- So this is the start of the eighth year of
the Neuroscience and Public Policy Seminar series.
And for me it's a very special start.
I don't pretend to be a moralist about society,
but I've long believed that women are not afforded
the recognition they need for their accomplishments
in science or other activities.
And for nearly two decades given all the lecture
series that I have been responsible for,
I have tried to put together a sequence in which
all the lectures were delivered by women
and this is happening this year for the first time
and I feel really, really good about that.
So, the first woman is Adina Roskies,
and she's from Dartmouth.
And she started out with a bachelor's degree
in humanities at Yale, with summa cum laude in the field.
Then she went to UCSD, got a master's in philosophy
master's in neuroscience, a PhD in neuroscience
and cognitive science and then she went to MIT
and got a PhD degree in philosophy.
And it's just amazing because she hasn't apparently
given up because in 2014 she went to Yale
and has a master's degree in legal studies.
The range of her interests as depicted in her
curriculum vitae are just unbelievable.
They range from something that I started out doing.
She was involved with somebody who was studying
projections of the retina in 1994 and published
a paper with him.
And then has migrated to papers on nueroethics,
morality, and recently co-authored a book titled
The Primer on Criminal Law and Neuroscience
with someone who was gonna lecture in this series
but it never happened.
Adina lists her major areas of competence as ethics,
epistemology, and metaphysics,
but she's a neuroscientist also.
And this kind of philosopher/neuroscientist
doesn't come along very often.
So, in her role as a professor of philosophy at
Dartmouth and acting chair of the program in
cognitive science, it's a great pleasure to introduce Adina.
She's going to talk about mind reading:
past, present, and future.
(applause)
- Thanks very much for the invitation.
I'm really happy to be here.
I was told about this program first by one of my
colleagues at Yale Law School who was a graduate of
the NPP program, Kim Farbota, and I don't know how many
of you know her but she's a very impressive person.
And so it was really nice to get the invitation.
And this is actually my first trip ever into Madison.
I've heard from many colleagues about what
a great university town it is, and I'm favorably impressed.
It really seems like a terrific place.
Okay, so today, as Ron said, I'm talking about
mind reading, and mind reading has a long history.
There are lots of old forms of mind reading.
It's something that people have wanted to
do for quite a long time.
So we have our swami, we have our fortune teller,
and even Mr. Spock has engaged in this kind of activity.
And there are reasons why we might want to
know the contents of other people's thoughts.
In particular, they matter to us, and we
don't have direct access.
The only access that we have to what other
people are thinking is typically what they will tell us.
but we can't always trust what they tell us
and so if there were some better way to do it,
it could be very useful.
The father of, in essence, technological mind reading,
the polygraph, was actually the same guy,
this is William Marston, the same guy who
invented Wonder Woman.
And it's maybe no accident that Wonder Woman
had this golden lasso, and the idea was that if
you got caught in her golden lasso,
you couldn't help but tell the truth.
And so Marston went on to develop the first kind
of golden lasso, and the idea was that if you
lied under the polygraph, people would be
able to know it.
But it's actually not been quite as good as
Wonder Woman at getting people to tell the truth.
So, in fact, a National Academy of Science report said,
after analyzing a lot of studies using the polygraph,
said that the research is unreliable, it's unscientific,
and it's biased.
They admitted that the polygraph was correct
in determining whether someone was lying
more often than chance.
So there was some kind of signal there,
but it was really so noisy of a signal that it wasn't
one that was sort of fit to be used.
And so they concluded that the polygraph is
neither scientifically valid nor especially effective
beyond it's ability to generate admissions.
And the way it generated admissions is essentially
because the people who were being tested by
the polygraph didn't realize how bad it was, and so,
thinking that in fact the polygraph would be able to
determine whether or not they we lying,
they just threw in the towel and decided to confess.
So, the question that we have now is whether
we're in that same situation.
So there's the old style mind reading,
and there's the new style mind reading.
And is this just a more technologically advanced
version of the polygraph, or is there something
new and really different about it?
So, I'm going to address a particular question here.
I'm actually not going to talk so much about lie detection,
although I'll mention it a little bit.
But what I want to talk about is reading mental content.
Understanding or discerning mental content
from functional MRI studies.
So, neural imaging, I think there are a lot of studies
that suggest it can tell us a lot of things that are
going on in the mind or the brain.
So it can tell us, in certain cases, whether people have
certain kinds of diseases or are developing certain
kinds of diseases or are prone to develop certain kinds
of diseases.
It can give us some information about character traits.
It can give us some information about unconscious
attitudes and biases.
It can tell us a lot about brain function.
But none of those things really are mental content.
And so what I want to focus on here is what can it tell
us about mental content.
So, first of all, we have to figure out what would
mind reading really entail, or what sorts of things
wouldn't be necessary in order for some technology
to effectively accomplish mind reading?
And so if you think about what reading is,
what we do is we map some sort of physical pattern
in the world to some kind of content,
you know, some kind of content.
So the firs thing we'd have to do is have a
technology that's able to recognize the physical pattern,
and then we'd have to figure out what the correct
mapping is to meaning.
And what would be involved in really doing
mind reading would not just be a brute force
mapping of one content to another.
But rather some sort of systematic content.
So if you think about what reading is there is
a system that we learn that allows us to figure
out what the meaning is of things that we've never
seen before because it's got this kind of set of rules
that it follows.
And so it's also not just systematic but it's generative.
That is, we can decode completely novel words
because we know the appropriate rules.
So we're looking for a systematic and generative
way of mapping content to some kind of signal in the brain.
And we also have to think about what kind
of meaning we might care about when doing,
what kind of content is important.
And so there are kind of coarse grained content,
things that I think neural imaging can already do,
and that is if you see activity in the amygdala,
you can infer some sort of fear response
If you see activity in certain areas of the brain,
you might think that there's some kind of working memory.
So there's sort of functional mapping to brain areas.
But when we think about mental content,
we're really thinking about what are
the meanings of thoughts.
Our thoughts are representable as propositions
or have propositional content.
That is, we might believe things about the world
or the future, et cetera, but that kind of content
is pretty fine grained.
So philosophers talk about representing propositions
of possible worlds and individuating content
even more finely than that.
So, equiangular and triangular are not the same things.
But they don't have the same fine grained meaning,
but they might map to exactly the same
propositional content.
So that's a philosopher's gloss, but when I'm talking
about mind reading, what I want to do is can I tell
the content of your thoughts such that if you reported
it to me, it would capture that fineness of grain meaning.
So I'm going to take you on a little bit of a tour.
What we can, what the history of mapping
mental content has been, and where we might
actually be able to go.
So, one of the first papers that I thought was
really impressive in this regard was a paper
by O'Craven and Kanwisher, where what they
did was show people either famous faces or places.
And it turns out that you have different areas in your
brain that respond strongly to faces and another
area that responds pretty strongly to places.
And so by showing people alternating pictures
of faces and places and looking at what parts of
their brains were active during the viewing of these things
and mapping those, they demonstrated that
you can actually see.
So these F episodes were when they were looking
at faces, and the P episodes were when they
were looking at places.
And you could see that the PPA, which is shown in blue,
is more active during the place visualization,
and the FFA, or the fusiform face area,
is more active during the face visualization.
And if they were just given the brain activity
and tried to then infer from the brain activity what
a person was looking at at that period of time,
they were able to do so with about 85% accuracy.
And that was very impressive because we didn't
really know prior to that that there were face areas
and place areas and that those were reliably correlated
with what a person was seeing at the time.
As it turned out, you can even have people
think about faces and places
and look at the activity in those areas and determine
what they were thinking at the time.
So this kind of work went even further.
But one thing to notice, though, is that they're
not discriminating that this is Woody Allen that they're
viewing or thinking of and this is Bill Clinton
and this is, I don't know what the great court is,
but we're not looking at the fine grain contents of
what they're looking at here but merely at the category
of what they're looking at here.
And so that's only, I think, a very first foray
into mind reading.
Here's another study that's also done in
Nancy Kanwisher's lab that I think was maybe even
more telling and more interesting, and that is a study
that purports not to only show you what somebody
is looking at but what they are conscience of given
the same stimulus activity.
So, in this case, people were given a rival stimulus,
a stimulus that creates binocular rivalry.
So if you show somebody in one eye a face, in this case
a red face, and in the other eye a green or maybe,
I guess, a green face and a red picture of a place,
so you have two different percepts in each eye,
what happens to your experience is not that you see
something that looks like this fused picture, but, rather,
you see a face and then it switches to a place
and then it switches back to a face and then
it switches to a place.
So you're only conscious at any given moment,
except during the transitions, of one of these stimuli
even though your eyes are being illuminated with
the very same stimulus the entire time.
And so what you have is the same visual stimulation
but different conscious experiences, and what they
showed is that during the rivalry condition,
if the person was just indicating what they were
experiencing at the time, the face and the place area
acted just as they acted when people were actually
presented with faces and places that is the stimulus
actually changed to be one or the other.
So this was really cool because one thing it
could suggest is that neural imaging allows you
to determine not just something about
the information in the brain but actually what
somebody is conscious of or what they're
experiencing at a given time.
And that would be really, really powerful.
The unfortunate caveat that was not made
nearly as clear in the written paper or any of
the commentary that followed from the paper is
that this experiment doesn't prove that you're looking,
that what functional MRI can do is get a conscious
experience rather than information content
because it's also consistent with the possibility that
what's going on in these rivalry processes is that
the information itself is being gated such that you're
only activating one part of the brain or the other
part of the brain further down, like the face area
and the place area is only being activated because
the information is only being sent upstream
during this rivalry.
So we don't actually know yet whether we can
tell the difference between information presence
and conscious percept from neural imaging.
But this actually is all sort of ancient history now,
the kinds of techniques that were used
in these kinds of studies.
We're just looking at what areas of the brain
are sort of blazingly active during apex of different
kinds of stimulation.
And in the early 2000s there was a development
that I think was really a game changer with
neural imaging, and that is multivariate techniques.
So ways of analyzing neural imaging data
and classifying it in a much more fine grained way.
So instead of just looking at what areas changed
their activity, what big chunks of brain regions change
their activity by a lot, you start looking at what are
the variations and patterns of activity
across different conditions.
So this might be an easier way to represent
what's going on.
So in this study, Jim Haxby showed people things like faces,
houses, chairs, and shoes, different categories of objects.
So they'd see a bunch of different faces or they'd
see a bunch of different houses.
And he noticed that the face area is not just
the only thing that's being activated when you see a face.
You're also seeing activity in the place area
and a bunch of different areas in the brain.
So you get a widespread pattern of activity when
you see faces, and you get a different widespread
pattern of activity when you see places and similarly
for shoes and chairs, et cetera.
And if you pay attention to the details of the pattern,
there are similarities across the pattern for
many of the faces and similarities among
the patterns for many of the places, et cetera, et cetera.
And you can train machine learning, using
machine learning algorithm to train a classifier to predict,
on the basis of a new pattern, which of these
other patterns it's most like.
So if you train the classifier on four classes of objects
and then give the classifier a new pattern and say, okay,
which of these four patterns is this one most like,
it can tell you.
And so, in this case, the pattern given is most like
the chair pattern, and, in fact, the classifier was given
a pattern generated during the viewing of chairs.
Okay, so this is what multivariate analysis tries to do.
And by looking at patterns instead of individual areas,
you can get a lot more fine grained information.
And so people at Carnegie Mellon showed people
different objects, like tools and houses, and trained
classifiers up on what they were looking at,
and then showed them a new object and the classifier
had to decide which of these categories are they looking at.
And they were pretty good within participants.
So they got up almost to 100%, pretty close to 100%,
on lots of different subjects.
But more interestingly, when they trained a classifier
on one subject or on a bunch of subjects and gave
them a new subject, the classifier was pretty good
at discriminating what category they were looking
at with that new subject.
So that suggests not just that you can train
the classifiers up on individuals but that it can tell you
something about how people in general represent
certain kinds of objects.
So this was another very impressive use of
multivariate pattern analysis.
And it gave certain kind of promise to the goal
of mind reading, but I think that it was, again,
not necessarily presented in the most realistic light.
So I'm going to show you a content, a clip from
a 60 Minutes interview that Lesley Stahl
did with this group.
And this is the kind of thing, insofar as this is
a neuroscience and public policy kind of lecture,
this is the kind of thing that should worry
the public policy people because the dissemination
of scientific knowledge has gone array.
So let me first play it for you, and then
we can talk about it.
- [Audience] Inside the scanner, Megan was shown
a series of 10 items and asked to think for a few seconds
about each one.
- If it all comes out right, when she's thinking hammer,
the computer will know she's thinking hammer.
- Right. - Okay, we're all done.
- So, Megan... - Yes?
- How was it? - It wasn't bad.
- Good.
- Within minutes, the computer, unaware of what
pictures Megan had been shown and working
only from her brain activity patterns as read out
by the scanner, was ready to tell us, in its own voice,
what it believed was the first object Megan had
been thinking about.
- I think the word is knife.
(bell dings)
- All right, one. - That's right.
- Then the second. - I think the word is hammer.
(bell dings) - All right.
- I think the word is window.
(bell dings) - It's perfect, right?
- So far. - And it continued to be.
Word after word.
- Apartment (bell dings)
- Ten out of 10.
Well done. Well done.
- Good thinking.
- And, of course, this is just the beginning.
- Exactly.
- Who knows what you're going to be able to read.
- That's right.
- It's a little scary, actually.
- That's our research program for the next five years.
- Okay, so, did anybody discover anything a little
bit fishy about what was going on?
- (inaudible)
- So the computer is always picking between two options.
So that's really good. And what are the two options?
The two options were always cross-category.
So, while it sounded like the computer could
actually read the content of the person's thought
or the object that they were visualizing, in fact
all it was doing is making a category discrimination.
It's either a house or a tool.
But because it was programmed to identify it by
the name that it was given, it sounded like it was
actually telling you which of these 10 different tools
it was seeing, and it wasn't doing that.
And that, I think, has been a problem, continues
to be a problem in the way these kinds of results
are disseminated, and I think it leads to both
misunderstanding of what the science can do
and mistrust because once the secret is let out,
people think, well, that wasn't very straightforward
of them, right?
And, in this case, I think it's 60 Minutes' fault for
not checking it or not having somebody else weigh in
and say, is this the best way to interpret
what's going on here?
And here's another little clip from Marcel Just.
- And we found that we could identify which object
they were thinking about from their
brain activation patterns.
- You're reading their mind.
- We're identifying the thought that's occurring.
- Whoa.
- It's incredible, just incredible.
- So I think it's unfortunate that scientists need to
pump up their data to the extent that it's so
sensational in order to get funding, but I think that
there really is this sense that they have to make
it seem so much better than it is.
I'm going to show you a few other kinds of results in
which people have said that they could read people's
minds or thoughts where, again, I think it's not at all
clear that they're doing that.
So here's a study that John Dylan Haynes did
where he claims the task that he had was telling
people I'm going to show you a couple numbers
and you have to decide whether you're going
to add or subtract these numbers.
And then he said on the basis of the brain imaging
data I can tell you whether you're intention
is to add or subtract.
And, basically, he did find an area that looked
different that gave predictive power over whether
people were intending to add or subtract.
But it isn't at all clear that what he found was their
intention to add or subtract or that the content
of the intention was add or subtract.
So again, in this case, there's a two-alternative
force choice.
And, secondly, all that the found is that there's
a difference in activity.
Well, if you have some number that you're intending,
you haven't seen yet the number but you're
intending to add or you're intending to subtract them,
one task is a lot harder, right?
So maybe what he found was just an
anticipation of difficulty.
There are a variety of things that the difference that
they found could mean and no indication that what
he's really found is the intention to add or subtract.
So what he's found is something in the brain,
some information in the brain the correlates
with the intention to add or subtract.
Okay, so that's sort of naysaying, but I actually don't
mean to be a naysayer in the sense that I think
what we can do now is really rather incredible.
So, there is an indication, for instance,
that there is sort of a generativity to the way
the brain represents information.
So here is a really ingenious study from
the CMU group, where what they did was imaged
people looking at a bunch or thinking of a bunch
of different kinds of nouns.
But then they built a model based on the statistics
of those kinds of nouns and their, the statistics with
which they're associated with different kinds
of verbs and essentially to derive some sort
of semantic model.
That is, certain kinds of nouns are nouns that you're
prone to eat or manipulate or sit on or push
or various kinds of of verbs.
We do things with nouns, and there are theories
that suggest that maybe the way we represent things
has to do with some of the things that we can use them for.
So they trained a classifier, now not just on the data,
but on the data and this model such that the classifier
based on the associated statistics of the words
had some sense of the mapping between lots of
different verbs and individual nouns.
And then they gave it--
So, here, just as an illustration.
Then they gave it a new word that it hadn't seen,
a new noun that it hadn't seen but for which it knew,
in which they programmed in what the statistics
were associated with that word.
So the word "celery" has some association in
the corpus of prose that they were using with
the verb eat and with taste and with fill and very much
weaker association with ride, and so knowing what
those statistics are and the kinds of other nouns
that it was trained on, it could make a prediction about
what the brain activity would be if a person
was thinking of celery.
And it turned out that that was actually pretty good.
So, here is the predicted pattern for celery,
and here is what celery looked like when they
imaged somebody thinking about celery.
And here's the predicted pattern for airplane,
and here's what somebody thinking about
an airplane looked like.
And you can see that there are places where
the higher activity looks fairly accurate.
It's not perfect, but it's not random chance.
And, in fact, they were much, much better
than chance in predicting what kind of brain
activity would be associated with various nouns
that the classifier had never been trained on.
And so that suggests that our brains do
do things systematically.
We don't know, this is just some random model that
they decided to try, but if we really could develop
a model of how our brains represent things
and then train classifiers on it, we maybe could get much
better at this.
So I think that there is some inroads in reading minds.
So, we can get at some of the semantic
content of individual words.
Although, just nouns does not a thought make, right.
So there are lots of elements of propositional
thought that this doesn't cover.
There are hints that there is a generative model,
and there is support for some of the theories
people have proposed for what the brain is actually
doing when it represents the world around it.
And, moreover, there's some evidence of
cross-subject validity.
So, I don't think brain, mind reading with brain
imaging is an impossibility, at least to a certain
degree of resolution, but we're not there for sure,
and there are a number of open questions.
So, one is, are these things reading content,
or are they just making mappings that,
in these very constrained situations, suggesting that,
well, I can just discriminate one brain pattern
from another brain pattern.
So merely the fact that you can do discriminations
doesn't mean that what you're looking at is
the content or the representation of content.
There's questions about how accurate these
kind of models can be.
Without the accurate model,
you're not going to be able to do a good job reading.
There is some cross-subject validity,
but there's a lot of variability.
I'll show a little bit more about that later.
Nouns is, again, just one element of things
we might be interested in.
One of the very important things,
for instance, is compositionality.
So you get very different propositional meanings
from "Mary bit the dog" and "the dog bit Mary."
You've got the very same elements, but
but the semantics is entirely different.
So those kinds of things are going to be problems
that we have to crack if we're really going to read
the content of thoughts.
Okay, so, that is one body of work.
There are other sorts of things that people have
done that are also really impressive.
So, one project that has been mostly carried out in
Jack Gallant's lab is trying to build up representations
or understand how our brains build up representations
from perceptual stimuli and being able to then look
at brain activity and try to reconstruct the stimuli,
the visual stimuli, that people are looking at.
So, if you show somebody an image,
you get a certain pattern of brain activity.
Can you, from the pattern of brain activity alone,
reconstruct what they must have been looking at?
And so what he's done is, using a bunch of filters
that are sort of stand-ins for receptive field properties
of visual cortex neurons, built models that can do things
with sort of limited libraries of images and classify them.
So, if you train, so what he did is train a classifier
on 120 images and found that the classifier could
correctly say which of the 120 images was being
looked at given the brain activity 92% correct.
If you expand that library to a thousand images,
you get 82% correct.
So it's actually quite a few more images
without that much of a loss in accuracy.
And the idea is that you could expand that library
almost indefinitely and still get above chance
accuracy in classifying the image.
What he's using here are Bayesian techniques.
So you have to have some kind of sense
of what the prior probability is.
So you have to use some kind of library of images.
Here's another figure from that.
That sort of shows how you can build up
complex images from the simple elements
or simple filters.
It's not purely bottom up because you have
to have these priors.
So you have to have a sense of what are
the possible range of images that we're looking at.
The question of cross-subject variability is one
that has been looked at quite a bit,
and here what you're seeing are images of the confluence
of activity across many people's brains.
So, what these guys did, Uri Hasson and his
colleagues did, was showed people movies.
It turns out that if you're trying to
understand how people represent things,
you get a lot more information when you show them
movies than when you show them pictures.
It's maybe not so surprising because we've
actually evolved to watch things that move,
not static images.
And so, if you show, I've forgotten, oh, Clint Eastwood...
What's that?
- The Good, the Bad, and the Ugly.
- Yeah, The Good, the Bad, and the Ugly.
If you show clips of that movie to people
and then correlate brain activity across
multiple subjects, you can see that it's
very highly correlated.
So the individual variation in at least a good
swath of cortex is quite small.
And you can get very reliable activations,
and from the activations you can actually decode
exactly what's going on in the movie there.
Is there a hand pulling the trigger of
the gun or various other things.
So there's a lot of functional specificity to things
in the movie scenes, including faces, houses,
hand movements, and other things,
and those kinds of correlations are
pretty robust across people.
And so Jack Gallant used this sort of thing to reconstruct
not just static images but also movie clips and,
again, has gotten pretty good accuracy.
And so what you see are what people actually saw.
And when I say pretty good accuracy, is that what
they actually saw bears some kind of relation to what
the reconstructed movie is, but not necessarily
the right relation.
So, we're making progress, but we're still by
no means able to reconstruct exactly what
people have seen.
And when you think about it in a forensic context,
this makes a lot of difference.
I mean, if what you can do is reconstruct a face
like that, that is not going to tell you the difference in
the identity between the five people in the line up
that you're looking at.
So those sorts of things, I think, again,
are not ready for prime-time.
This kind of approach has been done not
just in vision but other modality.
So, in addition, you might be interested in
trying to figure out whose voice was it
that someone has heard.
One thing I should point out is these reconstruction
things require that somebody's actually seeing
something or actually listening to something.
And, again, in the forensic context, that might
make it not very useful because most of
the time what you're interested in has happened in the past.
But how well can we discriminate auditory properties
from looking at brain imaging studies?
So, this study asked people to identify which of
three people are the speaker of this sound
and which of three vowel sounds are they making.
And the network could discriminate pretty well
which sounds and which speakers they were,
and that has been reported as showing that we can
reconstruct the auditory signal from brain imaging data.
But, again, if you think or look closely at the study,
it doesn't really show that.
What they were given is three people
and three vowel sounds, and that's not speech.
If you look at how people discriminated the three people,
what they did is they looked at the fundamental,
the diagnostic criteria and what is
the fundamental frequency.
So, if you had a male and a female in quite
different voices and you knew that it had to be
one of these three, you could tell which of the three it
was with fairly good accuracy.
But that's not the same as being able to identify
somebody without having a very small
stable of possibilities.
And similarly, being able to discriminate three
sounds is not the same as being able to decode speech.
So, again, I think this is indications that the brain
carries this kind of information in a way that's
recoverable to some extent but not to the extent
that it would need to be in order to be really
useful in a forensic context.
Memory might be the one place where
it could be really useful.
If you could figure out, if you could reconstruct
somebody's memory, that might be really good.
But here's another one of those sort of studies.
They had people look at video clips,
three different video clips, and then tried to identify
which of those three video clips people were thinking
about when they were recalling one of them.
And they could not-- they could do it with 45% accuracy,
where 33% accuracy is chance.
So it's somewhat above chance they could tell
which of three different video clips you were
thinking about, but that's not reconstructing a memory.
That's running an experiment.
So, there's a little bit of movement in that direction,
but, again, not enough.
One of the most interesting papers of studies
that people have done was to look at the limitations
of this multivariate analysis for looking at both subjective
and objective aspects of memory.
So, could you tell whether people were correctly
recalling things or mistaken about recalling things?
And it turns out that the imaging techniques
were relatively good at being able to tell whether
people were subjectively correct in saying this is a
new stimulus I haven't seen and I'm sure about it,
or this is a stimulus that I have seen and I'm
pretty sure about it.
You could tell the differences between the old memory,
a real memory and no memory in that case, a novel object.
But when people weren't certain about what they
actually had seen, so when they said that they were not
sure but they thought that they had seen the face before
or they're not sure but they thought that they hadn't seen
the face before, when you controlled
for those subjective aspects, there was no real
information given in the signal.
No indication of what the person actually had seen.
So, what you're really getting information about
is how confident are they that they had seen something.
So, this is just a demonstration of what they did.
They showed people before they went into the scanner
a bunch of different faces, and then they asked them,
can you classify these faces that we're going to show you
as new faces or old faces?
So here's someone that they'd seen before.
So, old, old, new, new.
And the reports could be correct that they said yes,
I've seen this one before, or here's one that they
had actually seen but they didn't remember having
seen that person.
Here is someone that they didn't see.
It's a new face, and they thought it was a new face.
And here's someone that they didn't see,
and they thought it was an old face.
So it's a false alarm.
And it turns out that if you just look at these
low confidence, false alarms, low confidence hits,
meaning that they had seen them but they weren't
so sure about it and that they hadn't seen them but
they thought they had seen them and weren't so
sure about it, there's a tiny bit of
information in the signal and none at all in the misses.
And so, again, what you're getting at is not
information about what they really saw.
You're not getting the objective truth.
You're getting some kind of information about
what they think they remember.
So, in memory classification, there was pretty poor
performance when they were trying to recover
objective familiarity in an implicit task and at
chance for objective familiarity when the subjective aspects
were controlled for.
So what you're getting is some sense
of subjective familiarity.
Now, despite this, I would say, not ready for prime-time
performance on these kinds of matters,
it hasn't stopped various companies from trying
to capitalize on the sensation of brain imaging.
So we have both Cephos and No Lie MRI who have
advertised their services as lie detection or,
alternatively, as truth telling, truth confirmation devices.
And some of the things they say are, again,
not really correct or overly sensational.
So they say the current accuracy is over 90%
and estimated to be 99% once product
development is complete.
And they're trying to get courts to admit
this kind of evidence.
The figures that they're using are based on studies
that they do in the laboratory that are supposed
to be studies about lying, but if you think about
what it would be to run a lying study in the laboratory,
to come in and have somebody lie, what you
have to tell them to do is lie.
That's not the same as lying.
That's ecologically quite a different phenomenon
that you're looking at.
So their 90% accuracy at determining,
distinguishing cases in which people who are told
to tell the truth tell the truth and told to lie
when they're told to lie.
It's not the same kind of thing as you would
encounter in the real world.
And here's another thing that I like.
So, they provide unbiased methods for
the detection of deception and other information.
The first and only direct measure of truth
verification and lie detection in human history.
So, how direct is this?
What are they looking at?
Well, they're looking at blood flow signals
that are complicatedly correlated with neural
activity that are delayed five seconds in time
that are at a resolution that is many differences
in magnitude from the level of the individual neuron.
So, it's not quite direct, but the claim is that
by measuring in the central nervous system,
you are one step removed or one step closer
to the source than measuring the autonomic response,
but it's not clear that you're losing very much
in that transfer.
So I just think these things are money making
deals but with very little scientific credibility.
So, there are lots of problems with fMRI lie detection.
I mentioned one, which is the ecological validity
and external validity worries.
That is, the kinds of experiments that you must do
in the laboratory or almost must do to make a
laboratory test feasible changed the task so much
that you're not really studying the phenomenon
you say that you're studying.
But there are lots of other problems, and that is
that all of these studies are done,
you get 90 or so percent accuracy when you
study groups, when you repeat the task over
and over and over again, but you can't decode
the data from a signal trial because it's too noisy.
Most of these studies have been done
with groups of people.
When you're using it in the real world,
you would be using it on an individual.
Individuals differ from the group in many kinds of ways,
and that individual variability adds more noise.
All of these things, in order to decide how reliable
they are, depend on the base rates of
the phenomenon in question.
So, for instance, if you were to take a test for cancer
and it came up positive, I think there's been a lot in
the news lately about how many false positives
there are when you take any kind of medical test.
And the false positive rate is going to depend on
how prevalent that disease is in the population
that's being measured.
And in order to decide how reliable and accurate
your technique is, you need to know something
about the base rate.
Well, lying is one of those things where we really have
no idea what the base rate is.
What is the base rate of lying, and it's going to depend
what's the base rate of lying in a population that's
sampled from your colleges students,
it's going to be one thing.
What's the base rate of lying in your population that
you're going to get from incarcerated people,
it's likely going to be another thing.
It's going to change depending on what's at stake.
So there's really no way to accurately measure
the reliability of these methods in a way that make
it suitable for forensic application.
So, anyway, I think there are lots of
problems with lie detection.
I don't think we know how much better we can get at this.
One worry is that it's not clear that deception is a thing.
That there's one kind of neural system that's going
to act the same way in every kind of case of deception.
There are some arguments for the use of it.
So, suppose we can get reliable information that even
though fMRI methods are not perfect, not even 90%,
suppose we can determine that they're better than
what juries do.
Is that a good argument for using them?
You might think yeah, the more accurate we can be,
the better it is, but there are other associated questions.
People know that juries are unreliable,
and people think that machines don't make mistakes.
So what kind of additional prejudicial aspect is using
a machine or using science to do this that you
wouldn't have if it's just the jury making the mistake?
There is also a question of whether there should be
a difference in treatment of imaging or any technique
for adding some sort of evidence that people
are telling the truth as opposed to adding
evidence to the fact that they're lying.
So, if the question is incriminating them versus
exculpating them, maybe we should be a little bit
more lenient in the exculpation case.
We want to believe that people are innocent
until proven guilty.
We want to have very strict standards for that,
but maybe we want to give everybody the benefit
of the doubt and want to take into account
evidence that supports the fact that they're
telling the truth even if we don't want to take
the evidence in support of the fact that they're lying.
So, I think those are policy questions that we
have to think about.
So now I'm just going to go through a few different
problems for mind reading that are going to be
problems no matter what technique
we want to use.
And one is that if we're going to make this a
useful technique in the law or for many other kinds
of applications, we don't want to have to build
a new dictionary for everybody.
What we want to do is map pattern to meaning.
It's really implausible that we could do
that for every individual.
So, the fact that people do have different
brains and might represent things
differently poses a problem.
Let's see, where's the--
So here's an indication of the kind of problem.
This is nine people doing the very same task,
and the main patterns of brain activation
for each of the nine.
And you can see that the red circle is the highest
intensity increases in this task,
and it's all over the place.
There's a huge amount of individual variability.
Here's what the group data says.
This is what's usually published.
Now, if you see somebody and you say,
look how different that individual's activation
pattern is to the group, therefore that person
is abnormal in some way.
There's nothing in the publication that shows you that,
in fact, he could be just as close to,
as variable as every other individual.
So you need to know what the variance is,
and that's something that's typically not reported
in scientific studies because, for what scientists
typically are interested in, that's not
the important part.
Scientists are interested in understanding how we,
in general, work.
They're not interested in understanding how this
particular person works differently from the next guy,
and that's what the law is interested in.
So there's a lot of issues in going between
the group data and the individual data that
would have to be addressed.
There's this problem of generativity.
So, what I showed you so far, the semantic
generativity that we've been able to achieve has
been really built on a toy theory,
and that's only the semantic part.
So we also need to understand syntactic generativity,
and there's very little progress made on
that problem so far.
There are problems about the resolution
of the techniques.
So it just may be that we don't have enough resolution
with functional MRI because when we look at a voxel,
we're looking at the signal from millions of neurons
to actually get at the level of fine grain content
that we would want from a mind reading technique.
Now, there are some techniques that have
already made some inroads into this problem,
and we don't know, it's sort of an empirical question,
how fine grain the signal needs to be in order to get
the kind of fine grained representational meaning
out that we want for something like mind reading.
We don't know how to distinguish the content
from the causal processes.
So, thinking back to this experiment where what
we saw was what people were conscious of at
the given moment, we don't know what
the conscious part is.
And it seems like what we care about when we're,
let's say, trying to decide what somebody's intention
was when they committed a crime is what they
were thinking, not sort of covert information
that's encoded in their brains but may
have had no impact on
their deliberation process.
So, I think there are a lot of problems
that still need to be solved.
There are a bunch of important questions
we need to answer.
So, how good is the technique?
How narrow is the hypothesis space?
So, if you look at a study and you don't notice
that they're only giving you a choice between
two categories and they make it sound like
they're giving you lots of individual things,
that's something that you need to pay attention to.
What are the assumptions that they relied on?
What are the technical limitations of the techniques?
There's a big issue about cooperation.
So, most, pretty much all functional MRI requires
that somebody be at least compliant enough that
they stay still in the scanner.
So there are easy ways to mess up brain scanning
just by moving a little bit.
So if you don't have a compliant subject,
you won't be able to get a signal.
If you're then trying to apply this kind of neural
imaging technique in cases in which people don't
want to comply, you're going to have a lot of problems.
Okay, so in the last few minutes I'll just turn a little bit
more to some of the ethical and legal issues that arise.
In general, I think that a lot of the,
there's very little law relevant to the mental,
to the issue of mental content.
And the law that there is often makes distinctions
that may not actually make sense once we
understand more about things,
and I'll talk a little bit about that.
I also think that the law is not particularly settled.
So where there is some kind of reference
to mental privacy,
it's mostly under the rubric of freedom of thought.
And freedom of thought may not be the same
thing as freedom to keep one's thought to oneself.
And then there's the overall issue that many of the laws
sort of depend upon what the cultural norms are,
and cultural norms with respect to privacy
are in flux right now.
So I'll talk about all of these things.
And then, beyond that, there's just the problems of
the admissibility of this kind of evidence.
So there are legal tests that scientific evidence has
to pass in order to be admitted into court.
In 2012, there was a case in which lie detection,
fMRI lie detection was introduced, and the court decided
that it didn't meet the Daubert standards,
which require that the theory that it's based
on must be testable, that there be a known error rate,
I already talked about some of the problems in
determining error rate, that the studies be peer
reviewed and generally accepted by
the scientific community.
And I should add that, in addition to this,
I think we have to be really careful.
So, while many of the studies are peer reviewed
and the technique is generally accepted,
it's generally accepted as a scientific technique.
That is, for we're trying to figure out how brains work,
not generally accepted as a means for detecting lies.
And so it's not okay to just ask, is it accepted
by scientists, but, is it accepted for what purpose?
And I think that that's an important thing to stress.
- [Voiceover] It's very interesting... - Oops.
- When you show someone a brain scan,
people just believe it.
It just reeks of credibility.
- Absolutely. Absolutely.
- And you telling me that's the area where people
add and subtract, I thought, well, of course, he knows.
- But I could have told you anything.
- I know. - So...
(laughter)
So, I didn't give this the proper introduction because
it started immediately, but even if it's admitted
as evidence, we have to be really careful about what
evidence it's for and whether we actually
know whether it's good.
So, just because it's possible to get a neural signal
doesn't necessarily mean that what people say it's
telling you is what it's telling you.
I'm going to very briefly talk about some of
the constitutional issues that might arise.
So there are a few different constitutional
amendments that might have something to say about
things like mind reading techniques in the courtroom
or the government's use of mind reading, for instance.
So the 4th Amendment says that the right of
the people to be secure in their persons, houses, papers,
and effects against unreasonable searches
and seizures will not be violated, no warrants
shall issue but upon probable cause supported
by oath or affirmation, blah, blah, blah.
So, it's not that searches and seizures are prohibited,
it's that unreasonable ones are prohibited
unless there's probable cause.
So, it's very likely that these things will be admitted
or that they will be introduced when there
is probable cause.
And this question of what is unreasonable is,
I think, a big one.
I don't really think that you would necessarily deem
brain imaging unreasonable, if it's something that
the person consented to, and, in general,
the court has held consent searches to be okay.
So, as I said, you're not going to be able to image
somebody if they don't consent because
they won't stay still.
If they do stay still, then they're consenting,
and then the unreasonable issue doesn't really arise.
So I don't think that there's anything in this
amendment that's really going to, let's say,
forbid the introduction of this kind of data in court.
I think what's much more likely to make a
difference is the standards for scientific evidence.
There are some cases in which you can imagine
evidence from neural imaging being collected in
ways that we might consider unreasonable.
That is, you might be able to tell things that people,
people may consent to a scan thinking that
you're looking at one thing and you might be
able to glean evidence about something else,
and that may be a violation of privacy that
the 4th Amendment might prohibit.
And there are some kinds of scans in which people
don't have to do anything at all, and you can find
out information about their brain that might be relevant.
So those are cases that I think this amendment
might come in, might be relevant to more than
regular neural imaging studies.
More interesting are the 5th Amendment issues.
So, the 5th Amendment says no person shall be
compelled in any criminal case to be a witness
against himself nor be deprived of life, liberty,
or property without due process of law.
And here, the compulsion is one issue,
but the being a witness against oneself is really
what I think is interesting.
So, what the court has said is that the 5th Amendment
really speaks against the self-incriminating
testimony but has always taken testimony
to be verbal testimony.
So, does that apply to brain scans where
you're not actually verbalizing at all?
You might think that reading somebody's thoughts
is equivalent to having somebody speak,
if you could do so, but, on the other hand,
what you're reading is just a pattern of brain activity.
And the court has said that fingerprinting is
physical evidence; it's not testimonial evidence.
And so this, if the court takes neural imaging
data to be physical evidence, then the
5th Amendment wouldn't apply.
And these things have not been decided, yet.
But what I think is becoming more and more
clear is this distinction between physical
and testimonial evidence is unworkable.
All evidence is physical evidence, in a sense, right?
Physically there's some mark on the world that is
providing a clue to something of interest,
and even testimonial evidence is going to disrupt
air particles and production of sound.
And I think that neural imaging is one place where
the disutility of this kind of distinction
really becomes apparent.
In any case, the right to-- the 5th Amendment
rights can be waived.
If people agree to a brain scan, that may just
be equivalent to waiving one's right to the 5th,
protections of the 5th.
And then there's 1st and 14th Amendment issues
about congress not making laws that abridge
the freedom of speech.
You might think neural imaging doesn't even go there,
we're not talking about speech, plus we're
only monitoring stuff.
We're not actually changing what people think.
But some of the court cases that have arisen since
then have actually identified substantive due process
as the right to be left alone.
So that's where I think 1st and 14th Amendment
issues might come up.
You might think imaging one's brain and reading
one's thoughts is a violation of people's right to
be left alone, and you may think that these things
actually have bearing, or the 1st Amendment might
have bearing on neural imaging, not because you
can influence what people think but that merely
the knowledge that somebody's thoughts might be
read by some kind of scanning device might actually
chill their ability so that that mind
reading technology might,
or the potential for admitting mind reading into
the courts might be chilling in such a way that this
amendment could be invoked.
But all of these things are actually dependent on
what reasonable expectations people might have,
and we used to have a reasonable expectation
of privacy that goes far beyond the privacy that
we actually enjoy these days.
And I think there's a lot going on with respect to NSA
monitoring emails and things like that that are calling
into question what reasonable expectations are,
and as people become more and more wired,
what the government might see as a reasonable
expectation of privacy may be further whittled down
that would also diminish the kinds of protections
against mind reading should it become possible.
- Do you think one day... - Oops, I will leave you with...
- Who knows how far in the future, there will
be a machine that will be able to read very
complex thought, like I hate so and so or
I love the ballet because?
- Definitely. Definitely.
And not in 20 years.
I think in three, five years.
- In three years?
- Well, five.
- So, this was taped more than five years ago.
(laughter)
And I can tell you we are not yet there.
So, you know, this may be a distant possibility,
but if anyone tells you differently, I think they
haven't looked carefully at the data.
So, I will leave you guys with that and answer
questions and thank you.
(applause)