>> Eric Horvitz: Okay, it’s an honor to host John Krakauer today from Johns Hopkins University. John is
the Professor of Neurology and Neuroscience at Johns Hopkins. He also directs a lab called Brain,
Learning, Animation, and Movement Lab. I think he conjured that up to have the acronym BLAM-lab.
When I was visiting Johns Hopkins, I give a talk there at CS and also School of Public Health, two
different talks that day. But I had a set of meetings in both places and of all the people that I met, John
stood out as being kind of out-of-the-box thinker. Kind of his interdisciplinary work crosses the borders
of, intersects with work and interests of many people at Microsoft Research. So I thought I’d just have
him out and we’d have some meetings scheduled and see what would happen, given the kind of
interesting things that John does, things that typically we don’t get exposed to at MSR.
So as far as background, John did his bachelor’s and master’s degree at Cambridge University. Then
went to Columbia where he did his medical school. He trained; he did a one year internship at Johns
Hopkins and then came back to Columbia to do a residency in Neurology. He’s been, and then followed
by a fellowship in motor control. His areas of interests, I’ll read these are experimental and
computational studies of motor control and motor learning in humans; tracking long-term motor skill
learning and its relation to higher cognitive processes such as decision making; prediction of motor
recovery after stroke; mechanisms of spontaneous motor recovery after stroke in humans and in mouse
models; and in new neuro-rehabilitation approaches for patients in the first three months after stroke.
He has lots of interesting clinical interests. But I was quite intrigued also by his interests in motor skills
learning and gaming. He has some interesting demos to show as well, so John.
>> John Krakauer: Thank you. Is this on? I’m not going to hear it right? Its broadcast that’s right. Well
Eric thank you very much for coming here this is a first for me, Pacific Northwest, Microsoft giving a talk,
you know it’s, makes me feel hip.
[laughter]
Ha, ha, so I’m, it’s nice to have this sort of, are there allowed to be questions during the…
>> Eric Horvitz: Yeah…
>> John Krakauer: Perfect, wonderful, so I’m going to talk about a lot of things that I’m just beginning to
try and tackle that puzzled me profoundly. I think it’s always interesting when one lists what one is, you
know. I don’t think it’s really easily defined the kind of work that we’re doing. Because the categories of
motor learning and cognition I think have been artificially separated. The word skill I think gets used in a
very colloquial sense. I think when you try and zero in on what we mean by all these terms it’s quite
hard. I think the philosopher that I’m working with right now said John don’t get so caught up with
definitions, just to keep them fuzzy and then show things. I think that’s probably the best thing to do.
So this is our labs website front page as of about a few days ago. I think the animator; we have this
wonderful resident animator called Kat McNally who did her degree at MICA which is an art school in
Baltimore. We hired her because of what we’re beginning to do in animation and gaming. She did this
sort of slightly phantasmagoric version of the old Hopkins facing the new Hopkins. We just have these
giant new buildings but it was just, we are the first lab I think in the Neurology Department to have a
resident artist and animator. So I’m very proud of her.
Okay, what am I going to talk about today? So I was involved in writing two reviews, one in two
thousand and eight, one in I think in two thousand and nine where they wanted you to have to sum up
the current thinking about motor learning and things like that. So Reza Shadmehr is also at Hopkins as a
computational motor control scientist and we wrote this. Then a year later the Olympics were coming
along and Nature Reviews Neuroscience wanted something about athletics. So they said would you
head up an article about what it is that we know about athletes and why are they so great? I said yes
because I knew that I wasn’t going to know what to say. Because what it made very apparent was that
when you say you do motor control and motor learning people in the real world think that you’re going
to tell them something interesting about what we’re all obsessed with which is sports. I mean for
reasons which are still unclear we pay people more to throw balls around or hit them than anything else
in human endeavor, right. If you ask yourselves right now why is that, you won’t be able to answer,
right.
Why do we like to watch car chases, ride roller coasters, watch Kung Fu movies, go to ballet, and watch
sports, I mean why? That is bizarre, right. We’re beginning to get at that and I think gaming might be a
way to actually formally test what it is about movement and watching it that is so pleasurable. That’s
what we’re beginning to move towards.
Okay, so about two weeks ago a reporter from Time Magazine called me up because he’s writing a piece
about LeBron James. He wanted to, and I think it’s something like the genius of LeBron James, it’s not
out yet. I hope I’m not doing anything wrong by mentioning it. The conversation began to unfold in a
way that there was this idea that there’s athletic and practical genius. Then there’s what we really think
genius is; you know Einstein and Newton. That somehow these are different things. I would argue that
that’s nonsense but we’ll get there.
But where did it all start? Where did the sort of scientific justification for this distinction come from? I
would say for those of you, I’m sure you’re all familiar it all started with this seminal observation that
Brenda Milner made in a patient called HM. So for those of you who aren’t familiar, HM was a patient
with intractable epilepsy who underwent bilateral temporal lobectomy for his intractable epilepsy,
unexpectedly it led to a profound amnesia, right. He would experience something, learn something, and
then swiftly forget it right after, right.
A whole slew of neuropsychological studies ensued but the famous one, I mean the one that led to the
distinctions I’m going to be talking about today was that when Brenda Milner asked him to do this
mirror tracing task where you basically have to trace with a pencil around the edges of a star in this case
and try to stay on, basically it’s a double edged star you have to keep the pencil between the tracks of
the perimeter of the star, doing it in a mirror with the vision of your hand obscured and then you just
look at the mean squared error and you and you just track across days and you get this clear savings
affect. In other words you’d come back on day two and you get better and day three, and yet each time
he was introduced the task he’d never done it before, what do I have to do, right? So in other words
this profound sort of doubled, this dissociation between having any recollection of ever having
performed this task and clearly showing memory of having done it before, so this led to the famous
distinction about separable memory systems in the brain.
It, we argue in a piece that I’ve written with the Philosopher Jason Stanley that his was really the
neuroscientific justification for the more sort of general cultural belief of the distinction between the
practical and the theoretical, right. There’s sort of explicit declarative cognition and then there’s this
sort of unconscious implicit motor side, alright. There from, that’s why these things separated, okay.
So, fine, now let’s take a step back and say well what do we really mean when we talk about motor
learning? Is that star tracing task really capturing everything that we intuitively imagine it means? Is it
really one mechanism or is multiple? So should we be thinking like this?
So if you were to go to the literature on motor learning. In fact, what the literature that we basically
reviewed quite exhaustively in that first article that I showed you of the two. People do these
adaptation like studies, right. So essentially you apply some sort of systematic perturbation and in this
case here’s a target, here’s a cursor representing your fingertip for example and you apply perturbation
where you aim for the target. Now there’s some systematic rotation on the cursor and you miss. Then
over time you adapt and so you move your hand over here and in goes the cursor. This is sort of the
computer generated version really of the mirror drawing task. It’s a classic adaptation task that there’s
some sort of systematic perturbation and you iteratively overtime bring down your errors and this is
what happens. You can fit single and double exponentials where the size of your correction seems to be
proportional to the size of the error, right. This is what people do force fields, prism goggles, and there’s
a vast literature on the way that we seem to implicitly reduce our errors in the setting of systematic
perturbations.
>>: By the way [inaudible] interacted. When I first saw some of this work I was thinking what’s the
intersection of this work with the CI kind of fix laws and the standard tools we have in MCI that might be
outside because it’s a different discipline outside of the studies like, that you see in motor adaptation
and maybe there’s opportunities there in itself for joining these, these images.
>> John Krakauer: Well I’ll be talking a little bit about speed accuracy in a minute actually because it’s
absolutely right, so.
>>: [inaudible] Psychology Departments also do this kind of work [inaudible]…
>> John Krakauer: Yes.
>>: And so I think very few, it’s recently well known to psychologists who study [indiscernible]
cognition…
>>: But you don’t, but you…
>>: How’s it used…
>>: Back in to [indiscernible] I’m saying typically have a standard simple tools for [indiscernible] target…
>>: [inaudible]
>>: Expedition work. It’s amazing that you could come up with…
>> John Krakauer: But it’s an interesting point that, you know psychologists have studied these tasks a
great deal. They, when it comes to motor learning the psychologists seem to really like sequence tasks
which I’ll talk about very briefly. Right, so you’ve got adaptation tasks which became more the prevue
of people from engineering, people interested in control theory, right. Then you had the sequence
learning of stuff and I’ll, then what I would say is that neither of those are capturing what we might
more colloquially think is skill which we’ll get to in a minute. So…
>>: Certainly people like J.J. Gibson for, you know psychology [inaudible].
>> John Krakauer: Absolutely…
>>: For human [indiscernible] department.
>> John Krakauer: Yes, yes and actually those divides are fascinating too…
>>: [inaudible]
>> John Krakauer: Right, yeah, so here’s, so I’m going to take you through some stuff that may seem
sort of odd to show you what you can do. In a sense take that HM dichotomy and hoist it by its own
petard, okay. So, and this has actually work done by Rich Ivory’s group and then Jordan Taylor is now at
Princeton and I’m working with them. This is stuff that we’ve done together to sort of try and push this
kind of paradigm to its limits and see how in fact even this kind of paradigm is heavily contaminated by
cognition. Cognition in the way just for now I’m just going to admit to a division and then try and
collapse it, right.
So here’s a sort of set up. You have a target, the red is the cursor, you start here and you just, a baseline
this is, you’re making zero error because there’s no perturbation, you just aim like this, you just have
some variability, okay. Then you apply rotation and now you try and move to the target and the cursor
goes off like this. You have this big first error of forty-five degrees and then you start to adapt. So here
you’re on your way and then you get into the target. Then you can have an after effect, you turn the
rotation off again and now you miss in the other direction, okay, it’s classic.
Now with a colleague of mine at Columbia, Pietro Mazzoni, we decided to see whether we could do sort
of reverse HM and bypass this slow implicit system by using the rest of your brain, right. So how would
you do that? We’d say well okay, here’s a series of targets and we’re going to space them by forty-five
degrees, which is exactly the spacing of the imposed rotation, okay. So, same as before you then, the
rotation turns on and you experience these two rotations. Then we say stop, we’ve imposed a rotation
of forty-five degrees. So why don’t you deliberately aim for the target that’s minus forty-five degrees
away and you’ll just instantly get into the target, alright. BAM you get right down in one trial, right.
So great this is what it is to be human. You can be given an instruction and you can bypass or override
this more primitive system presumably, right. No, you get worse and worse, right. It’s very spooky
actually, right. What’s going on? Then you say stop what you’re doing, just aim for the target like you
did before we started the experiment. We switch off the rotation you have an after effect. So here’s
the classic one I just showed you and here’s the experiment with the cognitive strategy, very, very
similar, right. You start, then you start adapting just like this. It really looks as though this curve is
simply been DC shifted and then there’s the after effect.
Now there’s some extra subtleties which we’ll get to which you may be noticing in terms of why isn’t the
after effect the size of the rotation given the fact that you’re making close to zero error here? Right, but
we’ll get to that. So what’s going on? Well and here’s another version of that experiment which we
reviewed in, recently. What this is suggesting is that even though the instruction was given in order to
adopt a strategy to not miss the target, the cursor has become a proxy for your hand; which means that,
when you say I’m going to aim to that cheating target and the cursor doesn’t go where you planned to
go with your hand that’s actually a prediction error, as far as the brain is concerned. Why didn’t my
hand go where I planned to aim it, alright?
So even though there’s a part of you saying I know there’s a rotation and I shouldn’t be bothered by that
planning error, there’s presumably a part of your brain and I’ll argue your cerebellum that says, ah that’s
an enormous prediction error, I planned to go that target and the curve didn’t go there, I need to close
that error, okay.
So what this is implying is that adaptation is being driven not by target errors but by sensory prediction
errors, right. If that’s true and there’s a work that I’ll briefly go through suggesting that to the degree
that it’s a forward model, with a forward model it’s some sort of neural network that can predict the
sensory consequences of commands and there’s a lot of work on that. That the cerebellum is the source
of this forward model, then perhaps you could paradoxically see that patients who can’t do forward
model learning that’s dependent on sensory prediction errors could actually cheat better than healthy
subjects, right. That’s exactly what you see. This is one of those paradoxical results were actually brain
injury makes you better at a task, right.
So here’s the, a group of controls doing exactly as we said, here you are at baseline, here’s the rotation,
you get given the strategy, you do fine, and then you drift. Here you do exactly the same thing and the
cerebella patients do a much better job of applying the strategy and staying on target, right. Just what
you’d predict if their deficient in a particular kind of motor learning called adaptation, which is forward
model dependent learning.
>>: You say cerebella damage obviously…
>> John Krakauer: This is…
>>: Cerebellum is a complicated machine…
>> John Krakauer: Yeah.
>>: Do you bet certain kind of…
>> John Krakauer: This is; these were done in patients with spinal cerebella atrophy type six. Which is a
degenerative condition which out of many of the spinal cerebella atrophies is more cerebella focused
and it just leads to loss of Purkinje cells, diffused loss of Purkinje cells.
>>: Yes.
>> John Krakauer: Yeah, in fact and you can sort of, this is…
>>: You didn’t test the people in advance to, to create a [indiscernible] that was limited in the same
ways with [indiscernible] ability to do prediction learning.
>> John Krakauer: Well it’s just that they all have clinically…
>>: [inaudible]
>> John Krakauer: Diagnosed SCA.
>>: [inaudible]
>> John Krakauer: Then here’s a group for example. So here’s controls doing this, and then you see that
when you try to do this regular learning in these patients they can’t do it, right. They’re just, they
haven’t lost all their Purkinje cells but you see that they’re impaired at just learning this kind of task. If
you do an imaging study of this which we’ve done, this is an old PET study. I don’t know why that’s
coming up in white there, it’s very strange. But you know you can see cerebella atibation performing
this task.
So the current orthodoxy represented by that first paper is that motor learning adaptation is some sort
of systems identification where if you’re tasked goals you have a control policy, you send motor controls
to the plant which leads to a change in state. At the same time you send a copy of the command to a
forward model and you have a predicted state. This leads to the ability to actually compute a prediction
error. Then somehow this prediction error dependent learning leads to an update of a control policy
represented by this thick red arrow. This is a diagram made by a Post Doc in my lab called Adrian Hath
who’s working very much on this distinction between this model-based learning and another kind of
learning, model free learning. You go from this prediction error which we think is in the cerebellum to a
control policy. This is adaptation; this is the orthodoxy, alright, and its model-based learning. All right
so this is what you’ll be seeing if you go perusing with literature on adaptation and looking at it from
computational standpoint.
Okay so we’re back to this. So you know to this, to encapsulate a little piece of the talk. This kind of
learning led to this dissociable memory systems and this kind of learning is thought to be driven by
prediction errors and cerebella dependent. But you may all be saying what’s this got to do with us going
to watch, this is Roger Federer in two thousand six, two thousand seven when he was on top of the
world. He’s had a little bit of a drop off at this point. Do you think that motor adaptation and all the
studies are telling us anything about this? I would say, no.
Because what does adaptation do? It gets you back; at best it gets you back to your baseline, right. So
in other words if I’m a basketball player and I’m transferred to a tournament on the moon and I adapt to
a change in gravity I’m not going to be any better at basketball on the moon. The best I can hope is to
be as good as I was once I’ve adapted to the change in G to the way I was on earth, right. But what we
admire is that these people are better over time than they were at baseline, right. But not enough to
return to baseline levels of performance and setting of perturbation.
So how do we deal with being better not just being as good as, right? Now that surprisingly is
understudied in the motor learning literature, right. So…
>>: In this picture what are you adapting to? You’re adapting to a very rich and dynamic, as you’re
learning tennis skills…
>> John Krakauer: No I’m not denying that you’re having to update potentially forward models of your
body, your plant, the racquet, you know and, I’m not saying that if you had a taxed year from SCA six or
cerebella infod you wouldn’t be able to play tennis, right. But being able to adapt and do systems
identification of your own body and balls and racquets is necessary but surely it’s not sufficient to
become really good at it, right.
So we decided, this is Lior, he’s now back at, he was a Post Doc in the lab and he’s now back in Israel,
and Pietro. We decided we’d come up with a simple, non-perturbation, non-adaptation related
experiment, okay, surprisingly understudied, okay. So Pietro and Lior came up with the arc-pointing task
where you really, so you have, you know there’s this retroflected mark on your hand, here’s an infrared
camera, and you simply have to, and I’m going to try and do it. You simply have to try and get the cursor
through this tube without hitting the edges, alright. You’ve all played some version of this, right. Now
what’s nice about this task, right, it’s a continuous, it’s a fast task but you have to make continuous
adjustments, you have a mixture feedback and feed forward, and it’s hard, right.
[Demo]
So I’m just going to, I hope this works, show you, whoops, aw, you miffed. That would be what would
be the first time you try and do it, right.
[Demo]
Right, so that’s what it’s like when you try to do it the first time, alright. Then you train over, in this case
they trained over five days, three to four days actually.
[Demo]
Pretty impressive, right, now the question is how do you do that? What is happening over the days of
training that allows you to do that? That’s what, he’s, whoever did that is the Federer of this task, right?
[laughter]
Now what’s interesting is what we did back in two thousand nine we applied it here to. Is we said we’re
going to operationally define this ability as the ability, and Eric mentioned Fitts to shift your speed
accuracy trade off function with practice. It’s actually fascinating that Fitts Law, you know this speed
accuracy trade off was the paper, original paper came out in nineteen fifty-three, right. Yet, and I say
this in all modesty we could not find an example of where people deliberately tried to derive a speed
accuracy trade off function before and after learning, alright. So what’s interesting here is, so this is the
movement time distribution over which people were making movements while training. Then what you
do is you deliberately test the speed accuracy trade off at imposed movement times outside of the
learning situation.
In other words, it’s not a good idea to just study learning during acquisition because all sorts of things
might be going on to change your movement time and your accuracy, not because of some limit but
because you’re exploring, for example. So what you want to do is you want to have a learning phase
and then before and after the learning phase you deliberately assay the speed accuracy trade off with a
metronome and see what people’s accuracy is at fourth movement times.
What’s interesting is here’s your day one speed accuracy trade off function. This is what it looks like
after training and you see this global shift, outside ranges notice that you actually trained in.
>>: Have you studied things like the training is fixed but you allow sleep or time to pass?
>> John Krakauer: Yeah, so in other words, well in this case this is done over days, right…
>>: But if you stop training and compare…
>> John Krakauer: Yeah, so…
>>: At one hour to include night of sleep.
>> John Krakauer: Yeah, right so there’s an entire literature on whether motor learning consolidates
with sleep or not, alright.
>>: [inaudible] ha, ha.
>> John Krakauer: Okay, and the current thinking and we haven’t done it with this task, is that there
doesn’t seem to be for adaptation tasks any effective sleep. In other words if you wait twelve hours on
the same day versus twelve hours overnight there’s no difference. If you look at the implicit
components of something called the SirA reaction time task there’s no difference. What seems to be
causing a difference is when there’s an explicit component to consolidation. Now consolidation has two
meanings in the neuroscience literature. One is resistance to interference by some other task and is
there a window after which the memory for task A is safe from interference from task B, versus the idea
of offline games. Meaning when you come back the next day your performance at the beginning is
actually incrementally better if DC shifted up from your last performance on the day before, right.
Now people confuse those two. But to the degree that it looks, there’s been a sleep related effect it
seems to be on explicit sequence tasks looking at the offline affect, right. But it’s, at the current time it’s
still fuzzy how important sleep is. But for sequence tasks that have a knowledge order component it
seems as though there’s an offline gain.
>>: I guess what I’m also getting at here the [indiscernible] cognitive, highly cognition like memory we
have models of short and long term, salvation models for its kind and masking interference. This
motor…
>> John Krakauer: Yes.
>>: Is it outside that?
>> John Krakauer: No, no, no, no, so other words it’s…
>>: In other words can you have a short term memory defect or long term memory defect…
>> John Krakauer: Yeah.
>>: With motor…
>> John Krakauer: Okay so in other words if they, it’s a contentious issue right now. My position that
I’m taking which is somewhat polemical is that adaptation is a short lived working memory like
phenomenon. That any long term motor learning is actually in the skill domain and is not error based,
its reward based.
>>: This is the part of your punch line, I guess…
>> John Krakauer: Well it’s a bit of a part of punch line, but it is the direction we’re going in. But that
causes a lot of people to be upset. Because they say wait a minute you can have prisms and you can
learn over time to sort of take the glasses on and off and you switch between internal models they call it
and things like that. I’m not going to talk a great deal about this but what we’re arguing is that piggy
backing on top of the adaptation system is another part of your brain watching the solution that the
adaptation system converges upon and goes, ooh, I’ll remember that solution. So that when you now
do it again you don’t have to go through the error based process that led you to that solution. Another
part of your brain remembers solution that was derived and that gets recalled.
Now recalling the solution that one system originally derived is not the same as saying that the initial
system has a long term memory, it provided a solution that another part of the brain then remembers.
So what we’d argue is that that other system that remembers solution that the short term system found
has a long term memory.
>>: Right but I guess who’d remember need something very different for cognitive, for recalling an
incident or, you know or an event…
>> John Krakauer: Yeah, so in other words it’s going to be…
>>: The message is going to remember a complex set of motor commands sounds very, very…
>> John Krakauer: Well, okay, so…
>>: Challenging.
>> John Krakauer: That’s right. So let’s just talk about this. So in order words I’m doing, so this is, I
don’t have a slide for this but I, can I draw it on here?
>> Eric Horvitz: Absolutely.
>> John Krakauer: So in other words if you learn over time that to get the cursor into the target your
hand has to incrementally end up going over here, right. You do that very slowly through this
adaptation process. This movement now is the solution to getting that cursor in that target. So now
you go away for a day or two and come back. I make you do it again and what happens when you look
at the learning curve instead of it looking like this you go kachunk like that. Now the question is what’s
this kachunk? Is it just a sort of parametric increasing in the rate of the original system that did the
learning or do you just start again and then say wait a minute I’ve been here before and have a kind of
implicit aha moment of having made these movements before.
What we’re arguing, we have, I don’t have, I can send you the paper is that there’s a system that does
remember what you did before. Now the question you asked is a very interesting one. What are you
exactly are you remembering? This movement by the way that you make to get the cursor in here is one
you’ve made all your life. In other words if I stuck a target here you could do this right away. There’s
nothing hard about this arm movement it’s just you need to associate this arm movement with getting
the cursor in, right.
All I’m saying is that even though you need a slow intro-process to get there which is adaptation or you
can use a strategy, like just aim over here. All I’m saying is that there may be a way to remember this
solution in hand space without having to go through this process again. In fact somebody would
erroneously say that’s a long term memory for this original process. I’m saying that’s not necessarily
true. Yes?
>>: Have you been able to distinguish between remembering the [indiscernible] forward model than
remembering the [indiscernible]?
>> John Krakauer: Yes, so, I’ll tell you, right. So the way we did it is we said okay, usually they’re
confounded, right. So what we did is we said alright what we’ll do is we’ll have people learn a, to two
targets where if you go to this target you have a rotation that’s negative but you end up converging with
hand over here, or you go to this target and it’s a positive rotation but you also end up converging over
here. So this is a plus thirty degree rotation and this is a minus thirty degree rotation, so two opposite
internal models, right. But it just so happens the way that we configured the experiment the hand
solution is the same for the two internal models.
Now if you believe that savings and interference are due to competition between internal models then
when you now come back you do the thirty degrees and now you come and do the minus thirty degrees
there should be no savings. But in fact what you see is if you do thirty degrees the first time and then
you do the minus you have a huge advantage doing minus thirty. Can’t be that you’re recalling the plus
thirty; you’re doing the opposite internal model. So what can be causing the savings? Well, it’s because
the hand movement of the thing in both cases. For that experiment to my mind very clearly showed
that what led to this improvement was actually the control policy and not the fact that you remembered
the mapping. Because in fact that wouldn’t help you it’s the opposite. Okay, so that is exactly what led
us to believe that it was a mistake to think that you were doing this through either revving up the initial
adaptation process or switching between maps, right.
So how are people doing this, right? So what people are doing when they bring down their speed, they
shift their speed accuracy trade off; this is a trajectory in that tube in normalized time. These are the
five movement times that you test the speed accuracy trade off. This is your trajectory variance. So this
is what you’re like on day one and unsurprisingly as you go faster you get this increase in variability, it
builds up over the course of the movement. This is signal dependent noise and then you compare this
to what happens on day five and you just see this unbelievable dramatic reduction in variability.
Now the question is how does the brain do this? In other words what is allowing this to happen with
practice, right? Now this is not systematic error. In other words in this task the mean trajectory that
you make on day one and day five are very similar so you’re already at the slow speed, like that slow
cursor that I showed you. That trajectory that you made slowly is not different to the trajectory that you
saw when you made it fast on average, right. So unlike adaptation where you’re having to make a new
mean movement, here the mean does not really change, what changes is the variance around it, okay.
So I’m saying that some people study systematic error and we were studying variable error.
Nevertheless you could argue that those two pieces are very implicit, right.
So where’s cognition? So before I get to that I just want to say that we imaged this. So in other words
we’ve done imaging experiments now and brain stimulation experiments to look at the neuro-substrates
of the systematic error process and this variable error process. This is what Lior Shmuelof did. This is
just submitted an FMRI study and there’s a lot of images but basically the main point is that because so
many people were studying adaptation unlike in the animal literature the human literature was
beginning to forget about motor cortex. But what we were interested in is we’re just interested in that
variability reduction process; where is that happening? Essentially the contra-lateral motor and premotor cortex and the ipsilateral cerebellum are activating for the learned task even when you control
for the performance. In other words you can’t study motor learning if you keep imaging the improved
performance. Because then the imaging change that you see is simply the change in the actual
performance not the learning process per se, right. It’s called the performance confound.
So what Lior did very cleverly is he used this task to clamp the trajectory and the speed that you moved
at. So that on day one and day five you were doing easy slow version of the task. But even though you
were performing the task the same way trial by trial on the two days the variance was different, the
second order part, and the activation was still there. So it would be a little bit quotient to if I’m sort of
standing here and a world class pianist, one of you might be very good pianist and that we both stood
up here and you had to guess which one of us was the world class pianist and all we were allowed to do
is press middle C at one hertz, right. You wouldn’t be able to tell which one of us was the, because I
clamped the performance, right. But if you imaged us I would say that for the same phenotype you
would see a difference. That’s exactly what Lior showed that by, there’s a difference in the extent of
activation in motor cortex on day one, day five, even though the performance is the same…
>>: And its [indiscernible] you said?
>> John Krakauer: Right. Huh?
>>: [inaudible] less of the…
>> John Krakauer: More.
>>: More [indiscernible]?
>> John Krakauer: So in other words, and so…
>>: You didn’t actually, said the actual magnitude yet that was changed.
>> John Krakauer: Well, okay so we don’t, these are statistical maps.
>>: Okay.
>> John Krakauer: Okay and you have to be very careful not to conflate changes in T maps with true
changes in extent of activation...
>>: Right.
>> John Krakauer: Right, so either you could be activating more voxels from day one too day five or the
same number of voxels but their degree of activation of that same number is increased. You can’t
decouple more activation the same voxels or more voxels. Now we did some analyses to suggest that
it’s more but when it comes to signal to noise there are two ways to increase your signal to noise, one is
more, right just have more neurons doing the job or you better recruit neurons tuned to the task that
you’re doing, right. Now if you look at the sensory literature it seems like you have these two phases.
You expand the population doing the task and then you select from that expanded population the more
specialized units, both of which could lead to reduction in increase in signal to noise, and one would
lead, you’d expect to lead to an increase in activation and then a sparse decrease in activation, right.
What we’re seeing here at least at this stage looks like it’s the increased part.
>>: So I guess there’s a sense of a well honed machine and that there’s less exploration and costly all
sorts of things so that it gets more efficient over time leaving less of all this stuff going on.
>> John Krakauer: Yeah so I would say that it probably depends on the task and when in the learning
process you are, alright. Here we were more interested in saying that for a task requires a shift in your
speed accuracy trade off and reduction in variability you should expect to see changes in the contralateral motor cortex, pre-motor cortex and cerebellum, and that’s what we saw. It’s, and in the current
stage of it, it looks like an increase in activation but ultimately I think you’re right, that’s what will
happen.
Then just too sort of go with that Janine Reis and Heidi Schambra when they were working with, in Lior
current [indiscernible] and I was collaborating with them. We wanted to see whether we could turn
people into super learners on a skill task that’s non-invasive brain stimulation. So this is the task. This
was devised Pablo Celnik who’s now at Hopkins too really spearheaded this. This task is very simple you
just have to, it’s isometric, you squeeze a fourth transducer, and you simply have to get the cursor into
this gate, come back, get it into that gate, don’t overshoot, come back. If you overshoot you lose the
whole trial. It’s extremely difficult, alright.
>>: [inaudible] menu control for circuits.
[laughter]
>> John Krakauer: Right, right, and it’s non-linear so what they did, right, and it’s non-linear. So
basically it’s not as though there’s an exponential function attached to this cursor so it’s very difficult,
right. So then what you do is you use, you isolate the hotspot and prime motor cortex using TMS over
the APB. So you just say okay we now know where we’re going to put the anodal transcranial direct
current stimulation. Basically you take a six volt battery and you stick on people’s head. This is TMS,
trans…
>>: [inaudible] describe this more, like who are these patients…
>> John Krakauer: These are healthy subjects…
>>: [inaudible]
>> John Krakauer: We only…
>>: [inaudible]
[laughter]
>> John Krakauer: No, no, no, no…
>>: Healthy [indiscernible].
>> John Krakauer: Well, transcranial magnetic stimulation…
>>: [inaudible]
>> John Krakauer: Transcranial magnetic stimulation you basically have an alternating current in the coil
which leads to a magnetic field which induces a current in the cortex and actually triggers spiking in
motor neurons, right. You get something called an MEP, a motor evoked potential, and it allows you to
say okay I’m over motor cortex. This is a hand task so you wanted to get activation of a muscle that
would be used in the task and that’s what Janine did.
>>: So how precise does that explain the [indiscernible] methodology? How precisely can you stimulate
muscles that way?
>> John Krakauer: You can stimulate individual muscles in the hand, right. Not all equally well but the
APB the abductor pollicis brevis and the…
>>: Is it safe?
>> John Krakauer: Yes, single pulse TMS is safe. You know things like phata burst and high frequency
repetitive TMS some people say shouldn’t be done in people with a history of epilepsy, maybe not after
an acute stroke. But lots of it’s been done now it’s very, very safe, yeah. Then what you do after you’ve
done the TMS you know do TDCS which is a direct current which isn’t thought to make neurons fire but
it may bring them closer to threshold. So when you voluntarily want them to go they’re more likely to
go…
>>: [inaudible] wrong?
>> John Krakauer: You could fodel TDCS but they’re not exactly antithetical to each other. It depends
on the placement and, that’s an interesting question we have that control here. But, and so then what
we did was we trained subjects. Janine, you know to do this kind of study, by the way the reason why
learning adaptation studies are so popular is because you can do them in a day, five minutes to adapt to
a prism. If you want to study long term learning you have to do what Janine and Heidi heroically did
which is you train over five days and then they followed up over three months. I mean these are the
kind of studies that are needed but they’re hard to get funded.
So stimulating them as they train and then following up. Okay, the idea being that if you stimulate
motor cortex and that’s the important substrate for learning a skill you can actually turn people into
super learners, right. Now that isn’t why we did this. We were interested in consolidation. Now…
>>: But what if the, why would you have hypothesis that like this general blasting of part of the cortex in
a part of the brain that’s probably doing structured things, very, very fine precision that that turning on
is a way of turning things…
>> John Krakauer: Yeah, yeah that’s a very good question. So the idea simply is that to the degree that
you get a delta for any change whether it’s an error or an update…
>>: [inaudible] change, okay, but…
>> John Krakauer: Right, so in other words…
>>: So you’re…
>> John Krakauer: The idea…
>>: You weren’t essentially super learning [inaudible]…
>> John Krakauer: The idea, well the idea is can you increase the gain of the update for a given error. In
other words if I, for example here you have a thirty degree error and in one trial you bring it down by
five degrees, why not bring it down by ten, right. In other words, now there are Bastian arguments to
why you don’t do one trial learning, right, based on are you correct in the error that you perceived? Do
you trust your ability to make a big change, right? So based on your confidence about the world,
confidence about your plant you optimize the size of your update based on your relative certainties.
>>: But looking at a whole number of hypotheses as to what’s going on in blast, mostly blast parts of
brain more blood flow, who knows more oxygen.
>> John Krakauer: You know and in fact to the degree that people have looked at what TDCS is doing in
slice preparation. So Janine and somebody she collaborated with Britta Fritch have done this work on a
slice preparation of motor corta. It seems to be enhancing ulti peda protein synthesis for example. So
your questions are very well taken and, but the idea just is could you just rev up a little bit the gain on
the deltas that you do in response to errors. But you’re right it’s not any way a given, and you’re right
this is what’s happened with deep brain stimulation for people with Parkinson’s, the idea that subtle
phasic changes can be mimicked by one big tonic step, right.
[laughter]
You’re absolutely right, okay. You, I’m sorry?
>>: Yes, I was just wondering if you compared your stimulation with changing the virtual gain. So having
that cursor respond with more sensitivity and compared with like what types of gain you’re getting out
of the stimulation…
>> John Krakauer: Yeah, so we’ve done a lot of, so there’s slightly different meanings to the word gain,
right. That would just be changing, you know for one centimeter change in your hand distance you, it’s
half a centimeter in cursor space. We’ve done a lot of work on that and brain stimulation.
What I’m talking about is when you do a little bit of protein synthesis and change a synaptic bouton on a
dendrite, right. That by doing TDCS the construction of that bouton is a little faster trial by trial because
of TDCS. So I’m talking about the gain on the plasticity process in the brain versus a gain in the
relationship between a cursor and they’re not the same thing, right.
Did you have a question? I’m sorry you…
>>: I was wondering how did you, did you actual test day six to eighty-five and then you just pick five
things you report on? How do you arrive at we’re going to follow up on the eighth, and fifteen, and
twenty-nine because it seems kind of [inaudible]?
>> John Krakauer: Yeah, yeah, no this was, I think these are averages of the days. No it wasn’t, it’s got
to be day twenty-nine, there was some jitter on it, alright, and it’s an average.
>>: So [indiscernible] terribly…
>> John Krakauer: So basically you’d…
>>: [inaudible] version of the story…
>> John Krakauer: So essentially on each of these five days Janine had them get anodal TDCS as they
practiced the task.
>>: And then what happened?
>> John Krakauer: And then we’ll show you what happened and then the follow up. But the follow up is
without stimulation, right. This is a measure of the shift in the speed accuracy trade off function. So in
other words the parameter that we called skill learning was a parameter that captured the shift globally
of the whole speed accuracy trade off function. This is people who were doing it without; this is people
doing it with, right. It’s not subtle, right.
>>: You’ll know you’re successful when the various support quotations don’t [indiscernible] anymore.
[laughter]
>> John Krakauer: I don’t, so does that, I get asked that every single time so does Pablo Celnik more
than I do because he’s really the expert on brain stimulation. There are ethical concerns because you
can’t test the urine for having had a battery in your head.
[laughter]
The, but, so in other words I don’t know how you’re going to do anything about this. You could check
peoples MEPs, in other words there’s, when you get TDCS transiently the excitability of motor cortex to
TMS goes up, but that’s transient, right. If you look at, I don’t have the plot but over time this advantage
at day five was still there three months later.
>>: I’m curious what’s your sense about a cup coffee, caffeine, nicotine, other kinds of…
>> John Krakauer: Yeah.
>>: Stimulants did the same kind of part of the cortex.
>> John Krakauer: Well, I mean that’s definitely true for the bold response, for example the functional
imaging. I don’t think that coffee is going to make you more skilled at learning…
>>: I don’t know about teas or coffee but some medications are…
>> John Krakauer: Oh yeah.
>>: Going to have some kind of effect.
>> John Krakauer: Yeah, I mean in fact, there’s so much I could show you but we’ve got some beautiful
work that Steve Zyler who is Assistant Professor at Hopkins on using Prozac in a mouse model of stroke.
What’s fascinating is that if you take a mouse and give it a stroke and then you train it within twentyfour hours of its stroke it can get back to the performance levels that are equivalent to before it was
given its stroke. If you just wait a week and then you train it again it never gets, it gets fifty percent up
and then it plateaus. It, having waited a week you’ve lost this window of plasticity…
>>: [inaudible] week, it’s immediately with the Prozac.
>> John Krakauer: But what he’s done if you give, no, no without Prozac and you start training…
>>: [inaudible]
>> John Krakauer: Right away you get back. If you wait a week, if you give the mouse Prozac after its
stroke but still wait a week before you start training…
>>: Awesome.
>> John Krakauer: It now gets back up to normal, right. It’s very interesting. So in other words the
intersection of brain stimulation giving drugs is going to, that’s what we’re trying to do in stroke right
now, right. It’s very exciting. Just Pablo Celnik and his lab this is just to show that in, I just showed you
enhancing learning by stimulating over M one for a skill task for the variable error kind of task. If you
stimulate over the cerebellum we’re using TDCS for the systematic error task you speed it up if you
stimulate over the cerebellum. But nothing happens if you stimulate over motor cortex.
So here’s a beautiful double disassociation to bring down the systematic errors you stimulate over the
cerebellum, nothing happens when you stimulate over motor cortex. However, if you stimulate over
motor cortex and then you look at the retention phase later now what was acquired by the cerebellum
is retained better by the motor cortex. So the link that we would argue between the error reduction
paradigms and the variability reduction paradigms is that even in the error reduction paradigms the
memory for the command may actually be formed in motor cortex, right. So, and I would make the
claim that when we’re studying skill we’re looking at the motor cortex and the motor cortex is also the
site for remembering what the cerebellum did, which is the title of Pablo Celnik’s paper actually.
So…
>>: When you talk about memory for the [indiscernible] what if you change the action to be instead of
thirty degrees, twenty-nine degrees, or twenty-five degrees. How quickly can you, are you literally…
>> John Krakauer: Right.
>>: Is there some parametric variation?
>> John Krakauer: We don’t…
>>: Generalization.
>> John Krakauer: Yeah generalization of model three learning…
>>: Yeah.
>> John Krakauer: Has not really been studied. Okay, there’s no, I mean people sort of interties think
that if you have a model like of a rotation then you should be able to apply that model anywhere. Right,
so I give you this target and it’s a thirty degree rotation or this target. Your model is rotate movement
by thirty degrees that should be global. So when you do gain adaptation it seems fairly global actually.
If it’s just learning a particular thing it’s easy to retrieve but the price you pay is that you don’t have to
do a lot of computation to generalize so it should not be generalized. But we don’t know if that’s true.
We don’t know what the generalization should be for model three.
So this is Jason Stanley. He’s a Professor at Rutgers. He’s moving to Yale now and he wrote this piece
which is fun to read in the Opinionator last year on this notion on The Practical and the Theoretical. He
argued that it’s a false distinction, right. This is actually a very nice, there’s a photographer called Steven
Pike who’s the main photographer for the New Yorker Magazine and he did a series of very famous
photographs of philosophers, very strange project that philosopher’s the ultimate thinking kind of
people and you think that you can work out what they’re like by taking photographs.
[laughter]
Anyway, so let’s, so, so far just to be clear I’ve talked about two kinds of learning, a sort of acuity
learning where you can bring down your variance and systematic error both of which seem to be
happening without you having to sort of intervene consciously, alright. But that can’t be right when it
comes to sport. That would drive Jason crazy if he thought that motor ability to just those two
components that I think I’ve shown you in the motor cortex of the cerebellum.
So let me just tell you about movement strategy. So this is a review that Jordan has done. You probably
know this story but this is about the high jump, okay. So this is the Scissor jump, it’s how people use to
do the high jump, alright. Then this happened. This is the Fosbury Flop. Now Fosbury wasn’t a
particularly good Scissor jumper but he had an, aha moment and decided to do that. Now if you look at
the world records, here’s the Scissors and there was the Western Roll, Parallel, Fosbury Flop. So what
I’m trying to say is you could argue that within anyone of these strategies you get learning of the kind
that I’ve been talking with you about thus far, right. But then you have this kachunk moment where
even if you’re not a particularly live athletic person just a cognitive switch in strategy will get you ahead
of all these really fit Western Rollers, and Parallel Straddlers, right.
So then that makes you ask well if you’re going to watch someone in like a Roger Federer where should
the credit go. Is it the adaptation stuff that I was telling you, talking to you about? Is it that acuity stuff
or is it that the really good athletes are sort of know something that other people don’t know, right.
Now as soon as you start couching things this way it just opens a Pandora’s Box, right. Because suddenly
you’re beginning to argue that cognition is having a huge role in the point scores. Then the distinction
between, is inventing the Fosbury Flop any less impressive than the theory of special relativity? Ha, ha.
>>: [inaudible] Fosbury Flop.
>>: Absolutely.
>> John Krakauer: So here we have them, thus everyone does this now, there is none of this. So,
working with Richard Ivry as a Chair of Psychology at Berkeley and Jordan Taylor at Princeton who is in
his lab and now we’re working together. We thought we’d start to look for Fosbury Floppishness even
in this so called totally implicit task, okay.
So, I’ve already told you what happens when you apply this strategy, you drift away. But look what
happens over time. I never told you this. I showed you the experiment to there but if the experiment
keeps going you overcome this sensory prediction error override and you override it in an intern. So
what is this? Right, so here you are cognitive strategy, success of cognitive strategy, failure of cognitive
strategy, success of something, right. So you have this dynamic push and pull going on presumably
between different parts of your brain, right.
Then you look at the after effect. So you say to people look don’t apply a strategy if you’re applying one
and the rotation turns off. Look your target error’s zero, the rotation is forty-five, your after effect is
only about fifteen degrees. So how did you take up the rest of this slack? It’s not implicit adaptation
because the after effect isn’t up here, right. So it suggests looking at it this way that something else is
going on to take up the rest of that error. So its evidence of two processes, one seems to be target error
dependent, the other one is sensory prediction error dependent. So you’ve got what I told you about
before, did the movement go as planned? This is the sensory prediction error. Did I achieve my goal?
This is performance and basically these two things are having a little duking it out, alright.
Essentially this is average behavior if you take a look at individual subjects you see a lot of differences
and strategies and what looks like an increase in variability which looks like exploration. So to cut a long
story short I’m not going to go, the idea is that is seems as though you get a strategy given. It fails and
then you go wait a minute I’m not going to stick to the strategy experimenter told me I’m going to take a
little walk about, right. Then the idea is you do a walk about and then you begin to have an incremental
walk about that begins to counter the adaptation strategy and they cancel each other out and you get
adaptation, zero target error.
You can assess the strategy by actually have a series of numbers. What you do in this experiment is you
have people adapt in the normal way but between each trial you say where would you point next? You
actually ask them to verbally report, alright. So if each target you rotate the numbers and you can get
the report. This is the instruction before moving tell me where you think you should aim to get your
cursor on the target? Now that question you could argue and we want it to be the case invokes the use
of a strategy, right. But it isn’t like the original experiment where you tell people aim to forty-five
degrees to the other target. All we say is before moving tell me where you think you should aim to get
your cursor on the target, right.
So that’s all that happens. You have the numbers, you get that instruction, and then you adapt. It looks
fairly economical, baseline, adaptation, after effect. But notice the after effect is not up here, right. So
then what you do is you turn the numbered spaces into degrees because they’re obviously they have
fixed intervals. Then you look at the mean report that people make across these trials. So you see that
they’re sort of exploring and zigzagging about and then they fix on this report that has a slope like this.
So this is empirical data of verbal report of where you’re aiming, right.
So it doesn’t look like an aiming strategy which is not zero and this model that I told you about are
working together, right. So you can decompose this target error in two, the aiming strategy, the model
adaptation minus the imposed rotation and that gives you where you should actually go, alright. So I’m
going to do this. I’m going to take this slide borrowed from Jordan. Take the empirical global
adaptation, take the report, subtract them, and low and behold you get this slow process where now
the after effect and where it reaches match, okay. So what you see here then is this we would argue is
the cerebella dependent forward model sensory prediction error process. This is the strategy which is
undergoing change. Notice how this counters that to keep that flat, right.
Now understand so you don’t get bogged down in the details. What we are showing you here is a kind
of task that has been tactically assumed, being entirely implicit and the modern day analog of what
made HM so famous which is the non-explicit learning process. It’s not even true that even this kind of
task has a strong cognitive component. Now what’s interesting, I don’t have the data here is if you don’t
put the numbers up and you don’t give the vague instruction you still see this component admittedly to
a lesser degree.
So what does it mean? It means that your brain has multiple ways to solve a problem even a task that
you think should be left to your implicit processes is contaminated by cognition. Depending on how you
frame the problem you actually will determine which system in your brain is using it, alright. So once
you start seeing this the whole breakdown between the practical and theoretical just doesn’t apply with
respect to task. It doesn’t mean that it’s not right to consider there are modules in the brain that are
operating that can be decomposed for any given task. But you mustn’t make the mistake of thinking
that those modules represent the jobs and the tasks that you do in the real world, alright.
We don’t know whether learning your French vocabulary or learning Math, or learning to do the high
jump or tennis are not all using those modules. That they all, you might be surprised that the weightings
on them…
>>: The cerebellum could be implicated in those other…
>> John Krakauer: Yes, in fact there are lots of projections from the lateral cerebella hemispheres which
fascinatingly have evolved along with an increase in the sides of the prefrontal cortex. If you look at
evolution it’s been the prefrontal cortex and the lateral cerebella hemisphere that have co-evolved.
There are lots of data now showing the involvement of the cerebellum in what people have considered
cognitive tasks, exactly, right. It’s a whole talk unto itself. So here’s the idea that these neural systems
are all converging and playing together and sort of coming out through motor cortex. That we really
have to reconsider the way we dichotomize these things.
Now that’s my last slide but I want to just tell you where we’re going. Okay, now those tasks, you know
the little cursor going through the loop, the rotated cursor are super simple. They’re the lab based
equivalence of pong, right. Then I showed you those two reviews that we wrote and one on athletes
and one on motor learning in the lab. How are we going to bridge the abyss between complex motor
tasks and the real world and these very simple learn within a day tasks, even the one I showed you of
five days?
So where we’re moving now and I think this is one of the things that I did talk to Eric about before. Is
that we’ve decided to move the lab onto a pad. That we think that gaming is the key to bridging lab
based motor learning and what most people think motor skill is in the real world. Now what I don’t
think that means, now there’s people like Daphne Bavelier who’s doing a lot of interesting work where
you take off the shelf video games and then you begin to look at things like selective spatial attention,
perceptual learning. But I don’t think that that’s the way to go. I think we need, now need to take the
capacity of doing, having a lab on a pad to do high-end longitudinal studies over much longer time
frames. That has not been done, right.
The clear way in my view is to think a priority about the component brain parts, the neural processes,
the computational processes, and the behaviors, and build them from the ground up in games. Alright,
now I’m just going to see if I can show you, so what we’re, so this is just a, first what we’re doing right
now is we have a grant to study traumatic brain injury. One of the problems with traumatic brain injury
is that after awhile subjects have subjective complaints after they recover that you can’t objectively
measure. I can’t concentrate as well, I’m not the same person, I’m not as clever as I was. A lot of time
they get considered that they have psychiatric diagnoses.
So we have a project that I’m doing with an intensive care neurologist, physician at Hopkins called
Robert Stevens. We have a grant from the Brain Science Institute and we call it Wake Up and Walk Out.
We’re interested in people who were in a comma after traumatic brain injury that walked out. The
reason I wanted to study these patients is they’re telling us something about recovery that they were in
a comma and yet they walked out of the hospital back into their lives, right. Then we want to track
them over time.
Now how do you track people in their lives, right? So we thought we would use, we would develop an
IPad game and we have a group in my lab now and I’m happy to answer questions about them
afterwards, where we’re really trying to do neuroscience longitudinally and high end with motor
learning and skill. It’s a big challenge because I’ll say this quite frankly you try to get the NSF or the NIH
to fund you where you say I want to follow two hundred people over a year to look at individual
differences in the, and they poof, you know. So this is why we’re sort of in the dark ages when it comes
to studying learning because it takes this kind of thing.
I think this is the way to do it, right. Having hundreds of thousands of people coming to your lab, you
know it’s, there’s no throughput. But this is a way to have high throughput. So what we did is this is just
a prototype of a game where we want to study the recovery of multi-tasking after brain injury. So
Premiet Roy and Omar Amard and the Artist Kat McNally have teamed up to start developing these
games to study these patients in.
So this is just an example of this, it’s not that, let me just increase the size. I think that works, oops, I
don’t know why it does that. I’ll keep it small. So this is a beautiful little thing. I mean this is an ant car.
This is a physically realized creature with its own physics. This is not an animation, okay. What these
patients have to do is to stay in the orange piece of the track and to not allow themselves to be
distracted by the little fighters that come. The idea is with your finger you have to hit the red one but
not hit the blue one. The idea is that when you’re trying to both pay attention to a contingent catcher
task and maintain accuracy, can you in fact do both? Now there’s very little literature on motor skill in
the setting of motor tasking, almost nothing actually.
So what’s fascinating is that you can, what we’re seeing in the primary data are that you get differential
affects on each of the tasks, right. At the moment in healthy subjects that we’ve piloted in they seem to
be able to maintain most of the time on track. But they start to fail at the contingent capture when they
actually have to also drive, right, and even though you look fine in both. So in other words the idea is
that the patients may look perfectly fine if you ask them to do the driving part and perfectly fine if you
do the contingent capture, and yet as soon as you ask them to do both they start to break down, alright.
Now this is extremely simple. It’s very beautiful, I mean the controls very smooth and people enjoy it
very much. But this is just the tip of the iceberg in terms of what you could begin to build up from the
ground in terms of cognitive components to learning.
If you go to our website, I don’t know if you can pull it up. I guess I could do it here, right, I’m online I’m
going to just show you our latest, just to tell you what we’re now doing, okay.
>>: So [indiscernible] don’t break down?
>> John Krakauer: No, no.
>>: There must be some level, right.
>> John Krakauer: Yes, you can, yes…
>>: Threshold.
>> John Krakauer: So this is just the new cover to our, the front page of our website. What we’re doing
now is this is an exoskeletal three D robot which basically can assist three D movement of the arm.
What we’ve been doing is we’ve been working with the aquarium in Baltimore. I don’t have video here
but it’s, because, you know I don’t know how. But basically what’s been created by our group are
unbelievably gorgeous synthetic fully realized animal simulations, alright. So the aquarium is at the
current time negotiating with us to maybe have an installation in the aquarium.
So the idea is that you end up living in animal space as a patient after injury. The movements you make
and we’re using the Connect, full disclosure, ha, ha, are now allowing patients to exist in an avatar space
with a fully realized animal. In this case dolphins while they get assistance with the [indiscernible] robot.
The level of pleasure and reward that this is giving in order for patients to explore space is unparalleled,
right.
Now we’re very excited about this and others are to. I think this is exactly what has been missing in the
neuroscience of learning and recovery is the confluence of computational principles, proper task
decomposition, and the whole world of motion capture, and reward and gaming, that essentially goes
unappreciated in the biomedical world. We presented this project to the President at Hopkins and
everyone was there, CS, Engineering, Neuroscience, and it, people suddenly and they said to us, you
know we suddenly think we’re seeing the future now, right. But in order to develop good science to
prove the intuition that all this gorgeousness and interactivity is real it requires publications and study.
It’s hard to do…
>>: So I’m still catching up on this, so you, the dolphins a robot?
>> John Krakauer: No.
>>: No…
>> John Krakauer: It’s a…
>>: So it’s a…
>> John Krakauer: Three D animation…
>>: Yeah.
>> John Krakauer: We haven’t canned it. What they’ve developed is a physically realized object that
responds in unexpected ways to inputs, right.
>>: So it moves in mapping the inputs on the exoskeleton to the three D simulation on the dolphin?
Okay.
>> John Krakauer: Whatever you said, ha, ha.
[laughter]
>>: [inaudible]
>>: Is there, have you studied, has anybody studied learning of the joint task situation over time learned
to get better at that?
>> John Krakauer: Yeah, so we’re doing that.
>>: [inaudible]. Is there a learning curve there?
>> John Krakauer: U-huh.
>>: How do you characterize that?
>> John Krakauer: You know it’s…
>>: Is it systems and methods?
>> John Krakauer: You know it’s so new…
>>: Yeah.
>> John Krakauer: That I, the only way we’re going to get to answer that question in my view is because
people are different. I mean some people take risks; other people are cautious, right. The expiration
exploitation trade off. Some people do it one way and some people sacrifice one versus the other. The
only way we’re going to know is to parameterize it like you suggested and have a high enough N to
begin to see. If you do what people do, I showed you the, what happens when you average the data.
The smooth turn around point that I showed that’s not what any individual does, right, it’s what
happens when you average the data.
>>: [inaudible]
>> John Krakauer: So, and that’s what all studies do. You have twelve subjects, fifteen subjects you
average the data. But what we’re beginning to notice as the tasks get more complex there are
idiosyncrasies that people show. They’re not arbitrary they are alternative ways to save or solve the
same problem. That’s what we need to, so in other words multi-tasking just a, if I were to say that I’m
going to use my prefrontal cortex, my motor cortex, and my cerebellum to do the task. I’m going to do,
you know twenty, ten, seventy waiting to do it and another person says I’m going to use all my
prefrontal cortex and I’m going to do no model based. Then you average the data for those two
subjects you get nothing.
>>: Right that means…
>> John Krakauer: That’s what we…
>>: There’s also waiting to neglect the score in example on the joint tasks. The question is there’s lots
of literature like Sterling’s work and so on joint official tasks…
>> John Krakauer: Right.
>>: Where dual tasks scenario we did with visual search that might [indiscernible] in terms of the
motions of imprompting and learning.
>> John Krakauer: Because dual tasks, I mean just because dual tasks have been done most of the time
to show automaticity that at a certain point you’re no longer interfered with by the dual task. The
emphasis has not been on seeing the dual task as a task in total that has to be learned, right. In other
words if tennis is a quadruple task, right, you have to be looking at the court, you have to be looking at
your opponent, you have to be deciding what you’re going to do, the score. People don’t say that the
overall task of tennis is a dual or multi-task interference. But, so it’s framed in the wrong way. What I’m
saying is everything that’s actually what we care about is a task made of components all of which need
to be learned together and that’s just not in the motor domain, it’s not studied even in the perceptual
the way people usually study interference. They’re not studying combination of the tasks.
It’s very hard to do, right. Because it means that you’re going to have to build the task from the ground
up, be able to parameterize its components, and then study enough people. It’s not been done and
that’s what we’d like to do. That’s what the gaming is for.
>>: [inaudible] question to [indiscernible] it’s a few [indiscernible].
>>: Since you [indiscernible] mentioned earlier some long term motor acquisition or skill acquisition.
Years ago there was work done for people learning to, yeah what did they do, what was the task for
people who were doing, it was learning morose code.
>> John Krakauer: Yes.
>>: There they studied individuals so you could see curves where individuals over the course of years…
>> John Krakauer: Yes.
>>: Learning it. You could see some interesting plateaus that looked sort of like your high jumping
example.
>> John Krakauer: Right, I mean…
>>: People have actually looked in detail at what might be happening during those sorts of plateaus and
[indiscernible].
>> John Krakauer: It does nothing, right. Because you see when you talk about those classic studies
they take very global distal measures. They’re not looking at the actual kinematics, the actual
movements that you’re making, right. In other words that’s a very, what we need is the ability to see
what the motor systems implementation of the learning is doing over long periods of time, right. When
their learning morose code no one cares at the way you actually tap the keys, right. It’s, what we need
is a way to actually get kinematics as well as the more global measure of performance and that just,
those studies have never, reaction time gets studied a lot, right. But reaction time, the meaning of the
reaction time to motor learning is extremely, we feel and this is controversial. I won’t show it, don’t
show these data that there is no relationship between the reaction time and the complexity of the
actual movement that you execute, right. So I don’t think it’s a very good measure. Yep?
>>: So just a [indiscernible] that if, how would you explain the studies [indiscernible] seventies where
they showed the movement complexity actually effects the reaction time just in planning movement. So
[indiscernible] movement and then [indiscernible] movement...
>> John Krakauer: Great question.
>>: And without even performing movement just in reaction time or [inaudible].
>> John Krakauer: Oh, right, okay good, great question. So first of all we would have to define what we
mean by complexity, right. You don’t want to be circular, right. You don’t want to say I’m going to call a
movement complex if the reaction time increases, right. So it would have to have some orthogonal
definition of complexity, agree upon that, and then start looking at the reaction time, okay.
Now the question you’re asking is where is the reaction time being spent? Is it, what am I going to do?
So in other words we’re doing an experiment right now with a patient, Aaron Wong and it’s very cool
you just have gates like this and you’re basically you’re in here and you have to get in there. So you go,
whoop like that and then you go okay what about this one? Whoop I have to do that, right.
Now you could argue and then you could have the easiest in the world which is that, choop. So you
could say alright I’m going to look at the reaction times for this versus the reaction times for that. Low
and behold the reaction time for this there’s a greater increasing reaction time, okay. Now why? Is it
because it’s more difficult to execute this movement or is it because you have to decide to make that
movement solve the problem? So if you now just show this and you give people the solution and just
ask them to trace out that solution the reaction time drops to that level, even though they’re still having
to make that complicated movement. Do you see what I’m saying?
So it suggests that what is happening in the reaction time and this is very preliminary is what do I need
to do, what trajectory is going to solve this problem? The actual execution of that trajectory isn’t
necessarily going to make that much more a demand on you than making that one. That’s the argument
we’d like to make. This is preliminary but that’s, it could be that there’s a little bit of a difference even
once you’ve control for that. But that’s not the bulk of the reaction time. That’s what we would argue.
>>: So John you mentioned the patient in, by name actually, what kind of patient is this and why would
you mention his name?
>> John Krakauer: HM?
>>: Well I think you said Alton Wong.
>> John Krakauer: Aaron Wong is a Post Doc…
[laughter]
Whose doing, yeah see, no, no, no, Aaron Wong is the Post Doc doing this experiment. No, no, no I’ve
been a doctor too long to make that mistake.
[laughter]
The, Aaron, no I was giving credit to a Post Doc who’s actually doing an experiment very much along…
>>: [inaudible]
>> John Krakauer: The lines of ESA.
>>: [inaudible]
>> John Krakauer: Yep.
>>: So there’s a lot of variability once people learn skills like high school or professional athlete. You
know sometimes they’ll be in the flow and be playing very well and sometimes it’s an off night or
something. Any idea cognitively of what’s going on there?
>> John Krakauer: Sorry.
>>: Come into your research at all?
>> John Krakauer: Right, so there’s this idea of the warm up decrement, right. So, no we’re wondering
about that about, there were three potential explanations for it, right. One is, is it physiologically you’re
not feeling, it’s not your skill in that task per se it’s just your bodies cold, you’re not feeling right. So
when people say for example to doctors you know I just don’t feel right today, I feel washed out, I’m just
not myself. Doctors go, ah, whatever, right.
[laughter]
But it’s turned out that there’s this whole new idea of the introceptive system which is coined as the
afferent version of your autonomic nervous system. There are nerve endings unmyelinated small nerve
endings everywhere in your body, in blood vessels, in the skin. Its where, it seems like you can detect
changes in PH, you can detect parasitic infestation, you just, you’re much more wired up for afferents.
Now it’s not labeled lines in so much that what happens is that all goes to your insular, it doesn’t go to
your sensory motor cortex. That’s where you feel disgust and these washed out feelings. So there is
this part of your brain which seems to read out in a global way the state that your body is in, right. Now
one thing that could happen is that there are days when athletes are just not in a good state. It’s, they
actually aren’t a hundred percent.
>>: The engine light comes on the dashboard…
>> John Krakauer: Right…
>>: Check, check, check engine.
>> John Krakauer: Then that leads…
>>: Ha, ha.
>> John Krakauer: To just a general reduction in the quality of your, it’s like having a cold if you’re, and
we know just to finish that there’s something, phenomenon after stoke called peeling the onion. So
you’re a patient, you’ve had a stroke, and you’ve recovered. Then you get a cold or you drink too many
glasses of wine, or you take a Benzodiazepine and your focal stroke symptoms come back. These
patients will go to the emergency room and be and you know it’s serious because oh my god you need
to give them TPA again, right. What you’re doing is you’re peeling the onion.
People at Columbia, Ron Rosard and Randy Marshall have been studying this by giving patients under
IRD conditions these benzo’s to get the recapitulate the deficit. So what you’re seeing then is a drug
that’s making you overall sedated or virus making you feel systemically ill leading to a focal deficit, right.
So I’m stretching it, I’m saying that imagine then that you don’t have to have a stroke or a virus, or UTI
but you’re just not feeling quite right and you’re picking that up with your intraseptal system. You’re
just not going to be ninety, hundred percent…
>>: [inaudible] further…
>> John Krakauer: And then, so that’s one. The other one is, is that you do something where people
choke, which is like a cognitive interference, which is that you start trying to use a module to solve a task
that you would be better off leaving to another one…
>>: Ha, ha.
>> John Krakauer: Right and so its match point, ha, ha. You know I’m about to win Wimbledon, what’s
this I’m holding, ha, ha?
[laughter]
Right, you know it’s, it’s a racket that’s right. You know and you’re, and that’s what some people think
choking is. It’s, you leave it to the modules and you override. So that’s the other possibility is that, just
like I’ve shown cooperation between modules you can get conflict between them and it may well be
that that conflict is the problem. Then the third one is, is that you actually have a warm up decrement.
You’re just not warmed up enough and so you should have spent another thirty minutes hitting before
you went onto the court. We all know about the warm up decrement. So those are the three things
that I think are doing it.
>> Eric Horvitz: And I’m sure of many slides in these days can actually figure out what the warm up
decrement actually even is…
>> John Krakauer: That’s a…
>> Eric Horvitz: [inaudible]
>> John Krakauer: Whole issue, right.
>> Eric Horvitz: Right, thanks very much.
>> John Krakauer: Right, not at all.
[applause]