Would you like to inspect the original subtitles? These are the user uploaded subtitles that are being translated:
1
00:00:01,100 --> 00:00:03,136
♪ ♪
2
00:00:04,970 --> 00:00:07,874
♪ ♪
3
00:00:07,907 --> 00:00:10,109
ANIL SETH:
The brain is one of
the most complex objects
4
00:00:10,142 --> 00:00:13,246
that we know of in the universe.
5
00:00:13,279 --> 00:00:16,382
BOBBY KASTHURI:
There are more connections
in your brain
6
00:00:16,415 --> 00:00:20,420
than there are stars
in the Milky Way galaxy.
7
00:00:20,453 --> 00:00:23,823
So, we literally walk around
with about 10,000 galaxies'
8
00:00:23,856 --> 00:00:25,758
worth of neuronal connections
9
00:00:25,791 --> 00:00:27,260
in one of our brains.
10
00:00:27,293 --> 00:00:29,262
HEATHER BERLIN:
That vast web of
connections creates you.
11
00:00:29,295 --> 00:00:30,963
But how?
12
00:00:30,996 --> 00:00:34,300
NANCY KANWISHER:
Figuring out how the brain
implements the mind
13
00:00:34,333 --> 00:00:37,103
is a massive challenge.
14
00:00:37,136 --> 00:00:38,404
♪ ♪
15
00:00:38,437 --> 00:00:39,672
SETH:
It seems as though
16
00:00:39,705 --> 00:00:41,841
the world just pours itself
into the mind
17
00:00:41,874 --> 00:00:44,644
through the transparent windows
of the eyes
18
00:00:44,677 --> 00:00:46,813
and the ears and,
and all our other senses.
19
00:00:49,381 --> 00:00:51,717
♪ ♪
20
00:00:51,750 --> 00:00:53,252
BERLIN:
But is what we see,
21
00:00:53,285 --> 00:00:54,987
hear,
22
00:00:55,020 --> 00:00:57,824
and feel real?
23
00:00:57,857 --> 00:01:00,059
You might think that the reality
24
00:01:00,092 --> 00:01:02,829
outside is actually
what you're perceiving.
25
00:01:02,862 --> 00:01:05,865
And the answer is no,
it really isn't.
26
00:01:05,898 --> 00:01:07,733
Almost at the very first moment,
27
00:01:07,766 --> 00:01:09,268
we are transforming reality.
28
00:01:09,301 --> 00:01:10,770
It feels so real
29
00:01:10,803 --> 00:01:12,405
because we don't know better.
30
00:01:12,438 --> 00:01:14,273
Think about illusions.
31
00:01:14,306 --> 00:01:15,875
ROSA LAFER-SOUSA:
Do you remember the dress?
32
00:01:15,908 --> 00:01:18,344
Of course-- it's like a
celebrity, the dress.
33
00:01:18,377 --> 00:01:19,946
A polarizing debate
34
00:01:19,979 --> 00:01:21,981
that took over the internet.
35
00:01:22,014 --> 00:01:23,516
White and gold.
36
00:01:22,014 --> 00:01:23,516
Blue and black.
37
00:01:23,549 --> 00:01:26,152
SETH:
Illusions are fascinating;
38
00:01:26,185 --> 00:01:27,286
they're like
fractures in the matrix.
39
00:01:27,319 --> 00:01:28,287
BERLIN:
Ow!
40
00:01:28,320 --> 00:01:30,456
Isn't that interesting?
41
00:01:28,320 --> 00:01:30,456
Whoa!
42
00:01:30,489 --> 00:01:32,291
They reveal to us that the way
43
00:01:32,324 --> 00:01:35,695
we perceive things isn't
necessarily the way they are.
44
00:01:35,728 --> 00:01:39,031
BERLIN:
Could you be
the biggest illusion of all?
45
00:01:39,064 --> 00:01:42,835
SUSANA MARTINEZ-CONDE:
Your sense of who you are
is an illusion
46
00:01:42,868 --> 00:01:45,105
as everything else;
you're no exception.
47
00:01:46,106 --> 00:01:49,542
BERLIN:
"Your Brain:
Perception Deception."
48
00:01:49,575 --> 00:01:51,511
Right now, on "NOVA."
49
00:01:51,544 --> 00:01:56,182
♪ ♪
50
00:02:03,756 --> 00:02:05,391
MAN:
Okay, rolling.
51
00:02:05,424 --> 00:02:11,030
♪ ♪
52
00:02:26,679 --> 00:02:29,082
Take three.
53
00:02:32,384 --> 00:02:35,254
BERLIN:
Have you ever thought
about what's real?
54
00:02:35,287 --> 00:02:37,624
(echoing)
55
00:02:44,029 --> 00:02:46,832
Somehow the whole world out
there gets inside my head.
56
00:02:46,865 --> 00:02:50,570
How do I know
what I see, what I hear,
57
00:02:50,603 --> 00:02:53,306
what I feel is right?
58
00:02:53,339 --> 00:02:55,541
It's a question
that's fascinated me
59
00:02:55,574 --> 00:02:58,711
ever since I was a little girl.
60
00:02:58,744 --> 00:03:01,147
I couldn't sleep one night,
61
00:03:01,180 --> 00:03:04,551
and I had this
thought for the first time:
62
00:03:08,520 --> 00:03:10,890
And then I thought, well,
even if I don't have a body,
63
00:03:10,923 --> 00:03:14,360
can I at least keep
my own inner thoughts?
64
00:03:14,393 --> 00:03:16,629
So I asked my dad the next day,
65
00:03:16,662 --> 00:03:19,532
"Dad, where do my thoughts come
from?"
66
00:03:19,565 --> 00:03:23,169
And he said,
"They come from your brain."
67
00:03:23,202 --> 00:03:24,570
(explosion echoes)
68
00:03:24,603 --> 00:03:26,405
♪ ♪
69
00:03:26,438 --> 00:03:29,309
"Your brain."
I was hooked.
70
00:03:30,342 --> 00:03:32,278
This bag of
jelly between my ears,
71
00:03:32,311 --> 00:03:34,013
how does it work?
72
00:03:34,046 --> 00:03:35,982
STANISLAS DEHAENE:
I think it's one of the ultimate
mysteries.
73
00:03:36,015 --> 00:03:38,718
How matter becomes thought.
74
00:03:40,286 --> 00:03:42,922
ANDRÉ FENTON:
To answer that question would be
perhaps
75
00:03:42,955 --> 00:03:44,958
the highest human
achievement to date.
76
00:03:46,425 --> 00:03:47,893
DANIELA SCHILLER:
I mean, forget about scientific
quest.
77
00:03:47,926 --> 00:03:49,729
It's a human quest.
78
00:04:03,542 --> 00:04:06,779
♪ ♪
79
00:04:06,812 --> 00:04:09,548
BERLIN:
To find answers,
80
00:04:09,581 --> 00:04:12,719
I became a neuroscientist
and a psychologist.
81
00:04:16,922 --> 00:04:19,025
I'm Heather Berlin,
82
00:04:19,058 --> 00:04:22,028
and my journey to understand
my brain begins with a question.
83
00:04:23,529 --> 00:04:25,531
How does the world out there,
84
00:04:25,564 --> 00:04:28,200
with all its beauty
and complexity,
85
00:04:28,233 --> 00:04:30,503
get inside our heads?
86
00:04:30,536 --> 00:04:33,739
♪ ♪
87
00:04:33,772 --> 00:04:35,041
Think about it.
88
00:04:35,074 --> 00:04:37,143
Imagine for a second
you're a brain,
89
00:04:37,176 --> 00:04:39,578
sealed inside your skull.
90
00:04:39,611 --> 00:04:42,882
There's no light, no sound.
91
00:04:44,383 --> 00:04:45,985
KASTHURI:
You're a massive collection
92
00:04:46,018 --> 00:04:48,788
of billions and billions
of cells that are living
93
00:04:48,821 --> 00:04:51,490
in this weird pond that
is entirely devoid
94
00:04:51,523 --> 00:04:54,260
of all of the sensations,
and that somehow,
95
00:04:54,293 --> 00:04:57,663
through chemistry
and electricity, all of these
96
00:04:57,696 --> 00:05:00,032
perceptions and memories
of the world
97
00:05:00,065 --> 00:05:01,701
originate in our brains.
98
00:05:03,335 --> 00:05:05,037
BERLIN:
All brains-- from the tiny fish
99
00:05:05,070 --> 00:05:08,341
to the enormous elephant--
contain microscopic cells
100
00:05:08,374 --> 00:05:12,345
called neurons, and one of their
jobs is to translate input
101
00:05:12,378 --> 00:05:14,580
from the external world,
whether that's light,
102
00:05:14,613 --> 00:05:17,249
heat, sound, or
pressure, for instance,
103
00:05:17,282 --> 00:05:21,220
into electrochemical signals
the organism can use to act.
104
00:05:21,253 --> 00:05:23,489
What might be surprising to you
is that
105
00:05:23,522 --> 00:05:26,058
as neurons process sensory
signals,
106
00:05:26,091 --> 00:05:28,527
they create an edited version
of reality,
107
00:05:28,560 --> 00:05:32,031
even on the most basic level.
108
00:05:32,064 --> 00:05:35,768
KASTHURI:
We're deciding to throw away 99%
of the world.
109
00:05:35,801 --> 00:05:38,838
Almost at the very first moment,
we are transforming reality
110
00:05:38,871 --> 00:05:40,940
into something we could use.
111
00:05:40,973 --> 00:05:44,777
BERLIN:
Neurons transform reality
by competing with each other.
112
00:05:44,810 --> 00:05:47,980
When a creature touches, smells,
sees, or hears something,
113
00:05:48,013 --> 00:05:51,517
its sensory neurons fire;
some a little, some a lot,
114
00:05:51,550 --> 00:05:54,186
depending on where the
physical signal is strongest.
115
00:05:54,219 --> 00:05:56,822
But follow those signals
down towards its brain,
116
00:05:56,855 --> 00:05:59,859
you'll see that the weaker ones
get stamped out.
117
00:06:01,193 --> 00:06:03,028
For simple brains,
say, the brain of a crab,
118
00:06:03,061 --> 00:06:07,266
a diffuse light to the eye
becomes a sharp beam.
119
00:06:07,299 --> 00:06:10,336
For more complex brains
like ours,
120
00:06:10,369 --> 00:06:12,171
it's in part what makes
you think
121
00:06:12,204 --> 00:06:15,741
that these two squares
are completely different colors,
122
00:06:15,774 --> 00:06:18,944
but actually, they're identical.
123
00:06:18,977 --> 00:06:24,283
♪ ♪
124
00:06:24,316 --> 00:06:26,585
MARTINEZ-CONDE:
Think about illusions.
125
00:06:26,618 --> 00:06:28,921
First, they're a lot of fun,
but as neuroscientists...
126
00:06:28,954 --> 00:06:31,357
Whoa...
127
00:06:31,390 --> 00:06:33,826
MARTINEZ-CONDE:
...illusions are very important
to us.
128
00:06:33,859 --> 00:06:38,164
Because of this discrepancy
between objective reality
129
00:06:38,197 --> 00:06:41,367
and subjective perception,
we can use these illusions
130
00:06:41,400 --> 00:06:45,571
as a handle to try to
understand what the brain
131
00:06:45,604 --> 00:06:48,507
is doing all the time.
132
00:06:48,540 --> 00:06:51,143
BERLIN:
Susana Martinez-Conde,
along with her partner
133
00:06:51,176 --> 00:06:52,511
and collaborator
Stephen Macknik,
134
00:06:52,544 --> 00:06:55,181
are among the
world's preeminent experts
135
00:06:55,214 --> 00:06:57,149
on illusions and perception,
136
00:06:57,182 --> 00:07:00,152
and what they tell us
about how the brain works.
137
00:07:00,185 --> 00:07:02,221
Ha, now what?
138
00:07:02,254 --> 00:07:03,522
(both laughing)
139
00:07:03,555 --> 00:07:05,024
MARTINEZ-CONDE:
To give a different example,
140
00:07:05,057 --> 00:07:09,395
Adelson's checkerboard illusion,
this is so striking because
141
00:07:09,428 --> 00:07:14,300
you see some of the checks
as dark and others as bright,
142
00:07:14,333 --> 00:07:18,904
but you realize that it is
exactly the same shade of gray.
143
00:07:18,937 --> 00:07:20,506
BERLIN:
Don't believe it?
144
00:07:20,539 --> 00:07:23,642
Look at the squares labeled
A and B.
145
00:07:23,675 --> 00:07:26,645
A looks darker,
right?
146
00:07:26,678 --> 00:07:29,048
Wrong-- that's the illusion.
147
00:07:29,081 --> 00:07:32,251
That's because your brain
is adjusting for the shadow.
148
00:07:32,284 --> 00:07:34,887
MARTINEZ-CONDE:
What's happening is that your
brain is considering
149
00:07:34,920 --> 00:07:39,391
the light source and basically
subtracting that light source
150
00:07:39,424 --> 00:07:41,594
from your resulting perception.
151
00:07:41,627 --> 00:07:44,497
Your brain is performing an
interpretation,
152
00:07:44,530 --> 00:07:47,700
a shortcut, if you will,
to arrive
153
00:07:47,733 --> 00:07:49,635
at a perception.
154
00:07:49,668 --> 00:07:53,038
BERLIN:
If the brain's shortcuts
distort reality this much,
155
00:07:53,071 --> 00:07:56,876
how much of the world
are we really seeing?
156
00:07:56,909 --> 00:07:58,511
STEPHEN MACKNIK:
What you need to understand
157
00:07:58,544 --> 00:08:02,448
is that we really can't see
most of the world around us.
158
00:08:02,481 --> 00:08:04,350
We're effectively blind
159
00:08:04,383 --> 00:08:06,886
to 99.9% of the world around us
at any given time.
160
00:08:06,919 --> 00:08:09,455
If you hold out your thumb
at arm's length...
161
00:08:09,488 --> 00:08:11,724
Mm-hmm.
162
00:08:09,488 --> 00:08:11,724
And you straighten your elbow
and you look at your thumbnail,
163
00:08:11,757 --> 00:08:14,660
your thumbnail is about
one degree of visual angle here,
164
00:08:14,693 --> 00:08:17,663
and it turns out
that that's the only place
165
00:08:17,696 --> 00:08:19,398
we can actually
see with 20/20 vision.
166
00:08:19,431 --> 00:08:21,967
Wow.
167
00:08:19,431 --> 00:08:21,967
And everywhere else,
we're legally blind.
168
00:08:22,000 --> 00:08:24,904
BERLIN:
It might sound hard to believe,
169
00:08:24,937 --> 00:08:27,473
but human vision is really
like this.
170
00:08:27,506 --> 00:08:29,408
You actually only see detail
171
00:08:29,441 --> 00:08:31,610
in about one percent
of your visual field.
172
00:08:31,643 --> 00:08:34,346
That's because only
a tiny portion of the world
173
00:08:34,379 --> 00:08:37,616
can be processed
in detail by the retina.
174
00:08:37,649 --> 00:08:40,753
It feels like I'm seeing
the whole world in 20/20 vision.
175
00:08:40,786 --> 00:08:44,123
And it's almost all completely
made up in your brain,
176
00:08:44,156 --> 00:08:47,526
based on assumptions and
models of how the world works
177
00:08:47,559 --> 00:08:50,129
and just a tiny bit of
high-quality visual information.
178
00:08:50,162 --> 00:08:51,830
Let me demonstrate this
to you.
179
00:08:51,863 --> 00:08:53,766
I know it's
kind of hard to believe...
180
00:08:51,863 --> 00:08:53,766
Mm-hmm.
181
00:08:53,799 --> 00:08:55,568
...because you've been
having your whole life
182
00:08:55,601 --> 00:08:56,602
where you feel like
everything's continuous.
183
00:08:55,601 --> 00:08:56,602
Yeah, show me the data.
184
00:08:56,635 --> 00:08:58,070
(laughs)
185
00:08:56,635 --> 00:08:58,070
Show me the evidence.
186
00:08:58,103 --> 00:09:00,506
Let's look at an eye-tracker
and look at your eyes
187
00:09:00,539 --> 00:09:01,874
and how they actually work.
188
00:09:01,907 --> 00:09:04,677
And if you put your head
in this headrest...
189
00:09:04,710 --> 00:09:07,313
Mm-hmm.
190
00:09:04,710 --> 00:09:07,313
...we'll point the camera
at your eyeballs
191
00:09:07,346 --> 00:09:08,714
and we'll actually be able
to see
192
00:09:08,747 --> 00:09:11,417
where your eyeballs
point during this demonstration.
193
00:09:11,450 --> 00:09:14,253
Feels like "Clockwork Orange."
(chuckles)
194
00:09:14,286 --> 00:09:16,589
"Buck lived at a big house
195
00:09:16,622 --> 00:09:18,991
in the sun-kissed
Santa Clara Valley."
196
00:09:20,058 --> 00:09:22,461
BERLIN:
First up, a reading demo.
197
00:09:22,494 --> 00:09:24,830
Though most of your screen
may be filled with Xs,
198
00:09:24,863 --> 00:09:27,499
to me, it just feels like
normal reading.
199
00:09:27,532 --> 00:09:29,301
I barely see the Xs,
200
00:09:29,334 --> 00:09:31,103
and that's because the display
of letters
201
00:09:31,136 --> 00:09:33,872
is tied to my eye movements.
202
00:09:33,905 --> 00:09:35,207
BERLIN:
Well, it's just, the words are
being
203
00:09:35,240 --> 00:09:37,042
revealed
depending on where I look.
204
00:09:37,075 --> 00:09:39,178
That's right,
so as you move your eyes...
205
00:09:39,211 --> 00:09:40,980
So weird.
206
00:09:39,211 --> 00:09:40,980
...the words are
revealed to you.
207
00:09:41,013 --> 00:09:43,649
But we don't move our eyes
in the same way you do.
208
00:09:43,682 --> 00:09:46,585
So we just see a bunch of Xs
most of the time.
209
00:09:46,618 --> 00:09:49,521
BERLIN:
What turns out to be critical
is my eye movements.
210
00:09:49,554 --> 00:09:53,525
MACKNIK:
So our eye movements program
what part of this
211
00:09:53,558 --> 00:09:55,327
high-quality
piece of visual real estate
212
00:09:55,360 --> 00:09:57,129
we're going to put
where and at what time.
213
00:09:57,162 --> 00:10:00,399
BERLIN:
The human eye moves about
three times per second.
214
00:10:00,432 --> 00:10:03,569
We take it for granted,
but without these movements,
215
00:10:03,602 --> 00:10:05,237
we'd be basically blind,
216
00:10:05,270 --> 00:10:07,272
as Steve is about to show me.
217
00:10:07,305 --> 00:10:09,475
MACKNIK:
In this demonstration,
it's the opposite.
218
00:10:09,508 --> 00:10:12,511
Here we're blocking what you
can possibly see, right?
219
00:10:12,544 --> 00:10:15,447
BERLIN:
Though you may see a whole scene
with a square moving around,
220
00:10:15,480 --> 00:10:17,716
all I see is the square!
221
00:10:17,749 --> 00:10:20,185
I can tell something is around
the edges, but it's blurry.
222
00:10:20,218 --> 00:10:23,656
Whenever I try to look,
the square moves with my eyes
223
00:10:23,689 --> 00:10:25,290
and it's blocked.
224
00:10:25,323 --> 00:10:27,526
BERLIN:
This is so frustrating,
this one.
225
00:10:27,559 --> 00:10:30,696
Who has their hand up in
this image?
226
00:10:32,030 --> 00:10:35,334
BERLIN:
Uh... I think that guy
down there?
227
00:10:35,367 --> 00:10:37,603
MACKNIK:
That's right, but it's very hard
for you to see,
228
00:10:37,636 --> 00:10:39,538
right?
229
00:10:37,636 --> 00:10:39,538
Every time I look at him,
it, yeah.
230
00:10:39,571 --> 00:10:43,342
It disappears because this
block, it blocks it.
231
00:10:43,375 --> 00:10:45,611
MACKNIK:
This actually is interesting
232
00:10:45,644 --> 00:10:48,480
because it's in high-quality
vision wherever you look,
233
00:10:48,513 --> 00:10:51,083
but it's blurry
in the surround.
234
00:10:51,116 --> 00:10:53,786
BERLIN:
Now the scene looks normal
to me,
235
00:10:53,819 --> 00:10:55,521
but mostly blurry to you
236
00:10:55,554 --> 00:10:57,690
because your eye movements
don't match mine.
237
00:10:57,723 --> 00:10:59,291
MACKNIK:
Wherever you happen to look,
238
00:10:59,324 --> 00:11:02,761
you have high-quality
image processing happening
239
00:11:02,794 --> 00:11:05,297
and the surround
is completely blurry.
240
00:11:05,330 --> 00:11:09,068
This kind of represents exactly
what your visual system
241
00:11:09,101 --> 00:11:11,904
looks like all the time
anyway.
242
00:11:11,937 --> 00:11:14,273
So why would our brains
be built this way?
243
00:11:14,306 --> 00:11:16,008
Well, think about what the
alternative is.
244
00:11:16,041 --> 00:11:17,276
What if
we didn't have eye movements?
245
00:11:17,309 --> 00:11:18,577
Well, if we didn't have
eye movements,
246
00:11:18,610 --> 00:11:20,612
and we just wanted to see
the entire world,
247
00:11:20,645 --> 00:11:21,914
we'd need to have our retinas
248
00:11:21,947 --> 00:11:23,682
see everything
in very high quality.
249
00:11:23,715 --> 00:11:25,918
Our brains would be
600 times bigger,
250
00:11:25,951 --> 00:11:28,721
and you gotta remember, the
visual system's our best sense.
251
00:11:28,754 --> 00:11:30,355
This is our richest sense.
252
00:11:30,388 --> 00:11:33,292
So our other senses are,
are even more impoverished.
253
00:11:33,325 --> 00:11:36,962
BERLIN:
Here's how your brain really
sees the world.
254
00:11:36,995 --> 00:11:38,697
It's easy to think it's
like this.
255
00:11:38,730 --> 00:11:41,600
You open your eyes
and the whole world pours in.
256
00:11:41,633 --> 00:11:43,569
But really, it's like this.
257
00:11:45,904 --> 00:11:48,474
Your eyes sample tiny pieces
of the world
258
00:11:48,507 --> 00:11:52,511
and the brain fills in the
rest-- constantly, all the time.
259
00:11:52,544 --> 00:11:53,912
KANWISHER:
We feel like we have
260
00:11:53,945 --> 00:11:57,549
this incredibly rich, wide,
full, detailed percept
261
00:11:57,582 --> 00:11:59,284
of what's going on
moment to moment,
262
00:11:59,317 --> 00:12:01,553
and that's probably pretty
illusory.
263
00:12:01,586 --> 00:12:04,089
What we're actually
aware of is a tiny subset
264
00:12:04,122 --> 00:12:06,225
of the information
that comes in through our eyes.
265
00:12:06,258 --> 00:12:08,227
BERLIN:
Don't believe it?
266
00:12:08,260 --> 00:12:09,728
Consider this:
267
00:12:09,761 --> 00:12:12,564
your optic nerve is what
connects your eye to your brain,
268
00:12:12,597 --> 00:12:15,200
and its location near the center
of your retina
269
00:12:15,233 --> 00:12:17,136
effectively creates a blind spot
270
00:12:17,169 --> 00:12:19,304
near the center of your
visual field.
271
00:12:19,337 --> 00:12:23,275
And yet, you don't experience
the blind spot-- why?
272
00:12:23,308 --> 00:12:26,011
The brain samples the area
near the blind spot
273
00:12:26,044 --> 00:12:29,014
and fills
in the gap with its best guess.
274
00:12:29,047 --> 00:12:30,682
KASTHURI:
It's probably not fair
275
00:12:30,715 --> 00:12:32,985
to say that we completely
confabulate the world,
276
00:12:33,018 --> 00:12:35,154
it's just
that we probably represent
277
00:12:35,187 --> 00:12:38,056
one percent of it at any
particular moment in time.
278
00:12:38,089 --> 00:12:41,393
So, it's a constant updating
between what I see with,
279
00:12:41,426 --> 00:12:44,630
versus what I remember, versus
what I expect.
280
00:12:44,663 --> 00:12:47,299
And it's that dance between
those three
281
00:12:47,332 --> 00:12:49,835
that actually gives us our
sense of reality.
282
00:12:49,868 --> 00:12:53,172
BERLIN:
And amazingly,
that edited reality--
283
00:12:53,205 --> 00:12:54,873
despite its limitations--
284
00:12:54,906 --> 00:12:56,742
serves us quite well.
285
00:12:56,775 --> 00:12:58,811
KASTHURI:
You might ask,
"If I'm just keeping track
286
00:12:58,844 --> 00:13:00,345
"of one percent of the
information in the world,
287
00:13:00,378 --> 00:13:02,581
how can I drive a car?"
288
00:13:02,614 --> 00:13:05,150
And it turns out that first one
percent
289
00:13:05,183 --> 00:13:06,819
of the information that comes
in from the world
290
00:13:06,852 --> 00:13:08,687
is actually an enormous
amount of information.
(chuckling)
291
00:13:08,720 --> 00:13:11,423
If we had to actually
pay attention
292
00:13:11,456 --> 00:13:14,960
to everything on the road at
the, at one particular time,
293
00:13:14,993 --> 00:13:17,396
it would take minutes,
maybe even longer,
294
00:13:17,429 --> 00:13:19,198
before I decide to turn
the wheel right
295
00:13:19,231 --> 00:13:21,633
or to turn the wheel left.
296
00:13:21,666 --> 00:13:23,168
♪ ♪
297
00:13:23,201 --> 00:13:25,871
BERLIN:
By understanding how
my senses really work,
298
00:13:25,904 --> 00:13:27,739
I'm getting a peek behind
the curtain:
299
00:13:27,772 --> 00:13:31,911
what my brain is really up
to outside of my awareness.
300
00:13:33,612 --> 00:13:37,449
MARTINEZ-CONDE:
Based on this very tiny amount
of information,
301
00:13:37,482 --> 00:13:41,053
we construct this
grand simulation
302
00:13:41,086 --> 00:13:44,857
of the visual world around us.
303
00:13:44,890 --> 00:13:48,160
It feels so real
because we don't know better.
304
00:13:49,294 --> 00:13:52,264
BERLIN:
And most of the time,
we all agree on that simulation.
305
00:13:52,297 --> 00:13:56,401
It's when we don't
that we can learn something.
306
00:13:56,434 --> 00:13:58,871
So do you remember
the dress?
307
00:13:56,434 --> 00:13:58,871
Of course.
308
00:13:58,904 --> 00:14:02,307
Did you see this dress
or this one?
309
00:14:02,340 --> 00:14:03,909
It's a simple question,
310
00:14:03,942 --> 00:14:06,345
but the answer has divided
friends and family.
311
00:14:06,378 --> 00:14:08,180
White and gold.
312
00:14:06,378 --> 00:14:08,180
Blue and black.
313
00:14:08,213 --> 00:14:10,015
I remember it caused
quite the stir, right?
314
00:14:10,048 --> 00:14:11,383
Massive stir.
315
00:14:11,416 --> 00:14:12,684
A polarizing debate
316
00:14:12,717 --> 00:14:14,586
that took over the internet.
317
00:14:14,619 --> 00:14:16,188
♪ ♪
318
00:14:16,221 --> 00:14:19,992
LAFER-SOUSA:
People had existential crises
over this image.
319
00:14:20,025 --> 00:14:21,560
People tweeted things like,
320
00:14:21,593 --> 00:14:23,262
"If that's not white and gold,
321
00:14:23,295 --> 00:14:24,897
my life has been a lie."
322
00:14:24,930 --> 00:14:26,865
Swear on your mother's
grave.
323
00:14:26,898 --> 00:14:28,467
Because that dress is white
and gold.
324
00:14:28,500 --> 00:14:29,635
Out of her (bleep) mind.
325
00:14:29,668 --> 00:14:31,236
LAFER-SOUSA:
Massive arguments.
326
00:14:31,269 --> 00:14:33,839
I watched videos of
people screaming at each other.
327
00:14:33,872 --> 00:14:35,240
GRAYSON DOLAN:
This is white, dude!
328
00:14:35,273 --> 00:14:37,309
ETHAN DOLAN:
White? That is dark blue!
329
00:14:37,342 --> 00:14:38,777
It's purple-blue!
330
00:14:38,810 --> 00:14:40,479
LAFER-SOUSA: I bet there was a
divorce here or there
331
00:14:40,512 --> 00:14:43,181
over this image.
332
00:14:43,214 --> 00:14:45,150
BERLIN: So when you first saw
that dress,
333
00:14:45,183 --> 00:14:47,953
as a, as a vision scientist,
what did you think?
334
00:14:47,986 --> 00:14:50,222
Well, when I first
saw the dress,
335
00:14:50,255 --> 00:14:52,224
I thought it was
blue and black.
336
00:14:52,257 --> 00:14:54,660
And I thought that the internet
was yanking my chain.
337
00:14:54,693 --> 00:14:57,362
Right-- to get the goat
of vision neuroscientists.
338
00:14:57,395 --> 00:14:59,097
Sure.
339
00:14:57,395 --> 00:14:59,097
(chuckles)
340
00:14:59,130 --> 00:15:01,166
But in the morning,
when I looked at my phone,
341
00:15:01,199 --> 00:15:03,268
I saw white and gold.
342
00:15:03,301 --> 00:15:05,304
And now,
of course, I was obsessed.
343
00:15:05,337 --> 00:15:07,539
So I said, "Well,
if this is an ambiguous image,
344
00:15:07,572 --> 00:15:09,841
all I have
to do is disambiguate it."
345
00:15:09,874 --> 00:15:12,010
So I set to work,
I got into Photoshop,
346
00:15:12,043 --> 00:15:16,048
cut out the dress, put it into
a scene with lots of rich cues.
347
00:15:16,081 --> 00:15:17,282
BERLIN:
Hm.
348
00:15:17,315 --> 00:15:18,850
LAFER-SOUSA:
And all of a sudden, boom:
349
00:15:18,883 --> 00:15:20,786
you can see the dress is
white and gold.
350
00:15:20,819 --> 00:15:22,487
Wow.
351
00:15:20,819 --> 00:15:22,487
Now, the pixels,
352
00:15:22,520 --> 00:15:23,989
the pixels that make up the
dress there,
353
00:15:24,022 --> 00:15:26,358
are identical
to the original image.
354
00:15:24,022 --> 00:15:26,358
Okay.
355
00:15:26,391 --> 00:15:28,927
BERLIN: Now, this doesn't work
for everybody,
356
00:15:28,960 --> 00:15:31,563
but for most, the visual context
357
00:15:31,596 --> 00:15:33,799
can make all the difference.
358
00:15:33,832 --> 00:15:36,201
LAFER-SOUSA:
What's different here is, her
skin is tinted blue,
359
00:15:36,234 --> 00:15:39,338
the background has blue light
cast on it,
360
00:15:39,371 --> 00:15:42,040
she's standing
in the shadow of that cube,
361
00:15:42,073 --> 00:15:45,177
and so your brain says,
"Aha, I need to ignore
362
00:15:45,210 --> 00:15:46,812
"some amount of blue light
363
00:15:46,845 --> 00:15:49,481
"that is in this signal that's
hitting my eye
364
00:15:49,514 --> 00:15:51,650
and render
this as white and gold."
365
00:15:51,683 --> 00:15:54,353
BERLIN:
And if we flip things around?
366
00:15:54,386 --> 00:15:57,990
LAFER-SOUSA:
Same dress, pasted it into
this other scene.
367
00:15:58,023 --> 00:16:01,393
Her skin is tinted yellow, the
background has a yellow cast,
368
00:16:01,426 --> 00:16:03,996
she's standing no longer
in the shadow but in the light.
369
00:16:04,029 --> 00:16:05,163
Boom! Blue and black.
370
00:16:05,196 --> 00:16:07,199
Amazing, that's really
amazing.
371
00:16:07,232 --> 00:16:08,500
So again, the dress,
372
00:16:08,533 --> 00:16:09,868
the pixels are exactly
the same.
373
00:16:09,901 --> 00:16:11,303
LAFER-SOUSA:
Identical.
374
00:16:11,336 --> 00:16:13,305
BERLIN:
The dress is a powerful example
375
00:16:13,338 --> 00:16:15,574
of how color really works
in the brain.
376
00:16:15,607 --> 00:16:17,809
Does that mean
we're creating color
377
00:16:17,842 --> 00:16:21,313
in our mind, or does color
actually exist in the world?
378
00:16:21,346 --> 00:16:23,281
Color takes place
in the brain,
379
00:16:23,314 --> 00:16:25,717
and I've prepared a little
illusion for you
380
00:16:25,750 --> 00:16:27,753
that should
convince you of this.
381
00:16:27,786 --> 00:16:30,756
LAFER-SOUSA:
So I have a picture
of four cars here.
382
00:16:30,789 --> 00:16:33,859
I want you to tell me,
what color are these cars?
383
00:16:33,892 --> 00:16:36,161
Let's start on the top left.
384
00:16:33,892 --> 00:16:36,161
BERLIN: Okay, so the one
on the left looks red,
385
00:16:36,194 --> 00:16:38,830
then the one
next to it looks blue.
386
00:16:38,863 --> 00:16:40,499
I'd say the one, the
bottom there,
387
00:16:40,532 --> 00:16:41,767
bottom left looks green,
388
00:16:41,800 --> 00:16:44,669
and then the one next to it
looks orange.
389
00:16:44,702 --> 00:16:45,971
LAFER-SOUSA:
Okay.
390
00:16:44,702 --> 00:16:45,971
Mm-hmm.
391
00:16:46,004 --> 00:16:47,105
What if I told you
392
00:16:47,138 --> 00:16:50,108
that all of those pixels
are not only gray,
393
00:16:50,141 --> 00:16:52,511
they are the same gray?
394
00:16:52,544 --> 00:16:54,212
BERLIN:
How is this possible?
395
00:16:54,245 --> 00:16:57,682
It's because the light
that enters your eyes,
396
00:16:57,715 --> 00:17:01,486
contrary to what you might have
learned in school, is not color.
397
00:17:01,519 --> 00:17:04,556
Color is an interpretation
of your brain.
398
00:17:04,589 --> 00:17:06,458
Here's how it works.
399
00:17:06,491 --> 00:17:08,627
Light shines on the world
400
00:17:08,660 --> 00:17:11,863
and bounces off objects--
this part you know.
401
00:17:11,896 --> 00:17:13,098
And light comes in
402
00:17:13,131 --> 00:17:14,366
different wavelengths,
403
00:17:14,399 --> 00:17:16,735
each corresponding
to a different color.
404
00:17:16,768 --> 00:17:18,437
What you might not have
heard in school
405
00:17:18,470 --> 00:17:20,372
is how those wavelengths change
406
00:17:20,405 --> 00:17:22,307
when they hit different
surfaces--
407
00:17:22,340 --> 00:17:24,576
rough, smooth, wet, et cetera.
408
00:17:24,609 --> 00:17:26,878
This signal that gets into
your eye
409
00:17:26,911 --> 00:17:30,115
is actually a product
of the reflective properties
410
00:17:30,148 --> 00:17:33,085
of the object and the wavelength
of light hitting it.
411
00:17:33,118 --> 00:17:35,287
Then that signal is focused
on the retina,
412
00:17:35,320 --> 00:17:37,055
the back of the eye,
413
00:17:37,088 --> 00:17:40,759
where we have about 130 million
light-sensitive cells.
414
00:17:40,792 --> 00:17:43,462
Three types called cones
are involved in color,
415
00:17:43,495 --> 00:17:46,398
each sensitive to different
wavelengths of light:
416
00:17:46,431 --> 00:17:48,467
long, medium, and short.
417
00:17:48,500 --> 00:17:51,036
But that light
still isn't color.
418
00:17:51,069 --> 00:17:53,271
For that to happen,
our brain has to take
419
00:17:53,304 --> 00:17:55,340
that three-piece code
from the retina
420
00:17:55,373 --> 00:17:59,945
and use the relative response
of the cones to encode color.
421
00:17:59,978 --> 00:18:03,982
It's not until that signal gets
to an area called V4
422
00:18:04,015 --> 00:18:06,718
that we get
a neural representation of color
423
00:18:06,751 --> 00:18:09,921
that corresponds
to our perceptual experience.
424
00:18:09,954 --> 00:18:12,424
So why would
our brains be built this way?
425
00:18:12,457 --> 00:18:14,893
Well, if our brains weren't
built this way,
426
00:18:14,926 --> 00:18:19,364
objects would appear to change
in color all the time,
427
00:18:19,397 --> 00:18:21,900
and that would render color
428
00:18:21,933 --> 00:18:24,469
a pretty useless signal in the
world.
429
00:18:24,502 --> 00:18:26,204
♪ ♪
430
00:18:26,237 --> 00:18:28,907
BERLIN:
That's because objects reflect
different wavelengths
431
00:18:28,940 --> 00:18:32,177
into your eye depending
on the lighting conditions.
432
00:18:32,210 --> 00:18:34,412
If your brain didn't compensate
for this,
433
00:18:34,445 --> 00:18:36,781
a red berry would appear gray
in a cave,
434
00:18:36,814 --> 00:18:39,885
blue at dawn,
and orange at dusk.
435
00:18:39,918 --> 00:18:42,420
But instead, your brain
carefully calibrates
436
00:18:42,453 --> 00:18:45,423
your experience to hold color
constant.
437
00:18:45,456 --> 00:18:48,226
Similarly,
color vision in other animals
438
00:18:48,259 --> 00:18:50,662
is tuned to their needs.
439
00:18:50,695 --> 00:18:54,166
SETH:
Different species have very
different kinds of color vision
440
00:18:54,199 --> 00:18:57,102
that are suited to their
particular environments
441
00:18:57,135 --> 00:19:00,038
and their particular challenges
for staying alive.
442
00:19:00,071 --> 00:19:03,975
BERLIN:
Dogs rely on smell, so they have
fewer types of cones,
443
00:19:04,008 --> 00:19:06,311
and thus see the world like
this.
444
00:19:06,344 --> 00:19:09,147
Birds need to recognize tiny
color differences
445
00:19:09,180 --> 00:19:10,715
from great distances.
446
00:19:10,748 --> 00:19:13,251
so they have an extra type
of cone that allows them
447
00:19:13,284 --> 00:19:14,719
to see more colors than we do.
448
00:19:14,752 --> 00:19:17,923
And bees need to find
flowers rich in nectar,
449
00:19:17,956 --> 00:19:21,960
so they see ultraviolet light
that's invisible to us.
450
00:19:21,993 --> 00:19:24,396
LAFER-SOUSA:
Color provides a
lot of valuable information
451
00:19:24,429 --> 00:19:26,031
about the world,
452
00:19:26,064 --> 00:19:28,967
but only if we can faithfully
extract
453
00:19:29,000 --> 00:19:32,037
something about the object.
454
00:19:32,070 --> 00:19:35,073
So we don't actually see color
as it is in the real world,
455
00:19:35,106 --> 00:19:39,177
we just see it in
terms of how it's useful for us?
456
00:19:39,210 --> 00:19:40,845
Absolutely.
457
00:19:40,878 --> 00:19:43,148
And the dress is probably
the best example of that.
458
00:19:43,181 --> 00:19:45,917
It's a really
powerful demonstration
459
00:19:45,950 --> 00:19:48,220
of how our color machinery
works.
460
00:19:48,253 --> 00:19:51,122
BERLIN:
So why do people see
this image
461
00:19:51,155 --> 00:19:52,891
of the dress differently?
462
00:19:52,924 --> 00:19:54,960
It comes down
to your brain's assumptions
463
00:19:54,993 --> 00:19:56,995
about the lighting conditions.
464
00:19:57,028 --> 00:19:59,631
It seems that the more
time you spend working indoors
465
00:19:59,664 --> 00:20:02,500
under artificial light,
which is predominantly yellow,
466
00:20:02,533 --> 00:20:05,804
the more likely you are to say
the dress is black and blue,
467
00:20:05,837 --> 00:20:08,907
because your brain assumes
it is lit by artificial light
468
00:20:08,940 --> 00:20:11,209
and subtracts out the yellow.
469
00:20:11,242 --> 00:20:13,111
Conversely, if you spend more
time
470
00:20:13,144 --> 00:20:15,280
in natural light,
which is bluer,
471
00:20:15,313 --> 00:20:18,950
you are more likely to see
it as white and gold.
472
00:20:18,983 --> 00:20:21,987
So then what is
the actual color of the dress?
473
00:20:22,020 --> 00:20:26,091
Well, Heather, I happen
to have brought it with me.
474
00:20:27,792 --> 00:20:29,894
(chuckles):
Wow.
475
00:20:27,792 --> 00:20:29,894
So what color is it?
476
00:20:29,927 --> 00:20:32,264
Uh, it's obviously,
I was right, blue and black.
477
00:20:32,297 --> 00:20:34,532
Team blue and black
for the win.
478
00:20:32,297 --> 00:20:34,532
Yes, yes.
479
00:20:34,565 --> 00:20:36,801
I can't believe this is the
actual dress.
480
00:20:36,834 --> 00:20:38,036
BERLIN:
I feel like I'm holding,
like...
481
00:20:38,069 --> 00:20:40,438
It's like a celebrity,
the dress.
482
00:20:40,471 --> 00:20:42,540
I know, it should be in a
museum, not in my closet.
483
00:20:40,471 --> 00:20:42,540
Yes!
484
00:20:42,573 --> 00:20:44,009
(both laugh)
485
00:20:44,042 --> 00:20:46,611
Before the dress,
people hadn't really realized
486
00:20:46,644 --> 00:20:48,913
we differ so much between
individuals.
487
00:20:48,946 --> 00:20:50,682
We're now quite used to the idea
488
00:20:50,715 --> 00:20:52,250
that we all
differ on the outside.
489
00:20:52,283 --> 00:20:55,353
We all have differences in skin
color, in height, in shape.
490
00:20:55,386 --> 00:20:57,622
But just as we all differ
on the outside,
491
00:20:57,655 --> 00:21:00,125
we all
differ on the inside, too.
492
00:21:00,158 --> 00:21:03,395
And this inner diversity
is very important.
493
00:21:03,428 --> 00:21:06,598
It gives us a certain humility
about our own ways of seeing.
494
00:21:06,631 --> 00:21:09,868
BERLIN:
Illusions give us a ringside
seat
495
00:21:09,901 --> 00:21:12,103
to watch how the brain creates
our world.
496
00:21:12,136 --> 00:21:14,806
And it's not
just the visual domain.
497
00:21:14,839 --> 00:21:17,008
Try listening to this.
498
00:21:17,041 --> 00:21:18,576
(staticky computerized voice
playing)
499
00:21:18,609 --> 00:21:20,111
Brainstorm, right?
500
00:21:20,144 --> 00:21:21,613
Simple enough.
501
00:21:21,646 --> 00:21:23,014
Now, listen to this.
502
00:21:23,047 --> 00:21:24,616
(staticky computerized voice
playing)
503
00:21:24,649 --> 00:21:25,950
Green needle.
504
00:21:25,983 --> 00:21:28,553
Okay, so you're thinking,
"What's the big deal?"
505
00:21:28,586 --> 00:21:32,023
But, what if I tell you that the
two audio clips I just played
506
00:21:32,056 --> 00:21:34,392
were exactly identical?
507
00:21:34,425 --> 00:21:38,129
For most people, what you hear
depends on which label you read.
508
00:21:38,162 --> 00:21:39,531
Sounds unbelievable?
509
00:21:39,564 --> 00:21:44,069
Here, try it again,
but this time, just read one.
510
00:21:44,102 --> 00:21:46,004
(staticky computerized voice
playing)
511
00:21:46,037 --> 00:21:49,808
Okay, now read the other
and listen again.
512
00:21:49,841 --> 00:21:52,310
(staticky computerized voice
playing)
513
00:21:52,343 --> 00:21:53,978
Now, when I first encountered
this,
514
00:21:54,011 --> 00:21:55,747
I was floored, too.
515
00:21:55,780 --> 00:21:57,949
Even though
I know what's going on.
516
00:21:57,982 --> 00:22:00,185
When your brain
encounters uncertainty,
517
00:22:00,218 --> 00:22:02,921
it fills in the gaps with its
best guess.
518
00:22:02,954 --> 00:22:05,557
In this case,
we have a degraded audio clip,
519
00:22:05,590 --> 00:22:08,293
and when you're primed with
a certain word to go with it,
520
00:22:08,326 --> 00:22:11,296
your brain automatically
jumps to the best fit.
521
00:22:11,329 --> 00:22:14,899
For most of us, we literally
hear what we want to hear.
522
00:22:14,932 --> 00:22:18,903
And it gets even worse--
let's try one more.
523
00:22:18,936 --> 00:22:21,406
Another internet sensation
524
00:22:21,439 --> 00:22:23,908
that lit up
debates across the country.
525
00:22:23,941 --> 00:22:28,713
(cleaner computerized voice
playing)
526
00:22:28,746 --> 00:22:30,615
Once and for all, is it yanny,
is it laurel?
527
00:22:30,648 --> 00:22:33,618
It's not yanny, it's laurel.
528
00:22:30,648 --> 00:22:33,618
It's yanny!
529
00:22:33,651 --> 00:22:35,653
Did you hear yanny?
530
00:22:35,686 --> 00:22:36,721
(cheering and applauding)
531
00:22:36,754 --> 00:22:38,056
Who heard laurel?
532
00:22:38,089 --> 00:22:39,357
(cheering more loudly)
533
00:22:39,390 --> 00:22:41,993
It is laurel and not yanny.
534
00:22:42,026 --> 00:22:44,329
(remix of computerized voice
playing)
535
00:22:44,362 --> 00:22:46,464
It's like that stupid dress
again
536
00:22:46,497 --> 00:22:48,700
all over, but in audio form!
537
00:22:48,733 --> 00:22:51,503
This is not saying "laurel,"
this is only saying "yanny."
538
00:22:51,536 --> 00:22:54,606
Exactly, it's laurel!
539
00:22:54,639 --> 00:22:57,642
Now, about half of you hear
yanny,
540
00:22:57,675 --> 00:22:59,511
and the other laurel,
541
00:22:59,544 --> 00:23:02,814
and unlike the first illusion,
I can't get most of you
542
00:23:02,847 --> 00:23:05,784
to experience
this one any other way.
543
00:23:05,817 --> 00:23:08,253
You're locked into your version
of reality.
544
00:23:08,286 --> 00:23:11,856
Experts aren't exactly sure why,
but some of us
545
00:23:11,889 --> 00:23:15,460
seem to pay more attention
to the low frequencies, laurel,
546
00:23:15,493 --> 00:23:17,495
and others to the high, yanny.
547
00:23:17,528 --> 00:23:20,465
The divide stems from the fact
that the audio file
548
00:23:20,498 --> 00:23:22,267
is an ambiguous signal made up
549
00:23:22,300 --> 00:23:24,335
of both high and low
frequencies.
550
00:23:24,368 --> 00:23:26,571
But by manipulating the
frequencies,
551
00:23:26,604 --> 00:23:29,140
I might be able to change what
you hear.
552
00:23:29,173 --> 00:23:31,376
(computerized voice playing
at mid frequency)
553
00:23:31,409 --> 00:23:32,377
High...
554
00:23:32,410 --> 00:23:34,779
(voice plays at high frequency)
555
00:23:34,812 --> 00:23:38,383
(voice plays at low frequency)
556
00:23:34,812 --> 00:23:38,383
Low.
557
00:23:38,416 --> 00:23:39,984
All of this goes to show
558
00:23:40,017 --> 00:23:41,252
how much the brain
559
00:23:41,285 --> 00:23:43,721
is an active interpreter
of sensory input.
560
00:23:43,754 --> 00:23:45,924
Our perception of
the external world
561
00:23:45,957 --> 00:23:49,561
is actually much less objective
than we'd like to believe.
562
00:23:49,594 --> 00:23:51,663
Most of the world around us is
very real,
563
00:23:51,696 --> 00:23:54,632
but you just never lived there,
okay?
564
00:23:54,665 --> 00:23:56,701
You lived in your mind,
565
00:23:56,734 --> 00:24:00,472
which is a perception of
that world that's being filtered
566
00:24:00,505 --> 00:24:03,374
through a bunch of salt
water sacks of proteins
567
00:24:03,407 --> 00:24:07,312
and electrochemical signals,
which can't possibly be making
568
00:24:07,345 --> 00:24:10,281
completely accurate
determinations
569
00:24:10,314 --> 00:24:13,184
of what's actually in the
outside world.
570
00:24:13,217 --> 00:24:16,521
Not convinced? Or maybe you're
just asking, "Why?"
571
00:24:16,554 --> 00:24:19,658
Well, try watching for when
the green dot flashes.
572
00:24:21,058 --> 00:24:23,161
Does it line up with the
red dot?
573
00:24:23,194 --> 00:24:24,562
If you are like most people,
574
00:24:24,595 --> 00:24:27,298
the red one always seems just a
little bit ahead.
575
00:24:27,331 --> 00:24:29,801
Now try again.
576
00:24:32,169 --> 00:24:36,074
The red dot and the green dot
are actually perfectly aligned.
577
00:24:36,107 --> 00:24:39,043
That's because some
neuroscientists would say
578
00:24:39,076 --> 00:24:40,712
that it's not your brain's job
579
00:24:40,745 --> 00:24:42,614
to perceive
the world accurately.
580
00:24:42,647 --> 00:24:45,717
Rather, its job is to
predict what happens next.
581
00:24:45,750 --> 00:24:49,554
To a certain extent,
you see what you expect to see:
582
00:24:49,587 --> 00:24:51,689
a predicted path of motion.
583
00:24:51,722 --> 00:24:54,158
And this is what helps
us hit a home run
584
00:24:54,191 --> 00:24:58,363
or flinch from a punch at just
the right moment.
585
00:25:00,665 --> 00:25:02,901
The brain is a predicting
machine.
586
00:25:02,934 --> 00:25:07,605
Given a set of circumstances
in this story at this moment,
587
00:25:07,638 --> 00:25:11,442
what are the likely plausible
588
00:25:11,475 --> 00:25:15,013
next events in the story?
589
00:25:15,046 --> 00:25:17,682
SETH:
The brain is
using sensory information
590
00:25:17,715 --> 00:25:19,450
to calibrate,
591
00:25:19,483 --> 00:25:21,853
update, to fine-tune these
predictions
592
00:25:21,886 --> 00:25:23,621
so they remain tied
593
00:25:23,654 --> 00:25:27,292
to reality in ways that are not
constrained by accuracy,
594
00:25:27,325 --> 00:25:30,061
but that are constrained by how
useful
595
00:25:30,094 --> 00:25:32,163
the brain's perceptual
predictions are
596
00:25:32,196 --> 00:25:34,299
in the business of staying
alive.
597
00:25:41,339 --> 00:25:43,808
BERLIN:
And to keep us alive,
the brain has evolved
598
00:25:43,841 --> 00:25:46,311
to look for signals
of potential danger.
599
00:25:46,344 --> 00:25:48,713
One of the most important
is pain,
600
00:25:48,746 --> 00:25:51,983
and as neuroscientist Theanne
Griffith is about to show me,
601
00:25:52,016 --> 00:25:54,886
sometimes that can be
a kind of illusion, as well.
602
00:25:54,919 --> 00:25:56,621
BERLIN:
So what is this?
603
00:25:56,654 --> 00:26:00,024
GRIFFITH:
This is a thermal grill.
604
00:25:56,654 --> 00:26:00,024
Okay.
605
00:26:00,057 --> 00:26:02,460
This is a machine
that could give us some insight
606
00:26:02,493 --> 00:26:05,496
as to how pain works in your
brain.
607
00:26:05,529 --> 00:26:07,365
All right, this is making me
nervous already
608
00:26:07,398 --> 00:26:08,900
as I'm getting strapped in!
609
00:26:07,398 --> 00:26:08,900
(Griffith laughs)
610
00:26:08,933 --> 00:26:11,536
GRIFFITH:
Don't worry,
it's all an illusion, actually.
611
00:26:11,569 --> 00:26:13,404
Okay.
612
00:26:13,437 --> 00:26:16,274
And it's comprised of these
different metal bars
613
00:26:16,307 --> 00:26:18,309
that are either set
to a cold or warm temperature.
614
00:26:18,342 --> 00:26:21,212
So why don't you go
ahead and touch that first bar?
615
00:26:21,245 --> 00:26:23,381
It's warm, right?
616
00:26:23,414 --> 00:26:25,016
And then the next bar?
617
00:26:25,049 --> 00:26:26,751
Cold.
618
00:26:25,049 --> 00:26:26,751
Mm-hmm.
619
00:26:26,784 --> 00:26:28,453
GRIFFITH:
And then the next one, warm.
620
00:26:28,486 --> 00:26:30,188
You see? So they're alternating
cold, warm, cold, warm.
621
00:26:28,486 --> 00:26:30,188
BERLIN: Mm-hmm.
622
00:26:30,221 --> 00:26:31,789
Now, you want to see what
happens
623
00:26:31,822 --> 00:26:33,391
when you put your hand down?
624
00:26:33,424 --> 00:26:35,193
Not necessarily.
625
00:26:33,424 --> 00:26:35,193
(both laugh)
626
00:26:35,226 --> 00:26:37,662
Go ahead.
627
00:26:35,226 --> 00:26:37,662
Okay.
628
00:26:37,695 --> 00:26:39,030
Okay, here we go.
629
00:26:39,063 --> 00:26:40,565
BERLIN:
Ow!
630
00:26:40,598 --> 00:26:42,266
GRIFFITH:
Right? Isn't that interesting?
631
00:26:40,598 --> 00:26:42,266
Whoa!
632
00:26:42,299 --> 00:26:44,202
Yeah,
what is going on there?
633
00:26:44,235 --> 00:26:45,937
It sort of feels cold at first,
but then...
634
00:26:45,970 --> 00:26:48,272
Then it gets this kind of
burning sensation, right?
635
00:26:48,305 --> 00:26:49,941
Yes, very much so.
636
00:26:49,974 --> 00:26:52,910
BERLIN:
It feels super-hot,
like I'm getting burnt.
637
00:26:52,943 --> 00:26:56,147
So it's not 100% clear
exactly how this is happening.
638
00:26:56,180 --> 00:26:58,383
But what we think might be going
on
639
00:26:58,416 --> 00:27:00,151
is that, basically, your brain
640
00:27:00,184 --> 00:27:02,020
is getting a little bit
confused.
641
00:27:02,053 --> 00:27:03,087
Okay.
642
00:27:02,053 --> 00:27:03,087
It's feeling cold,
643
00:27:03,120 --> 00:27:05,356
and it's also feeling warmth.
644
00:27:05,389 --> 00:27:09,027
And somehow, it's interpreting
these two signals as pain.
645
00:27:09,060 --> 00:27:13,264
BERLIN:
Here's what neuroscientists
think is going on.
646
00:27:13,297 --> 00:27:16,067
In your hands, you have
separate sensors for heat,
647
00:27:16,100 --> 00:27:18,269
cold, and pain.
648
00:27:18,302 --> 00:27:21,339
Normally, when you touch
something slightly cold,
649
00:27:21,372 --> 00:27:24,242
both your cold and pain sensors
are activated,
650
00:27:24,275 --> 00:27:26,344
but the cold ones
override the signals
651
00:27:26,377 --> 00:27:27,845
from the pain sensors,
652
00:27:27,878 --> 00:27:30,281
telling your brain there's
nothing to worry about.
653
00:27:30,314 --> 00:27:32,517
Unless, in this very
unnatural scenario
654
00:27:32,550 --> 00:27:35,119
with the thermal grill,
you happen to be touching
655
00:27:35,152 --> 00:27:37,789
something warm at the same time.
656
00:27:37,822 --> 00:27:40,892
Here, the heat signals
cancel out the cold ones,
657
00:27:40,925 --> 00:27:43,594
leaving you with just
the pain ones activated,
658
00:27:43,627 --> 00:27:47,065
telling your brain, "Ouch!"
659
00:27:47,098 --> 00:27:49,300
So in that respect,
is pain real?
660
00:27:49,333 --> 00:27:51,169
Mm-hmm.
661
00:27:49,333 --> 00:27:51,169
Or is it just an illusion
662
00:27:51,202 --> 00:27:52,437
or a construct of the brain?
663
00:27:52,470 --> 00:27:54,038
That's a really good
question.
664
00:27:54,071 --> 00:27:57,542
So noxious stimuli
is, is a real thing, right?
665
00:27:57,575 --> 00:27:59,377
If you stick
your hand in boiling water,
666
00:27:59,410 --> 00:28:00,978
that's an aversive stimulus.
667
00:28:01,011 --> 00:28:06,184
The perception
of a noxious stimuli is real.
668
00:28:01,011 --> 00:28:06,184
Mm-hmm.
669
00:28:06,217 --> 00:28:08,419
Pain is more of a construct,
right?
670
00:28:06,217 --> 00:28:08,419
Mm-hmm.
671
00:28:08,452 --> 00:28:10,755
And it can vary from
individual to individual.
672
00:28:10,788 --> 00:28:14,625
EMERY BROWN:
Pain is a construct of the
brain.
673
00:28:14,658 --> 00:28:16,494
How do we know that?
674
00:28:16,527 --> 00:28:19,130
You touch a needle, right?
And prick your finger.
675
00:28:19,163 --> 00:28:21,933
We can draw the anatomy
of what just happened.
676
00:28:21,966 --> 00:28:25,303
We have very well-defined
pathways
677
00:28:25,336 --> 00:28:27,538
saying, "This is pain
information."
678
00:28:27,571 --> 00:28:33,077
We don't interpret it as pain
until it hits your brain.
679
00:28:33,110 --> 00:28:35,413
BERLIN: Pain, not unlike the
experience of color,
680
00:28:35,446 --> 00:28:37,949
is a construct of the mind.
681
00:28:37,982 --> 00:28:39,250
Mama! Mama!
682
00:28:39,283 --> 00:28:41,052
BERLIN: But just because pain is
in your brain
683
00:28:41,085 --> 00:28:43,988
doesn't make it any
less critical for survival.
684
00:28:44,021 --> 00:28:45,623
GRIFFITH: Pain is a
very important...
685
00:28:45,656 --> 00:28:49,293
(gasps)
686
00:28:45,656 --> 00:28:49,293
...learning mechanism
for children.
687
00:28:49,326 --> 00:28:51,896
They learn what behaviors
they can engage in that are safe
688
00:28:51,929 --> 00:28:54,665
and what behaviors, well,
they should not engage in
689
00:28:54,698 --> 00:28:57,468
because they could cause
them bodily harm.
690
00:28:57,501 --> 00:29:00,605
And there's, um, uh, different
mutations that people can have
691
00:29:00,638 --> 00:29:02,673
in certain proteins
that make them
692
00:29:02,706 --> 00:29:04,442
completely insensitive to pain.
693
00:29:04,475 --> 00:29:06,644
And so kids do things
like bite on their lips
694
00:29:06,677 --> 00:29:09,213
or on their fingers
when they're very young,
695
00:29:09,246 --> 00:29:12,283
and as they get older,
can engage in risky behavior.
696
00:29:12,316 --> 00:29:16,821
So pain is extremely
important for us to feel.
697
00:29:16,854 --> 00:29:20,324
SETH:
Illusions are fascinating.
698
00:29:20,357 --> 00:29:22,493
They're like fractures in the
matrix.
699
00:29:22,526 --> 00:29:25,596
They reveal to us that the way
we perceive things
700
00:29:25,629 --> 00:29:28,232
isn't necessarily the way they
are.
701
00:29:28,265 --> 00:29:31,736
MACKNIK:
Illusions help us find the
cracks in the mortar
702
00:29:31,769 --> 00:29:34,105
of that world we've built for
ourselves,
703
00:29:34,138 --> 00:29:37,575
and understand what it is our
world is actually made out of
704
00:29:37,608 --> 00:29:39,443
and what the brain is actually
doing.
705
00:29:39,476 --> 00:29:41,913
So most people think of the
brain
706
00:29:41,946 --> 00:29:45,449
reconstructing the world more
or less verbatim.
707
00:29:45,482 --> 00:29:47,485
(dog growling)
708
00:29:45,482 --> 00:29:47,485
But that's just not true.
709
00:29:47,518 --> 00:29:49,220
What it's actually doing is,
710
00:29:49,253 --> 00:29:50,621
it's getting very little
information
711
00:29:50,654 --> 00:29:53,624
and it's using that very little
information
712
00:29:53,657 --> 00:29:56,361
to make a big, grand model
of the world.
713
00:29:57,395 --> 00:30:00,998
MARTINEZ-CONDE:
We cannot process the vast
amount of information
714
00:30:01,031 --> 00:30:05,937
that is constantly bombarding
our senses.
715
00:30:05,970 --> 00:30:08,306
Illusions, you can
think of them as shortcuts.
716
00:30:08,339 --> 00:30:11,809
Shortcuts make us faster,
717
00:30:11,842 --> 00:30:15,346
more efficient
with less resources.
718
00:30:15,379 --> 00:30:18,049
Based on
these snippets of information,
719
00:30:18,082 --> 00:30:23,521
we build this more
complex simulation of reality.
720
00:30:23,554 --> 00:30:25,923
And that simulation
of the world is
721
00:30:25,956 --> 00:30:27,091
what we call consciousness.
722
00:30:29,393 --> 00:30:31,195
BERLIN:
Consciousness.
723
00:30:31,228 --> 00:30:34,832
(alarm buzzing)
724
00:30:31,228 --> 00:30:34,832
We take it for granted,
but every time you wake up,
725
00:30:34,865 --> 00:30:38,870
(alarm stops)
726
00:30:34,865 --> 00:30:38,870
your brain stitches together
all your sensory inputs--
727
00:30:38,903 --> 00:30:40,771
the sound of a distant train...
728
00:30:40,804 --> 00:30:42,540
(train whistle blowing)
729
00:30:42,573 --> 00:30:44,775
...the smell of coffee,
730
00:30:44,808 --> 00:30:47,678
the warmth of the sun--
731
00:30:47,711 --> 00:30:50,214
into an experience of the world.
732
00:30:50,247 --> 00:30:52,049
And that experience,
733
00:30:52,082 --> 00:30:54,285
that awareness of the world,
734
00:30:54,318 --> 00:30:57,622
is what scientists
call consciousness.
735
00:30:57,655 --> 00:31:00,958
In neuroscience,
consciousness is the Holy Grail.
736
00:31:00,991 --> 00:31:03,761
Humans have been fascinated by
consciousness
737
00:31:03,794 --> 00:31:06,631
for thousands of years,
probably much longer than that.
(chuckling)
738
00:31:06,664 --> 00:31:08,432
Take three.
739
00:31:08,465 --> 00:31:10,534
Now, of course,
the word "consciousness"
740
00:31:10,567 --> 00:31:12,470
means a lot of
things to different people.
741
00:31:12,503 --> 00:31:14,639
To some, consciousness means
being awake,
742
00:31:14,672 --> 00:31:16,073
as opposed to asleep.
743
00:31:16,106 --> 00:31:19,644
Or self-aware, or the
contents of my thoughts.
744
00:31:19,677 --> 00:31:22,780
But that's not how we
neuroscientists think about it.
745
00:31:22,813 --> 00:31:27,685
We think of it as something
much more basic--
746
00:31:27,718 --> 00:31:30,288
it's just internal experience.
747
00:31:30,321 --> 00:31:33,591
It feels like
something to see the color red.
748
00:31:33,624 --> 00:31:35,860
To taste a strawberry.
749
00:31:35,893 --> 00:31:37,194
(thunder rumbling)
750
00:31:37,227 --> 00:31:39,997
To hear the crack of thunder.
751
00:31:40,030 --> 00:31:43,501
(thunder crashing)
752
00:31:43,534 --> 00:31:46,904
SETH:
We are complicated
biological creatures,
753
00:31:46,937 --> 00:31:49,206
but the most central
feature of our lives
754
00:31:49,239 --> 00:31:51,475
is that we are
conscious creatures, too.
755
00:31:51,508 --> 00:31:53,210
When I open my eyes,
756
00:31:53,243 --> 00:31:56,447
it's not just that my brain does
some sophisticated processing
757
00:31:56,480 --> 00:31:57,548
of the visual information.
758
00:31:57,581 --> 00:32:00,284
I have an experience.
759
00:32:00,317 --> 00:32:02,320
BERLIN:
During the course of my journey,
760
00:32:02,353 --> 00:32:04,221
I've seen how our experience
of reality
761
00:32:04,254 --> 00:32:05,856
is not what it seems.
762
00:32:05,889 --> 00:32:07,291
If my conscious awareness
763
00:32:07,324 --> 00:32:10,828
is built from my perceptions,
flawed as they may be,
764
00:32:10,861 --> 00:32:15,333
how does that work
and what does it mean?
765
00:32:15,366 --> 00:32:18,302
♪ ♪
766
00:32:18,335 --> 00:32:20,171
So please take a seat.
767
00:32:20,204 --> 00:32:23,507
BERLIN:
Some of the first clues trickled
in from people like this.
768
00:32:23,540 --> 00:32:25,343
LORELLA BATTELLI:
Put your chin on the
chin rest.
769
00:32:25,376 --> 00:32:26,911
BROWN:
A lot of very valuable
information
770
00:32:26,944 --> 00:32:30,681
comes from patients who've had,
part of the brain's damaged.
771
00:32:30,714 --> 00:32:34,352
By piecing these various parts
together, seeing what was lost,
772
00:32:34,385 --> 00:32:36,287
we've come to appreciate
the role
773
00:32:36,320 --> 00:32:37,955
that these various brain regions
play
774
00:32:37,988 --> 00:32:39,857
in the creation of
consciousness.
775
00:32:39,890 --> 00:32:41,826
We're going to
calibrate your eyes first.
776
00:32:41,859 --> 00:32:44,095
SETH:
A powerful example of this
777
00:32:44,128 --> 00:32:46,230
is the phenomenon of blindsight.
778
00:32:46,263 --> 00:32:49,633
BATTELLI:
This is a patient who had a
stroke
779
00:32:49,666 --> 00:32:51,268
in her visual areas,
780
00:32:51,301 --> 00:32:52,970
in the back of the brain.
781
00:32:53,003 --> 00:32:55,406
And this stroke is
affecting her visual field.
782
00:32:55,439 --> 00:32:59,043
BERLIN:
Three years ago,
she felt a pounding in her head.
783
00:32:59,076 --> 00:33:01,345
WOMAN:
I had what
I thought was a migraine.
784
00:33:01,378 --> 00:33:04,181
I actually went
to the emergency room
785
00:33:04,214 --> 00:33:06,617
because I'm walking around with
this area where I can't see.
786
00:33:06,650 --> 00:33:09,153
BERLIN:
The stroke damaged
a piece of the brain
787
00:33:09,186 --> 00:33:11,756
devoted to vision,
leaving her with an apparent
788
00:33:11,789 --> 00:33:13,491
total blind spot.
789
00:33:13,524 --> 00:33:16,594
WOMAN:
That blind spot, it's enough
that if you're driving,
790
00:33:16,627 --> 00:33:20,965
an oncoming car disappears into
it.
791
00:33:20,998 --> 00:33:24,802
It's a little anxiety-producing
and, and things like that.
792
00:33:24,835 --> 00:33:26,570
BERLIN:
In everyday tasks,
793
00:33:26,603 --> 00:33:28,973
her eye movements make up
the difference.
794
00:33:29,006 --> 00:33:32,309
But what happens when
she doesn't move her eyes?
795
00:33:32,342 --> 00:33:34,578
Neuroscientist Lorella
Battelli wants to find out,
796
00:33:34,611 --> 00:33:38,716
so she developed a clever series
of experiments to pin down,
797
00:33:38,749 --> 00:33:40,785
just how blind is
she really in that spot?
798
00:33:42,152 --> 00:33:43,654
BATTELLI:
We're using EyeLink,
799
00:33:43,687 --> 00:33:46,057
which is the eye-tracking system
800
00:33:46,090 --> 00:33:47,892
to make sure she doesn't move
the eyes.
801
00:33:47,925 --> 00:33:50,261
BERLIN:
She keeps her eyes focused on
the center spot.
802
00:33:50,294 --> 00:33:52,763
Every time she hears a beep,
803
00:33:52,796 --> 00:33:54,665
she has to say if those little
dots inside the circle
804
00:33:54,698 --> 00:33:56,901
are moving to the left
or to the right.
805
00:33:56,934 --> 00:33:58,836
Left.
806
00:33:58,869 --> 00:34:01,939
BERLIN:
The eye tracker checks that
she's not shifting her gaze.
807
00:34:03,140 --> 00:34:04,575
WOMAN:
Right.
808
00:34:04,608 --> 00:34:06,477
When you're doing tests like
this,
809
00:34:06,510 --> 00:34:09,480
that blind area, what does that
kind of look like for you?
810
00:34:09,513 --> 00:34:11,348
What does it feel like for you?
811
00:34:11,381 --> 00:34:15,753
When that target pops up
in my blind area,
812
00:34:15,786 --> 00:34:17,388
I don't see it.
813
00:34:17,421 --> 00:34:19,657
(machine beeps)
814
00:34:19,690 --> 00:34:21,559
Left.
815
00:34:21,592 --> 00:34:23,494
BERLIN:
Strangely, even though she says
she doesn't see
816
00:34:23,527 --> 00:34:25,329
anything in the blind spot,
817
00:34:25,362 --> 00:34:26,897
she gets it right more often
than not.
818
00:34:26,930 --> 00:34:28,933
WOMAN:
Right.
819
00:34:28,966 --> 00:34:30,201
BERLIN:
So that some information
820
00:34:30,234 --> 00:34:31,268
is getting in.
821
00:34:30,234 --> 00:34:31,268
Yeah, so...
822
00:34:31,301 --> 00:34:33,737
But they're not consciously
seeing it,
823
00:34:33,770 --> 00:34:35,639
but they can respond
to it in different ways.
824
00:34:33,770 --> 00:34:35,639
Exactly.
825
00:34:35,672 --> 00:34:38,776
BATTELLI:
Even if they say,
"I didn't see anything."
826
00:34:38,809 --> 00:34:40,945
Left.
827
00:34:38,809 --> 00:34:40,945
But you tell them,
"Please, just tell me
828
00:34:40,978 --> 00:34:42,947
whether you saw it or not,"
829
00:34:42,980 --> 00:34:44,882
then their response
would be above chance.
830
00:34:44,915 --> 00:34:47,718
BERLIN:
So just keep your eyes closed,
okay?
831
00:34:47,751 --> 00:34:49,720
And...
832
00:34:49,753 --> 00:34:51,755
BERLIN:
What's going on?
833
00:34:51,788 --> 00:34:54,125
To probe deeper,
Lorella lets me give the patient
834
00:34:54,158 --> 00:34:56,994
a different
version of the challenge.
835
00:34:57,027 --> 00:34:59,630
I put a miniature
screwdriver in her blind spot.
836
00:34:59,663 --> 00:35:02,867
BERLIN:
Okay, I'm going to have
you open your eyes and fixate.
837
00:35:02,900 --> 00:35:04,936
Okay.
838
00:35:02,900 --> 00:35:04,936
Okay.
839
00:35:07,037 --> 00:35:08,239
I see nothing.
840
00:35:08,272 --> 00:35:09,240
You see nothing.
841
00:35:09,273 --> 00:35:10,474
Nothing.
842
00:35:09,273 --> 00:35:10,474
Okay.
843
00:35:10,507 --> 00:35:11,775
BERLIN:
Even though she says
844
00:35:11,808 --> 00:35:14,445
she sees nothing,
look at which tool she picks.
845
00:35:14,478 --> 00:35:16,380
Now turn over there
and look at the objects.
846
00:35:16,413 --> 00:35:17,414
Tell me what you think
you saw.
847
00:35:17,447 --> 00:35:20,151
WOMAN: The screwdriver.
848
00:35:17,447 --> 00:35:20,151
BERLIN: Yep.
849
00:35:20,184 --> 00:35:22,219
BERLIN:
Now try another one.
850
00:35:22,252 --> 00:35:25,322
BERLIN:
Next, I display a tiny wrench.
851
00:35:25,355 --> 00:35:29,760
♪ ♪
852
00:35:29,793 --> 00:35:31,328
Did you see anything?
853
00:35:31,361 --> 00:35:33,097
No.
854
00:35:31,361 --> 00:35:33,097
No? Okay.
855
00:35:33,130 --> 00:35:37,067
Look over there and guess
what you think you, was there.
856
00:35:37,100 --> 00:35:40,271
I think the wrench?
857
00:35:37,100 --> 00:35:40,271
Yep.
858
00:35:40,304 --> 00:35:42,506
WOMAN:
I'm going to guess
the scissors.
859
00:35:42,539 --> 00:35:44,675
Yeah, good job, great,
scissors.
860
00:35:44,708 --> 00:35:46,010
BERLIN:
Time and time again,
861
00:35:46,043 --> 00:35:47,878
she makes the right choice.
862
00:35:47,911 --> 00:35:49,813
Amazing, so, you know,
it seems
863
00:35:49,846 --> 00:35:52,016
to me that you're saying you're
not seeing anything,
864
00:35:52,049 --> 00:35:54,885
yet when
I'm asking you to choose,
865
00:35:54,918 --> 00:35:56,954
you're pretty much getting it
correct,
866
00:35:56,987 --> 00:35:58,455
so something is getting in.
867
00:35:58,488 --> 00:36:01,091
BERLIN:
How is this possible?
868
00:36:01,124 --> 00:36:04,028
It's as if she sees
the tools, but doesn't know it.
869
00:36:04,061 --> 00:36:05,863
BATTELLI:
They actually saw something.
870
00:36:05,896 --> 00:36:08,399
Mm-hmm, certainly.
871
00:36:05,896 --> 00:36:08,399
But they're
not entirely aware of it.
872
00:36:08,432 --> 00:36:10,568
Information is getting in,
affecting our behavior
873
00:36:10,601 --> 00:36:12,369
and how we're responding
to the world around us,
874
00:36:12,402 --> 00:36:14,338
without there being
a conscious perception
875
00:36:14,371 --> 00:36:16,707
of that piece
of visual information.
876
00:36:14,371 --> 00:36:16,707
Correct.
877
00:36:16,740 --> 00:36:18,742
WOMAN:
I'm gonna go
with the hammer again.
878
00:36:18,775 --> 00:36:23,480
BERLIN:
Until patients like this,
we scientists had never seen
879
00:36:23,513 --> 00:36:26,116
perception separate
from conscious experience.
880
00:36:26,149 --> 00:36:28,219
And this tells us that
perception
881
00:36:28,252 --> 00:36:31,522
and consciousness are separate
things in the brain.
882
00:36:31,555 --> 00:36:33,424
But it also has me wondering,
883
00:36:33,457 --> 00:36:35,759
if someone can still use
visual information
884
00:36:35,792 --> 00:36:37,695
without awareness of it,
885
00:36:37,728 --> 00:36:39,763
why do we have
consciousness at all?
886
00:36:39,796 --> 00:36:42,132
What is consciousness for?
887
00:36:42,165 --> 00:36:44,368
A clue might come from babies.
888
00:36:44,401 --> 00:36:46,370
(cooing)
889
00:36:46,403 --> 00:36:48,305
ALISON GOPNIK:
Babies-- everything we know
suggests
890
00:36:48,338 --> 00:36:50,174
that they're born conscious.
891
00:36:50,207 --> 00:36:51,442
They're certainly taking
in information
892
00:36:51,475 --> 00:36:52,710
from the time they're born.
893
00:36:52,743 --> 00:36:55,112
REBECCA SAXE:
They are making rational choices
894
00:36:55,145 --> 00:36:58,649
about what they learn
from extremely early on.
895
00:36:58,682 --> 00:37:02,319
And they are forming memories
of their specific surroundings,
896
00:37:02,352 --> 00:37:03,687
of their parents,
897
00:37:03,720 --> 00:37:05,756
of their important
relationships.
898
00:37:05,789 --> 00:37:08,125
We can see that
in their behavior.
899
00:37:08,158 --> 00:37:11,996
BERLIN:
And all that behavior
burns a lot of fuel.
900
00:37:12,029 --> 00:37:14,832
GOPNIK:
Brains are expensive computing
gadgets.
901
00:37:14,865 --> 00:37:16,300
So while you're just sitting
here,
902
00:37:16,333 --> 00:37:19,036
your brain is using up about 20%
of all the calories
903
00:37:19,069 --> 00:37:21,071
that you have, so
it's using up quite a bit.
904
00:37:21,104 --> 00:37:23,240
But if you think about
a two-year-old,
905
00:37:23,273 --> 00:37:27,144
his brain is using
60% of his calories.
906
00:37:27,177 --> 00:37:32,783
So almost all that food is just
going to keep his brain going.
907
00:37:32,816 --> 00:37:36,020
BERLIN:
To understand why young brains
might need so much more fuel,
908
00:37:36,053 --> 00:37:38,789
check out the connections
in a toddler's brain
909
00:37:38,822 --> 00:37:40,591
versus an adult's.
910
00:37:40,624 --> 00:37:44,595
A two-year-old's brain has about
two quadrillion synapses.
911
00:37:44,628 --> 00:37:49,533
By the time they hit adulthood,
that number is cut in half.
912
00:37:49,566 --> 00:37:51,602
So if you think about the
difference between
913
00:37:51,635 --> 00:37:54,038
the baby brain, the child's
brain, and the adult brain,
914
00:37:54,071 --> 00:37:58,075
the child's brain is more like
back country roads
915
00:37:58,108 --> 00:38:00,010
where you have little,
tiny roads
916
00:38:00,043 --> 00:38:02,713
that are going
from one village to the next.
917
00:38:02,746 --> 00:38:04,815
None of
them are very efficient.
918
00:38:04,848 --> 00:38:05,983
There's not a lot of traffic,
919
00:38:06,016 --> 00:38:07,885
and the traffic doesn't go very
quickly,
920
00:38:07,918 --> 00:38:09,853
but they connect lots
and lots of different places.
921
00:38:09,886 --> 00:38:12,556
And the adult brain is more
like superhighways
922
00:38:12,589 --> 00:38:15,326
that get you from one place
to another very quickly,
923
00:38:15,359 --> 00:38:17,061
and take a lot of traffic,
924
00:38:17,094 --> 00:38:19,763
but don't connect as many
different places.
925
00:38:19,796 --> 00:38:22,266
BERLIN:
As we age,
in the interest of efficiency,
926
00:38:22,299 --> 00:38:25,069
we strengthen the connections
that are useful to us
927
00:38:25,102 --> 00:38:27,204
and prune the rest.
928
00:38:27,237 --> 00:38:30,374
MACKNIK:
You basically take neurons you
don't need, you get rid of them.
929
00:38:30,407 --> 00:38:33,477
And what you have
now is a very lean machine
930
00:38:33,510 --> 00:38:38,416
that does certain things
and it does them very well.
931
00:38:39,483 --> 00:38:42,052
GOPNIK:
We see this early brain
that's very exploratory,
932
00:38:42,085 --> 00:38:43,854
that has lots and lots of
potential,
933
00:38:43,887 --> 00:38:45,055
lots of possibilities.
934
00:38:45,088 --> 00:38:47,358
Not very good at
putting on your jacket
935
00:38:47,391 --> 00:38:49,426
and getting out to
preschool in the morning.
936
00:38:49,459 --> 00:38:50,961
And then we have this
later brain
937
00:38:50,994 --> 00:38:52,730
that's very good at doing
things.
938
00:38:52,763 --> 00:38:54,098
Not so good at changing,
939
00:38:54,131 --> 00:38:55,699
not so good at taking in new
information,
940
00:38:55,732 --> 00:38:56,967
not so good at doing something
new.
941
00:38:57,000 --> 00:38:59,670
BERLIN:
What this suggests is that maybe
942
00:38:59,703 --> 00:39:01,171
what consciousness is for
943
00:39:01,204 --> 00:39:03,507
is choosing what's
important for us to be aware of
944
00:39:03,540 --> 00:39:05,042
at any given moment.
945
00:39:05,075 --> 00:39:07,544
Kind of like a spotlight.
946
00:39:07,577 --> 00:39:10,047
GOPNICK:
For adults,
it's as if consciousness is
947
00:39:10,080 --> 00:39:11,548
this bright spotlight in
one place
948
00:39:11,581 --> 00:39:12,950
and everything around it is
dark.
949
00:39:14,951 --> 00:39:16,587
BERLIN:
While for children and babies,
950
00:39:16,620 --> 00:39:18,255
it would be
more like a flood light,
951
00:39:18,288 --> 00:39:20,557
where nearly everything is
illuminated.
952
00:39:20,590 --> 00:39:24,228
GOPNIK:
You're conscious of a lot more
that's going on.
953
00:39:24,261 --> 00:39:27,097
BERLIN:
Consciousness may
be like an amplifier,
954
00:39:27,130 --> 00:39:29,666
boosting the important
signals over the noise.
955
00:39:29,699 --> 00:39:31,736
Is there any evidence?
956
00:39:33,136 --> 00:39:35,372
This is where FMRI comes in,
957
00:39:35,405 --> 00:39:38,008
a special tool in neuroscience
that takes pictures of the brain
958
00:39:38,041 --> 00:39:40,677
while it's doing something
to map where blood flow
959
00:39:40,710 --> 00:39:41,912
is in high demand.
960
00:39:41,945 --> 00:39:44,582
The result is
a map of brain activity.
961
00:39:45,749 --> 00:39:48,552
So where is consciousness
in the brain
962
00:39:48,585 --> 00:39:51,522
and how does it work?
963
00:39:51,555 --> 00:39:53,390
To find out,
neuroscientists designed
964
00:39:53,423 --> 00:39:56,460
a clever series of
experiments that go like this.
965
00:39:56,493 --> 00:39:58,729
They start by flashing a word
966
00:39:58,762 --> 00:40:01,198
on a screen for about
30 milliseconds.
967
00:40:01,231 --> 00:40:02,199
(computer beeps)
968
00:40:02,232 --> 00:40:03,600
DEHAENE:
You flash this word,
969
00:40:03,633 --> 00:40:06,170
and the person is not
able to see the word at all.
970
00:40:06,203 --> 00:40:07,871
She says, "There was no word."
971
00:40:07,904 --> 00:40:12,075
BERLIN:
But in the FMRI scanner,
the visual cortex is activated,
972
00:40:12,108 --> 00:40:15,612
even though people say
they don't see anything.
973
00:40:15,645 --> 00:40:18,682
So the trick here is to find the
threshold.
974
00:40:18,715 --> 00:40:21,518
Find the timing where sometimes
975
00:40:21,551 --> 00:40:24,188
people consciously see the
image.
976
00:40:24,221 --> 00:40:28,892
DEHAENE:
So if you make now
the word a little bit longer,
977
00:40:28,925 --> 00:40:30,461
suddenly, the person says,
978
00:40:30,494 --> 00:40:32,129
"Oh, well,
there is a word, obviously."
979
00:40:32,162 --> 00:40:34,364
And it's completely visible.
980
00:40:34,397 --> 00:40:36,767
There is really a sort
of all or none phenomenon.
981
00:40:36,800 --> 00:40:38,569
Either you see it or you don't.
982
00:40:38,602 --> 00:40:40,804
And once you've done that,
983
00:40:40,837 --> 00:40:42,439
you've got
a really powerful window
984
00:40:42,472 --> 00:40:45,209
onto the neural correlates of
conscious perception.
985
00:40:45,242 --> 00:40:47,678
BERLIN:
When that happens,
986
00:40:47,711 --> 00:40:49,646
suddenly, a suite of different
parts of the brain
987
00:40:49,679 --> 00:40:52,983
shows a surge in activity:
the parietal cortex,
988
00:40:53,016 --> 00:40:54,751
which integrates the senses,
989
00:40:54,784 --> 00:40:56,386
the anterior cingulate,
990
00:40:56,419 --> 00:40:58,388
which modulates
drive and decision-making,
991
00:40:58,421 --> 00:41:00,023
and the prefrontal cortex,
992
00:41:00,056 --> 00:41:03,961
which handles reasoning
and higher-order cognition.
993
00:41:03,994 --> 00:41:06,663
An ignition of
distributed brain areas
994
00:41:06,696 --> 00:41:09,600
that come online together,
speak to each other,
995
00:41:09,633 --> 00:41:12,769
and broadcast this information
to the rest of the brain.
996
00:41:12,802 --> 00:41:14,104
And this is what
we think is occurring
997
00:41:14,137 --> 00:41:15,639
during conscious perception.
998
00:41:17,407 --> 00:41:19,076
BERLIN:
According to some experts,
999
00:41:19,109 --> 00:41:21,211
this communication
between brain regions
1000
00:41:21,244 --> 00:41:23,481
is the signature
of consciousness.
1001
00:41:24,848 --> 00:41:27,751
This discovery could have
real-world applications
1002
00:41:27,784 --> 00:41:29,386
in matters of life and death.
1003
00:41:29,419 --> 00:41:31,255
But that would take one
more step:
1004
00:41:31,288 --> 00:41:34,858
figuring out how
to measure consciousness.
1005
00:41:34,891 --> 00:41:36,727
♪ ♪
1006
00:41:36,760 --> 00:41:38,795
SETH:
In science, when we struggle
to understand
1007
00:41:38,828 --> 00:41:40,597
a phenomenon
that seems quite mysterious,
1008
00:41:40,630 --> 00:41:44,234
it's often really important
to be able to measure it.
1009
00:41:44,267 --> 00:41:46,537
So a few hundred years
ago,
1010
00:41:46,570 --> 00:41:49,139
this happened with heat-- you
know, it was the development
1011
00:41:49,172 --> 00:41:53,077
of thermometers that catalyzed
our understanding.
1012
00:41:53,911 --> 00:41:56,079
Could something work for
consciousness the same way?
1013
00:41:56,112 --> 00:41:58,248
Could we have
a consciousness-ometer
1014
00:41:58,281 --> 00:41:59,650
that will lead us
1015
00:41:59,683 --> 00:42:02,886
to a deeper understanding of
what consciousness is?
1016
00:42:02,919 --> 00:42:06,256
BERLIN:
That deeper understanding
could transform
1017
00:42:06,289 --> 00:42:08,625
the treatment of people with
brain injuries.
1018
00:42:08,658 --> 00:42:11,595
BRIAN EDLOW:
So every year,
over a million people worldwide
1019
00:42:11,628 --> 00:42:15,265
will come into an intensive care
unit unresponsive, comatose.
1020
00:42:15,298 --> 00:42:18,135
The challenge that we face
is that our bedside exam--
1021
00:42:18,168 --> 00:42:20,070
asking the person
to open their eyes,
1022
00:42:20,103 --> 00:42:22,506
pinching them and
seeing if they'll respond,
1023
00:42:22,539 --> 00:42:24,474
seeing if they move their arms
and their legs--
1024
00:42:24,507 --> 00:42:27,578
that bedside exam
is fundamentally limited.
1025
00:42:27,611 --> 00:42:30,380
BERLIN:
Limited because it often misses
people
1026
00:42:30,413 --> 00:42:32,182
who are actually conscious.
1027
00:42:32,215 --> 00:42:34,918
In this case,
we have a healthy volunteer
1028
00:42:34,951 --> 00:42:36,720
from the "NOVA" team,
1029
00:42:36,753 --> 00:42:38,322
but what if
she were unresponsive?
1030
00:42:38,355 --> 00:42:41,058
How would we ever know
if she was conscious?
1031
00:42:41,091 --> 00:42:43,126
Brian Edlow's team
at Mass. General Hospital
1032
00:42:43,159 --> 00:42:46,163
is testing a new technique
to find out.
1033
00:42:46,196 --> 00:42:48,298
So tell me, what are you doing
here?
1034
00:42:48,331 --> 00:42:51,001
EDLOW:
We are pinging the brain
with a magnetic pulse
1035
00:42:51,034 --> 00:42:53,503
and looking for
an electrical echo.
1036
00:42:53,536 --> 00:42:57,207
The ping is transcranial
magnetic stimulation,
1037
00:42:57,240 --> 00:42:58,842
or TMS.
1038
00:42:58,875 --> 00:43:01,812
The echo is the key;
if it dies out quickly,
1039
00:43:01,845 --> 00:43:04,114
the patient is unconscious--
they might be in a coma,
1040
00:43:04,147 --> 00:43:06,149
deep sleep, or under anesthesia.
1041
00:43:06,182 --> 00:43:09,720
If instead the echo rings out
across the brain
1042
00:43:09,753 --> 00:43:11,622
and becomes more complex,
1043
00:43:11,655 --> 00:43:14,558
the patient is likely
to be conscious and aware,
1044
00:43:14,591 --> 00:43:17,160
even if they appear
unresponsive.
1045
00:43:17,193 --> 00:43:18,228
EDLOW:
The analogy that we like
to use
1046
00:43:18,261 --> 00:43:20,964
is throwing a pebble in a lake.
1047
00:43:20,997 --> 00:43:24,368
So the pebble represents the TMS
pulse to stimulate the brain,
1048
00:43:24,401 --> 00:43:27,137
and the brain waves,
the electrical ripples
1049
00:43:27,170 --> 00:43:32,209
that emanate from that pulse,
represent the waves in the lake.
1050
00:43:32,242 --> 00:43:34,411
The more
complex those waves are,
1051
00:43:34,444 --> 00:43:36,446
and the longer duration,
1052
00:43:36,479 --> 00:43:38,549
the more likely that person is
to be conscious.
1053
00:43:40,050 --> 00:43:43,453
BERLIN:
To detect those brain waves,
Edlow's team uses E.E.G.,
1054
00:43:43,486 --> 00:43:45,956
a tool that measures
electrical activity
1055
00:43:45,989 --> 00:43:48,792
in the brain, to quantify
the amount of complexity
1056
00:43:48,825 --> 00:43:51,128
a patient's brain bounces back.
1057
00:43:51,161 --> 00:43:52,462
Here's how it works.
1058
00:43:52,495 --> 00:43:53,697
♪ ♪
1059
00:43:53,730 --> 00:43:55,832
All neurons,
when poked by a magnet,
1060
00:43:55,865 --> 00:43:59,036
will kick back an electrical
signal that looks like this:
1061
00:43:59,069 --> 00:44:00,671
a brain wave.
1062
00:44:00,704 --> 00:44:02,773
But if the surrounding neurons
aren't healthy,
1063
00:44:02,806 --> 00:44:06,176
those brain waves won't get
very far.
1064
00:44:06,209 --> 00:44:08,412
It turns out that
in conscious people,
1065
00:44:08,445 --> 00:44:10,647
even those
who appear unresponsive,
1066
00:44:10,680 --> 00:44:13,917
not only do those brain
waves spread all over the brain,
1067
00:44:13,950 --> 00:44:17,320
but they become more complex
too-- it's as if,
1068
00:44:17,353 --> 00:44:19,423
to use music as the analogy,
1069
00:44:19,456 --> 00:44:22,893
what starts as a single repeated
note by a few neurons
1070
00:44:22,926 --> 00:44:26,697
eventually turns into
a coordinated symphony
1071
00:44:26,730 --> 00:44:28,465
of millions.
1072
00:44:28,498 --> 00:44:30,901
♪ ♪
1073
00:44:30,934 --> 00:44:34,871
DEHAENE:
We find is that there
is this explosion of complexity
1074
00:44:34,904 --> 00:44:36,473
only when
the person is conscious.
1075
00:44:36,506 --> 00:44:38,008
This complexity,
1076
00:44:38,041 --> 00:44:40,143
the way different brain areas
speak to each other,
1077
00:44:40,176 --> 00:44:43,046
is a signature,
a marker of consciousness.
1078
00:44:43,079 --> 00:44:46,349
BERLIN:
Studies of hundreds of
patients in various states--
1079
00:44:46,382 --> 00:44:48,985
from deep sleep to anesthesia
to coma--
1080
00:44:49,018 --> 00:44:52,723
have enabled scientists
to develop a complexity scale.
1081
00:44:52,756 --> 00:44:54,691
A score above
a certain threshold
1082
00:44:54,724 --> 00:44:59,262
means you are conscious or have
the capacity for consciousness.
1083
00:44:59,295 --> 00:45:00,931
EDLOW:
Multiple studies have now shown
1084
00:45:00,964 --> 00:45:04,735
that 15% to 20% of patients who
appear unresponsive,
1085
00:45:04,768 --> 00:45:07,704
they don't express themselves on
our behavioral exam,
1086
00:45:07,737 --> 00:45:09,473
they are actually conscious.
1087
00:45:09,506 --> 00:45:11,341
So, you know,
will this be able
1088
00:45:11,374 --> 00:45:13,510
to help
people in those states?
1089
00:45:13,543 --> 00:45:15,112
When we speak to families
1090
00:45:15,145 --> 00:45:16,980
about what matters to them most,
1091
00:45:17,013 --> 00:45:20,217
it is that patient's
current level of consciousness
1092
00:45:20,250 --> 00:45:24,221
and their potential for future
recovery of consciousness.
1093
00:45:24,254 --> 00:45:25,789
If families were to have
1094
00:45:25,822 --> 00:45:28,358
that information,
it could fundamentally affect
1095
00:45:28,391 --> 00:45:29,626
the decisions they make about
1096
00:45:29,659 --> 00:45:31,361
whether to continue
life-sustaining therapy.
1097
00:45:33,129 --> 00:45:37,534
DEHAENE:
Progress in the clinic
is extremely real and fast,
1098
00:45:37,567 --> 00:45:39,836
and people realize that the
problem of consciousness
1099
00:45:39,869 --> 00:45:41,404
is starting to be solved.
1100
00:45:41,437 --> 00:45:44,307
♪ ♪
1101
00:45:44,340 --> 00:45:46,209
BERLIN:
Starting to be solved,
1102
00:45:46,242 --> 00:45:47,978
because while we have
several clues
1103
00:45:48,011 --> 00:45:50,213
about how conscious awareness
might work in the brain,
1104
00:45:50,246 --> 00:45:52,616
this is only the beginning.
1105
00:45:52,649 --> 00:45:55,519
How all of those pieces
of brain activation add up
1106
00:45:55,552 --> 00:45:58,789
to you, a distinct individual
with a sense of self,
1107
00:45:58,822 --> 00:46:00,190
is still a mystery.
1108
00:46:00,223 --> 00:46:02,793
I know my brain creates
an internal experience
1109
00:46:02,826 --> 00:46:05,295
by knitting together bits
of sensory information,
1110
00:46:05,328 --> 00:46:07,264
filling in the gaps with its
best guess
1111
00:46:07,297 --> 00:46:09,099
of what's out there in the
world,
1112
00:46:09,132 --> 00:46:12,269
but what are those guesses
based on?
1113
00:46:12,302 --> 00:46:14,805
Memory.
1114
00:46:14,838 --> 00:46:18,708
Each of us has a life rich
with experiences to draw from.
1115
00:46:18,741 --> 00:46:21,111
Where we were born,
went to school,
1116
00:46:21,144 --> 00:46:23,246
who we fell in love with.
1117
00:46:23,279 --> 00:46:25,982
Memories are the cornerstone
of our identities,
1118
00:46:26,015 --> 00:46:27,450
but as it turns out,
1119
00:46:27,483 --> 00:46:30,487
they have
a very shaky foundation.
1120
00:46:30,520 --> 00:46:32,923
I could swear by it,
1121
00:46:32,956 --> 00:46:35,192
and would pass every
lie detector test, that...
1122
00:46:35,225 --> 00:46:36,693
I had met Mother Teresa.
1123
00:46:36,726 --> 00:46:37,794
But I hadn't.
1124
00:46:37,827 --> 00:46:39,362
Something that I wanted to
happen
1125
00:46:39,395 --> 00:46:40,631
but it never did happen.
1126
00:46:43,199 --> 00:46:44,734
SCHILLER:
The stories we tell
1127
00:46:44,767 --> 00:46:48,405
ourselves, or what we consider
our memory, is a construction.
1128
00:46:48,438 --> 00:46:50,640
We create these representations.
1129
00:46:50,673 --> 00:46:53,710
And they're very dynamic,
they constantly change.
1130
00:46:53,743 --> 00:46:55,912
You're kind of living a revision
1131
00:46:55,945 --> 00:46:58,348
of the story of your life,
constantly.
1132
00:46:59,349 --> 00:47:01,351
SETH:
The more
often we recall things,
1133
00:47:01,384 --> 00:47:03,820
the less objectively accurate
1134
00:47:03,853 --> 00:47:05,189
our memories become.
1135
00:47:06,190 --> 00:47:08,525
BERLIN:
It turns out that every
time you a recall a memory--
1136
00:47:08,558 --> 00:47:10,660
your first kiss,
1137
00:47:10,693 --> 00:47:12,429
graduating from college,
1138
00:47:12,462 --> 00:47:14,464
the death of a loved one--
1139
00:47:14,497 --> 00:47:19,069
the very act of recollection
makes it vulnerable to change.
1140
00:47:19,102 --> 00:47:20,937
SCHILLER:
So when you experience
a new event,
1141
00:47:20,970 --> 00:47:23,473
it has to be
stored in the brain.
1142
00:47:23,506 --> 00:47:25,008
And then, we used to think that
1143
00:47:25,041 --> 00:47:26,676
whenever you think about that
event,
1144
00:47:26,709 --> 00:47:30,447
you retrieve the same original
memory.
1145
00:47:30,480 --> 00:47:34,184
But what we got to
realize in the last few decades
1146
00:47:34,217 --> 00:47:36,353
is that whenever
you retrieve a memory,
1147
00:47:36,386 --> 00:47:38,588
it goes back
to an unstable state.
1148
00:47:38,621 --> 00:47:44,027
BERLIN:
In 2000, memory scientist
Eric Kandel won the Nobel Prize
1149
00:47:44,060 --> 00:47:47,497
for showing that each memory
creates new synapses,
1150
00:47:47,530 --> 00:47:49,332
connections
that store the memory.
1151
00:47:49,365 --> 00:47:53,036
But what happens
when you recall it?
1152
00:47:53,069 --> 00:47:54,838
Every time you remember it,
1153
00:47:54,871 --> 00:47:56,539
you bring it up into your
working memory
1154
00:47:56,572 --> 00:47:57,774
and you perceive it,
1155
00:47:57,807 --> 00:47:59,943
and you destroy the long-term
memory.
1156
00:47:59,976 --> 00:48:02,913
And you actually have
1157
00:48:02,946 --> 00:48:04,948
to recast it into long-term
memory
1158
00:48:04,981 --> 00:48:06,683
when you re-remember it.
1159
00:48:06,716 --> 00:48:08,685
So every single time
you remember something,
1160
00:48:08,718 --> 00:48:10,320
you actually add more
noise to it,
1161
00:48:10,353 --> 00:48:12,689
so that it's more and more
and more false
1162
00:48:12,722 --> 00:48:14,624
throughout time.
1163
00:48:14,657 --> 00:48:17,627
BERLIN:
This mechanism,
called reconsolidation,
1164
00:48:17,660 --> 00:48:19,296
was first discovered in rodents,
1165
00:48:19,329 --> 00:48:22,265
where neuroscientists witnessed
what happens
1166
00:48:22,298 --> 00:48:24,301
when a memory gets recollected:
1167
00:48:24,334 --> 00:48:26,937
for the memory to return
to long-term storage,
1168
00:48:26,970 --> 00:48:31,474
the connections between neurons
actually have to get rebuilt.
1169
00:48:31,507 --> 00:48:34,044
Recent experiments
have suggested
1170
00:48:34,077 --> 00:48:37,113
this is likely a mechanism in
human brains, as well,
1171
00:48:37,146 --> 00:48:40,650
because certain drugs known
to disrupt reconsolidation
1172
00:48:40,683 --> 00:48:43,587
have been shown
to alter human memories.
1173
00:48:44,721 --> 00:48:48,758
FENTON:
We're stuck with the problem
of, how do we know what is true?
1174
00:48:48,791 --> 00:48:51,027
How do we know what's real?
1175
00:48:51,060 --> 00:48:52,896
And maybe
1176
00:48:52,929 --> 00:48:55,665
part of the recognition is,
some of those things
1177
00:48:55,698 --> 00:48:58,835
don't matter as
much as we think they do.
1178
00:49:01,471 --> 00:49:04,341
SCHILLER:
If we think about the fact that
maybe our memories are not
1179
00:49:04,374 --> 00:49:05,942
as they originally happened,
1180
00:49:05,975 --> 00:49:09,212
it could be a scary thought,
because then, who are we?
1181
00:49:09,245 --> 00:49:12,382
I think you need
to think about it as something
1182
00:49:12,415 --> 00:49:15,618
more liberating,
because if you're stuck
1183
00:49:15,651 --> 00:49:17,687
with original representations,
1184
00:49:17,720 --> 00:49:19,423
you're kind
of stuck in the past.
1185
00:49:20,524 --> 00:49:24,294
BERLIN:
Just like our perceptions,
our sense of self is dynamic,
1186
00:49:24,327 --> 00:49:26,863
built to serve
us in the present.
1187
00:49:26,896 --> 00:49:28,732
SETH:
Our experience of, of self
1188
00:49:28,765 --> 00:49:31,034
is a construction at all sorts
of different levels.
1189
00:49:31,067 --> 00:49:33,503
What the brain is doing,
is interested in,
1190
00:49:33,536 --> 00:49:36,973
is weaving
together a kind of story.
1191
00:49:37,006 --> 00:49:40,643
FENTON:
The brain is
a storytelling machine, right?
1192
00:49:40,676 --> 00:49:43,980
It's a machine that's
designed to make predictions.
1193
00:49:44,013 --> 00:49:47,751
KASTHURI:
The narratives
that we tell ourselves
1194
00:49:47,784 --> 00:49:49,819
are the biggest illusions
that we ever participate in.
1195
00:49:49,852 --> 00:49:52,255
Your sense of who you are is an
illusion,
1196
00:49:52,288 --> 00:49:55,325
as everything else--
you're no exception.
1197
00:49:56,492 --> 00:49:59,262
BERLIN:
But if even our sense
of self is an illusion,
1198
00:49:59,295 --> 00:50:01,564
where does that leave us?
1199
00:50:01,597 --> 00:50:04,601
MARTINEZ-CONDE (chuckles):
Trust the illusion,
that's the only thing
1200
00:50:04,634 --> 00:50:07,137
that we can be sure of,
1201
00:50:07,170 --> 00:50:12,142
that what
we perceive is not what's there.
1202
00:50:17,447 --> 00:50:19,516
BERLIN:
So all these years later,
1203
00:50:19,549 --> 00:50:22,552
in my quest to understand where
my thoughts come from
1204
00:50:22,585 --> 00:50:24,387
and how my brain works,
1205
00:50:24,420 --> 00:50:27,457
I've learned that my brain
is an exquisite machine
1206
00:50:27,490 --> 00:50:31,061
that perceives reality
in the service of survival,
1207
00:50:31,094 --> 00:50:33,029
not accuracy.
1208
00:50:33,062 --> 00:50:35,732
The world I carry inside of my
head
1209
00:50:35,765 --> 00:50:37,333
is a construction of my
brain
1210
00:50:37,366 --> 00:50:40,003
built on bits of sensory
information
1211
00:50:40,036 --> 00:50:41,905
woven together with memory
1212
00:50:41,938 --> 00:50:44,974
to create a conscious
experience.
1213
00:50:45,007 --> 00:50:47,410
Now, to some this might sound
scary,
1214
00:50:47,443 --> 00:50:50,146
but to me, it's inspiring.
1215
00:50:50,179 --> 00:50:53,817
♪ ♪
1216
00:50:53,850 --> 00:50:55,819
SETH:
The simple act of
opening our eyes
1217
00:50:55,852 --> 00:50:57,620
and seeing a world,
1218
00:50:57,653 --> 00:50:59,923
we should not
take that for granted.
1219
00:50:59,956 --> 00:51:01,858
And in realizing
1220
00:51:01,891 --> 00:51:05,695
what a miracle of neural
computation
1221
00:51:05,728 --> 00:51:07,263
is going on under the hood,
1222
00:51:07,296 --> 00:51:09,699
to give us even the simplest
experiences,
1223
00:51:09,732 --> 00:51:11,267
I think this adds value,
1224
00:51:11,300 --> 00:51:13,970
it adds meaning,
it adds depth to our lives.
1225
00:51:14,003 --> 00:51:15,772
♪ ♪
1226
00:51:15,805 --> 00:51:18,308
DEHAENE:
I think it's liberating
to understand
1227
00:51:18,341 --> 00:51:23,113
that we rise from
this organization of matter.
1228
00:51:23,146 --> 00:51:25,815
It means that we can
be a little bit more humble.
1229
00:51:25,848 --> 00:51:29,385
We are gorgeous machines
designed by evolution
1230
00:51:29,418 --> 00:51:34,524
as well as by our environment,
education, friends, families.
1231
00:51:34,557 --> 00:51:36,560
All of that is
inscribed in our brains.
1232
00:51:39,195 --> 00:51:42,832
SAXE:
Sometimes when I wonder what I'm
doing with my life,
1233
00:51:42,865 --> 00:51:46,035
I think how is it that a spatial
1234
00:51:46,068 --> 00:51:48,271
and temporal pattern
of electrical signals
1235
00:51:48,304 --> 00:51:52,041
passing between cells in
our brains makes us who we are?
1236
00:51:52,074 --> 00:51:55,478
That just being
a part of the team
1237
00:51:55,511 --> 00:52:00,250
asking that question
is worth keeping going for.
1238
00:52:00,283 --> 00:52:04,020
♪ ♪
1239
00:52:13,396 --> 00:52:20,937
♪ ♪
1240
00:52:24,774 --> 00:52:32,315
♪ ♪
1241
00:52:33,950 --> 00:52:41,491
♪ ♪
1242
00:52:43,125 --> 00:52:50,667
♪ ♪
1243
00:52:56,405 --> 00:53:03,580
♪ ♪
90605
Can't find what you're looking for?
Get subtitles in any language from opensubtitles.com, and translate them here.