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NANCY KANWISHER: All right, it's 11:05.
Let's get started.
So the agenda for today, we're doing this whole thing
on the methods in human cognitive neuroscience.
And I'm illustrating those methods
with the case of face perception.
Not just because I'm into face perception,
but it's a particularly rich domain of research
where there's lots to say about it from all
these different methods.
And so last time, we talked a bit
about applying Marr's computational theory
level to face perception.
We talked a teeny bit about some behavioral data
and a little bit about functional MRI.
What I'm going to do today is quickly
zoom through a speeded-up review of those things,
and then we're going to get to some of these other methods.
And there's a quiz at the end.
All right?
OK, so methods in any field of science
are just there to enable us to answer scientific questions.
They're not to impress our friends
with all the fancy things we know how to do
or our colleagues.
They're just to answer questions.
And so you always have to start with the questions.
And so last time, I listed a bunch of questions.
Not all of them, but a bunch of questions
one would really want to know about face perception
if we were to understand how it works in the brain.
And last time, we focused on these first three.
So let me just do a super quick review.
The questions at the level of Marr's computational theory,
we ask, what is the problem that's being solved
and why is that important to the organism?
What is the input, what is the output?
How much you get from that input to that output, right?
So for the case of face perception,
here's a very simple version of it.
Here's an example of the input.
It goes in, hits the retina.
The stuff that we want to understand happens in here,
and you have an output.
OK, so just even thinking about it that way,
we can already just see, with common sense,
that one of the big challenges in solving this problem
is that faces look different every time you see them.
The lighting changes, the orientation
of the face changes, the hair changes, the mood changes,
all this stuff happens.
People put on makeup, they shave off their facial hair,
they do all these things to make it a big challenge
to recognize faces.
And yet, we manage really well.
So how do we do that?
Well, our field has many methods to address this question.
Last time, I talked about one little example
of a behavioral study-- simple, cognitive psychology
study measuring behavior-- where we showed that the way
people solve this problem is fundamentally
different with people they know well
and people they don't know well.
So I showed an example that all of you
presumably would have no trouble determining
that those are all pictures of the same person,
even though at the pixel level they're wildly different.
And yet, you have a hell of a time saying which of those
images are of the same people and which aren't.
And so the point is that our ability
to extract this invariant representation, that
is to figure out abstractly who is that,
is really-- well, to figure out that any of these images
are the same as each other is much better for familiar
than unfamiliar faces.
And that means we don't have a perfectly general ability
to take any face and abstract out this completely
image-independent version of it.
That's what invariant representation is.
Yeah?
AUDIENCE: For the case of the Dutch politicians,
did they ever do the study on people
who were super recognizers?
NANCY KANWISHER: I don't know about that,
but they did do it on people who are
professional TSA-type people.
AUDIENCE: OK.
NANCY KANWISHER: Right?
And I'll tell you guys about that later.
But you could think about whether you
think it might work better with those people or not.
OK, everybody get this general point here?
All right.
So I skipped over another simple behavioral finding
last time that I want to mention now.
And that is an extremely low tech--
charmingly low tech, and yet, I think very powerful-- discovery
about face perception.
One of the most important original bits
of evidence that face perception might
be a different thing in the brain came from a PhD thesis
in this department by a guy named Robert Yin.
And he used the extremely high tech equipment
of a stopwatch and paper.
OK, so what did he do?
He presented faces to people upright.
And he said, study these 20 faces.
And then he tested them later.
Did you see this face?
And then he did the exact same experiment
on a different set of faces, but they were all upside down.
Studied upside down and tested upside down.
And what did he find?
He found what's known as the face inversion effect.
Namely, people do much worse at this task
when the faces are upside down.
Here's errors for inverted upside down,
errors for upright at this task.
Even though, importantly, they were studied and tested
upside down or studied and tested inverted--
upright.
OK, everybody got what this shows?
OK, so that's cool that this face inversion--
but the further cool thing is he showed that this face inversion
effect is greater for faces than for other kinds of stimuli.
So he tested lots of other things,
including houses and stick figures.
And he showed that that cost, when
you turn the stimuli upside down,
is greater, that difference is greater
for faces than for other classes of stimuli.
So what that suggests is that face recognition may just
work differently in some deep way from recognition
of other classes of stimuli.
And Robert Yin actually inferred in his PhD thesis--
way, way back before any imaging method--
that maybe there are special parts
of the brain for face recognition.
And maybe face recognition is just a totally different thing,
that's why it is more affected by inversion
than recognition of other kinds of things.
Was there a question back there?
Yeah.
AUDIENCE: I was going to ask, could that just be
because faces are much more complex than houses or stick
figures and that--
NANCY KANWISHER: Good question.
Hang on to--
AUDIENCE: --backwards.
NANCY KANWISHER: Good question.
That's a very good question.
And many people have tried to grapple with that.
And actually, about 10 years ago,
the idea that this disproportionate effect
for faces was standard textbook, completely accepted.
And now there's another round of people doubting it
with other kinds of stimuli.
So it's kind of ongoing.
It's a very robust difference, but to say exactly what it
is about face stimuli versus other kinds of things that
is responsible for that difference,
you can imagine it's subtle.
For the purposes of this course, I'm
trying to not quite lie to you guys,
but give you the most standard view without freighting you
with every possible objection to every little thing.
Because pretty much every finding,
there's somebody who has a beef with it.
We'll tell you, that's not really
true because blah-di-blah.
OK?
So yes, there's a little bit of debate going on
about this right now.
But for the purposes of this course,
it's pretty damn rock solid, at least as an empirical result.
All right.
So there's in fact lots of versions of the face inversion
effect.
One you may have seen before but which is very amusing.
If you look at faces like this that are upside down,
they look sort of normal.
But then if you rotate them, you realize
there's something deeply weird going on.
So the point is, you're much more
sensitive to those grotesquely distorted faces
when you see them right side up than when you see them
upside down.
So that's another version of the face inversion effect,
and there are many, many incarnations of this effect.
You'll see another one later in the lecture.
So where did we get last time with these questions?
We got that one of the major, if not the major central challenge
in face recognition at a computational level,
is the fact that we deal with huge image variation
each time we see a face.
And yet, somehow we're able to grapple with it.
So to understand how face recognition works will
be to understand, what is the code,
ultimately-- nobody knows right now--
but what is a code running in our heads that
enables us to do that?
What is our mental representation
of a face that enables us to deal with this problem?
By looking at behavioral data, we got some evidence
from the Dutch politician study that whatever
that representation is that we extract from faces,
it's not independent of the particular image.
It's not that we have some platonic ideal of the face
that we can extract from any face
that lands on our retina, platonic ideal of that person's
face, right?
So whatever we're doing, it's not completely invariant,
because we can't do that so well with unfamiliar faces.
Also, as I just showed you-- related,
but not exactly the same point--
our mental representations of faces
are very sensitive to the orientation of the face
more than our mental representations
of other classes of stimuli.
So those are just very simple insights about whatever
our representations of faces are in our heads,
just from simple behavioral data.
OK, so let me just review some of the strengths and weaknesses
of simple behavioral methods.
Strengths are, they're good for characterizing
the internal representation, right?
Not with huge computational precision,
they're more like with gisty kind of ideas.
They're not very invariant, they depend
on the orientation, right?
That's not very precise, but it's a whole lot better
than nothing.
That's what I mean by at least qualitatively.
They're good for disassociating mental phenomena.
So you've already seen that when the inversion effect,
it happens more for faces than other things.
So that already starts to tell us, OK, maybe
whatever the code in our head is that we
use for face recognition, maybe it's
pretty different than the code that we use in our head
to recognize objects.
OK, it's also cheap.
It's really cheap.
Much cheaper than all the other methods.
OK, weaknesses-- behavioral methods
alone don't have any relationship to the brain,
at least without doing extra work.
And it's not that they're useless until you link them
to the, brain it's just that the brain is
a whole source of other data.
And it's nice to link them, because then you
can connect with all those other data.
Also, behavioral data are pretty sparse.
For the most part, you have accuracy and reaction time,
and that's it.
And that's just not a whole lot of data to work with.
You have to actually be much smarter
to be a behavioral cognitive psychologist,
than you have to be a cognitive neuroscientist,
where you have much richer data to reason from.
Cognitive psychologists really have very, very clever designs
because they're taking this extremely limited data
and trying to pull out interesting insights
about mental function.
Another way of looking at that is,
here's an eyeball and a bunch of processing going over stages
and a response, right?
With behavioral data, all you have is that response.
But presumably, for most of the mental processes that
go on in our heads, there are many different stages
of processing where different things are going on.
Computations tend to have multiple stages
and unfold over time.
And all we have is the output.
So really, what we want to be able to do
is characterize the whole sequence of processes.
And it's not that you can't get insights
about some of those intermediates
from behavioral data, it's just much more challenging.
So if we had a way to look at those things independently,
wouldn't that be awesome?
OK, so there's lots of ways to do that.
And a particularly good one is functional MRI.
So as I mentioned before--
I mentioned this very briefly--
this very early experiment that I did way back asking
whether there is a region of the brain that's selectively
involved in processing faces.
And I'm going to put a slightly different spin on it
from what I put before.
It's the same experiment, same data,
but I want to emphasize more the logic
of the experimental design because you guys will
be designing an experiment on a different topic due Monday
night.
That we're going to discuss Sunday night-- that we're going
to discuss in class on Monday.
So we start with a hypothesis that there's
a region of the brain that's selectively
responsive to faces.
That's the hypothesis.
The way we test it is to pop people in a scanner
and show them faces and objects.
The data that I showed you before
is that this little patch of the brain--
remember, this is a horizontal slice, back of the head,
left and right are flipped.
So that little region in me is right about in there.
Everybody oriented?
OK.
That region responds much more to faces than objects.
Is that clear to everybody what that is?
OK.
So yes, you see that in most subjects.
So yes, there's a bit that responds more to faces
than objects.
But now, let's consider the hypothesis
that that region is really selective to faces per se.
And the way you evaluate whether these data,
how strongly these data support that hypothesis--
they're certainly consistent with it,
but do they nail that hypothesis fully--
is to consider, are there any other alternative
accounts we can think of that are consistent with these data
and different from that hypothesis?
Is that clear?
It's really important.
That's just the whole kernel of scientific thinking
and evaluating evidence is asking yourself that question.
Is there any other way we could get those data where
that hypothesis wasn't true?
And if so, you've got to grapple with it.
So what you do next is you think up alternative hypotheses
to the one you started with, that
is different accounts of the same data.
And so in our case, you guys suggested a whole bunch,
I suggested a bunch.
And then the next thing I showed you
is that we can test those alternative hypotheses,
at least these ones here, by first--
what we did was, I didn't really emphasize this
before-- but we reran that experiment
in a new bunch of subjects, each subject individually.
We found in each subject the little bit that does this.
We write down exactly where that is in that person's brain.
Now that we found that region-- that's called a localizer
run, because we're finding that region in each subject
individually-- now we can ask it new questions.
And so the new questions we asked it last time
was to present faces and hands.
And we found, oh, that region right there responds like this.
So the key ideas here is that we can identify
that region in each subject individually
with a functional scan.
The reason that's important-- which I'll carry on about
in more detail later--
is that the exact location of that region varies from one
subject to the next.
So if we just grab the whole fusiform
gyrus or the whole lateral side of the fusiform
gyrus in each subject, we'll get lots of stuff
that is that region and lots of cortical neighbors
that's something else.
And if we took the exact location
of that region in my brain and registered it
to any of your brains and said, OK,
let's take the part of your brain
that registers spatially as well as we can with mine,
we're not going to exactly get the right bit.
So to study that thing, we've got
to first find it functionally.
And then we can ask it new questions.
Does that make sense?
OK, if anybody's unclear about that,
I have actually online talks that
go through the whole logic of this in painful detail.
And I'm happy to answer other questions about it later.
OK, so I put the word conditions in red
because somebody asked one of the TAs what a condition was.
And that's not stupid, I should have made that clear.
This is just experimental design gobbledygook that means any--
OK, what is the definition of condition?
In an experimental design, you have
things that you are manipulating and measuring.
So in this case, we're manipulating the stimulus.
And we're measuring the magnitude of response
in the fusiform face area or in the brain.
So what we're manipulating, in this case,
is the stimulus condition.
So that would be one condition, that's
another condition, that's another condition.
Does that make sense?
OK, so for your experimental design assignment for Monday
night, you will be designing one or more experiments.
And you will be describing exactly what conditions you are
going to test.
Everybody clear on that?
OK.
All right, so these data enable us
to rule out those hypotheses.
And now what you could ask, OK, once you
get more data like this, have you completely
nailed that hypothesis?
Is there just no way that hypothesis could be wrong now
given these data and those data?
And I'll let you percolate on that.
There are ways it could be wrong,
but you have to work harder to come up with them.
OK, so skipping ahead, just to give you the gist.
This field has been going on for a long time.
And there are now many, many studies--
100 maybe even, I don't know, God, maybe even thousands,
I don't know-- studies of this region in which--
and so this is sort of a summary statement from a long time ago.
In my lab, we've tested the response of this region
to lots of different kinds of stimuli.
With that same method, localize it
in each subject, measure its response when people
look at that kind of stimulus,
And so what we know now was that this region
is found in roughly the same location in pretty
much every normal subject.
It responds more to faces than to any other kind of stimuli
anyone has ever tested.
Let me just give you one example here.
If you haven't seen this stimulus before,
raise your hand if you can tell what it is.
Raise your hand if you can tell what that is.
OK, some of you didn't quite get it yet.
If you don't see it, don't worry.
There's nothing wrong with you.
It's a little subtle.
It's a face in profile, eyes, nose, mouth.
Everyone got it?
OK, so here's the thing.
That's the same stimulus, it's just upside down.
Another version of the face inversion effect.
In this case, you can't even make yourself see the face
when it's upside down.
If you think you see the upside down version of the face,
you probably have the wrong bits.
The thing you think is a nose probably isn't, et cetera.
OK, so this is an extreme version of the face inversion
effect.
And it's a gift to an experimental psychologist.
Why is that such a gift?
Because it's the same damn stimulus.
But in one case you see a face, in another case you don't.
All we did was tip it upside down.
And the response of the fusiform face area is much stronger
to the upright version when you see the face than
to the inverted version when you don't.
So that enables us to stifle a whole line of attack
from all of these hard core vision people who
early on said, Kanwisher, your face area
isn't really selective for faces.
It's selective for these spatial frequencies or those,
that kind of contrast, or this kind of shading information.
It's like, no, same stimulus.
It's just upside down.
It makes all the difference.
It's really whether you see a face or not.
Yeah?
AUDIENCE: When you were measuring
the response of that example, did
you have it so that at first, when people that the first time
didn't recognize it and then you told them?
NANCY KANWISHER: We did that later.
Not in this experiment, but we did that later.
AUDIENCE: Are they looked see like what changed [INAUDIBLE]??
NANCY KANWISHER: OK, so it's a great question.
And there's a lot you could do with that.
And actually, I think other people have published
studies like that since.
I can't quite remember who all has done it.
But what we did was most of our subjects,
especially in the context of a whole experiment,
we chose stimuli so that most people could see the face
in most of the upright stimuli and most people could not
see the face in most of the inverted stimuli.
It wasn't perfect at all.
They didn't see faces in all of the upright ones
and they didn't fail to see them in all of the inverted ones.
And that's probably why this difference in response
is not 2 to 1, but it's close.
But you could do lots of other experiments like that,
and you should think about what kinds of designs
would be good ones to do and what it would
enable you to test exactly.
All right.
So OK, I'm, as usual taking too long to do things
so I'm just going to throw out some questions for you
to percolate on and we will come back to them later
in the course.
Do these data-- the fact that you can see this so robustly
in all subjects and that all this evidence suggests
it's really very selective for faces--
does that tell us that this region is innate?
It's in the same place, more or less, in pretty much everyone.
Does that mean it's innate?
Think about it, OK?
It's not immediately obvious.
Another question, does the fact that this thing responds so
selectively to faces in pretty much everyone
mean that it's necessary for face recognition?
What do you guys think about that?
In the sense of, does that necessarily
mean that if you lost that thing,
you wouldn't be able to recognize faces?
Isabelle.
Is that Isabelle?
AUDIENCE: Yes.
Well, I would think to really test that hypothesis,
you'd have to find someone that [INAUDIBLE]
in that specific area.
NANCY KANWISHER: Exactly.
Exactly.
Exactly, and we'll talk more about that in a moment.
The critical thing is that it's fabulous and powerful and cool
to be able to find this thing in everybody,
measure its response.
It's taken us very far.
But just the fact that people have that thing
doesn't tell us that you need it for face recognition.
It just tells you it turns on when you recognize faces.
This is really important.
We'll keep coming around to this.
Does this tell us how face recognition actually
works in the human brain?
No.
I mean, it's important, but it's barely step zero.
Unfortunately, the field is kind of still
at step zero for most things.
Step zero's better than I guess, I don't know,
maybe I should call it step one.
Anyway, it's something, but doesn't tell us how it works.
OK.
All right, so advantages and disadvantages
of functional MRI.
Advantages, it is, as I mentioned
last time, the best spatial resolution
available for studies on normal subjects
without opening their heads.
That's what it means to say noninvasive.
Disadvantages, as I just said, we
don't know-- just because we see a response there doesn't mean
that that region is causally involved in perception
or cognition or experience.
We don't know exactly what is going on at a neural level
underlying that bold response, that blood flow change.
It could be any metabolic change, not necessarily
neuronal spiking.
So it's a little bit-- it's very indirect
and a little imprecise.
Spatial resolution is much better
than anything else in humans, but it's appallingly bad
compared to anything that people who work on animals can do
or they routinely record from individual neurons
or even dendrites on a neuron.
We are summing over hundreds of thousands
of neurons in each pixel or voxel
that we measure with functional MRI.
It's very expensive.
It's a little cheaper than that here, but in most places
it's more than $600 an hour.
That is a lot.
There are other-- there are parts of the brain where
it's really hard to get any signal
for various physics-y reasons.
And it makes a loud noise, which is not always a problem,
but it's a problem for some things like scanning infants
or like doing auditory experiments.
The temporal resolution is not even close to the time
scale on which vision happens.
So vision is really fast and functional MRI is really slow.
Right?
It's slow, why is it slow?
Yeah.
AUDIENCE: Blood levels take time to change.
NANCY KANWISHER: Yeah.
Just takes a long time for blood flow
to change after the increase in neural activity.
All right.
OK, so back to our questions that we're
asking about face perception.
Where do we get with functional MRI?
Well, actually from both behavior and functional MRI,
it kind of looks like we have a distinct system
for recognizing faces than for recognizing everything else.
I don't think we've totally nailed it.
Yes.
AUDIENCE: So quick question regarding the fMRI.
So the resolution is field of a couple of seconds?
[INAUDIBLE]?
NANCY KANWISHER: Yeah, some people
would say you could get it down to a couple hundred
milliseconds but that's debated.
You have to go to great lengths to do that.
Normal functional MRI, a couple of seconds at best.
Yeah.
All right.
So let's consider this next question.
How fast does face recognition happen?
Now, that may seem like a completely arbitrary question
to ask, but it's not.
Remember, we're trying to understand
the computations that are running in your head
when you recognize faces.
And you might imagine some computations
that are iterative-- that involve multiple repeated
testing of hypotheses, generative models, whatever--
things that involve lots of iterated feedback versus things
where you just have a feed forward sweep up
the visual system.
And so there might be very different time scales
for those different kinds of mental processes.
So we just went through this.
Functional MRI is not going to answer this question.
It's just not.
It's a bummer, but that's life.
We're adults, we're going to just move
on and use a different method.
OK, so there's a bunch of different methods.
One is kind of been around forever.
You glue electrodes on the head, right?
Sometimes you push the hair apart
or try to find bald people and glue electrodes right on there.
And you can use, in the old days, about 10 electrodes,
or you can use in more modern devices
these nets with a few hundred electrodes
that you settle onto the head.
And so then you just measure directly electrical potentials
right on the scalp.
So what's cool about that is it's totally non-invasive.
And it gives you a beautiful online temporal measure
of underlying neural activity.
What's not so cool about it is that electrical potentials blur
all over the scalp and the spatial resolution
is really awful.
So the analogy has been made that it
would be like sticking a microphone
on the inside of the top of a football stadium
and collecting audio there.
You would know when a touchdown was scored.
There's a lot of noise all over.
It's like, OK, there's an event, we detected that event, right?
You might be able to tell a touchdown from something else.
I don't know about football so I can't tell you what else.
Anyway, something else, some other event that could happen.
OK, so that will be useful for some things, but kind of crude.
But you'd have a hell of a time telling anything else,
like what one person is saying to another person
in the bleachers.
So that's the old analogy.
This is changing slightly, and we'll get to that later.
But first, I want to briefly mention
one of the assigned readings that I just
hoped you guys could figure out on your own.
But just in case you were confused about it,
the point I wanted you to get from the Thorpe reading
is he's asking how quickly can we tell if an image contains
an animal or not?
It's a kind of way to say, how fast is object recognition?
So what does he do?
He has people look at a bunch of images and they press this
button if it has an animal in this button if it doesn't.
Really simple task.
So first question is, why not just use those reaction times?
We can measure how fast it takes for people to press a button
after the image comes on.
Why not just use that?
Does that tell us how fast object recognition occurs?
Yeah, Jimmy.
AUDIENCE: It doesn't because if you perceive that and then
it also activates the motor neurons
and it takes time to respond.
NANCY KANWISHER: Yeah, you have to take all that time to figure
out, OK, I see the animal.
OK, which button is that?
And then which finger do I push?
And then you've got to send a signal all the way down
here, conduction velocity all the way down to your finger,
that takes a long time.
And so it includes all that motor stuff
in with the perceptual stuff.
We could make some guesses about how long that motor
stuff takes, but it's still not very precise.
So the point of the Thorpe paper is
they're basically trying to collect a reaction time out
of the neurons in the head, right?
So they're trying to actually collect--
it's essentially what they're collecting
in this case is more of the motor response
because they're collecting responses over frontal lobes,
right?
And we haven't talked about this much.
But all of the visual stuff we've been talking about all
happens in the back of the head.
More motor planning stuff mostly happens
in the front of the head.
And so they're collecting responses out of here,
averaging over a bunch of frontal responses.
And they see the average response when there's
an animal-- this is just potential average
over those frontal electrodes-- is like this.
And when there's no animal it's like that.
And so what does that tell us about how fast people can
distinguish whether an image has an animal or not?
Yes?
Yeah.
AUDIENCE: It's less than that number.
NANCY KANWISHER: Less than?
AUDIENCE: 150, 160.
NANCY KANWISHER: OK, why less than 150?
AUDIENCE: I've read the paper so it's kind of cheating, so.
NANCY KANWISHER: That's OK.
That's good.
That's fine.
Go ahead.
AUDIENCE: It gives you around--
the 150 second is giving you a [INAUDIBLE]
saying some process has been registered
and now you're trying to do something else
in the case of non-animals.
NANCY KANWISHER: Right.
AUDIENCE: So the deviation starts
getting you that OK, two different actions have
started taking place.
NANCY KANWISHER: Yep.
AUDIENCE: So by that time, the image
ought to have been sort of fully processed.
So that should be something less than that number.
NANCY KANWISHER: Yeah.
Yeah, did everybody get that?
It's actually quite subtle.
So the key thing is, these curves
diverge right there at 150.
So that tells you that by 150 milliseconds,
something in your brain is happening different if there's
an animal and not an animal.
That's the key question.
But what is that something?
It may be your motor preparation of the response.
In that case, the actual visual part
happened before, because you wouldn't
know which button to press if you hadn't already
recognized it.
So it's an upper bound for when that process happened,
because maybe it happened before and we're
looking at a later stage, OK?
Does that make sense?
But also, it's an upper bound for the beginning of that
process.
Because the fact that those electrode responses have
diverged doesn't mean you've finished processing
whether it's an animal or not.
So it's kind of a subtle business reasoning from this.
OK, so that's all that.
So that's a case with detecting animals.
What about faces, to get back to our theme for today?
Yes, you can learn about the speed of face detection
at least with the ERPs.
And so here's the first paper that did that back in 1996.
They had electrodes where are these?
Just right around here and here.
I actually have those electrode locations tattooed on my scalp,
color-coded anyway.
Yes?
AUDIENCE: Is ERP just the same as an EEG, just
in a specific plan e?
NANCY KANWISHER: Yes, exactly.
It's the same as an EEG except what you do
is you time lock the data collection to stimulus onset.
So it actually stands for Event-Related Potential.
And the reason it's event-related
is you collect all those trials and you time
lock to stimulus onset, and then you signal average.
I had a slide on that but I took it out.
It was too detailed.
But that's exactly the idea, yeah.
So here, stimulus onset is right around here.
This is time going this way.
And what you see-- it's hard to see here,
but the faces are right there.
And at 170 milliseconds after stimulus onset,
there's a bigger bump for faces at an electrode
approximately here.
And even more so--
actually, even more so over the right hemisphere right there.
Compared to cars and scrambled faces and stuff like that.
Yeah?
AUDIENCE: What is ERP exactly measuring?
Is it just activity?
NANCY KANWISHER: Yeah.
So again, it's electrodes glued on your scalp
or just stuck there with some kind of icky gel.
And so they're just measuring potentials.
And so the idea is that's neural activity somewhere
underneath those electrodes, but maybe anywhere within inches.
Like a long-- probably average is over much of the whole lobe
underneath.
So it's very spatially blurry, but it's
giving you summed idea of activity under that electrode.
Make sense?
Electrical activity, because it's
the direct electrical consequence of neural activity,
it's very precisely time locked, unlike functional MRI, which
is going by way of blood flow.
OK, so that tells us that we have a face-specific response
at 170 milliseconds.
And that's sort of more evidence that there
might be something special in the brain for face recognition.
That's useful.
It tells us that faces are discriminated from non-faces,
or they've begun to be discriminated
from non-faces by 170 milliseconds
after the stimulus comes on.
Make sense?
OK, now do we know whether the signals coming
from the fusiform face area?
No, we have no idea.
It's probably somewhere in the back of the head,
because you get it better with electrodes
back here than electrodes up here.
But that's about it.
That's all you can tell.
So can we do a little bit better localizing
the source of that signal?
Well, maybe a hair better using a very similar method
called magnetoencephalography.
So this is a picture that Chris Brewer took of Leyla Isik
postdoc in my lab, and me and the MEG system.
This is in on the other side of the building.
So MEG is a lot like EEG and ERPs
except that it detects magnetic fields, not electric fields.
And it does this by having these several hundred devices that
are placed right next to your head
in this big hairdryer thing.
There's 300 devices in there that measure
teeny tiny magnetic field changes that
happen with neural activity.
And the crux of the idea is this is a cross-section
through the brain.
So remember in Graybiel's dissection, this is cortex here
and this is underlying.
What is this stuff underneath it?
Sorry?
AUDIENCE: White matter.
NANCY KANWISHER: White matter, yeah.
Well, those are all the fibers.
OK, so the activity that underlies perception
and cognition mostly happens in the gray matter
where the cell bodies are.
And so a lot of that activity goes in a direction
perpendicular to the cortical orientation
with these cells that cross the cortical surface like that.
So if you remember 8.02--
if you have activity that's going
through the cortex like this, right hand rule,
the magnetic field here is going to be
a consequence of that electrical activity in this direction.
It's going to mostly stay within the cortex.
Everybody see how that's true?
That's not so great, because our detectors are out there,
outside the cortex.
However, consider the activity that's in the sulcus in here,
in this fold of the brain.
Electrical activity in this direction, right hand rule,
will stick outside the brain.
And we can detect it with our magnetic sensors.
Does that make sense?
So you can sort of see most cortical activity
better if it's in a sulcus, or at least in part
of the cortical surface that's perpendicular to the scalp
where the detectors are just because of the orientation
in the right hand rule.
OK, so it primarily sees activity in the folds or sulci,
not in the outer bumps gyri.
Field strengths are minuscule as a consequence
of neural activity.
So the fields we measure are 10 to the minus 13th Tesla,
a million times weaker than the Earth's magnetic field.
So you can imagine that if you set up an MEG system
you need a lot of shielding.
We had a whole rigmarole when the MEG system was set up
in this building because it's right near the subway
and the train.
And so there are many, many layers
of copper shielding to protect it.
So we can detect these teeny tiny magnetic fields
from the brain's activity separated
from the noise of the outside world, which
is much greater in magnitude.
OK, so-- all right.
So actually, MEG was invented here at MIT
by this guy, David Cohen.
And this is the first MEG device ever built, very cool,
way back in 1968.
And what can it tell us about face perception?
Well, a lot.
I'll give you just one rudimentary example.
That M170 that you can detect with scalp electrodes,
you can also detect with magnetic sensors on the head.
So here's some of our data from a long time ago.
This is the strength of the magnetic field
at sites right about out here.
And you can see a face-selective response also
at 170 milliseconds, just like you can with scalp electrodes.
So that tells us that at least you've
started to detect faces by 170 milliseconds.
That's pretty fast.
And again, it's more evidence that there's
specialized machinery.
These data don't yet go beyond the EEG data, the ERP data
from electrical potentials.
But they might, in principle, and there's
lots of ongoing work trying to do that.
OK, overview, advantages of these methods,
both EEG and MEG.
They're non-invasive-- that means you
don't need to open the head.
A very good thing, especially if you're the subject.
They have very good temporal resolution.
And if we want to see computations
unfolding over time in the brain, this is a good way.
I just said why we'd I care about that.
OK, so far-- well, never mind, I'm going to skip this point.
Not that important.
We will get back and do more sophisticated things with EEG
and MEG in subsequent lectures.
Disadvantages-- spatial resolution is terrible.
And this is another kind of ill-posed problem.
So just as the brain is facing lots
of ill-posed problems in perception and cognition,
we scientists are facing ill-posed problems
when we collect electrical or magnetic activity at the scalp
and try to infer the exact location in the brain
where it's coming from.
It's a similar problem to the problem of invariant object
recognition.
There are many possible configurations
of sources in the brain that could give rise
to the same set of electrical and magnetic fields out
of the scalp.
And that means it's ill-posed.
We don't have a way to get a unique solution.
So all that to say we can't figure out the exact sources.
We can make some guesses, but it's not very good.
So what do we do?
Just give up?
No, we use another method.
So here's an amazing method.
This is the one method in humans that
gives us high resolution in both space and time.
And that's when we have the very rare opportunity
to record directly from inside the human brain.
This happens only in the context of neurosurgery.
So neurosurgical patients-- like this guy here,
who you'll meet in a little bit--
this guy had intractable epilepsy.
And most people with epilepsy are
treated well by drugs that suppress seizures.
But some people are just not responsive to drugs.
And if the seizures are bad enough,
they can be totally life disrupting.
If they happen dozens of times a day,
you just can't live a normal life.
And under those rather extreme circumstances,
sometimes the best option is neurosurgery.
That is, trying to find the source of those seizures
and trying to remove it surgically.
OK, so you hope you never have to go through this or anyone
you care about has to go through it.
It's no picnic.
But actually, this surgical treatment
is often very effective.
So when neurosurgeons decide to do this,
they have to remove a whole piece of skull bone
to get access to the brain.
They have to go through what structure
that Ann Graybiel showed you in her dissection the other day.
What do you have to--
after you take off the a skull patch?
Yes.
AUDIENCE: Dura mater.
NANCY KANWISHER: Dura mater, exactly.
That nice big piece of white, leathery stuff
that was sitting over the surface of the brain.
So you to take off a piece of skull,
then you need to cut through and push apart the dura.
And then what they sometimes do is stick electrodes straight
on the surface of the brain.
And they do that for two reasons.
One, if they have enough of them sampled far enough apart,
they can kind of triangulate and figure out where
is the source of the seizure.
So the patient hangs out in the hospital for a week
or so with these electrodes in their head waiting
to have seizures.
And then when they have a seizure,
the clinicians can figure out where
the source is so they know what bit to cut out.
The other reason to do this is to map functions.
Because once the surgeons decide they have to go in and cut,
they want to try to not cut out any
of the most important parts.
I don't know what it means to have unimportant
parts of the brain, but they try to avoid language regions
and stuff like that because then patients really
notice if they lose those things or motor control regions.
OK, so they map out functions where they
might be planning their route.
OK, make sense?
Now, some of these patients are very kind and generous
to the world and say, yes, you scientists
can measure responses in my brain
while I look at your damn stimuli.
And so whenever we can, we ask them please, please, please,
can we show you some pictures or play you some tones
or have you read some sentences while we
record from your brain.
And some of those patients very kindly let us do that.
And that gives us the most amazing data
you can get from human brains.
So for example, I had a rare opportunity
to do this a few years ago from this lovely guy who
was undergoing neurosurgery in Japan.
And while he had electrodes in his brain, a colleague of mine
was there and emailed me and said,
look where these electrodes are--
right near regions I care about--
do you want to show us some stimuli
and we'll record responses from those electrodes?
And I said, damn straight I want to send you some stimuli.
So my students and I stayed up for a couple of days
and made some stimuli and shot them to Japan
and got some responses from those very electrodes.
And here they are.
So this is a strip of two parallel strips of electrodes
right along the fusiform gyrus, right
where the fusiform face area should be in most people.
And here are the responses of each of those electrodes.
174 is here, what's 173 and so forth.
And what you see is this batch of electrodes right here--
this is a response when the patient was looking at faces.
And these are the responses when they
looked at a whole bunch of different kinds of stimuli.
Objects, and this guy is Japanese
so we showed him Kana and Kanji and digit strings
and other kinds of stuff.
Very low response to those other things.
This is a extremely selective response.
It's much more selective than you see with functional MRI
because we were recording directly
from the surface of the brain.
Further, we have time information.
This axis here is time, and you can
see that that response-- well, you can't see the axis,
but that response starts up at around 1:30
milliseconds and peaks up there at around 170.
Everybody clear what we're seeing here
and why this is so vastly better than either functional MRI
or MEG or ERPs or anything else?
Make sense?
OK, so these are very, very precious data.
OK, nonetheless, the electrodes in this case
are about 2 millimeters across, each electrode.
And that is about the size of a functional MRI
pixel or voxel, a little bit smaller.
It has less blurring because functional MRI blurs spatially
because it's looking at blood flow.
So this is a more precise spatial measurement
than functional MRI, but it is still
averaging over probably tens of thousands of neurons,
down from hundreds of thousands of neurons with functional MRI.
So can we ever get responses from individual neurons
in the human brain?
Yes, occasionally.
In fact, a paper came out on the bioRxiv a couple of months ago.
I was on this guy's PhD thesis defense.
And this is a guy who works with a neurosurgeon on Long Island.
And this neurosurgeon specializes
in epilepsy neurosurgery.
And he's very interested in not damaging people's ability
to recognize faces.
And so he sticks electrodes to map out neural activity
and to discover seizure foci.
Before the neurosurgery, he sticks
electrodes in parts of the brain near the fusiform face area.
So this is a slice like this through the brain.
I showed you before horizontal slices, OK, so left and right
are flipped, that region is right in there, everybody
oriented with this picture here?
So this is an MRI image of this person.
It was scanned with functional MRI
before the electrodes were put in.
And that shows you their fusiform face area right there.
So now, the neurosurgeons put in electrodes
for clinical reasons, but the electrodes this surgeon uses
have these little tiny micro wires
that come out of the tip of the electrode that
enable him to record from individual neurons.
And so these guys, for the first time,
have recorded from individual neurons in the fusiform face
area in humans.
And here's an example of one of these neurons.
So here are the different stimuli here.
A bunch of different face stimuli, body stimuli, houses,
patterns, and tools.
And this shows you time across here.
Each one of those dots is--
this is all the response of a single neuron that's been
identified in a human brain.
Each dot is an action potential, is a spike out of that neuron.
So you can see them happening over time here
to all the faces.
And this is an average amount of activity
to all of the faces and average amount of activity
to all the other stimuli.
Make sense?
So that's pretty breathtaking to me
because I've been using these very indirect methods
for a long time, inferring that they must result
from the average across a lot of neurons doing that,
but it's pretty awesome to actually see
individual neurons doing that.
Yeah?
OK.
Here's the time course of responses just averaging
over this raster over time, showing you
a similar time course to what I've shown before.
And in this guy's thesis, he found three other face
selective neurons in the FFA, but the electrodes
are so rarely in the right location
that they only have a few in this whole thesis,
and there they are.
Yeah?
AUDIENCE: Even if we could measure individual neurons,
we don't really know which neuron it is, right?
If I wanted to go back and find the same neuron Again,
That's pretty much impossible.
NANCY KANWISHER: Forget it.
Yep.
So people like me who almost never
get to see responses from individual neurons
in human brains have kind of neuron envy.
It's like everyone else in this building has--
they can measure stuff from dendrites or ion
channels or individual neurons.
They can do all this amazing stuff.
But actually, there are a lot of limitations in those methods
too.
And you just put your finger on one of them.
So they're like, OK they found those neurons,
there are four neurons.
We can't go back and find those neurons again.
That's that, right?
And they're probably subtly different in different brains,
right?
So it's cool and powerful but has still has many limitations.
OK, does this tell us that these neurons are involved
in discriminating one face from another
or just detecting faces?
Can we tell from these data?
Are they just saying, here's a face
or are they saying, that's Joe?
AUDIENCE: Did they have different conditions
for different people?
NANCY KANWISHER: These are different faces here.
What do you think?
What are these neurons doing?
Yeah?
AUDIENCE: They're just recognizing faces [INAUDIBLE]..
NANCY KANWISHER: You mean just detecting?
No, just say more.
What do you think they're doing?
AUDIENCE: They're just selecting for faces.
There's no evidence to show that they
distinguished different faces.
NANCY KANWISHER: Well, how about this?
These are different faces here.
AUDIENCE: But one could ask, if it does involve them sort
of acknowledging what faces, did they
have to put a name to the face?
NANCY KANWISHER: Nope, they're just
sitting there looking at stuff.
So bottom line is, we don't know from this.
It could be just responding and saying essentially,
there's a face.
But the fact that there's different responses
to different faces suggests that maybe there's
some information in there.
If you ran some machine learning code on this,
you could tell a little bit, which face was being presented.
Because those neurons are responding differently
to different faces.
Yeah?
AUDIENCE: Is it really like if they just
showed the same face repeatedly, wouldn't it just
be like [INAUDIBLE]?
NANCY KANWISHER: OK, very good question.
Very good question.
That's why I said suggest, right?
You're absolutely right.
That could be just noise.
It could be that if you presented
the same face every time you'd get that same distribution.
You're exactly right.
And so we will talk--
not next time, I think Wednesday next week.
But anyways, very soon we'll talk
about methods that enable us to exactly deal with that question
and ask, is there actually information
in this pattern of response across neurons or voxels
or whatever it is?
Or is that just the noise of variation?
Yeah?
OK.
AUDIENCE: But how many neurons are in [INAUDIBLE]??
NANCY KANWISHER: Oh good question.
Let's see.
I would say, I think a few million.
So let's think about it.
Each voxel is about a half a million,
and they are typically maybe like 30 voxels, something
like that.
Somewhere on the order of 20 million, something like that.
I mean, with huge error bars.
OK, so this is cool and tantalizing,
but it doesn't even tell us what these neurons-- what exactly
they're participating in.
It doesn't tell us if those neurons are telling
that person which face is there or maybe what facial expression
the person has or how old they are
or whether they're male or female or God knows what else,
right?
And it certainly doesn't tell us how those neurons
get that information.
Still, it's cool.
OK, so intracranial recording, both with the grids
that I showed you and the single unit version.
Advantages are, this is the only method
in humans that has both pretty good spatial resolution
and temporal resolution at the same time.
Disadvantage-- well, you need to have a craniotomy, which
is no picnic, to put it mildly.
You need to have a huge piece of your skull removed
and neurosurgery.
And that means that the only times
we get to do this are when it's required clinically
and everything is under control of the doctors,
as it should be.
So the doctors make all the choices
about where the electrodes go, and we just
get to sit in the background and say, please, please,
please, look at these stimuli, but try not to hassle
the patients too much.
Right now there's a patient in Albany,
New York who has electrodes right
over a really exciting part of the brain
to us that I'll talk about in a few months.
This patient has electrodes that respond specifically to music.
We will talk about that later.
It's pretty amazing.
And for the last couple of days, Dana and I
have been-- mostly Dana has been collecting stimuli
because we really want to ask questions
about the response of those electrodes.
And this patient is not too thrilled
listening to our stimuli.
So we finally said, oh, OK, tell the patient
they can just do Instagram on their phone
and we'll play the stimuli in the background.
So hopefully, we'll have cool data from that soon.
OK, so to say that these data are limited and hard to control
is an understatement.
We basically can't control it at all.
All we can control occasionally is the stimuli.
And it also, like functional MRI,
just because we see those beautiful responses,
it doesn't tell us how those responses
are connected to behavior.
So that's a real challenge.
So that won't do.
We need to get beyond this problem.
I keep saying this method is great,
but it doesn't tell us the causal role
of that neural phenomenon in cognition and behavior.
As scientists, science is all about discovering
causal mechanisms.
We're not just interested in what is correlated with what,
we want to know what's causing what.
That's really of the essence, and so we
need to do better here.
So what are we going to do?
Somebody mentioned a while ago, maybe it
was Isabelle, that one of the ways
to do that and ask whether the face area is causally involved
in face perception is to look at a case
where the face area is altered.
So there's a bunch of ways to do that.
And one of them--
OK, that's just a review.
We said faces are recognized fast
but we haven't learned much more.
How do we test causality?
OK, patients with focal brain damage.
Here is a patient.
These are vertical slices through the back
of this patient's head.
OK, let me get oriented.
The slice is maybe this here.
And as you go rightward, you're marching back
in the brain like that.
Everybody oriented?
What's this thing right there?
AUDIENCE: Cerebellum.
NANCY KANWISHER: Yeah, cerebellum, right.
That thing right there is this patient's lesion
that spans several slices going back like that.
And this patient's lesion looks a whole lot like my FFA.
There's my FFA, greater response to faces than objects,
on similar slices.
We don't have functional MRI from this patient
so we don't know exactly where this guy's FFA was.
But there's a good bet that it was blitzed by that lesion,
because it's right in the zone where it usually lands.
And this patient can't recognize faces at all.
And importantly, the patient is absolutely
normal at recognizing objects.
No problem whatsoever at recognizing objects.
How does this take us beyond functional MRI?
Yeah?
AUDIENCE: It implies causation.
NANCY KANWISHER: Speak up.
AUDIENCE: It implies causation.
NANCY KANWISHER: Yeah, say more.
What does it tell us?
AUDIENCE: So because of the fact that area's damaged
and then it makes it to not be able to recognize faces
and I can see that there's causality that oh,
that area's [INAUDIBLE].
NANCY KANWISHER: Exactly.
Exactly.
It says you need that bit to recognize faces.
But also says something else.
What else does it?
AUDIENCE: That you don't need it for recognizing objects.
NANCY KANWISHER: You don't need it for recognizing objects.
So this is actually really strong evidence
that that bit of brain is very specialized for face
recognition.
Specialized and necessary for face recognition.
OK, so--
AUDIENCE: Can that person still detect faces?
NANCY KANWISHER: Oh, yes.
Good question, absolutely.
OK, so let me just distinguish-- this person here has
prosopagnosia-- that means a selective deficit in face
recognition--
like Jacob Hodes, who I described yesterday,
who has no brain damage whatsoever
but has just never been able to recognize faces at any point
in his life.
So this syndrome can arise just from some weird developmental
thing where you're atypical and you're just really bad at it,
or it can result from damage to that part of the brain.
So now we're talking about the case of damage,
but in both cases, people with prosopagnosia
have no problem knowing that a face is a face.
They just don't know who it is.
Yeah?
AUDIENCE: Has there ever been a case
of problems of people who can't recognize faces who
[INAUDIBLE]?
NANCY KANWISHER: Indeed.
Indeed.
Jacob Hodes, who I talked about last time who is just
absolutely awful at face recognition,
including family members, close friends,
can't do it, like not at all.
He has a very normal looking fusiform face area.
So after I told you that I had that conversation with him
a dozen years ago or something like that, I scanned him.
And he had a beautiful fusiform face area, like textbook.
It looked-- well, looked like mine, which is a damn fine one
if I do say so myself.
And I looked at that and I went, oh shit.
I better publish this before someone else does.
And I didn't get my act together,
and then a whole bunch of papers came out saying,
oh, people with developmental prosopagnosia
have normal looking face areas.
Take that, Kanwisher.
What do you say about that?
And it was a little shocking.
But upon further reflection, it's not really devastating,
right?
I mean, it's bracing, it's informative.
But it tells you that having a face area that
is a region that responds more to faces and objects
isn't sufficient for normal face recognition, right?
You need other stuff.
What might that other stuff be?
Well, the circuits in there need to work right.
It's not enough to just respond more to faces and objects.
To recognize faces, they need to be able to distinguish faces
from objects.
We don't know if that's working right.
What else do you need?
AUDIENCE: Memory.
NANCY KANWISHER: Memory, absolutely.
Yes, you need to remember faces.
What else?
AUDIENCE: [INAUDIBLE]
NANCY KANWISHER: Could be, but in Jacob's case,
it was close friends he couldn't recognize.
So what's another possible account
of how could he have a normal face area and yeah?
David?
AUDIENCE: It might be a gap between recognizing a face
and connecting that to recognizing a person.
NANCY KANWISHER: Yeah.
Yeah.
Or to put that neuroanatomically,
you got to get the information out of there.
maybe, for all we know, that little face area
is working perfectly.
Maybe that face area knows who that person is, in a sense.
But if the connection's out of that brain
region to the rest of the brain are messed up,
it doesn't do you any good.
You need to be able to read that information out and act
on the basis of it.
Anyway, that's a big sidebar.
Point is, you can have prosopagnosia
either as just a developmental disorder
or as a result of brain damage.
Oh, God, I knew this was going to happen.
All right, so OK, so very briefly, it messes up ability
to discriminate and recognize faces, not your ability
to detect a face, right?
So as [INAUDIBLE] had asked, it's
not just you can't tell the thing is a face,
they're fine with that.
Importantly, they are normal and voice recognition.
So it's not that they're confused
about distinguishing one person from another.
They can do it fine from audition, just not from vision.
In the rare cases where the lesion is small,
it can be very specific, leaving object recognition intact.
More often, there's kind of a blurry mess.
You have a big lesion and a bunch of things are affected.
OK, so we've talked about that.
OK now, it's very important in neuropsychology reasoning--
like we want to say, OK, that's really powerful,
the case of prosopagnosia.
You lose that bit, you can't recognize faces.
And that establishes a kind of causality
that we didn't have before with just functional MRI.
But is that sufficient to say that that region is specialized
for face recognition only?
It's not.
Whenever I ask this, the answer's no.
Your task is to say, why?
How could you have great difficulty at recognizing faces
and be OK at object recognition?
And yet, not have a deficit that's specific to faces?
How might that arise?
You guys have suggested this hypothesis in different context
before.
Yes?
You look like you know.
No?
AUDIENCE: But I just you could do other things.
It doesn't have to only be for facial recognition.
Because it response to animals [INAUDIBLE],, right?
NANCY KANWISHER: Sort of.
But the question here is--
OK, let's just start bare bones.
You have a lesion, you get around fine in the world,
you can do everything else but you have a real problem
recognizing faces.
Does that mean that the region lesioned is specialized
for face recognition per se?
AUDIENCE: There might be other [INAUDIBLE]..
NANCY KANWISHER: That's true.
There could be other things going on, absolutely.
But let's suppose they're not.
Let's suppose you had good reason to think there weren't.
Yeah?
AUDIENCE: It could be--
it'd be a path--
it could be one point in a pathway.
NANCY KANWISHER: That's true.
It could be totally a point in a pathway.
Absolutely, that's another account.
What else?
AUDIENCE: Well, I couldn't get last comment, so.
NANCY KANWISHER: He said maybe you damage a pathway.
Yeah?
AUDIENCE: Maybe there's some other function
we haven't tested in that person.
NANCY KANWISHER: All these are very good
alternative hypotheses.
You guys are very good at this.
The one I'm fishing for is, maybe
face recognition is just harder than object recognition.
Maybe the part that's damaged is just generically involved
in object recognition, but you damage
part of the object recognition system and face recognition
takes a bigger hit because it's harder.
Right?
Does that make sense?
Do you see how the case of prosopagnosia
is consistent with that?
So that means we cannot infer from these data alone that that
region's specialized for face recognition.
Now, we can do various things like test
them on really hard versions of object recognition.
And people have done that.
But there's another kind of data that are really powerful here.
And that's when we have the opposite syndrome.
So there's only a couple of cases of this.
The best one is called CK, published in a paper in 1997.
You don't need to remember that.
The point about this is that this guy
has the opposite syndrome.
He's severely impaired at object recognition.
He can't tell a chair from a table
from a car from a toaster, but he's
100% normal at face recognition.
Totally normal at face recognition.
In fact, better than average.
Do you see how that's in some ways
even more powerful evidence that face recognition goes
on in specialized brain machinery
than the case of prosopagnosia?
Face recognition isn't even a special thing
that sits on top of normal object recognition.
It's a totally different pathway.
You can have no ability to recognize objects
and your OK a face recognition.
Does everybody see how that's really powerful?
And how those two kinds of evidence
together are vastly more powerful than either one alone.
Well, that's called a double association.
We'll skip all of that for now.
Doubled associations are particularly powerful
examples--
powerful forms of evidence in cognitive
neuroscience where we have opposite syndromes that
collectively make it really hard to wiggle out and come up
with alternative accounts other than
that there's a bit of brain that's really specialized
for face recognition.
It's not just that face recognition is harder,
or else you'd never get this syndrome.
All right, I just wanted to finish that point.
OK now, how much time do I have until the quiz?
15 minutes, OK good.
AUDIENCE: 13.
NANCY KANWISHER: OK, good.
We're going to skip over TMS.
I'm sorry about that, guys.
Someday I'll learn to time things in a lecture.
Actually, I knew this was going to happen, I just--
we'll get back to TMS later.
And we will skip to the most amazing method
in all of cognitive neuroscience for which-- we're
going to come back to this dude who you met before who
has the face selective responses in that part of his brain.
Remember how I said that even though these data are
gorgeous and spectacular and the only way
we can get high spatial and temporal resolution together,
but they don't tell us causality?
Right?
That's true here?
Resolution doesn't get you causality.
To test the causal role of something,
you need to mess with it.
So it turns out that sometimes the neurosurgeons electrically
stimulate through those same electrodes.
And they do that to test the function of those regions
causally.
They also do it to test their hypotheses about the location
of the seizure foci.
So in those rare cases, where you
have a patient like this with selective electrodes
like that where the clinicians decide that they are going
to electrically stimulate through some
of those electrodes, then we're in a position
to kind of have it all scientifically, right?
I don't mean to be so crude.
This is a horrible situation for that lovely guy to be in,
but scientifically, it's extremely powerful.
So I'm going to show you--
we did in fact have an opportunity.
The same guys in Japan emailed me
and said, OK, we're going to be stimulating that electrode.
What do we do?
And I said, OK, have him look at faces
and have them look at other objects
and ask him if anything changes.
And I'm going to show you a video of what
happens when that goes on.
OK, here we go.
Oh, and I need to turn on the audio.
OK, he's getting stimulated right there and he says--
[VIDEO PLAYBACK]
- [NON-ENGLISH SPEECH]
NANCY KANWISHER: He's such a good subject, this guy.
- One more time.
- [NON-ENGLISH SPEECH]
- His eyes.
- [NON-ENGLISH SPEECH]
NANCY KANWISHER: OK, that tells us
that that region is causally involved in face perception.
Is it causally involved in perception
of things that aren't faces?
He's getting stimulated in the same electrode.
He doesn't know that there's a face area.
- [NON-ENGLISH SPEECH]
NANCY KANWISHER: He doesn't know which
electrode is being stimulated.
- [NON-ENGLISH SPEECH]
NANCY KANWISHER: This is a Kanji character on a card here.
- [NON-ENGLISH SPEECH]
- One more time.
- [NON-ENGLISH SPEECH]
[END PLAYBACK]
NANCY KANWISHER: Awesome, huh?
What did we just learn?
AUDIENCE: You can trigger it.
NANCY KANWISHER: You can trigger it, yeah.
Yeah.
So what does that tell us about the function of that region?
Why is this-- I mean, it's amazing to see, no question,
but what does it tell us scientifically?
AUDIENCE: It's specific.
NANCY KANWISHER: Yeah.
How does it tell us that it's specific?
AUDIENCE: Because when you stimulate it,
it particularly sees a face.
NANCY KANWISHER: Yeah.
And what happens when he's looking
at things that aren't faces?
AUDIENCE: [INAUDIBLE]
NANCY KANWISHER: Yeah.
So if that region was causally involved
in perception of things that aren't faces,
you might think that it would distort--
the box would look different or the ball would look different
or the Kanji would look different.
It doesn't, there's just a face on top.
So I think that's very strong evidence
that that region is not only causally involved
in face perception, but very specifically causally involved
in face perception only.
Everybody get that?
Do I have to stop?
OK.
OK, I have another video.
Consider-- and we'll get back to this later--
consider other alternative hypotheses to this.
This is pretty powerful.
This is more powerful than most of the other things
I showed you, but there's always ways
to come up with alternative hypotheses,
and that's the business we're in here.
So be percolating on what other control
conditions you'd want from this guy
to really believe these data.