WEBVTT

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[SQUEAKING]

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[RUSTLING]

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[CLICKING]

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NANCY KANWISHER: So this
is the line-up for today.

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We're going to be talking
about language today

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and on Wednesday.

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But I want to start
with something

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that I gave very short shrift
at the end of lecture last time.

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And I'm going to
give it short shrift

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again, but in a
slightly different way.

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You'll need this for the
reading, which hopefully you've

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already tried, started.

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Representational
similarity analysis

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is subtle and rich
and interesting.

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And it's taken me
years of revisiting

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it to get its full force.

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So just keep going at it and
hopefully every time you'll

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get it a little better.

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So let me try another
brief version of this.

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So representational
similarity analysis

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is like a generalized case
of multiple voxel pattern

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analysis that applies
to other methods

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and characterizes a
bigger conceptual space.

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So to remind you, multiple
voxel pattern analysis

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with functional MRI
is this business where

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you split your data in half.

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So you have one
set of scans where

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people are looking at, say, dogs
and another set where they're

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looking at cats, and a whole
other separate replication

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where they're looking
at dogs and cats.

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You look at the
pattern of response

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across voxels in each of those
four conditions, dog 1, dog

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2, cat 1, cat 2.

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And you ask if the pattern
is more similar for the two

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different splits of the
data in the same condition,

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dog 1, dog 2, and cat 1, cat
2, the diagonal here, than

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in the two cases where they're
different, dogs to cats.

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Everybody remember that?

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If you're having trouble with
this, come see me or the TAs.

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That's not good.

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So now, that's MVPA.

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And you can use that to ask
of a given region of interest

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in the brain or the whole brain,
if the pattern of response

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in that region can
distinguish between class A

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and Class B. That's
what it's good for.

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So that's worth knowing,
but it's impoverished.

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It's binary.

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I mean, cats versus dogs.

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It's a dopey example I choose.

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But whatever you choose, it's
just going to be two things.

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It only takes you so far
in characterizing what's

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represented in that region.

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You can make it richer if
you force it to generalize.

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So if these two are a smaller
size and a different viewpoint

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from those, and it
still works, then we

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show that there's generality.

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Train on one kind of condition,
test on a slightly different

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version of them.

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That tests the invariants.

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That's richer and
more interesting.

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But even so, it's limited.

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So representational
similarity analysis

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is a bigger, richer way of
characterizing representations

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by looking at the
pattern of response

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across multiple conditions, not
just two and their variations.

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So instead of
something like this,

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we'd have something like
this with a whole bunch

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of different stimuli
or conditions

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that we scan people on.

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And then we look at all
the pairwise combinations--

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how similar is dog to
cat, how similar is it

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to pig or horse or table
or chair or whatever.

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So then we have all of
these pairwise similarities,

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which gives us a richer idea
of what's going on there.

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And so now we don't have to
choose a binary classification

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in there.

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We can look at
that entire space.

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We can think of this
whole space as our proxy

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for what is represented in
that region of the brain.

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So now that's cool.

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So everybody get the
gist of how this set

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of pairwise similarities
in a region of the brain

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is a richer idea of what's
going on in that region

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and what it cares about?

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Everybody got that?

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Now, chunk that
matrix as one thing.

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That's a representation
of what's represented

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in this part of the brain.

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But now we can take that
unit and we can say,

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we can do the same thing on a
totally different kind of data.

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So here's what we just did.

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Here's like some region
of the brain, voxels.

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We can do the same
thing in behavior.

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Now we can say,
OK, you rate for me

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how similar is a dog to a cat
on a scale from one to 10.

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I don't know, six or something.

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How similar is a cat to a pig?

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Four, I don't know.

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You can see, you imagine you
get some similarity space.

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You could just
get people to rate

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them and you could make
a whole new matrix here.

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Now you're characterizing
your conceptual space

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over those same
items behaviorally

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by asking people how
similar each thing are.

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Here, we're comparing
similarity of patterns

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of responses across voxels.

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Here, we're doing it by
asking how similar it

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seems to people behaviorally.

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Everybody get how that's a
similar kind of enterprise?

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Or, we could record from
neurons in monkey brains

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and show them the same pictures.

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And just look at the
response across, say,

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100 neurons in the monkey brain
to a dog and a cat and a pig

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and so forth.

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And then we could,
ask how similar

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is a response across neurons
in the monkey to each pair

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of stimuli, just as we did that
across each pair of stimuli

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across voxels.

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Everybody got that?

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So in each case, we're
getting a matrix like this.

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Now, we can do the
totally cool-- oh, sorry,

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we're not quite yet.

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We can also do that not
just on functional MRI

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voxels in the whole
brain or in one region,

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but we can make
separate matrices.

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These are obviously
all fake data.

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I didn't take the trouble
to make different matrices

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for each, right.

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But we can make
different matrices

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for different
regions of interest

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in the brain, one for each.

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Voxels here, what's their
pairwise set of similarities

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across those stimuli?

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Voxels over here, what's their
pairwise set of similarities?

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Now, we can correlate these
matrices to each other.

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So we can say, for
example, we had

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a bunch of people do
ratings and give us

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their behavioral similarities
based over these stimuli.

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And then we looked in
some region of the brain

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and got the brain's similarity
space and their responses

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across voxels.

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How similar are
those to each other?

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So it's like we've
moved up a level.

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Each matrix is a
set of correlations

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between each pair of stimuli.

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But then once we have
that set of correlations,

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we can take the whole matrix and
correlate it to another matrix.

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This would be a way of asking
in some region of the brain

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how well does the representation
in this chunk of brain match

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people's subjective impression
of that similarity space

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when you ask them about it.

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Everybody see how that's a
way to ask that question?

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We can also relate
functional MRI voxels

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to neurophysiology
responses across neurons.

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We can ask how
similar is your FFAs--

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let's not take the FFA--

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your LO that likes object
shape, how similar is its shape

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space in your brain
measured with functional MRI

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to shape space in this
part of the monkey's brain

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registered with neurophysiology.

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It's pretty cosmic, right.

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We're asking if the monkey
sees the world the same way you

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do, in a sense, for this
method, by using these matrices

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and asking how similar they
are across species and methods.

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Yeah?

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AUDIENCE: So are the function
for [INAUDIBLE] similarity.

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All of them are the same or?

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NANCY KANWISHER: You could
do whatever you like.

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So you can do garden
variety functional MRI

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like we've been talking about
in here just like the Haxby

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thing from 2001.

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That's when it all
started, right.

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Just get a vector across
voxels for one condition,

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a vector across voxels
for the two condition,

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and correlate them.

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You can do that in
responses across neurons.

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But you can also do
more exotic things.

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You can train a linear
classifier on a bunch of voxels

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and say, how well can it
decode the response to pig

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to the response to dog.

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And you can put that
number in that cell.

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So you can do it different
ways, any measure of similarity.

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Or, very confusingly,
there's an increasing trend

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to talk about dissimilarity,
not similarity,

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by subtracting the
r values from 1.

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I find that annoying, but
it's all over the literature.

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And who cares whether it's
similarity or dissimilarity.

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Doesn't really matter.

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They're both ways of collecting
a representational space.

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Yeah?

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AUDIENCE: Are there any
caveats into the [INAUDIBLE]

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that we should be
available, since this

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is like a correlation
of coordination.

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NANCY KANWISHER: Oh, a million.

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You're supposed to
Fisher transform it

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and do all that garbage.

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And we're not
discussing that in here.

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I'm just trying to
give you the idea.

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I don't mean to be dismissive.

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I'm skipping over
all of that stuff

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to just give you the
gist of the idea.

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For purposes in this class,
you could just eyeball

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that and that.

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And you'd say, oh,
they're really ident--

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no, they're not identical.

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I guess, I did switch it.

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I did switch a few of them, oh.

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OK, anyway, whatever.

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For purposes in this class,
you could just eyeball them.

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Mathematically,
an r-value-- we're

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leaving out all the details.

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Yeah, OK.

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And, of course, we
can compare behavior

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in a person to physiology in a
monkey, or behavior in a monkey

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to physiology in a monkey.

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And here's one thing you
need for the reading.

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I hope it didn't
already stump you.

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It's in a tiny part
of one of the figures.

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We could make up a hypothesis
of what's represented here.

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We might say, hey, consider
this patch of brain.

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Maybe it represents the
animate/inanimate distinction.

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In the ideal case,
that would mean all

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it knows is animals
versus non animals.

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And so that would
mean this should

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be the representational
similarity space.

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If these are all the
animals, they're all

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exactly the same as each other.

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All the non-animals are
the same as each other.

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But any animal and any
non-animal are different.

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So this is a hypothesized
similarity space

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of our guess of
what's represented

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in a region, a model
of what we think

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is represented in a region.

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And we can correlate that
to any of these matrices

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to ask whether our hypothesis
of what's in there is right.

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Does that make sense?

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OK.

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So why is that so-- oh, this
whole thing so totally cool?

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It enables us to compare
representational spaces

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across regions of
interest in the brain--

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the FFA to the PPA, do they
have similar representational

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spaces--

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across subject
groups-- this batch

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of subjects and that
batch of subjects--

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without having to align voxels.

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We're not aligning voxels.

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We've left voxels behind.

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We're only using these matrices.

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We can do it across
species, across methods,

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and across hypothesized
models of what we

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think is going on, like that.

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So more generally, this
probes representations

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in a richer way.

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We don't need to have just
10 or whatever I put there.

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We could have, if we keep
subjects in the scanner long

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enough, or monkeys in
the lab long enough,

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we can get hundreds of stimuli
and really characterize

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a rich space.

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And we're looking at not just
two discriminations, but lots.

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The key requirement for
representational similarity

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analysis, to be able to
do all this cool stuff,

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is the axes need to be the same.

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So the stimuli that you're
getting the similarity of

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need to be the same in
the person doing behavior,

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the person doing MRI, the monkey
doing physiology, the model.

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If the axes are not
the same, then there's

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no way to correlate
the matrices.

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Make sense?

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We'll keep coming at
this again and again.

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You'll see it in the
paper for tomorrow night.

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And we'll come at it again
in class on Wednesday.

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So that was all catch-up.

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So today, we are going
to talk about language.

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And let's start by reflecting
on what an amazing thing

00:12:20.080 --> 00:12:21.490
language is.

00:12:21.490 --> 00:12:25.610
So right now, there's a
miraculous thing going on.

00:12:25.610 --> 00:12:31.090
I'm taking some weird, abstract,
hard-to-grasp, even for me,

00:12:31.090 --> 00:12:33.550
ideas someplace in my head--

00:12:33.550 --> 00:12:36.070
god knows where,
somewhere in there--

00:12:36.070 --> 00:12:38.440
and I'm trying to
take those ideas

00:12:38.440 --> 00:12:42.010
and translate them into this
bunch of noises coming out

00:12:42.010 --> 00:12:42.760
my mouth.

00:12:42.760 --> 00:12:44.230
That's already
pretty astonishing.

00:12:44.230 --> 00:12:45.260
Like, what?

00:12:45.260 --> 00:12:46.780
What does that idea look like?

00:12:46.780 --> 00:12:47.710
Who the hell knows?

00:12:47.710 --> 00:12:49.720
How do you take an
abstract idea and turn it

00:12:49.720 --> 00:12:51.250
into a string of sounds?

00:12:51.250 --> 00:12:52.600
That's wild.

00:12:52.600 --> 00:12:54.610
Nobody really knows
pretty much a damn thing

00:12:54.610 --> 00:12:57.250
about how that works,
fascinating mystery.

00:12:57.250 --> 00:12:59.800
But then that bunch
of noises is going

00:12:59.800 --> 00:13:03.700
through the air and producing,
let's hope, pretty similar

00:13:03.700 --> 00:13:05.830
ideas in your head.

00:13:05.830 --> 00:13:07.622
Wow.

00:13:07.622 --> 00:13:08.830
We do this all day every day.

00:13:08.830 --> 00:13:09.670
Big deal.

00:13:09.670 --> 00:13:11.320
But it's astonishing.

00:13:11.320 --> 00:13:15.190
It's just astonishing
that that works at all.

00:13:15.190 --> 00:13:16.970
So that's the
essence of language.

00:13:16.970 --> 00:13:19.060
That's why it's so cool.

00:13:19.060 --> 00:13:22.580
And let's think about how we're
going to think about this.

00:13:22.580 --> 00:13:26.650
So the first thing to note is
language is universally human.

00:13:26.650 --> 00:13:30.820
All neurologically intact
humans have language.

00:13:30.820 --> 00:13:33.430
There are about 7,000
languages in the world.

00:13:33.430 --> 00:13:36.670
Sadly, this number is
shrinking all the time.

00:13:36.670 --> 00:13:40.930
They are all richly expressive,
including sign languages.

00:13:40.930 --> 00:13:43.060
There are no kind of
impoverished languages

00:13:43.060 --> 00:13:45.400
that don't capture
the full richness

00:13:45.400 --> 00:13:48.100
of expressible human experience.

00:13:48.100 --> 00:13:51.490
They're all equally rich.

00:13:51.490 --> 00:13:54.540
Language is uniquely human.

00:13:54.540 --> 00:13:57.360
Yes, chimps and
parrots can accomplish

00:13:57.360 --> 00:14:00.180
all kinds of cool things,
especially if you train them

00:14:00.180 --> 00:14:01.890
extensively.

00:14:01.890 --> 00:14:05.550
But what they have is not
anything really like language.

00:14:05.550 --> 00:14:08.940
And to give you a
vivid sense of this,

00:14:08.940 --> 00:14:12.240
let's look at Chaser,
the Border Collie.

00:14:12.240 --> 00:14:14.490
And what I want you to think
about as you look at this

00:14:14.490 --> 00:14:17.370
little video of Chaser the
Border Collie is what is

00:14:17.370 --> 00:14:19.680
the difference between
your language abilities

00:14:19.680 --> 00:14:20.550
and Chaser's.

00:14:20.550 --> 00:14:23.070
Chaser is pretty
damned impressive,

00:14:23.070 --> 00:14:24.420
but you are more impressive.

00:14:24.420 --> 00:14:26.460
So watch it and enjoy
and think about how it's

00:14:26.460 --> 00:14:28.841
different from what you do.

00:14:28.841 --> 00:14:29.508
[VIDEO PLAYBACK]

00:14:29.508 --> 00:14:32.880
- Some of us burst with
pride if our dogs can respond

00:14:32.880 --> 00:14:34.930
to two or three commands.

00:14:34.930 --> 00:14:37.080
But what if we haven't
begun to understand

00:14:37.080 --> 00:14:41.340
the possibilities of what the
animal mind can really do?

00:14:41.340 --> 00:14:44.460
Our friend, astrophysicist
Neil deGrasse Tyson,

00:14:44.460 --> 00:14:46.530
is host of Nova Science Now.

00:14:46.530 --> 00:14:49.833
And he brings us big
news from the frontier.

00:14:49.833 --> 00:14:51.000
- Walk up, walk up, walk up.

00:14:51.000 --> 00:14:54.990
- Meet Chaser, beloved
six-year-old Border Collie

00:14:54.990 --> 00:14:56.820
of Psychology
Professor John Pilley.

00:14:56.820 --> 00:14:58.200
- Good girl.

00:14:58.200 --> 00:15:01.620
She was born to live in
the Scottish mountains--

00:15:01.620 --> 00:15:03.270
Chase, toe, toe, toe--

00:15:03.270 --> 00:15:04.830
and herd sheep.

00:15:04.830 --> 00:15:05.370
Go, go.

00:15:05.370 --> 00:15:08.220
- John has taught chaser
to tend an extremely large.

00:15:08.220 --> 00:15:12.280
if unconventional
herd, of 1,000 toys.

00:15:12.280 --> 00:15:14.520
And she knows the name of
every single one of these?

00:15:14.520 --> 00:15:15.420
- I hope.

00:15:15.420 --> 00:15:18.510
- I find this hard to believe,
so I test Chaser's memory

00:15:18.510 --> 00:15:19.950
with a random sampling.

00:15:19.950 --> 00:15:21.780
Chaser, find Inky.

00:15:26.340 --> 00:15:28.800
Well, she got one right.

00:15:28.800 --> 00:15:31.560
Find Seal.

00:15:31.560 --> 00:15:34.600
Whoa, and that one too.

00:15:34.600 --> 00:15:36.930
In fact, she got all nine right.

00:15:36.930 --> 00:15:39.720
But what about a new toy
she's never seen or heard

00:15:39.720 --> 00:15:40.440
the name of?

00:15:40.440 --> 00:15:43.950
Chaser's never seen Darwin,
hasn't even ever heard the name

00:15:43.950 --> 00:15:45.480
Darwin.

00:15:45.480 --> 00:15:48.690
So we're going to see if she
picks out Darwin by inference.

00:15:48.690 --> 00:15:51.220
Find Darwin.

00:15:56.390 --> 00:15:58.510
I have to ask her again.

00:15:58.510 --> 00:16:04.430
OK, Chaser, Chaser, Chaser,
Chaser, find Darwin.

00:16:06.950 --> 00:16:08.370
(EXCITEDLY) Darwin!

00:16:08.370 --> 00:16:10.050
He's got Darwin!

00:16:10.050 --> 00:16:11.220
She did it.

00:16:11.220 --> 00:16:13.380
Chaser's never seen
that doll before,

00:16:13.380 --> 00:16:17.940
yet she settled on the one toy
she didn't know by deduction.

00:16:17.940 --> 00:16:20.460
It's similar to the way
children learn language.

00:16:20.460 --> 00:16:23.940
But how does Chaser's ability
compare with other species?

00:16:23.940 --> 00:16:27.090
Besides us, chimps and bonobos
are the animal kingdom's

00:16:27.090 --> 00:16:29.460
top linguists, capable of
learning sign language,

00:16:29.460 --> 00:16:31.350
but very slowly.

00:16:31.350 --> 00:16:33.480
They can solve some
sophisticated problems,

00:16:33.480 --> 00:16:35.730
but they don't always pay
close attention to humans.

00:16:35.730 --> 00:16:36.355
- Is he coming?

00:16:36.355 --> 00:16:39.163
- When I see my dog, my
dog wants me to be around.

00:16:39.163 --> 00:16:41.080
Whereas a bonobo and
chimpanzee, they need me.

00:16:41.080 --> 00:16:42.780
They're basically like,
hey, you got any food.

00:16:42.780 --> 00:16:43.980
Can I get any food off of you?

00:16:43.980 --> 00:16:45.765
They're not interested
in making me happy.

00:16:45.765 --> 00:16:48.090
- Since dogs do
like to please us,

00:16:48.090 --> 00:16:51.450
that humans need to find a way
to tap the potential in all

00:16:51.450 --> 00:16:52.710
of our dogs.

00:16:52.710 --> 00:16:54.420
OK, put it in the tub.

00:16:54.420 --> 00:16:56.490
And dogs like Chaser
are just waiting for us

00:16:56.490 --> 00:16:58.980
to discover all
that they can do.

00:16:58.980 --> 00:17:02.100
[GRUNTS] Smart dog.

00:17:02.100 --> 00:17:04.440
- And Neil deGrasse
Tyson is here

00:17:04.440 --> 00:17:07.530
with the astonishing
Chaser here.

00:17:07.530 --> 00:17:12.264
Tell me what you learned about
animal behavior and child

00:17:12.264 --> 00:17:12.764
behavior.

00:17:12.764 --> 00:17:15.119
- Who would have
thought that the animals

00:17:15.119 --> 00:17:17.940
are capable of this much
display of intellect.

00:17:17.940 --> 00:17:21.280
I think we like thinking of
humans as top of some ladder

00:17:21.280 --> 00:17:23.490
and don't even imagine
that other animals could

00:17:23.490 --> 00:17:25.230
even approximate what we do.

00:17:25.230 --> 00:17:27.390
- All right, I think
we all want to see.

00:17:27.390 --> 00:17:28.755
- You want the demo.

00:17:28.755 --> 00:17:29.380
- Can we do it?

00:17:29.380 --> 00:17:30.000
- A demo of this.

00:17:30.000 --> 00:17:30.690
- Do you think we can it.

00:17:30.690 --> 00:17:30.780
- Sure.

00:17:30.780 --> 00:17:31.210
We can try it.

00:17:31.297 --> 00:17:32.297
- This is so astounding.

00:17:32.297 --> 00:17:33.540
Can we take away the stool.

00:17:33.540 --> 00:17:33.795
- Sure.

00:17:33.795 --> 00:17:34.290
Let's try this.

00:17:34.290 --> 00:17:35.207
- We'll give it a try.

00:17:35.207 --> 00:17:35.810
[INAUDIBLE]

00:17:35.810 --> 00:17:37.080
Thanks.

00:17:37.080 --> 00:17:38.590
All right, so we get down?

00:17:38.590 --> 00:17:39.840
- Let's get down on dog level.

00:17:39.840 --> 00:17:40.650
That's always better.

00:17:40.650 --> 00:17:41.692
- All right, [INAUDIBLE].

00:17:41.692 --> 00:17:45.532
- OK, Chaser, find Goose.

00:17:45.532 --> 00:17:48.969
[STUFFED TOY SQUEALING]

00:17:48.969 --> 00:17:49.680
- OK.

00:17:49.680 --> 00:17:50.580
- Can I do this one?

00:17:50.580 --> 00:17:51.497
- You can do this one.

00:17:51.497 --> 00:17:54.430
- Chaser, Chaser, find ABC.

00:18:01.480 --> 00:18:04.240
ABC-- you did it!

00:18:04.240 --> 00:18:04.960
We thank you.

00:18:04.960 --> 00:18:08.080
And we want everyone to know
that it's a truly remarkable

00:18:08.080 --> 00:18:09.190
NOVA tonight.

00:18:09.190 --> 00:18:13.090
Four wheels reporting tonight
on NOVA Science Now on PBS.

00:18:13.090 --> 00:18:18.318
And to you and your brilliant
dogs at home, goodnight.

00:18:18.318 --> 00:18:18.901
[END PLAYBACK]

00:18:18.901 --> 00:18:20.380
NANCY KANWISHER: OK.

00:18:20.380 --> 00:18:22.540
She's a very good girl.

00:18:22.540 --> 00:18:29.290
And she knows a lot of nouns,
right, 1,000 nouns, apparently.

00:18:29.290 --> 00:18:33.107
But what can't she do
that you guys can do?

00:18:33.107 --> 00:18:33.815
Is this language?

00:18:37.890 --> 00:18:38.475
Yeah?

00:18:38.475 --> 00:18:39.990
AUDIENCE: It's word
identification.

00:18:39.990 --> 00:18:41.125
It's not language.

00:18:41.125 --> 00:18:43.120
You modify actions
[INAUDIBLE] language

00:18:43.120 --> 00:18:47.020
to be able to put verbs
and nouns together.

00:18:47.020 --> 00:18:49.720
NANCY KANWISHER: That's good--
verbs and nouns together.

00:18:49.720 --> 00:18:51.250
What else?

00:18:51.250 --> 00:18:52.000
Yeah, [INAUDIBLE]?

00:18:52.000 --> 00:18:53.710
AUDIENCE: It's
fortification of things.

00:18:53.710 --> 00:18:56.170
If they were like a bigger
ABC and a smaller ABC

00:18:56.170 --> 00:18:59.886
type of thing, that distinction
wouldn't be possible.

00:18:59.886 --> 00:19:01.990
NANCY KANWISHER: Alex the
Parrot can do that one.

00:19:01.990 --> 00:19:03.160
I don't have the
video of Alex, and I

00:19:03.160 --> 00:19:04.743
don't want to get
too hung up on this,

00:19:04.743 --> 00:19:07.580
but some animals can
do that kind of stuff.

00:19:07.580 --> 00:19:08.080
What else?

00:19:11.590 --> 00:19:12.090
Yeah?

00:19:12.090 --> 00:19:13.140
AUDIENCE: Yeah,
it's probably closer

00:19:13.140 --> 00:19:14.850
to like sound
identification. like,

00:19:14.850 --> 00:19:18.280
how I can identify the sound of
a train or the sound of a car.

00:19:18.280 --> 00:19:20.280
NANCY KANWISHER: So just
some rudimentary thing,

00:19:20.280 --> 00:19:22.950
like, visual form and sound.

00:19:22.950 --> 00:19:25.551
How about when she found Darwin?

00:19:25.551 --> 00:19:26.732
AUDIENCE: [INAUDIBLE].

00:19:26.732 --> 00:19:27.690
NANCY KANWISHER: Sorry?

00:19:27.690 --> 00:19:30.107
AUDIENCE: Wasn't that case
just, like you said, deduction?

00:19:30.107 --> 00:19:32.145
It was just like, it
wasn't any of the words.

00:19:32.145 --> 00:19:35.010
NANCY KANWISHER: That's
right, that's right.

00:19:35.010 --> 00:19:37.320
But that's pretty
impressive, isn't it?

00:19:37.320 --> 00:19:39.690
Turns out, kids use that rule
too in learning language.

00:19:39.690 --> 00:19:42.390
It's a whole set
of studies of how

00:19:42.390 --> 00:19:44.940
kids use rules to try to figure
out what people are referring

00:19:44.940 --> 00:19:46.232
to when they learn novel words.

00:19:46.232 --> 00:19:48.510
And that's one of the
things that kids use.

00:19:48.510 --> 00:19:50.430
If there's a thing
here that I don't know

00:19:50.430 --> 00:19:52.470
and somebody's saying a
sound here I don't know,

00:19:52.470 --> 00:19:54.480
that thing probably
goes with the sound.

00:19:54.480 --> 00:19:55.125
Yeah?

00:19:55.125 --> 00:19:57.570
AUDIENCE: I was about to say,
I took 9.85 last semester.

00:19:57.570 --> 00:19:59.250
We talked about like
an exact experiment

00:19:59.250 --> 00:20:02.730
where kids were able to
learn the words of toys

00:20:02.730 --> 00:20:04.530
that were like
not English words,

00:20:04.530 --> 00:20:05.710
but like "dax" and stuff.

00:20:05.710 --> 00:20:07.912
But then when they were
given like a new object,

00:20:07.912 --> 00:20:09.870
they would be able to
identify it as different.

00:20:09.870 --> 00:20:10.890
NANCY KANWISHER: Exactly.

00:20:10.890 --> 00:20:12.182
It's called mutual exclusivity.

00:20:12.182 --> 00:20:14.790
And that's exactly what
Chaser is showing here.

00:20:14.790 --> 00:20:17.730
OK, so pretty
impressive, but not fully

00:20:17.730 --> 00:20:21.180
language, more like
memorizing a bunch of nouns

00:20:21.180 --> 00:20:26.400
plus mutual exclusivity plus
some other stuff, maybe.

00:20:26.400 --> 00:20:31.770
She certainly can't understand
who did what to who and why.

00:20:31.770 --> 00:20:33.510
This is not even
in the ballpark.

00:20:33.510 --> 00:20:37.350
This is the essence of what
we talk to each other about is

00:20:37.350 --> 00:20:40.710
this kind of stuff, all kinds
of complicated relationships

00:20:40.710 --> 00:20:46.200
between different concepts that
we communicate in language.

00:20:46.200 --> 00:20:49.980
So animals in-- not
just taught English,

00:20:49.980 --> 00:20:51.900
but animals in their
natural environments

00:20:51.900 --> 00:20:55.260
communicate in rich and
detailed ways with each other.

00:20:55.260 --> 00:20:59.860
But usually in each case,
about a very restricted domain.

00:20:59.860 --> 00:21:02.760
What kind of danger is around?

00:21:02.760 --> 00:21:04.530
What kind of food
source is around?

00:21:04.530 --> 00:21:06.930
Those basic kinds
of narrow things

00:21:06.930 --> 00:21:08.850
that are of survival
value, those

00:21:08.850 --> 00:21:11.970
are the things that animal
communication systems usually

00:21:11.970 --> 00:21:12.840
deal with.

00:21:12.840 --> 00:21:15.360
And in contrast,
human languages are

00:21:15.360 --> 00:21:17.970
open-ended and compositional.

00:21:17.970 --> 00:21:20.250
Compositional means
that we combine

00:21:20.250 --> 00:21:23.730
words to say new things,
things no human being

00:21:23.730 --> 00:21:26.040
has ever said before.

00:21:26.040 --> 00:21:29.700
So that you don't
see in animals.

00:21:29.700 --> 00:21:31.710
So what is language cognitively?

00:21:31.710 --> 00:21:36.070
That is, what do you have
to know to know a language?

00:21:36.070 --> 00:21:37.810
Bunch of basic things.

00:21:37.810 --> 00:21:40.090
One is phonology, the
sounds of language.

00:21:40.090 --> 00:21:42.900
We've talked about this a bit in
the case of speech perception.

00:21:42.900 --> 00:21:46.380
Just hearing the difference
between a ba and a pa,

00:21:46.380 --> 00:21:48.570
or seeing the
equivalent gesture.

00:21:48.570 --> 00:21:51.540
American Sign Language
is a fully expressive

00:21:51.540 --> 00:21:52.710
natural language.

00:21:52.710 --> 00:21:55.710
And there the phonemes
are different pieces

00:21:55.710 --> 00:21:58.350
of hand movements rather
than sounds, but function

00:21:58.350 --> 00:22:00.810
as phonemes all the same.

00:22:00.810 --> 00:22:03.510
And we talked about a
region of the brain that

00:22:03.510 --> 00:22:07.830
responds very specifically
to speech sounds in humans.

00:22:07.830 --> 00:22:09.810
Moving up into the
language system,

00:22:09.810 --> 00:22:12.595
that's just an input system--

00:22:12.595 --> 00:22:14.970
and by the way, we also talked
about the visual word form

00:22:14.970 --> 00:22:18.812
area, a very recent addition to
the input system in language.

00:22:18.812 --> 00:22:20.520
But that's only a few
thousand years old.

00:22:20.520 --> 00:22:23.640
It's really phonology that's the
native form of language that's

00:22:23.640 --> 00:22:27.180
been around for tens, if
not hundreds, of thousands

00:22:27.180 --> 00:22:29.290
of years in human evolution.

00:22:29.290 --> 00:22:32.010
So semantics, we need
to know what words mean.

00:22:32.010 --> 00:22:33.240
That's lexical semantics.

00:22:33.240 --> 00:22:34.740
But we also need
to know how meaning

00:22:34.740 --> 00:22:36.690
arises when words go together.

00:22:39.740 --> 00:22:41.510
And related to how
words go together,

00:22:41.510 --> 00:22:44.150
we need to know about
the syntax of a language.

00:22:44.150 --> 00:22:48.170
That is, the structure
or grammar of a language.

00:22:48.170 --> 00:22:50.600
And so each language
has a set of rules

00:22:50.600 --> 00:22:54.170
about how you string words
together in that language.

00:22:54.170 --> 00:22:57.050
And usually central to that--
it's not the only thing,

00:22:57.050 --> 00:22:59.360
but a central part of
that-- is word order.

00:22:59.360 --> 00:23:02.270
And that whole set of rules
for how you string together

00:23:02.270 --> 00:23:04.730
words, following
word order rules,

00:23:04.730 --> 00:23:08.070
determines the meaning
of the string of words.

00:23:08.070 --> 00:23:13.610
For example, shark bites man is
different than man bites shark.

00:23:13.610 --> 00:23:15.620
And that just comes
out of the syntax

00:23:15.620 --> 00:23:19.310
that we know that in English
in this kind of construction

00:23:19.310 --> 00:23:22.790
the first word is going to
be the agent, the one who's

00:23:22.790 --> 00:23:23.530
doing the thing.

00:23:23.530 --> 00:23:26.030
And the third word is going to
be the patient, the one who's

00:23:26.030 --> 00:23:27.710
receiving the doing.

00:23:27.710 --> 00:23:30.200
And that's just built
into your language system,

00:23:30.200 --> 00:23:33.990
that you know that implicitly.

00:23:33.990 --> 00:23:36.300
There's also the
pragmatics of language.

00:23:36.300 --> 00:23:39.690
That is, how we understand
what somebody actually

00:23:39.690 --> 00:23:41.640
means when they say
something to us,

00:23:41.640 --> 00:23:44.040
which isn't always
just a function

00:23:44.040 --> 00:23:47.340
of the actual string of words
coming out of their mouth.

00:23:47.340 --> 00:23:51.870
So if somebody says it will be
awesome if you pass the salt,

00:23:51.870 --> 00:23:54.120
it's not all that
awesome to have the salt.

00:23:54.120 --> 00:23:56.760
It really means,
please pass the salt.

00:23:56.760 --> 00:23:59.550
The pragmatics of the situation
tells you the actual intent.

00:24:02.210 --> 00:24:04.730
And so to do pragmatics
involves thinking

00:24:04.730 --> 00:24:07.577
about the other person's intent,
what are they thinking, what

00:24:07.577 --> 00:24:09.410
do they want, what's
going on in their head,

00:24:09.410 --> 00:24:11.300
and using all that
background knowledge

00:24:11.300 --> 00:24:16.690
to constrain what do they mean
by this particular utterance.

00:24:16.690 --> 00:24:19.720
So let's just survey
of the main pieces

00:24:19.720 --> 00:24:21.490
of what we mean by language.

00:24:21.490 --> 00:24:23.350
But for the next
two lectures, we're

00:24:23.350 --> 00:24:24.820
going to focus on
the core, which

00:24:24.820 --> 00:24:27.820
is syntax and semantics,
this stuff in here.

00:24:27.820 --> 00:24:29.950
And I will sloppily
use the word "language"

00:24:29.950 --> 00:24:34.420
to refer to this stuff,
not all the other stuff.

00:24:34.420 --> 00:24:37.787
And we'll focus really on
the sentence understanding.

00:24:37.787 --> 00:24:40.120
So what do we want to know
about sentence understanding?

00:24:40.120 --> 00:24:43.540
Well, the first thing we want
to know is, is it even a thing.

00:24:43.540 --> 00:24:46.855
Is language a thing separate
from the rest of thought?

00:24:49.700 --> 00:24:52.295
Second thing we want to know
is, if it is at least something

00:24:52.295 --> 00:24:55.760
of kind of a thing,
does language itself

00:24:55.760 --> 00:24:58.590
have component
structure within it?

00:24:58.590 --> 00:25:00.590
Are there different parts
of the language system

00:25:00.590 --> 00:25:04.570
that maybe do different things?

00:25:04.570 --> 00:25:06.700
And if so, what is
represented and computed

00:25:06.700 --> 00:25:08.990
in each of those parts?

00:25:08.990 --> 00:25:13.798
And third, how do we represent
meaning in the brain?

00:25:13.798 --> 00:25:16.090
So these are the things we'll
address over the next two

00:25:16.090 --> 00:25:16.900
lectures.

00:25:16.900 --> 00:25:18.370
And let's start
with this question

00:25:18.370 --> 00:25:21.190
that'll probably take up
the bulk of this lecture.

00:25:21.190 --> 00:25:24.820
Is language distinct
from the rest of thought?

00:25:24.820 --> 00:25:27.190
Another way of putting
this, a more familiar way,

00:25:27.190 --> 00:25:30.100
is to ask, what is the
relationship between language

00:25:30.100 --> 00:25:31.060
and thought?

00:25:31.060 --> 00:25:35.895
Or even more pointedly, could
you think without language?

00:25:35.895 --> 00:25:37.520
Probably, every one
of you has wondered

00:25:37.520 --> 00:25:38.940
about that at some point.

00:25:38.940 --> 00:25:40.910
So take like two or
three minutes, talk

00:25:40.910 --> 00:25:43.485
to your neighbors about this,
see if you can figure out

00:25:43.485 --> 00:25:45.110
whether you can think
without language,

00:25:45.110 --> 00:25:47.690
and then let's
pool your insights.

00:25:47.690 --> 00:25:48.830
Talk, think.

00:25:50.778 --> 00:25:51.752
[SIDE CONVERSATIONS]

00:25:51.752 --> 00:25:53.920
NANCY KANWISHER: OK, if
you guys all nailed it,

00:25:53.920 --> 00:25:56.530
I'm sure you solve
the whole thing.

00:25:56.530 --> 00:25:59.300
People have been talking about
this for probably millennia.

00:25:59.300 --> 00:26:03.380
So, what do you guys think?

00:26:03.380 --> 00:26:05.725
What were some of your
reflections on this question?

00:26:08.910 --> 00:26:09.660
Come on, you guys.

00:26:09.660 --> 00:26:11.430
Yes, Carly?

00:26:11.430 --> 00:26:13.530
AUDIENCE: I said
I think that they

00:26:13.530 --> 00:26:17.010
could think without language
because of like we talked

00:26:17.010 --> 00:26:19.560
previously about how
[INAUDIBLE] babies

00:26:19.560 --> 00:26:21.640
are given very complex thought.

00:26:21.640 --> 00:26:26.880
But, like, he was arguing
that the whale research,

00:26:26.880 --> 00:26:29.170
there's also the thing
that babies kind of form

00:26:29.170 --> 00:26:31.060
their own language that
we don't understand,

00:26:31.060 --> 00:26:32.703
but I don't think [INAUDIBLE].

00:26:32.703 --> 00:26:33.870
NANCY KANWISHER: Not really.

00:26:33.870 --> 00:26:35.880
If you take three-month-old
babies-- not really.

00:26:35.880 --> 00:26:39.120
So perfect, absolutely,
you can hear this.

00:26:39.120 --> 00:26:41.010
Babies can think.

00:26:41.010 --> 00:26:42.592
You take 9.85,
you'll learn more.

00:26:42.592 --> 00:26:44.550
They can really think
about all kinds of stuff.

00:26:44.550 --> 00:26:47.640
It's really amazing how
much they understand.

00:26:47.640 --> 00:26:51.160
And at three to six months,
there's little or no language.

00:26:51.160 --> 00:26:53.580
So there's a beautiful case
of thinking without language.

00:26:53.580 --> 00:26:54.300
Yeah, David?

00:26:54.300 --> 00:26:57.235
AUDIENCE: On the
other side, if you

00:26:57.235 --> 00:26:59.610
don't give a name to something,
if you don't give it word

00:26:59.610 --> 00:27:01.820
to something, then it's
hard to really know it.

00:27:01.820 --> 00:27:04.950
Like, maybe there are
20 different types

00:27:04.950 --> 00:27:06.700
of the color green.

00:27:06.700 --> 00:27:09.110
And if you don't decide
to call one of them

00:27:09.110 --> 00:27:11.630
olive and another
one khaki green

00:27:11.630 --> 00:27:13.302
or something like that, then--

00:27:13.302 --> 00:27:15.427
NANCY KANWISHER: Then you
can't see the difference?

00:27:15.427 --> 00:27:17.580
AUDIENCE: Well, well, I
don't know if you'd ever

00:27:17.580 --> 00:27:19.140
think of the difference.

00:27:19.140 --> 00:27:21.227
NANCY KANWISHER: OK,
let's think about this.

00:27:21.227 --> 00:27:22.810
Do you think could
see the difference?

00:27:22.810 --> 00:27:27.210
Suppose I held up an olive
patch and a khaki patch to you.

00:27:27.210 --> 00:27:29.940
And for whatever reason, you
had been raised with deprivation

00:27:29.940 --> 00:27:31.350
of the words olive and khaki.

00:27:31.350 --> 00:27:33.600
AUDIENCE: But somehow it's
not about just a perception

00:27:33.600 --> 00:27:34.140
question.

00:27:34.140 --> 00:27:35.460
It's about remembering.

00:27:35.460 --> 00:27:37.437
NANCY KANWISHER:
Yeah, bingo, bingo.

00:27:37.437 --> 00:27:39.270
So that's roughly what
the literature shows.

00:27:39.270 --> 00:27:40.228
Anya, help me out here.

00:27:40.228 --> 00:27:41.400
I forgot to look this up.

00:27:41.400 --> 00:27:43.980
The literature still show
that perceptually you can

00:27:43.980 --> 00:27:45.147
discriminate them just fine.

00:27:45.147 --> 00:27:46.813
It doesn't make a
damn bit of difference

00:27:46.813 --> 00:27:48.190
if you have words for it.

00:27:48.190 --> 00:27:49.630
But if you have to remember it--

00:27:49.630 --> 00:27:50.130
sorry.

00:27:50.130 --> 00:27:51.010
AUDIENCE: Faster.

00:27:51.010 --> 00:27:53.670
NANCY KANWISHER: Faster, faster.

00:27:53.670 --> 00:27:55.830
But accuracy in D
prime, I don't think

00:27:55.830 --> 00:27:59.970
is different,
maybe a little bit.

00:27:59.970 --> 00:28:00.580
Oops, caught.

00:28:00.580 --> 00:28:01.580
I meant to look this up.

00:28:01.580 --> 00:28:03.630
I knew this is going to come up.

00:28:03.630 --> 00:28:05.820
Write me an email to
look this up and help

00:28:05.820 --> 00:28:07.013
me find the relevant stuff.

00:28:07.013 --> 00:28:09.180
Anyway, doesn't make a huge
difference perceptually,

00:28:09.180 --> 00:28:11.710
but it does if you have
to remember it for later.

00:28:11.710 --> 00:28:12.210
Yeah?

00:28:12.210 --> 00:28:13.890
AUDIENCE: That's actually what
I say because I'm actually

00:28:13.890 --> 00:28:16.020
reproducing the
experiment that found

00:28:16.020 --> 00:28:18.410
that there was a difference
in color [INAUDIBLE]..

00:28:18.410 --> 00:28:19.680
NANCY KANWISHER: Aha, aha.

00:28:19.680 --> 00:28:20.180
What?

00:28:20.180 --> 00:28:21.495
In perception or memory?

00:28:21.495 --> 00:28:23.640
AUDIENCE: So they found that--

00:28:23.640 --> 00:28:25.275
I believe it was--

00:28:25.275 --> 00:28:27.900
NANCY KANWISHER: Because there's
been a long history with this.

00:28:27.900 --> 00:28:29.970
They find one thing and
they-- that's partly why I'm--

00:28:29.970 --> 00:28:31.900
AUDIENCE: It's like a
difference in the reaction time.

00:28:31.900 --> 00:28:34.230
Interesting enough, they
found that if they introduce

00:28:34.230 --> 00:28:36.638
interference in their
linguistic system,

00:28:36.638 --> 00:28:37.930
then that difference went away.

00:28:37.930 --> 00:28:39.480
So that's evidence
that the language

00:28:39.480 --> 00:28:40.742
is causing the difference.

00:28:40.742 --> 00:28:43.200
NANCY KANWISHER: And that's in
a perceptual discrimination.

00:28:43.200 --> 00:28:43.510
OK.

00:28:43.510 --> 00:28:44.677
AUDIENCE: It's pretty small.

00:28:44.677 --> 00:28:47.910
NANCY KANWISHER: Yeah, yeah,
well, behavioral, well, yeah,

00:28:47.910 --> 00:28:49.350
effects often are.

00:28:49.350 --> 00:28:50.070
Yeah, Isabel?

00:28:50.070 --> 00:28:52.900
AUDIENCE: I remember one of
the first neuroscience talks I

00:28:52.900 --> 00:28:57.680
went to in college was a woman
who had been [INAUDIBLE]..

00:28:57.680 --> 00:28:59.590
She got in a terrible
stroke and she's

00:28:59.590 --> 00:29:03.630
suffering from aphasia
[INAUDIBLE] the speaking part

00:29:03.630 --> 00:29:05.320
and forgot all the
language she learned.

00:29:05.320 --> 00:29:09.108
It took over her over a
year to regain [INAUDIBLE]..

00:29:09.108 --> 00:29:12.412
And I remember the
question that I asked was,

00:29:12.412 --> 00:29:16.832
you have this really
terrible pain [INAUDIBLE]..

00:29:16.832 --> 00:29:18.540
But what did your
inner voice sound like?

00:29:18.540 --> 00:29:21.840
And she said, well, I don't
really have one, [INAUDIBLE]..

00:29:21.840 --> 00:29:23.340
And then she said,
well, I must have

00:29:23.340 --> 00:29:25.350
thought in images and feelings.

00:29:25.350 --> 00:29:28.740
And the interesting
thing that I experienced

00:29:28.740 --> 00:29:32.130
when I was relearning to talk
was that, the more English

00:29:32.130 --> 00:29:34.890
I learned, the more my
thoughts was with grammar.

00:29:34.890 --> 00:29:36.870
So I still could
have these thoughts,

00:29:36.870 --> 00:29:40.340
but they were formulated
in a different way

00:29:40.340 --> 00:29:43.230
than they were when
I had [INAUDIBLE]

00:29:43.230 --> 00:29:45.023
the structured
language department.

00:29:45.023 --> 00:29:46.440
NANCY KANWISHER:
OK, that's great.

00:29:46.440 --> 00:29:50.550
So we're going to learn more
about all of that, absolutely.

00:29:50.550 --> 00:29:52.590
OK, very good.

00:29:52.590 --> 00:29:54.750
So cool question, not obvious.

00:29:54.750 --> 00:29:56.680
Let's see what the data say.

00:29:56.680 --> 00:29:59.522
So first of all, you
guys talked about babies

00:29:59.522 --> 00:30:00.480
and how they can think.

00:30:00.480 --> 00:30:04.600
But animals can think too, maybe
not fully as richly as we can,

00:30:04.600 --> 00:30:07.350
but they can think in all
kinds of subtle, rich ways.

00:30:07.350 --> 00:30:08.730
And animals don't have language.

00:30:08.730 --> 00:30:10.890
And so that's another
case, animals and infants.

00:30:10.890 --> 00:30:12.892
And I'm mentioning
numerosity because these

00:30:12.892 --> 00:30:14.850
are things we happen to
have mentioned in here.

00:30:14.850 --> 00:30:17.130
Remember, the approximate
number system.

00:30:17.130 --> 00:30:18.420
Animals are great at that.

00:30:18.420 --> 00:30:20.670
Very young infants are
greater that when they

00:30:20.670 --> 00:30:22.860
don't have language at all.

00:30:22.860 --> 00:30:25.620
Also, by the way,
people whose language

00:30:25.620 --> 00:30:29.370
do not have any number
words whatsoever

00:30:29.370 --> 00:30:31.740
can do approximate numerosity.

00:30:31.740 --> 00:30:36.220
So here's a cool study from Ted
Gibson's lab a few years ago.

00:30:36.220 --> 00:30:39.930
They went down into
remote parts of the Amazon

00:30:39.930 --> 00:30:42.390
to study this group
of people, the Piraha.

00:30:42.390 --> 00:30:44.760
Here they are in their canoe.

00:30:44.760 --> 00:30:49.790
They are a hunter-gatherer tribe
of just a few hundred people.

00:30:49.790 --> 00:30:51.540
Their language is, as
far as linguists can

00:30:51.540 --> 00:30:53.530
tell, unrelated to anyone else.

00:30:53.530 --> 00:30:55.170
And it has no number words.

00:30:55.170 --> 00:30:57.120
There's a whole
dispute about that,

00:30:57.120 --> 00:30:58.890
but the current view
is there are really

00:30:58.890 --> 00:31:02.880
no number words at all,
not even for zero or one.

00:31:02.880 --> 00:31:06.600
So how do they do at
approximate magnitude?

00:31:06.600 --> 00:31:07.980
Well, let's see.

00:31:07.980 --> 00:31:12.450
So here is the testing
session down in the Amazon.

00:31:12.450 --> 00:31:16.500
And this is the experimenter
lining up a bunch of, I think,

00:31:16.500 --> 00:31:17.650
they're batteries.

00:31:17.650 --> 00:31:19.920
And this guy is asked to
match the number of balloons

00:31:19.920 --> 00:31:21.045
to the number of batteries.

00:31:21.045 --> 00:31:23.670
And he has to do it aligned
this way so he can't just

00:31:23.670 --> 00:31:25.290
put them one next to the other.

00:31:25.290 --> 00:31:28.290
If you let him, he'll put
them one next to the other.

00:31:28.290 --> 00:31:32.220
But this is designed
to test it better.

00:31:32.220 --> 00:31:33.690
And he puts down four balloons.

00:31:33.690 --> 00:31:34.065
[VIDEO PLAYBACK]

00:31:34.065 --> 00:31:34.898
[SIDE CONVERSATIONS]

00:31:34.898 --> 00:31:36.690
Bingo, very good.

00:31:36.690 --> 00:31:41.730
OK, no number words
in his language.

00:31:41.730 --> 00:31:47.666
What about this case?

00:31:47.666 --> 00:31:48.907
- Hi, people.

00:31:48.907 --> 00:31:50.740
NANCY KANWISHER: Oh,
the plot is thickening.

00:31:50.740 --> 00:31:55.852
- Six or five, [INAUDIBLE]

00:31:55.852 --> 00:31:58.310
- [INAUDIBLE] lot of thread.

00:31:58.310 --> 00:31:59.350
[INAUDIBLE] of thread.

00:31:59.350 --> 00:32:02.390
NANCY KANWISHER: He laughs,
he thinks that's pretty funny.

00:32:02.390 --> 00:32:03.452
But watch.

00:32:03.452 --> 00:32:05.745
- Five, five.

00:32:05.745 --> 00:32:07.550
NANCY KANWISHER:
[? Valiant ?] goes ahead.

00:32:07.550 --> 00:32:11.302
- [INAUDIBLE] I think
it is [INAUDIBLE] five.

00:32:11.302 --> 00:32:14.040
Lots, lots.

00:32:14.040 --> 00:32:19.810
[INAUDIBLE] and intensifier,
like lots and lots.

00:32:19.810 --> 00:32:21.200
- You're doing well.

00:32:33.325 --> 00:32:35.105
NANCY KANWISHER:
Right, right, right.

00:32:35.105 --> 00:32:35.327
- There you go.

00:32:35.327 --> 00:32:35.820
- Good.

00:32:35.820 --> 00:32:36.820
- You can see which one.

00:32:36.820 --> 00:32:37.820
- Nine-- nine, 10.

00:32:37.820 --> 00:32:38.320
- 10?

00:32:38.320 --> 00:32:38.820
That was 10?

00:32:38.820 --> 00:32:39.745
[END PLAYBACK]

00:32:39.745 --> 00:32:42.210
NANCY KANWISHER: So I
think he gave nine for 10,

00:32:42.210 --> 00:32:45.460
or something like that.

00:32:45.460 --> 00:32:47.615
Anyway, if I had any of
you guys do this task

00:32:47.615 --> 00:32:48.990
and I prevented
you from counting

00:32:48.990 --> 00:32:51.210
by having you do verbal
shadowing or something else

00:32:51.210 --> 00:32:53.010
to tie up your
language system, you

00:32:53.010 --> 00:32:55.980
would do exactly the
same as this guy does.

00:32:55.980 --> 00:32:59.580
So the approximate number
system doesn't require language,

00:32:59.580 --> 00:33:01.830
doesn't require number
words in your language

00:33:01.830 --> 00:33:03.120
to get the concept.

00:33:03.120 --> 00:33:06.756
And it doesn't require use
of language to do the task.

00:33:06.756 --> 00:33:08.842
AUDIENCE: He saw
him put [INAUDIBLE]??

00:33:08.842 --> 00:33:09.800
NANCY KANWISHER: Sorry?

00:33:09.800 --> 00:33:11.560
AUDIENCE: He actually
saw him put all of them?

00:33:11.560 --> 00:33:11.960
He saw?

00:33:11.960 --> 00:33:13.543
NANCY KANWISHER:
Yeah, just like you--

00:33:13.543 --> 00:33:14.680
AUDIENCE: [INAUDIBLE].

00:33:14.680 --> 00:33:16.270
NANCY KANWISHER: I mean, that's
the actual experiment being

00:33:16.270 --> 00:33:17.187
conducted right there.

00:33:19.970 --> 00:33:23.330
OK, so we've just argued that
at least the approximate number

00:33:23.330 --> 00:33:27.200
system is present in animals
who don't have number words,

00:33:27.200 --> 00:33:32.340
infants who don't, and adults
who don't have number words.

00:33:32.340 --> 00:33:34.730
What about other
aspects of thought?

00:33:34.730 --> 00:33:36.965
And what can we learn from
studying brain disorders,

00:33:36.965 --> 00:33:41.332
as Isabel mentioned a moment
ago, a very rich source.

00:33:41.332 --> 00:33:43.040
So here's the question
we're considering.

00:33:43.040 --> 00:33:45.890
We're taking language and
thought, or cognition,

00:33:45.890 --> 00:33:48.710
and we're asking whether they're
totally separate in the mind

00:33:48.710 --> 00:33:52.910
and brain or whether they're
totally the same thing

00:33:52.910 --> 00:33:55.790
or whether there's some
relationship that they're

00:33:55.790 --> 00:33:57.320
somewhat different.

00:33:57.320 --> 00:33:58.890
So that's the question.

00:33:58.890 --> 00:34:00.500
What do we learn
from brain disorders?

00:34:00.500 --> 00:34:03.720
Well, let's start with
developmental disorders.

00:34:03.720 --> 00:34:06.800
And there are unfortunately
a large number of these.

00:34:06.800 --> 00:34:09.350
For example, there
are language savants,

00:34:09.350 --> 00:34:12.199
people with Down syndrome,
Williams syndrome,

00:34:12.199 --> 00:34:13.440
Turner syndrome.

00:34:13.440 --> 00:34:15.949
These are all
developmental disorders

00:34:15.949 --> 00:34:20.330
in which people have
very low IQs, but,

00:34:20.330 --> 00:34:23.780
notably, in each of these
cases, very good language.

00:34:23.780 --> 00:34:26.690
Perhaps the most striking
is Williams syndrome.

00:34:26.690 --> 00:34:28.620
These kids are remarkable.

00:34:28.620 --> 00:34:30.230
They have very low IQs.

00:34:30.230 --> 00:34:33.739
They can't do the most basic
spatial reasoning tasks.

00:34:33.739 --> 00:34:35.570
They can't cross
the street safely.

00:34:35.570 --> 00:34:39.230
They can't live
independently at all.

00:34:39.230 --> 00:34:41.060
And yet they're highly social.

00:34:41.060 --> 00:34:45.350
And their language is almost
indistinguishable from any

00:34:45.350 --> 00:34:46.730
of yours.

00:34:46.730 --> 00:34:48.290
Not quite-- if you
test them subtly,

00:34:48.290 --> 00:34:51.530
can find some differences,
but it is rich and complex.

00:34:51.530 --> 00:34:52.550
And it's bizarre.

00:34:52.550 --> 00:34:54.650
Because you'd think if
your thoughts are so

00:34:54.650 --> 00:34:56.690
impoverished because
your IQ is low,

00:34:56.690 --> 00:34:58.255
how could you have
rich language.

00:34:58.255 --> 00:35:00.380
But that's the weird thing
about Williams syndrome.

00:35:00.380 --> 00:35:02.810
Their language is extremely
rich and, in fact,

00:35:02.810 --> 00:35:05.580
poetic and quite
beautiful and expressive.

00:35:05.580 --> 00:35:08.600
So that's really
surprising and suggests

00:35:08.600 --> 00:35:13.310
that you can have quite severely
impaired cognition and very

00:35:13.310 --> 00:35:14.610
good language.

00:35:14.610 --> 00:35:16.878
So that's the first crack
that these things are

00:35:16.878 --> 00:35:18.170
more separate than you'd guess.

00:35:18.170 --> 00:35:21.530
Actually, I find this one more
surprising than all the others.

00:35:21.530 --> 00:35:25.280
But in cases of
brain damage, which

00:35:25.280 --> 00:35:30.330
was the first mental function
localized in the brain.

00:35:30.330 --> 00:35:31.970
So this is
historically important.

00:35:31.970 --> 00:35:35.510
Way back in 1861,
Paul Broca stood up

00:35:35.510 --> 00:35:37.970
in front of the Anthropology
Society of Paris

00:35:37.970 --> 00:35:40.610
and he announced that
the left frontal lobe

00:35:40.610 --> 00:35:42.680
was the seat of speech.

00:35:42.680 --> 00:35:46.490
And this is on the basis
of his patient Tan, who

00:35:46.490 --> 00:35:48.380
had a big nasty
lesion right there

00:35:48.380 --> 00:35:51.650
in what became known
as Broca's area.

00:35:51.650 --> 00:35:55.190
Tan was his name because,
after that lesion, that was all

00:35:55.190 --> 00:35:57.410
he could say.

00:35:57.410 --> 00:36:01.100
So this is back when
the mainstream view

00:36:01.100 --> 00:36:03.470
was very much against
localization of function

00:36:03.470 --> 00:36:04.490
in the brain.

00:36:04.490 --> 00:36:06.570
There were people like
Franz Josef Gall who

00:36:06.570 --> 00:36:08.987
were going around saying that
different parts of the brain

00:36:08.987 --> 00:36:11.480
did very different things,
but Gall was kind of a nut

00:36:11.480 --> 00:36:14.660
and he was not taken seriously
by the academic elite,

00:36:14.660 --> 00:36:19.460
whereas Broca was a fancy
member of the French academic

00:36:19.460 --> 00:36:21.530
societies and a muckety muck.

00:36:21.530 --> 00:36:23.960
And when he announced that
the left frontal lobe is

00:36:23.960 --> 00:36:26.510
the seat of speech, everybody
had to pay attention.

00:36:26.510 --> 00:36:27.990
So it was big stuff.

00:36:27.990 --> 00:36:32.960
Importantly, Broca noted that
Tan wasn't globally impaired

00:36:32.960 --> 00:36:36.110
at thinking, that Tan could
do all kinds of things,

00:36:36.110 --> 00:36:37.800
even though he could not speak.

00:36:37.800 --> 00:36:41.960
So he was already onto this
critical idea way back in 1861.

00:36:41.960 --> 00:36:44.600
And he's just the most
famous in that group.

00:36:44.600 --> 00:36:46.850
There were a bunch of people
before him in the decades

00:36:46.850 --> 00:36:51.380
before who were reporting
similar kinds of associations.

00:36:51.380 --> 00:36:56.030
So what would it be like to have
intact thought despite impaired

00:36:56.030 --> 00:36:57.320
language?

00:36:57.320 --> 00:36:59.915
So Isabel mentioned, asking
somebody who had a stroke.

00:37:04.080 --> 00:37:04.770
OK, Great.

00:37:04.770 --> 00:37:06.910
So here's another case.

00:37:06.910 --> 00:37:09.570
This is a case of this
guy here, Tom Lubbock,

00:37:09.570 --> 00:37:13.620
who died a few years
ago from a brain tumor

00:37:13.620 --> 00:37:17.460
in his temporal lobe that
destroyed most of his language,

00:37:17.460 --> 00:37:19.450
but it destroyed it gradually.

00:37:19.450 --> 00:37:20.970
And this guy was a writer.

00:37:20.970 --> 00:37:25.380
He was an art critic for
a major English paper.

00:37:25.380 --> 00:37:28.830
And as he started to lose
language, he wrote about it,

00:37:28.830 --> 00:37:31.400
and he wrote about
it very beautifully.

00:37:31.400 --> 00:37:35.210
And he said, "my language to
describe things in the world

00:37:35.210 --> 00:37:38.120
is very small, limited.

00:37:38.120 --> 00:37:39.770
My thoughts when I
look at the world

00:37:39.770 --> 00:37:42.590
are vast, limitless, and normal.

00:37:42.590 --> 00:37:44.810
Same as they ever were.

00:37:44.810 --> 00:37:46.580
My experience of
the world is not

00:37:46.580 --> 00:37:52.230
made less by lack of language
but is essentially unchanged."

00:37:52.230 --> 00:37:55.490
So that's a very powerful and
surprising piece of writing.

00:37:55.490 --> 00:37:57.568
It's a little bit
mysterious, because here's

00:37:57.568 --> 00:37:59.360
this guy writing
beautifully and telling us

00:37:59.360 --> 00:38:00.840
his language is impaired.

00:38:00.840 --> 00:38:03.960
So his idea of language
impairment may not be mine.

00:38:03.960 --> 00:38:06.020
I wish I could write that well.

00:38:06.020 --> 00:38:08.690
Nonetheless, he's
clearly reflecting

00:38:08.690 --> 00:38:13.955
on what is a very big loss of
his previous language ability.

00:38:13.955 --> 00:38:15.830
And I'm sure it was very
painstaking to write

00:38:15.830 --> 00:38:16.980
these sentences.

00:38:16.980 --> 00:38:18.980
And he's still telling
us that, even though he's

00:38:18.980 --> 00:38:22.770
lost a lot of language, it has
not changed his experience.

00:38:22.770 --> 00:38:25.190
So that's just one
subjective impression.

00:38:25.190 --> 00:38:27.530
So that argues against
this extreme view

00:38:27.530 --> 00:38:32.010
that they're the same thing,
but it leaves a lot of slop.

00:38:32.010 --> 00:38:32.510
Yes?

00:38:32.510 --> 00:38:35.720
AUDIENCE: [INAUDIBLE]
because he had a [INAUDIBLE]

00:38:35.720 --> 00:38:39.560
of speaking and learning
about the word before.

00:38:39.560 --> 00:38:40.660
NANCY KANWISHER: Yes.

00:38:40.660 --> 00:38:41.940
A very important point.

00:38:41.940 --> 00:38:43.040
Absolutely.

00:38:43.040 --> 00:38:44.570
So this is a case
of somebody who

00:38:44.570 --> 00:38:48.950
had a lesion in mid-life
40, 50, something like that.

00:38:48.950 --> 00:38:53.420
He had a whole lifetime of using
language to learn and bootstrap

00:38:53.420 --> 00:38:54.420
all of cognition.

00:38:54.420 --> 00:38:55.850
So absolutely we
have to separate

00:38:55.850 --> 00:38:57.020
two different questions.

00:38:57.020 --> 00:39:02.090
Do you need language to become a
normal, intelligent, functional

00:39:02.090 --> 00:39:04.467
human being?

00:39:04.467 --> 00:39:06.050
Do you need it
throughout development?

00:39:06.050 --> 00:39:10.280
Or, once you've developed, do
you still need it to think?

00:39:10.280 --> 00:39:12.260
And those are two very
different questions.

00:39:12.260 --> 00:39:15.470
And, in fact, absolutely you
need language to develop.

00:39:15.470 --> 00:39:19.430
If you reflect for a moment
on all the things you know,

00:39:19.430 --> 00:39:23.360
take a quick mental inventory,
survey all the things you know,

00:39:23.360 --> 00:39:27.830
it's a lot of things, almost
all of those you learned

00:39:27.830 --> 00:39:29.270
because somebody told you.

00:39:31.900 --> 00:39:34.930
Most of what we know
we learn from language.

00:39:34.930 --> 00:39:36.340
Maybe you read about it.

00:39:36.340 --> 00:39:40.620
But that's somebody telling
you in a different way.

00:39:40.620 --> 00:39:43.980
So language is crucial for
development of cognition

00:39:43.980 --> 00:39:44.760
and for learning.

00:39:44.760 --> 00:39:45.990
Absolutely.

00:39:45.990 --> 00:39:48.120
But now we're asking
a different question

00:39:48.120 --> 00:39:50.160
of whether you need
it, whether it's

00:39:50.160 --> 00:39:53.920
the same thing in adulthood.

00:39:53.920 --> 00:39:57.040
So this guy is a
little bit complicated,

00:39:57.040 --> 00:39:59.530
because he obviously still
has a lot of language left.

00:39:59.530 --> 00:40:01.840
Let's consider
cases of people who

00:40:01.840 --> 00:40:05.480
have essentially no language
due to brain damage.

00:40:05.480 --> 00:40:07.970
So this is known
as global aphasia.

00:40:07.970 --> 00:40:10.810
And Rosemary Varley in
England has been studying

00:40:10.810 --> 00:40:11.867
a group of three people--

00:40:11.867 --> 00:40:13.450
I think she's got a
few more, but here

00:40:13.450 --> 00:40:15.340
are her three main ones--

00:40:15.340 --> 00:40:16.520
who have global aphasia.

00:40:16.520 --> 00:40:18.550
And she's been studying
them for a few years.

00:40:18.550 --> 00:40:21.460
And, sorry, it doesn't
show here at all.

00:40:21.460 --> 00:40:23.200
Sorry about this
lousy projector.

00:40:23.200 --> 00:40:24.010
Shows on my screen.

00:40:24.010 --> 00:40:26.530
They're big, nasty,
lesions taking up

00:40:26.530 --> 00:40:29.680
a lot of the left
hemisphere and basically

00:40:29.680 --> 00:40:33.640
knocking out essentially
all the language regions

00:40:33.640 --> 00:40:35.320
in these three individuals.

00:40:35.320 --> 00:40:38.020
And here's their performance on
a bunch of different language

00:40:38.020 --> 00:40:39.610
tasks.

00:40:39.610 --> 00:40:43.090
They have to look at
a picture and name it.

00:40:43.090 --> 00:40:46.150
They have to understand
reversible sentences.

00:40:46.150 --> 00:40:49.588
That's like boy kiss girl
versus girl kiss boy.

00:40:49.588 --> 00:40:51.130
They need to know
who did the kissing

00:40:51.130 --> 00:40:54.990
and who got kissed, right,
and a whole bunch of questions

00:40:54.990 --> 00:40:55.490
like that.

00:40:55.490 --> 00:40:58.270
And they are at chance
at every one of these.

00:40:58.270 --> 00:41:00.970
So these are people-- not
just people who can't speak.

00:41:00.970 --> 00:41:03.490
They're people who can't
speak or understand

00:41:03.490 --> 00:41:06.280
language pretty much at all.

00:41:06.280 --> 00:41:09.100
So it's as close as we can
get to a case of a person who

00:41:09.100 --> 00:41:11.530
has no language ability.

00:41:11.530 --> 00:41:15.370
So can these people think?

00:41:15.370 --> 00:41:18.870
So Rosemary Varley
has done paper

00:41:18.870 --> 00:41:22.200
after paper in which she finds
clever ways to communicate

00:41:22.200 --> 00:41:24.510
tasks to these people to find
out what kind of thinking

00:41:24.510 --> 00:41:25.680
they're capable of.

00:41:25.680 --> 00:41:27.390
Here's one.

00:41:27.390 --> 00:41:30.563
You have to order this
series of pictures.

00:41:30.563 --> 00:41:31.980
So look at it for
a second and you

00:41:31.980 --> 00:41:38.760
can figure out that it goes
basically from to left.

00:41:38.760 --> 00:41:42.000
So can people with global
aphasia do this task?

00:41:42.000 --> 00:41:47.140
Yes, they're perfect at
it, no problem whatsoever.

00:41:47.140 --> 00:41:49.140
Now you might dispute,
is that cause and effect.

00:41:49.140 --> 00:41:50.408
Is it knowledge of sequences?

00:41:50.408 --> 00:41:51.200
Are they different?

00:41:51.200 --> 00:41:51.742
I don't know.

00:41:51.742 --> 00:41:54.920
But anyway, it's a
pretty rich task here.

00:41:54.920 --> 00:41:56.960
Here's another task.

00:41:56.960 --> 00:41:58.940
Look at these pictures
and tell which

00:41:58.940 --> 00:42:00.710
of them are things
you know and which

00:42:00.710 --> 00:42:04.280
of them are things you have
never seen before that I drew.

00:42:07.710 --> 00:42:10.325
Takes a moment, but
you can figure it out.

00:42:10.325 --> 00:42:11.700
Top three things
are real things,

00:42:11.700 --> 00:42:15.210
and those three things
are things I drew.

00:42:15.210 --> 00:42:17.347
So we could ask, does a
person with globalization

00:42:17.347 --> 00:42:18.180
know the difference.

00:42:18.180 --> 00:42:20.333
Basically, do you have
to be able to name

00:42:20.333 --> 00:42:22.500
things to know the difference
of what's a real thing

00:42:22.500 --> 00:42:24.300
and what's not?

00:42:24.300 --> 00:42:27.180
Here's another task.

00:42:27.180 --> 00:42:30.820
Which of these is
the plausible event?

00:42:30.820 --> 00:42:34.630
That's more complicated, because
here we just need to know,

00:42:34.630 --> 00:42:36.280
is that a real
thing that I know.

00:42:36.280 --> 00:42:38.380
Here we need to know,
who's doing what to who,

00:42:38.380 --> 00:42:39.760
and does it make sense?

00:42:39.760 --> 00:42:41.768
So it taps world
knowledge, figuring out

00:42:41.768 --> 00:42:43.810
who's doing what to whom,
which many people think

00:42:43.810 --> 00:42:46.040
is at the core of language.

00:42:46.040 --> 00:42:49.900
So how do people with
global aphasia do?

00:42:49.900 --> 00:42:52.210
Perfectly at both
of these things.

00:42:52.210 --> 00:42:55.420
Well, not perfectly, but the
same as control subjects.

00:42:55.420 --> 00:42:56.620
Yeah, Carly?

00:42:56.620 --> 00:42:57.820
AUDIENCE: I'm just confused.

00:42:57.820 --> 00:43:00.250
Like, how do you
get the question

00:43:00.250 --> 00:43:02.140
across what they need to do?

00:43:02.140 --> 00:43:03.723
NANCY KANWISHER: I
don't know exactly,

00:43:03.723 --> 00:43:11.110
but you do something
like, for example.

00:43:11.110 --> 00:43:12.430
Do you ever play charades?

00:43:12.430 --> 00:43:13.275
Like that.

00:43:13.275 --> 00:43:17.740
AUDIENCE: So, like,
it's not exactly--

00:43:17.740 --> 00:43:19.690
couldn't someone
argue that there's

00:43:19.690 --> 00:43:24.446
actions that you're doing or
some kind of form of language?

00:43:24.446 --> 00:43:26.300
NANCY KANWISHER:
They're communication.

00:43:26.300 --> 00:43:28.030
They're not language.

00:43:28.030 --> 00:43:30.280
So when we say language,
we really mean language.

00:43:30.280 --> 00:43:32.110
Not necessarily
noises coming out of

00:43:32.110 --> 00:43:35.345
the mouth, because American
Sign Language counts.

00:43:35.345 --> 00:43:37.720
And I didn't have time to put
that in this lecture, which

00:43:37.720 --> 00:43:40.090
is a damn shame, because it
really does count in every way

00:43:40.090 --> 00:43:42.520
and is very interesting and
uses similar neural structures

00:43:42.520 --> 00:43:44.710
and all that stuff.

00:43:44.710 --> 00:43:47.140
But language is different
than communication.

00:43:47.140 --> 00:43:50.270
There's all kinds of
ways of communicating.

00:43:50.270 --> 00:43:50.770
Yeah?

00:43:50.770 --> 00:43:53.267
AUDIENCE: And how old
are these patients again?

00:43:53.267 --> 00:43:54.850
NANCY KANWISHER: I
don't know exactly,

00:43:54.850 --> 00:43:57.010
but it's almost always strokes.

00:43:57.010 --> 00:43:58.337
They're probably 40 to 60.

00:43:58.337 --> 00:43:59.920
AUDIENCE: So it's
definitely an adult.

00:43:59.920 --> 00:44:03.313
I mean, it's not a infant thing.

00:44:03.313 --> 00:44:04.480
NANCY KANWISHER: No, no, no.

00:44:04.480 --> 00:44:07.510
These are all people who
had brain damage in midlife

00:44:07.510 --> 00:44:09.910
or later in life.

00:44:09.910 --> 00:44:13.660
So that's pretty impressive, OK.

00:44:13.660 --> 00:44:17.590
So, basically, these
people with global aphasia

00:44:17.590 --> 00:44:22.150
are able to do every single
task that Rosemary Varley has

00:44:22.150 --> 00:44:23.290
tested them on.

00:44:23.290 --> 00:44:27.320
So I just showed you
causality, nonverbal meaning.

00:44:27.320 --> 00:44:28.150
Here's a cool one.

00:44:28.150 --> 00:44:30.790
Remember reorientation--
you should.

00:44:30.790 --> 00:44:32.350
May well be on the final exam.

00:44:32.350 --> 00:44:35.680
To remind you, I did a
whole most of a lecture

00:44:35.680 --> 00:44:37.630
on this thing about
reorientation.

00:44:37.630 --> 00:44:41.860
Remember, rats and infants,
if you hide food there and put

00:44:41.860 --> 00:44:45.700
them in this box, they later
go 50-50 to the two corners,

00:44:45.700 --> 00:44:48.400
even though that wall
should disambiguate which

00:44:48.400 --> 00:44:50.530
is the exactly correct corner.

00:44:50.530 --> 00:44:52.240
They should always go here.

00:44:52.240 --> 00:44:56.170
They have the knowledge that
it's there, but they go 50-50.

00:44:56.170 --> 00:44:58.090
And, remember, I
said that Liz Spelke

00:44:58.090 --> 00:45:00.820
has this interesting argument
that the key thing you

00:45:00.820 --> 00:45:04.210
need to be able to solve
that task is language.

00:45:04.210 --> 00:45:06.118
Because, in fact,
if you test adults

00:45:06.118 --> 00:45:07.660
and you tie up their
language system,

00:45:07.660 --> 00:45:10.030
they behave like
infants and rats.

00:45:10.030 --> 00:45:12.190
But if you don't tie up
their language system,

00:45:12.190 --> 00:45:15.430
they can do the task, which is
pretty suggestive that language

00:45:15.430 --> 00:45:16.990
is a crux of the matter.

00:45:16.990 --> 00:45:20.560
However, the global aphasic
do this task just fine.

00:45:23.240 --> 00:45:26.810
So now, we have to go to
[? Min ?] Young's hypothesis,

00:45:26.810 --> 00:45:30.610
which is that maybe the role
of language in reorientation

00:45:30.610 --> 00:45:33.730
is learning about that
whole spatial system

00:45:33.730 --> 00:45:36.580
during childhood, which the
global aphasics could do,

00:45:36.580 --> 00:45:41.200
not maintaining the ability
once you've gained it.

00:45:41.200 --> 00:45:42.280
All right, they can do--

00:45:42.280 --> 00:45:43.905
I won't give you all
the data on this--

00:45:43.905 --> 00:45:45.490
but they can do
arithmetic tasks,

00:45:45.490 --> 00:45:48.340
logic tasks, algebra tasks.

00:45:48.340 --> 00:45:50.200
They appreciate music.

00:45:50.200 --> 00:45:54.130
They can think about what
other people are thinking.

00:45:54.130 --> 00:45:57.130
So everything that all
that kind of high level

00:45:57.130 --> 00:46:00.010
abstract quintessentially
human abilities

00:46:00.010 --> 00:46:03.100
that we are impressed with
ourselves for being able to do,

00:46:03.100 --> 00:46:05.380
these people can do
without language.

00:46:05.380 --> 00:46:10.170
So language and thought
are not the same thing.

00:46:10.170 --> 00:46:12.430
You can still think in
lots of different ways,

00:46:12.430 --> 00:46:14.610
even after you lose language.

00:46:14.610 --> 00:46:17.670
On the other hand, as has
already been brought up,

00:46:17.670 --> 00:46:20.795
global aphasics had
language during development.

00:46:20.795 --> 00:46:22.920
So saying that you don't
need it as an adult is not

00:46:22.920 --> 00:46:25.510
the same as saying you don't
need it during development.

00:46:25.510 --> 00:46:28.800
You absolutely do need
it during development,

00:46:28.800 --> 00:46:32.170
because it's a key way
we learn about the world.

00:46:32.170 --> 00:46:36.030
And for example, there are
studies from Rebecca Sachs' lab

00:46:36.030 --> 00:46:39.420
showing that deaf kids
who learn language later--

00:46:39.420 --> 00:46:42.250
for example, if they're
born not to deaf parents

00:46:42.250 --> 00:46:45.210
but to hearing parents who
don't cotton on to the fact

00:46:45.210 --> 00:46:48.060
that it's important for
them to learn ASL early,

00:46:48.060 --> 00:46:51.030
and hence they don't get
language until later,

00:46:51.030 --> 00:46:53.820
those kids are not as
good at understanding

00:46:53.820 --> 00:46:56.520
what other people are thinking,
something that we usually

00:46:56.520 --> 00:46:57.765
learn about through language.

00:47:02.370 --> 00:47:05.610
Further, even though
I'm making a big deal

00:47:05.610 --> 00:47:07.650
about how you can
think without language,

00:47:07.650 --> 00:47:12.600
I'm not saying that language
is irrelevant to thinking.

00:47:12.600 --> 00:47:14.220
Every time I write
a grant proposal,

00:47:14.220 --> 00:47:16.320
I think, oh god, I have
all these ideas in my head

00:47:16.320 --> 00:47:17.700
and now I have to
waste weeks and weeks

00:47:17.700 --> 00:47:19.408
and weeks, blah, blah,
blah, putting them

00:47:19.408 --> 00:47:23.100
all down on paper to try to
get money to fund my habit.

00:47:23.100 --> 00:47:24.990
And then I get into
like sentence 3

00:47:24.990 --> 00:47:26.910
and I suddenly
realize, oh, oh no, I

00:47:26.910 --> 00:47:29.160
haven't been thinking
about this clearly at all.

00:47:29.160 --> 00:47:31.440
So this is my very
informal introspection

00:47:31.440 --> 00:47:33.690
on the role of language
in my own thinking.

00:47:33.690 --> 00:47:36.060
Like, even when I think
there's a clear thought,

00:47:36.060 --> 00:47:37.710
the same thing happens when
I go to prepare a lecture.

00:47:37.710 --> 00:47:39.335
It's, like, oh yeah,
I know this stuff.

00:47:39.335 --> 00:47:41.860
But I put together some
slides, I'm like slide 2,

00:47:41.860 --> 00:47:45.000
no, I don't really
know this stuff.

00:47:45.000 --> 00:47:48.810
So there is some role for
language and thinking.

00:47:48.810 --> 00:47:52.440
And I'll give you
one example here.

00:47:52.440 --> 00:47:54.660
One of the many things
that language can do

00:47:54.660 --> 00:47:58.350
is to make information
more salient.

00:47:58.350 --> 00:48:02.070
So right now, close your eyes,
everyone close your eyes.

00:48:02.070 --> 00:48:04.860
I mean it, I see
if they're open.

00:48:04.860 --> 00:48:09.220
While keeping your eyes
closed, point south.

00:48:09.220 --> 00:48:11.430
You may not exactly
know where south

00:48:11.430 --> 00:48:12.870
is, but make a good guess.

00:48:15.590 --> 00:48:17.600
Use your whole arm
so everyone can

00:48:17.600 --> 00:48:19.170
see when they open their eyes.

00:48:22.220 --> 00:48:24.290
Keep pointing, but now
you can open your eyes

00:48:24.290 --> 00:48:25.430
and you can look
around and see where

00:48:25.430 --> 00:48:26.513
everyone else is pointing.

00:48:26.513 --> 00:48:29.900
You guys are not bad,
not bad, not bad.

00:48:29.900 --> 00:48:33.530
But we got some over here, we're
a little turned around here.

00:48:33.530 --> 00:48:35.135
Anyway, it's roughly over there.

00:48:37.790 --> 00:48:40.490
So, yeah, hang
on, wait a second.

00:48:40.490 --> 00:48:41.260
Yes, right.

00:48:43.890 --> 00:48:44.592
Well, hang on.

00:48:44.592 --> 00:48:45.800
Yeah, right, it's over there.

00:48:48.890 --> 00:48:51.290
So your vector
average was closer

00:48:51.290 --> 00:48:56.100
to the true thing than a
random vector, but not so hot.

00:48:56.100 --> 00:48:58.830
If your language forced
you to keep track of this,

00:48:58.830 --> 00:49:00.450
you'd be better at it.

00:49:00.450 --> 00:49:04.410
And we know that from the
case of the Pormpuraaw,

00:49:04.410 --> 00:49:07.050
these guys here, who
live in Australia.

00:49:07.050 --> 00:49:08.820
It's Aboriginal people.

00:49:08.820 --> 00:49:13.500
And they spend a lot
of time going around

00:49:13.500 --> 00:49:15.940
in the remote
outback of Australia,

00:49:15.940 --> 00:49:17.700
where they need to
know where they are.

00:49:17.700 --> 00:49:21.090
And who is going where
and when is really

00:49:21.090 --> 00:49:22.950
of the essence in
their lives and

00:49:22.950 --> 00:49:24.760
in their social interactions.

00:49:24.760 --> 00:49:26.640
So when they run
into each other,

00:49:26.640 --> 00:49:29.460
they don't say, hi, how are you.

00:49:29.460 --> 00:49:32.670
Instead, they say,
which way are you going.

00:49:32.670 --> 00:49:35.550
And a typical answer
might be, "North northwest

00:49:35.550 --> 00:49:37.500
in the middle distance,
how about you?"

00:49:40.820 --> 00:49:43.430
They don't talk about
things being left or right

00:49:43.430 --> 00:49:45.590
or behind them,
reference frames that

00:49:45.590 --> 00:49:48.650
have to do with the person's
own body, which are frankly

00:49:48.650 --> 00:49:50.330
really stupid reference frames.

00:49:50.330 --> 00:49:52.610
Because I can say this
thing is to the left.

00:49:52.610 --> 00:49:54.920
And then I turn, and now
it's not to the left anymore.

00:49:54.920 --> 00:49:57.260
Like, how stupid is that, right?

00:49:57.260 --> 00:49:59.210
These guys have a
much better system.

00:49:59.210 --> 00:50:00.710
They would rather
say, oh, "You have

00:50:00.710 --> 00:50:04.150
a bug on your southeast leg."

00:50:04.150 --> 00:50:05.840
Right, OK.

00:50:05.840 --> 00:50:08.780
So these guys, people
who speak this language,

00:50:08.780 --> 00:50:11.630
they have to be aware of
absolute compass directions

00:50:11.630 --> 00:50:14.870
all the time just to speak.

00:50:14.870 --> 00:50:18.230
And so they're oriented
all the time, unlike us.

00:50:18.230 --> 00:50:20.060
And in that sense,
their language

00:50:20.060 --> 00:50:22.920
makes salient certain
kinds of information.

00:50:22.920 --> 00:50:24.852
It's not that we can't
think about direction.

00:50:24.852 --> 00:50:26.810
It's just that most of
the time we're not aware

00:50:26.810 --> 00:50:29.143
because our language doesn't
force us to think about it.

00:50:31.760 --> 00:50:34.770
So interim summary.

00:50:34.770 --> 00:50:37.250
We've been asking this
question of whether thought

00:50:37.250 --> 00:50:39.897
is separate from and
possible without language.

00:50:39.897 --> 00:50:41.480
Before you guys take
off, you wrote it

00:50:41.480 --> 00:50:43.010
on the board, this
board right here?

00:50:43.010 --> 00:50:44.690
Awesome.

00:50:44.690 --> 00:50:47.190
You guys need to tell me when
there's time to take the quiz.

00:50:47.190 --> 00:50:49.398
So you're going to have
seven minutes because there's

00:50:49.398 --> 00:50:50.240
seven questions.

00:50:50.240 --> 00:50:59.180
And so at 12:18 let me know and
I will turn the board around.

00:50:59.180 --> 00:51:01.670
OK, 12:17 because it'll take
me a minute to turn around.

00:51:01.670 --> 00:51:02.840
All right, thank you.

00:51:02.840 --> 00:51:05.390
Take notes, tell
me about that time.

00:51:05.390 --> 00:51:07.490
So here's a question
we've been engaging in,

00:51:07.490 --> 00:51:10.370
is thought separate from and
possible without language?

00:51:10.370 --> 00:51:13.490
And the literature from
neuroscience psych patients

00:51:13.490 --> 00:51:16.550
says, yes, absolutely,
they're totally separate.

00:51:16.550 --> 00:51:20.460
Global aphasics have many forms
of thought without language.

00:51:20.460 --> 00:51:24.910
So given that, what would you
predict from functional MRI?

00:51:24.910 --> 00:51:27.720
So if I told you, which is
true, that these are the brain

00:51:27.720 --> 00:51:30.930
regions that are active
during language tasks,

00:51:30.930 --> 00:51:34.690
for example, when you understand
the meaning of a sentence,

00:51:34.690 --> 00:51:35.800
what would you predict?

00:51:35.800 --> 00:51:39.550
Should they be activated
only by language, not

00:51:39.550 --> 00:51:41.440
by non-linguistic tasks?

00:51:41.440 --> 00:51:42.320
What do you think?

00:51:42.320 --> 00:51:43.653
Take a moment to think about it.

00:51:43.653 --> 00:51:46.070
These are the regions that are
engaged when you understand

00:51:46.070 --> 00:51:47.200
the meaning of a sentence.

00:51:47.200 --> 00:51:49.840
Would you expect them to be
engaged based on what I've just

00:51:49.840 --> 00:51:53.260
told you when you do
mental arithmetic, when

00:51:53.260 --> 00:51:57.040
you think about
spatial orientations,

00:51:57.040 --> 00:52:00.290
when you appreciate music?

00:52:00.290 --> 00:52:01.437
No, right.

00:52:01.437 --> 00:52:03.020
If they're separate,
they're separate.

00:52:03.020 --> 00:52:04.895
They should go on in
different brain regions.

00:52:04.895 --> 00:52:06.440
Everybody have that intuition?

00:52:06.440 --> 00:52:07.985
No, you don't have
that intuition?

00:52:07.985 --> 00:52:10.200
AUDIENCE: No.

00:52:10.200 --> 00:52:14.360
I mean, do you think about
things in terms of words,

00:52:14.360 --> 00:52:16.913
even as a mental crutch,
even if you didn't have to?

00:52:16.913 --> 00:52:18.830
NANCY KANWISHER: OK,
fair enough, fair enough.

00:52:18.830 --> 00:52:20.970
So it doesn't nail this case.

00:52:20.970 --> 00:52:24.500
It could well be that you
have separate systems for all

00:52:24.500 --> 00:52:27.900
those other things, but you
still lean on the system.

00:52:27.900 --> 00:52:30.350
Not necessarily, but
you use it sometimes.

00:52:30.350 --> 00:52:31.790
In fact, there's
evidence for that

00:52:31.790 --> 00:52:34.550
that we won't get to today.

00:52:34.550 --> 00:52:39.320
But the initial thought is,
you don't need to activate it.

00:52:39.320 --> 00:52:41.090
Well, here's a surprise.

00:52:41.090 --> 00:52:44.750
Up until recently, pretty
much the whole brain imaging

00:52:44.750 --> 00:52:47.330
literature says that
language overlaps

00:52:47.330 --> 00:52:49.690
with all of these
things in the brain,

00:52:49.690 --> 00:52:51.440
that the activations
overlap in the brain.

00:52:51.440 --> 00:52:53.360
They're all the same thing.

00:52:53.360 --> 00:52:57.110
That's been the received
story for 20 years or so

00:52:57.110 --> 00:52:58.580
of brain imaging.

00:52:58.580 --> 00:53:01.200
And that just does not fit
with the patient literature.

00:53:01.200 --> 00:53:03.020
So we have a conundrum.

00:53:03.020 --> 00:53:05.640
Here are just a few examples.

00:53:05.640 --> 00:53:09.650
Stan Dehaene says, "arithmetic
recruits networks involved

00:53:09.650 --> 00:53:12.680
in word association processes."

00:53:12.680 --> 00:53:14.330
People who study
music say regions

00:53:14.330 --> 00:53:16.800
such as Broca's area
and Wernicke's area,

00:53:16.800 --> 00:53:19.280
which have been considered
specific to language,

00:53:19.280 --> 00:53:22.460
are also activated by
certain aspects of music.

00:53:22.460 --> 00:53:24.530
Thus, the idea of
language specificity

00:53:24.530 --> 00:53:26.675
has been called into
question, and on and on.

00:53:26.675 --> 00:53:27.800
There's a million of these.

00:53:27.800 --> 00:53:30.700
I just put a few
of them up there.

00:53:30.700 --> 00:53:33.010
So what's going on.

00:53:33.010 --> 00:53:35.060
How are we going to
resolve this contradiction?

00:53:35.060 --> 00:53:37.490
On the one hand, the
patient literature

00:53:37.490 --> 00:53:39.490
suggests that language
is separate from the rest

00:53:39.490 --> 00:53:40.150
of thought.

00:53:40.150 --> 00:53:42.950
And on the other hand, most
of the neuroimaging literature

00:53:42.950 --> 00:53:44.950
says that if you look at
those language regions,

00:53:44.950 --> 00:53:48.200
you find them activated in all
these other kinds of things.

00:53:48.200 --> 00:53:51.820
One hypothesis is David's,
that they're activated but not

00:53:51.820 --> 00:53:53.380
essentially so.

00:53:53.380 --> 00:53:55.957
But there's another hypothesis.

00:53:55.957 --> 00:53:58.290
And that is that there's a
methodological flaw with most

00:53:58.290 --> 00:54:01.340
of the prior research.

00:54:01.340 --> 00:54:03.410
What is that
methodological flaw?

00:54:03.410 --> 00:54:05.420
It's an inappropriate
use of something

00:54:05.420 --> 00:54:06.560
called a group analysis.

00:54:06.560 --> 00:54:08.600
I've alluded to this
a few times briefly,

00:54:08.600 --> 00:54:10.430
but let me do it for real now.

00:54:10.430 --> 00:54:13.880
Let me first say, it's
not that a group analysis

00:54:13.880 --> 00:54:15.680
with functional MRI
is an evil thing that

00:54:15.680 --> 00:54:16.790
should never be done.

00:54:16.790 --> 00:54:17.960
They have uses.

00:54:17.960 --> 00:54:19.850
But particularly
for the question

00:54:19.850 --> 00:54:23.030
of asking whether common
regions of the brain

00:54:23.030 --> 00:54:24.860
are engaged in two
different tasks,

00:54:24.860 --> 00:54:27.380
it is not a good method,
for the following reason.

00:54:27.380 --> 00:54:29.930
So, first, let's say,
what is a group analysis.

00:54:29.930 --> 00:54:31.550
With functional
MRI, it just means--

00:54:31.550 --> 00:54:33.633
and, again, I'm going to
be very sketchy with this

00:54:33.633 --> 00:54:35.913
because this is not an actual
hands-on methods class.

00:54:35.913 --> 00:54:37.580
I'm just trying to
get you to understand

00:54:37.580 --> 00:54:39.200
the gist of the methods.

00:54:39.200 --> 00:54:41.120
You take a bunch
of scanned brains

00:54:41.120 --> 00:54:44.510
and you align them in a
common space as best you can.

00:54:44.510 --> 00:54:46.640
You can't do it
perfectly because brains

00:54:46.640 --> 00:54:50.240
are anatomically different
from one person to the next.

00:54:50.240 --> 00:54:53.480
But you do your best to
align them as best you can.

00:54:53.480 --> 00:54:57.110
Then you do an analysis
across those aligned brains.

00:54:57.110 --> 00:55:00.470
And you ask, what is consistent
across this group of subjects.

00:55:00.470 --> 00:55:02.300
That's a very useful
question to ask.

00:55:02.300 --> 00:55:05.180
If we want to know overall
what are the brain regions that

00:55:05.180 --> 00:55:07.880
are consistently activated
when you understand language

00:55:07.880 --> 00:55:09.410
across this whole
group of subjects,

00:55:09.410 --> 00:55:11.620
that's a good use
of a group analysis.

00:55:11.620 --> 00:55:13.370
You'll find that picture
I just showed you

00:55:13.370 --> 00:55:15.537
before with stuff going
down the left temporal lobe,

00:55:15.537 --> 00:55:17.270
a bunch of left
frontal lobe stuff.

00:55:17.270 --> 00:55:19.250
And that will be a
very blurry picture

00:55:19.250 --> 00:55:21.902
of the regions that are most
consistent across subjects.

00:55:21.902 --> 00:55:22.610
Yes, [INAUDIBLE].

00:55:22.610 --> 00:55:24.980
AUDIENCE: Do you line
them anatomically

00:55:24.980 --> 00:55:26.990
as you new [INAUDIBLE]
side to each other?

00:55:26.990 --> 00:55:29.070
Or do you line
them functionally,

00:55:29.070 --> 00:55:32.090
so you can look at the scans
in the functional [? region? ?]

00:55:32.090 --> 00:55:34.940
NANCY KANWISHER: So therein
lies a universe of options.

00:55:34.940 --> 00:55:37.040
What I'm talking about
now is a group analysis

00:55:37.040 --> 00:55:39.050
is aligning them anatomically.

00:55:39.050 --> 00:55:40.850
And that's where the
problem comes in.

00:55:40.850 --> 00:55:42.683
And where we're going
to go from that is you

00:55:42.683 --> 00:55:44.570
need to align them functionally.

00:55:44.570 --> 00:55:47.900
If you just align
them anatomically,

00:55:47.900 --> 00:55:50.000
then the following can happen.

00:55:50.000 --> 00:55:52.430
So you do a standard
group analysis

00:55:52.430 --> 00:55:55.310
and you say, for example,
let's do a language task

00:55:55.310 --> 00:55:57.860
and arithmetic task
and a music task.

00:55:57.860 --> 00:55:59.720
And let's suppose
you find this--

00:55:59.720 --> 00:56:03.860
basically, Broca's area vicinity
is activated in an overlapping

00:56:03.860 --> 00:56:06.050
fashion in all three.

00:56:06.050 --> 00:56:10.310
Each of those is based on an
analysis of 12 or 20 subjects

00:56:10.310 --> 00:56:12.830
aligned as best we can.

00:56:12.830 --> 00:56:14.810
So that's basically what
the literature shows

00:56:14.810 --> 00:56:17.390
is lots of stuff like that.

00:56:17.390 --> 00:56:18.770
But here's the problem.

00:56:18.770 --> 00:56:21.860
You can get that result
in a group analysis,

00:56:21.860 --> 00:56:25.370
even if the actual
data looks like this

00:56:25.370 --> 00:56:27.740
in each individual subject.

00:56:27.740 --> 00:56:31.430
No overlap at all
in any subject,

00:56:31.430 --> 00:56:34.470
but those regions are in
slightly different locations.

00:56:34.470 --> 00:56:39.340
And so if you average
across this, you get that.

00:56:39.340 --> 00:56:41.300
Everybody see the problem?

00:56:41.300 --> 00:56:43.870
So it's not that it's a bad
idea to do a group analysis.

00:56:43.870 --> 00:56:45.940
It's a nice, initial,
blurry picture

00:56:45.940 --> 00:56:48.970
of the approximate
consistent locations

00:56:48.970 --> 00:56:50.650
in the brain for a given task.

00:56:50.650 --> 00:56:53.290
The problem is when you
say, oh, there's overlap,

00:56:53.290 --> 00:56:55.330
therefore they're
the same thing,

00:56:55.330 --> 00:56:57.040
because you can get
this result even

00:56:57.040 --> 00:57:00.940
if there's no overlap
in any subject at all.

00:57:00.940 --> 00:57:02.890
So the whole literature
did this for 20 years

00:57:02.890 --> 00:57:04.660
and made all this talk
about how language

00:57:04.660 --> 00:57:06.670
is on top of everything
else in the brain.

00:57:06.670 --> 00:57:09.160
And for a long time I was
sitting by the sidelines

00:57:09.160 --> 00:57:11.740
going, oh my god.

00:57:11.740 --> 00:57:15.763
And then, eventually,
Ev Fedorenko came along

00:57:15.763 --> 00:57:16.930
and she knew about language.

00:57:16.930 --> 00:57:19.390
And I said, let's figure
out, maybe they're right,

00:57:19.390 --> 00:57:22.610
maybe that's true, or
maybe it's like this.

00:57:22.610 --> 00:57:24.850
Let's find out.

00:57:24.850 --> 00:57:27.250
So how do we do that?

00:57:27.250 --> 00:57:29.410
What you do is exactly
what [INAUDIBLE]

00:57:29.410 --> 00:57:30.520
mentioned a moment ago.

00:57:30.520 --> 00:57:33.970
You align them not
anatomically but functionally.

00:57:33.970 --> 00:57:36.580
That's a whole reason to use
functional reasons of interest.

00:57:36.580 --> 00:57:38.860
We've encountered this
before when I was carrying on

00:57:38.860 --> 00:57:41.800
about why we do functional
localizers with the fusiform

00:57:41.800 --> 00:57:42.310
face area.

00:57:42.310 --> 00:57:43.600
This is the same deal.

00:57:43.600 --> 00:57:46.675
It's just that that insight
started in the back of the head

00:57:46.675 --> 00:57:48.550
and hasn't reached the
front of the head yet,

00:57:48.550 --> 00:57:49.820
or it's about here.

00:57:49.820 --> 00:57:51.028
So some people get it here.

00:57:51.028 --> 00:57:53.320
And the farther forward you
go, the less people realize

00:57:53.320 --> 00:57:55.390
this is an issue, which
is really ridiculous,

00:57:55.390 --> 00:57:57.900
because it gets more and more
important as you go this way.

00:57:57.900 --> 00:57:59.650
Some stuff is actually
aligned in the back

00:57:59.650 --> 00:58:01.330
and nothing is
aligned in the front.

00:58:01.330 --> 00:58:03.280
Anyway, so what do you do?

00:58:03.280 --> 00:58:06.910
You do just what we did with the
FFA and all the other regions.

00:58:06.910 --> 00:58:09.550
One, in each subject
individually,

00:58:09.550 --> 00:58:12.370
you identify those
language regions.

00:58:12.370 --> 00:58:13.687
You run some localizer.

00:58:13.687 --> 00:58:15.520
It's like, OK, I got
this and that and that.

00:58:15.520 --> 00:58:17.740
And then once you
identified them,

00:58:17.740 --> 00:58:20.620
you can ask, OK, does that
region in that subject

00:58:20.620 --> 00:58:22.060
show activation for arithmetic.

00:58:22.060 --> 00:58:25.090
No, that's next door,
right, et cetera.

00:58:25.090 --> 00:58:25.990
Everybody got this?

00:58:25.990 --> 00:58:27.520
This is really important.

00:58:27.520 --> 00:58:30.380
I guess just because
I'm obsessed with it.

00:58:30.380 --> 00:58:32.380
I honestly don't know if
it's globally important

00:58:32.380 --> 00:58:33.963
or if it's just my
personal obsession,

00:58:33.963 --> 00:58:35.770
but you need to know
it for this course.

00:58:35.770 --> 00:58:38.560
We'll leave it at that.

00:58:38.560 --> 00:58:41.020
So this is standard in
people who study vision

00:58:41.020 --> 00:58:43.660
and it's less standard in people
who work in other domains.

00:58:43.660 --> 00:58:46.720
But they're slowly cottoning on.

00:58:46.720 --> 00:58:49.780
So how do we identify language
regions in each subject

00:58:49.780 --> 00:58:51.310
individually?

00:58:51.310 --> 00:58:53.500
There are lots of
possible ways to do this.

00:58:53.500 --> 00:58:56.320
But here's the way
I'm going to show you

00:58:56.320 --> 00:58:59.180
that's been used a bunch
by Fedorenko and others.

00:58:59.180 --> 00:59:02.410
So we start by saying, OK,
let's find candidate brain

00:59:02.410 --> 00:59:06.070
regions that respond to
language, which I told you,

00:59:06.070 --> 00:59:07.840
by language, I mean
sentence understanding

00:59:07.840 --> 00:59:09.590
for present purposes.

00:59:09.590 --> 00:59:11.680
So if we want to look at
sentence understanding,

00:59:11.680 --> 00:59:13.700
we've got to start with
sentence understanding.

00:59:13.700 --> 00:59:15.340
So if you look at
the screen, you'll

00:59:15.340 --> 00:59:16.930
see some of the stimuli we use.

00:59:20.870 --> 00:59:24.200
So subject is lying in the
scanner and they see that.

00:59:24.200 --> 00:59:26.220
And then we can either
give them a task or not.

00:59:26.220 --> 00:59:28.800
And we'll talk about
that in a second.

00:59:28.800 --> 00:59:30.380
What are we going
to compare it to?

00:59:30.380 --> 00:59:32.422
Well, there are lots and
lots of different things

00:59:32.422 --> 00:59:34.920
we could compare it to that
control for different things.

00:59:34.920 --> 00:59:38.825
But we started off with
this, if you read this here.

00:59:44.410 --> 00:59:47.080
So the idea is, it's
visually similar.

00:59:47.080 --> 00:59:48.910
You can hear the
sounds in your head.

00:59:48.910 --> 00:59:51.070
You can pronounce those
things to yourself.

00:59:51.070 --> 00:59:55.720
But there's really no
syntax and no meaning--

00:59:55.720 --> 00:59:59.770
not perfect, but a first pass.

00:59:59.770 --> 01:00:02.290
So when you do that,
you get activations

01:00:02.290 --> 01:00:03.160
that look like this.

01:00:03.160 --> 01:00:04.660
Here are four
different subjects.

01:00:04.660 --> 01:00:07.120
And you can see they're
very systematic things.

01:00:07.120 --> 01:00:08.290
See these three blobs--

01:00:08.290 --> 01:00:10.270
boom, boom, boom,
boom, boom, boom--

01:00:10.270 --> 01:00:13.180
in each subject, and a bunch
of stuff in the temporal lobe

01:00:13.180 --> 01:00:14.770
like that in each subject.

01:00:14.770 --> 01:00:19.880
They're quite systematic but
absolutely not identical.

01:00:19.880 --> 01:00:22.820
All right, so, yeah,
it's just what I did.

01:00:22.820 --> 01:00:24.120
So now what do you do next?

01:00:24.120 --> 01:00:25.670
Well, we just made
this up, sentences

01:00:25.670 --> 01:00:26.765
versus non-word strings.

01:00:26.765 --> 01:00:29.270
Well, who says that's
a good thing to do?

01:00:29.270 --> 01:00:31.040
So the next thing
you do is you've

01:00:31.040 --> 01:00:34.250
got to validate your localizer
task to make sure it isn't just

01:00:34.250 --> 01:00:35.820
like trivial in some sense.

01:00:35.820 --> 01:00:39.200
So the first question
is, is it reliable?

01:00:39.200 --> 01:00:41.930
So, here's session 1,
three different subjects'

01:00:41.930 --> 01:00:43.370
activations.

01:00:43.370 --> 01:00:45.050
Well, just scan them again.

01:00:45.050 --> 01:00:47.925
There's a lot of talk about
fancy statistics, blah, blah

01:00:47.925 --> 01:00:48.590
blah.

01:00:48.590 --> 01:00:51.060
Just scan them again.

01:00:51.060 --> 01:00:54.390
Wow, look how similar
these two little hot spots,

01:00:54.390 --> 01:00:55.860
this elongated one.

01:00:55.860 --> 01:00:58.380
I mean, it's
remarkable, extremely

01:00:58.380 --> 01:01:01.230
reliable within a
subject, and yet somewhat

01:01:01.230 --> 01:01:06.170
different across subjects,
so check one, reliable.

01:01:06.170 --> 01:01:09.850
More interestingly, does
it generalize across task

01:01:09.850 --> 01:01:11.650
and presentation modality?

01:01:11.650 --> 01:01:13.635
So before we just had
people reading sentences.

01:01:13.635 --> 01:01:15.010
And I keep saying,
reading is not

01:01:15.010 --> 01:01:17.540
the native form of language.

01:01:17.540 --> 01:01:20.050
So let's replicate that reading.

01:01:20.050 --> 01:01:22.068
And now we're adding
a memory task.

01:01:22.068 --> 01:01:24.610
So at the end of each string,
a little probe comes up and you

01:01:24.610 --> 01:01:28.120
have to say, was this word
or a non-word in the previous

01:01:28.120 --> 01:01:29.200
thing--

01:01:29.200 --> 01:01:31.360
sequence.

01:01:31.360 --> 01:01:34.870
And let's compare that to just
listening to the sentences.

01:01:34.870 --> 01:01:37.570
Wow, look how similar.

01:01:37.570 --> 01:01:42.070
So that tells us that we're
not studying reading or speech.

01:01:42.070 --> 01:01:45.010
We're studying language
after those things converge.

01:01:45.010 --> 01:01:48.220
Those regions don't care if you
saw a word or heard the word.

01:01:48.220 --> 01:01:50.137
They just care if
you're representing

01:01:50.137 --> 01:01:51.220
the meaning of a sentence.

01:01:51.220 --> 01:01:54.610
Everybody with me
why that's important?

01:01:54.610 --> 01:01:56.950
All right, check, check.

01:01:56.950 --> 01:01:58.630
Does it generalize
across languages?

01:01:58.630 --> 01:02:02.110
Suppose you're bilingual and
speak two different languages.

01:02:02.110 --> 01:02:05.590
Here's two subjects who speak
both English and Spanish.

01:02:05.590 --> 01:02:08.350
Wow, look how similar.

01:02:08.350 --> 01:02:10.390
So it's really
language in general,

01:02:10.390 --> 01:02:15.310
not English or Spanish
or a particular language.

01:02:15.310 --> 01:02:18.070
Does it generalize
across materials?

01:02:18.070 --> 01:02:20.598
So we could have reading
sentences versus non-words

01:02:20.598 --> 01:02:22.390
that we've been talking
about here with two

01:02:22.390 --> 01:02:24.610
different runs in one subject.

01:02:24.610 --> 01:02:26.380
Are we going have
subjects listening

01:02:26.380 --> 01:02:28.602
to speech versus degraded
speech, like this?

01:02:28.602 --> 01:02:29.560
Here's the speech case.

01:02:29.560 --> 01:02:30.227
[VIDEO PLAYBACK]

01:02:30.227 --> 01:02:32.470
- During my days
of house arrest,

01:02:32.470 --> 01:02:36.250
it felt as though I were no
longer part of the real world.

01:02:36.250 --> 01:02:37.855
NANCY KANWISHER:
OK, versus this.

01:02:37.855 --> 01:02:43.727
- [INAUDIBLE]

01:02:43.727 --> 01:02:44.310
[END PLAYBACK]

01:02:44.310 --> 01:02:45.770
NANCY KANWISHER: OK,
so very degraded.

01:02:45.770 --> 01:02:47.430
You can't understand
what's being said,

01:02:47.430 --> 01:02:50.720
but it has similar prosody
and some similar structure.

01:02:50.720 --> 01:02:53.900
And the point is, you get
very similar activations

01:02:53.900 --> 01:02:58.190
with those very different
kinds of contrasts.

01:02:58.190 --> 01:03:01.480
So now we have really
validated this thing.

01:03:01.480 --> 01:03:03.650
It checks out in all
the ways it should.

01:03:03.650 --> 01:03:05.780
It doesn't care about modality.

01:03:05.780 --> 01:03:09.650
It does care about meaning.

01:03:09.650 --> 01:03:11.610
And it's highly reliable.

01:03:11.610 --> 01:03:13.520
So now we can put it to use.

01:03:13.520 --> 01:03:17.120
Now we can ask, what does
each of those regions do?

01:03:17.120 --> 01:03:20.660
All right, so to do
that, in each participant

01:03:20.660 --> 01:03:24.170
then we find those regions
with this localizer.

01:03:24.170 --> 01:03:25.790
Now let me just
step back a second.

01:03:25.790 --> 01:03:28.610
There's nothing magic about
this localizer per se.

01:03:28.610 --> 01:03:31.160
When you want to
study something,

01:03:31.160 --> 01:03:32.510
you use common sense.

01:03:32.510 --> 01:03:34.850
You try something,
you validate it.

01:03:34.850 --> 01:03:37.248
It may turn out later that
of the thing that we thought

01:03:37.248 --> 01:03:39.290
we were identifying language
with this localizer,

01:03:39.290 --> 01:03:40.500
it's got this other stuff.

01:03:40.500 --> 01:03:42.368
And then maybe you
refine your localizer

01:03:42.368 --> 01:03:43.410
into something different.

01:03:43.410 --> 01:03:45.740
So it's not that this is
the only possible way.

01:03:45.740 --> 01:03:49.250
It was just a sensible approach.

01:03:49.250 --> 01:03:51.320
So you use this to
find those regions.

01:03:51.320 --> 01:03:53.540
Here they are in
these four subjects.

01:03:53.540 --> 01:03:57.313
And now, you can
say, let's find.

01:03:57.313 --> 01:03:58.730
So you have to
figure out some way

01:03:58.730 --> 01:04:01.850
to say that thing corresponds
to that to that to that.

01:04:01.850 --> 01:04:03.440
And there's a
whole bunch of math

01:04:03.440 --> 01:04:05.420
that was invented to do that.

01:04:05.420 --> 01:04:08.480
You can basically see
it with your eyeballs

01:04:08.480 --> 01:04:11.510
that those guys roughly
correspond and those guys

01:04:11.510 --> 01:04:13.220
roughly correspond.

01:04:13.220 --> 01:04:15.480
The math is just
a way to do that.

01:04:15.480 --> 01:04:17.225
And then once you've
found that region,

01:04:17.225 --> 01:04:19.850
you can measure its response in
a whole bunch of new conditions

01:04:19.850 --> 01:04:22.820
and ask what it does.

01:04:22.820 --> 01:04:27.680
And in particular, so this is
different from a group analysis

01:04:27.680 --> 01:04:29.810
where you don't
identify those regions.

01:04:29.810 --> 01:04:34.070
You just choose
regions anatomically

01:04:34.070 --> 01:04:37.760
So if we just align them and
said, OK, that's a region,

01:04:37.760 --> 01:04:39.980
well, we don't have much of
the language stuff there,

01:04:39.980 --> 01:04:42.560
not much there, a lot
there, not much there.

01:04:42.560 --> 01:04:44.360
OK, that's not great.

01:04:44.360 --> 01:04:46.640
Then we take another
one and we define this.

01:04:46.640 --> 01:04:47.420
This is a problem.

01:04:47.420 --> 01:04:49.160
No language stuff
here, lots of language

01:04:49.160 --> 01:04:50.750
stuff there, none and lots.

01:04:50.750 --> 01:04:52.880
Not good.

01:04:52.880 --> 01:04:54.560
Everybody see how
that's a problem?

01:04:54.560 --> 01:04:56.960
OK, I guess I'm flogging this.

01:04:56.960 --> 01:04:58.820
We can move on now.

01:04:58.820 --> 01:05:02.030
But the main problems
with the group analysis

01:05:02.030 --> 01:05:05.360
are you might fail to detect
neural activity that's actually

01:05:05.360 --> 01:05:07.880
there, because it doesn't align
well enough across subjects

01:05:07.880 --> 01:05:09.560
and so it doesn't
reach threshold.

01:05:09.560 --> 01:05:11.450
It's not consistent.

01:05:11.450 --> 01:05:13.910
But for present purposes,
the more relevant problem

01:05:13.910 --> 01:05:17.930
is, you might fail to
distinguish between two

01:05:17.930 --> 01:05:19.790
different functions,
because they

01:05:19.790 --> 01:05:24.830
invariably coexist within
that region or not.

01:05:24.830 --> 01:05:27.290
So we're not doing that
for present purposes.

01:05:27.290 --> 01:05:29.150
Instead, we're
going to now go back

01:05:29.150 --> 01:05:32.720
to the conundrum of why do
the patient studies suggest

01:05:32.720 --> 01:05:35.180
that language is distinct
from the rest of thought,

01:05:35.180 --> 01:05:37.130
but the past
functional MRI studies

01:05:37.130 --> 01:05:39.320
suggest that language
overlaps with other functions

01:05:39.320 --> 01:05:40.640
in the brain.

01:05:40.640 --> 01:05:43.040
And we're going to
consider the hypothesis

01:05:43.040 --> 01:05:44.960
that if you study
individual brains

01:05:44.960 --> 01:05:48.680
and localize those regions
individually in each subject,

01:05:48.680 --> 01:05:51.950
then the story
might be different.

01:05:51.950 --> 01:05:53.030
And it is.

01:05:53.030 --> 01:05:55.310
So here's the task
that Fedorenko and I

01:05:55.310 --> 01:05:56.610
did a few years ago.

01:05:56.610 --> 01:05:58.557
We came up with seven
different tasks.

01:05:58.557 --> 01:06:00.140
I won't bore you
with all the details.

01:06:00.140 --> 01:06:01.500
It doesn't really matter.

01:06:01.500 --> 01:06:04.610
We just had lots of stuff,
arithmetic, spatial working

01:06:04.610 --> 01:06:07.010
memory, various
cognitive control tasks,

01:06:07.010 --> 01:06:09.560
working memory tests,
all kinds of stuff,

01:06:09.560 --> 01:06:12.560
focusing on things that--
music, focusing on stuff

01:06:12.560 --> 01:06:14.330
that other people
had said overlaps

01:06:14.330 --> 01:06:17.193
with language in the brain.

01:06:17.193 --> 01:06:18.860
And so first thing
is you've got to make

01:06:18.860 --> 01:06:21.520
sure those other tasks
actually produce activations,

01:06:21.520 --> 01:06:24.020
because it's easy to make up a
task and have it not do much,

01:06:24.020 --> 01:06:26.010
and then that's not
very interesting.

01:06:26.010 --> 01:06:29.450
So, yes, each one of those tasks
produce lots of activation.

01:06:29.450 --> 01:06:31.040
Look at all that red stuff.

01:06:31.040 --> 01:06:34.580
Looks like a bunch of pizzas.

01:06:34.580 --> 01:06:36.170
So they produce
good activations.

01:06:36.170 --> 01:06:38.342
Now the question is, do
those activations overlap

01:06:38.342 --> 01:06:39.425
with the language regions.

01:06:42.720 --> 01:06:45.130
So let's consider two of them.

01:06:45.130 --> 01:06:46.710
This is basically
Wernicke's area

01:06:46.710 --> 01:06:49.800
and Broca's area, two
well-known language regions,

01:06:49.800 --> 01:06:52.420
identified individually
in each subject

01:06:52.420 --> 01:06:55.800
and now averaging the response
over all the conditions.

01:06:55.800 --> 01:06:57.300
Here's a response
when subjects read

01:06:57.300 --> 01:07:01.680
sentences and non-word strings,
sentences and non-word strings.

01:07:01.680 --> 01:07:03.550
That's how we define
those regions,

01:07:03.550 --> 01:07:05.302
but this is in data
that wasn't actually

01:07:05.302 --> 01:07:06.510
used to define those regions.

01:07:06.510 --> 01:07:10.198
We held out some data and
just cross-validated it.

01:07:10.198 --> 01:07:12.240
Now the question is, how
do those regions respond

01:07:12.240 --> 01:07:15.180
to all of these other things?

01:07:15.180 --> 01:07:18.480
They don't, pretty much at all.

01:07:21.600 --> 01:07:23.190
So notice what's happened here.

01:07:23.190 --> 01:07:26.220
The prior literature
shows massive overlap

01:07:26.220 --> 01:07:28.680
between language and
all these other things.

01:07:28.680 --> 01:07:32.100
In our data, when you identify
those language regions

01:07:32.100 --> 01:07:34.450
in each subject
individually and measure

01:07:34.450 --> 01:07:36.450
the magnitude of response
in those other things,

01:07:36.450 --> 01:07:38.470
they don't respond.

01:07:38.470 --> 01:07:41.280
So this shows stunning
specificity of the language

01:07:41.280 --> 01:07:43.530
regions consistent
with the picture that

01:07:43.530 --> 01:07:45.150
comes from the
patient literature,

01:07:45.150 --> 01:07:46.950
from studies of brain damage.

01:07:46.950 --> 01:07:48.750
Language really is
separate in the brain

01:07:48.750 --> 01:07:52.310
from all of these things
Everybody get that picture?

01:07:52.310 --> 01:07:54.060
And the reason the
literature had it wrong

01:07:54.060 --> 01:07:56.760
is they were mushing brains
together and blurring

01:07:56.760 --> 01:08:01.140
the hell out of their data
and drawing wrong conclusions.

01:08:01.140 --> 01:08:04.470
I'm speeding up because I
don't want to run out of time.

01:08:04.470 --> 01:08:06.930
So we started with
these questions here.

01:08:06.930 --> 01:08:08.880
Is language distinct
from the rest of thought?

01:08:08.880 --> 01:08:12.240
I'm saying, yes, language may
be necessary to learn to think.

01:08:12.240 --> 01:08:13.960
And it is indeed.

01:08:13.960 --> 01:08:17.550
But the evidence from
the neurological patients

01:08:17.550 --> 01:08:18.810
is pretty powerful.

01:08:18.810 --> 01:08:21.000
Global aphasics with
pretty much no language

01:08:21.000 --> 01:08:24.250
can think in myriad,
sophisticated ways.

01:08:24.250 --> 01:08:27.060
And when you do your
functional MRI studies right,

01:08:27.060 --> 01:08:29.100
you find that the language
regions in the brain,

01:08:29.100 --> 01:08:34.210
in fact, are not active during
non-linguistic thinking.

01:08:34.210 --> 01:08:36.180
Make sense?

01:08:36.180 --> 01:08:38.270
Questions?

01:08:38.270 --> 01:08:40.840
Wow, I finished on time.