This is the Convergent Science Network podcast. Leading researchers in the domain
of neuroscience, brain theory and technology are interviewed by Paul Vershoor and Tony Prescott.
This is Paul Vershoor with the Convergent Science Network podcast.
And I'm having a conversation with Moshe Bar, who's a
speaker at our summer school in in
barcelona and and moshe you you started
your presentation with um a view
on perception where where you brought in let's say a key role for let's say
prediction and top-down components yes so what's what's the key observation
and the key insight there well you mean what brought me to this conclusion to
this presentation yeah Yeah.
So first of all, it's not only mine, so I can't claim complete pioneering movement here.
But the idea here is that we've known for decades that most connections,
if not all, in the brain are reciprocal. They go in both directions.
And still the idea of feedback hasn't been incorporated into mainstream thinking
about the brain sufficiently.
So you see it here and there. But when you just open any textbook on perception
and cognition, you see some kind of an artificial boundary between perception and cognition.
There's first perception that you can think of as the analysis of physical signals
coming through to the brain through the senses.
And once we understand what is it that we perceive, cognition kicks in.
That's the old traditional view.
Cognition kicks in as in memory, attention allocation, executive decisions.
Decisions, and so forth.
But there's actually no real reason why there will be a boundary between cognition and perception.
And our and other's idea is that actually cognition and perception are intertwined
and they help each other whenever possible.
And therefore, the bottom line is that what we see and what we perceive from
our environment is to a large extent affected by cognition and by memory and
by what we expect and what are our goals So, cognition,
perception is not purely affected by the senses, but rather also with top-down information.
But now, would you be able to give a number to that? Would you say,
look, under normal- 50-50.
50-50. Yeah. It doesn't vary with task demands. Well, it does vary.
It does vary. No, of course it doesn't.
I think zero to a hundred. Okay. So we spoke, if you remember,
towards the end of the talk, people asked about meditation.
And I think people that do meditation are actually 100% bottom up.
I think they quiet down completely the top-down effects.
And there are interesting findings that I think Similac or Eastwood,
that's the name of the authors of these interesting studies that we called in the lab the Zen studies.
They're not ours, but we just really love these findings.
And in these specific search tasks, I believe, they ask people to just lean
back, relax, and let the display come to them.
And people improve their performance just by the fact that they're kind of quieting
down their top-down expectations.
So you look for Ts among Els, and you perform better when you just lean back
and relax and don't think about anything.
Thing so there are some cases where expectations actually might
be bad for you especially when you when you have no uh
basis for your expectations and they're completely uh
like me in the stock market right if i lose my expertise there
i'll lose my my house so so whenever you don't have any expertise or whenever
there is a situation that's completely novel or completely not you not based
on the past then it's better to quiet off the expectations and this is a case
where um I would imagine it's 100% bottom-up and 0% top-down.
Let's think about a case where it's only predictions, or only top-down.
I think it's when you plan, right?
When you plan, you're kind of only planning. You don't have the input.
You just sit down now and plan your dinner. You don't have any input other than from within.
So I guess, yeah, I think it's roughly between 0% to 100%. Right.
So here we have the proactive brain. okay now the proactive brain would actually,
in some form, go from sensor states to predictions.
But now you used different kinds of techniques and human subjects like fMRI
and EEG and so on to try to sort of disentangle these pathways a little bit.
And a surprising result that you presented was that in some sense,
if you want prediction pathways, that in some sense we jump outside sort of
the traditional areas of visual processing.
So, it doesn't seem to be restricted to just, let's say, the ventral stream
and the temporal lobe, but also to involve frontal areas.
So, it seems rather surprising to now include more frontal areas in perception.
So, how should I think about that?
So, these findings don't necessarily mean that the prefrontal cortex is suddenly
involved in perception per se, but it just supports what I said at the beginning
of our conversation, that perception and cognition are not separated.
And the role of prefrontal cortex in helping perception be accomplished is an
example of cognition helping perception.
So something about these high-level areas in the prefrontal cortex and maybe
anterior temporal cortex and other regions send down initial guesses to help
the perception of the signal to be accomplished in a quicker and more efficient manner.
So the involvement of the OFC specifically, orbital frontal cortex.
As we discussed yesterday, seems to be polysensory.
So it's not that OFC is a visual area all of a sudden, but it's an area that helps predicting.
And it helps predicting even though we didn't test it, but I believe that it
predicts also in other modalities such as olfaction.
And it also predicts in higher level events
not only detecting objects but also preparing
for a new situation or a new script that's coming
up so an OFC as we know
is also connected with the limbic system and people think about
affect and about reward in the context of OFC and
we think that the reason people see activation in this prefrontal region
specifically in the OFC in tasks that
involve reward and affect as
well as in our own experimental predictions is because what
unites all these specific all these different
processes even though they seem separated there's some
common element to all of them which is a predictive element so
when you're estimating a reward or an
effective value you think about the future you think about what would it give
me or what how would it punish me or what did we do from for me or for anybody
what would be the outcome of a certain
choice so i think the ofc um should more wholly uh be seen as a as a.
Primary player in thinking about the future and predictions rather
in being specific to vision or to affect
or to reward okay but then so that means ofc
which is sort of integrating information from
many sources include including vision is is
making predictions as i say a behavioral time scale so it
would be seconds not milliseconds or both
both i mean in our experiments it
was tens of of milliseconds so yeah so uh
i don't know about the longer range but i
i'm pretty sure that ofc is recruited also when people
do what's called effective forecasting when people try to
predict what would a trip to the um bahamas
do to you if you like if you're going in half a year from now so it's far away
but still you can estimate even though people show that effective forecasting
is something that we're not so good at and um but nevertheless i think that
ofc is response is responsive or is involved in all time scales of predictions
not necessarily is there any,
upper bound to that or that would also go to hours and days,
as I said with the VK I didn't do an experiment so I'm just speculating I understand
yeah but I don't see any reason why it would go,
elsewhere this type of process to depending on time scale it will go to another
area I think if there's a region that knows how to do this it can be employed
in all time scales I don't see why not but.
But I can't say more than speculate yeah but then but
but there's another element or piece of
the puzzle that that that you also revealed which is that it's
not this ofc in some sense gets gets
a very let's say rough impression of the world right
it's not that it gets a high resolution kind of impression but it's a fairly
rough understanding of the world so so what's going on there so in the in the
case of our experiments which was in vision what you're referring to was a low
spatial frequency or you can think of as a blurred picture of reality,
meaning that we indeed, as you said, OFC is not.
Sensitive to the details, it's not informed of the details, it just gets the gist.
It gets the gist of a picture, of a scene, of a situation, and it's enough to
direct the more specified expert
type of cortex, in this case the visual cortex, how to behave, what to.
Focus the analysis on.
So it's enough to say, hey, there is a blob in the upper right corner which
is not expected, I guess he should go there and analyze it thoroughly because
it might be something falling on my head or something like this.
So you're right completely that what we're saying is that it has a gist level understanding.
But as I showed, I really like this picture of a street with a car in the middle.
And all of you guys knew that it was a car, even though the picture is severely
blurred. So our ability to understand these blobs keeps surprising me.
And I came to the conclusion where give me a context and a low spatial frequency
image, and I know what everything in the picture is.
Of course, if you want to know a type of a car or identity of a person or something
like this, you'll need the details. I'm not saying the details are useless.
But for everyday quick decisions, low spatial frequency seems sufficient in
many instances. So your prediction would be that this would also hold for other modalities.
Like if we look at audition for instance, it would also be, let's say,
some low-pass filtered version of an auditory world that would enter OFC.
Yeah, this would be my prediction, but I would love seeing it done by somebody. Of course.
But then why would it rely on this low-pass filtered version of the world?
Yeah, because it seems, look, maybe you gain a few dozen milliseconds because
you can rely on sort of a bit faster pathways to get the information.
So here we go, OFC, we gain 10 milliseconds as compared to a fast processing
or a high resolution processing pathway.
So why would those 10 milliseconds be so crucial? Yes.
So I used to think like this too, and it puzzled me.
Why would, you know, it's not 10 milliseconds, a few tens of milliseconds,
but still, I just recognizing a chair, why would I need any heads?
I mean, why would I need any advanced warning? warning so um
there are two things here first it's the most trivial
question answer which is uh this ability has
evolved mostly for uh uh
survival related more crucial uh type
of recognition like ledoux's example john uh example with a snake in the woods
that you want to know if it's a snake you don't care if it's a snake or a hose
or just a type of a of a branch you just when I run away because in some very coarse level,
it already looks suspicious, so we're safer if we just run away from it and
analyze their high spatial frequencies later on.
So in that case, getting half a second head start over the snake might be beneficial.
But I think there's something unfair about thinking, oh, you know what,
the HSF will bring all the information anyway, why do I need to rush?
The thing is that I don't think that there's much that can be done on an image
with high spatial frequencies only.
I have some pictures that I didn't show yesterday, but kind of high spatial
frequency only images that you can make sense out of them.
You can look at them for hours, and you can't make sense out of them.
So you need the low spatial frequencies, but actually it goes back to your question
of why do we need them earlier. So I guess I'll stick to the first answer, which is, yeah.
But that seems very funny, right? Okay, the results are there,
so we don't have to debate the data. Right.
But if you think about the brain as some sort of layered structure,
where indeed you have structures like the amygdala that Joe Ledoux has been working quite a bit on,
and there the interpretation, oh, the amygdala is actually really a fast,
let's say, an alarm detector detector that rapidly prepares
you for for action to to defend yourself against
threats in the world and now it seems ofc in
this interpretation is like rather redundant compared to
such a really fast responder like the amygdala so i think that they share the
information as we know they are connected heavily and i think they both share
i i suspect they both share the same information including low spatial frequencies
we've shown in other studies where um.
It was initially an unrated study, but somehow it became linked to it.
It was a study about first impressions, how people judge other people's faces.
And we show the faces. We want to see how fast, how first are first impressions.
And we show them faster and faster and faster. And we found that people,
even in the masked presentations of faces, in 39 milliseconds,
were already able to recognize and categorize them as threatening versus non-threatening.
Well we didn't know what's the actual personality of these people so this was
just correlated with much longer presentations of a few seconds so 39 milliseconds
were enough for people to be,
accurate in their first impressions to the extent that first impressions are accurate.
Um then the follow-up experiment was to filter those faces because our assumption
or our hypothesis was that this extraction of features from faces to infer certain
you know threatness or whether it's threatening or not,
was based on low spatial frequencies, of course.
So that's how we feel. That's exactly what we found, that people judge faces
quickly and they use low spatial frequencies for this judgment and not the high spatial frequencies.
So it's not only the speed, but also the content that makes them...
And we found that this activation was in the amygdala.
So it was... And I think others, I'm pretty sure, actually, that others have
shown sensitivity to low spatial frequencies in the amygdala.
So here we're talking about an OFC that's in a way an extension of the amygdala.
You can say it's redundant, but you can also think about its anatomical connections
and how it's a polysensory hub that gets information and sends information to
so many modalities at the same time.
So I think it's important that if the amygdala has this upper area information that can help...
Make quick decisions, share this information with this area that can… This is interesting, right?
Because originally, when you look at the story superficially,
so from the outside, it looks like it's a story focusing on visual perception.
So you think, okay, we have sort of a visual hierarchy, and that sort of sends
information to the orbital frontal cortex.
And orbital frontal cortex might be then generating predictions back to this
sort of visual hierarchy to help it sort of resolve all sort of recognition problems.
But if analyzed in these terms, it sounds much more like, let's say,
a parallel processing stream that is capitalizing on subcortical processing like by the amygdala.
So which of these two views would you lean to?
Would you say both or it's indeed orbital frontal is more driven by the subcortical
pathways or is it capitalizing other input streams?
I think, and I'm sure that you'll share this view, that the brain is pretty
opportunistic in the sense that it will use any information it could to solve a certain problem.
So I'm not ready to commit to only subcortical or only cortical.
I think it uses everything possible.
We know from the anatomy that it gets more of this subcortical information.
So I'm happy to say that OFC or to hypothesize that OFC indeed benefits from subcortical pathways.
But at the same time, we know that dorsal pathways that have this Magno-Serac
type of information project to OFC, and I don't see why OFC has to commit only to subcortical.
And in a way, now you're bringing me to.
Think aloud about another advantage of OFC over amygdala.
Not advantage in saying, I mean, we're not comparing them, but information that
OFC has that amygdala doesn't, which is more this cortical information that
it's getting from the dorsal and from other pathways.
So it can combine, integrate information from more sources than just amygdala.
Well, in some sense, OFC would be well-placed to modulate the amygdala and its
responses to the stimuli.
Because amygdala needs this kind of control. Exactly. Or jumping all the time,
seeing snakes everywhere.
But that might mean that this interpretation of you exploiting information in
the OFC to resolve the perception problem might actually not really be the right emphasis.
So maybe OFC is just exploiting this low-frequency information,
accumulating predictions, building up context information about the world for
more general, let's say, action planning, something along these lines.
Some people talk about this, and there's a chapter or a paper that I wrote with
Lisa Feldman Barrett that talks exactly about how these predictions actually prepare the body.
It's more in the context of affect, but I won't be surprised if these predictions
that are generated or triggered by the OFC are then disseminated not only to
perception, but just anybody,
any taker, any area that wants to benefit or can benefit from it or get it included.
Of course, preparation for action.
But now, okay, so now we have OFC. OFC has been building up predictions, right?
And predictions, as you said earlier, at multiple timescales.
So, and then the example could be, let's say, the blurry car in the blurry street.
So now I can say, okay, car in the street, and I have the following predictions
about the building collapsing and Superman coming out of the phone booth and so on, right? Yeah.
But now, if you think about context or contextual memory, you very quickly end
up with structures like the hippocampus.
So, first we looked at now the comparison with amygdala.
But in some sense, you could then also argue, if OFC gives me contextual information,
in some sense, I seem to be repeating a bit the job of my hippocampus,
which we know is very much dedicated to the formation of these episodic memories.
So, look, I am in this street and I see the car and the building collapsed and so on.
So what's the added value now of OFC if I compare it to this episode of memory?
At some point, you start representing what I'm saying, and you start saying something else.
And I didn't really claim that OFC does context. It doesn't activate context.
It triggers predictions.
I'm not even sure that the predictions themselves are the OFC,
or rather it sends instructions to the relevant cortex what kind of predictions
or what kind of representations are relevant.
I just bring them online and make it a prediction by bringing them online.
Line so uh in in the studies i showed the second half of my talk uh where i
talked about context it wasn't the ofc anymore that was involved it was another
prefrontal region the medial prefrontal cortex that was part of a network that
included the the the empty the medial temporal lobe.
Especially the paripocampal cortex and the retrosplenial area
with a posterior cingulate so there it plays it's different in the role of the
OFC and we're still studying actually up to late night late last night I was
corresponding with two people in my lab about a review that we're writing and
we try to figure out what does the.
Prefrontal cortex does in this in this.
What does it do in this type of a network of contexts? There are different hypotheses.
It's still speculative, so I'm not sure you want to hear them,
but we think there are different types of contextual activations in the medial
temporal lobe and in the retrosplenial,
which some will be more sensitive to the specifics of the context.
When I tell you the context of a kitchen, and you can think about five,
seven items, but you're not committing to a specific appearance.
You know, the fridge can be stainless steel, it can be white,
it could be on the left, it could be on the right.
You know some basic stuff like that the sink is concave and it will be on the
level of the counter, but other specific features you're not committing to because
you need to see the actual exemplar on the specific kitchen.
Then these blobs, these slots are being filled with actual specifics.
Specifics so there is a coarse or abstract representation of
of a context people in the cognitive psychology in the
past call it schema for example so there is
a schema or a frame of of a specific context and it's yet to be filled you know
there is a kitchen oven and a sink and a refrigerator there but you don't know
where and you don't know how exactly they look so you give some basic information
so one part of this network is sensitive to the to the schema and we think it's
the retrospinal cortex,
and together with Elisa Eminov and Dan Schachter, we published.
Some studies that support this, but the specific appearance and the specific
properties of the context frame is filled up with details, and this happens
more in the parhypocampal cortex and possibly also in the hippocampus.
So this is more sensitive to the end and with interactions in the visual cortex
in the sense of a visual context.
What the medial prefrontal cortex does here is, again, we think some sort of
an integration, but we're far from
being able to make it explicit but the concept would be something like,
A frontal area gives you more, let's say, a frame for integration,
a more abstract kind of representational scheme, which indeed you might fill
in also partially with predictions coming from your orbital frontal cortex, what have you.
But now to fill in those hooks with concrete information, you have to rely on
areas like parahippocampal area or even hippocampus itself more than a visual
hierarchy in case of vision. This is the concept.
So frontal more, let's say, an abstract framework, there's a frame in which
you would integrate, and sort of preceding areas really providing you with that content.
Well, yeah, what I was saying is that the retrosplenial, actually,
the medial parietal is the one that involves the more abstract representation,
the more abstract of a context frame, and the prefrontal cortex does some kind of integration.
And we shouldn't forget also the time dimension, the temporal domain where actually
things in context don't necessarily happen simultaneously.
Right, I wanted to ask you about that, because you present some interesting
results on that, which had to do with how these different areas actually establish
specific phase relationships in their responses.
Right. So how is that informative about their interactions?
That's very good. Just before I get into this, I don't want the previous point to be lost.
What I was talking about there was that context can be a spatial context of
things that happen at the same time together in the same environment.
But there's also a temporal dimension of things happening after things or before
things. So there's some kind of temporal order.
So a context is that if you hear a certain sound, you expect another sound afterwards, right?
So context can be in space. It could be in time.
And back to your question, this phase lock analysis that we're doing,
especially with MEG, it's much
easier to do it because the signal has such better temporal resolution,
it allows us to infer, we can't really conclude, but suggest and infer patterns
of connectivity that this can also be done with DCM and other methods, also with MRI.
But with MEG, we could look for areas that are co-activated with the same phase
or with a fixed phase difference.
And from this, we suspect that these areas do something together.
They're active at the same time in the same pattern.
And we took it a step further, and I think it relates to a question you asked
yesterday, and we wanted to test causality.
So if area A and area B are...
Activated in the same pattern, does it mean that one affects the other or the
other way around, or there's a third factor here, or they're just synchronized as an epiphenomenon?
Using tools such as Granger causality, we could test which area affects the other.
I really like these demonstrations, even though, again, nothing here is completely
conclusive because this is highly suggestive, that certain areas speak and affect other areas.
And it's interesting that in MEG experiment of this context network that I mentioned,
I didn't present these results yesterday but we find that objects that are highly contextual,
like the roulette that I showed yesterday or a bowling pin, that are highly
diagnostic of a specific context, this network of three main nodes is active
and also is highly correlated, highly phase-locked.
These three nodes are highly phase-locked.
If you show an object like scissors or a cherry that is not highly contextual,
it's as common in our environment, even more common than a roulette,
but it's as common and as we equate in all dimensions that we can think of,
this network might be somewhat activated because because all of us have some
associations with everything else,
but it's not as diagnostic and as consistent.
As a result, these nodes are not as synchronized.
So supporting our suggestion that this network is related to contextual associations.
But it's essentially a three-node system, right? Yeah.
Would each node provide a specific component of that context information in
this case, following the framework we discussed earlier? Yeah. Okay.
So that's the framework, that's the network that I was trying to ascribe functions
to each node where we're talking about a schema or an abstract context frame
with slots that are yet to be filled,
and then another node that provides the actual features, the actual properties,
and the third node, maybe the prefrontal cortex, that does the integration of
some sort and maybe utilizes this information to deploy other areas with predictions.
But now the synchronization across these three nodes,
is, let's say, a straightforward form of synchronization? Let's say they all
start to oscillate in sync, and that's it?
Or do you see a more, let's say, fine-tuning?
Let's say you will only see a synchronization within a certain frequency range
and not in others. And this is, again, node-specific.
Well, I wish we could be so elaborate in this first analysis,
but I definitely can tell you that the synchrony was in specific frequency bands,
which I can't recall now, but it's a PNAS paper that came out a year or two ago.
So it was specific to frequency band. It didn't just happen all over.
But I wish I could tell, and maybe in the future we will be able to,
say how this synchronization is modulated with different information.
There are some intriguing demonstrations that started, like many other good
things, started with old cognitive psychology.
So for example, if I give you the word bank, you can activate two types of context
frames as we call them context frames.
One of them is a bank with money and with tellers and all these things.
The other one is the river bank, right? When you think about fishing,
jumping in the water, vacation.
So for a given second or moment, there are two context frames that are active.
And then if I tell you bank water, then it kind of disambiguates it and you
know it's the bank with the river, not the other bank.
So you suppress one context frame, you activate the other, and you're more committed to it now.
So I bet if you could look, and we did something like this with vision also, with.
I bet if you looked at the synchrony of this network, within this network,
during this process, you would see some adjustments being made as two context
frames are activated, one of them is suppressed, the other one is committed to.
So I would expect this synchrony, if it's related to the actual function,
to be changed based on this process. Right.
But now, would you... Now, there are two interpretations of this, right?
Because you could say, well, what you call the context frame could be like an
emergent property, if you want, of the synchronized activity across your three
nodes, or you could localize it within one of the nodes and say,
and the other guys are sort of piping information into it.
So, could you make... Is it possible to distinguish between these two interpretations?
Not at this point. I think they're equally likely, yeah.
Do you have a preference for any of these two interpretations?
I like my interpretation with black
coffee. No, I don't have any personal preference. I think that they're...
I mean, given what we know about prefrontal cortex, if I had to put money on
something, I would put money on the interpretation that says that everything
feeds into the prefrontal cortex and then it decides what to do and guides other areas.
But the data doesn't show it yet, so I can't commit to it. Right.
Okay. So now you also, after dealing with this issue of a context network that
we now discussed, right?
So what are the ingredients of context in this?
Are there boundaries to context information that you would consider in this network?
For instance, you could say, well, context does not include information about
self because context is only oriented towards the outside world.
This could be a boundary.
Yeah, that's interesting, because for a while we had a little debate with a
group of people that ascribed specifically to the parahippocampal cortex role in place.
And initially the criticism was, hey, what you found as contextual activation
in parahippocampal is actually place in spatial information.
So for this, which was legitimate criticism, for this we had to design experiments that,
differentiated between spatial context and context that's more abstract,
like a picture of Cupid and a heart-shaped chocolate box.
So both of them has to do with romance, and you hardly see them both together.
I never saw Cupid in my life.
So you never see them together in the same place, or justice,
or other contexts that are not as physically bound to each other and not necessarily
spatially related to each other.
So we proved our point that context, and especially in this network,
is not limited to space-related context, but it did make us think about,
okay, so what is context if it's not appearing together?
Because it's so tempting and so easy to think about context as space,
as things that happen together and...
And I started to think pretty intensely about, okay, what defines context if it's not space?
And another construction definition that I'm currently liking the best is that
actually to contextual relation is,
or contextual related are all the items that are activated together.
So rather than looking at the environment, it's looking at the brain.
All the things that are activated together as a result of one of them appearing
or occurring are contextually related.
So if I show you a picture of Cupid, you activate other things that are related to it in your mind.
And these are the things, that's the definition of context in my mind.
Now, self can and cannot be, I mean, depending on the instance,
because if you want to think about.
The context of a cold shower right now, you would imagine yourself in the shower,
I guess, and you can... How much more can you do in a cold shower?
The beach okay so so
me in a culture okay so then it's not self it's uh yes
but i think that self is just in that regard might
be another another uh item that could be in and out but if you define it so
broadly then context start to coincide with something like working memory i
guess because you're saying anything that is active in the brain well as a result
of of uh relation not as a result
of you know if i give you a telephone number and you put it in your working
memory that's not because the digits are contextually
related but just to finish the point about the self
if you don't mind sure uh we have to be aware also of other people's findings
here that uh this network of it's a medial network that we call the contextual
network with the three nodes the prefrontal the medial temporal and the medial
parietal as i showed yesterday also highly
overlaps with the default network.
Right. And when the default network, so we treat them almost as the same.
And the default network has received a lot of attention recently of people trying
to explain its function, trying to find a function. And one of the prominent functions,
theories, other than our own account of saying, oh, the default network is engaged
in associative activation and the generation of predictions,
as in planning and simulations.
But some groups think that the default network does self-referential processes,
things that relate to self.
So in a way, they'll answer your question, sure, context is self,
because it's the same network. It's activated both by context and by self.
Yeah, but now if we slowly move then to this possible similarity with the default network.
So is the default network for you an epiphenomenon or it's sort of a real feature
of brain dynamics that we should explain?
Oh, it's definitely a real feature and it's intended. I mean,
you know, you can't have a third of the brain active so vigorously,
wasting so much energy as an epiphenomenon or something that's useless.
I'm sure that in my logic, at least, or in my thinking, it has to serve a function.
And in this case, this actually is what led me to think about the brain as proactive
and as being continuously on the move to be ready, to be preparing for the future.
And there are some interesting metaphors that I like to give but they might
have became cliches by now one of them is for example you know playing I like
to play squash and you know in squash you have to be always moving even if it's
your other if your opponent's.
Turn, you're still moving your feet. It's much easier to move into action from
a moving, and you know this probably better than I do.
And in one of the introductions I wrote to either the book on predictions or
the special issue we had, or one of them anyway, I compared the brain to F-16.
Showing my past with the Air Force.
And this type of fighter jets that, because they have to be so agile and so
maneuverable, their steady state, in a way, is not steady at all. It's to be really wild.
And it's unlike jumbo that you want to be the Boeing. You want to be steady
and not to have any abrupt movements.
Here, in order to be agile, you want to start with a system that's easily transforming
to one position versus another.
Other so of course it's a metaphor with a lot of caveats
so don't take it too seriously but but that's how
i like to think about the default network as preparing you and
it's much harder it seems to to go
into action or into cognition or into some kind of a
mental operation from a brain dead
position to rather than something that's already
preparing always thinking always on the move and of
course there are also i think the default network involves uh a
different time scales of planning and of thinking so when
you stack in traffic you don't your default network is not
only thinking about the next second because this will be boring
it will be just like this second but uh it
also thinks about this afternoon when you arrive home or this evening or or
your dinner or you're going out with friends afterwards or playing with the
kids it also involves thinking about the conference you're going to in two weeks
or something that's so different time scales and And there's something interesting
that I want to say about this,
even though I don't think I discussed this in the talk yesterday.
It's the issue of simulations and the experiences that they,
experiences in quotation marks, that they afford us.
So as we all agree, I think, we store in our memory,
our experiences and we store them with a
primary reason well there's no proof that that's the primary reason but it seems
that it's the primary reason or at least a primary reason of being able to use
this experience in the future right so our behavior in the upcoming seconds
or minutes or years is based on what we've learned and encoded so in a way experience helps us.
Store scripts that sometimes can be action plans or motor plans and sometimes
it could be in in conversations, sometimes it could be in dance,
in playing basketball, or anything like this.
So we use our experience, our memories, for the future.
Now, the default network, this area of just sitting there and stuck in traffic,
or waiting for your doctor, or being in a shower.
You create new experiences, again, with quotation marks.
So you make simulations, You sit on a plane.
I have a funny example, but what can I say? That's what went through my mind.
I'm sitting on a plane and reading, reviewing some manuscript.
And I'm thinking, what would happen? It was a very long flight.
What would happen if this door opens up and all of us are starting to fall?
Right? So I'm thinking, oh, I'll take this blanket that's on my lap and I'll use it as a parachute.
Shoot but oh it might slip from my hands you
know if i'll be sweating or whatever how do i make holes oh i have this
pen that i'm holding now so of course this chances of
this happening is one in a zillion right but let's say less real uh simulations
we also think about other things now right what happens if uh i don't know what
the electricity uh um uh yeah so uh we'll be able to manage, right?
But if, let's say, if we both now simulate this in our mind,
we think, oh, we'll just use the iPhone for making some light,
and then we go out and call somebody, right?
If it happens now, we'll be more ready than people who didn't simulate this.
They'll probably also do it, but just a little later.
So these simulations are an amazing way of creating experiences without experimenting.
So Popper said that he lets
his hypothesis die in his
in his behalf so you generate all this
hypothesis and you choose the right one and that's the one
you store and now when there's a situation if
it happens then you're ready for it even though you haven't experienced it
so i think this is very powerful it's it almost allows you to just sit in your
couch and experience everything in life and just store it and be ready for right
but would it be surprising that then this default network is a relatively large
number of neurons are involved in this,
but still it's only a relatively small subset of your whole brain.
And it's not necessarily engaging neurons,
Let's say all possible areas that might provide you with memory or with contextual information.
Or for instance, you could think about the so-called mirror mechanisms,
which again would be running in different systems than this default state network.
So are you then saying that the brain is running a number of substrates that support simulation?
Or are they sort of interlinked in some way?
That's an interesting question. So first of all, the classically defined default
network does involve the medial temporal lobe.
So, you know, an area that, but we know that the entire brain is busy doing
memory. So it's not only that.
So I would suspect that if your simulation involves smell, it will recruit at
least momentarily the olfactory cortex or auditory cortex, depending on what
you're doing in the visual cortex.
The way the default network is defined is averaged across many subjects and many situations.
So I would suspect that all these momentary recruitments of specific cortices
or expert cortices is kind of washed out in the averaging.
It's a possibility. And what you see is the major mechanism of these simulations,
but you don't see these extensions that are recruited on and off. Right.
But then how do you see the role of, let's say, subcortical structures to this.
Default network, if it's a big simulator of the brain?
Yeah. So I think it just falls under the category of what we said now about specific cortices.
So if you simulate a situation that might be scary, I won't be surprised if
you're recruiting your amygdala during the process.
But I don't think that traditionally the amygdala will be part of this default
network only when the simulation or the content of your thoughts pertains to
anything that the amygdala does.
Right. So that would mean what we now call the default network is really like,
let's say, a very rough, let's say, backbone of the simulation structure.
And then let's say when you are alert and acting, it gets sort of blown up into
your context network or sort of starts to elaborate into this context network.
And then when you become inactive and relaxed again, it sort of falls back in
this default state mode again. And this would be roughly the model.
Right. Right. Okay. So, but if I.
So other views on the brain have focused more on, let's say,
sentence-safe-like theories, where you would say, well, cognition relies very
much on, let's say, the integrative properties of basal ganglia.
So in your mind, let's say it's the default network that has this sort of core
integrative powers that provide us with, let's say, the contents of cognition
and possibly consciousness.
Is this really the starting point of that or not? Yeah, and people have talked
about the default network in the context of consciousness, but I want it to
be clear that you make it almost sound like we know what's going on.
No, you're the expert. I'm posting the questions.
But gradually, we're starting both to believe that we know what we're talking about.
So it has to be clear that we don't, and that the same type of simulations,
I won't be surprised if different types of simulations are happening in the
basal ganglia or other areas.
So there's a lot of work that still needs to be done in order to better carve
these different processes.
What I said was pertaining specifically to this default network,
but it doesn't exclude similar processes from taking place in other structures.
What is the overlap, really? So the default network has largely been characterized using fMRI, right?
Yeah, even though it started with PET. But that was the first demonstration.
Yeah, you're right. But that means slow signals, right?
So what do we know about, let's say, the correlates of this default network
at faster timescales? Right.
So people are only now starting to look at the default network in high temporal
resolution modalities such as MEG and EEG.
And one of the reasons is that its majority is in the medial surface,
which is hard to kind of disentangle in methods such as MEG.
But now as methods with MEG improved, we can start differentiating different medial structures.
But I don't think, at least I'm not aware of the temporal characteristics of
the default network other than it's active all the time.
But how does it change over time?
It'll be interesting to explore, yeah. Okay.
But then in the last part of your talk, you sort of moved away.
But okay, there was sort of, if you want, an associative link from the...
We go back to orbital frontal cortex.
We go also back to, let's say, the role of orbital the frontal cortex in
in affective processing possibly mood and
then you made this step towards depression and
then the study of depression and that's how this this sounded a bit surprising
because a bit like okay how does this now relate to this notion of contact so
so what is the relationship between let's say contextual processing prediction
and now depression yeah so um.
It started, this link started by me reading somewhere that people in depression,
and I had no interest in depression or in psychiatric disorders back in the
day, to my embarrassment, but I was reading this interesting notion that people
in depression have a hard time incorporating context.
They don't analyze the broad context.
And with all our focus on context, I felt the need and obligation to try to
explain what in depression is related to this context.
And we started thinking more and more about symptoms, especially cognitive symptoms
and symptoms that characterize the pattern of thinking of depressed people.
So for example, depressed people tend to ruminate.
So they stuck in the same topic or the same thought over and over and over.
It's a cyclical type of thinking.
And this immediately sounded like the opposite of broad associative activation.
So we expect and we know that the healthy brain goes from one thought to another,
and it's very associative.
So unless we're busy and focused on a very narrow task momentarily,
the brain really goes from one thought to another.
And here we have a population that is focused on the same topic in a clinical
manner. They really go on and on and on.
So this already showed some kind of linking.
And from there, we started looking for all this.
We've accumulated all this, some of it's circumstantial evidence and some of
it is fresh evidence from my lab that shows relationship between mood and associative
activation, specifically as it relates to context and to predictions,
associative as in context and is prediction.
So when I say circumstantial evidence is that when you have a hypothesis like
this, that this population will suffer from a lack of foresight and you open
the literature and you see a couple, well not enough, but still a couple of
demonstrations that people with.
With depression show deficiency in foresight that supports this idea, but we also show that,
first of all, we can, together with Malia Mason, a paper that came out in JAP
a couple of years ago, that shows that if you make even healthy individuals
think narrowly versus think broadly,
it can improve their mood significantly,
not in a major way, but it's definitely statistically significant.
So that's another way of supporting this idea.
And overall, we find more and more... So for example, we showed with MRI,
we haven't published this yet, but we show with MRI that people with depression
don't recruit the context network with the same efficiency.
So we're thinking that, or other thing that I showed yesterday,
that treatment of depression affects regions that we activate also with the context stimuli.
So there's all this evidence that kind of converge, and we would like to bring
it to fruition one day and actually help people.
But for the time being it's more an experimental and it's something that's more in the lab and,
showing that I mean the crux of this hypothesis is that broad association improves
mood and narrow associations do not and it also relates to inhibition because
as I showed yesterday it's hard to show it without a slide but.
That part of the reason we suspect it's a hypothesis it's not proven yet But
part of the reason why their brain is so ruminative and so focused and not associative
is because of hyperinhibition that,
according to this hypothesis, comes from prefrontal cortex structures.
So this hyperinhibition makes them less associative.
It doesn't let their thinking process broaden up like the healthy brain.
So if we could affect levels of inhibition or if we can train people to think
more broadly, maybe we can bring about some alleviation of their symptoms in
depression. But as I said before, it's very far from this and we're still on the basic level.
What's the causality there exactly? Because in some you're saying like,
okay, we have, let's say, control from frontal areas on the parahippocampal
area, which is, let's say, more an associative kind of network.
And this, let's say, allows more or less, let's say, lateral thinking,
if you want, or let's say more or less flexibility in an associative process.
And if you restrict this too much, you get rumination. And then this leads to
mood disorders like depression.
This is the causality that you have in mind, right? Yes.
I could also argue that it might be the other way around because you've got
to look, mood is related to effective processing, to neuromodulation.
Neuromodulation has a quite direct impact on, let's say, lateral interactions in cortical area.
So a mood disorder leads to a deregulation of the neuromodulation that allows
lateral interactions among, let's say, associative states in the parahippocampal area.
Could you exclude that interpretation?
No, actually, I embrace it. I think that we talked yesterday about this chicken
and egg, as he called it, the issue of it could start from the molecules and
come all the way up to pattern of thinking.
But what we do here and bring to the table is the optimism, so to speak,
of the thought that you can actually start the other way around.
You can start from the highest possible level of pattern and thinking.
And maybe by training and changing the pattern of thinking to be more broadly
associative, you can affect the depressed brain all the way down to the same
molecules, the same dopamine and serotonin.
So it might be naive, but it's worth trying.
And that's what we do. So just imagine that this hierarchy goes both ways.
But then this also, I guess, takes inspiration from the fact that we know that
cognitive therapy works pretty well.
On a large group of depression patients is that
indeed the case the only reservation i have is with the word large
so the problem with cognitive behavioral therapy is that it's effective but
only for a very small percentage of people so you have to understand i think
i told somebody like yesterday that after i wrote my first paper about depression
a colleague good friend of mine said i could tell you never had depression because
uh you have to to know this population better in order to.
Be able to explain this behavior.
And you realize that people in severe depression are not really motivated to
even go to a psychiatrist, let alone sit on a couch and introspect and think
about their pattern of thinking and now fighting this pattern of thinking.
So anything that addresses this population or attempts to improve their situation
has to be minimally demanding because it's a population that the nature of their
symptoms is not to be involved and not to be active.
So, a method such as CBT, I think, is on the right track, but what it misses
is the larger part of the population of depressed that just cannot be engaged.
So, the question is, can we take elements of CBT and break them down in a way
that's less demanding? And I think in a way, there are some overlaps between
what we're saying and what's CBT.
And I think just based on what we know about the brain, maybe we can just broaden
their thinking pattern without, you know, no questions asked.
Don't introspect and don't fight your thoughts and don't change the topics or
the enemy in your thinking pattern.
Just be associative. Just, you know,
play with these games that we'll be developing or something like this.
And this will be more automatic and less demanding and
less asking less of the patients and maybe
it will be efficient for a broader part of the population
but do you have any evidence today that would suggest that that will work well
other than showing that the press don't recruit a contextual network well of
course you can ask who knows if it's a trainable maybe this this is a one -way ticket.
And the other thing that we've shown is that with healthy individuals,
we can improve mood by associative thinking.
What is missing is to show that
depressed patients are less depressed with broader associative thinking.
But that's something we're working on, but it's not something that happens fast.
But now there are also correlations between, let's say, just physical exercise
and the alleviation of depression, which would argue against your proposal. No, it's not.
Maybe that's why you and I are so happy because of running, even though you run much more.
But there's actually a big wave of publications, including in popular media,
the New York Times likes to write about it, about the effects of running on mood.
But independently, there's this issue of neurogenesis, the growth of new neurons
in dentate gyrus within the hippocampus.
And of course, it's somewhat controversial, or at least still not yet to be
proven completely, that these new neurons actually assume a function.
But assuming that neurogenesis does something, there is clear evidence that
running improves or facilitates the growth of new neurons in the dentate gyrus.
Interestingly, fluoxetine and SSRIs like Prozac also increase the growth of
neurons in the dented gyro.
So we have two things that are parallel and affecting this critical structure
in the hippocampus in a similar manner.
So take SSRIs or take running, it almost exerts the same...
Well, I can't of course commit to this, I didn't do the research and I'm not
sure they They are comparable in terms of magnitude, but it's tempting to think of how exercise,
especially aerobic exercise, weights don't exert the same effect on neurogenesis.
But what's interesting about it, it would be, let's say, the argument earlier
was to say, look, we can take a cognitive route.
We can try to change thinking patterns and from there affect mood.
Right. Well, the exercise example would say it is more, let's say,
from the bottom up, from the body itself and action itself, so completely non-cognitive,
that you can also affect mood.
Right. So we're getting into an interesting topic that I'd like to talk about.
If we have time, sure, I can expand on this.
So I think that, see that the SSRIs, like Prozac, affect, if I remember the
numbers correctly, about 30 to 50% of the patients.
So you ask yourself, well, these are brains. Brains are brains.
Why would some respond to this SRS and some are not?
And I think it goes back to the lifestyle of the press, as I said before.
And I think this is completely speculative. So nobody writes down anything I say now.
So the idea there is that all this criticism about neurogenesis,
how do we know that these neurons assume a function?
Well, I think that if they grow in the morning because you took drugs or you
ran, I mean, SSRIs, or you ran, and you have new neurons in your dentate gyrus
now, they won't connect to any network unless you engage them.
Otherwise, you go to sleep, they die, and then in the morning you start this process again.
So you need to engage them. So in order for improvement from SSRIs or from running,
I think two things have to happen.
You grow new neurons and you connect them to existing networks.
And I think they are connected to existing networks by.
Excuse me, doing something else in addition to just having them grow.
So learn something, play something, et cetera, do social interactions.
So I think that only running won't be enough.
If you just run and then you go back home and you just lie on the couch until
tomorrow morning when you run again, I don't think you'll have the mood benefit.
If you just take SSRIs and you sit on your couch and not do anything,
maybe that's a population that is not benefiting from SSRIs.
So I think you have to be both socially or intellectually engaged at the same
time to give these new neurons a better chance of being engaged and being recruited to a network.
And just one slightly unrelated but still important to emphasize is that I didn't
talk about the fact that this standard JAR is, I mean, it's part of the default network.
The hippocampus is part of the same network that's being activated by predictions,
by the default network, by contextual associations.
So that's pretty intriguing that this growth of neurons and effect on mood come
from a structure that both loses its volume and mass with depression.
And also aging, by the way.
So who knows, it might affect, might help Alzheimer's in the future,
or cognitive decline with aging.
But in any event, with the depression, we hope that this associative thinking,
combined with maybe running or combined with what we just said,
that depressed people don't do much, so make them go out and run. Well, it depends.
It's a spectrum, and some people will do more than others, and I think many
are interested in alleviating their symptoms.
But there would be an interesting, let's say, difference within the SSRI case
and And the running case, because in the running case, you would also be driving
your grid cells because you're moving in space, providing an input into the dentate gyrus.
So while with the SSRIs, that would not be the case.
So that means in the running case, you might actually also facilitate the embedding
of these neurons in active networks.
Well, for purely SSRIs, that would not be the case. Yeah, that's an interesting
line of thought. and I think it has to be tested more rigorously.
I've been thinking myself about the difference of the benefits for running if
it's running outside versus running on treadmill. I hate running on treadmill.
I wasn't thinking like you about the entorhinal and the grid cells,
which is an interesting line of thought and I have to consider it too.
I was thinking more about the change of scenery and how it, in a way,
activates more and more concepts or maybe also associations.
So it's kind of, it's also intellectually pleasing in a sense that it engages
more and more items in your cortex than just running in front of CNN,
you know, on the treadmill.
So if they run in front of flow fields, basically, and green fields and the
television. So you can try this in your virtual reality lab, right? Yeah, exactly.
Just to benefit from treadmill, treadmill with...
Vr or just running outside of course outside has
multiple effects it's also fresh air and all
this absolutely yeah but okay that's
in the future but now do you believe that how do you
see the basic science you're doing on this um depression or
let's say the networks that are correlated with
with depression like the default network and the context network how
is that basic science translating in an impact in
the clinic right now how do you see that you mean beyond
what we're you're trying to do yeah no well the work
you're currently doing when do you see that really impacting the
clinic when it's going to hit the clinic in some form well i
can't predict you know as much as i would like predictions i
can't predict this but we're definitely uh
engaged in collaborations with clinicians and with psychiatric uh um like the
dcrp at mgh with maurizio fava and other big and important and smart psychiatrists
that are interested which which also shows you the state of the interaction
between basic level neuroscience and psychiatry.
I think that psychiatry could benefit from knowledge that we are acquiring in neuroscience.
And the other way around, that neuroscientists can make their work more relevant
and more applicable if they understand more of what's going on in the clinic.
But I can't commit to how quickly. First of all, we have to show in the lab
that we can improve the state of depressed individuals with our methods and
maybe improve our methods.
There have been attempts to make our ideas commercialized, but I wasn't too
ecstatic about it just because I want to see more proofs.
And once we have this in the lab, I think that we would like to make it widely
available for people who can benefit from this. Okay. So Moshe, to finish up.
So you made quite a tour through the brain in some sense. Brought the associative.
Exactly. And also revealing some really core properties of the brain.
So given your experience in brain research and understanding of the mind,
what would be Moshe's law?
Well, it would be make no laws ahead of time.
I don't know. I mean, if I had a law in mind now,
it probably would have been um yeah no
it's this is
your chance uh well it will be when it's proven
it will be my chance not when i'm predicting it but
yeah no love for you really okay so there's no mushes law not now okay and then
what's what's the key prediction that you feel most strongly about today that
so if i come to visit you there in the outskirts of tel aviv no that's yeah
we are or five years from now,
and say, okay, Moshe, this was your prediction you made in 2012, September.
What's this one prediction that you feel most passionate about today?
That the state of psychiatric patients will be much better. Okay, very good.
Moshe Baer, thank you very much for this conversation. Thank you. It was a pleasure.
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