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NANCY KANWISHER: So seeing
where animals are going,

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so you can avoid them if
they're coming after you

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00:00:16,379 --> 00:00:19,020
or so you can catch them if
you're going after them, right?

4
00:00:19,020 --> 00:00:23,160
One of the arguably
uniquely human abilities

5
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is precision throwing, right?

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00:00:25,560 --> 00:00:26,940
No other animal can do that.

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That's a very human thing.

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Although, visual motion is
shared with lots of ability

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to see motion is shared
with lots of animals.

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What else did you notice?

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What else seemed funny or harder
to discern with stop motion?

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00:00:40,200 --> 00:00:41,070
Yeah?

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AUDIENCE: We care about small
details like [INAUDIBLE]

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to understand what
the person is seeing.

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NANCY KANWISHER: Yeah.

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Yeah, so I was
making notes to self.

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I haven't done that demo before.

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But in future, it would
be really good to have

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the audio quality terrible.

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Because if the audio
quality is terrible,

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you would lean more
on lip reading.

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And we might have noticed more.

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But it's really hard to
do that probably even

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at relatively fast flicker rates
because that motion information

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is important.

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Absolutely.

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What else?

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How about beyond
just lip reading?

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What else did you notice about
the faces, mine or Jim's?

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Could you-- yeah?

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AUDIENCE: They were static.

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So it was kind of hard
to tell like emotion

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because a lot of the
ways we express emotion

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is very nuanced.

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NANCY KANWISHER: Exactly.

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Exactly.

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Facial expressions
are incredibly subtle.

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Like little
microexpressions flicker

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across the face in a tenth
of a second and go away,

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and you guys detect them.

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Like we're very, very
sensitive to those things.

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Sometimes if you see somebody
in a hallway and, for a moment,

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there's an expression that
flickers across their face

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00:01:54,850 --> 00:01:56,530
and then they give
you a normal smile,

45
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but you can tell
from that expression

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that actually they
didn't want to see you,

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00:01:59,840 --> 00:02:01,150
for whatever reason, right?

48
00:02:01,150 --> 00:02:02,290
We catch those things.

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00:02:02,290 --> 00:02:03,832
We're really, really
good at catching

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00:02:03,832 --> 00:02:05,560
those little
fleeting expressions.

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And those probably have to
do with not just sampling

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with fine temporal frequency
but probably seeing

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00:02:09,940 --> 00:02:12,670
the direction of motion of
each little part of the face.

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OK?

55
00:02:13,480 --> 00:02:15,940
OK, so this is just
common sense reasoning

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about what we might
have motion for.

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OK?

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And so you guys got all the
things that I had in mind.

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OK, so now the next question,
just kind of thought question,

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speculation question, given
these many different things

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that make motion important
to us, biologically,

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ecologically, in
our daily lives,

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maybe that's important
enough that we

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might allocate special brain
machinery to processing motion.

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What do you think?

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Important enough?

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00:02:45,850 --> 00:02:48,550
Could you get by if you lived
in a strobe world all the time?

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00:02:48,550 --> 00:02:49,930
Could you survive just fine?

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Hard to say, right?

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Might be hard.

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I mean, we probably don't need
to go hunting down predators.

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But you walk across
Vassar Street.

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And there's some pretty
dangerous predators

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00:03:01,850 --> 00:03:04,100
coming down Vassar Street
in the way of cars, right?

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00:03:04,100 --> 00:03:05,690
You need to know
where they're going

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00:03:05,690 --> 00:03:07,482
and whether you can
cross in front of them.

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So it's actually pretty
hard to live life

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without being able
to see motion.

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And I'll tell you about a woman
who has that experience later

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00:03:14,540 --> 00:03:16,040
in the lecture.

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OK, next question,
just think about this.

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I'm not going to test
you on it or anything.

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It's not the topic
of this course.

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But it's a perspective
you should take.

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Imagine that this
were a CS course

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and I gave you a
segment of video.

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And your task was to
write some code that

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00:03:33,950 --> 00:03:36,770
takes that video input
and says whether objects

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00:03:36,770 --> 00:03:40,430
are moving in that movie or
says which objects are moving

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00:03:40,430 --> 00:03:43,010
or how much they're moving or
what direction they're moving.

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00:03:43,010 --> 00:03:45,290
What kind of code
would you have to write

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00:03:45,290 --> 00:03:48,740
to take that video input
to try to figure that out?

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00:03:48,740 --> 00:03:50,148
OK, so just think about that.

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00:03:50,148 --> 00:03:52,190
We're not going to be
writing code in this class.

95
00:03:52,190 --> 00:03:53,898
But a lot of what
we're going to be doing

96
00:03:53,898 --> 00:03:56,330
is thinking about,
how do you take

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00:03:56,330 --> 00:03:58,610
this kind of perceptual
input and come out

98
00:03:58,610 --> 00:04:00,350
with that kind of
perceptual inference?

99
00:04:00,350 --> 00:04:02,780
And what kinds of
computations would

100
00:04:02,780 --> 00:04:06,380
have to go on in between whether
those computations are going on

101
00:04:06,380 --> 00:04:09,260
in code that you guys write
or in a piece of brain that's

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00:04:09,260 --> 00:04:10,700
doing that computation?

103
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And thinking about how
you might write the code

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00:04:13,430 --> 00:04:15,080
gives you really
important insights

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00:04:15,080 --> 00:04:17,209
about what the brain
might be doing.

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00:04:17,209 --> 00:04:18,200
OK?

107
00:04:18,200 --> 00:04:20,610
All right, so that's the
point of all of that.

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The Marr reading talks
about all of this.

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And the key point we're
trying to get here

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is that you can't
understand perception

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without thinking about what
each perceptual inference is

112
00:04:30,290 --> 00:04:33,020
necessary for ecologically
in daily lives

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and about the computational
challenges involved

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in making that inference.

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OK?

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So we'll get back to all
that next week and beyond.

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But meanwhile, here's
the agenda for today.

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So here's the agenda.

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We just did the demo.

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We're now going to skip and do
some neuroanatomy, absolutely

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bare basics.

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Because on Wednesday, we
have this amazing opportunity

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to have one of the most famous
neuroscientists in the world

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do a dissection of
a real human brain

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right here right
in front of you.

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It's going to be awesome.

127
00:05:03,920 --> 00:05:07,640
And I don't want to
waste that opportunity

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or embarrass ourselves
by having people not

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know the bare basics.

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So we're going to
do the bare basics.

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It's all stuff you should
know from 900 and 901.

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And I'm going to
whip through it fast,

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so we can get to more
interesting stuff

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and get back to visual motion.

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OK?

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That's the agenda.

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All right, so some
absolute bare basics

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of the brain, the human brain
contains about 100 billion,

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10 to the 11th neurons.

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00:05:30,530 --> 00:05:32,250
And that's a very big number.

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00:05:32,250 --> 00:05:34,850
That's such a big number it's
approximately Jeff Bezos'

142
00:05:34,850 --> 00:05:35,700
worth.

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Well, it was until Mackenzie
got into the picture.

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So we'll see.

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00:05:39,088 --> 00:05:40,880
No, you don't need to
remember this number.

146
00:05:40,880 --> 00:05:43,190
Just know it's a
really big number.

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Basics of a neuron,
here's a neuron.

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A neuron is a cell like
any other cell in the body.

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It's got a cell body
and a nucleus, just

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00:05:49,520 --> 00:05:50,922
like any other
cell in your body.

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But the thing that's
distinctive about a neuron

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is it has a big long
process called an axon.

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It's got a bunch of dendrites,
the little processes,

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the little thingies
near the cell body.

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And out at the tip of the axon,
that's your classic neuron.

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Many neurons have
a myelin sheath,

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a layer of rolled up fat
around the axon made up

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of other cells.

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That makes the axon conduct
neural signals faster.

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OK, you should know all that.

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00:06:16,160 --> 00:06:17,955
I'm not trying to insult
your intelligence.

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I'm just trying to make sure
everybody's with the program

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here.

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OK, so you have thousands
of synapses on each neuron.

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And that means you have-- to
put it technically-- a shitload

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of synapses in your brain.

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OK?

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Another important point, the
brain runs on a mere 20 watts.

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And if you're not impressed
with that, reflect on the fact

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that IBM's Watson
runs on 20,000 watts.

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So one of the cool things
about the human brain

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is not just all
the awesome stuff

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that we can do that
still no computer can do,

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that I talked about
last time, but also

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how incredibly
energetically efficiently

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we do it with our human brains.

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So most of this course is
going to talk about the cortex.

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That's all the stuff on
the outside of the brain.

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That's that sheet
wrapping around

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the outside of the brain,
that folded outer surface.

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It's approximately the size
and area of a large pizza.

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But there are lots of
other important bits too.

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And I'm going to just
do whirlwind tour

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of those other bits now.

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OK, so you can
think of the brain

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as composed of four major
kinds of components.

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Deep down in the
bottom of the brain,

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you have the brain stem, where
the spinal cord comes in here.

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And the rest of the
brain is up there.

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And the brain stem
is right down here.

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And the cerebellum, this
little cauliflower like thing

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that sits out right back there.

193
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And in the middle
of the brain, you

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have the limbic system
with a whole bunch

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of subcortical regions.

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And we'll talk about a
few of those in a moment.

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And you have white matter,
all the cables and connections

198
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that go from one part of
the brain to another part.

199
00:07:51,020 --> 00:07:53,610
This is an actual
dissected human brain.

200
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And all those kind of
weird fibrous things

201
00:07:55,830 --> 00:07:59,640
are bundles of axons connecting
remote parts of the brain

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to each other.

203
00:08:00,460 --> 00:08:03,040
You can see them in
gross dissection.

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OK?

205
00:08:04,710 --> 00:08:06,310
And of course, you
have the cortex.

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OK, so these are just four
major things to think about.

207
00:08:09,125 --> 00:08:11,250
And before we spend the
rest of the course on that,

208
00:08:11,250 --> 00:08:13,050
we're going to do just
a teeny little bit

209
00:08:13,050 --> 00:08:15,300
on the other major bits.

210
00:08:15,300 --> 00:08:16,570
OK, and I'm going fast.

211
00:08:16,570 --> 00:08:19,050
So just stop me if any
of this isn't clear.

212
00:08:19,050 --> 00:08:21,960
All right, so the reason
we're doing this in part

213
00:08:21,960 --> 00:08:24,390
is that, with a
dissection of a brain,

214
00:08:24,390 --> 00:08:26,520
some of the main
things you see are

215
00:08:26,520 --> 00:08:28,320
those subcortical
structures, right?

216
00:08:28,320 --> 00:08:31,620
And so even though the course
is going to focus on the cortex,

217
00:08:31,620 --> 00:08:34,110
each little different bit of
the cortex to the naked eye

218
00:08:34,110 --> 00:08:35,940
looks like any other
bit of the cortex.

219
00:08:35,940 --> 00:08:38,940
It's the subcortical stuff
that looks different, right?

220
00:08:38,940 --> 00:08:40,830
So that's why we're doing this.

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00:08:40,830 --> 00:08:43,500
OK, bare basics
on the brain stem,

222
00:08:43,500 --> 00:08:45,480
you can think of it
as a bunch of relays

223
00:08:45,480 --> 00:08:49,410
in here, different centers that
connect information coming up

224
00:08:49,410 --> 00:08:53,590
from the spinal cord and send
it through into the cerebellum.

225
00:08:53,590 --> 00:08:57,060
So it's, in many ways, the most
primitive part of the brain.

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00:08:57,060 --> 00:08:59,340
That means it's
shared with animals

227
00:08:59,340 --> 00:09:00,930
that branched off
from us very far

228
00:09:00,930 --> 00:09:03,540
back in mammalian evolution.

229
00:09:03,540 --> 00:09:05,740
But it's also essential to life.

230
00:09:05,740 --> 00:09:06,240
OK?

231
00:09:06,240 --> 00:09:09,582
So you can get by with
most of your cortex gone.

232
00:09:09,582 --> 00:09:11,040
Like you may not
have a lot of fun.

233
00:09:11,040 --> 00:09:12,707
You may not really
know what's going on.

234
00:09:12,707 --> 00:09:14,100
But you will stay alive.

235
00:09:14,100 --> 00:09:16,890
But you can't get by without
your brain stem, right?

236
00:09:16,890 --> 00:09:20,850
It controls all kinds of basic
crucial bodily functions,

237
00:09:20,850 --> 00:09:24,390
like breathing, consciousness,
temperature regulation,

238
00:09:24,390 --> 00:09:24,930
et cetera.

239
00:09:24,930 --> 00:09:27,660
So it's not interesting
cognitively.

240
00:09:27,660 --> 00:09:30,420
But it's crucial for life.

241
00:09:30,420 --> 00:09:33,810
Cerebellum, this
beautiful thing here,

242
00:09:33,810 --> 00:09:37,380
it's basically involved
in motor coordination.

243
00:09:37,380 --> 00:09:39,450
But from there on out,
there's a huge debate

244
00:09:39,450 --> 00:09:41,380
about its possible
role in cognition.

245
00:09:41,380 --> 00:09:43,440
And so there's lots of
brain-imaging studies

246
00:09:43,440 --> 00:09:46,140
where people find that
the cerebellum is engaged

247
00:09:46,140 --> 00:09:48,210
in all kinds of
things from aspects

248
00:09:48,210 --> 00:09:51,120
of perception up through aspects
of language understanding.

249
00:09:51,120 --> 00:09:55,290
You can find activations
in brain-imaging studies.

250
00:09:55,290 --> 00:09:57,930
Nonetheless, the best
guess is that you actually

251
00:09:57,930 --> 00:10:01,510
don't need a cerebellum
for any of this.

252
00:10:01,510 --> 00:10:03,133
So if anybody's
interested, I'm going

253
00:10:03,133 --> 00:10:04,800
to actually try to
remember to put it up

254
00:10:04,800 --> 00:10:06,258
as an optional
reading on the site.

255
00:10:06,258 --> 00:10:08,550
There's a recent
article in The Atlantic

256
00:10:08,550 --> 00:10:11,910
or The New Yorker about a
kid who had no cerebellum.

257
00:10:11,910 --> 00:10:15,330
And he learned to
walk late and slow.

258
00:10:15,330 --> 00:10:16,707
Nobody knew what
his problem was.

259
00:10:16,707 --> 00:10:18,540
But he learned to do
pretty much everything.

260
00:10:18,540 --> 00:10:20,070
Like he's pretty much fine.

261
00:10:20,070 --> 00:10:22,480
His motor coordination
isn't great, but he's fine.

262
00:10:22,480 --> 00:10:23,100
Yeah?

263
00:10:23,100 --> 00:10:25,100
AUDIENCE: How would you
define the consciousness

264
00:10:25,100 --> 00:10:25,890
in this context?

265
00:10:25,890 --> 00:10:27,060
NANCY KANWISHER: Oh,
that's a good question.

266
00:10:27,060 --> 00:10:28,343
And it's a big question.

267
00:10:28,343 --> 00:10:30,510
And it's a question that
nobody knows how to answer,

268
00:10:30,510 --> 00:10:31,540
not just me.

269
00:10:31,540 --> 00:10:34,410
So Christof Koch,
who does more work

270
00:10:34,410 --> 00:10:35,910
on the neural basis
of consciousness

271
00:10:35,910 --> 00:10:37,590
than just about
anybody, has been

272
00:10:37,590 --> 00:10:40,090
going around saying,
for about 15 years,

273
00:10:40,090 --> 00:10:42,485
we must not get stuck on
a premature definition

274
00:10:42,485 --> 00:10:43,860
of consciousness
because we don't

275
00:10:43,860 --> 00:10:46,193
know what that thing is that
we're trying to understand.

276
00:10:46,193 --> 00:10:50,360
So I'll hide behind Christof's
parry of that question

277
00:10:50,360 --> 00:10:52,360
and say we'll talk about
it later in the course.

278
00:10:52,360 --> 00:10:54,960
But there are many
different ways

279
00:10:54,960 --> 00:10:57,840
of defining it from the
difference between being awake

280
00:10:57,840 --> 00:11:00,240
versus asleep, which is
some of the functions that

281
00:11:00,240 --> 00:11:04,500
go on here, the difference
between being knocked out

282
00:11:04,500 --> 00:11:06,678
and completely unconscious
under general anesthesia,

283
00:11:06,678 --> 00:11:08,220
which is different
from being asleep.

284
00:11:08,220 --> 00:11:10,020
Those kind of states
of consciousness

285
00:11:10,020 --> 00:11:13,980
are regulated, in
part, in here, yeah.

286
00:11:13,980 --> 00:11:16,380
OK, so you can get by
without a cerebellum.

287
00:11:16,380 --> 00:11:19,260
But it's not recommended.

288
00:11:19,260 --> 00:11:23,310
Moving right along, all
those subcortical bits, we're

289
00:11:23,310 --> 00:11:25,830
just going to talk about three
of the most important ones,

290
00:11:25,830 --> 00:11:28,350
the thalamus, this
big guy right smack

291
00:11:28,350 --> 00:11:31,290
in the middle of the brain,
very large structure,

292
00:11:31,290 --> 00:11:33,690
the hippocampus,
and the amygdala.

293
00:11:33,690 --> 00:11:35,670
OK, let's talk
about the thalamus.

294
00:11:35,670 --> 00:11:37,680
Think about the thalamus
as a Grand Central

295
00:11:37,680 --> 00:11:41,040
Station of the brain, OK,
with all of these connections

296
00:11:41,040 --> 00:11:42,960
going to all those
parts of cortex

297
00:11:42,960 --> 00:11:45,910
coming in and out of
the thalamus like that.

298
00:11:45,910 --> 00:11:46,980
OK?

299
00:11:46,980 --> 00:11:50,460
So one of the key things
about the thalamus

300
00:11:50,460 --> 00:11:53,640
is that most of the
incoming sensory information

301
00:11:53,640 --> 00:11:57,570
goes by way of the thalamus
en route to the cortex.

302
00:11:57,570 --> 00:11:58,110
OK?

303
00:11:58,110 --> 00:12:00,780
So if you start with
your ear, there's

304
00:12:00,780 --> 00:12:02,700
sensory endings in
your ear that we'll

305
00:12:02,700 --> 00:12:04,290
talk about later in the term.

306
00:12:04,290 --> 00:12:07,380
And they send neurons into
this, the thalamus here,

307
00:12:07,380 --> 00:12:09,935
this yellow thing, through
a bunch of different stages.

308
00:12:09,935 --> 00:12:11,310
They make a stop
in the thalamus.

309
00:12:11,310 --> 00:12:13,440
And then they come up
here to this green patch,

310
00:12:13,440 --> 00:12:15,480
which is auditory cortex.

311
00:12:15,480 --> 00:12:16,620
OK?

312
00:12:16,620 --> 00:12:20,520
Similarly,
somatosensory endings,

313
00:12:20,520 --> 00:12:22,560
touch sensors in
your skin that enable

314
00:12:22,560 --> 00:12:25,380
you to feel when
you're being touched

315
00:12:25,380 --> 00:12:27,750
come in through the skin.

316
00:12:27,750 --> 00:12:30,150
And they make a stop
in the thalamus.

317
00:12:30,150 --> 00:12:34,140
And then they go up to
somatosensory cortex up there.

318
00:12:34,140 --> 00:12:35,580
OK?

319
00:12:35,580 --> 00:12:40,290
Similarly, visual signals
that come in from your eyes

320
00:12:40,290 --> 00:12:45,510
make a stop in the thalamus and
then go up to visual cortex.

321
00:12:45,510 --> 00:12:48,270
OK, what's the name of the
structure in the thalamus

322
00:12:48,270 --> 00:12:50,478
that those axons
make a synapse in?

323
00:12:50,478 --> 00:12:52,520
Coming up from the eyes,
you make a synapse here.

324
00:12:52,520 --> 00:12:54,620
And you go up to visual cortex.

325
00:12:54,620 --> 00:12:55,470
AUDIENCE: LGN.

326
00:12:55,470 --> 00:12:56,720
NANCY KANWISHER: LGN, perfect.

327
00:12:56,720 --> 00:12:57,470
What does it stand for?

328
00:12:57,470 --> 00:12:58,740
AUDIENCE: Lateral
geniculate nucleus.

329
00:12:58,740 --> 00:12:59,220
NANCY KANWISHER: Perfect.

330
00:12:59,220 --> 00:13:00,450
OK, you should know that.

331
00:13:00,450 --> 00:13:03,930
This is review from 900, 901.

332
00:13:03,930 --> 00:13:06,580
OK, yes?

333
00:13:06,580 --> 00:13:07,080
Sorry.

334
00:13:07,080 --> 00:13:12,150
OK, which sensory modality does
not go through the thalamus

335
00:13:12,150 --> 00:13:14,370
en route to cortex
between the sensory nerve

336
00:13:14,370 --> 00:13:17,450
endings and the cortex?

337
00:13:17,450 --> 00:13:17,950
Sorry?

338
00:13:17,950 --> 00:13:18,730
AUDIENCE: Olfactory.

339
00:13:18,730 --> 00:13:19,570
NANCY KANWISHER: Yes.

340
00:13:19,570 --> 00:13:20,080
Yes.

341
00:13:20,080 --> 00:13:21,122
You guys are on the ball.

342
00:13:21,122 --> 00:13:23,830
Yes, olfactory system is
the one sensory modality

343
00:13:23,830 --> 00:13:26,090
that doesn't make a
stop in the cortex.

344
00:13:26,090 --> 00:13:27,340
You can sort of see that here.

345
00:13:27,340 --> 00:13:31,240
From the nose, it goes straight
up into olfactory cortex

346
00:13:31,240 --> 00:13:33,280
right there.

347
00:13:33,280 --> 00:13:36,670
All right, so that's
the standard view

348
00:13:36,670 --> 00:13:38,890
of the thalamus is this
kind of like relay station

349
00:13:38,890 --> 00:13:41,680
where all the external sensory
information comes in there,

350
00:13:41,680 --> 00:13:43,810
makes a stop, and then
goes up to cortex.

351
00:13:43,810 --> 00:13:44,710
OK?

352
00:13:44,710 --> 00:13:46,400
That's my thalamus act.

353
00:13:46,400 --> 00:13:46,900
Boom.

354
00:13:46,900 --> 00:13:47,620
Like that, right?

355
00:13:47,620 --> 00:13:48,520
OK.

356
00:13:48,520 --> 00:13:51,790
But, increasingly,
there's evidence

357
00:13:51,790 --> 00:13:54,280
that the thalamus is much
more than a relay station.

358
00:13:54,280 --> 00:13:56,380
And why would you bother
with a relay anyway?

359
00:13:56,380 --> 00:13:57,340
Kind of doesn't mean anything.

360
00:13:57,340 --> 00:13:58,360
Kind of means like
we don't know what's

361
00:13:58,360 --> 00:14:00,220
going on here because
you wouldn't just make

362
00:14:00,220 --> 00:14:02,970
a synapse for no reason, right?

363
00:14:02,970 --> 00:14:05,098
OK, and so the
first thing to note,

364
00:14:05,098 --> 00:14:06,640
is there are lots
of connections that

365
00:14:06,640 --> 00:14:08,420
go back down the other way?

366
00:14:08,420 --> 00:14:10,720
There are 10 times
as many connections

367
00:14:10,720 --> 00:14:12,880
that go from primary
visual cortex

368
00:14:12,880 --> 00:14:15,610
right here in me, right
here in this guy in red,

369
00:14:15,610 --> 00:14:19,360
there are 10 times as many
that go backwards down

370
00:14:19,360 --> 00:14:23,680
to the thalamus as go forwards.

371
00:14:23,680 --> 00:14:25,210
That's mind blowing, right?

372
00:14:25,210 --> 00:14:27,580
Information comes from the
eyes up into the brain.

373
00:14:27,580 --> 00:14:30,493
What the hell are those things
doing going backwards, OK?

374
00:14:30,493 --> 00:14:32,660
Well, they're doing all
kinds of interesting things.

375
00:14:32,660 --> 00:14:35,118
So that's the first indication
that the thalamus isn't just

376
00:14:35,118 --> 00:14:39,080
relaying stuff in a
stupid, passive way.

377
00:14:39,080 --> 00:14:40,630
And the second
whole line of work,

378
00:14:40,630 --> 00:14:42,160
which many people
are working on,

379
00:14:42,160 --> 00:14:45,040
but I think some of the most
awesome work on this topic

380
00:14:45,040 --> 00:14:48,430
is done by our own Mike
Halassa in this department.

381
00:14:48,430 --> 00:14:50,290
And he does these
incredible studies

382
00:14:50,290 --> 00:14:53,200
that you can do in mice with
these spectacular methods

383
00:14:53,200 --> 00:14:56,530
that we can't use in humans,
where he can really take apart

384
00:14:56,530 --> 00:14:59,020
the circuit and
magnificent detail.

385
00:14:59,020 --> 00:15:00,610
And he's showing
that the thalamus

386
00:15:00,610 --> 00:15:04,180
is involved in all kinds
of high-level cognitive

387
00:15:04,180 --> 00:15:05,920
computations in mice.

388
00:15:05,920 --> 00:15:07,150
It's really stunning work.

389
00:15:07,150 --> 00:15:09,820
When the mice have to switch
from doing one task to another,

390
00:15:09,820 --> 00:15:14,170
the thalamus plays a key role in
gating the flow of information

391
00:15:14,170 --> 00:15:18,040
from one cortical
region to another, OK?

392
00:15:18,040 --> 00:15:20,650
All right, moving along,
the hippocampus, I you

393
00:15:20,650 --> 00:15:21,820
guys all learned about this.

394
00:15:21,820 --> 00:15:23,560
The number one gripe
in this department

395
00:15:23,560 --> 00:15:25,690
as we learn about
H.M. in every course.

396
00:15:25,690 --> 00:15:26,990
So that's going to happen here.

397
00:15:26,990 --> 00:15:28,720
But it's going to
last about 20 seconds.

398
00:15:28,720 --> 00:15:29,770
So here goes.

399
00:15:29,770 --> 00:15:32,207
That's a normal slice
of the brain like this.

400
00:15:32,207 --> 00:15:33,790
Here's the hippocampus
on either side.

401
00:15:33,790 --> 00:15:37,660
It's like a whole curled up deal
right there and right there.

402
00:15:37,660 --> 00:15:41,080
And here is H.M.'s brain,
the famous H.M., who

403
00:15:41,080 --> 00:15:44,110
had surgery to remove his
hippocampus on both sides,

404
00:15:44,110 --> 00:15:47,950
and completely lost his episodic
memory for anything that

405
00:15:47,950 --> 00:15:49,730
happened after his surgery.

406
00:15:49,730 --> 00:15:50,230
OK?

407
00:15:50,230 --> 00:15:53,440
You all remember that, right?

408
00:15:53,440 --> 00:15:55,570
If anybody hasn't heard
of H.M., send me an email.

409
00:15:55,570 --> 00:15:57,580
And I'll give you some
background reading.

410
00:15:57,580 --> 00:16:00,870
OK, so very loosely,
the hippocampus

411
00:16:00,870 --> 00:16:03,910
involved both in this kind
of long-term episodic memory

412
00:16:03,910 --> 00:16:05,200
that H.M. lost.

413
00:16:05,200 --> 00:16:08,050
And it also plays a
key role in navigation,

414
00:16:08,050 --> 00:16:12,230
which we'll talk about in
great detail in a few weeks.

415
00:16:12,230 --> 00:16:14,200
And I just want to say
that some cases are

416
00:16:14,200 --> 00:16:16,960
even more extreme than H.M.

417
00:16:16,960 --> 00:16:19,490
So there's a case of
Lonni Sue Johnson.

418
00:16:19,490 --> 00:16:21,340
And I am trying to
get you guys a video.

419
00:16:21,340 --> 00:16:22,550
And I didn't get it in time.

420
00:16:22,550 --> 00:16:25,175
But I'll show it to you later in
the term if you're interested.

421
00:16:25,175 --> 00:16:28,000
Lonni Sue Johnson
had a viral infection

422
00:16:28,000 --> 00:16:29,600
that went up into her brain.

423
00:16:29,600 --> 00:16:32,080
She was an extremely
accomplished person.

424
00:16:32,080 --> 00:16:34,510
She did illustrations on
the cover of The New Yorker.

425
00:16:34,510 --> 00:16:35,440
She was a pilot.

426
00:16:35,440 --> 00:16:37,900
She had her own
farm in which she

427
00:16:37,900 --> 00:16:42,430
raised lots of stuff, a
very smart, interesting,

428
00:16:42,430 --> 00:16:46,360
multitalented woman, who
had this terrible tragedy

429
00:16:46,360 --> 00:16:49,240
of getting viral
encephalitis at I

430
00:16:49,240 --> 00:16:51,130
don't know what
age, but middle age.

431
00:16:51,130 --> 00:16:56,440
And she now does not remember
a single event in her life.

432
00:16:56,440 --> 00:16:57,550
She's smart.

433
00:16:57,550 --> 00:16:58,720
She's funny.

434
00:16:58,720 --> 00:17:01,330
Her personality
is totally intact.

435
00:17:01,330 --> 00:17:02,920
She can answer questions.

436
00:17:02,920 --> 00:17:03,580
She can paint.

437
00:17:03,580 --> 00:17:05,270
She can do all kinds of things.

438
00:17:05,270 --> 00:17:08,440
But she does not remember
a single event in her life.

439
00:17:08,440 --> 00:17:10,180
That's pretty astonishing.

440
00:17:10,180 --> 00:17:13,359
Reflect on what it means
to have the sense of self

441
00:17:13,359 --> 00:17:15,310
if you don't remember
anything in your life.

442
00:17:15,310 --> 00:17:15,810
Yeah?

443
00:17:15,810 --> 00:17:17,780
AUDIENCE: Can she
remember her name?

444
00:17:17,780 --> 00:17:18,710
NANCY KANWISHER:
That's a good question.

445
00:17:18,710 --> 00:17:19,420
I'm not sure she.

446
00:17:19,420 --> 00:17:20,980
Might know her-- yes,
she does know her name.

447
00:17:20,980 --> 00:17:22,720
Actually, it is
evident in this video.

448
00:17:22,720 --> 00:17:25,270
But the video, well, so
she doesn't remember.

449
00:17:25,270 --> 00:17:27,069
At one point in this
video, she's asked,

450
00:17:27,069 --> 00:17:29,170
were you ever married?

451
00:17:29,170 --> 00:17:32,890
And she's lovely and sweet and
gentle and kind of low key.

452
00:17:32,890 --> 00:17:35,200
And she's like, you know,
just don't remember.

453
00:17:35,200 --> 00:17:36,040
I might have been.

454
00:17:36,040 --> 00:17:37,210
I might have been.

455
00:17:37,210 --> 00:17:39,590
She was married for 10 years.

456
00:17:39,590 --> 00:17:41,590
So that's the hippocampus.

457
00:17:41,590 --> 00:17:42,170
Important.

458
00:17:42,170 --> 00:17:43,503
You don't want to lose that one.

459
00:17:43,503 --> 00:17:44,590
Yeah?

460
00:17:44,590 --> 00:17:47,170
AUDIENCE: About H.M.,
if the hippocampus

461
00:17:47,170 --> 00:17:50,590
is used in long-term memory,
why is it that it being removed

462
00:17:50,590 --> 00:17:54,267
caused him to not form memories?

463
00:17:54,267 --> 00:17:56,350
NANCY KANWISHER: Well, so
long-term memory means--

464
00:17:56,350 --> 00:17:57,880
it's a vague term.

465
00:17:57,880 --> 00:18:00,550
It means the formation
and retrieval

466
00:18:00,550 --> 00:18:02,780
of memories that are
going to last a long time.

467
00:18:02,780 --> 00:18:05,320
So in H.M.'s case, he can
access a lot of the memories

468
00:18:05,320 --> 00:18:07,030
from before his injury.

469
00:18:07,030 --> 00:18:10,060
In Lonni Sue's case,
she can't do even that.

470
00:18:10,060 --> 00:18:11,860
OK?

471
00:18:11,860 --> 00:18:16,300
All right, the
amygdala, OK, amygdala

472
00:18:16,300 --> 00:18:18,520
is a Greek word
that means almond.

473
00:18:18,520 --> 00:18:22,330
Because the amygdala is the
size and shape of an almond.

474
00:18:22,330 --> 00:18:26,170
And so just for fun, we're
passing around some almonds,

475
00:18:26,170 --> 00:18:28,360
my favorite kind.

476
00:18:28,360 --> 00:18:30,070
Have some almonds
and pass them around.

477
00:18:30,070 --> 00:18:35,080
All right, OK, so
the amygdala is

478
00:18:35,080 --> 00:18:38,500
involved in experiencing
and recognizing emotions,

479
00:18:38,500 --> 00:18:40,000
especially fear.

480
00:18:40,000 --> 00:18:42,460
The simple statement that you
should remember about what

481
00:18:42,460 --> 00:18:46,090
the amygdala does is just
remember the four F's.

482
00:18:46,090 --> 00:18:49,870
You guys all know about the
four F's, fighting, fleeing,

483
00:18:49,870 --> 00:18:51,040
feeding, and mating.

484
00:18:53,890 --> 00:18:59,080
OK, patient SM lost her
amygdala on both sides.

485
00:18:59,080 --> 00:18:59,980
OK?

486
00:18:59,980 --> 00:19:02,590
She cannot experience fear.

487
00:19:02,590 --> 00:19:05,290
She doesn't recognize
fear on facial expressions

488
00:19:05,290 --> 00:19:06,500
of other people.

489
00:19:06,500 --> 00:19:09,340
And she doesn't
experience fear herself.

490
00:19:09,340 --> 00:19:10,150
OK?

491
00:19:10,150 --> 00:19:13,780
And so that's the
striking piece of evidence

492
00:19:13,780 --> 00:19:15,280
on what the amygdala does.

493
00:19:15,280 --> 00:19:18,790
Her face recognition is
normal, recognizing identities.

494
00:19:18,790 --> 00:19:20,540
Her IQ is normal.

495
00:19:20,540 --> 00:19:23,710
She's overly trusting
of other people.

496
00:19:23,710 --> 00:19:26,013
OK?

497
00:19:26,013 --> 00:19:27,430
OK, so that's all
you need to know

498
00:19:27,430 --> 00:19:29,950
about the amygdala for now.

499
00:19:29,950 --> 00:19:33,160
OK, let's talk about white
matter, just brief review.

500
00:19:33,160 --> 00:19:36,280
Here's a kind of tunnel
through a piece of cortex.

501
00:19:36,280 --> 00:19:38,650
OK, so my brain cortex is
wrapping around like that.

502
00:19:38,650 --> 00:19:41,560
If we took a piece like
this, just took a segment out

503
00:19:41,560 --> 00:19:43,840
like that, this is the
outside of the brain up there.

504
00:19:43,840 --> 00:19:45,610
Cortex runs like this.

505
00:19:45,610 --> 00:19:48,340
And gray matter is the
stuff on the outer surface

506
00:19:48,340 --> 00:19:51,430
that's full of cell bodies, OK?

507
00:19:51,430 --> 00:19:54,580
White matter are the
axons, the processes

508
00:19:54,580 --> 00:19:56,200
that come out of
those cell bodies

509
00:19:56,200 --> 00:19:58,610
and travel elsewhere
in the brain.

510
00:19:58,610 --> 00:19:59,110
OK?

511
00:19:59,110 --> 00:20:00,490
Everybody clear on that?

512
00:20:00,490 --> 00:20:02,860
OK, so we got gray matter up
here and white matter down

513
00:20:02,860 --> 00:20:06,370
there, mostly myelinated axons
that have that layer of fat

514
00:20:06,370 --> 00:20:09,010
to make them conduct fast.

515
00:20:09,010 --> 00:20:13,220
And so you'll see bundles of
white matter in the dissection.

516
00:20:13,220 --> 00:20:15,790
And so here's an
actual photograph

517
00:20:15,790 --> 00:20:17,050
of the slice through a brain.

518
00:20:17,050 --> 00:20:19,960
So all that white stuff
up there is white matter.

519
00:20:19,960 --> 00:20:22,240
OK, and so you might
say, well, that's

520
00:20:22,240 --> 00:20:24,190
just a big bunch of wires.

521
00:20:24,190 --> 00:20:25,880
Who cares about that?

522
00:20:25,880 --> 00:20:26,860
That's a good question.

523
00:20:26,860 --> 00:20:29,560
But actually, the wires
are pretty damn interesting

524
00:20:29,560 --> 00:20:31,540
and pretty fundamental.

525
00:20:31,540 --> 00:20:33,370
And so I'll just give
you a few reasons.

526
00:20:33,370 --> 00:20:34,960
And you don't need to
memorize every one of these.

527
00:20:34,960 --> 00:20:37,430
I'm trying to give you a gist
of why we might care about this.

528
00:20:37,430 --> 00:20:39,763
And then there will be a whole
other lecture on networks

529
00:20:39,763 --> 00:20:41,680
and connectivity
later in the course.

530
00:20:41,680 --> 00:20:45,640
Well, first of all, white matter
is 45% of the human brain, OK?

531
00:20:45,640 --> 00:20:48,160
So it takes up a lot of space,
all those wires connecting

532
00:20:48,160 --> 00:20:50,440
one bit to another bit.

533
00:20:50,440 --> 00:20:53,620
And I would say we cannot
possibly understand the cortex

534
00:20:53,620 --> 00:20:56,860
and how it works or any little
piece of it without knowing

535
00:20:56,860 --> 00:20:59,530
the connectivity of each
piece to each other bit

536
00:20:59,530 --> 00:21:00,880
of the cortex, right?

537
00:21:00,880 --> 00:21:03,910
Imagine trying to understand
a computer or a circuit

538
00:21:03,910 --> 00:21:06,850
without being able to see the
connections between the bits.

539
00:21:06,850 --> 00:21:08,223
Like it would drive you crazy.

540
00:21:08,223 --> 00:21:10,390
That's the situation we're
in now in human cognitive

541
00:21:10,390 --> 00:21:10,960
neuroscience.

542
00:21:10,960 --> 00:21:12,550
It, frankly, drives me insane.

543
00:21:12,550 --> 00:21:13,660
But that's where we are.

544
00:21:16,570 --> 00:21:19,570
Next thing, the
long-range connectivity

545
00:21:19,570 --> 00:21:21,970
of each little bit of cortex,
some little bit right there

546
00:21:21,970 --> 00:21:24,790
in my brain, is
connected to some bunch

547
00:21:24,790 --> 00:21:26,950
of other remote
regions in my brain.

548
00:21:26,950 --> 00:21:29,230
And that particular
set of connections

549
00:21:29,230 --> 00:21:31,970
is distinctive for
that patch of cortex.

550
00:21:31,970 --> 00:21:34,780
So you can think of it as
a connectivity fingerprint

551
00:21:34,780 --> 00:21:36,382
of a patch of cortex.

552
00:21:36,382 --> 00:21:38,590
OK, so one of the ways that
the different bits differ

553
00:21:38,590 --> 00:21:40,930
from each other is by
way of their connectivity

554
00:21:40,930 --> 00:21:41,705
fingerprints.

555
00:21:41,705 --> 00:21:43,330
And I'm going to skip
the rest of these

556
00:21:43,330 --> 00:21:45,250
because we're going to
get back to them later.

557
00:21:45,250 --> 00:21:46,625
And I'm going to
run out of time.

558
00:21:46,625 --> 00:21:51,010
And I'm going to assign the
TAs to sound the gong at 12:15.

559
00:21:51,010 --> 00:21:51,730
OK?

560
00:21:51,730 --> 00:21:52,900
Good.

561
00:21:52,900 --> 00:21:55,220
All right, now we're
up to the cortex.

562
00:21:55,220 --> 00:21:58,000
This is really,
laughably, shallow.

563
00:21:58,000 --> 00:22:00,650
But whatever, that's
what we're doing here.

564
00:22:00,650 --> 00:22:01,570
So here's this cortex.

565
00:22:01,570 --> 00:22:03,362
And as I mentioned,
it's a whole big sheet.

566
00:22:03,362 --> 00:22:05,650
And the different bits look
really similar if you just

567
00:22:05,650 --> 00:22:07,300
look at them or slice them up.

568
00:22:07,300 --> 00:22:08,860
So how are we
going to figure out

569
00:22:08,860 --> 00:22:11,350
how this thing is organized?

570
00:22:11,350 --> 00:22:15,170
Well, OK, now we're up
here talking about cortex.

571
00:22:15,170 --> 00:22:17,410
All right, let's start
with the easy parts,

572
00:22:17,410 --> 00:22:19,240
which you've already seen.

573
00:22:19,240 --> 00:22:20,830
You've already
seen this up here.

574
00:22:20,830 --> 00:22:27,550
These colored bits, visual
cortex, auditory cortex,

575
00:22:27,550 --> 00:22:33,250
somatosensory cortex, gustatory
taste cortex, those bits

576
00:22:33,250 --> 00:22:35,380
are like the easy
parts of cortex.

577
00:22:35,380 --> 00:22:37,930
Those are called
primary sensory regions.

578
00:22:37,930 --> 00:22:40,790
There's also motor cortex right
in front of sensory cortex.

579
00:22:40,790 --> 00:22:43,630
So those are the
primary regions.

580
00:22:43,630 --> 00:22:45,340
They're primary in
the sense of this

581
00:22:45,340 --> 00:22:48,310
is the first place that
sensory information lands up

582
00:22:48,310 --> 00:22:51,820
at the cortex coming up
from the senses, right?

583
00:22:51,820 --> 00:22:57,670
OK, and all of that input is
wired through what structure?

584
00:23:00,248 --> 00:23:01,040
AUDIENCE: Thalamus.

585
00:23:01,040 --> 00:23:01,915
NANCY KANWISHER: Yes.

586
00:23:01,915 --> 00:23:03,380
Thank you.

587
00:23:03,380 --> 00:23:06,440
So how are these
regions organized?

588
00:23:06,440 --> 00:23:08,150
Well, they have maps.

589
00:23:08,150 --> 00:23:10,640
Every one of these
regions has a map.

590
00:23:10,640 --> 00:23:12,840
And each of them has a
map of a different thing.

591
00:23:12,840 --> 00:23:14,830
So let's start
with visual cortex,

592
00:23:14,830 --> 00:23:16,580
and we're going to
talk about the map that

593
00:23:16,580 --> 00:23:18,210
lives in visual cortex.

594
00:23:18,210 --> 00:23:20,780
But the prior condition
for understanding that map

595
00:23:20,780 --> 00:23:23,533
is to understand the concept
of receptive field, which

596
00:23:23,533 --> 00:23:24,200
you should know.

597
00:23:24,200 --> 00:23:27,020
So I'm going to whip
through it quickly.

598
00:23:27,020 --> 00:23:29,510
OK, so here is how
you map the receptive

599
00:23:29,510 --> 00:23:33,480
field as a property of an
individual cell in a brain.

600
00:23:33,480 --> 00:23:33,980
OK?

601
00:23:33,980 --> 00:23:36,950
So the classic way in
animal neuroscience

602
00:23:36,950 --> 00:23:39,680
is you place an
electrode in the brain

603
00:23:39,680 --> 00:23:42,810
next to a neuron in
monkey visual cortex.

604
00:23:42,810 --> 00:23:43,310
OK?

605
00:23:43,310 --> 00:23:44,180
So here's this monkey.

606
00:23:44,180 --> 00:23:45,847
He's got an electrode
right in his brain

607
00:23:45,847 --> 00:23:47,660
right next to a neuron
in visual cortex.

608
00:23:47,660 --> 00:23:51,260
And every time that neuron
fires, you get a spike.

609
00:23:51,260 --> 00:23:52,430
You hear a spike.

610
00:23:52,430 --> 00:23:56,990
OK, now you train the monkey
to stare at a fixation spot

611
00:23:56,990 --> 00:23:59,030
without moving its eyes.

612
00:23:59,030 --> 00:24:01,520
OK, I can do this with
humans without training you.

613
00:24:01,520 --> 00:24:04,290
I can just tell you, look
at the tip of my nose.

614
00:24:04,290 --> 00:24:06,667
OK, so keep your eyes
on the tip of my nose.

615
00:24:06,667 --> 00:24:08,250
I can see if you're
looking elsewhere.

616
00:24:08,250 --> 00:24:09,590
So look at the tip of my nose.

617
00:24:09,590 --> 00:24:10,280
OK?

618
00:24:10,280 --> 00:24:11,860
OK, so you train a
monkey to do that.

619
00:24:11,860 --> 00:24:12,860
That takes a few months.

620
00:24:12,860 --> 00:24:14,150
And then they can do that.

621
00:24:14,150 --> 00:24:16,970
And then while recording
from neurons in his brain,

622
00:24:16,970 --> 00:24:19,460
you put stimuli over here,
put a flash over there

623
00:24:19,460 --> 00:24:22,850
or a flash over here or a flash
over here or a flash over here.

624
00:24:22,850 --> 00:24:24,350
OK, you can stop
looking at my nose.

625
00:24:24,350 --> 00:24:26,630
It's not all that fabulous
a nose, I realize.

626
00:24:26,630 --> 00:24:32,840
OK, so a receptive field is
the place in the visual world

627
00:24:32,840 --> 00:24:35,460
that makes a given neuron fire.

628
00:24:35,460 --> 00:24:35,960
OK?

629
00:24:35,960 --> 00:24:37,550
So if there's a
neuron in your brain

630
00:24:37,550 --> 00:24:40,700
that responds to a flash here
but not a flash here or here

631
00:24:40,700 --> 00:24:44,150
or here or here, the
receptive field of that neuron

632
00:24:44,150 --> 00:24:45,410
is right there.

633
00:24:45,410 --> 00:24:47,180
Everybody got that idea?

634
00:24:47,180 --> 00:24:49,670
OK, so in visual
cortex, neurons have

635
00:24:49,670 --> 00:24:50,890
restricted receptive fields.

636
00:24:50,890 --> 00:24:53,390
They don't respond to anything
anywhere in the visual field.

637
00:24:53,390 --> 00:24:56,480
They respond to a
particular place in space.

638
00:24:56,480 --> 00:24:58,940
OK, if that's confusing
at all, ask a question.

639
00:24:58,940 --> 00:25:01,200
Because it will come
up again and again.

640
00:25:01,200 --> 00:25:03,200
All right, so that's what
the rest of this slide

641
00:25:03,200 --> 00:25:04,100
says, what I just said.

642
00:25:04,100 --> 00:25:04,620
Blah, blah, blah.

643
00:25:04,620 --> 00:25:05,060
It doesn't matter.

644
00:25:05,060 --> 00:25:06,170
That's a receptive field.

645
00:25:06,170 --> 00:25:09,740
Different visual neurons have
different receptive fields

646
00:25:09,740 --> 00:25:11,870
for different parts of space.

647
00:25:11,870 --> 00:25:13,940
Now here comes the
important idea.

648
00:25:13,940 --> 00:25:16,940
In visual cortex,
two neurons that

649
00:25:16,940 --> 00:25:19,580
are next to each
other in visual cortex

650
00:25:19,580 --> 00:25:22,490
have nearby receptive fields.

651
00:25:22,490 --> 00:25:23,990
OK?

652
00:25:23,990 --> 00:25:26,870
So that's the
concept of retinotopy

653
00:25:26,870 --> 00:25:29,100
or the map in visual cortex.

654
00:25:29,100 --> 00:25:31,670
So you basically have a
map of the visual world

655
00:25:31,670 --> 00:25:36,073
in your visual cortex because
there's this systematic layout

656
00:25:36,073 --> 00:25:37,490
just like you have
in your retina.

657
00:25:37,490 --> 00:25:39,602
In your retina, visual
information comes in.

658
00:25:39,602 --> 00:25:41,810
And because of optics,
different parts of your retina

659
00:25:41,810 --> 00:25:43,700
respond to different
parts of the image.

660
00:25:43,700 --> 00:25:47,300
But that information is
propagated back through the LGN

661
00:25:47,300 --> 00:25:49,670
up to primary visual
cortex where you still

662
00:25:49,670 --> 00:25:53,750
have a map of the visual space
up in primary visual cortex.

663
00:25:53,750 --> 00:25:55,130
OK?

664
00:25:55,130 --> 00:25:58,850
So that map is called
retinotopic in visual cortex

665
00:25:58,850 --> 00:26:02,550
because it's oriented
like the retina.

666
00:26:02,550 --> 00:26:05,210
And so here's a particularly
kind of gruesome but very

667
00:26:05,210 --> 00:26:08,900
literal depiction of this
property of retinotopy

668
00:26:08,900 --> 00:26:10,370
in a monkey brain.

669
00:26:10,370 --> 00:26:13,640
This is an experiment done
very long ago by Roger Tootell.

670
00:26:13,640 --> 00:26:19,880
And what he did was he used
a method called deoxyglucose.

671
00:26:19,880 --> 00:26:23,060
And so what deoxyglucose
is a molecule that's

672
00:26:23,060 --> 00:26:25,160
a whole lot like glucose.

673
00:26:25,160 --> 00:26:27,720
But it's got one little
change in the molecule,

674
00:26:27,720 --> 00:26:29,990
which means it gets stuck
on the metabolic chain.

675
00:26:29,990 --> 00:26:33,260
And so it gets taken up by cells
that want to take up glucose.

676
00:26:33,260 --> 00:26:36,020
And then it gets stuck in
there and can't be broken down.

677
00:26:36,020 --> 00:26:39,870
So it builds up in cells that
are metabolically active.

678
00:26:39,870 --> 00:26:40,370
OK?

679
00:26:40,370 --> 00:26:42,110
So you can put a
little radioactive

680
00:26:42,110 --> 00:26:47,330
tracer on deoxyglucose, inject
it into a person or an animal.

681
00:26:47,330 --> 00:26:51,140
And what happens is it builds
up with this radioactive tag

682
00:26:51,140 --> 00:26:53,210
on all the cells
that were active.

683
00:26:53,210 --> 00:26:54,600
Make sense?

684
00:26:54,600 --> 00:26:58,790
OK, so Tootell did an
experiment where he had

685
00:26:58,790 --> 00:27:00,590
the monkey fixate on a spot.

686
00:27:00,590 --> 00:27:02,550
And he presented
this stimulus here.

687
00:27:02,550 --> 00:27:04,160
So the monkey's
fixating right there.

688
00:27:04,160 --> 00:27:06,950
And this stimulus is
flashing on and off.

689
00:27:06,950 --> 00:27:10,400
He injects the radioactive
deoxyglucose into the monkey

690
00:27:10,400 --> 00:27:13,580
while the monkey's
looking at this.

691
00:27:13,580 --> 00:27:16,940
And then, I'm sorry to say, he
killed the monkey, rolled out

692
00:27:16,940 --> 00:27:19,040
visual cortex into a sheet.

693
00:27:19,040 --> 00:27:21,090
And there it is.

694
00:27:21,090 --> 00:27:23,600
And you can see the
bullseye pattern

695
00:27:23,600 --> 00:27:26,570
that the monkey was looking
at across the surface

696
00:27:26,570 --> 00:27:28,400
of visual cortex.

697
00:27:28,400 --> 00:27:30,740
Does everybody get that?

698
00:27:30,740 --> 00:27:32,810
OK, so that shows
you very literally

699
00:27:32,810 --> 00:27:35,240
what a retinotopic
map is in the brain.

700
00:27:35,240 --> 00:27:38,120
It's just like the map of the
visual world in the retina.

701
00:27:38,120 --> 00:27:40,220
But there it is up in
the back of the brain.

702
00:27:40,220 --> 00:27:42,320
And humans have this too.

703
00:27:42,320 --> 00:27:44,300
OK?

704
00:27:44,300 --> 00:27:47,090
And so this can be shown in
humans with functional MRI.

705
00:27:47,090 --> 00:27:49,970
We'll talk later more about
the methods of functional MRI.

706
00:27:49,970 --> 00:27:53,480
But here's a very
high-resolution functional MRI

707
00:27:53,480 --> 00:27:57,170
experiment done by some
people over MGH Charlestown.

708
00:27:57,170 --> 00:27:59,330
By the way, when I
have names on slides,

709
00:27:59,330 --> 00:28:02,660
it's just because, in science,
we don't get paid that much.

710
00:28:02,660 --> 00:28:05,600
And so our credit for our cool
data is kind of all we have.

711
00:28:05,600 --> 00:28:08,150
And so I can't stand to
talk about other people's

712
00:28:08,150 --> 00:28:10,005
cool experiments without
giving them credit.

713
00:28:10,005 --> 00:28:11,630
I do not expect you
to learn the names.

714
00:28:11,630 --> 00:28:13,417
It's just my little
personal tic that I

715
00:28:13,417 --> 00:28:15,500
need to have their name
there to give them credit,

716
00:28:15,500 --> 00:28:17,167
even though you don't
know who they are.

717
00:28:17,167 --> 00:28:18,080
OK.

718
00:28:18,080 --> 00:28:20,930
OK, so what this guy
John Polimeni did

719
00:28:20,930 --> 00:28:24,110
was show human subjects
this stimulus here.

720
00:28:24,110 --> 00:28:25,790
They were fixating right there.

721
00:28:25,790 --> 00:28:28,070
And the stimulus is
flickering with the dots kind

722
00:28:28,070 --> 00:28:29,330
of dancing around.

723
00:28:29,330 --> 00:28:31,910
And then he looked on
the back in visual cortex

724
00:28:31,910 --> 00:28:36,470
on the surface of the brain,
and he sees an M there.

725
00:28:36,470 --> 00:28:37,790
It's the same stimulus.

726
00:28:37,790 --> 00:28:39,560
It's just flipped
upside down, which

727
00:28:39,560 --> 00:28:40,728
is not deep or interesting.

728
00:28:40,728 --> 00:28:42,770
The cortex has to be
oriented one way or another.

729
00:28:42,770 --> 00:28:45,830
The brain doesn't care whether
you turn it around, right?

730
00:28:45,830 --> 00:28:47,690
And your map of visual
space is upside down

731
00:28:47,690 --> 00:28:49,220
in the back of the head.

732
00:28:49,220 --> 00:28:51,980
And you see that M. Does
everybody get how that also

733
00:28:51,980 --> 00:28:55,130
shows retinotopic
properties in the brain

734
00:28:55,130 --> 00:28:56,510
in human visual cortex?

735
00:28:56,510 --> 00:28:58,820
OK.

736
00:28:58,820 --> 00:29:01,670
All right, so the key
idea of retinotopy

737
00:29:01,670 --> 00:29:04,340
is that adjacent parts
of the visual field

738
00:29:04,340 --> 00:29:07,260
are mapped to adjacent
parts of the cortex.

739
00:29:07,260 --> 00:29:11,600
All right, OK, a little bit of
terminology just because people

740
00:29:11,600 --> 00:29:13,340
are fast and loose
with these things.

741
00:29:13,340 --> 00:29:17,030
I've already referred to V1
and primary visual cortex.

742
00:29:17,030 --> 00:29:18,950
It's also sometimes
called striate cortex.

743
00:29:18,950 --> 00:29:20,550
It's all the same thing.

744
00:29:20,550 --> 00:29:22,220
It's the part of
the visual cortex

745
00:29:22,220 --> 00:29:25,130
where the information first
comes up from the LGN right

746
00:29:25,130 --> 00:29:26,640
back here.

747
00:29:26,640 --> 00:29:28,230
So in me, it's right there.

748
00:29:28,230 --> 00:29:30,650
Most of it is in the space
between the two hemispheres.

749
00:29:30,650 --> 00:29:33,210
But a little bit
sticks out on the side.

750
00:29:33,210 --> 00:29:35,630
So in this person, that
yellowy orange stuff,

751
00:29:35,630 --> 00:29:37,400
that's primary
visual cortex, which

752
00:29:37,400 --> 00:29:39,690
is the same as V1
and striate cortex.

753
00:29:39,690 --> 00:29:40,190
OK?

754
00:29:40,190 --> 00:29:42,650
That's just terminology.

755
00:29:42,650 --> 00:29:45,260
All right, just as we have
maps for visual space,

756
00:29:45,260 --> 00:29:47,580
we have maps for touch space.

757
00:29:47,580 --> 00:29:49,790
And so you've probably
seen this diagram here

758
00:29:49,790 --> 00:29:53,570
of the map of touch space going
across somatosensory cortex

759
00:29:53,570 --> 00:29:54,390
like this.

760
00:29:54,390 --> 00:29:56,510
So this is a picture
of a slice like that,

761
00:29:56,510 --> 00:29:59,210
showing you which parts of the
body are mapped out to which

762
00:29:59,210 --> 00:30:00,680
parts of space.

763
00:30:00,680 --> 00:30:02,810
And you can see that
particularly important parts

764
00:30:02,810 --> 00:30:05,990
of the body get
bigger bits of cortex.

765
00:30:05,990 --> 00:30:06,890
Yeah?

766
00:30:06,890 --> 00:30:11,150
OK, just as we have visual
maps and touch maps,

767
00:30:11,150 --> 00:30:14,040
we have auditory maps
in auditory cortex,

768
00:30:14,040 --> 00:30:17,420
which is right on the top of
the temporal lobe right in here.

769
00:30:17,420 --> 00:30:20,120
And what's mapped out
in auditory cortex

770
00:30:20,120 --> 00:30:23,150
is auditory frequency,
high versus low

771
00:30:23,150 --> 00:30:26,100
versus high
frequencies of sound.

772
00:30:26,100 --> 00:30:29,120
And so you see that here's a
piece of auditory cortex in one

773
00:30:29,120 --> 00:30:31,640
subject, showing
you regions that

774
00:30:31,640 --> 00:30:34,640
respond to high frequencies, low
frequencies, high frequencies.

775
00:30:34,640 --> 00:30:36,830
Here it is another
subject, high, low, high,

776
00:30:36,830 --> 00:30:39,320
another subject,
high, low, high.

777
00:30:39,320 --> 00:30:44,030
OK, so the point of all of this
is that primary somatosensory

778
00:30:44,030 --> 00:30:45,350
cortex has maps.

779
00:30:45,350 --> 00:30:46,700
Everybody clear on this?

780
00:30:46,700 --> 00:30:50,510
The different sensory modalities
map different dimensions.

781
00:30:50,510 --> 00:30:53,870
OK, so what about
the rest of cortex?

782
00:30:53,870 --> 00:30:55,610
Like you can see,
most of the cortex

783
00:30:55,610 --> 00:30:58,970
is not primary sensory cortex.

784
00:30:58,970 --> 00:31:00,980
Is the rest of cortex just mush?

785
00:31:00,980 --> 00:31:04,130
Or are there separate bits
like primary sensory areas?

786
00:31:06,980 --> 00:31:10,340
And if so, do those
other bits have maps?

787
00:31:10,340 --> 00:31:14,450
And if so, what
are those maps of?

788
00:31:14,450 --> 00:31:15,140
OK?

789
00:31:15,140 --> 00:31:17,060
We just took you
from 100 years ago

790
00:31:17,060 --> 00:31:20,480
to the cutting edge of the field
is asking this question in lots

791
00:31:20,480 --> 00:31:22,340
of different ways right now.

792
00:31:22,340 --> 00:31:24,770
OK?

793
00:31:24,770 --> 00:31:28,160
OK, let's back up and ask,
what counts as a cortical area

794
00:31:28,160 --> 00:31:28,910
anyway?

795
00:31:28,910 --> 00:31:32,150
I just posited that these
primary sensory regions

796
00:31:32,150 --> 00:31:34,050
count as distinct things.

797
00:31:34,050 --> 00:31:35,810
They're like the things, right?

798
00:31:35,810 --> 00:31:37,450
They're separate
things in the brain.

799
00:31:37,450 --> 00:31:37,950
OK?

800
00:31:37,950 --> 00:31:40,370
And if for no other reason,
then they get direct input

801
00:31:40,370 --> 00:31:42,680
from the thalamus, right?

802
00:31:42,680 --> 00:31:46,970
OK, but let's back up
and ask, what exactly

803
00:31:46,970 --> 00:31:48,560
is a cortical area?

804
00:31:48,560 --> 00:31:51,800
And we're going to
consider this question

805
00:31:51,800 --> 00:31:54,860
by considering the three
key criteria for what

806
00:31:54,860 --> 00:31:57,110
counts as a cortical area.

807
00:31:57,110 --> 00:32:00,470
OK, the first one is that
that region of cortex

808
00:32:00,470 --> 00:32:04,040
is distinct from its
neighbors in function.

809
00:32:04,040 --> 00:32:06,230
Neurons there fire in
response to something

810
00:32:06,230 --> 00:32:08,587
different from the neurons
in the neighboring region.

811
00:32:08,587 --> 00:32:09,920
OK, that's very vague right now.

812
00:32:09,920 --> 00:32:12,950
But we'll illustrate that.

813
00:32:12,950 --> 00:32:13,730
The next one is--

814
00:32:13,730 --> 00:32:17,120
I mentioned this before--
each distinct region of cortex

815
00:32:17,120 --> 00:32:19,190
has a different
set of connections

816
00:32:19,190 --> 00:32:20,430
to other parts of the brain.

817
00:32:20,430 --> 00:32:23,300
It has a distinct
connectivity fingerprint.

818
00:32:23,300 --> 00:32:24,770
OK?

819
00:32:24,770 --> 00:32:28,760
And the third thing is,
for at least some regions

820
00:32:28,760 --> 00:32:30,980
of the cortex, they're
physically different.

821
00:32:30,980 --> 00:32:33,560
If you slice them up and stain
them and look at them really

822
00:32:33,560 --> 00:32:35,270
carefully, they
might look a little

823
00:32:35,270 --> 00:32:37,850
different than other
bits of the cortex.

824
00:32:37,850 --> 00:32:38,690
OK?

825
00:32:38,690 --> 00:32:40,760
So those are three of
the key criteria that

826
00:32:40,760 --> 00:32:44,120
have been used to say, this bit
of cortex, it's a thing, right?

827
00:32:44,120 --> 00:32:46,280
It's distinct, right?

828
00:32:46,280 --> 00:32:49,760
OK, so let's look at
the classic example

829
00:32:49,760 --> 00:32:51,440
beyond those primary regions.

830
00:32:51,440 --> 00:32:53,180
Those are the most
classic regions.

831
00:32:53,180 --> 00:32:54,950
Those are the primary regions
we've already talked about.

832
00:32:54,950 --> 00:32:57,000
Those are the ones nobody
would fight you on that.

833
00:32:57,000 --> 00:32:58,070
This one is next in line.

834
00:32:58,070 --> 00:33:00,490
Nobody would fight
you if you say,

835
00:33:00,490 --> 00:33:03,502
visual area MT, that's an area.

836
00:33:03,502 --> 00:33:04,210
Well, they might.

837
00:33:04,210 --> 00:33:05,410
But most people wouldn't.

838
00:33:05,410 --> 00:33:09,700
OK, and then from there on out,
it's all fighting all the time.

839
00:33:09,700 --> 00:33:12,430
OK, so let's talk
about visual area MT.

840
00:33:12,430 --> 00:33:14,960
It's a little patch of the
cortex in a monkey brain.

841
00:33:14,960 --> 00:33:17,080
This is a side view
of a monkey brain.

842
00:33:17,080 --> 00:33:21,220
And in this human brain, it's
that little patch right there.

843
00:33:21,220 --> 00:33:26,500
OK, so this region
meets all the criteria

844
00:33:26,500 --> 00:33:29,030
to be a distinct visual area.

845
00:33:29,030 --> 00:33:30,800
So how do we know this?

846
00:33:30,800 --> 00:33:33,440
Well, we know this from lots
and lots of different methods.

847
00:33:33,440 --> 00:33:35,732
So I'm going to whip through
a few of those to give you

848
00:33:35,732 --> 00:33:37,570
a gist of how we
can find evidence

849
00:33:37,570 --> 00:33:39,430
that that region is
distinct in functional

850
00:33:39,430 --> 00:33:43,960
connectivity and the physical
stuff, sometimes called

851
00:33:43,960 --> 00:33:45,850
cytoarchitecture.

852
00:33:45,850 --> 00:33:47,170
OK?

853
00:33:47,170 --> 00:33:49,270
All right, function,
how would we

854
00:33:49,270 --> 00:33:51,820
know that region has
a different function?

855
00:33:51,820 --> 00:33:54,280
Well, one way,
the classic way is

856
00:33:54,280 --> 00:33:57,880
to record from individual
neurons in monkey brains.

857
00:33:57,880 --> 00:34:01,600
So if you stick a neuron
into monkey visual cortex

858
00:34:01,600 --> 00:34:04,127
while the monkey is
looking at the stimulus

859
00:34:04,127 --> 00:34:05,710
that I'll show you
in a second, you'll

860
00:34:05,710 --> 00:34:07,930
hear the responses of
an individual neuron.

861
00:34:07,930 --> 00:34:10,780
Each click will be the response
of an individual neuron

862
00:34:10,780 --> 00:34:12,530
to the stimulus.

863
00:34:12,530 --> 00:34:17,790
So let's play this thing, except
it's not making any sound.

864
00:34:17,790 --> 00:34:18,929
Chris, can you help me?

865
00:34:22,469 --> 00:34:24,126
Oh, right.

866
00:34:24,126 --> 00:34:24,626
Duh.

867
00:34:45,690 --> 00:34:48,929
That part, OK, see when the
bar of light moves this way,

868
00:34:48,929 --> 00:34:53,643
it makes a lot of firing and
not when it moves the other way?

869
00:34:53,643 --> 00:34:54,810
Let's watch it for a second.

870
00:34:54,810 --> 00:34:55,980
Watch the bar move again.

871
00:35:12,235 --> 00:35:12,735
See?

872
00:35:12,735 --> 00:35:13,740
It responds less
when it's moving

873
00:35:13,740 --> 00:35:15,090
in a different direction.

874
00:35:15,090 --> 00:35:15,882
Everybody got that?

875
00:35:18,870 --> 00:35:23,400
What is this area
right there called?

876
00:35:23,400 --> 00:35:26,754
Yeah, this area right
here in the middle.

877
00:35:26,754 --> 00:35:27,630
AUDIENCE: [INAUDIBLE]

878
00:35:27,630 --> 00:35:28,500
NANCY KANWISHER: Exactly.

879
00:35:28,500 --> 00:35:29,625
That's the receptive field.

880
00:35:29,625 --> 00:35:34,440
That's the part of visual space
that makes this neuron fire.

881
00:35:34,440 --> 00:35:37,590
OK, this neuron also has a
property called direction.

882
00:35:37,590 --> 00:35:39,540
It's sensitive to
motion, as you see.

883
00:35:39,540 --> 00:35:43,710
But it's also specific to
specific directions of motion.

884
00:35:43,710 --> 00:35:45,570
Everybody see that?

885
00:35:45,570 --> 00:35:50,280
OK, so that's a
direction-selective neuron

886
00:35:50,280 --> 00:35:52,590
in monkey area MT.

887
00:35:52,590 --> 00:35:55,560
And here's a way of showing,
with data, what you guys just

888
00:35:55,560 --> 00:35:56,370
saw.

889
00:35:56,370 --> 00:35:58,380
This is a map of
different directions

890
00:35:58,380 --> 00:35:59,860
in polar coordinates.

891
00:35:59,860 --> 00:36:02,160
And this shows you how much--

892
00:36:02,160 --> 00:36:04,960
this is a single cell
being described here.

893
00:36:04,960 --> 00:36:07,230
This is the direction
selectivity of that cell,

894
00:36:07,230 --> 00:36:09,810
showing you that when
the stimulus moves

895
00:36:09,810 --> 00:36:11,970
in this direction, you
get a lot of firing.

896
00:36:11,970 --> 00:36:14,550
When it moves in this
direction, you get less firing.

897
00:36:14,550 --> 00:36:17,250
And can everybody see how this
plot shows you the direction

898
00:36:17,250 --> 00:36:18,720
selectivity of that cell?

899
00:36:18,720 --> 00:36:20,010
Make sense?

900
00:36:20,010 --> 00:36:20,880
Right.

901
00:36:20,880 --> 00:36:23,910
OK, so that shows you what
you just saw in the movie.

902
00:36:23,910 --> 00:36:27,480
So this is one way to establish
the function of visual area

903
00:36:27,480 --> 00:36:30,300
MT is stick electrodes in
there and record directly

904
00:36:30,300 --> 00:36:33,810
from them when a monkey looks
at different kinds of stimuli.

905
00:36:33,810 --> 00:36:36,840
And you see direction
selectivity when you do that.

906
00:36:36,840 --> 00:36:41,970
OK, further, if you actually
do this systematically,

907
00:36:41,970 --> 00:36:47,520
moving across next door
bits of monkey area MT,

908
00:36:47,520 --> 00:36:49,890
what you find is that,
as we said before,

909
00:36:49,890 --> 00:36:54,390
nearby bits of cortex respond
to similar things, in this case,

910
00:36:54,390 --> 00:36:56,380
to similar directions of motion.

911
00:36:56,380 --> 00:36:57,660
So here's a little diagram.

912
00:36:57,660 --> 00:37:01,830
As you move across the cortex,
you see a systematic change

913
00:37:01,830 --> 00:37:03,780
in the direction
selectivity of neurons

914
00:37:03,780 --> 00:37:05,770
as you move across the cortex.

915
00:37:05,770 --> 00:37:10,380
So in MT, we have a map
of direction preference,

916
00:37:10,380 --> 00:37:13,530
just as we had a map
of spatial location

917
00:37:13,530 --> 00:37:15,060
in primary visual cortex.

918
00:37:15,060 --> 00:37:16,830
Make sense?

919
00:37:16,830 --> 00:37:20,260
OK, now because those neurons
are clustered like that--

920
00:37:20,260 --> 00:37:21,810
I forget what my next point was.

921
00:37:21,810 --> 00:37:22,050
No.

922
00:37:22,050 --> 00:37:22,380
Never mind.

923
00:37:22,380 --> 00:37:23,505
We'll get that in a second.

924
00:37:23,505 --> 00:37:24,780
OK, what about humans?

925
00:37:24,780 --> 00:37:26,140
OK, so here's a monkey brain.

926
00:37:26,140 --> 00:37:27,810
Here's a neuron
in a monkey brain.

927
00:37:27,810 --> 00:37:28,860
What about humans?

928
00:37:28,860 --> 00:37:30,915
Can we record from
single neurons in humans?

929
00:37:30,915 --> 00:37:31,770
What do you think?

930
00:37:34,857 --> 00:37:35,940
Do we ever get to do that?

931
00:37:38,820 --> 00:37:39,442
Yeah?

932
00:37:39,442 --> 00:37:40,770
AUDIENCE: Like neurosurgeons.

933
00:37:40,770 --> 00:37:41,710
NANCY KANWISHER: Yeah.

934
00:37:41,710 --> 00:37:42,800
Yeah.

935
00:37:42,800 --> 00:37:45,380
Neurosurgeons,
very occasionally,

936
00:37:45,380 --> 00:37:48,110
enable us to record
from individual neurons

937
00:37:48,110 --> 00:37:49,310
in human brains.

938
00:37:49,310 --> 00:37:51,710
It's the most awesome data ever.

939
00:37:51,710 --> 00:37:55,040
Of course, we only do it
when the neurosurgeons

940
00:37:55,040 --> 00:37:56,780
have decided, for
clinical reasons,

941
00:37:56,780 --> 00:37:58,340
to put electrodes
in human brains.

942
00:37:58,340 --> 00:38:02,360
They need to do this to map
out epilepsy before surgery.

943
00:38:02,360 --> 00:38:04,395
And sometimes those
patients are super nice

944
00:38:04,395 --> 00:38:06,020
and say, yes, I'll
look at your stimuli

945
00:38:06,020 --> 00:38:09,390
or listen to your stimuli while
you record from my neurons.

946
00:38:09,390 --> 00:38:11,390
And then we get the
most awesome data ever.

947
00:38:11,390 --> 00:38:12,728
But it's very, very rare.

948
00:38:12,728 --> 00:38:14,270
I don't know of any
data where people

949
00:38:14,270 --> 00:38:17,610
have reported individual
neurons in area MT in humans.

950
00:38:17,610 --> 00:38:18,110
Yeah?

951
00:38:18,110 --> 00:38:19,950
AUDIENCE: So how
powerful should an fMRI

952
00:38:19,950 --> 00:38:22,910
be to be able to record
such information?

953
00:38:22,910 --> 00:38:24,620
NANCY KANWISHER: Oh,
we're getting there.

954
00:38:24,620 --> 00:38:28,340
OK, so given that
we, very rarely,

955
00:38:28,340 --> 00:38:31,680
get to record from
individual neurons in humans

956
00:38:31,680 --> 00:38:34,310
and we want to more
generally if there is

957
00:38:34,310 --> 00:38:36,860
an MT in humans, what do we do?

958
00:38:36,860 --> 00:38:39,410
We pop subjects
in an MRI scanner.

959
00:38:39,410 --> 00:38:44,120
And we show them moving
dots or stationary dots.

960
00:38:44,120 --> 00:38:46,173
And we scan them
with functional MRI.

961
00:38:46,173 --> 00:38:47,840
We'll go through the
details of how this

962
00:38:47,840 --> 00:38:49,590
works more in future lectures.

963
00:38:49,590 --> 00:38:51,350
But what you see,
basically, is this

964
00:38:51,350 --> 00:38:53,450
is a slice through
the brain like this.

965
00:38:53,450 --> 00:38:58,100
And you see this region
right here responds more

966
00:38:58,100 --> 00:38:59,850
to the moving dots.

967
00:38:59,850 --> 00:39:00,750
This is the response.

968
00:39:00,750 --> 00:39:01,790
This is time here.

969
00:39:01,790 --> 00:39:03,980
This is when the moving
dots are on high response.

970
00:39:03,980 --> 00:39:06,560
And then when it switches
to stationary dots,

971
00:39:06,560 --> 00:39:08,510
the response drops.

972
00:39:08,510 --> 00:39:11,000
OK, so with functional
MRI, you can also

973
00:39:11,000 --> 00:39:14,000
find the visual area empty
by the higher response

974
00:39:14,000 --> 00:39:15,602
to moving than stationary dots.

975
00:39:15,602 --> 00:39:17,060
Does that make
sense, more or less?

976
00:39:17,060 --> 00:39:18,977
I mean, I'm not giving
you any of the details.

977
00:39:18,977 --> 00:39:20,600
But for now, they
don't really matter.

978
00:39:20,600 --> 00:39:23,420
OK, so that's cool.

979
00:39:23,420 --> 00:39:27,320
But does that tell us
that neurons in human MT

980
00:39:27,320 --> 00:39:29,330
are specific for the
direction of motion?

981
00:39:37,155 --> 00:39:37,655
Yes?

982
00:39:37,655 --> 00:39:40,580
AUDIENCE: Are the moving dots
moving to a specific location?

983
00:39:40,580 --> 00:39:42,788
NANCY KANWISHER: They're
moving in all the directions

984
00:39:42,788 --> 00:39:45,250
you see here.

985
00:39:45,250 --> 00:39:46,540
No, it doesn't.

986
00:39:46,540 --> 00:39:49,660
It tells us it's sensitive
to the presence of motion

987
00:39:49,660 --> 00:39:51,970
but not the direction of motion.

988
00:39:51,970 --> 00:39:53,260
OK?

989
00:39:53,260 --> 00:39:56,650
So if we want to really know,
is human MT like monkey MT

990
00:39:56,650 --> 00:39:59,560
or is this really human
MT, we want to know,

991
00:39:59,560 --> 00:40:02,380
are the neurons in there not
just responsive to motion

992
00:40:02,380 --> 00:40:04,840
but are neurons specific
for particular directions

993
00:40:04,840 --> 00:40:07,480
of motion, OK?

994
00:40:07,480 --> 00:40:10,360
So how would we do that?

995
00:40:10,360 --> 00:40:13,270
OK, well, there's lots
of ways of doing that.

996
00:40:13,270 --> 00:40:14,950
But actually, one of
the charming things

997
00:40:14,950 --> 00:40:17,327
is you can do that
without an MRI scanner.

998
00:40:17,327 --> 00:40:18,910
That is it won't
tell you whether it's

999
00:40:18,910 --> 00:40:19,785
MT you're looking at.

1000
00:40:19,785 --> 00:40:22,210
But we can ask the question
of whether your brains have

1001
00:40:22,210 --> 00:40:25,460
neurons that are tuned
for particular directions.

1002
00:40:25,460 --> 00:40:30,370
So for this demo, I want you
to fixate right in the center.

1003
00:40:30,370 --> 00:40:32,810
And do not move your
eyes from that dot.

1004
00:40:32,810 --> 00:40:34,585
And I'm going to keep
talking for a while,

1005
00:40:34,585 --> 00:40:37,420
while you keep fixating
right on that dot.

1006
00:40:37,420 --> 00:40:39,520
And so what I'm
going to show you

1007
00:40:39,520 --> 00:40:41,420
is something called
an after effect.

1008
00:40:41,420 --> 00:40:45,070
This is also known as the
psychophysicist's electrode.

1009
00:40:45,070 --> 00:40:48,310
Psychophysicists are people
who just measure behavior.

1010
00:40:48,310 --> 00:40:50,740
And from behavior,
they can infer

1011
00:40:50,740 --> 00:40:52,450
how individual neurons work.

1012
00:40:52,450 --> 00:40:54,520
And that is about as
awesome as it gets.

1013
00:40:54,520 --> 00:40:56,705
That's much more impressive
than just recording

1014
00:40:56,705 --> 00:40:57,580
from the damn neuron.

1015
00:40:57,580 --> 00:40:59,980
Inferring from
very indirect data

1016
00:40:59,980 --> 00:41:02,880
how the neuron works from
behavior, now, that is pretty--

1017
00:41:02,880 --> 00:41:03,380
oops.

1018
00:41:03,380 --> 00:41:04,390
OK, sorry.

1019
00:41:04,390 --> 00:41:07,420
Look directly at my face.

1020
00:41:07,420 --> 00:41:08,410
You see anything?

1021
00:41:08,410 --> 00:41:09,680
I didn't see it stop.

1022
00:41:09,680 --> 00:41:11,770
OK, we're going
to-- oh, here we go.

1023
00:41:11,770 --> 00:41:12,280
Oh, right.

1024
00:41:12,280 --> 00:41:14,000
OK, just fixate on
the center again.

1025
00:41:14,000 --> 00:41:14,500
Sorry.

1026
00:41:14,500 --> 00:41:17,020
I forgot this guy
was going to stop.

1027
00:41:17,020 --> 00:41:19,930
So keep looking at the center.

1028
00:41:19,930 --> 00:41:22,247
And then when it
stops in a little bit,

1029
00:41:22,247 --> 00:41:23,830
then keep your eyes
right on that dot.

1030
00:41:23,830 --> 00:41:25,038
And you can see what happens.

1031
00:41:25,038 --> 00:41:26,163
AUDIENCE: [INAUDIBLE]

1032
00:41:26,163 --> 00:41:27,580
NANCY KANWISHER:
Oh, that's right.

1033
00:41:27,580 --> 00:41:28,450
Good point.

1034
00:41:28,450 --> 00:41:29,950
Yes, right now,
it's alternating.

1035
00:41:29,950 --> 00:41:30,540
Nothing's going to happen.

1036
00:41:30,540 --> 00:41:31,150
But that's OK.

1037
00:41:31,150 --> 00:41:32,858
We're going to have
the whole experience.

1038
00:41:32,858 --> 00:41:35,206
Keep fixating on the dot.

1039
00:41:35,206 --> 00:41:38,000
It's good the TAs
are on the ball.

1040
00:41:38,000 --> 00:41:39,930
OK, fixate on the dot.

1041
00:41:39,930 --> 00:41:42,040
Anybody see anything?

1042
00:41:42,040 --> 00:41:43,240
Not really.

1043
00:41:43,240 --> 00:41:43,740
That's OK.

1044
00:41:43,740 --> 00:41:45,602
You're not supposed to.

1045
00:41:45,602 --> 00:41:46,810
That's the control condition.

1046
00:41:46,810 --> 00:41:48,700
It was alternating directions.

1047
00:41:48,700 --> 00:41:49,550
OK?

1048
00:41:49,550 --> 00:41:52,570
So I think it's going
to start moving again.

1049
00:41:52,570 --> 00:41:53,260
I'm not sure.

1050
00:41:53,260 --> 00:41:54,160
Let's go back.

1051
00:41:54,160 --> 00:41:55,420
Let's just start it again.

1052
00:41:55,420 --> 00:41:57,045
OK, I'm sorry I blew
it the first time.

1053
00:41:57,045 --> 00:41:58,300
But let's just get this right.

1054
00:41:58,300 --> 00:42:01,960
OK, fixate on the center
and just keep your eyes

1055
00:42:01,960 --> 00:42:04,100
right on that center.

1056
00:42:04,100 --> 00:42:07,090
So this one, it's
not alternating.

1057
00:42:07,090 --> 00:42:10,300
And it's going to do this
for around 30 seconds.

1058
00:42:10,300 --> 00:42:13,233
And so the whole point of
this is a way with behavior

1059
00:42:13,233 --> 00:42:14,650
to ask the question
of whether you

1060
00:42:14,650 --> 00:42:18,070
have neurons in your brain
tuned to specific directions

1061
00:42:18,070 --> 00:42:19,510
of motion.

1062
00:42:19,510 --> 00:42:22,300
And something as low-tech
and simple as an aftereffect

1063
00:42:22,300 --> 00:42:23,290
can tell you that.

1064
00:42:26,100 --> 00:42:26,655
Keep looking.

1065
00:42:29,760 --> 00:42:31,400
Did you guys see anything?

1066
00:42:31,400 --> 00:42:33,860
What did you see?

1067
00:42:33,860 --> 00:42:35,060
What happened?

1068
00:42:35,060 --> 00:42:39,170
AUDIENCE: It wasn't
moving exactly [INAUDIBLE]

1069
00:42:39,170 --> 00:42:40,490
NANCY KANWISHER: Uh huh.

1070
00:42:40,490 --> 00:42:41,750
Well, it actually should--

1071
00:42:41,750 --> 00:42:43,250
well, now it's doing
something else.

1072
00:42:43,250 --> 00:42:44,630
But it should shrink at the end.

1073
00:42:44,630 --> 00:42:46,230
Did you guys see it shrink?

1074
00:42:46,230 --> 00:42:47,690
OK, so that's an after effect.

1075
00:42:47,690 --> 00:42:49,400
And the simple
version of the story

1076
00:42:49,400 --> 00:42:51,530
is that you are tiring
out your neurons that

1077
00:42:51,530 --> 00:42:53,510
are sensitive to outward
motion while you stare

1078
00:42:53,510 --> 00:42:55,050
at all that outward motion.

1079
00:42:55,050 --> 00:42:57,770
And after you kind of burn
them out and exhaust them,

1080
00:42:57,770 --> 00:42:59,780
then when you look at
something stationary,

1081
00:42:59,780 --> 00:43:02,300
it looks like it's going inward.

1082
00:43:02,300 --> 00:43:03,410
OK?

1083
00:43:03,410 --> 00:43:06,200
And the general idea is
you have pools of neuron--

1084
00:43:06,200 --> 00:43:08,120
the easiest way to
account for that is you

1085
00:43:08,120 --> 00:43:11,340
have pools of neurons tuned
for different directions.

1086
00:43:11,340 --> 00:43:13,430
And that's why, if you
tire out one batch,

1087
00:43:13,430 --> 00:43:15,720
you have a net signal
in the other direction.

1088
00:43:15,720 --> 00:43:16,910
Does that make sense?

1089
00:43:16,910 --> 00:43:19,130
This is all very relevant
to your assignment which

1090
00:43:19,130 --> 00:43:21,410
is due tomorrow night at 6:00.

1091
00:43:21,410 --> 00:43:24,400
This phenomenon was used in the
scanner for that experiment.

1092
00:43:24,400 --> 00:43:28,670
You can think about how you
would use this phenomenon

1093
00:43:28,670 --> 00:43:32,480
to ask whether there's direction
selectivity, not just responses

1094
00:43:32,480 --> 00:43:34,670
to motion, in human MT.

1095
00:43:34,670 --> 00:43:35,782
Yeah?

1096
00:43:35,782 --> 00:43:37,490
AUDIENCE: I'm just a
little bit confused.

1097
00:43:37,490 --> 00:43:40,970
So even when an image
is completely still,

1098
00:43:40,970 --> 00:43:43,340
like even if you're
not detecting motion,

1099
00:43:43,340 --> 00:43:45,110
those neurons are still firing?

1100
00:43:47,073 --> 00:43:48,740
NANCY KANWISHER:
That's a good question.

1101
00:43:48,740 --> 00:43:52,373
But most likely,
the simple cases--

1102
00:43:52,373 --> 00:43:54,290
this may have not worked
beautifully, in part,

1103
00:43:54,290 --> 00:43:57,410
because I screwed it up and
didn't notice when it stopped.

1104
00:43:57,410 --> 00:44:00,290
But if it works well, you should
get a pretty powerful sense

1105
00:44:00,290 --> 00:44:03,050
that after you see it
expanding, then when it's still,

1106
00:44:03,050 --> 00:44:05,100
it should seem to
be contracting.

1107
00:44:05,100 --> 00:44:06,980
So when that happens--

1108
00:44:06,980 --> 00:44:10,400
the reading assigned for
today, tomorrow night

1109
00:44:10,400 --> 00:44:12,050
tells you what
happens in your brain

1110
00:44:12,050 --> 00:44:15,320
during that time when you are
looking at stationary stimuli

1111
00:44:15,320 --> 00:44:17,678
but experiencing motion.

1112
00:44:17,678 --> 00:44:19,220
So there's no motion
in the stimulus.

1113
00:44:19,220 --> 00:44:21,300
But there's motion
in your percept.

1114
00:44:21,300 --> 00:44:21,800
OK?

1115
00:44:21,800 --> 00:44:23,430
So that's the question.

1116
00:44:23,430 --> 00:44:23,930
All right?

1117
00:44:23,930 --> 00:44:25,520
So read the paper and find out.

1118
00:44:25,520 --> 00:44:27,490
Yeah?

1119
00:44:27,490 --> 00:44:33,880
All right, so all of that tells
us just that there are neurons

1120
00:44:33,880 --> 00:44:36,400
someplace in your brain that
are sensitive to the direction

1121
00:44:36,400 --> 00:44:37,270
of motion.

1122
00:44:37,270 --> 00:44:40,900
It doesn't tell us that
they're in MT in particular.

1123
00:44:40,900 --> 00:44:43,330
But the assigned reading
will talk about that.

1124
00:44:43,330 --> 00:44:45,040
OK?

1125
00:44:45,040 --> 00:44:48,130
Right, a further
bit of evidence is

1126
00:44:48,130 --> 00:44:51,520
remember I said how, in
monkeys, next door bits in MT

1127
00:44:51,520 --> 00:44:54,820
have similar
direction selectivity.

1128
00:44:54,820 --> 00:44:58,030
That means you can also
inject an electrical signal

1129
00:44:58,030 --> 00:45:02,140
in a little patch of MT and
give the monkey a net percept

1130
00:45:02,140 --> 00:45:04,540
of a direction of motion.

1131
00:45:04,540 --> 00:45:05,980
OK?

1132
00:45:05,980 --> 00:45:08,690
If all the neurons were
scrambled around spatially,

1133
00:45:08,690 --> 00:45:11,860
so that there was no clustering
of neurons sensitive to, say,

1134
00:45:11,860 --> 00:45:14,080
this direction of
motion, then stimulation

1135
00:45:14,080 --> 00:45:15,298
wouldn't do anything.

1136
00:45:15,298 --> 00:45:16,840
But if you train a
monkey to tell you

1137
00:45:16,840 --> 00:45:18,693
what direction of
motion he's seeing

1138
00:45:18,693 --> 00:45:20,860
and you show him just random
dots that aren't moving

1139
00:45:20,860 --> 00:45:24,100
in any direction and you
stimulate one little patch,

1140
00:45:24,100 --> 00:45:26,740
it'll tell you the
direction of motion

1141
00:45:26,740 --> 00:45:29,590
of the neurons in
that little patch.

1142
00:45:29,590 --> 00:45:32,620
And that is much more
powerful evidence

1143
00:45:32,620 --> 00:45:37,180
that that region is not
only responsive to motion

1144
00:45:37,180 --> 00:45:41,090
but causally involved in
your perception of motion.

1145
00:45:41,090 --> 00:45:41,590
OK?

1146
00:45:41,590 --> 00:45:45,400
I'm a little obsessed with this
distinction between recording

1147
00:45:45,400 --> 00:45:47,480
responses and
establishing causality.

1148
00:45:47,480 --> 00:45:49,820
So we'll go over this
in more detail later.

1149
00:45:49,820 --> 00:45:52,850
But I want you to start
getting used to that idea.

1150
00:45:52,850 --> 00:45:59,920
Another way to test the causal
role of area MT in motion

1151
00:45:59,920 --> 00:46:04,060
is with patients with
brain damage in area MT.

1152
00:46:04,060 --> 00:46:06,250
So there's one
famous patient who

1153
00:46:06,250 --> 00:46:08,440
had brain damage
right there, which

1154
00:46:08,440 --> 00:46:10,900
is right where MT usually is.

1155
00:46:10,900 --> 00:46:12,970
And she could not see motion.

1156
00:46:12,970 --> 00:46:14,590
And she reports
all kinds of things

1157
00:46:14,590 --> 00:46:18,190
like difficulty crossing the
street, difficulty catching

1158
00:46:18,190 --> 00:46:22,180
balls, difficulty
pouring water into a cup,

1159
00:46:22,180 --> 00:46:25,180
OK, just as you
guys saw earlier.

1160
00:46:25,180 --> 00:46:28,600
That's called
akinetopsia, right?

1161
00:46:28,600 --> 00:46:29,950
Kinetics, motion.

1162
00:46:29,950 --> 00:46:32,050
A, not motion, right?

1163
00:46:32,050 --> 00:46:34,000
Opsia, eyes.

1164
00:46:34,000 --> 00:46:36,100
OK.

1165
00:46:36,100 --> 00:46:38,980
All right, so I started
with these criteria

1166
00:46:38,980 --> 00:46:43,240
for what makes something
a distinct area.

1167
00:46:43,240 --> 00:46:45,525
And one piece of
evidence is function.

1168
00:46:45,525 --> 00:46:46,900
And I just give
you a whole bunch

1169
00:46:46,900 --> 00:46:49,390
of different kinds of
evidence for distinct function

1170
00:46:49,390 --> 00:46:52,150
and visual area MT,
that it's specifically

1171
00:46:52,150 --> 00:46:55,030
involved in motion processing.

1172
00:46:55,030 --> 00:46:57,643
And the two other criteria,
which are getting short shrift,

1173
00:46:57,643 --> 00:46:58,810
but I'll just toss them off.

1174
00:46:58,810 --> 00:47:00,200
And we'll return to them.

1175
00:47:00,200 --> 00:47:03,980
One is the distinct
connectivity of that region.

1176
00:47:03,980 --> 00:47:07,120
OK, so you may have seen
this horrific wiring diagram

1177
00:47:07,120 --> 00:47:08,860
of visual cortex in monkeys.

1178
00:47:08,860 --> 00:47:11,830
I think it comes up in like
half the talks in classes

1179
00:47:11,830 --> 00:47:13,450
in my field.

1180
00:47:13,450 --> 00:47:15,500
This is the one down here.

1181
00:47:15,500 --> 00:47:17,890
And so there's lots and lots
of different visual areas.

1182
00:47:17,890 --> 00:47:20,290
And there's a whole
fancy wiring diagram.

1183
00:47:20,290 --> 00:47:24,303
And smack in the middle of this
diagram, that's visual area MT.

1184
00:47:24,303 --> 00:47:25,970
And if you blow this
up and stare at it,

1185
00:47:25,970 --> 00:47:29,200
you'll see that MT has a
particular set of connections

1186
00:47:29,200 --> 00:47:31,660
to other visual
regions in cortex.

1187
00:47:31,660 --> 00:47:33,633
And its particular
set of connections

1188
00:47:33,633 --> 00:47:35,050
are different from
the connections

1189
00:47:35,050 --> 00:47:36,910
of any of those other regions.

1190
00:47:36,910 --> 00:47:40,390
It's part of its connectivity
fingerprint or signature.

1191
00:47:40,390 --> 00:47:43,540
And that's another piece of
evidence that it's a thing.

1192
00:47:43,540 --> 00:47:44,050
OK?

1193
00:47:44,050 --> 00:47:47,080
It's not just another like
amorphous bit of cortex.

1194
00:47:47,080 --> 00:47:50,930
It's a particular
thing in the brain.

1195
00:47:50,930 --> 00:47:53,140
And finally, you might
wonder, is that bit

1196
00:47:53,140 --> 00:47:54,945
of cortex physically different?

1197
00:47:54,945 --> 00:47:56,320
Are the cells in
there different?

1198
00:47:56,320 --> 00:48:00,610
Are the layers of cortex
different in any way?

1199
00:48:00,610 --> 00:48:04,450
And you may remember,
from probably 900,

1200
00:48:04,450 --> 00:48:05,890
about Brodmann areas.

1201
00:48:05,890 --> 00:48:08,020
Like this dude
Korbinian Brodmann

1202
00:48:08,020 --> 00:48:10,870
sliced up lots of dead
brains, looked at them

1203
00:48:10,870 --> 00:48:13,870
under a microscope,
and argued that there

1204
00:48:13,870 --> 00:48:16,180
were 52 different
parts just from what

1205
00:48:16,180 --> 00:48:18,740
it looked like if you slice
them up under a microscope.

1206
00:48:18,740 --> 00:48:19,240
OK?

1207
00:48:19,240 --> 00:48:21,220
So we called those
Brodmann areas.

1208
00:48:21,220 --> 00:48:24,010
And area 17, this
primary visual cortex,

1209
00:48:24,010 --> 00:48:27,040
comes from Brodmann's
terminology.

1210
00:48:27,040 --> 00:48:29,560
And so he argued that there--

1211
00:48:29,560 --> 00:48:32,170
he thought these were
distinct organs in the brain.

1212
00:48:32,170 --> 00:48:35,620
And he even inferred the
specific histological

1213
00:48:35,620 --> 00:48:37,450
differentiation of
the cortical areas

1214
00:48:37,450 --> 00:48:40,120
proves irrefutably their
specific functional

1215
00:48:40,120 --> 00:48:41,440
differentiation.

1216
00:48:41,440 --> 00:48:42,220
Well, it doesn't.

1217
00:48:42,220 --> 00:48:43,210
But never mind.

1218
00:48:43,210 --> 00:48:44,950
Kind of sounded good.

1219
00:48:44,950 --> 00:48:47,440
Anyway, that was his idea.

1220
00:48:47,440 --> 00:48:50,080
And these kinds
of distinct, kind

1221
00:48:50,080 --> 00:48:53,320
of cellular, physical,
anatomical differences

1222
00:48:53,320 --> 00:48:57,370
are very salient for
primary cortical areas

1223
00:48:57,370 --> 00:49:01,150
for vision and audition
and touch and motor cortex.

1224
00:49:01,150 --> 00:49:04,700
But they're much muckier
for lots of other areas.

1225
00:49:04,700 --> 00:49:07,360
One important exception,
which is why we chose this,

1226
00:49:07,360 --> 00:49:09,070
is area MT.

1227
00:49:09,070 --> 00:49:10,582
And so I'll end in one minute.

1228
00:49:10,582 --> 00:49:12,290
But just to tell you
where this is going,

1229
00:49:12,290 --> 00:49:14,800
this is a flattened piece
of monkey cortex rolled out

1230
00:49:14,800 --> 00:49:16,160
like with a baking roller.

1231
00:49:16,160 --> 00:49:16,660
No.

1232
00:49:16,660 --> 00:49:17,290
I don't know.

1233
00:49:17,290 --> 00:49:18,350
Something like that.

1234
00:49:18,350 --> 00:49:19,750
So here's monkey cortex.

1235
00:49:19,750 --> 00:49:20,877
And there's V1 and V2.

1236
00:49:20,877 --> 00:49:21,710
And it's a big mess.

1237
00:49:21,710 --> 00:49:24,670
But that big dark blob,
this bit of cortex

1238
00:49:24,670 --> 00:49:28,260
is stained with something
called cytochrome oxidase.

1239
00:49:28,260 --> 00:49:30,840
And that indicates
metabolic activity.

1240
00:49:30,840 --> 00:49:35,130
MT neurons are very highly
metabolically active.

1241
00:49:35,130 --> 00:49:38,110
And so here's a map
of visual cortex.

1242
00:49:38,110 --> 00:49:40,590
And that exactly is area MT.

1243
00:49:40,590 --> 00:49:44,430
So area MT actually
is histologically

1244
00:49:44,430 --> 00:49:48,600
or cytoarchitectonically
different from its neighbors

1245
00:49:48,600 --> 00:49:53,220
and fits all of the criteria
for a cortical area.

1246
00:49:53,220 --> 00:49:53,760
OK?

1247
00:49:53,760 --> 00:49:55,360
I went one minute over.

1248
00:49:55,360 --> 00:49:57,360
I realize I threw out
a lot of terminology.

1249
00:49:57,360 --> 00:49:58,950
I don't want you to
memorize too much.

1250
00:49:58,950 --> 00:50:00,575
So I made a list of
the kinds of things

1251
00:50:00,575 --> 00:50:02,940
that you should understand
from this lecture, the things

1252
00:50:02,940 --> 00:50:05,330
that I think are important.