Your brain."
I was hooked.

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This bag of
jelly between my ears,

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how does it work?

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STANISLAS DEHAENE:
I think it's one of the ultimate
mysteries.

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How matter becomes thought.

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ANDRÉ FENTON:
To answer that question would be
perhaps

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the highest human
achievement to date.

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DANIELA SCHILLER:
I mean, forget about scientific
quest.

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It's a human quest.

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♪ ♪

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BERLIN:
To find answers,

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I became a neuroscientist
and a psychologist.

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I'm Heather Berlin,

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and my journey to understand
my brain begins with a question.

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How does the world out there,

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with all its beauty
and complexity,

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get inside our heads?

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♪ ♪

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Think about it.

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Imagine for a second
you're a brain,

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sealed inside your skull.

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There's no light, no sound.

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KASTHURI:
You're a massive collection

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of billions and billions
of cells that are living

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in this weird pond that
is entirely devoid

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of all of the sensations,
and that somehow,

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through chemistry
and electricity, all of these

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perceptions and memories
of the world

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originate in our brains.

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BERLIN:
All brains-- from the tiny fish

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to the enormous elephant--
contain microscopic cells

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called neurons, and one of their
jobs is to translate input

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from the external world,
whether that's light,

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heat, sound, or
pressure, for instance,

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into electrochemical signals
the organism can use to act.

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What might be surprising to you
is that

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as neurons process sensory
signals,

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they create an edited version
of reality,

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even on the most basic level.

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KASTHURI:
We're deciding to throw away 99%
of the world.

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Almost at the very first moment,
we are transforming reality

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into something we could use.

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BERLIN:
Neurons transform reality
by competing with each other.

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When a creature touches, smells,
sees, or hears something,

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its sensory neurons fire;
some a little, some a lot,

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depending on where the
physical signal is strongest.

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But follow those signals
down towards its brain,

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you'll see that the weaker ones
get stamped out.

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For simple brains,
say, the brain of a crab,

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a diffuse light to the eye
becomes a sharp beam.

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For more complex brains
like ours,

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it's in part what makes
you think

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that these two squares
are completely different colors,

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but actually, they're identical.

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♪ ♪

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MARTINEZ-CONDE:
Think about illusions.

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First, they're a lot of fun,
but as neuroscientists...

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Whoa...

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MARTINEZ-CONDE:
...illusions are very important
to us.

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Because of this discrepancy
between objective reality

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and subjective perception,
we can use these illusions

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as a handle to try to
understand what the brain

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is doing all the time.

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BERLIN:
Susana Martinez-Conde,
along with her partner

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and collaborator
Stephen Macknik,

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are among the
world's preeminent experts

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on illusions and perception,

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and what they tell us
about how the brain works.

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Ha, now what?

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(both laughing)

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MARTINEZ-CONDE:
To give a different example,

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Adelson's checkerboard illusion,
this is so striking because

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you see some of the checks
as dark and others as bright,

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but you realize that it is
exactly the same shade of gray.

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BERLIN:
Don't believe it?

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Look at the squares labeled
A and B.

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A looks darker,
right?

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Wrong-- that's the illusion.

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That's because your brain
is adjusting for the shadow.

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MARTINEZ-CONDE:
What's happening is that your
brain is considering

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the light source and basically
subtracting that light source

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from your resulting perception.

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Your brain is performing an
interpretation,

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a shortcut, if you will,
to arrive

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at a perception.

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BERLIN:
If the brain's shortcuts
distort reality this much,

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how much of the world
are we really seeing?

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STEPHEN MACKNIK:
What you need to understand

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is that we really can't see
most of the world around us.

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We're effectively blind

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to 99.9% of the world around us
at any given time.

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If you hold out your thumb
at arm's length...

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Mm-hmm.
And you straighten your elbow
and you look at your thumbnail,

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your thumbnail is about
one degree of visual angle here,

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and it turns out
that that's the only place

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we can actually
see with 20/20 vision.

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Wow.
And everywhere else,
we're legally blind.

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BERLIN:
It might sound hard to believe,

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but human vision is really
like this.

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You actually only see detail

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in about one percent
of your visual field.

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That's because only
a tiny portion of the world

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can be processed
in detail by the retina.

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It feels like I'm seeing
the whole world in 20/20 vision.

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And it's almost all completely
made up in your brain,

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based on assumptions and
models of how the world works

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and just a tiny bit of
high-quality visual information.

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Let me demonstrate this
to you.

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I know it's
kind of hard to believe...
Mm-hmm.

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...because you've been
having your whole life

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where you feel like
everything's continuous.
Yeah, show me the data.

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(laughs)
Show me the evidence.

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Let's look at an eye-tracker
and look at your eyes

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and how they actually work.

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And if you put your head
in this headrest...

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Mm-hmm.
...we'll point the camera
at your eyeballs

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and we'll actually be able
to see

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where your eyeballs
point during this demonstration.

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Feels like "Clockwork Orange."
(chuckles)

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"Buck lived at a big house

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in the sun-kissed
Santa Clara Valley."

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BERLIN:
First up, a reading demo.

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Though most of your screen
may be filled with Xs,

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to me, it just feels like
normal reading.

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I barely see the Xs,

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and that's because the display
of letters

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is tied to my eye movements.

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BERLIN:
Well, it's just, the words are
being

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revealed
depending on where I look.

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That's right,
so as you move your eyes...

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So weird.
...the words are
revealed to you.

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But we don't move our eyes
in the same way you do.

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So we just see a bunch of Xs
most of the time.

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BERLIN:
What turns out to be critical
is my eye movements.

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MACKNIK:
So our eye movements program
what part of this

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high-quality
piece of visual real estate

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we're going to put
where and at what time.

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BERLIN:
The human eye moves about
three times per second.

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We take it for granted,
but without these movements,

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we'd be basically blind,

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as Steve is about to show me.

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MACKNIK:
In this demonstration,
it's the opposite.

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Here we're blocking what you
can possibly see, right?

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BERLIN:
Though you may see a whole scene
with a square moving around,

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all I see is the square!

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I can tell something is around
the edges, but it's blurry.

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Whenever I try to look,
the square moves with my eyes

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and it's blocked.

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BERLIN:
This is so frustrating,
this one.

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Who has their hand up in
this image?

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BERLIN:
Uh... I think that guy
down there?

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MACKNIK:
That's right, but it's very hard
for you to see,

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right?
Every time I look at him,
it, yeah.

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It disappears because this
block, it blocks it.

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MACKNIK:
This actually is interesting

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because it's in high-quality
vision wherever you look,

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but it's blurry
in the surround.

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BERLIN:
Now the scene looks normal
to me,

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but mostly blurry to you

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because your eye movements
don't match mine.

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MACKNIK:
Wherever you happen to look,

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you have high-quality
image processing happening

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and the surround
is completely blurry.

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This kind of represents exactly
what your visual system

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looks like all the time
anyway.

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So why would our brains
be built this way?

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Well, think about what the
alternative is.

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What if
we didn't have eye movements?

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Well, if we didn't have
eye movements,

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and we just wanted to see
the entire world,

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we'd need to have our retinas

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see everything
in very high quality.

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Our brains would be
600 times bigger,

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and you gotta remember, the
visual system's our best sense.

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This is our richest sense.

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So our other senses are,
are even more impoverished.

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BERLIN:
Here's how your brain really
sees the world.

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It's easy to think it's
like this.

245
00:11:47,700 --> 00:11:50,600
You open your eyes
and the whole world pours in.

246
00:11:50,600 --> 00:11:52,533
But really, it's like this.

247
00:11:54,866 --> 00:11:57,466
Your eyes sample tiny pieces
of the world

248
00:11:57,466 --> 00:12:01,566
and the brain fills in the
rest-- constantly, all the time.

249
00:12:01,566 --> 00:12:02,966
KANWISHER:
We feel like we have

250
00:12:02,966 --> 00:12:06,600
this incredibly rich, wide,
full, detailed percept

251
00:12:06,600 --> 00:12:08,333
of what's going on
moment to moment,

252
00:12:08,333 --> 00:12:10,600
and that's probably pretty
illusory.

253
00:12:10,600 --> 00:12:13,133
 
What we're actually
aware of is a tiny subset

254
00:12:13,133 --> 00:12:15,266
of the information
that comes in through our eyes.

255
00:12:15,266 --> 00:12:17,266
BERLIN:
Don't believe it?

256
00:12:17,266 --> 00:12:18,766
Consider this:

257
00:12:18,766 --> 00:12:21,600
your optic nerve is what
connects your eye to your brain,

258
00:12:21,600 --> 00:12:24,233
and its location near the center
of your retina

259
00:12:24,233 --> 00:12:26,166
effectively creates a blind spot

260
00:12:26,166 --> 00:12:28,333
near the center of your
visual field.

261
00:12:28,333 --> 00:12:32,300
And yet, you don't experience
the blind spot-- why?

262
00:12:32,300 --> 00:12:35,033
The brain samples the area
near the blind spot

263
00:12:35,033 --> 00:12:38,033
and fills
in the gap with its best guess.

264
00:12:38,033 --> 00:12:39,700
KASTHURI:
It's probably not fair

265
00:12:39,700 --> 00:12:42,000
to say that we completely
confabulate the world,

266
00:12:42,000 --> 00:12:44,166
it's just
that we probably represent

267
00:12:44,166 --> 00:12:47,066
one percent of it at any
particular moment in time.

268
00:12:47,066 --> 00:12:50,400
So, it's a constant updating
between what I see with,

269
00:12:50,400 --> 00:12:53,633
versus what I remember, versus
what I expect.

270
00:12:53,633 --> 00:12:56,300
And it's that dance between
those three

271
00:12:56,300 --> 00:12:58,833
that actually gives us our
sense of reality.

272
00:12:58,833 --> 00:13:02,233
BERLIN:
And amazingly,
that edited reality--

273
00:13:02,233 --> 00:13:03,933
despite its limitations--

274
00:13:03,933 --> 00:13:05,800
serves us quite well.

275
00:13:05,800 --> 00:13:07,866
KASTHURI:
You might ask,
"If I'm just keeping track

276
00:13:07,866 --> 00:13:09,400
"of one percent of the
information in the world,

277
00:13:09,400 --> 00:13:11,633
how can I drive a car?"

278
00:13:11,633 --> 00:13:14,200
And it turns out that first one
percent

279
00:13:14,200 --> 00:13:15,866
of the information that comes
in from the world

280
00:13:15,866 --> 00:13:17,733
is actually an enormous
amount of information.
(chuckling)

281
00:13:17,733 --> 00:13:20,466
If we had to actually
pay attention

282
00:13:20,466 --> 00:13:24,000
to everything on the road at
the, at one particular time,

283
00:13:24,000 --> 00:13:26,433
it would take minutes,
maybe even longer,

284
00:13:26,433 --> 00:13:28,233
before I decide to turn
the wheel right

285
00:13:28,233 --> 00:13:30,666
or to turn the wheel left.

286
00:13:30,666 --> 00:13:32,200
♪ ♪

287
00:13:32,200 --> 00:13:34,900
BERLIN:
By understanding how
my senses really work,

288
00:13:34,900 --> 00:13:36,766
I'm getting a peek behind
the curtain:

289
00:13:36,766 --> 00:13:40,900
what my brain is really up
to outside of my awareness.

290
00:13:42,600 --> 00:13:46,466
MARTINEZ-CONDE:
Based on this very tiny amount
of information,

291
00:13:46,466 --> 00:13:50,066
we construct this
grand simulation

292
00:13:50,066 --> 00:13:53,866
of the visual world around us.

293
00:13:53,866 --> 00:13:57,133
It feels so real
because we don't know better.

294
00:13:58,266 --> 00:14:01,333
BERLIN:
And most of the time,
we all agree on that simulation.

295
00:14:01,333 --> 00:14:05,466
It's when we don't
that we can learn something.

296
00:14:05,466 --> 00:14:07,933
So do you remember
the dress?
Of course.

297
00:14:07,933 --> 00:14:11,366
Did you see this dress
or this one?

298
00:14:11,366 --> 00:14:12,966
It's a simple question,

299
00:14:12,966 --> 00:14:15,400
but the answer has divided
friends and family.

300
00:14:15,400 --> 00:14:17,233
White and gold.
Blue and black.

301
00:14:17,233 --> 00:14:19,066
I remember it caused
quite the stir, right?

302
00:14:19,066 --> 00:14:20,433
Massive stir.

303
00:14:20,433 --> 00:14:21,733
A polarizing debate

304
00:14:21,733 --> 00:14:23,633
that took over the internet.

305
00:14:23,633 --> 00:14:25,233
♪ ♪

306
00:14:25,233 --> 00:14:29,033
LAFER-SOUSA:
People had existential crises
over this image.

307
00:14:29,033 --> 00:14:30,600
People tweeted things like,

308
00:14:30,600 --> 00:14:32,300
"If that's not white and gold,

309
00:14:32,300 --> 00:14:33,933
my life has been a lie."

310
00:14:33,933 --> 00:14:35,900
Swear on your mother's
grave.

311
00:14:35,900 --> 00:14:37,500
Because that dress is white
and gold.

312
00:14:37,500 --> 00:14:38,666
Out of her (bleep) mind.

313
00:14:38,666 --> 00:14:40,266
LAFER-SOUSA:
Massive arguments.

314
00:14:40,266 --> 00:14:42,866
I watched videos of
people screaming at each other.

315
00:14:42,866 --> 00:14:44,266
GRAYSON DOLAN:
This is white, dude!

316
00:14:44,266 --> 00:14:46,333
ETHAN DOLAN:
White? That is dark blue!

317
00:14:46,333 --> 00:14:47,800
It's purple-blue!

318
00:14:47,800 --> 00:14:49,500
LAFER-SOUSA: I bet there was a
divorce here or there

319
00:14:49,500 --> 00:14:52,200
over this image.

320
00:14:52,200 --> 00:14:54,166
BERLIN: So when you first saw
that dress,

321
00:14:54,166 --> 00:14:56,966
as a, as a vision scientist,
what did you think?

322
00:14:56,966 --> 00:14:59,233
Well, when I first
saw the dress,

323
00:14:59,233 --> 00:15:01,300
I thought it was
blue and black.

324
00:15:01,300 --> 00:15:03,733
And I thought that the internet
was yanking my chain.

325
00:15:03,733 --> 00:15:06,433
Right-- to get the goat
of vision neuroscientists.

326
00:15:06,433 --> 00:15:08,166
Sure.
(chuckles)

327
00:15:08,166 --> 00:15:10,233
But in the morning,
when I looked at my phone,

328
00:15:10,233 --> 00:15:12,333
I saw white and gold.

329
00:15:12,333 --> 00:15:14,366
And now,
of course, I was obsessed.

330
00:15:14,366 --> 00:15:16,600
So I said, "Well,
if this is an ambiguous image,

331
00:15:16,600 --> 00:15:18,900
all I have
to do is disambiguate it."

332
00:15:18,900 --> 00:15:21,066
So I set to work,
I got into Photoshop,

333
00:15:21,066 --> 00:15:25,100
cut out the dress, put it into
a scene with lots of rich cues.

334
00:15:25,100 --> 00:15:26,333
BERLIN:
Hm.

335
00:15:26,333 --> 00:15:27,900
LAFER-SOUSA:
And all of a sudden, boom:

336
00:15:27,900 --> 00:15:29,833
you can see the dress is
white and gold.

337
00:15:29,833 --> 00:15:31,533
Wow.
Now, the pixels,

338
00:15:31,533 --> 00:15:33,033
the pixels that make up the
dress there,

339
00:15:33,033 --> 00:15:35,400
are identical
to the original image.
Okay.

340
00:15:35,400 --> 00:15:37,966
BERLIN: Now, this doesn't work
for everybody,

341
00:15:37,966 --> 00:15:40,600
but for most, the visual context

342
00:15:40,600 --> 00:15:42,833
can make all the difference.

343
00:15:42,833 --> 00:15:45,233
LAFER-SOUSA:
What's different here is, her
skin is tinted blue,

344
00:15:45,233 --> 00:15:48,366
the background has blue light
cast on it,

345
00:15:48,366 --> 00:15:51,066
she's standing
in the shadow of that cube,

346
00:15:51,066 --> 00:15:54,200
and so your brain says,
"Aha, I need to ignore

347
00:15:54,200 --> 00:15:55,833
"some amount of blue light

348
00:15:55,833 --> 00:15:58,500
"that is in this signal that's
hitting my eye

349
00:15:58,500 --> 00:16:00,733
and render
this as white and gold."

350
00:16:00,733 --> 00:16:03,433
BERLIN:
And if we flip things around?

351
00:16:03,433 --> 00:16:07,066
LAFER-SOUSA:
Same dress, pasted it into
this other scene.

352
00:16:07,066 --> 00:16:10,466
Her skin is tinted yellow, the
background has a yellow cast,

353
00:16:10,466 --> 00:16:13,066
she's standing no longer
in the shadow but in the light.

354
00:16:13,066 --> 00:16:14,233
Boom! Blue and black.

355
00:16:14,233 --> 00:16:16,266
Amazing, that's really
amazing.

356
00:16:16,266 --> 00:16:17,566
So again, the dress,

357
00:16:17,566 --> 00:16:18,933
the pixels are exactly
the same.

358
00:16:18,933 --> 00:16:20,366
LAFER-SOUSA:
Identical.

359
00:16:20,366 --> 00:16:22,366
BERLIN:
The dress is a powerful example

360
00:16:22,366 --> 00:16:24,633
of how color really works
in the brain.

361
00:16:24,633 --> 00:16:26,866
Does that mean
we're creating color

362
00:16:26,866 --> 00:16:30,366
in our mind, or does color
actually exist in the world?

363
00:16:30,366 --> 00:16:32,333
Color takes place
in the brain,

364
00:16:32,333 --> 00:16:34,766
and I've prepared a little
illusion for you

365
00:16:34,766 --> 00:16:36,800
that should
convince you of this.

366
00:16:36,800 --> 00:16:39,800
LAFER-SOUSA:
So I have a picture
of four cars here.

367
00:16:39,800 --> 00:16:42,900
I want you to tell me,
what color are these cars?

368
00:16:42,900 --> 00:16:45,200
Let's start on the top left.
BERLIN: Okay, so the one
on the left looks red,

369
00:16:45,200 --> 00:16:47,866
then the one
next to it looks blue.

370
00:16:47,866 --> 00:16:49,533
I'd say the one, the
bottom there,

371
00:16:49,533 --> 00:16:50,800
bottom left looks green,

372
00:16:50,800 --> 00:16:53,700
and then the one next to it
looks orange.

373
00:16:53,700 --> 00:16:55,000
LAFER-SOUSA:
Okay.
Mm-hmm.

374
00:16:55,000 --> 00:16:56,133
What if I told you

375
00:16:56,133 --> 00:16:59,133
that all of those pixels
are not only gray,

376
00:16:59,133 --> 00:17:01,600
they are the same gray?

377
00:17:01,600 --> 00:17:03,300
BERLIN:
How is this possible?

378
00:17:03,300 --> 00:17:06,766
It's because the light
that enters your eyes,

379
00:17:06,766 --> 00:17:10,566
contrary to what you might have
learned in school, is not color.

380
00:17:10,566 --> 00:17:13,633
Color is an interpretation
of your brain.

381
00:17:13,633 --> 00:17:15,533
Here's how it works.

382
00:17:15,533 --> 00:17:17,700
Light shines on the world

383
00:17:17,700 --> 00:17:20,933
and bounces off objects--
this part you know.

384
00:17:20,933 --> 00:17:22,166
And light comes in

385
00:17:22,166 --> 00:17:23,433
different wavelengths,

386
00:17:23,433 --> 00:17:25,800
each corresponding
to a different color.

387
00:17:25,800 --> 00:17:27,500
What you might not have
heard in school

388
00:17:27,500 --> 00:17:29,433
is how those wavelengths change

389
00:17:29,433 --> 00:17:31,366
when they hit different
surfaces--

390
00:17:31,366 --> 00:17:33,633
rough, smooth, wet, et cetera.

391
00:17:33,633 --> 00:17:35,933
This signal that gets into
your eye

392
00:17:35,933 --> 00:17:39,166
is actually a product
of the reflective properties

393
00:17:39,166 --> 00:17:42,133
of the object and the wavelength
of light hitting it.

394
00:17:42,133 --> 00:17:44,333
Then that signal is focused
on the retina,

395
00:17:44,333 --> 00:17:46,100
the back of the eye,

396
00:17:46,100 --> 00:17:49,800
where we have about 130 million
light-sensitive cells.

397
00:17:49,800 --> 00:17:52,500
Three types called cones
are involved in color,

398
00:17:52,500 --> 00:17:55,433
each sensitive to different
wavelengths of light:

399
00:17:55,433 --> 00:17:57,500
long, medium, and short.

400
00:17:57,500 --> 00:18:00,133
But that light
still isn't color.

401
00:18:00,133 --> 00:18:02,366
For that to happen,
our brain has to take

402
00:18:02,366 --> 00:18:04,433
that three-piece code
from the retina

403
00:18:04,433 --> 00:18:09,033
and use the relative response
of the cones to encode color.

404
00:18:09,033 --> 00:18:13,066
It's not until that signal gets
to an area called V4

405
00:18:13,066 --> 00:18:15,800
that we get
a neural representation of color

406
00:18:15,800 --> 00:18:19,000
that corresponds
to our perceptual experience.

407
00:18:19,000 --> 00:18:21,500
So why would
our brains be built this way?

408
00:18:21,500 --> 00:18:23,966
Well, if our brains weren't
built this way,

409
00:18:23,966 --> 00:18:28,433
objects would appear to change
in color all the time,

410
00:18:28,433 --> 00:18:30,966
and that would render color

411
00:18:30,966 --> 00:18:33,533
a pretty useless signal in the
world.

412
00:18:33,533 --> 00:18:35,266
♪ ♪

413
00:18:35,266 --> 00:18:37,966
BERLIN:
That's because objects reflect
different wavelengths

414
00:18:37,966 --> 00:18:41,233
into your eye depending
on the lighting conditions.

415
00:18:41,233 --> 00:18:43,466
If your brain didn't compensate
for this,

416
00:18:43,466 --> 00:18:45,833
a red berry would appear gray
in a cave,

417
00:18:45,833 --> 00:18:48,933
blue at dawn,
and orange at dusk.

418
00:18:48,933 --> 00:18:51,466
But instead, your brain
carefully calibrates

419
00:18:51,466 --> 00:18:54,466
your experience to hold color
constant.

420
00:18:54,466 --> 00:18:57,266
Similarly,
color vision in other animals

421
00:18:57,266 --> 00:18:59,700
is tuned to their needs.

422
00:18:59,700 --> 00:19:03,266
SETH:
Different species have very
different kinds of color vision

423
00:19:03,266 --> 00:19:06,200
that are suited to their
particular environments

424
00:19:06,200 --> 00:19:09,133
and their particular challenges
for staying alive.

425
00:19:09,133 --> 00:19:13,066
BERLIN:
Dogs rely on smell, so they have
fewer types of cones,

426
00:19:13,066 --> 00:19:15,400
and thus see the world like
this.

427
00:19:15,400 --> 00:19:18,233
Birds need to recognize tiny
color differences

428
00:19:18,233 --> 00:19:19,800
from great distances.

429
00:19:19,800 --> 00:19:22,333
so they have an extra type
of cone that allows them

430
00:19:22,333 --> 00:19:23,800
to see more colors than we do.

431
00:19:23,800 --> 00:19:27,000
And bees need to find
flowers rich in nectar,

432
00:19:27,000 --> 00:19:31,033
so they see ultraviolet light
that's invisible to us.

433
00:19:31,033 --> 00:19:33,466
LAFER-SOUSA:
Color provides a
lot of valuable information

434
00:19:33,466 --> 00:19:35,100
about the world,

435
00:19:35,100 --> 00:19:38,033
but only if we can faithfully
extract

436
00:19:38,033 --> 00:19:41,100
something about the object.

437
00:19:41,100 --> 00:19:44,133
So we don't actually see color
as it is in the real world,

438
00:19:44,133 --> 00:19:48,233
we just see it in
terms of how it's useful for us?

439
00:19:48,233 --> 00:19:49,900
Absolutely.

440
00:19:49,900 --> 00:19:52,200
And the dress is probably
the best example of that.

441
00:19:52,200 --> 00:19:54,966
It's a really
powerful demonstration

442
00:19:54,966 --> 00:19:57,266
of how our color machinery
works.

443
00:19:57,266 --> 00:20:00,166
BERLIN:
So why do people see
this image

444
00:20:00,166 --> 00:20:01,933
of the dress differently?

445
00:20:01,933 --> 00:20:04,000
It comes down
to your brain's assumptions

446
00:20:04,000 --> 00:20:06,033
about the lighting conditions.

447
00:20:06,033 --> 00:20:08,666
It seems that the more
time you spend working indoors

448
00:20:08,666 --> 00:20:11,533
under artificial light,
which is predominantly yellow,

449
00:20:11,533 --> 00:20:14,833
the more likely you are to say
the dress is black and blue,

450
00:20:14,833 --> 00:20:17,933
because your brain assumes
it is lit by artificial light

451
00:20:17,933 --> 00:20:20,233
and subtracts out the yellow.

452
00:20:20,233 --> 00:20:22,133
Conversely, if you spend more
time

453
00:20:22,133 --> 00:20:24,300
in natural light,
which is bluer,

454
00:20:24,300 --> 00:20:27,966
you are more likely to see
it as white and gold.

455
00:20:27,966 --> 00:20:31,000
So then what is
the actual color of the dress?

456
00:20:31,000 --> 00:20:35,066
Well, Heather, I happen
to have brought it with me.

457
00:20:36,766 --> 00:20:38,900
(chuckles):
Wow.
So what color is it?

458
00:20:38,900 --> 00:20:41,266
Uh, it's obviously,
I was right, blue and black.

459
00:20:41,266 --> 00:20:43,533
Team blue and black
for the win.
Yes, yes.

460
00:20:43,533 --> 00:20:45,800
I can't believe this is the
actual dress.

461
00:20:45,800 --> 00:20:47,033
BERLIN:
I feel like I'm holding,
like...

462
00:20:47,033 --> 00:20:49,433
It's like a celebrity,
the dress.

463
00:20:49,433 --> 00:20:51,533
I know, it should be in a
museum, not in my closet.
Yes!

464
00:20:51,533 --> 00:20:53,000
(both laugh)

465
00:20:53,000 --> 00:20:55,600
Before the dress,
people hadn't really realized

466
00:20:55,600 --> 00:20:57,900
we differ so much between
individuals.

467
00:20:57,900 --> 00:20:59,666
We're now quite used to the idea

468
00:20:59,666 --> 00:21:01,300
that we all
differ on the outside.

469
00:21:01,300 --> 00:21:04,400
We all have differences in skin
color, in height, in shape.

470
00:21:04,400 --> 00:21:06,666
But just as we all differ
on the outside,

471
00:21:06,666 --> 00:21:09,166
we all
differ on the inside, too.

472
00:21:09,166 --> 00:21:12,433
And this inner diversity
is very important.

473
00:21:12,433 --> 00:21:15,633
It gives us a certain humility
about our own ways of seeing.

474
00:21:15,633 --> 00:21:18,900
BERLIN:
Illusions give us a ringside
seat

475
00:21:18,900 --> 00:21:21,133
to watch how the brain creates
our world.

476
00:21:21,133 --> 00:21:23,833
And it's not
just the visual domain.

477
00:21:23,833 --> 00:21:26,033
Try listening to this.

478
00:21:26,033 --> 00:21:27,600
(staticky computerized voice
playing)

479
00:21:27,600 --> 00:21:29,133
Brainstorm, right?

480
00:21:29,133 --> 00:21:30,633
Simple enough.

481
00:21:30,633 --> 00:21:32,033
Now, listen to this.

482
00:21:32,033 --> 00:21:33,633
(staticky computerized voice
playing)

483
00:21:33,633 --> 00:21:34,966
Green needle.

484
00:21:34,966 --> 00:21:37,566
Okay, so you're thinking,
"What's the big deal?"

485
00:21:37,566 --> 00:21:41,033
But, what if I tell you that the
two audio clips I just played

486
00:21:41,033 --> 00:21:43,400
were exactly identical?

487
00:21:43,400 --> 00:21:47,133
For most people, what you hear
depends on which label you read.

488
00:21:47,133 --> 00:21:48,533
Sounds unbelievable?

489
00:21:48,533 --> 00:21:53,066
Here, try it again,
but this time, just read one.

490
00:21:53,066 --> 00:21:55,000
(staticky computerized voice
playing)

491
00:21:55,000 --> 00:21:58,800
Okay, now read the other
and listen again.

492
00:21:58,800 --> 00:22:01,366
(staticky computerized voice
playing)

493
00:22:01,366 --> 00:22:03,033
Now, when I first encountered
this,

494
00:22:03,033 --> 00:22:04,800
I was floored, too.

495
00:22:04,800 --> 00:22:07,000
Even though
I know what's going on.

496
00:22:07,000 --> 00:22:09,233
When your brain
encounters uncertainty,

497
00:22:09,233 --> 00:22:11,966
it fills in the gaps with its
best guess.

498
00:22:11,966 --> 00:22:14,600
In this case,
we have a degraded audio clip,

499
00:22:14,600 --> 00:22:17,333
and when you're primed with
a certain word to go with it,

500
00:22:17,333 --> 00:22:20,333
your brain automatically
jumps to the best fit.

501
00:22:20,333 --> 00:22:23,933
For most of us, we literally
hear what we want to hear.

502
00:22:23,933 --> 00:22:27,933
And it gets even worse--
let's try one more.

503
00:22:27,933 --> 00:22:30,433
Another internet sensation

504
00:22:30,433 --> 00:22:32,933
that lit up
debates across the country.

505
00:22:32,933 --> 00:22:37,733
(cleaner computerized voice
playing)

506
00:22:37,733 --> 00:22:39,633
Once and for all, is it yanny,
is it laurel?

507
00:22:39,633 --> 00:22:42,633
It's not yanny, it's laurel.
It's yanny!

508
00:22:42,633 --> 00:22:44,666
Did you hear yanny?

509
00:22:44,666 --> 00:22:45,733
(cheering and applauding)

510
00:22:45,733 --> 00:22:47,066
Who heard laurel?

511
00:22:47,066 --> 00:22:48,366
(cheering more loudly)

512
00:22:48,366 --> 00:22:51,000
It is laurel and not yanny.

513
00:22:51,000 --> 00:22:53,333
(remix of computerized voice
playing)

514
00:22:53,333 --> 00:22:55,466
It's like that stupid dress
again

515
00:22:55,466 --> 00:22:57,700
all over, but in audio form!

516
00:22:57,700 --> 00:23:00,566
This is not saying "laurel,"
this is only saying "yanny."

517
00:23:00,566 --> 00:23:03,666
Exactly, it's laurel!

518
00:23:03,666 --> 00:23:06,700
Now, about half of you hear
yanny,

519
00:23:06,700 --> 00:23:08,566
and the other laurel,

520
00:23:08,566 --> 00:23:11,866
and unlike the first illusion,
I can't get most of you

521
00:23:11,866 --> 00:23:14,833
to experience
this one any other way.

522
00:23:14,833 --> 00:23:17,300
You're locked into your version
of reality.

523
00:23:17,300 --> 00:23:20,900
Experts aren't exactly sure why,
but some of us

524
00:23:20,900 --> 00:23:24,500
seem to pay more attention
to the low frequencies, laurel,

525
00:23:24,500 --> 00:23:26,533
and others to the high, yanny.

526
00:23:26,533 --> 00:23:29,500
The divide stems from the fact
that the audio file

527
00:23:29,500 --> 00:23:31,300
is an ambiguous signal made up

528
00:23:31,300 --> 00:23:33,366
of both high and low
frequencies.

529
00:23:33,366 --> 00:23:35,600
But by manipulating the
frequencies,

530
00:23:35,600 --> 00:23:38,166
I might be able to change what
you hear.

531
00:23:38,166 --> 00:23:40,400
(computerized voice playing
at mid frequency)

532
00:23:40,400 --> 00:23:41,400
High...

533
00:23:41,400 --> 00:23:43,800
(voice plays at high frequency)

534
00:23:43,800 --> 00:23:47,400
(voice plays at low frequency)
Low.

535
00:23:47,400 --> 00:23:49,000
All of this goes to show

536
00:23:49,000 --> 00:23:50,266
how much the brain

537
00:23:50,266 --> 00:23:52,733
is an active interpreter
of sensory input.

538
00:23:52,733 --> 00:23:54,933
Our perception of
the external world

539
00:23:54,933 --> 00:23:58,566
is actually much less objective
than we'd like to believe.

540
00:23:58,566 --> 00:24:00,733
Most of the world around us is
very real,

541
00:24:00,733 --> 00:24:03,700
but you just never lived there,
okay?

542
00:24:03,700 --> 00:24:05,766
You lived in your mind,

543
00:24:05,766 --> 00:24:09,533
which is a perception of
that world that's being filtered

544
00:24:09,533 --> 00:24:12,433
through a bunch of salt
water sacks of proteins

545
00:24:12,433 --> 00:24:16,366
and electrochemical signals,
which can't possibly be making

546
00:24:16,366 --> 00:24:19,333
completely accurate
determinations

547
00:24:19,333 --> 00:24:22,233
of what's actually in the
outside world.

548
00:24:22,233 --> 00:24:25,566
Not convinced? Or maybe you're
just asking, "Why?"

549
00:24:25,566 --> 00:24:28,666
Well, try watching for when
the green dot flashes.

550
00:24:30,066 --> 00:24:32,200
Does it line up with the
red dot?

551
00:24:32,200 --> 00:24:33,600
If you are like most people,

552
00:24:33,600 --> 00:24:36,333
the red one always seems just a
little bit ahead.

553
00:24:36,333 --> 00:24:38,800
Now try again.

554
00:24:41,166 --> 00:24:45,100
The red dot and the green dot
are actually perfectly aligned.

555
00:24:45,100 --> 00:24:48,066
That's because some
neuroscientists would say

556
00:24:48,066 --> 00:24:49,733
that it's not your brain's job

557
00:24:49,733 --> 00:24:51,633
to perceive
the world accurately.

558
00:24:51,633 --> 00:24:54,733
Rather, its job is to
predict what happens next.

559
00:24:54,733 --> 00:24:58,566
To a certain extent,
you see what you expect to see:

560
00:24:58,566 --> 00:25:00,766
a predicted path of motion.

561
00:25:00,766 --> 00:25:03,233
And this is what helps
us hit a home run

562
00:25:03,233 --> 00:25:07,400
or flinch from a punch at just
the right moment.

563
00:25:09,700 --> 00:25:11,966
The brain is a predicting
machine.

564
00:25:11,966 --> 00:25:16,666
Given a set of circumstances
in this story at this moment,

565
00:25:16,666 --> 00:25:20,500
what are the likely plausible

566
00:25:20,500 --> 00:25:24,066
next events in the story?

567
00:25:24,066 --> 00:25:26,733
SETH:
The brain is
using sensory information

568
00:25:26,733 --> 00:25:28,500
to calibrate,

569
00:25:28,500 --> 00:25:30,900
update, to fine-tune these
predictions

570
00:25:30,900 --> 00:25:32,666
so they remain tied

571
00:25:32,666 --> 00:25:36,333
to reality in ways that are not
constrained by accuracy,

572
00:25:36,333 --> 00:25:39,100
but that are constrained by how
useful

573
00:25:39,100 --> 00:25:41,200
the brain's perceptual
predictions are

574
00:25:41,200 --> 00:25:43,300
in the business of staying
alive.

575
00:25:50,333 --> 00:25:52,833
BERLIN:
And to keep us alive,
the brain has evolved

576
00:25:52,833 --> 00:25:55,333
to look for signals
of potential danger.

577
00:25:55,333 --> 00:25:57,733
One of the most important
is pain,

578
00:25:57,733 --> 00:26:01,066
and as neuroscientist Theanne
Griffith is about to show me,

579
00:26:01,066 --> 00:26:03,966
sometimes that can be
a kind of illusion, as well.

580
00:26:03,966 --> 00:26:05,700
BERLIN:
So what is this?

581
00:26:05,700 --> 00:26:09,100
GRIFFITH:
This is a thermal grill.
Okay.

582
00:26:09,100 --> 00:26:11,533
This is a machine
that could give us some insight

583
00:26:11,533 --> 00:26:14,566
as to how pain works in your
brain.

584
00:26:14,566 --> 00:26:16,433
All right, this is making me
nervous already

585
00:26:16,433 --> 00:26:17,966
as I'm getting strapped in!
(Griffith laughs)

586
00:26:17,966 --> 00:26:20,600
GRIFFITH:
Don't worry,
it's all an illusion, actually.

587
00:26:20,600 --> 00:26:22,466
Okay.

588
00:26:22,466 --> 00:26:25,333
And it's comprised of these
different metal bars

589
00:26:25,333 --> 00:26:27,366
that are either set
to a cold or warm temperature.

590
00:26:27,366 --> 00:26:30,266
So why don't you go
ahead and touch that first bar?

591
00:26:30,266 --> 00:26:32,433
It's warm, right?

592
00:26:32,433 --> 00:26:34,066
And then the next bar?

593
00:26:34,066 --> 00:26:35,800
Cold.
Mm-hmm.

594
00:26:35,800 --> 00:26:37,500
GRIFFITH:
And then the next one, warm.

595
00:26:37,500 --> 00:26:39,233
You see? So they're alternating
cold, warm, cold, warm.
BERLIN: Mm-hmm.

596
00:26:39,233 --> 00:26:40,833
Now, you want to see what
happens

597
00:26:40,833 --> 00:26:42,433
when you put your hand down?

598
00:26:42,433 --> 00:26:44,233
Not necessarily.
(both laugh)

599
00:26:44,233 --> 00:26:46,700
Go ahead.
Okay.

600
00:26:46,700 --> 00:26:48,066
Okay, here we go.

601
00:26:48,066 --> 00:26:49,600
BERLIN:
Ow!

602
00:26:49,600 --> 00:26:51,300
GRIFFITH:
Right? Isn't that interesting?
Whoa!

603
00:26:51,300 --> 00:26:53,233
Yeah,
what is going on there?

604
00:26:53,233 --> 00:26:54,966
It sort of feels cold at first,
but then...

605
00:26:54,966 --> 00:26:57,300
Then it gets this kind of
burning sensation, right?

606
00:26:57,300 --> 00:26:58,966
Yes, very much so.

607
00:26:58,966 --> 00:27:02,000
BERLIN:
It feels super-hot,
like I'm getting burnt.

608
00:27:02,000 --> 00:27:05,233
So it's not 100% clear
exactly how this is happening.

609
00:27:05,233 --> 00:27:07,466
But what we think might be going
on

610
00:27:07,466 --> 00:27:09,233
is that, basically, your brain

611
00:27:09,233 --> 00:27:11,100
is getting a little bit
confused.

612
00:27:11,100 --> 00:27:12,166
Okay.
It's feeling cold,

613
00:27:12,166 --> 00:27:14,433
and it's also feeling warmth.

614
00:27:14,433 --> 00:27:18,100
And somehow, it's interpreting
these two signals as pain.

615
00:27:18,100 --> 00:27:22,333
BERLIN:
Here's what neuroscientists
think is going on.

616
00:27:22,333 --> 00:27:25,133
In your hands, you have
separate sensors for heat,

617
00:27:25,133 --> 00:27:27,333
cold, and pain.

618
00:27:27,333 --> 00:27:30,400
Normally, when you touch
something slightly cold,

619
00:27:30,400 --> 00:27:33,300
both your cold and pain sensors
are activated,

620
00:27:33,300 --> 00:27:35,400
but the cold ones
override the signals

621
00:27:35,400 --> 00:27:36,900
from the pain sensors,

622
00:27:36,900 --> 00:27:39,333
telling your brain there's
nothing to worry about.

623
00:27:39,333 --> 00:27:41,566
Unless, in this very
unnatural scenario

624
00:27:41,566 --> 00:27:44,166
with the thermal grill,
you happen to be touching

625
00:27:44,166 --> 00:27:46,833
something warm at the same time.

626
00:27:46,833 --> 00:27:49,933
Here, the heat signals
cancel out the cold ones,

627
00:27:49,933 --> 00:27:52,633
leaving you with just
the pain ones activated,

628
00:27:52,633 --> 00:27:56,100
telling your brain, "Ouch!"

629
00:27:56,100 --> 00:27:58,333
So in that respect,
is pain real?

630
00:27:58,333 --> 00:28:00,266
Mm-hmm.
Or is it just an illusion

631
00:28:00,266 --> 00:28:01,533
or a construct of the brain?

632
00:28:01,533 --> 00:28:03,133
That's a really good
question.

633
00:28:03,133 --> 00:28:06,633
So noxious stimuli
is, is a real thing, right?

634
00:28:06,633 --> 00:28:08,466
If you stick
your hand in boiling water,

635
00:28:08,466 --> 00:28:10,066
that's an aversive stimulus.

636
00:28:10,066 --> 00:28:15,266
The perception
of a noxious stimuli is real.
Mm-hmm.

637
00:28:15,266 --> 00:28:17,500
Pain is more of a construct,
right?
Mm-hmm.

638
00:28:17,500 --> 00:28:19,833
And it can vary from
individual to individual.

639
00:28:19,833 --> 00:28:23,700
EMERY BROWN:
Pain is a construct of the
brain.

640
00:28:23,700 --> 00:28:25,566
How do we know that?

641
00:28:25,566 --> 00:28:28,200
You touch a needle, right?
And prick your finger.

642
00:28:28,200 --> 00:28:31,000
We can draw the anatomy
of what just happened.

643
00:28:31,000 --> 00:28:34,366
We have very well-defined
pathways

644
00:28:34,366 --> 00:28:36,600
saying, "This is pain
information."

645
00:28:36,600 --> 00:28:42,133
We don't interpret it as pain
until it hits your brain.

646
00:28:42,133 --> 00:28:44,466
BERLIN: Pain, not unlike the
experience of color,

647
00:28:44,466 --> 00:28:47,000
is a construct of the mind.

648
00:28:47,000 --> 00:28:48,300
Mama! Mama!

649
00:28:48,300 --> 00:28:50,100
BERLIN: But just because pain is
in your brain

650
00:28:50,100 --> 00:28:53,033
doesn't make it any
less critical for survival.

651
00:28:53,033 --> 00:28:54,666
GRIFFITH: Pain is a
very important...

652
00:28:54,666 --> 00:28:58,333
(gasps)
...learning mechanism
for children.

653
00:28:58,333 --> 00:29:01,000
They learn what behaviors
they can engage in that are safe

654
00:29:01,000 --> 00:29:03,766
and what behaviors, well,
they should not engage in

655
00:29:03,766 --> 00:29:06,566
because they could cause
them bodily harm.

656
00:29:06,566 --> 00:29:09,700
And there's, um, uh, different
mutations that people can have

657
00:29:09,700 --> 00:29:11,766
in certain proteins
that make them

658
00:29:11,766 --> 00:29:13,533
completely insensitive to pain.

659
00:29:13,533 --> 00:29:15,733
And so kids do things
like bite on their lips

660
00:29:15,733 --> 00:29:18,300
or on their fingers
when they're very young,

661
00:29:18,300 --> 00:29:21,366
and as they get older,
can engage in risky behavior.

662
00:29:21,366 --> 00:29:25,900
So pain is extremely
important for us to feel.

663
00:29:25,900 --> 00:29:29,400
SETH:
Illusions are fascinating.

664
00:29:29,400 --> 00:29:31,566
They're like fractures in the
matrix.

665
00:29:31,566 --> 00:29:34,666
They reveal to us that the way
we perceive things

666
00:29:34,666 --> 00:29:37,300
isn't necessarily the way they
are.

667
00:29:37,300 --> 00:29:40,800
MACKNIK:
Illusions help us find the
cracks in the mortar

668
00:29:40,800 --> 00:29:43,166
of that world we've built for
ourselves,

669
00:29:43,166 --> 00:29:46,633
and understand what it is our
world is actually made out of

670
00:29:46,633 --> 00:29:48,500
and what the brain is actually
doing.

671
00:29:48,500 --> 00:29:50,966
So most people think of the
brain

672
00:29:50,966 --> 00:29:54,500
reconstructing the world more
or less verbatim.

673
00:29:54,500 --> 00:29:56,533
(dog growling)
But that's just not true.

674
00:29:56,533 --> 00:29:58,266
What it's actually doing is,

675
00:29:58,266 --> 00:29:59,666
it's getting very little
information

676
00:29:59,666 --> 00:30:02,666
and it's using that very little
information

677
00:30:02,666 --> 00:30:05,366
to make a big, grand model
of the world.

678
00:30:06,400 --> 00:30:10,033
MARTINEZ-CONDE:
We cannot process the vast
amount of information

679
00:30:10,033 --> 00:30:14,966
that is constantly bombarding
our senses.

680
00:30:14,966 --> 00:30:17,333
Illusions, you can
think of them as shortcuts.

681
00:30:17,333 --> 00:30:20,833
Shortcuts make us faster,

682
00:30:20,833 --> 00:30:24,366
more efficient
with less resources.

683
00:30:24,366 --> 00:30:27,066
 
Based on
these snippets of information,

684
00:30:27,066 --> 00:30:32,533
we build this more
complex simulation of reality.

685
00:30:32,533 --> 00:30:34,933
And that simulation
of the world is

686
00:30:34,933 --> 00:30:36,066
what we call consciousness.

687
00:30:38,366 --> 00:30:40,200
BERLIN:
Consciousness.

688
00:30:40,200 --> 00:30:43,833
(alarm buzzing)
We take it for granted,
but every time you wake up,

689
00:30:43,833 --> 00:30:47,866
(alarm stops)
your brain stitches together
all your sensory inputs--

690
00:30:47,866 --> 00:30:49,766
the sound of a distant train...

691
00:30:49,766 --> 00:30:51,533
(train whistle blowing)

692
00:30:51,533 --> 00:30:53,766
...the smell of coffee,

693
00:30:53,766 --> 00:30:56,666
the warmth of the sun--

694
00:30:56,666 --> 00:30:59,200
into an experience of the world.

695
00:30:59,200 --> 00:31:01,100
And that experience,

696
00:31:01,100 --> 00:31:03,333
that awareness of the world,

697
00:31:03,333 --> 00:31:06,666
is what scientists
call consciousness.

698
00:31:06,666 --> 00:31:10,000
In neuroscience,
consciousness is the Holy Grail.

699
00:31:10,000 --> 00:31:12,800
Humans have been fascinated by
consciousness

700
00:31:12,800 --> 00:31:15,666
for thousands of years,
probably much longer than that.
(chuckling)

701
00:31:15,666 --> 00:31:17,466
Take three.

702
00:31:17,466 --> 00:31:19,566
Now, of course,
the word "consciousness"

703
00:31:19,566 --> 00:31:21,500
means a lot of
things to different people.

704
00:31:21,500 --> 00:31:23,666
To some, consciousness means
being awake,

705
00:31:23,666 --> 00:31:25,100
as opposed to asleep.

706
00:31:25,100 --> 00:31:28,666
Or self-aware, or the
contents of my thoughts.

707
00:31:28,666 --> 00:31:31,800
But that's not how we
neuroscientists think about it.

708
00:31:31,800 --> 00:31:36,700
We think of it as something
much more basic--

709
00:31:36,700 --> 00:31:39,300
it's just internal experience.

710
00:31:39,300 --> 00:31:42,600
It feels like
something to see the color red.

711
00:31:42,600 --> 00:31:44,866
To taste a strawberry.

712
00:31:44,866 --> 00:31:46,200
(thunder rumbling)

713
00:31:46,200 --> 00:31:49,000
To hear the crack of thunder.

714
00:31:49,000 --> 00:31:52,500
(thunder crashing)

715
00:31:52,500 --> 00:31:55,900
SETH:
We are complicated
biological creatures,

716
00:31:55,900 --> 00:31:58,200
but the most central
feature of our lives

717
00:31:58,200 --> 00:32:00,533
is that we are
conscious creatures, too.

718
00:32:00,533 --> 00:32:02,266
When I open my eyes,

719
00:32:02,266 --> 00:32:05,500
it's not just that my brain does
some sophisticated processing

720
00:32:05,500 --> 00:32:06,600
of the visual information.

721
00:32:06,600 --> 00:32:09,333
I have an experience.

722
00:32:09,333 --> 00:32:11,366
BERLIN:
During the course of my journey,

723
00:32:11,366 --> 00:32:13,266
I've seen how our experience
of reality

724
00:32:13,266 --> 00:32:14,900
is not what it seems.

725
00:32:14,900 --> 00:32:16,333
If my conscious awareness

726
00:32:16,333 --> 00:32:19,866
is built from my perceptions,
flawed as they may be,

727
00:32:19,866 --> 00:32:24,366
how does that work
and what does it mean?

728
00:32:24,366 --> 00:32:27,333
♪ ♪

729
00:32:27,333 --> 00:32:29,200
So please take a seat.

730
00:32:29,200 --> 00:32:32,533
BERLIN:
Some of the first clues trickled
in from people like this.

731
00:32:32,533 --> 00:32:34,366
LORELLA BATTELLI:
Put your chin on the
chin rest.

732
00:32:34,366 --> 00:32:35,933
BROWN:
A lot of very valuable
information

733
00:32:35,933 --> 00:32:39,700
comes from patients who've had,
part of the brain's damaged.

734
00:32:39,700 --> 00:32:43,366
By piecing these various parts
together, seeing what was lost,

735
00:32:43,366 --> 00:32:45,300
we've come to appreciate
the role

736
00:32:45,300 --> 00:32:46,966
that these various brain regions
play

737
00:32:46,966 --> 00:32:48,866
in the creation of
consciousness.

738
00:32:48,866 --> 00:32:50,833
We're going to
calibrate your eyes first.

739
00:32:50,833 --> 00:32:53,100
SETH:
A powerful example of this

740
00:32:53,100 --> 00:32:55,233
is the phenomenon of blindsight.

741
00:32:55,233 --> 00:32:58,633
BATTELLI:
This is a patient who had a
stroke

742
00:32:58,633 --> 00:33:00,333
in her visual areas,

743
00:33:00,333 --> 00:33:02,033
in the back of the brain.

744
00:33:02,033 --> 00:33:04,466
And this stroke is
affecting her visual field.

745
00:33:04,466 --> 00:33:08,100
BERLIN:
Three years ago,
she felt a pounding in her head.

746
00:33:08,100 --> 00:33:10,400
WOMAN:
I had what
I thought was a migraine.

747
00:33:10,400 --> 00:33:13,233
I actually went
to the emergency room

748
00:33:13,233 --> 00:33:15,666
because I'm walking around with
this area where I can't see.

749
00:33:15,666 --> 00:33:18,200
BERLIN:
The stroke damaged
a piece of the brain

750
00:33:18,200 --> 00:33:20,800
devoted to vision,
leaving her with an apparent

751
00:33:20,800 --> 00:33:22,533
total blind spot.

752
00:33:22,533 --> 00:33:25,633
WOMAN:
That blind spot, it's enough
that if you're driving,

753
00:33:25,633 --> 00:33:30,000
an oncoming car disappears into
it.

754
00:33:30,000 --> 00:33:33,833
It's a little anxiety-producing
and, and things like that.

755
00:33:33,833 --> 00:33:35,600
BERLIN:
In everyday tasks,

756
00:33:35,600 --> 00:33:38,000
her eye movements make up
the difference.

757
00:33:38,000 --> 00:33:41,333
But what happens when
she doesn't move her eyes?

758
00:33:41,333 --> 00:33:43,600
Neuroscientist Lorella
Battelli wants to find out,

759
00:33:43,600 --> 00:33:47,733
so she developed a clever series
of experiments to pin down,

760
00:33:47,733 --> 00:33:49,766
just how blind is
she really in that spot?

761
00:33:51,133 --> 00:33:52,666
BATTELLI:
We're using EyeLink,

762
00:33:52,666 --> 00:33:55,066
which is the eye-tracking system

763
00:33:55,066 --> 00:33:56,900
to make sure she doesn't move
the eyes.

764
00:33:56,900 --> 00:33:59,266
BERLIN:
She keeps her eyes focused on
the center spot.

765
00:33:59,266 --> 00:34:01,833
Every time she hears a beep,

766
00:34:01,833 --> 00:34:03,733
she has to say if those little
dots inside the circle

767
00:34:03,733 --> 00:34:05,966
are moving to the left
or to the right.

768
00:34:05,966 --> 00:34:07,900
Left.

769
00:34:07,900 --> 00:34:10,966
BERLIN:
The eye tracker checks that
she's not shifting her gaze.

770
00:34:12,166 --> 00:34:13,633
WOMAN:
Right.

771
00:34:13,633 --> 00:34:15,533
When you're doing tests like
this,

772
00:34:15,533 --> 00:34:18,533
that blind area, what does that
kind of look like for you?

773
00:34:18,533 --> 00:34:20,400
What does it feel like for you?

774
00:34:20,400 --> 00:34:24,800
When that target pops up
in my blind area,

775
00:34:24,800 --> 00:34:26,433
I don't see it.

776
00:34:26,433 --> 00:34:28,700
(machine beeps)

777
00:34:28,700 --> 00:34:30,600
Left.

778
00:34:30,600 --> 00:34:32,533
BERLIN:
Strangely, even though she says
she doesn't see

779
00:34:32,533 --> 00:34:34,366
anything in the blind spot,

780
00:34:34,366 --> 00:34:35,933
she gets it right more often
than not.

781
00:34:35,933 --> 00:34:37,966
WOMAN:
Right.

782
00:34:37,966 --> 00:34:39,233
BERLIN:
So that some information

783
00:34:39,233 --> 00:34:40,300
is getting in.
Yeah, so...

784
00:34:40,300 --> 00:34:42,766
But they're not consciously
seeing it,

785
00:34:42,766 --> 00:34:44,666
but they can respond
to it in different ways.
Exactly.

786
00:34:44,666 --> 00:34:47,800
BATTELLI:
Even if they say,
"I didn't see anything."

787
00:34:47,800 --> 00:34:49,966
Left.
But you tell them,
"Please, just tell me

788
00:34:49,966 --> 00:34:51,966
whether you saw it or not,"

789
00:34:51,966 --> 00:34:53,900
then their response
would be above chance.

790
00:34:53,900 --> 00:34:56,733
BERLIN:
So just keep your eyes closed,
okay?

791
00:34:56,733 --> 00:34:58,733
And...

792
00:34:58,733 --> 00:35:00,833
BERLIN:
What's going on?

793
00:35:00,833 --> 00:35:03,200
To probe deeper,
Lorella lets me give the patient

794
00:35:03,200 --> 00:35:06,066
a different
version of the challenge.

795
00:35:06,066 --> 00:35:08,700
I put a miniature
screwdriver in her blind spot.

796
00:35:08,700 --> 00:35:11,933
BERLIN:
Okay, I'm going to have
you open your eyes and fixate.

797
00:35:11,933 --> 00:35:13,966
Okay.
Okay.

798
00:35:16,066 --> 00:35:17,300
I see nothing.

799
00:35:17,300 --> 00:35:18,300
You see nothing.

800
00:35:18,300 --> 00:35:19,533
Nothing.
Okay.

801
00:35:19,533 --> 00:35:20,833
BERLIN:
Even though she says

802
00:35:20,833 --> 00:35:23,500
she sees nothing,
look at which tool she picks.

803
00:35:23,500 --> 00:35:25,433
Now turn over there
and look at the objects.

804
00:35:25,433 --> 00:35:26,466
Tell me what you think
you saw.

805
00:35:26,466 --> 00:35:29,200
WOMAN: The screwdriver.
BERLIN: Yep.

806
00:35:29,200 --> 00:35:31,266
BERLIN:
Now try another one.

807
00:35:31,266 --> 00:35:34,366
BERLIN:
Next, I display a tiny wrench.

808
00:35:34,366 --> 00:35:38,800
♪ ♪

809
00:35:38,800 --> 00:35:40,366
Did you see anything?

810
00:35:40,366 --> 00:35:42,133
No.
No? Okay.

811
00:35:42,133 --> 00:35:46,100
Look over there and guess
what you think you, was there.

812
00:35:46,100 --> 00:35:49,300
I think the wrench?
Yep.

813
00:35:49,300 --> 00:35:51,533
WOMAN:
I'm going to guess
the scissors.

814
00:35:51,533 --> 00:35:53,700
Yeah, good job, great,
scissors.

815
00:35:53,700 --> 00:35:55,033
BERLIN:
Time and time again,

816
00:35:55,033 --> 00:35:56,900
she makes the right choice.

817
00:35:56,900 --> 00:35:58,833
Amazing, so, you know,
it seems

818
00:35:58,833 --> 00:36:01,100
to me that you're saying you're
not seeing anything,

819
00:36:01,100 --> 00:36:03,966
yet when
I'm asking you to choose,

820
00:36:03,966 --> 00:36:06,033
you're pretty much getting it
correct,

821
00:36:06,033 --> 00:36:07,533
so something is getting in.

822
00:36:07,533 --> 00:36:10,166
BERLIN:
How is this possible?

823
00:36:10,166 --> 00:36:13,100
It's as if she sees
the tools, but doesn't know it.

824
00:36:13,100 --> 00:36:14,933
BATTELLI:
They actually saw something.

825
00:36:14,933 --> 00:36:17,466
Mm-hmm, certainly.
But they're
not entirely aware of it.

826
00:36:17,466 --> 00:36:19,633
Information is getting in,
affecting our behavior

827
00:36:19,633 --> 00:36:21,433
and how we're responding
to the world around us,

828
00:36:21,433 --> 00:36:23,400
without there being
a conscious perception

829
00:36:23,400 --> 00:36:25,766
of that piece
of visual information.
Correct.

830
00:36:25,766 --> 00:36:27,800
WOMAN:
I'm gonna go
with the hammer again.

831
00:36:27,800 --> 00:36:32,533
BERLIN:
Until patients like this,
we scientists had never seen

832
00:36:32,533 --> 00:36:35,166
perception separate
from conscious experience.

833
00:36:35,166 --> 00:36:37,266
And this tells us that
perception

834
00:36:37,266 --> 00:36:40,566
and consciousness are separate
things in the brain.

835
00:36:40,566 --> 00:36:42,466
But it also has me wondering,

836
00:36:42,466 --> 00:36:44,800
if someone can still use
visual information

837
00:36:44,800 --> 00:36:46,733
without awareness of it,

838
00:36:46,733 --> 00:36:48,800
why do we have
consciousness at all?

839
00:36:48,800 --> 00:36:51,166
What is consciousness for?

840
00:36:51,166 --> 00:36:53,400
A clue might come from babies.

841
00:36:53,400 --> 00:36:55,400
(cooing)

842
00:36:55,400 --> 00:36:57,333
ALISON GOPNIK:
Babies-- everything we know
suggests

843
00:36:57,333 --> 00:36:59,200
that they're born conscious.

844
00:36:59,200 --> 00:37:00,533
They're certainly taking
in information

845
00:37:00,533 --> 00:37:01,800
from the time they're born.

846
00:37:01,800 --> 00:37:04,200
REBECCA SAXE:
They are making rational choices

847
00:37:04,200 --> 00:37:07,733
about what they learn
from extremely early on.

848
00:37:07,733 --> 00:37:11,400
And they are forming memories
of their specific surroundings,

849
00:37:11,400 --> 00:37:12,766
of their parents,

850
00:37:12,766 --> 00:37:14,833
of their important
relationships.

851
00:37:14,833 --> 00:37:17,200
We can see that
in their behavior.

852
00:37:17,200 --> 00:37:21,066
BERLIN:
And all that behavior
burns a lot of fuel.

853
00:37:21,066 --> 00:37:23,900
GOPNIK:
Brains are expensive computing
gadgets.

854
00:37:23,900 --> 00:37:25,366
So while you're just sitting
here,

855
00:37:25,366 --> 00:37:28,100
your brain is using up about 20%
of all the calories

856
00:37:28,100 --> 00:37:30,133
that you have, so
it's using up quite a bit.

857
00:37:30,133 --> 00:37:32,300
But if you think about
a two-year-old,

858
00:37:32,300 --> 00:37:36,200
his brain is using
60% of his calories.

859
00:37:36,200 --> 00:37:41,833
So almost all that food is just
going to keep his brain going.

860
00:37:41,833 --> 00:37:45,066
BERLIN:
To understand why young brains
might need so much more fuel,

861
00:37:45,066 --> 00:37:47,833
check out the connections
in a toddler's brain

862
00:37:47,833 --> 00:37:49,633
versus an adult's.

863
00:37:49,633 --> 00:37:53,633
A two-year-old's brain has about
two quadrillion synapses.

864
00:37:53,633 --> 00:37:58,566
By the time they hit adulthood,
that number is cut in half.

865
00:37:58,566 --> 00:38:00,700
So if you think about the
difference between

866
00:38:00,700 --> 00:38:03,133
the baby brain, the child's
brain, and the adult brain,

867
00:38:03,133 --> 00:38:07,166
the child's brain is more like
back country roads

868
00:38:07,166 --> 00:38:09,100
where you have little,
tiny roads

869
00:38:09,100 --> 00:38:11,800
that are going
from one village to the next.

870
00:38:11,800 --> 00:38:13,900
None of
them are very efficient.

871
00:38:13,900 --> 00:38:15,066
There's not a lot of traffic,

872
00:38:15,066 --> 00:38:16,966
and the traffic doesn't go very
quickly,

873
00:38:16,966 --> 00:38:18,933
but they connect lots
and lots of different places.

874
00:38:18,933 --> 00:38:21,633
And the adult brain is more
like superhighways

875
00:38:21,633 --> 00:38:24,400
that get you from one place
to another very quickly,

876
00:38:24,400 --> 00:38:26,133
and take a lot of traffic,

877
00:38:26,133 --> 00:38:28,833
but don't connect as many
different places.

878
00:38:28,833 --> 00:38:31,333
BERLIN:
As we age,
in the interest of efficiency,

879
00:38:31,333 --> 00:38:34,133
we strengthen the connections
that are useful to us

880
00:38:34,133 --> 00:38:36,266
and prune the rest.

881
00:38:36,266 --> 00:38:39,433
MACKNIK:
You basically take neurons you
don't need, you get rid of them.

882
00:38:39,433 --> 00:38:42,533
And what you have
now is a very lean machine

883
00:38:42,533 --> 00:38:47,433
that does certain things
and it does them very well.

884
00:38:48,500 --> 00:38:51,100
GOPNIK:
We see this early brain
that's very exploratory,

885
00:38:51,100 --> 00:38:52,900
that has lots and lots of
potential,

886
00:38:52,900 --> 00:38:54,100
lots of possibilities.

887
00:38:54,100 --> 00:38:56,400
Not very good at
putting on your jacket

888
00:38:56,400 --> 00:38:58,466
and getting out to
preschool in the morning.

889
00:38:58,466 --> 00:39:00,000
And then we have this
later brain

890
00:39:00,000 --> 00:39:01,833
that's very good at doing
things.

891
00:39:01,833 --> 00:39:03,200
Not so good at changing,

892
00:39:03,200 --> 00:39:04,800
not so good at taking in new
information,

893
00:39:04,800 --> 00:39:06,066
not so good at doing something
new.

894
00:39:06,066 --> 00:39:08,766
BERLIN:
What this suggests is that maybe

895
00:39:08,766 --> 00:39:10,266
what consciousness is for

896
00:39:10,266 --> 00:39:12,600
is choosing what's
important for us to be aware of

897
00:39:12,600 --> 00:39:14,133
at any given moment.

898
00:39:14,133 --> 00:39:16,633
Kind of like a spotlight.

899
00:39:16,633 --> 00:39:19,133
GOPNICK:
For adults,
it's as if consciousness is

900
00:39:19,133 --> 00:39:20,633
this bright spotlight in
one place

901
00:39:20,633 --> 00:39:22,000
and everything around it is
dark.

902
00:39:24,000 --> 00:39:25,666
BERLIN:
While for children and babies,

903
00:39:25,666 --> 00:39:27,333
it would be
more like a flood light,

904
00:39:27,333 --> 00:39:29,633
where nearly everything is
illuminated.

905
00:39:29,633 --> 00:39:33,300
GOPNIK:
You're conscious of a lot more
that's going on.

906
00:39:33,300 --> 00:39:36,166
BERLIN:
Consciousness may
be like an amplifier,

907
00:39:36,166 --> 00:39:38,733
boosting the important
signals over the noise.

908
00:39:38,733 --> 00:39:40,766
Is there any evidence?

909
00:39:42,166 --> 00:39:44,433
This is where FMRI comes in,

910
00:39:44,433 --> 00:39:47,066
a special tool in neuroscience
that takes pictures of the brain

911
00:39:47,066 --> 00:39:49,733
while it's doing something
to map where blood flow

912
00:39:49,733 --> 00:39:50,966
is in high demand.

913
00:39:50,966 --> 00:39:53,600
The result is
a map of brain activity.

914
00:39:54,766 --> 00:39:57,600
So where is consciousness
in the brain

915
00:39:57,600 --> 00:40:00,566
and how does it work?

916
00:40:00,566 --> 00:40:02,433
To find out,
neuroscientists designed

917
00:40:02,433 --> 00:40:05,500
a clever series of
experiments that go like this.

918
00:40:05,500 --> 00:40:07,766
They start by flashing a word

919
00:40:07,766 --> 00:40:10,233
on a screen for about
30 milliseconds.

920
00:40:10,233 --> 00:40:11,233
(computer beeps)

921
00:40:11,233 --> 00:40:12,633
DEHAENE:
You flash this word,

922
00:40:12,633 --> 00:40:15,200
and the person is not
able to see the word at all.

923
00:40:15,200 --> 00:40:16,900
She says, "There was no word."

924
00:40:16,900 --> 00:40:21,100
 
BERLIN:
But in the FMRI scanner,
the visual cortex is activated,

925
00:40:21,100 --> 00:40:24,633
even though people say
they don't see anything.

926
00:40:24,633 --> 00:40:27,700
So the trick here is to find the
threshold.

927
00:40:27,700 --> 00:40:30,533
Find the timing where sometimes

928
00:40:30,533 --> 00:40:33,200
people consciously see the
image.

929
00:40:33,200 --> 00:40:37,900
DEHAENE:
So if you make now
the word a little bit longer,

930
00:40:37,900 --> 00:40:39,466
suddenly, the person says,

931
00:40:39,466 --> 00:40:41,133
"Oh, well,
there is a word, obviously."

932
00:40:41,133 --> 00:40:43,366
And it's completely visible.

933
00:40:43,366 --> 00:40:45,766
There is really a sort
of all or none phenomenon.

934
00:40:45,766 --> 00:40:47,566
Either you see it or you don't.

935
00:40:47,566 --> 00:40:49,800
And once you've done that,

936
00:40:49,800 --> 00:40:51,433
you've got
a really powerful window

937
00:40:51,433 --> 00:40:54,200
onto the neural correlates of
conscious perception.

938
00:40:54,200 --> 00:40:56,666
BERLIN:
When that happens,

939
00:40:56,666 --> 00:40:58,633
suddenly, a suite of different
parts of the brain

940
00:40:58,633 --> 00:41:02,033
shows a surge in activity:
the parietal cortex,

941
00:41:02,033 --> 00:41:03,800
which integrates the senses,

942
00:41:03,800 --> 00:41:05,433
the anterior cingulate,

943
00:41:05,433 --> 00:41:07,433
which modulates
drive and decision-making,

944
00:41:07,433 --> 00:41:09,066
and the prefrontal cortex,

945
00:41:09,066 --> 00:41:13,000
which handles reasoning
and higher-order cognition.

946
00:41:13,000 --> 00:41:15,700
An ignition of
distributed brain areas

947
00:41:15,700 --> 00:41:18,633
that come online together,
speak to each other,

948
00:41:18,633 --> 00:41:21,800
and broadcast this information
to the rest of the brain.

949
00:41:21,800 --> 00:41:23,133
And this is what
we think is occurring

950
00:41:23,133 --> 00:41:24,633
during conscious perception.

951
00:41:26,400 --> 00:41:28,100
BERLIN:
According to some experts,

952
00:41:28,100 --> 00:41:30,233
this communication
between brain regions

953
00:41:30,233 --> 00:41:32,466
is the signature
of consciousness.

954
00:41:33,833 --> 00:41:36,766
This discovery could have
real-world applications

955
00:41:36,766 --> 00:41:38,400
in matters of life and death.

956
00:41:38,400 --> 00:41:40,266
But that would take one
more step:

957
00:41:40,266 --> 00:41:43,866
figuring out how
to measure consciousness.

958
00:41:43,866 --> 00:41:45,733
♪ ♪

959
00:41:45,733 --> 00:41:47,800
SETH:
In science, when we struggle
to understand

960
00:41:47,800 --> 00:41:49,600
a phenomenon
that seems quite mysterious,

961
00:41:49,600 --> 00:41:53,233
it's often really important
to be able to measure it.

962
00:41:53,233 --> 00:41:55,533
So a few hundred years
ago,

963
00:41:55,533 --> 00:41:58,133
this happened with heat-- you
know, it was the development

964
00:41:58,133 --> 00:42:02,100
of thermometers that catalyzed
our understanding.

965
00:42:02,933 --> 00:42:05,133
Could something work for
consciousness the same way?

966
00:42:05,133 --> 00:42:07,300
Could we have
a consciousness-ometer

967
00:42:07,300 --> 00:42:08,700
that will lead us

968
00:42:08,700 --> 00:42:11,933
to a deeper understanding of
what consciousness is?

969
00:42:11,933 --> 00:42:15,300
BERLIN:
That deeper understanding
could transform

970
00:42:15,300 --> 00:42:17,666
the treatment of people with
brain injuries.

971
00:42:17,666 --> 00:42:20,633
BRIAN EDLOW:
So every year,
over a million people worldwide

972
00:42:20,633 --> 00:42:24,300
will come into an intensive care
unit unresponsive, comatose.

973
00:42:24,300 --> 00:42:27,166
The challenge that we face
is that our bedside exam--

974
00:42:27,166 --> 00:42:29,100
asking the person
to open their eyes,

975
00:42:29,100 --> 00:42:31,533
pinching them and
seeing if they'll respond,

976
00:42:31,533 --> 00:42:33,500
seeing if they move their arms
and their legs--

977
00:42:33,500 --> 00:42:36,600
that bedside exam
is fundamentally limited.

978
00:42:36,600 --> 00:42:39,400
BERLIN:
Limited because it often misses
people

979
00:42:39,400 --> 00:42:41,200
who are actually conscious.

980
00:42:41,200 --> 00:42:43,933
In this case,
we have a healthy volunteer

981
00:42:43,933 --> 00:42:45,733
from the "NOVA" team,

982
00:42:45,733 --> 00:42:47,333
but what if
she were unresponsive?

983
00:42:47,333 --> 00:42:50,066
How would we ever know
if she was conscious?

984
00:42:50,066 --> 00:42:52,133
Brian Edlow's team
at Mass. General Hospital

985
00:42:52,133 --> 00:42:55,166
is testing a new technique
to find out.

986
00:42:55,166 --> 00:42:57,300
So tell me, what are you doing
here?

987
00:42:57,300 --> 00:43:00,000
EDLOW:
We are pinging the brain
with a magnetic pulse

988
00:43:00,000 --> 00:43:02,566
and looking for
an electrical echo.

989
00:43:02,566 --> 00:43:06,266
The ping is transcranial
magnetic stimulation,

990
00:43:06,266 --> 00:43:07,900
or TMS.

991
00:43:07,900 --> 00:43:10,866
The echo is the key;
if it dies out quickly,

992
00:43:10,866 --> 00:43:13,166
the patient is unconscious--
they might be in a coma,

993
00:43:13,166 --> 00:43:15,200
deep sleep, or under anesthesia.

994
00:43:15,200 --> 00:43:18,766
If instead the echo rings out
across the brain

995
00:43:18,766 --> 00:43:20,666
and becomes more complex,

996
00:43:20,666 --> 00:43:23,600
the patient is likely
to be conscious and aware,

997
00:43:23,600 --> 00:43:26,200
even if they appear
unresponsive.

998
00:43:26,200 --> 00:43:27,266
EDLOW:
The analogy that we like
to use

999
00:43:27,266 --> 00:43:30,000
is throwing a pebble in a lake.

1000
00:43:30,000 --> 00:43:33,400
So the pebble represents the TMS
pulse to stimulate the brain,

1001
00:43:33,400 --> 00:43:36,166
and the brain waves,
the electrical ripples

1002
00:43:36,166 --> 00:43:41,233
that emanate from that pulse,
represent the waves in the lake.

1003
00:43:41,233 --> 00:43:43,433
The more
complex those waves are,

1004
00:43:43,433 --> 00:43:45,466
and the longer duration,

1005
00:43:45,466 --> 00:43:47,533
the more likely that person is
to be conscious.

1006
00:43:49,033 --> 00:43:52,466
BERLIN:
To detect those brain waves,
Edlow's team uses E.E.G.,

1007
00:43:52,466 --> 00:43:54,966
a tool that measures
electrical activity

1008
00:43:54,966 --> 00:43:57,800
in the brain, to quantify
the amount of complexity

1009
00:43:57,800 --> 00:44:00,200
a patient's brain bounces back.

1010
00:44:00,200 --> 00:44:01,533
Here's how it works.

1011
00:44:01,533 --> 00:44:02,766
♪ ♪

1012
00:44:02,766 --> 00:44:04,900
All neurons,
when poked by a magnet,

1013
00:44:04,900 --> 00:44:08,100
will kick back an electrical
signal that looks like this:

1014
00:44:08,100 --> 00:44:09,733
a brain wave.

1015
00:44:09,733 --> 00:44:11,833
But if the surrounding neurons
aren't healthy,

1016
00:44:11,833 --> 00:44:15,233
those brain waves won't get
very far.

1017
00:44:15,233 --> 00:44:17,466
It turns out that
in conscious people,

1018
00:44:17,466 --> 00:44:19,700
even those
who appear unresponsive,

1019
00:44:19,700 --> 00:44:22,966
not only do those brain
waves spread all over the brain,

1020
00:44:22,966 --> 00:44:26,366
but they become more complex
too-- it's as if,

1021
00:44:26,366 --> 00:44:28,466
to use music as the analogy,

1022
00:44:28,466 --> 00:44:31,933
what starts as a single repeated
note by a few neurons

1023
00:44:31,933 --> 00:44:35,733
eventually turns into
a coordinated symphony

1024
00:44:35,733 --> 00:44:37,500
of millions.

1025
00:44:37,500 --> 00:44:39,933
♪ ♪

1026
00:44:39,933 --> 00:44:43,900
DEHAENE:
We find is that there
is this explosion of complexity

1027
00:44:43,900 --> 00:44:45,500
only when
the person is conscious.

1028
00:44:45,500 --> 00:44:47,033
This complexity,

1029
00:44:47,033 --> 00:44:49,166
the way different brain areas
speak to each other,

1030
00:44:49,166 --> 00:44:52,066
is a signature,
a marker of consciousness.

1031
00:44:52,066 --> 00:44:55,366
BERLIN:
Studies of hundreds of
patients in various states--

1032
00:44:55,366 --> 00:44:58,000
from deep sleep to anesthesia
to coma--

1033
00:44:58,000 --> 00:45:01,800
have enabled scientists
to develop a complexity scale.

1034
00:45:01,800 --> 00:45:03,766
A score above
a certain threshold

1035
00:45:03,766 --> 00:45:08,333
means you are conscious or have
the capacity for consciousness.

1036
00:45:08,333 --> 00:45:10,000
EDLOW:
Multiple studies have now shown

1037
00:45:10,000 --> 00:45:13,800
that 15% to 20% of patients who
appear unresponsive,

1038
00:45:13,800 --> 00:45:16,766
they don't express themselves on
our behavioral exam,

1039
00:45:16,766 --> 00:45:18,533
they are actually conscious.

1040
00:45:18,533 --> 00:45:20,400
So, you know,
will this be able

1041
00:45:20,400 --> 00:45:22,566
to help
people in those states?

1042
00:45:22,566 --> 00:45:24,166
When we speak to families

1043
00:45:24,166 --> 00:45:26,033
about what matters to them most,

1044
00:45:26,033 --> 00:45:29,266
it is that patient's
current level of consciousness

1045
00:45:29,266 --> 00:45:33,266
and their potential for future
recovery of consciousness.

1046
00:45:33,266 --> 00:45:34,833
If families were to have

1047
00:45:34,833 --> 00:45:37,400
that information,
it could fundamentally affect

1048
00:45:37,400 --> 00:45:38,666
the decisions they make about

1049
00:45:38,666 --> 00:45:40,366
whether to continue
life-sustaining therapy.

1050
00:45:42,133 --> 00:45:46,566
DEHAENE:
Progress in the clinic
is extremely real and fast,

1051
00:45:46,566 --> 00:45:48,866
and people realize that the
problem of consciousness

1052
00:45:48,866 --> 00:45:50,433
is starting to be solved.

1053
00:45:50,433 --> 00:45:53,333
♪ ♪

1054
00:45:53,333 --> 00:45:55,233
BERLIN:
Starting to be solved,

1055
00:45:55,233 --> 00:45:57,000
because while we have
several clues

1056
00:45:57,000 --> 00:45:59,233
about how conscious awareness
might work in the brain,

1057
00:45:59,233 --> 00:46:01,700
this is only the beginning.

1058
00:46:01,700 --> 00:46:04,600
How all of those pieces
of brain activation add up

1059
00:46:04,600 --> 00:46:07,866
to you, a distinct individual
with a sense of self,

1060
00:46:07,866 --> 00:46:09,266
is still a mystery.

1061
00:46:09,266 --> 00:46:11,866
I know my brain creates
an internal experience

1062
00:46:11,866 --> 00:46:14,366
by knitting together bits
of sensory information,

1063
00:46:14,366 --> 00:46:16,333
filling in the gaps with its
best guess

1064
00:46:16,333 --> 00:46:18,166
of what's out there in the
world,

1065
00:46:18,166 --> 00:46:21,333
but what are those guesses
based on?

1066
00:46:21,333 --> 00:46:23,866
Memory.

1067
00:46:23,866 --> 00:46:27,766
Each of us has a life rich
with experiences to draw from.

1068
00:46:27,766 --> 00:46:30,166
Where we were born,
went to school,

1069
00:46:30,166 --> 00:46:32,300
who we fell in love with.

1070
00:46:32,300 --> 00:46:35,033
Memories are the cornerstone
of our identities,

1071
00:46:35,033 --> 00:46:36,500
but as it turns out,

1072
00:46:36,500 --> 00:46:39,533
they have
a very shaky foundation.

1073
00:46:39,533 --> 00:46:41,966
I could swear by it,

1074
00:46:41,966 --> 00:46:44,233
and would pass every
lie detector test, that...

1075
00:46:44,233 --> 00:46:45,733
I had met Mother Teresa.

1076
00:46:45,733 --> 00:46:46,833
But I hadn't.

1077
00:46:46,833 --> 00:46:48,400
Something that I wanted to
happen

1078
00:46:48,400 --> 00:46:49,633
but it never did happen.

1079
00:46:52,200 --> 00:46:53,766
SCHILLER:
The stories we tell

1080
00:46:53,766 --> 00:46:57,433
ourselves, or what we consider
our memory, is a construction.

1081
00:46:57,433 --> 00:46:59,666
We create these representations.

1082
00:46:59,666 --> 00:47:02,800
And they're very dynamic,
they constantly change.

1083
00:47:02,800 --> 00:47:05,000
You're kind of living a revision

1084
00:47:05,000 --> 00:47:07,400
of the story of your life,
constantly.

1085
00:47:08,400 --> 00:47:10,433
SETH:
The more
often we recall things,

1086
00:47:10,433 --> 00:47:12,900
the less objectively accurate

1087
00:47:12,900 --> 00:47:14,233
our memories become.

1088
00:47:15,233 --> 00:47:17,600
BERLIN:
It turns out that every
time you a recall a memory--

1089
00:47:17,600 --> 00:47:19,733
your first kiss,

1090
00:47:19,733 --> 00:47:21,500
graduating from college,

1091
00:47:21,500 --> 00:47:23,533
the death of a loved one--

1092
00:47:23,533 --> 00:47:28,133
the very act of recollection
makes it vulnerable to change.

1093
00:47:28,133 --> 00:47:30,000
SCHILLER:
So when you experience
a new event,

1094
00:47:30,000 --> 00:47:32,533
it has to be
stored in the brain.

1095
00:47:32,533 --> 00:47:34,066
And then, we used to think that

1096
00:47:34,066 --> 00:47:35,733
whenever you think about that
event,

1097
00:47:35,733 --> 00:47:39,500
you retrieve the same original
memory.

1098
00:47:39,500 --> 00:47:43,233
But what we got to
realize in the last few decades

1099
00:47:43,233 --> 00:47:45,400
is that whenever
you retrieve a memory,

1100
00:47:45,400 --> 00:47:47,633
it goes back
to an unstable state.

1101
00:47:47,633 --> 00:47:53,066
BERLIN:
In 2000, memory scientist
Eric Kandel won the Nobel Prize

1102
00:47:53,066 --> 00:47:56,533
for showing that each memory
creates new synapses,

1103
00:47:56,533 --> 00:47:58,366
connections
that store the memory.

1104
00:47:58,366 --> 00:48:02,133
But what happens
when you recall it?

1105
00:48:02,133 --> 00:48:03,933
Every time you remember it,

1106
00:48:03,933 --> 00:48:05,633
you bring it up into your
working memory

1107
00:48:05,633 --> 00:48:06,866
and you perceive it,

1108
00:48:06,866 --> 00:48:09,033
and you destroy the long-term
memory.

1109
00:48:09,033 --> 00:48:12,000
And you actually have

1110
00:48:12,000 --> 00:48:14,033
to recast it into long-term
memory

1111
00:48:14,033 --> 00:48:15,766
when you re-remember it.

1112
00:48:15,766 --> 00:48:17,766
So every single time
you remember something,

1113
00:48:17,766 --> 00:48:19,400
you actually add more
noise to it,

1114
00:48:19,400 --> 00:48:21,766
so that it's more and more
and more false

1115
00:48:21,766 --> 00:48:23,700
throughout time.

1116
00:48:23,700 --> 00:48:26,700
BERLIN:
This mechanism,
called reconsolidation,

1117
00:48:26,700 --> 00:48:28,366
was first discovered in rodents,

1118
00:48:28,366 --> 00:48:31,333
where neuroscientists witnessed
what happens

1119
00:48:31,333 --> 00:48:33,366
when a memory gets recollected:

1120
00:48:33,366 --> 00:48:36,000
for the memory to return
to long-term storage,

1121
00:48:36,000 --> 00:48:40,533
the connections between neurons
actually have to get rebuilt.

1122
00:48:40,533 --> 00:48:43,100
Recent experiments
have suggested

1123
00:48:43,100 --> 00:48:46,166
this is likely a mechanism in
human brains, as well,

1124
00:48:46,166 --> 00:48:49,700
because certain drugs known
to disrupt reconsolidation

1125
00:48:49,700 --> 00:48:52,600
have been shown
to alter human memories.

1126
00:48:53,733 --> 00:48:57,800
FENTON:
We're stuck with the problem
of, how do we know what is true?

1127
00:48:57,800 --> 00:49:00,133
How do we know what's real?

1128
00:49:00,133 --> 00:49:02,000
And maybe

1129
00:49:02,000 --> 00:49:04,766
part of the recognition is,
some of those things

1130
00:49:04,766 --> 00:49:07,900
don't matter as
much as we think they do.

1131
00:49:10,533 --> 00:49:13,433
SCHILLER:
If we think about the fact that
maybe our memories are not

1132
00:49:13,433 --> 00:49:15,033
as they originally happened,

1133
00:49:15,033 --> 00:49:18,300
it could be a scary thought,
because then, who are we?

1134
00:49:18,300 --> 00:49:21,466
I think you need
to think about it as something

1135
00:49:21,466 --> 00:49:24,700
more liberating,
because if you're stuck

1136
00:49:24,700 --> 00:49:26,766
with original representations,

1137
00:49:26,766 --> 00:49:28,466
you're kind
of stuck in the past.

1138
00:49:29,566 --> 00:49:33,366
BERLIN:
Just like our perceptions,
our sense of self is dynamic,

1139
00:49:33,366 --> 00:49:35,933
built to serve
us in the present.

1140
00:49:35,933 --> 00:49:37,800
SETH:
Our experience of, of self

1141
00:49:37,800 --> 00:49:40,100
is a construction at all sorts
of different levels.

1142
00:49:40,100 --> 00:49:42,566
What the brain is doing,
is interested in,

1143
00:49:42,566 --> 00:49:46,033
is weaving
together a kind of story.

1144
00:49:46,033 --> 00:49:49,700
FENTON:
The brain is
a storytelling machine, right?

1145
00:49:49,700 --> 00:49:53,033
It's a machine that's
designed to make predictions.

1146
00:49:53,033 --> 00:49:56,800
KASTHURI:
The narratives
that we tell ourselves

1147
00:49:56,800 --> 00:49:58,866
are the biggest illusions
that we ever participate in.

1148
00:49:58,866 --> 00:50:01,300
Your sense of who you are is an
illusion,

1149
00:50:01,300 --> 00:50:04,333
as everything else--
you're no exception.

1150
00:50:05,500 --> 00:50:08,300
BERLIN:
But if even our sense
of self is an illusion,

1151
00:50:08,300 --> 00:50:10,600
where does that leave us?

1152
00:50:10,600 --> 00:50:13,633
MARTINEZ-CONDE (chuckles):
Trust the illusion,
that's the only thing

1153
00:50:13,633 --> 00:50:16,166
that we can be sure of,

1154
00:50:16,166 --> 00:50:21,133
that what
we perceive is not what's there.

1155
00:50:26,433 --> 00:50:28,533
BERLIN:
So all these years later,

1156
00:50:28,533 --> 00:50:31,566
in my quest to understand where
my thoughts come from

1157
00:50:31,566 --> 00:50:33,400
and how my brain works,

1158
00:50:33,400 --> 00:50:36,466
I've learned that my brain
is an exquisite machine

1159
00:50:36,466 --> 00:50:40,066
that perceives reality
in the service of survival,

1160
00:50:40,066 --> 00:50:42,033
not accuracy.

1161
00:50:42,033 --> 00:50:44,733
The world I carry inside of my
head

1162
00:50:44,733 --> 00:50:46,333
is a construction of my
brain

1163
00:50:46,333 --> 00:50:49,000
built on bits of sensory
information

1164
00:50:49,000 --> 00:50:50,900
woven together with memory

1165
00:50:50,900 --> 00:50:53,966
to create a conscious
experience.

1166
00:50:53,966 --> 00:50:56,400
Now, to some this might sound
scary,

1167
00:50:56,400 --> 00:50:59,133
but to me, it's inspiring.

1168
00:50:59,133 --> 00:51:02,866
♪ ♪

1169
00:51:02,866 --> 00:51:04,866
SETH:
The simple act of
opening our eyes

1170
00:51:04,866 --> 00:51:06,666
and seeing a world,

1171
00:51:06,666 --> 00:51:08,966
we should not
take that for granted.

1172
00:51:08,966 --> 00:51:10,900
And in realizing

1173
00:51:10,900 --> 00:51:14,733
what a miracle of neural
computation

1174
00:51:14,733 --> 00:51:16,300
is going on under the hood,

1175
00:51:16,300 --> 00:51:18,733
to give us even the simplest
experiences,

1176
00:51:18,733 --> 00:51:20,300
I think this adds value,

1177
00:51:20,300 --> 00:51:23,000
it adds meaning,
it adds depth to our lives.

1178
00:51:23,000 --> 00:51:24,800
♪ ♪

1179
00:51:24,800 --> 00:51:27,333
DEHAENE:
I think it's liberating
to understand

1180
00:51:27,333 --> 00:51:32,133
that we rise from
this organization of matter.

1181
00:51:32,133 --> 00:51:34,833
It means that we can
be a little bit more humble.

1182
00:51:34,833 --> 00:51:38,400
We are gorgeous machines
designed by evolution

1183
00:51:38,400 --> 00:51:43,533
as well as by our environment,
education, friends, families.

1184
00:51:43,533 --> 00:51:45,533
 
All of that is
inscribed in our brains.

1185
00:51:48,166 --> 00:51:51,833
SAXE:
Sometimes when I wonder what I'm
doing with my life,

1186
00:51:51,833 --> 00:51:55,033
I think how is it that a spatial

1187
00:51:55,033 --> 00:51:57,266
and temporal pattern
of electrical signals

1188
00:51:57,266 --> 00:52:01,100
passing between cells in
our brains makes us who we are?

1189
00:52:01,100 --> 00:52:04,533
That just being
a part of the team

1190
00:52:04,533 --> 00:52:09,300
asking that question
is worth keeping going for.

1191
00:52:09,300 --> 00:52:13,033
♪ ♪

1192
00:52:31,400 --> 00:52:38,933
♪ ♪

1193
00:52:42,766 --> 00:52:50,300
♪ ♪

1194
00:52:51,933 --> 00:52:59,466
♪ ♪

1195
00:53:01,166 --> 00:53:08,700
♪ ♪

1196
00:53:14,433 --> 00:53:21,600
♪ ♪