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NANCY KANWISHER: So
I'm going to talk today
about a couple of things.
I had a hell of a
time constructing
a nice clean narrative arc to
everything I wanted to say.
And so I finally just
decided, the hell with it.
I'm just going to be honest.
There's several
different pieces.
They don't make a narrative arc.
That's life.
I want to address what I see
as a sort of macroscopic view
of the organization
of the human brain
is giving us a kind
of picture of what
I'm going to call
the architecture
of human intelligence.
We're trying to understand
intelligence in this class.
And so I think the overall
organization of the human
brain-- in which we've made a
lot of progress in the last 20
years--
gives us a kind of
macro picture of what
the pieces of the system are.
So I'll talk about that.
And then I'll also--
if I talk fast enough--
do a kind of whirlwind
introduction through the basic
methods of human cognitive
neuroscience using face
recognition as an example
to illustrate what each
of the methods
can and cannot do.
So that's the agenda.
It's going to be pretty basic.
So if you've heard
me speak before,
you've probably
heard a lot of this.
Anyway, the key
question we're trying
to address in this course
is, how does the brain
produce intelligent behavior?
And how may we be
able to replicate that
intelligence in machines?
So there's, of course,
a million different ways
to go at that question.
And you can go at it from a
kind of computational angle,
a coding perspective, from a
fine-grained neural circuit
perspective.
But I'm going to do something
that's kind of in between.
because those are the things we
can approach in human brains.
And it's really
human intelligence
we want to understand.
It's a sum of human
intelligences.
A lot of it are things
that we share with animals,
but some of it is not.
And so I think it's important
to be able to approach this
not just from the perspectives
of animal research, magnificent
as those methods are,
but to also see what we
can learn about human brains.
OK.
So I'll talk a bit about the
overall functional architecture
of the human brain.
What are the basic
pieces of the system?
And then I'll get into
some different methods
and what they tell us
about face perception.
OK.
So at the most general level,
we can ask whether human
intelligence-- as people have
been asking for centuries,
actually--
whether human intelligence
is the product
of a bunch of very special
purpose components,
each optimized to solve
a specific problem,
kind of like this
device here, where
you have a saw for cutting wood,
scissors for cutting paper.
Saws don't work
that well on paper,
and scissors don't
work that well on wood.
Or whether human intelligence
is a product of some more
generic, all-purpose
computational power
that makes us generically
smart without optimizing us
for any particular task.
And just to
foreshadow the answer,
as in all questions in
psychology, the answer is both.
But we'll do that
in some detail.
Before we get into
that, who cares?
And I'd say, first of all,
this kind of macro level
question about functional
components of the human mind
and brain matters for
a bunch of reasons.
First of all, I just think it's
one of the most basic questions
we can ask about ourselves--
about who we are--
is to ask what the basic
pieces are of our minds.
Second, more pragmatically,
this kind of divide and conquer
research strategy has
been effective in lots
of different fields
that are trying
to understand a complex system.
What do you do with this
incredibly complex system,
where you just can't even
figure out how to get started?
Well, one sensible
way to get started
is first figure out what
its pieces are and then
maybe try to figure out how
each of the pieces work.
And then maybe some day,
maybe not in my lifetime,
figure out how they
all work together
in some coordinated fashion.
And third, somewhat more
subtly, of course, we
want to know not just
what the pieces are,
but what the computations
that are performed in each
of those pieces and what the
representations extracted
in each piece are.
And I think even just a
functional characterization
of the scope of a
particular brain region
already gives us
some important clues
about the kinds of
computations that go on there.
So if we find that there's
a part of the brain that's
primarily involved
in face recognition,
not in reading visually
presented words,
recognizing scenes, or
recognizing objects,
that already gives us
some clues about the kinds
of computations that would
be appropriate for that scope
of task.
So if you tried to write
the code to do that,
you'd be writing very
different code if it only
had to do face recognition
versus if it also
had to be able to
recognize words and scenes
and objects presented visually.
OK.
So that's my list
of the main reasons.
And of course, there are
heaps of different ways
to investigate this
question, and I'll
mention some of those
in the second half.
But I want to start
with Spearman,
who published a paper
in 1904 in the American
Journal of Psychology.
This article was sandwiched
between a discussion
of the soul and an
article on the psychology
of the English sparrow.
And in this article, Spearman
did the following low tech
but fascinating thing.
He tested a whole bunch of
kids in two different schools
on a wide variety
of different tasks.
And this included scholastic
achievement type things.
He got exam grades
from each student
in a bunch of different classes.
And he measured a whole
bunch of other kinds
of psychological
abilities, including
some very psychophysical
perceptual discrimination
abilities.
How well could
people discriminate
the loudness of two
different tones,
the brightness of two
different flashes of light,
the weight of two
different pieces of stuff?
And what he found-- well,
before I tell you what he found,
what would you expect with this?
Should we expect a correlation
between your ability
to discriminate two
different loudnesses
and, say, your math score in
grade five on a math exam?
Spearman's main result is
that most pairs of tasks
were correlated with each other.
That is, if you
were good at one,
you're good at the others--
even tasks that
seemingly had very little
to do with each other.
And this is the basis
of the whole idea
of g, which is the
general factor, which
is what led to the whole
idea of IQ and IQ testing.
And in America, we're very
uptight about the idea of IQ.
Brits don't seem to have
a problem with this idea.
They're very enthusiastic about
the idea and always have been.
But aside from all the social
uses and misuses of IQ tests,
the point is there's actually a
deep discovery about psychology
that Spearman made from the
fact that all of these tasks
were correlated with each other.
He didn't know what it was, kind
of like Gregor Mendel inferring
genes without knowing anything
about molecular biology.
Spearman just inferred
there's something general
about the human
intellect such that there
are these strong
correlations across tasks.
OK, so that's g.
But less well known
about Spearman's work
is he also talked about
the specific factor, s.
And s was the fact that
although the broad result
of his experiments was that most
pairs of tasks were correlated,
there were some tasks that
weren't so strongly correlated
with others, and that you could
factor those out and discover
some mental abilities that
weren't just broadly shared
across subjects.
And I think this kind of
foreshadows everything
that we see with functional MRI.
There's a lot of specific
s's, and there's also some g.
And you can see those in
different brain regions,
as I will detail next.
Another method was invented
by Franz Joseph Gall.
And he argued that there are
distinct mental faculties that
live in different parts of
the brain, which I think
is more or less
right, as I'll argue.
But Gall lived in the 1700s, and
he didn't have an MRI machine.
So he did the best he
could, which wasn't so hot.
He invented the infamous
method of phrenology.
He felt the bumps on
the skull and tried
to relate those to
specific abilities
of different individuals,
and from this,
inferred 27 mental faculties.
My favorites are and
amativeness, filial piety,
and veneration.
And so there's a kernel
of the right idea,
but kind of the wrong method.
And another method that
was a very early one
was the method of
studying the loss
of specific mental abilities
after brain damage.
And so Flourens,
who's often credited
as being the first
experimental neuroscientist,
went around making lesions
in pigeons and rabbits
and then tested them
on various things.
And he didn't really
find much difference
in what parts of the
brain he took out
for their mental abilities.
Maybe that's because he wasn't
such a hot experimental--
he didn't have great
experimental methods.
In any case, he argued that
all sensory and volitional
faculties exist in the
cerebral hemispheres
and must be regarded as
occupying concurrently
the same seat in
those structures.
In other words, everything
is on top of everything else
in the brain.
So that was a sort of
dominant view for a while.
People thought Gall
was kind of a crackpot,
even though he wrote
very popular books
and went around Europe
giving popular lectures
that huge numbers
of people attended.
The respectable
intellectual society
didn't take him seriously.
In fact, the whole idea of
localization of function
wasn't taken seriously
until Paul Broca,
a member of the French
Academy, stood up
in front of the Society of
Anthropology in Paris in 1861
and announced that the
left frontal lobe is
the seat of speech.
And this was based
on his patient
Tan, whose brain is shown here.
Tan was named Tan
because that was
all he could say after damage to
his left inferior frontal lobe.
And Broca pointed
out that Tan had
lots of other mental
faculties preserved,
and it was simply speech
that was disrupted.
And from this was one of
the first respectable people
to argue for
localization of function.
OK.
So this research
program goes on.
And by the end of
the 20th century,
there's pretty much agreement
that basic sensory and motor
functions do
exhibit localization
of function in the brain.
There are different regions
for basic visual processing,
auditory processing,
and so forth.
And that was no
longer controversial.
But the whole question
of whether higher level
mental functions were localized
and distinct parts of the brain
was controversial then and
remains controversial now.
And so the method I'll
focus on is functional MRI,
because I think it's played
a huge role in addressing
this question at this
macroscopic level.
And I think you guys know
what an MRI machine is.
In case anybody has been
on Mars for a while,
the important part
is its measure
is a very indirect measure
of neural activity by way
of a long causal chain.
Neurons fire, you incur
metabolic cost, and blood flow
changes to that region.
Blood flow increase more than
compensates for oxygen use,
producing a local
decrease rather than
the expected increase
in deoxyhemoglobin
relative to oxyhemoglobin.
Those two are
magnetically different.
That's what the MRI
machine detects.
It's very indirect,
so it's remarkable it
works as well as it does.
And it's currently the
best noninvasive method
we have in humans in terms
of spatial resolution, not
temporal resolution.
OK.
So many of you are already
diving into the details of some
of the data we collected.
But in case you're
on other projects
and are coming
from other fields,
the basic format of the data in
a typical functional MRI study
is you have tens of thousands
of three dimensional pixels
or voxels that you scan.
And typically, you
sample the whole set
once every two seconds or so.
You can push it and do
it every second or less
under special circumstances.
You can have more voxels by
sampling at higher resolution,
but that's a ballpark of the
format of the kind of movie
you can get of brain activity.
OK.
So a few things about the
method and its limitations,
because they're really important
in terms of what you can learn
from functional MRI
and what you can't.
So first of all,
this is a timeline.
My x-axis, even though it's
invisible, is time in seconds.
And so if you imagine looking at
V1 and presenting a brief, say,
tenth of a second high contrast
flash of a checkerboard, what
we know from neurophysiology
is that neurons fire
within 100 milliseconds
of a visual onset.
The information gets right
up there really fast.
The BOLD, which stands for
Blood Oxygenation Level
Dependent, or
functional MRI response,
is way lagged behind this.
So the neurons are firing
way over here in this graph,
essentially at time zero--
a tenth of a second.
But the MRI response is
six seconds later, OK?
So it's really slow.
And that has a bunch of
implications about what we can
and cannot learn from it.
So first of all,
because it's so slow,
we can't resolve the steps in
a computation for fast systems
like vision and hearing and
language understanding--
systems for which we have
dedicated machinery that's
highly efficient where you can
recognize the gist of a scene
within a quarter
of a second of one
it flashes on a screen
in front of you.
And similarly, you understand
the meaning of a sentence
so rapidly that
you've already parsed
much of the sentence well
before the sentence is over.
So these are extremely efficient
rapid mental processes.
That means the component steps
in those mental processes
happen over a few
tens of milliseconds.
And we're way off in temporal
resolution with functional MRI.
All of those things are squashed
together on top of each other.
That's a drag.
That's just life.
We can't see those
individual components steps
with functional MRI.
The second thing is that
the spatial resolution
is the best that we have in
humans noninvasively right now,
but it's absolutely
awful compared
to what you can do in animals.
So I missed Jim
DiCarlo's talk yesterday,
but those methods
are spectacular.
You can record from
individual neurons,
record their precise
activity with beautiful time
information.
In contrast, functional
MRI is like the dark ages.
We have, typically,
hundreds of thousands
of neurons in each voxel.
So the real miracle
of functional MRI
is that we ever see anything at
all rather than just garbage,
because you're summing over
so many neurons at once.
And it's just a lucky
fact of the organization
of the human brain that
you have clusterings
of neurons with similar
responsal activities
and similar functions
at such a macro grain
that you can see some
stuff with functional MRI,
although you miss a lot as well.
The third important limit of
functional MRI that comes out
of just a consideration of
what the method measures
is that you can only really see
differences between conditions
with functional MRI.
The magnitude of the MRI
response in a voxel at a time
point is meaningless.
It might be 563, and
that's all it means.
Nothing, right?
It means nothing.
It's just the intensity
of the MRI signal.
The only way to make
it mean something
is to compare it
to something else--
usually two different tasks
or two different stimuli.
And so you can go far
with that, but it's
important to realize you
can't train translate it
into any kind of absolute
measure of neural activity.
It's only a relative
measure of strength
of neural activity between two
or more different conditions.
OK.
And the final deep
limitation of functional MRI
is that we use this convenient
phrase "neural activity."
It's very convenient,
because it's extremely vague.
And fittingly so,
because we don't
know exactly what kind
of neural activity
is driving the BOLD response.
It could be spikes
or action potentials.
It could be synaptic activity
that doesn't lead to spikes.
It could be tonic inhibition.
It could be all kinds
of different things.
Anything that's
metabolically expensive
is likely to increase
the blood flow response.
In practice, when people
have looked at it,
it's very nicely correlated
with firing rate--
with some bumps and caveats, so
you can never be totally sure.
But it's a pretty good
proxy for firing rate.
You just need to remember
in the back of your mind
that it could be
other stuff too.
The final, very important
caveat is that functional MRI--
like most other methods
where you're just
recording neural activity in
a variety of different ways--
you're just watching.
You're not intervening.
And that means you're not
measuring the causal role
of the things you measure.
And that's very
important, because it
could be that everything
you measure is just
completely epiphenomenal and
has absolutely nothing to do
with behavior.
So in practice, that's
unlikely that you
have all this systematic
stuff for no reason,
but you need to keep in mind
that functional MRI affords
no window at all into the causal
role of different regions.
For that, you need to complement
it with other methods.
So despite all
these limitations,
I think functional MRI has had
a huge impact on the field.
And admittedly,
I'm biased, but I
think it's one of
these things where
as it happens, we get so
used to a result the minute
it gets published.
It was like, oh, yeah, right.
One of these, one
of those, so what?
But I think it's
important to step back,
so I made a bunch of
pictures to show you
why I think this is important.
OK.
Here is Penfield's functional
map of the human brain,
published in 1957, a
year before I was born.
And he has six--
count them, six-- functional
regions labeled in there.
You probably can't see them.
But it's the basic sensory and
motor regions, visual cortex,
auditory cortex, motor cortex,
speech appear in Broca's area,
and then my favorite is this
word that says interpretive.
Nice.
OK.
Anyway, this was based
on electrical recording
and stimulation in
patients with epilepsy who
were undergoing brain surgery.
Actually a very powerful method,
but that's where it got him.
He published this near
the end of his career.
And that's nice, but
it's pretty rudimentary.
OK, now, cut to
1990, immediately
before the advent
of functional MRI.
And this is really
crude-- the black outlines
are the basic sensory
and motor regions.
And I've added a couple
of big colored blobs
for regions that had been
identified by studying patients
with brain damage.
So even from Broca
and Wernicke, it
was known that approximately
these regions were involved
in language, because
people with damage
there lost their
language abilities.
You get whacked in
your parietal lobe,
you have weird
attentional problems,
like neglecting the left half
of space and stuff like that.
If you have damage
somewhere to the back end
of the right
hemisphere, you might
lose face recognition ability.
These things were known by
around 1990, not much else.
That's basically the functional
map of the brain in 1990.
That probably seems like
ancient history to a lot of you,
but not to me.
OK.
Here we are today.
There's a lot of stuff
we've learned, right?
There a lot of particular
parts of the human brain
whose function has been
characterized quite precisely.
Not in the sense that we know
the precise circuits in there
or that we can very
precisely characterize
the representations
or computations,
but to the sense that we know
that a region may be very
selectively involved,
for example,
in thinking about what
other people are thinking.
A totally remarkable
result that Rebecca Saxe
who discovered it will tell
you about when she's here
next week.
So that was completely
unknown even 15 years ago,
let alone back in 1990.
And likewise, most of
these other regions
were either known in
the blurriest sense
or not with this precision.
So I think even though
this is very limited,
and it's kind of
step zero in trying
to understand the human brain,
I think it's important progress.
And I think to push
a little farther,
I'd like to see this
as an admittedly very
blurry but still a picture
of the architecture
of human intelligence.
What are the basic pieces?
What is it we have in here
to work with when we think?
We have these basic pieces--
a bunch more that haven't been
discovered yet, and a lot more
that we need to know
about each of these
and how they interact
and all of that,
but a reasonable beginning.
So that's my story here for fun.
This is me with a bunch of
functional regions identified
in my brain.
And so the argument
I'm making here
is that the human mind
and brain contains
a set of highly
specialized components,
each solving a different
specific problem,
and that each of these regions
is present in essentially
every normal person.
It's just part of the basic
architecture of the human mind
and brain.
Now, this view is pretty simple.
But nonetheless,
it's often confused
with a whole bunch
of other things
that people think
are the same thing
and that aren't, so it's
starting to drive me insane.
So I'm going to
take five minutes
and go through the things
this does not mean.
And I hope this doesn't
insult your intelligence,
but it's amazing how in the
current literature in the field
people conflate these things.
So I'm talking about
functional specificity, which
is the question of whether this
particular region right here
is engaged in pretty
selectively in just
that particular
mental process and not
lots of other mental process.
That's what I mean by
functional specificity.
That's a different idea
than anatomical specificity.
Anatomical specificity
would say it is only
this region that's involved,
and nothing else is involved.
That's a different question.
How specific is this region
versus are there other regions
that do something similar?
Also an interesting question,
but a different one.
I'm going to go
through this fast.
So if any of it doesn't make
sense, just raise your hand
and I'll explain it more.
Yet another idea
is the necessity
of a brain region for
a particular function.
That's actually
what we really want
to know with the functional
specificity question-- is not
just does it only
turn on when you do x,
but do you absolutely
need it for x?
And so that's actually
a central question
that's closely connected.
It's really part of
functional specificities.
It's the causal question.
It's different from the
question of sufficiency.
Is a given brain
region sufficient
for a mental process?
Well, I think that's just kind
of a wrong headed question,
because nothing's
ever sufficient.
It's just kind of
a confused idea.
What would that mean?
That would mean we excise my
face area, we put it in a dish,
keep all the neurons alive.
Let's pretend we can do that.
I'm sure Ed Boyden
could figure out
how to do that in a weekend.
And so we have this
thing alive in a dish,
can it do face recognition?
Well, of course not.
You got to get the information
in there in the right format.
And if information
doesn't get out and inform
the rest of a brain, it doesn't
house a face percept, right?
So you need things
to be connected up,
and you need lots of other
brain regions to be involved.
So let's distinguish
whether this brain region is
functionally specific for
a process from whether it's
sufficient for
the whole process.
Of course it's not sufficient.
All right.
I know you guys would
never say anything so dumb.
OK.
A question of connectivity--
so people often say,
oh, well, this region
is part of a network, period.
And my reaction is, duh.
Of course it's
part of a network.
Everything's part of a network.
In no way does that
engage with the question
of whether that region
is functionally specific.
A functionally specific region
of course is part of a network.
It talks to other brain regions.
Those other brain regions
may play an important role
in its processing, sure.
At the very least, they're
necessary for getting
the information in
and out and using it.
OK?
OK.
All right.
The final thing that
people confuse it
with functional
specificity is innateness.
This is a very
different concept.
Just because we have
some particular part
of the brain for which we
make it really strong evidence
that it's very specifically
involved in mental process x,
that's cool.
That's important.
That's completely orthogonal
to how it got wired up
and whether it's innately
specified in the genome
that whole circuit--
or whether that
circuit is instructed
by experience over
development, or as
in the usual case, very
complicated combinations
of those two.
So just to remind you that
functional specificity
is a different question
from innateness.
And one way you can
see that very clearly
is to consider the case
of the visual word form
area, about which I'll show
you some data in a moment.
The visual word form
area responds selectively
to words and letter strings
in an orthography you know,
not an orthography
you don't know.
It's very anatomically
stereotyped.
Mine is approximately
right there,
and so is yours in your brain.
And it responds to
orthographies you know.
If you can read
Arabic and Hebrew,
yours also responds
when you look
at words in Arabic and Hebrew.
If you can't, it doesn't, or
it responds a whole lot less.
So that's a function of your
individual experience, not
your ancestor's experience.
It has strong
functional specificity,
and yet, its functional
specificity is not innate.
So this idea that
I'm staking out here
has become kind of unpopular.
It's very trendy
to say, of course
we know the brain doesn't
have specialized components.
So for example, here's
from a textbook.
Scott Huettel-- unlike
the phrenologists
who believe this very stupid
idea that very complex traits
are associated with
discrete brain regions,
modern researchers recognize
that a single brain
region may participate in
more than one function.
Well, he built in the hedge
word "may," so we can't really
have a fight.
But he's trying to stake
out this different view .
Lisa Feldman Barrett--
I haven't met her,
but she's driving me
insane, most recently
by proclaiming all kinds of
things in The New York Times
just a few weeks ago.
Quote, "in general, the
workings of the brain
are not one to one,
whereby a given
region has a distinct
psychological purpose."
Well, she's got hedge
words "in general."
We all have hedge words.
But basically, what
she's reasoning from
is the fact that her data
suggests that specific emotions
don't inhabit specific
brain regions from the idea
that the whole brain has no
localization of function.
Well, that's idiotic.
It's just idiotic, right?
So I hope that people will stop
these fast and lose arguments.
But here's my favorite--
this old coot Uttal.
I know this is going
to be on the web,
and here I am carrying on as
if we are-- anyway, whatever.
This guy cracks me up.
He's been publishing.
Every year, he
publishes another book
going after functional MRI.
Any studies using
brain images that
report single areas of
activation exclusively
associated with a
particular cognitive process
should be a priori considered
to be artifacts of the arbitrary
threshold set by
the investigators
and seriously questioned.
You go.
So anyway, that's fun.
Anyway, my point is just that
we should engage in the data,
right?
This isn't like an
ideology, where we can just
proclaim our opinions.
There are data that speak to it.
So let me show you some of mine.
OK.
So what would be evidence
of functional specificity?
There are lots of
ways of doing it.
The way I like to do it is
something called a functional
region of interest method.
The problem is
that although there
are very systematic regularities
in the functional organization
of the brain, each of these
regions that I'm talking about
is in approximately
the same location
in each normal subject.
Their actual location varies
a bit from subject to subject.
So if you do the standard
thing of aligning brains
and averaging across them,
you get a lot of mush,
and yet there isn't much mush
in each subject individually.
And so to deal
with that problem--
and to deal with a bunch
of other problems--
we use something
called a functional
region of interest method.
And that means if you want
to study a given region,
you find it in that
subject individually.
And then once you've found it
with a simple contrast-- you
want to find a face
region, you find a region
that responds more when
people look at faces
than when they look at objects.
Now you found it
in that subject.
It's these 85 voxels right
there in that subject.
Now we run a new experiment to
test more interesting questions
about it, and we measure the
response in those voxels.
OK?
That also has the advantage that
the data you plot and look at
is independent of the way
you found those voxels--
a very important problem
in a lot of functional
neuroimaging, where people have
non-independent statistical
problems with their
data analysis.
If you have a functional region
of interest that's localized
independently of the
data you look at in it,
you get out of that problem.
It's also a huge
benefit, because one
of the central problems with
functional brain imaging,
which I think has
led to the fact
that a large percent of the
published neuroimaging findings
are probably noise,
is that there are just
too many degrees of freedom.
You have tens of
thousands of voxels.
You have loads of different
places to look and ways
to analyze your data.
One of the things I love
dearly about the functional
region of interest method
is that you tie your hands
in a really good way, right?
So you specify in
advance exactly where
you're going to look, and you
specify exactly how you're
going to quantify the response.
And so you have no
degrees of freedom,
and that gives you a huge
statistical advantage.
And it means you're less likely
to be inadvertently publishing
papers on noise.
OK.
So that's the functional
region of interest method.
We've done loads of
these experiments.
Here's just from a current
experiment in my lab
being conducted
by Zeynep Saygin.
She's actually looking at
connectivity of different brain
regions using a different
method I probably
won't have time to talk about.
It's very cool.
But in the process,
she's run a whole bunch
of functional localizers.
And so we can look in her data
at the response of the fusiform
face area to a whole bunch
of different conditions.
So these are a bunch of
auditory language conditions,
so, OK, not too surprising.
It doesn't respond
very much to those.
They're presented
auditorily, but these are all
visual stimuli here.
The two yellow bars are faces.
This is line drawings of faces.
This is color video
clips of faces--
strong responses to both.
And all of these
other conditions--
line drawings of objects, movies
of objects, movies of scenes,
scrambled objects, words,
scrambled words, bodies--
all produce much
lower responses.
OK?
So I would say this is
pretty strong selectivity.
It's been tested against
lots of alternatives,
only a tiny percent of
which are shown here.
As I mentioned before, it's
present in more or less
the same place and pretty
much every normal subject.
I think it's just a basic
piece of mental architecture.
Now, this is a very
simple univariate measure.
We're just measuring
the very crude thing
of the overall magnitude
of MRI response
in that region to
these conditions.
There are legitimate
counter-arguments
to the simple-minded
view I'm putting forth,
and we should consider them.
I think the most
important one comes
from pattern analysis methods,
which I will talk about
if I get there.
And importantly, these
data don't tell us
about the causal
role of that region.
We'll return to those.
However, the point
is, before we blithely
say it's not fashionable to talk
about functional specificity,
we need counterarguments
to data like this.
They're pretty strong.
And that's just one example,
to show you just a few others
from Zeynep's paper.
OK, so this is what
I just showed you,
but I'm in the same experiment.
We can look at
other brain regions.
OK.
So this is a bottom
surface of the brain
there, so this is the occipital
pole, front of the head, bottom
of the temporal lobe.
That face area is the region
in yellow in this subject.
This purple region is
that visual word form area
that I mentioned, and here
is its response magnitude
across a whole
bunch of subjects,
localizing and then
independently testing.
The purple bars are when
subjects are looking
at visually presented words.
And again, all these
other conditions--
faces, objects, bodies,
scenes, listening to words,
all of those things--
much lower response.
In the same experiment, we can
also look at a set of regions
that respond to speech.
I mentioned those very
briefly in my introduction
a few days ago.
These are regions a number
of people have found.
In this case, they're
immediately below
or lateral to primary
auditory cortex in humans,
interestingly situated right
between primary auditory cortex
and language sensitive regions.
Right between is the set
of regions that respond
to the sounds of speech--
not to the content of language,
but the sounds of speech.
And so this is when people
are saying stuff like,
"ba da ga ba da ga."
So they're just
lying in the scanner,
saying, "ba da ga ba da ga."
And here's when they're
tapping their fingers
in a systematic order.
Here's when they're
listening to sentences.
Importantly, this
is when they're
listening to
jabberwocky gobbledygook
that's meaningless.
So no meaning, but phonemes--
same response.
That's what tells us that
this region is involved
in processing the
sounds of speech,
not the content of language,
and load everything else.
So other things-- moving
outside of perceptual regions,
you might say, OK, fine.
Perception is an
inherently modular process.
There's different kinds
of perceptual problems,
that make sense.
But high level cognition--
we wouldn't really
have functional
specificity for that.
But oh, yes, we do.
Here are some language regions.
There's a bunch of them in
the temporal and frontal lobe
that have been known
since Wernicke and Broca.
But now, with functional
MRI, we can identify them
in individual subjects and
go back and repeatedly query
them and say, are
they involved in all
of these other mental processes?
So this is now the response
in a language region--
so identified, here's
the response when
you're listening to sentences.
This is when you're listening
to jabberwocky nonsense strings.
Here's when you're saying
"ba da ga ba da ga."
It's not just speech sounds.
Here's when you're listening to
synthetically decomposed speech
sounds that are
acoustically very
similar to the
jabberwocky speech.
It's just not interested
in those things.
It seems to be
interested in something
more like the meaning
of a sentence.
And just to show you some
other data we have on this,
this is data from Ev Fedorenko,
who has tested this region.
Now, this is sort of
roughly Broca's area,
the main mental functions
that people have argued
overlap in the
brain with language.
Namely-- sorry, this is
probably hard to see here,
but arithmetic, so we
have difficult and easy
mental arithmetic.
Intact and scrambled
music in pink.
A bunch of working
memory tasks--
spatial working memory and
verbal working memory--
and a bunch of cognitive
control tasks--
just kind of an
attention demanding task
where you have to switch between
tasks and stuff like that.
And here is the response
profile in that region.
Reading sentences,
reading non-word strings.
All of those other
tasks, both the difficult
and the easy version--
no response at all.
That's extreme functional
specificity, right?
It's not that we've
tested everything,
there's more to be done.
But the first pass querying
of do those language regions
engage in all of these other
things that people thought
might overlap with language?
The answer is no, they don't.
And I think that's really
deep and interesting,
because it means that this
basic question that we all
start asking ourselves
when we're young
is, what is the relationship
between language and thought?
I know Liz disagrees
with me somewhat on this.
That's because she's
very articulate,
and she doesn't feel the
difference between an idea
and its articulation.
I'm less articulate.
It's very obvious to me
they're different things.
No, it's not the only reason.
She has data, too, and
it'd be fun to discuss.
But I think there's a
vast gulf between the two
in that many different
aspects of cognition
can proceed just fine
without language regions.
And actually, the
stronger evidence for that
comes not from these
functional MRI data,
striking as I think they
are, but from patient data.
So Rosemary Varley in England
has been testing patients
with global aphasia.
This is this very
tragic, horrible thing
that happens in patients who
have massive left hemisphere
strokes that pretty
much take out
essentially all of their
language abilities.
Those people she has shown
are intact in their navigation
abilities, their arithmetic
abilities, their ability
to solve logic
problems, their ability
to think about what other
people are thinking,
their ability to appreciate
music, and so on and so forth.
So I think there's really
a very big difference
between a major
part of the system
that you need to understand
the meaning of a sentence
and all of those other
aspects of thought.
This is just showing
you what I mean
by functional specificity--
what the basic first
order evidence is.
And these are just
the regions that we
happen to have in this study
so I could make a new slide.
But for lots of other perceptual
and cognitive functions,
people have found quite
specific brain regions
for perceiving bodies and
scenes, of course, motion.
The area MT has been
studied for a long time--
regions that are
quite specifically
involved in processing shape.
We've been studying color
processing regions recently.
They're not as
selective for color
as some of these other
regions, but they're
very anatomically consistent.
And things I mentioned before in
my brief introduction-- regions
that are specifically involved
in processing pitch information
and music information,
and as you'll
hear next week from
Rebecca Saxe, theory
of mind or thinking about
other people's thoughts.
And so there's quite a
litany of mental functions
that have brain regions that
are quite specifically engaged
to that mental function.
And each of these--
to varying degrees, but to
some appreciable degree--
have corroborating
evidence from patients
who have that specific deficit.
So that shows that
each of these is
likely to be not only
activated during,
but causally involved
in its mental function.
And as I mentioned, there are
actually good counter-arguments
to some of the things
I've been making
that are worth discussing.
I think the pattern analysis
data is the strongest.
Oh, and I do need to
take a few more minutes.
Just like five or something?
OK.
So all of that's
to say, so here's
roughly where we are now.
There are counter-arguments, but
loose talk about, oh, there's
no localization of
function in the brain.
You got to engage
with us at first
and give me a serious
counter-argument.
OK.
Finally, I want to say that
it's not that the whole brain is
like this, right?
There are big gray patches
where we haven't figured
out what it's doing, but there
are also substantial patches
that have already
been shown to be,
in some sense, the
opposite of this.
Regions that are engaged in
almost any difficult task
you do at all.
And I think this is a
very interesting part
of the whole story of the
architecture of intelligence,
so I'm going to take five
minutes and tell you about it.
This work is primarily the
work of John Duncan in England.
And he's been pointing
out for about 15 years
that there are regions in the
parietal and frontal lobe shown
here that are engaged in pretty
much any difficult task you do.
Any time you increase the
difficulty of a task--
whether it's perceptual
or high level cognitive--
those regions turn
on differentially.
And so that's why he calls
them multiple demand.
They respond to multiple
different kinds of demand.
Duncan argues that
these regions are
related to fluid intelligence.
So remember Spearman,
who I started with,
who talks about the
general factor, g.
Well, Duncan thinks
that basically, this
is the seat of g--
these regions here-- to
oversimplify his argument.
There's multiple sources
of evidence for that.
And one is, well,
they're strongly
activated when you do
classic g-loading tasks.
That's not that surprising.
They're activated in all
different kinds of tasks.
More interestingly, he
did a large patient study,
where they found 80 or
so neuropsych patients
in their patient database.
And they identified the
locus of the brain damage
in each of those patients.
And what they did was they
measured post-injury IQ.
They estimated from a variety
of sources pre-injury IQ.
And they asked, how much
does your IQ go down
after brain damage
as a function of one,
the volume of tissue you
lost in the brain damage,
and two, the locus of tissue?
And basically, what
he finds is if you
lose tissue in these
regions, your IQ goes down.
If you lose tissue
elsewhere, you
may become paralyzed or
aphasic or prosopagnosia.
Your IQ does not go down.
In fact, he made a kind
of ghoulish calculation
that you lose 6 and 1/2
IQ points for every 10
cubic centimeters of
this region of cortex,
and almost nothing for
the rest of the brain.
So this is kind of crude.
It's very imperfect what you
can get from patient study,
but I think it's intriguing.
And so his suggestion is that
in addition to these highly
specialized cortical
regions that we
use for these particular
important tasks,
we also have this kind of
general purpose machinery that
makes us generically smart.
And I'm going to skip around.
We've tested this
more seriously.
He did group analyses,
which I don't like.
We did it in
collaboration with him
with individual
subject analyses,
the most precise measurements
we could make, and boy,
is he right.
I mean, even to
the voxel you can
find that these
regions are engaged
in seven or eight very,
very different kinds
of cognitive demand--
all activate the same
voxel differentially.
So the basic story I'm
putting forth here--
without the second half of my
talk, I'm sorry about that--
is that at a macro
scale, the architecture
of human intelligence is that
we have these special purpose
bits for a smallish number of
important mental functions,
not all of them innate--
maybe some of them.
In addition, we have some
general purpose machinery.
There's loads more
that we don't know
from the precise computations
that go on in these things,
to their connectivity, to the
actual precise representations
that you can see with the neural
code if you could measure it,
which we can't in
humans, to the timing
of these complex interactions,
which of them are uniquely
human, which of them are
also present in monkeys.
And I don't have time
to go find the slide,
but one of the things
we've been doing recently
is looking in the
ventral visual pathway
at the organization of face,
place, and color selectivity.
And what we see is that--
we is me and Bevil Conway
and Rosa Lafer-Sousa.
Bevil and Rosa had
previously shown
that on the lateral
surface in the monkey,
you have three bands
of selectivity.
So it goes face selectivity,
color selectivity,
place selectivity,
and three bands
on the side of the
temporal lobe in monkeys.
We find this in humans.
You have exactly the same
organization in the same order,
but it's rolled around on the
ventral surface of the brain
in the same order--
face, color, place-- on
the bottom of the brain.
So we think that whole
broad region is homologous
between monkeys and humans.
It just rolled
around on the bottom.
Maybe it got pushed over
[AUDIO OUT] something.
And that's not exactly
a novel argument.
Actually, Winrich wrote a paper
suggesting this a while back,
and I think we're starting
to see those homologies.
And the reason
that's important is
that it means that all these
questions we desperately
want to answer about the
causal role, connectivity,
population codes, [AUDIO OUT]
interactions between regions,
development-- all
of that that we
can't answer very
well in humans,
Winrich can answer in monkeys.
And after a break, he will
tell you about all of that.
[APPLAUSE]
