This is the Convergent Science Network podcast. Leading researchers in the domain
of neuroscience, brain theory and technology are interviewed by Paul Vichure and Tony Prescott.
So this is Paul Vichure for the Convergent Science Network podcast together with Tony Prescott.
Today, we're speaking with Aaron Scherner. Welcome, Aaron, to our podcast.
You were speaking today in our BCBT summer school, and you very much delved
into the physiology of behavior, of volition, of free will.
So do you think we can say anything meaningful about free will at all from a
sort of scientific perspective?
I think in theory it's possible to do so, but I'm not sure if we've said anything meaningful yet.
Okay um but now free will
is a constant complex uh concept as
she also indicated to your talk um so is
that the construct is that is that the natural category of
a follow ryle is that what we should be investigating is
that the right starting point or would you rephrase it
and use a concept like volition or decision
making i i i tend to
use the the the phrase conscious will uh
because yeah i think i think free will
is something that if you if you take the
the hardcore definition of free
will which is uh some uncaused cause uh
that's not something you can really grapple with uh scientifically but you can
ask questions about the relationship between conscious events and actions and
ask if there's any kind of causal relationship between those two.
But in some sense, then you first have to also grapple with the so-called heart
problem, right? Because now you link it to conscious states.
And so how do we access these conscious states in an independent way?
Yeah, that's, I mean, that I think is a direction that we're going in.
It's not something that anyone has done in the past for that very reason.
Uh but we do now have some
uh very reliable correlates
of conscious perception and so we
could potentially start looking at those uh as
indicators of the the presence of a conscious
intention and ask whether or not it has the right relationship with the subsequent
action or at least the neural activity that that brings the action about it's
still you're very cautious is right now yeah how you approach this and i understand
that it's it's a conceptual minefield so exactly but still you have to leave
some of the mystery to have something to explain.
So have you just scraped off too much now of
of the free will volition phenomenon well we
we we scraped away maybe we
scraped away the the mystery about the meaning of this
uh signature the the readiness potential uh which
we thought uh was a sign of a decision uh
and uh what what we've shown is that uh
this may not be the sign of a decision right it
it it uh might be the sign of
a random process uh and if we if if
we're using this uh this phenomenon
this readiness potential as a temporal marker against which to to to compare
the time of other events well uh we we We may have been deceiving ourselves
by interpreting it in that way, interpreting it as a decision.
But now we, so we already raised that quite a bit, right? But just first,
I don't want to be too flippant about it.
You know, you might still believe it is flippant, but.
You said, well, instead of free will, I want to speak of conscious will, right?
But you left the notion will in there as a construct, right?
Can we do anything with this construct will? Also from a physiological perspective,
as a physiologist, is will a useful construct? Can we do anything with that?
Well, if it maps onto something like an intention or an urge,
something, then yes, I think so.
So I use the word will in this context to be almost identical with an intention.
Which is, you could say, a mental state or a neural state that represents a
commitment to some course of action.
An urge is sort of, in the way that I think people would think about it,
is almost something which is unconscious.
Unconscious you know i felt an urge you know i don't know where it came from but,
something urged me to go to do this um so the
origin of the urge the origin of the urge i guess is unconscious but
the urge itself is so so the
uh so there's an interesting question you
could talk about talk about sort of unconscious will potentially as
well yeah in fact that's something uh it's very interesting that you bring that
up because it's just it's something that i I was just recently debating with
colleagues and with some philosophers who really like this idea of the concept
of an unconscious intention.
But my problem with it is how would you know an unconscious intention if you saw one?
I don't know. Behavior? Action? Arousal?
Maybe. me well i think it's it speaks to the
whole question of agency you know the
fact that we have an experience of being agents is
why we have this idea of free will in
the first place you know if we did things without
feeling we were making choices we we
wouldn't need to have will but we will explains
why we have a uh a feeling of agency so
i guess and that feeling of agency is both unconscious and
there is a conscious version of it i think it's
important though to distinguish agency from volition
uh where so age i think we can distinguish between the two i think we we can
say that agency is the the uh that doesn't have to be the feeling but the knowledge
that my action caused some effect in the world,
whereas volition is that my thought caused my action.
Uh my my desire or my intention uh was responsible for bringing that action about.
You also usually know this distinction between let's say secondary and
or primary secondary desires right so you you might
have an urge but you must actually desire to execute or follow up on that urge
but so it's a multi-level process that you commit yourself to and so so in an
And unconscious or subconscious urge or desire would never be identified as such by the agent.
It just plays out underneath the radar.
Right. Right. So, yeah, you always require by necessity this idea of a secondary
or a primary sort of level of urges or desires.
Right. And then what you also need is an agent that actually owns these desires.
But then there's also what's called the reason responsiveness.
Right. so that you can deliberate on that desire.
So these are ingredients you would need to then lead up to volition. To volition, right.
Reason's responsiveness is an important one that I think in the past has been
largely neglected, but that now everyone is talking about, which I think it's quite fair.
It's true that if we want to give something this label of volition,
it should have this property of responding to reasons this is right and of course
this is also your trajectory right because you move to link the study of volition
more closely with decision making which of course goes in the direction of.
Deliberation and reasoning.
But the other ingredient you need is the ability to do otherwise.
So if I act following my urge and desire, I must also be able to understand
that I have alternatives. I could have done otherwise.
Right, I could have done otherwise. Yeah, this is an important one as well.
Yeah, and one that's very important from a philosophical point of view,
But it's very difficult empirically to show that you could have done otherwise.
Absolutely. Yeah. Okay, so this is this conceptual environment of free will,
which is complex and minefield, as you said.
That, but it's now, now you, you cut through that with, with,
with a laser in some sense, by really zooming in on a very specific physiological
feature of, of voluntary action, which is this readiness potential that was discovered,
in the sixties, um, so why, why do you think the readiness potential as such
is, is actually telling us something about, about free will, um,
I don't think it is. I think that's the issue, really.
I don't think it tells us anything about free will one way or another.
I think it tells us something about how the motor system works,
some potentially interesting things, but I don't think it's a temporal marker
for a decision or a temporal marker for a commitment. it.
Because this would go back to these classical experiments on the Bonitov potential,
that then fed into the Libet experiments, and you laid this out for us in quite
some detail, the Cornelio-Brandicchi 60s experiments.
And the big mystery that appeared with Libet was like, well, if I look at.
The neural signals or the neural, the correlates, the neural correlates of decision-making,
and I compare that to people's ability to declare where they are in the decision-making
process in terms of making decisions or committing,
to actually make the action, it is, there's, there's a delay, right?
So, so the brain only knows what it's going to do before you know it,
which is of course already implying a dualism in some sense, right?
So, so are you saying it was a surprise? There was no surprise? rise. Come again?
When you say it doesn't tell us much about evolution, people are tremendously
impressed with this result, right?
There have been many attempts to replicate it, and no one could actually have
any successful replications of it, and it's very much accepted as a physiological
feature of decision-making.
Yeah, it replicates extremely well. Yeah, exactly. So in that sense,
it's a physiological fact. Yeah.
Yeah. But then a huge amount of trees have been killed to produce the papers,
the paper on which many words have been written now about what this means with respect to free will.
Because well, the brain knows what you're going to do before you know it,
and therefore, there's no free will.
So where did that interpretation go wrong? Because, okay, you have many methodological
concerns, right, we can look at.
But in some sense why were people so easily misled about this at the time,
well because it it it reliably
very reliably precedes self-initiated movement and so the the i think the first
and it may be sensible conclusion to come to is that that this is uh this is
the brain's way of getting ready to initiate a movement of of of planning and
in preparation for movement.
But of course, the kind of data that we use to get it out, it's all correlational.
So you could say, well, we've made the mistake of, the simple mistake of confusing
correlation for causation.
It's a bit more than that, but it's that as well. Right.
But isn't there also a hidden prior in this whole debate at this stage,
right, before we delve into physiology physiology and the methodology.
That there is an implicit dualism in the interpretation.
There is this whole idea like, oh, every action at any timescale has to be caused
by a conscious state, has to be preceded, therefore, by a conscious state,
which seems an arbitrary assumption.
Yeah, well, no, I think with the readiness potential, what people are saying
is that every action, or at least every voluntary self-initiated action,
has to be caused by this same preceding neural state,
not necessarily conscious.
Conscious right um so i think the conscious
bit you can you can you can separate that out and in fact and
and and we intentionally evaded the
topic of consciousness when we first started working with this because we just
wanted to ask is this signature this readiness potential the cause of the movement
does this represent the brain getting ready to move uh and uh i think that's
what we That's what we, if you want to say,
debunked in a way.
Okay, so here was the redness potential. It's a slow buildup of activity that
peaks just before you initiate a movement.
It precedes your ability to declare where you are in a position in the decision-making process.
And so the first way you debunked that story is to say, well,
there's no unitary process underlying this.
In some sense, it's an artifact of just averaging many observations, right?
Because the idea would be that if you have spontaneous fluctuations in the brain,
and these spontaneous fluctuations are sort of slightly biased by some form
of evidence, let's say, perceptual evidence about a task or an internally generated cue, then the,
Over many trials, this will go to some average state. That will exactly look
like what you call a maintenance potential.
But at heart, it is essentially just an integration of a highly noisy signal.
But even on single trials, it's the case. You don't really have to average trials
to run into this problem.
Problem um when you when you select a single trial based on the time of the movement itself,
um you've in a way selected a biased sample right because that that little piece
of data that you're looking at ends with a movement uh and if you want to understand
how movement works You also want to know what happens when there is no movement.
So, of course, in this case, because you have this biased sample,
you see everything through the lens of, well, this is what happens before a movement.
And you're you will if you
if you look at that you'll recover in the average or on
individual trials a tendency for there to be a a
ramping phenomenon ramp up to in on the assumption that this is a threshold
crossing type of phenomenon right and you also test that hypothesis right by
asking your subjects to respond as quickly as they could where you would queue
you would queue the response, right?
And then you would sort of link
the queuing to where they would be in that integration process, right?
So you would assume there's some decision threshold, and if they're further
away from the decision threshold, you would predict if you now force somebody
to respond, the reaction time should be longer than they are forced to respond
when they're closer in this direction.
When they just happen to be closer, right? Of course, yeah. And this then just
depends on this sort of highly variable fluctuation.
Right, right, right. There's a lot of data fluctuation who sourced you,
you don't know about. So-
So with that, you came to this idea that you could explain the readiness potential,
and also the performance from the perspective of a so-called drift diffusion model.
Actually, I integrate this noisy signal.
When I hit threshold, I'm going. It's highly variable.
Averaging across trials gives me something that looks like this readiness potential.
Potential but um then when
you tested that on your on your subjects you could
show that that your model that implements this kind
of integration of a noisy fluctuation when you
compared that to the eg signal you got from your from your
subjects that it gave
you gave you a similar kind of response right so you see that indeed if you
compare fast and slow response trials you see that this integration process
has reached a different level and has some offset that's the difference between
these two traces that you extract yeah and importantly there's a difference,
uh not just before the movement but before the queue you make
the movement and and so these are these these uh
interruptions are random i don't even
know when the interruptions were going to happen nobody the computer only knows
right so uh what you see as a as a as a difference in electrical potential preceding
the cue can't possibly be a preparatory process because you can't prepare for
a movement that you don't know you're going to make. Right.
That's an important insight from this model that indeed there is no preparation.
But if you compare the model to the actual physiology, there is indeed a difference in the offset.
But in terms of the details of how the trace evolves, it's not identical.
So if you would really lay the traces on top of each other, I would do some
sort of correlation measure.
I would not get one. Right. It's qualitative.
What the model gives you relative to the reality is qualitative.
Right. So for instance, the model gives a much stronger decay in the trace than
you observe in your subjects.
While for the fast trials, it seems very constant in the model,
practically constant, the level, while in your subject it is still sort of sloping
down. There's some form that you get, right? So the difference is here.
Now, this in itself is not a criticism because model doesn't have to be identical
to what you measure, but when is a model good enough?
Right, so to what extent did you feel this model was good enough to explain that data?
Well, I think it was good enough because it made a novel prediction,
prediction uh and albeit qualitative
but it it it told us we should find a
difference between fast and slow responses uh in
this particular direction uh and that
we found now the fact that it doesn't map on to the reality exactly that leaves
uh some more questions for us to grapple with uh in the next iteration of experience
but it also leaves the door open and in some sense in the discussion part of
your talk, as we came to that,
I could also say, well, maybe the model that explains the data could even be simpler.
Maybe it doesn't need to be drift-to-fusion. Right. I gave an example, yeah.
Another example, maybe I could have just an oscillator that can exist in two modes, right?
And it's a high-energy mode and a low-energy mode.
And this would then account for the difference between the two traces.
And it completely depends on intrinsic property and this flip it between the two.
Right. It would be a completely solipsistic model, but in terms of the physiology,
I could also account for this difference in offset, right? Right.
So, why this drift-to-fusion? So, drift-to-fusion is not the simplest model.
Um, well, you can go simpler. I mean, what you need, what I said,
uh, after the talk, when we were, when we were discussing this,
what you need at a minimum is pink noise, how you get that pink noise.
Maybe it, maybe you can just say, well, I don't know how, I don't care how I got it. It's just there.
Uh, the drift diffusion model gives you a sort of principled way that's grounded
in prior research that gives, that gives you this pink noise. Right.
Um, but then. Okay, so now we have an alternative explanation,
right, of the retinence potential.
So how many studies have really confirmed, you think, in your mind, the model you propose?
Five or six different studies have come out that support the theory in one way or another.
Another um so an important one
was the 2014 study by murakami with rats
who found a ramping like activity uh in
the premotor cortex of rats when they were
doing a task where they could basically spontaneously stop
waiting for a big reward and just go immediately get
a small reward correct yeah okay
yeah you mentioned you mentioned that that result was um but
ramping activity as such is that sufficient as a
signature well it's okay so uh i
spoke very quickly it was not just ramping activity but
it was ramping activity that consistently reached
the same level just at the moment that the that the rat left the waiting station
and went to get the reward uh so it at least it's very consistent with the the
idea of a of an accumulator of the this would be the output put of an accumulator, right?
We would expect it to look just like that.
I mean, you pointed to some interesting evidence from crayfish and rat as well,
that I guess suggests that this readiness potential is something that's common to brains. Yeah.
And therefore, probably if the crayfish is doing it, what we normally think
of as conscious volition isn't going to be a factor.
Yeah, I would agree with you. I mean, and you also talked about the idea that
this experiment, actually, there is a weak imperative, you said, to me.
So it's almost that if people are making a conscious choice,
it's when they walk into the experiment and agree to do this task. Exactly.
And the task is designed so that specifically you're asked to suspend volition
and wait for that urge to come.
So it's really a task that's weighted against having any conscious volition
that we would normally think of.
As conscious volition you know sort of choosing to
buy a house or move country or something like these are
conscious choices um whereas
deciding to lift your finger in this task or whatever is is it's really designed
to to remove anything but the most minimal of conscious choice yeah i think
so i mean it's to that extent is it really that surprising i mean for you the
result isn't uh surprising but i guess you thought though the.
Why why had the scientific community become so obsessed with this yourself yeah
well i think it i mean it is it is just a matter of lifting your finger it is
a very yeah it is a very uh simple,
act um.
Yeah, I'm sorry, I lost my train of thought. Well, I mean, people have pointed
to Labette as evidence against conscious control.
But if you're a defender of conscious control, you can just say this experiment
isn't representative of what we mean by conscious will.
You know, it's the minimal amount of conscious will.
So even if Labette was right and you were wrong, this doesn't really tell us
anything about conscious volitional actions.
Yeah, I would agree. I mean, you need a signature of a decision,
and then you need to know the relationship between the time of that decision
and the time of your conscious decision.
And if that signature is not reliable or doesn't mean what you think it means,
then you can't make those inferences.
So given that that's the case, that the Lebet experiment isn't a useful experiment
for doing this, and the readiness potential isn't a useful signal,
what would be another way of getting at conscious volition that we could imagine
experimentally that would be more powerful?
I think a more powerful way that I alluded to at the end of the talk is to use
a closed loop feedback system, or if you want to call it a brain-computer interface,
to drive some external signal directly from cortical activity.
And then you can ask questions about what happens when the feedback from,
you might say an intention,
if you will, comes far earlier than you expected it to, far earlier than your brain expected it to.
And that can tell you some things about conscious, at least the conscious feeling of volition.
But still, it would then depend on reportability, you know.
You'd have to give a report, yeah. Yeah. Yeah, so it doesn't,
in the context of saying no report paradigm, this doesn't work. Right.
But the other thing is that, you like this more common result, which is interesting.
Okay, we see some integration to threshold. We see how nicely lined up before
this sort of self-initiated action to move from one port in the task as a mouse
to the feeding port or the reward port, okay?
But look at premotor core type, right? Well, usually also in this whole debate
on demolition, people point more to, let's say, medial frontal structures, SMA.
So more advanced in this hierarchy, right?
So is it not an issue that we're getting a little bit, let's
say unclear but also localization of these phenomena no I think the area of
cortex that they were looking at in the rat m2 I think is the rodent is the
rat homologue of the SMA if I'm not mistaken all right,
so we have a reinterpretation of the Leavitt experiment and sometimes you're
saying it's less magical than it looks like but still there is some source of
let's say spontaneous activity that comes from somewhere,
so is the spontaneous activity an echo of something that you might want to call,
volition a sound agent that is trying to manipulate this or are you really thinking
about but just noise that is floating around in neural circuits.
I think it's the latter. I think it's just noise.
I think you need more than that to explain and to account for volition.
So what are the properties of neural noise in these carnival circuits?
Well, one of the most important properties is that it's temporally autocorrelated.
Uh and that uh that is
key in order to give you the the kind
of result that we it's temporary auto-correlated temporally auto-correlated
in another way of saying it's pink it's pink
noise it's not white noise uh so how much physiological evidence is there that
there's pink noise in in these parts of the brain there's there's noise in the
brain and in behavior tends to be pink uh in fact i would turn that around and
challenge you to find white noise Anywhere in the brain.
Sure. I would go more to the other direction. I'm not a big believer in noise.
Noise just means there's a source of variability that you haven't identified yet.
Ah, yes. So this is more where I was going with that.
So to say pink noise, I think just means, well, we lump a lot of things together
and it looks like pink noise.
Right? But it either means there's some dynamical state that evolves with the memory.
So as long as you have a dynamical system with some kind of memory,
then you have your pink noise.
So in the way you decompose the render's potential into, let's say,
multiple highly variable traces, maybe they themselves can again be decomposed
in something more mechanistic that we can understand that's more deterministic than pink noise itself.
Is that reasonable? Or you think there's really a a pink noise source in the brain somewhere.
Oh, I see. No, I wouldn't say that there's a pink noise source in the brain
somewhere, but it isn't.
I think one of the interesting things about the model is that this kind of simple
accumulator, its output has a 1 over F power spectrum. It's pink.
And that, as I mentioned before, that helps to sort of ground it in some neurophysiology
that has been well characterized in perception research, in decision making research in general.
So we have an instance where a very different kind of decision,
a decision that is not made on the basis of a stimulus, at least not one that
you have right there at hand,
is governed maybe by the same kind of mechanism that all decisions are.
The brain didn't have to reinvent the wheel for this kind of decision.
So now we have a reinterpretation of the readiness potential.
In some sense, you've deconstructed a notion of volition.
And you said, well, we can just think about it in the same way we think about decision-making.
And it doesn't really matter whether you want to call it volition or not.
It's a decision-making process that we're looking at. Yeah, yeah.
And then there are more recent results. And you point, for instance,
to this work by Fried, but also Soon and others.
Who then started to add more of the black box science to the whole story.
So now we're going to say, okay, can I, as opposed to do a post-op analysis,
I was going to say, can I predict decisions given some classifier extracting
features from my physiology?
And I couldn't do this in a free case on single cells. Right.
And in the case of Soon, you can do that looking at f and y.
Right. Right. So again, it was a bit of a game changer, right?
It's this whole black box approach.
And then surprisingly, these approaches seem to work.
They could predict what the decision was going to be by the subject with some
improved performance as compared to random.
For Freed, it was 75% or something like this, right?
And they could predict 500 milliseconds before the action was initiated of what the subject would do.
But for Soon, it was even more extreme, that's with Dylan Hines.
More extreme, it could be up to six or six to eight seconds or something like
that, some extreme time window. With fMRI, yeah.
And then the point was, okay, it almost seems to violate the basic principles
of the universe, right? Because now I can.
Look into the future. Yeah, I mean, what I think would have violated the basic
principles of the universe would be if you couldn't classify what someone was
going to do based on prior brain activity.
That would be strange, right? As if that decision just emerged out of ether.
But say, seconds might be a bit long. They can predict it before you even get the cue.
Yeah, well, I mean, let's say you're talking about a right or a left-hand movement.
I mean, it's, it's, it doesn't seem that unusual to me to think that several
seconds beforehand, your brain might be in a state that, uh,
will tend to bias it slightly towards one or the other.
Um, so I, I, I don't find that to be too hard to believe.
So also eight seconds, you would find not too hard to believe in,
in the case of the SUD experiments.
Yeah. I mean, no, I don't find it too hard to believe. Okay.
Okay. Okay, but that's interesting, right? Because with that,
you're saying that these fluctuations that would sort of bias you in one way
or the other have really a rather long history in the dynamics of the brain.
Yeah, I guess that's what that points to, is that if it is related to the phenomenon
of autocorrelation, so it has a relatively sluggish time constant.
That would also reduce the states that the system can then occupy.
Yeah, although bear in mind that you're classifying slightly better than chance.
I forget exactly how we're at 58% or 60% correct, where 50% is just a random guess. Right.
It would be a very different story, I guess, if it was 80% or 90%. Right.
So when you came across this free-to-result with the single cells using the
support vector machine,
or or soon with this fmi classifier at
the time were you were you shocked by that or surprised or this
was also already done for you within the realm
of the expected um when i
when i saw freed result uh i i wasn't too surprised uh i was already at that
time i was already working on i was already doing this work uh and had already
come to a lot of these conclusions when i When I saw the work of Soon,
that was much earlier, that was in 2008, I think.
Yeah, I was initially pretty blown away by that.
And later...
Well sort of revised uh revised my
my way of interpreting uh the results
yeah right because in the second in the last part of your talk
you focus very much on also the methodological challenges that you face with
this kind of analysis right and uh and you pointed out the number of caveats
like for instance uh if you use a sliding time window to make your your estimates
is you better be very clear where you put the reference to estimate.
Yeah, you want to align your time axis to the leading edge of the window,
not the middle or, God forbid, the tail end of the window, right?
Because you don't want your classifier to be able to peek into the very future
that it's trying to predict.
Right, exactly. That would be cheating. Yeah, absolutely.
And then you'll also look at setting up the right reference conditions,
like look at conditions without movement, the width movement, right?
Yeah, yeah. Those are all the old correlation in the signals that we're analyzing.
So when all these caveats point to the fact that they're overestimating these time windows,
if there's an error, the error is an overestimation of a time window in which
you can reliably predict.
You mean, if I understand you correctly, you're
saying we're overestimating how far back yeah yeah
i'm not sure it means that um because
i think uh for example in the soon study they weren't really
using a sliding window they were just classifying at each at each tr at each
point in time before the movement um no but still they would have another correlation
to then take care of yes although i believe they were they they did one reanalysis of some of those data,
trying to rule out the possibility that this was just driven by autocorrelation.
And at least their conclusion was that it couldn't be entirely accounted for by autocorrelation.
But that, of course, leaves open the possibility that it could partly be. Right.
But in your own analysis that you presented to also show the relevance of these
adjustments to the analysis method,
you showed that the whole shape of the, that you already called the scourge
of the banana, the whole shape of this randomness potential starts to disappear, right?
And at this transition point, at which you can start to predict,
it looks much more discrete or very, very punctuated in time. Yeah.
It's just less gradual. When you have properly controlled conditions where, as I mentioned before,
if you want to talk about predicting the onset of movement, you want to have
data that include movements and you want to also have data that don't include
movements as a control, right?
Right. And ideally, you'd want to have these two and compare them.
So in a more recent experiment that we're now just working on getting published, we did just that.
So we used an experimental paradigm where you end up with data epochs that either
terminate with a movement or terminate without a movement, but are well matched in other respects.
And when you apply a sliding window analysis using pattern classifier to that kind of data.
You're using a very powerful
classifier uh we couldn't tell apart movement from
non-movement epochs until the very last moment
until until basically the moment at which the movement was was
was beginning uh and it certainly was was
no fault of the classifier itself because at and after the time of movement
the classifier was at at nearly perfect performance uh so you couldn't say oh
well it's that's just because your classifier isn't good enough well appears
to be good enough in some sense isn't the consequence of that.
The shape that we gave to the randomness potential is more an artifact of,
let's say, insufficiently tuned methods than that's really in the signal of the brain itself.
Like also your examples, if you have a corrected signal,
if you take out the autocorrelation effect, that suddenly the meaningful part
of your signal starts to look very different.
It's much more than, let's say, an S-shaped kind of response,
a more compressed transition point than the traditional randomness potential.
So having you with that sort of deconstructed the randomness potential as an
artifact of insufficient methods. I think you can say that, yeah.
I think you can call it an artifact. If you want an artifact of time-locking,
at least a tendency to time-lock to crests in an autocorrelated time series. Right, yeah.
And if you time-lock to crests in an autocorrelated time series,
you recover the autocorrelation function, which looks like a slow curve. Right, exactly.
So you've talked about having the right kinds of controls,
Patrick Haggart who was here last year did a big experiment I'm sure you know
his work on this has developed some quite beautifully controlled paradigms for comparing,
volitional with stimulus driven,
responses I think he was using sort of random noise movement and you had to
say which correctional dots were going in.
And his study, I think, also made it a bit more sort of mattered whether or
not you, what response you gave, because there was actually a monetary reward.
So there's a bit more of an incentive to do the right thing or to do the most rational thing.
And his data suggested that in the case of volitional actions,
you see a reduction in the amount of noise.
That's right. You see a slow reduction in variability over time as you approach the movement.
So what do you think of that result, and how would you interpret it?
Well, I would really interpret it in the same way. That is to say that that
signal, that variability,
can vary in much the same way that the time series can vary in an autocorrelated fashion.
So the same logic that gets you the readiness potential in the experiments I did could get you this.
Let's say decrease in variability but why would it be different between the
volitional and the instructed uh trials so well because the the uh in in the volitional case,
uh the time the precise time of the movement right uh tends to be uh biased
slightly by so it depends on this underlying process right whereas the cued
in in the case of a instructed,
uh, movement.
That's not the case. So that's one of the key aspects of this explanation is that, uh, when the, uh,
the way I say it is that when the imperative to move is weak or absent,
meaning when this precise moment at which you move is not dictated by some stimulus,
uh, then you have this question, okay, uh, I'm going to move approximately now, right?
Uh, and so here goes, as I move, and you're left with this question of,
well, why did I move just right then when I moved and not 200 milliseconds earlier or later, right?
There is some freedom there to play with.
And if the answer to that question is, well, there are spontaneous fluctuations
in neural activity and they bias the precise moment at which you move,
then if that's the case, then you're guaranteed to recover a slow gradual buildup.
But I don't really see how this answers Tony's challenge because Tony's referring
to the Peter Haggard claiming... Patrick. Patrick, sorry.
Voluntary control, lower variability. Cube control, higher variability, right?
Or no reduction in variability. Okay.
But then cube control, then we can go back to the literal decision-making.
When you start to drive, you sort of drive the integrators, right?
And as soon as you start to add a drive to these integrators,
you start to overcome the more spontaneous fluctuation.
So you would expect to see lower variability there.
So I don't really see how the explanation... Well, that's what you get.
You get a decrease in variability.
No, it was a voluntary case of this lower variability. Right, right. Yeah.
Well, I was making the example, if you go to the decision-making literature
where everything is cubed, right?
Right. Then also for your model, if I start to drive your model with an external
input coming from an external queue, I would start to reduce variability.
Yeah, and you would abolish this early tail readiness potential.
So this is, okay, my claim, and this one goes used now.
If you take the Haggard challenge, you say, okay, volition, higher variability.
No, volition, lower variability, queued, higher variability.
But if you take your standard drift diffusion model with some,
let's say, noise if I now start to get an external Q as an additional input,
I start to reduce the noise effect right?
So isn't that the encountering to it from this drift diffusion perspective?
It's true that it does pose a bit of a challenge but I think it's not.
I think the case is not at all closed by that evidence because again Again,
that variability could itself be fluctuating in a random way.
Okay, we're leaving a bit of a cop-out now. Well, essentially,
I mean, that's one, I would say, to be fair, that's one of the weaknesses of
the model or the explanation is that you can always keep saying,
well, that could also vary, right?
That could also fluctuate spontaneously. Simultaneously. So if somebody comes
up with a new phenomenon.
Oh, we just have another free parameter, right? And now we have the fluctuation we need.
You said, well, that could fluctuate as well.
But maybe another response could be, those to the Tony's challenge,
in some of the people always ignore that, that,
the agent itself is represented, is sending a signal to a decision-making stage.
Mm-hmm. Right? So, you get a vision that the parietal areas,
right, around the temporal parietal junction that are implicated in states of
self and agency are generating internal cues, right?
Right, right, right. So, in some sense, so the argument would then be internal
cues have a higher gain, let's say, than externally generated cues.
And then, that way, I might account for it. But that's what you said.
But I think Patrick's result doesn't imply necessarily that that's due to conscious will, as you said.
It could be due to some other internal attentional process.
He's agnostic. Patrick would be agnostic about that.
So it doesn't necessarily help the people who want to claim a role for conscious
volition, which I think Patrick is one of those.
So it could be some other unconscious that's happening, maybe it's linked to attention in some way.
Is attention relevant there?
I don't know, why do you bring up attention? It's very confusing.
Because you're in a task where you're having to make a decision.
So you're focusing on, am I going to, in his task, interrupt this trial,
go to the next trial? Well, that's something you're fixating on.
And also you're instructed. And you're instructed to be. Yeah,
well, there's money involved. So you make more money if you make good choices.
So that would be a reason for being more focused, which might reduce your neural noise.
Okay, that's fair enough. But then...
So now we're having a problem right because we have many problems but let's start with
aaron's problem first and so
so okay great we do fantastically well here's libid
look very confusing conscious will is an illusion uh
what's the causal relation between conscious states and
action and so on right you resolve that conundrum
by saying well you just missed you have misinterpreted your
signal right essentially right yeah well what's uh what's the
relation between the readiness potential and action yeah sure
yeah so you have said look you have over
interpreted an artifact yeah right
and you can you can reduce the whole thing into very single model where we just
have spontaneous fluctuations that that have a little memory so they can add
up to to some decisions i showed right and it brings in the realm of the standard
a decision yeah I mean it accounts for the data in a parsimonious way right but then.
Then you start to get worried about all the methodologies involved and also
these crazy long-range predictions people can make about decisions up to eight
seconds before they happen which
led to all sort of further speculation certainly in the popular press,
and then you start to apply all sorts of methodological caveats to say well
Well, actually, the real signal might look very different from the randomness potential.
The real signal looks much more like a rather constant baseline state that shows
a very rapid transition to a sort of an upstate shortly before the decision. Yeah. Correct. Yeah.
But with that, you invalidate your earlier drift-diffusion model. That's now irrelevant.
Doesn't invalidate it right in fact it it it the
idea is that it confirms it uh so the
the uh the you say two
competing camps or two competing hypotheses are for an early decision which
is you would might say i mean he's not here for us to ask him but i would say
as libit's view the decision is early the the the neural what i call the neural
decision right uh to it to contrast it with the conscious decision.
This is an early decision, whereas our model says, well, no,
the decision only happens, and Murakami used the same model,
or the same kind of model, essentially.
The decision doesn't happen until the threshold is crossed, and that happens
very late in the game, very close to the time of the movement.
And so that if you were, the upshot of that, one prediction that it makes is
that if you were if you compare movement to no movement uh they should look very similar until,
the very last moment and until uh that that threshold if you will has been crossed right so.
We started with a very complex picture on free will and now we end up with something
relatively pragmatic physiological controllable some sense right we're in the
decision-making decision-making domain, yeah?
Is that where you want to be? Or do you want to, again, jump out of that and
go back to this fundamental question around conscious will, as you call it?
Because sometimes, now it's out, it's out in the picture, right? Doesn't matter, do you?
Now we're looking at decision-making about self-generated actions.
Yeah, I think we should stay firmly grounded in the world of decision-making.
And self-generated actions.
And I think there's more that we can do with this model and models like it.
I think even if ultimately the model is all wrong.
I think just the idea of taking this kind of phenomenon and bringing it down
to earth and grounding it in a computational model was an important step.
And we've already gone beyond with this same model.
We've done something very simple, which is just to add a second threshold,
slightly lower than the threshold for activating movement, let's say.
That we we say well this this lower
threshold represents sort of self-monitoring process
so that when when we
cross that lower threshold some information
is generated uh to the
effect that well a movement is very likely to
happen very soon because i'm very close to the
threshold um and using that
uh simple variant uh of the
model we were able to confirm makes and confirm some predictions about uh the
conscious urge to move what but a bit called w time uh for the will um so on
uh on trials where the subject waited a longer time to produce a movement meant,
the assumption is that the ramping signal was ramping not as steeply as in trials
when the subject responded early.
And so the delay between the crossing of those two thresholds would be longer.
And that would tell us something about the relationship between subjects W time,
when they felt they had the urge to move, and the movement itself, and that prediction.
But that's something quite disturbing about.
Our notion of free will, which what we have in Chobha Chitral now as my moment
of choice, I decided to make a movement,
turns out to be just some internal monitoring system reporting that a bit of
my brain that I'm not aware of has passed the threshold and I'm going to make
a movement whether I urge it or not.
That's what it may be. So it's really quite a frightening conclusion for people
who would like to be even free will because it shows that,
these feelings that we have about will could just
be completely wrong because that because you would you would have a feeling
that i've willed them but it turns out you haven't something in your brain has
happened and this has popped into consciousness and you've interpreted that
as well if it's true for that event it could be true for all sorts of other
decisions you make in your your life,
that some bit of your brain made something happen. And then it was reported up to consciousness.
Although I wouldn't say that it's, uh, you, you, you said that maybe that feeling was wrong.
Uh, but I wouldn't say interpretation of it that we put on it is that we made a choice.
Ah, I see. So mens rea doesn't hold that.
In that model no not very well yeah so so that's an important point because
in the trust of the feeling i was having like you you invited us to some big
party um with lots of uh booze and,
god knows what then entertainment but then you open a door and then it's an
empty room and there's no no no drinks no snacks no nothing yeah decision making
yeah so haven't you haven't you
now actually throwing out the dancers with the bath water, um,
because of the limitations of your methods?
I mean, the, the goal here was not to prove that free will exists, right.
But was, was to shed some light on how this all works. No, you shed some light
on it by turning the light off.
Well, then you turned the light
on and the room was empty. The room was empty. I think, I don't know.
When the risk is, he actually turned on the lights in another room.
Right. We have shifted. We have shifted now somewhere else.
No, I think this is where we thought the party was. You put the lights on.
There is no party. There's no will.
There's just these things causing stuff to happen, and you heard about it.
Yeah. So, I mean, what do we do with that? But it was deeply dissatisfactory.
Well, only if you wanted to believe in conscious will.
Because you believed that there was a party to go to. That's right.
I like parties, but the thing is there are things, there are things like,
like mens rea and there are, there are costs of failure, right?
So if we really believe with Wagner and his friends that, that conscious will
is an illusion and, or not operational,
there's a huge cost associated with that because we're dehumanizing ourselves
and we're giving up the sense of responsibility for action. The cost of that is huge.
And if you do that to protect yourself.
Relatively primitive scientific models and methods, then I think that's a very naive move.
Because I do believe, given the cost of failure, we ought to critically question the models we use.
And we already know drift diffusion is great.
We do really boneheaded tasks as a macaque monkey for months on end.
Great. We get drift diffusion.
But as soon as As I make the decision test, it's a little bit more complicated.
Drift and fusion is not predictive of anything.
If you give a complex brain a stupid test, you get a stupid code.
And the stupid code can be deciphered by boneheaded neuroscientists.
But the thing is, if you give a complex brain a complex task, it uses complex codes.
And that's where the beauty is. And that's where the scientific challenges are.
So what I feel now, we're a bit on the slippery slope. Oh, my methods are not really helping me.
Let's just rephrase the problem. let's scrape away all the complexity so at
least I can hold on to the method that has given me tenure.
And it's not for you, okay, but the socialness of the field, right?
Drift diffusion drives me mad indeed because I think it is distorting our understanding
of decision making and certainly of voluntary decision making.
I don't think you can just point to drift diffusion for this.
So all you need is for that noise to be auto-associated, yeah?
So that's all you need for this result to happen and the problem for your version is that you want to.
Have some role for that experience of making the choice and this result shows
that you can have an experience of making the choice without having made any
choice in consciousness that matters so so that i mean there may be other situations
in which you make a choice that matters,
but in this situation,
you have the qualia, you have the experience of making a choice,
But no, we've shown that... I appreciate your intent to mediate here,
whether to take offense to your summary.
Because the point I was making earlier, Aaron is showing to us that pink noise
doesn't do the job, unless it's some really special case of it,
because he shows a much more S-shaped transition, right?
Where you don't have sort of gradual accumulation of anything.
There's accumulation of nothing and a very rapid switch. Well,
that's when we squeeze the pink noise out of the picture.
Exactly. Yeah. But that would be the real ground truth of the decision-making signal.
Abrupt. Yes. Late and abrupt. So it looks very different than this sort of autocorrelation.
That autocorrelation signals some background thing that happens has nothing
to do with the decision-making process.
Right. This is the consequence.
It's, I mean, it is, it's part and parcel of the decision-making process,
but it's just that But when we look at it through the lens of this event-locked
averaging, we see it in that misleading way.
But it's like a non-specific contributor.
Yeah. It's not a decisive contributor decision, right? Right.
So, but I think it would be, as scientists, we must also be willing to say, well, we don't know.
We don't know, but it's more than decision-making, because for the free will case,
there is a layer of the ability to reflect upon the decision,
to own the urges to act, to not only act,
but to also, and I want to act, I own this urge to act, and I experience that,
and I can declare it, and I could have done something else.
But there are many requirements of which the decision-making process is one
of the requirements, but it's not the whole story. It's only one of the necessary conditions.
And there I think we should be careful. So yes, studying decision-making can
be fantastically useful.
Drift diffusion models can be a great tool to do that, but they're not the whole story.
So this is why I got a little bit excited, because I feel like,
oh, we have to be careful not to collapse the complexity of voluntary action
into the more reduced view of decision-making,
which is only a necessary condition i mean i think what what we
could agree is that in this task it's a very minimal task for
any kind of volitional control and uh i think what aaron's done is produced
the sort of uh the minimal model of that and occam's razor said well why should
there be anything more if this random process that's also correlated is enough
to explain the result then.
Quite possibly all there is the disturbing thing for you
is that the quality are associated with it
are like the quality are associated with other decisions so i think the pressure
is now on people who want to defend you know sort of some strong role for consciousness
and decision making to find another paradigm where where you can demonstrate
that because you mean where volition then comes
in as a separate factor, or what?
Yes. Yeah, well, I mean, or in this paradigm, to show that it's more than just
this minimal model, this, I mean, that there might be something else that's
happening in those last few hundred milliseconds.
Right. Would you agree with the summary there? Yeah, and I think,
I mean, I think one of the, again, one of the things that, that is to be recommended
about the, about this model is its simplicity. It's a very simple model,
and it accounts for the data.
And that puts, I think that reverses the burden of proof, in a sense, now to the other side.
Say well, why would millions of years of evolution result in a human who lives
one full second behind their own self decisions?
Oh, I have an answer to that, but that's not an interview. But the thing is,
we already know, as you know, Libet, he understood the consequence of his observation
or the implications of his interpretation.
So he said, well, maybe the free will acts in holding back the action,
in interrupting the action, stopping the action. Right, right.
And actually, it turns out, if you start to do tasks where you have stop signals,
so like countermanding, then drift-of-fusion models stop working.
They're not predictive of anything. Okay.
So you really have your counter-example.
As soon as you start to voluntarily withhold action because you get the stop
signal, then just integration to threshold is not explanatory in any way of
the performance of a macaque monkey.
But the other way to recover your notion of human and of choice is to embrace
the unconscious as part of the self and a source of your decision-making. Oh, absolutely.
And if you do that, then none of this is a worry because you just say,
well, I am not just my conscious process.
I'm the whole of what my brain is doing in the context of the body.
And that collective system is making sensible decisions. And some of them are
being reported to consciousness.
It shouldn't bother me if most of them aren't as long as they're good decisions.
As long as your conscious urge is a meaningful best guess on the part of your
brain as to the decision that you made and the time that you made it.
And if consciousness is able to monitor that and say, well, hang on a minute,
we made a poor choice there.
Maybe we can, maybe there's a role for consciousness If you guys could finally read my papers.
I know. I'm telling you what was in your papers. Ah, thank you.
You know that I see consciousness and also volition act towards the future.
And all real-time control, as the ones you manipulate as experiments, is all subconscious.
And consciousness is catching up because consciousness tries to reconfigure
you for the future. So you will yourself into the future.
And that's the big mistake people make. They think real-time control,
because they never program robots to control anything.
They think real-time control is conscious.
It cannot be. It has to be automated quick, because that's your survival system.
It has to act in real-time, and anything can happen to you.
But what you then do, you reassess, and that's why the monitoring also comes in.
You have to reassess, you revalue, you extract norms from your environment,
and you reconfigure your real-time control, so in the future,
you can will yourself to be better.
So in that sense, indeed, this is I'm not scared by that we're converging on
a common view that would be really scary so Aaron, so now that we all agree
and are humming along and you promised to read all my papers.
So you have been wading through this really difficult territory this minefield
of volition and you're still standing which is amazing, that's a real accomplishment
so you're a great scientist,
So given that experience and these accomplishments, what is Aaron's law that
we should follow to understand the brain?
Well, I think what we should start looking at and what hasn't been the case
in the past is look at instances where conscious states might actually play
a role in an upcoming decision.
Decision so there's i i i think
that in literature there's been a maybe because it's
easier to do maybe because it's sexier but there's been a bias
toward finding examples of things that you can do without consciousness um and
that that leaves open the possibility that there are some things that you do
need consciousness for uh but we haven't been spending our time uh investigating
So how does that allude to the law?
Ah, I don't know about a law. Look, that's a little mug or something.
It's like 10 words. So it's Aaron's law. Aaron's law is some actions,
some volitional actions might actually be conscious,
might actually have a conscious antecedent, let's say.
Um so we've we've we've
looked at a lot of examples of of tasks where uh
you you consciousness seems
not to be involved so aaron's law is some consciousness
might play a role sometimes yeah sometimes maybe rarely but maybe sometimes
right all right uh the other thing is so we were in in paris recently for a
new machines conference which was fantastic and Tony likes Paris now,
so he wants to go back to Paris,
and he will come visit you four years from now.
Okay. And he will come and check, he will come to the notebook and check whether
you have actually falsified or confirmed the key hypothesis for your scientific program.
So what's the key hypothesis you would like to see tested in that time frame?
Four years. Four years. Prediction, yeah.
Or maybe by then the channel tower will be filled up with concrete.
Yeah. Two years. Maybe. Two years, yeah. Okay. Earlier.
I think what I'd like to see done is a test of whether or not,
to the extent that this can be done, whether or not one can draw a causal arrow
between conscious states and actions.
And if we could get a handle on how to come up with a paradigm to test that,
I think that would add sort of counterweight to the kind of research that's
been done over the past decades.
What's the specific prediction then from that?
Ah, I guess it's true. I didn't give you a prediction. That's all right.
Yeah. So I've paid enough attention.
Okay, so, yeah. So, okay, a big prediction is this.
You can, what the model, one of the things that the model says is that the,
let's say the character of the noise will in part determine the shape of the readiness potential.
So if you, let's start with describing the power spectrum of pink noise has
this one over F slope to it.
So that the slope of the power spectrum gives you what we what you could call
the character of the noise or the degree of autocorrelation in the noise.
So one prediction that follows from that highly counter counterintuitive prediction,
or at least a novel prediction,
is that the character of the noise, the 1 over f exponent of the noise,
should predict at an individual subject level the shape of the readiness potential.
Isn't that the retrodiction? Don't
you really know that, given the results you have? No. No, not at all.
So, no, that's very much a prediction and a strong one.
It's difficult to test, but it is testable. All right. Tony, you wrote it down?
It's on record on tape. Yeah. Aaron
Sugar, thank you very much for this conversation. Thank you for having me.
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