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visit MIT OpenCourseWare at ocw.mit.edu. 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]
