Ep94 "How does the brain construct reality?"

Do you perceive red the same way I do? What is wrong with the textbook model of vision? Why do brains have so many internal feedback loops? And what does any of this have to do with Plato’s cave, Ernest Hemingway, or artificial neural networks that perceive dogs everywhere? Join Eagleman with guest Anil Seth, author of “Being You”, to explore the scientific problem of consciousness.

Episode Transcript

Speaker 1 (00:05):
What is consciousness? Do you perceive the color red the
same way that I do on the inside? What is
wrong with the standard textbook version of vision? Why do
you have so many feedback loops in the brain? And
what does any of this have to do with Ernest
Hemingway or Plato's cave or artificial neural networks that.

Speaker 2 (00:26):
See dogs everywhere?

Speaker 1 (00:31):
Welcome to inner Cosmos with me David Eagleman, I mean
neuroscientists and author at Stanford.

Speaker 2 (00:36):
And in these episodes we sail.

Speaker 1 (00:38):
Deeply into our three pound universe to understand why and
how our lives look the way they do. Today's episode
is about the sense of being alive, and in a moment,

(00:59):
I'm going to bring in my colleague annal Seth, who
wrote a great book called Being You, which is all
about this neuroscientific problem. Now, by way of setting the
table for this, you may say, what is the neuroscientific
problem of consciousness? Well, it's simply this. It feels like
something to be you, and that feeling flickers to life

(01:24):
when you wake up in the morning, and it's not
there when you're in a deep sleep or under anesthesia,
and we're not sure how that happens. Our brains are
made up of tens of billions of very sophisticated processing
units neurons that are all operating together in a giant network.
But just because something has a lot of pieces and

(01:46):
parts doesn't tell you anything about why it's conscious, why
there's any subjective experience. If you get the right tools
and take your cover off your iPhone, you'll find that
it has a chip which has nineteen billion transistors. Now
just think about the interactions and the almost speed of

(02:07):
light signaling in that rich, sweeping electronic landscape.

Speaker 2 (02:12):
But we don't have any meaningful reason.

Speaker 1 (02:16):
To believe that your phone has consciousness or that it
would be like something to be a phone. In other words,
when your phone plays a funny video on the screen,
do you think it feels amused or is it more
likely that it's zero's and ones moving around in a
deterministic way through these billions of pathways when it gets

(02:38):
an email from your boss, does it feel stressed when
it registers the receipt of a text message? Does your
phone have the capacity to feel sad? Probably not? But
how do we make a more rigorous assessment of the question.
How could we know what is conscious and what is
not until we have we have tighter constraints on what

(03:02):
consciousness is. This is the problem that neuroscience faces. How
is it that all our billions of cells hook up
in just such a way that we have consciousness? In
other words, it feels like something to be us now.
Not so long ago. In neuroscience, like thirty years ago,
this problem of subjective experience was essentially not talked about.

(03:26):
People generally felt it was too squishy, and they talked
about how the brain worked, but not about why we
have subjective experience. But things started to change around the
nineteen nineties when some great minds started devoting themselves to
taking this problem seriously. And two of those minds happened

(03:46):
to be Nobel laureates in San Diego. One was Francis
Crick and one was Gerald Adelman, and they worked in
neighboring institutions. And it happened that when I was a
young post doc, I got to work with Krick, and
just across the way was another young postdoc working with Adelman.
His name was Annal Seth, and we both were reared

(04:07):
in this environment where it made sense to tackle the
problem of subjective experience. The mission was how can we
work towards a scientific understanding of what consciousness is and
how brains give rise to it.

Speaker 2 (04:22):
Annal Seth is now.

Speaker 1 (04:24):
A professor of cognitive and Computational neuroscience at the University
of Sussex, where he also directs the Sussex Center for
Consciousness Science. In twenty seventeen he gave a very popular
Ted talk called Your Brain Hallucinates Your Conscious Reality, and
in twenty twenty one he published a book called Being You.

Speaker 2 (04:42):
A New Science of Consciousness.

Speaker 1 (04:45):
So I called him up to share his views on perception,
consciousness and reality.

Speaker 2 (04:50):
Here's my interview with annal Seth. What is consciousness?

Speaker 3 (04:57):
Well, of course we could yan you and I have
just this for many, many hours, and philosophers for centuries,
and so you've got to be pragmatic and I define consciousness.
So I follow this a philosopher Thomas Nagel, who I
think put it very beautifully and very simply, and his
idea was that for a conscious organism, there is something
it is like to be that organism. It feels like

(05:19):
something to be me, and it feels like something to
be you. It's a bit circular, right, There's there's experiencing happening.
But I think it's I think there's something useful about
that because it doesn't mix up consciousness with other things
like intelligence or language or a particular sense of identity.
It's just any kind of experience whatsoever. So at least

(05:39):
we know what we're talking about. You know, it's also
what goes away under something like general anesthesia and then
comes back. That for me, is a nice starting point
for what consciousness is. So we know roughly what we're
talking about, and then it's the Then the approach is, well,
how do we explain it in terms of neuroscience, biology, physics, whatever,

(06:00):
How does it happen? Why is it the way it is?
And here my approach has been again quite pragmatic. Instead
of trying to find some sort of magic Eureka solution
that magic's conscious experience out of neurons or atoms or
quantum fields or whatever it might be, let's just take
a different approach and accept that consciousness exists, because there'll

(06:23):
be some philosophers that try and tell you it doesn't
even really exist, and that it has certain properties. Different
experiences feel different ways, and emotion feels different from a
visual experience, So let's try to understand how and why
these experiences are the way they are. And every experience
has various things in common too, like every experience is

(06:44):
unified more than more than the sum of its many parts.
So my approach is to try to find ways of
bridging between description of the brain in some way collection
of neurons or areas or whatever, and descriptions of experience.
And this is where this idea of controlled hallucination comes in.

(07:05):
And I'm sure we'll dig more into it, but very
very simply, the idea is that instead of our experience
of the world and the body sort of pouring itself
into the brain through the transparent windows of the senses,
in fact perception works the other way around. It's not
a new idea, it's it's very old that perception is

(07:26):
a process of inference. The brain is locked inside this
bony vault of a skull. It's dark and silent in there,
and so it has to make sense of sensory signals
which don't have colors or shapes or labels, and they're
uncertain that they're ambiguous and noisy with respect to whatever's
going on in the world and the body. So the

(07:47):
brain's always casting out predictions about the causes of its
sensory signals, and using sensory signals to calibrate to update
these predictions. And the claim here is that that that's
what we experience. The brain doesn't read out the world
from the outside in. It's always actively constructing the world
from the top down or the inside out. This is

(08:10):
why I think the term controlled hallucination is useful, because
we tend to think of hallucinations as things that are
internally generated, and I think that that's true for all
our experiences. It's just that our normal perceptual experiences are
controlled by calibrated, by yoked to, geared to the world
and the body in ways that are useful. So perception

(08:33):
is a controlled hallucination, and consciousness is a collection of perceptions.

Speaker 1 (08:37):
So let's double click on a couple things there. So
first let's go back to the history. Let's say Kan't
and Helmholtz and think about what were the first clues
that people got in thinking about this idea that we're
not seeing reality as it is out there, but instead
it's something of a construction.

Speaker 4 (08:58):
I think you're right.

Speaker 3 (08:58):
The history of this is super fascinating and it's I
think not given enough credits sometimes and you can go
right back to Plato, I think in his allegory of
the cave, where you have all these prisoners in a cave.
They're chained to the walls, and all they can see
are shadows cast on the wall of the cave by
the light of a fire, and they take the shadows

(09:20):
to be real because that's all they have access to,
and they don't really know that there's anything out there,
you know, that is actually responsible for the shadows. So
I think that's a great starting point because our brain
is in a bit of a similar situation to the
prisoners in the cave. You know, they don't The brain
doesn't have any direct access to anything really, the body,

(09:41):
the world, whatever, so it has to make its best
guess of what's going on on the basis of things
like shadows, and then can't I think for me is
always the reference point. I always keep coming back to
his idea of the newmnen. So there is a reality.
I'm often sometimes misunderstood as denying that there's a real

(10:03):
world because I've used this word hallucination.

Speaker 4 (10:06):
But no, not at all. Now there is a real world.

Speaker 3 (10:08):
I think there is objective reality at least to answer
that question, you'd better ask a physicist rather than me.
But the world that we experience is never identical to
that world. It's always an interpretation. In Kant's way of thinking,
the newmenon reality as it really is is always hidden

(10:32):
behind a sensory veil or a kind of inferential curtain,
as I might describe it now. And then, Yeah, there
were many hints I think initially in vision. I think
most of the early workin this was done by vision.
There's Ibanel Heitm, the Arabian scientist and polymath, did a
lot of work hundreds of years ago basically arguing that

(10:55):
perception cannot be this direct readoubt of the world because
the relation is so in direct and you know, things
seen to obey regularities that can't be explained purely in
terms of the sensory data. Like when you take a
piece of white paper from insider room to outside room,
it still seems white.

Speaker 4 (11:14):
You know, how does that happen?

Speaker 3 (11:16):
It's because the brain isn't just reading off the light
that comes into the eyes. It's trying to figure out
what's causing the light.

Speaker 2 (11:24):
Just to double click on that.

Speaker 1 (11:25):
So if you're under let's say fluorescent lights, and then
you walk under the sunlight. What's actually bouncing off the
paper and hitting your eyes. There's a different wavelength, and
yet we see it as white, and well, this is
color constancy.

Speaker 3 (11:38):
The brain is always after utility. You know, we perceive
the world in a way that evolution has decided is
useful for us. The novelist deny as Nin put it
beautifully when she said, you know, we do not see
things as they are. We see them as we are.
That's what the brain is gunning for.

Speaker 4 (11:56):
That.

Speaker 3 (11:56):
And then there's this trade off between the brains prior
expectations or beliefs about what's going on, and how much
the brain decides to update its beliefs, expectations, predictions with
new sensory data. Sometimes it can update quite quickly, pay
a lot of attention to this data. Sometimes it can

(12:17):
pay less attention. In fact, attention is exactly that process
in my mind anyway. Attention is exactly the process of
balancing how much incoming sensory information is able to update
the brain's best guess controlled hallucination of what's actually happening.

Speaker 1 (12:36):
Right, So people like Can't and Helmholtz and others all
the way back to platos who point out have thought about, hey,
maybe we're not seeing reality as it is, but we're
seeing something.

Speaker 2 (12:45):
That we are having.

Speaker 1 (12:47):
To construction the inside because the brain is isolated. So
then by the nineteen sixties, the neuroscientist Donald McKay noticed something.
I don't know if other people had noticed this before,
but he noticed that the the you know, the amount
of input to the visual cortex in the back of
the brain that's coming from the eyes is actually really small.

(13:09):
I think the estimates now or that it's five percent
of the input to the visual cortex comes from the
eyeballs and all the rest is this sort of feedback.
So let's come back to this issue how you think
about this with predictions and what the brain is doing.

Speaker 3 (13:26):
That's a great place to start, because that's such a paradox,
isn't it. I Mean when I was starting out, and
I think you and I started in neuroscience around the
same time, and the textbooks I remember reading just describe
perception as this bottom up, inside out process. You know,
signals would come into the retina early parts of the
visual cortex right at the back of the brain would

(13:48):
fish out really simple features like lines and edges, and
then you got this picture of information marching deeper and
deeper into the brain, sort of more complex things being
fished out. And then somehow the brain put all these
pieces back together, and that led to this experience.

Speaker 4 (14:05):
Of the world.

Speaker 3 (14:07):
Probably if you open a textbook now, you will probably
see something quite similar. I think this has been a very,
very pervasive idea despite this long and rich history from
Plato to Cant to Helmholt and so on.

Speaker 1 (14:21):
In sixty something years since Mackay published his paper.

Speaker 3 (14:25):
Is sixty years since Mackay's observation, which is hard to
understand in this classical view, Right, if perception is done
in this bottom up direction, why do you have so
many connections mark going the other way from the brain
back out to the census.

Speaker 4 (14:41):
It doesn't make any sense.

Speaker 3 (14:43):
But from the perspective of perception as this constructive process,
it makes much more sense. So digging into it a
bit more so far, you know, we've kind of outlined
that the brain has to make this inference about what's
out there on the basis of noisy and ambiguous sensory
signals without labels. Now, in mathematics we call this Bayesian inference.

(15:07):
It's a process of reasoning under conditions of uncertainty, or
in general, how you should update what you believe when
you get new data. And this is a very very
general formulation. The Reverend Thomas Bays and Laplace figured out
the maths hundreds of years ago, and it's been used
to do all kinds of things like figure out where

(15:28):
to look for missing nuclear submarines, or even figure out
how likely you are to have a disease if you've
got particular symptoms. It's always when things are uncertain that
this mathematics is useful. It's the same deal with perception.
The brain is trying to figure out what it should
believe now about what's out there, given some new information

(15:48):
from the census. Now, the problem is that actually doing
Bayesian inference is really really hard. In fact, it's almost
impossible to do it exactly. So the brain, any good
biological evolutionary hack has figured out an approximation, a very
general approximation. And this approximation is in the business we

(16:12):
call it predictive coding or predictive processing. And what this
means is that the brain effectively has some kind of
model of the world and the body, and it uses
that model to generate predictions about the sensory information that
should be coming in. And these predictions they cascade in

(16:34):
this top down direction and back out to the sensory
surfaces through all these connections that Mackay identified, and then
the sensory signals instead of being read out by the brain,
they just serve as prediction errors. They report the difference
between what the brain expects and what it gets you

(16:56):
at every level from the retina to the early parts
of the visual cortex all the way up. And if
the brain then follows a very very simple rule, which
is just either make an action or update its prediction
to try and minimize these prediction errors. So it's just
trying to minimize and trying to reduce these prediction era

(17:17):
sensory signals, then collectively its predictions will be a very
very very good approximation to Baysing inference. So we get
a picture in which the brain is constantly casting out
predictions into the world, the sensory signals update these predictions,

(17:39):
and there's always this simple mechanism which the brain is
just trying to minimize prediction error. And as a result,
what happens is the brain is able to make a
best guess about what's out there in the world or
in the body, and then the claim is, well, that
that's what we perceive.

Speaker 2 (18:13):
So let me just summarize.

Speaker 1 (18:14):
So the idea is the brain can't know exactly what's
out there, so it's making its best guesses and then saying, oh, wow,
that guess was really off, that guess was a little closer,
and so on, and as it matches the incoming data,
it gets to refine its model that way until it
has a reasonably good model. And so one of the
ideas that's been floating around in neuroscience for a while,

(18:36):
but again I don't think this makes it to the
textbooks is this idea that all of the data that
we see in the century courtices is actually the error.
It's the part that you didn't get right. And if
you were actually getting everything one hundred percent right, you'd have,
you know, golden silence going on in there. But the
world is complicated and things are always happening, not in
the way you can predict away. But this is the

(18:58):
frame shift that's real required to see this sort of thing.
So coming back to why you call this a controlled hallucination,
let's do it this way. Which is we don't need
our eyes at all to have rich visual experience, right.
You have dreams inn when your eyes are closed, right,

(19:18):
And so the idea is that maybe you are dreaming,
you're producing all the stuff on the inside, but use
the word controlled to indicate, Look, you're anchored to what's going.

Speaker 2 (19:30):
On in the outside.

Speaker 1 (19:31):
You've got this, you know, let's say five percent of
data coming in from the central world, and that anchors
you somewhat. And that's what you mean, my controlled hallucination
control dreaming in a sense.

Speaker 3 (19:43):
That's exactly right. In fact, if you describe perception as
a kind of controlled hallucination, then you can just flip
it around and you can say when you're hallucinating as
we would normally use it. You know, when someone sees
something that's not there, indeed, when they're dreaming, well you
can just call that uncontrolled perception. And I think the
point here is that there's a continuity. They're not completely

(20:05):
different categories. So, you know, we used to thinking of
dreams and hallucinations as coming more from the inside than
the outside. And it's true that in normal perception the
outside world plays a bigger role. But fundamentally, I think
it's the same kind of process. It's the same dance,
this exchange of prediction and prediction error, but you change

(20:29):
exactly how this dance plays out, and you can sort
of sweep through all these different ways of experiencing, whether
it's normal perception or dreaming or hallucination or something else.

Speaker 1 (20:40):
Now use the terms controlled and uncontrolled, but in fact
it's more of a spectrum. I'd imagine right where a
controlled hallucination is I'm looking for something on my desk,
I have to really pay attention, and so in that sense,
it's really controlled by what's in the outside world. Here
it's more controlled, whereas other times it's less and less

(21:02):
controlled when I'm you know, imagining something or just seeing whatever.
And you can imagine in cases like Charles Bonet syndrome,
as people are going blind, they have formed visual hallucinations.
They think they see somebody walking in or doing a
bunch of dancers in the street or something, even though
that's not there. So it's sort of like a spectrum

(21:22):
of how much control the outside world has.

Speaker 3 (21:26):
That's exactly right, And in fact, one of the powers
of this approach is that you can use it to
better understand what's going on in conditions like Charles Bonne,
where people have hallucinations. One of the things we did
in my research group last year was we built computational
models of this process of prediction and prediction error and

(21:47):
so on, and we tweak the model in different ways
to try and simulate different kinds of hallucination. So Charles
bona syndrome where indeed people often see patterns, but also
people wandering around. Then there's in Parkinson's disease, people often
have quite rich and complex hallucinations. And then in psychedelics

(22:11):
you get all kinds of different hallucinations too that seem
sometimes seem to emerge out of things that are already
there in the environment. That clouds can become animals or people,
things like that. So we've been able to use this
approach to really drill down and understand not only how
these hallucinations happen, but how they and why they differ

(22:33):
from each other. And what we did was we actually
went to people who have these kinds of hallucinations in
real life and we asked them to judge the output
of the model so that we could test whether our
you know, our computational model of these hallucinations was right.
You know, would somebody with Parkinson's disease pick the output

(22:53):
that we generated when we were trying to simulate Parkinson's
hallucinations and more or less that that works well. So
we're beginning to be able to really characterize and at
the level of what the brain is doing these different
kinds of hallucinations.

Speaker 1 (23:10):
You know, one of the things that I talked about
in my book Incognito was this possibility that all of
us might be having hallucinations all the time, but we
don't know it. So for example, you know, it's something
on my desk here, or you know, I think my
dog's over there when he's not, and so on, and
we only notice it when there's a clear indication that

(23:34):
it's not true, either because someone else tells us, or
I go look for my dog and realize that was
a bag on the floor, not my dog, or so on.
But probably this is happening all the time. I want
to drill in on what you said about how you
go out to different people with psychedelics or with Parkinson's
disease and ask them and find out the degree to
which this match is. What you're doing is taking things like,

(23:56):
for example, in your Ted talk a while ago.

Speaker 2 (23:58):
You used Google deep.

Speaker 1 (24:00):
Dream to take footage, and Google deep dream sort of
has these psychedelical aist nations where it sees dogs, faces
and everything, So tell us about that.

Speaker 4 (24:09):
That's exactly right.

Speaker 3 (24:10):
So this is I think it's a nice story because
we basically did their own to start with, just because
it was a lot of fun. You know, we had
this virtual reality lab. One of my post docs is
very good at coding these things up, and we just thought,
why can't we try and make a situation where you know,
at the time, people had been just taking photos of

(24:32):
bowls of pasta and then putting them through Google deep Dream,
and they'd all become you know, they'd grow, loads of
puppy heads would be there in the in the bowl
of pasta. And we just thought, should we just try
and do this in virtual reality just because? And so
we took a panoramic movie, so three hundred and sixty
degree movie, and we put every frame through an adaptation

(24:54):
of this deep dream algorithm so that you would get
framed frame continuity. So it was it was not an
easy to do.

Speaker 1 (25:00):
Can I for just one second just over on sclear
The reason Google deep dreams sees puppy faces everywhere is
because that's its expectation. It's busy and prior is that
it's looking for puppy faces, and so that's why that's
why it sees it everywhere.

Speaker 2 (25:17):
If you even vaguely have like two dots.

Speaker 1 (25:20):
In the line or something, it says, oh, there's a
puppy face.

Speaker 2 (25:22):
Okay, keep doing that.

Speaker 3 (25:24):
That's exactly right. And of course it doesn't have to
be puppies. You could you could give the network another
expectation and basically fix another node or part of the network,
and then it's expecting something else. And that's actually the key.
So once we'd done this with Sussex campus, and we
did it with the dog expectations, so suddenly people would

(25:46):
have all these strange experiences of Sussex University campus, all
these dogs coming out of the walls and the windows
and the sky and and we tried it and it
was fun, and it just struck us when we were
thinking about this, what would there be any actual scientific
utility in it? And what we realized was that in
a lot of psychology experiments, people focus, for good reasons,

(26:09):
on very constrained situations. You know, they ask people was
the dot moving to the left or the right, or
was the patch of light there or not there, or
something very very simple, so that you can make very
precise measurements. But these things are very far from the
richness of everyday conscious experience. In the end, that's what
we want to understand. So what we realized was what

(26:31):
we built was not a model of any kind of
cognition of what people think or any kind of behavior
what they do. What we built was a model of experience.
We built a model of particular way of encountering the world,
and not many people have really been doing that. In fact,
it was probably one of the first examples of doing

(26:54):
something like this, and it was really just for fun.
So from that starting point, then you're point is exactly right.
So that initial network was set up to project expectations
of dogs into everything. But so what we then did
was we moved on from the Google Deep Dream algorithm
to some more complicated neural network architecture that we could

(27:17):
tweak in different ways so that we could begin to
simulate different kinds of hallucinations. So some hallucinations are very
rich and very complex, others are very simple and very geometric.
Some hallucinations appear out of nowhere, you know, they just spontaneous.
Theorize other hallucinations are transformations of things that are already there.

(27:41):
So we were able to kind of create this space
we could move around in to generate these different kinds
of experience.

Speaker 2 (27:49):
So tell us what you learned from that.

Speaker 3 (27:51):
So what we were able to do this is with
my colleagues David Schwartzman and ks Ksuzuki.

Speaker 4 (27:57):
They did all this work. By the way, I want
to make that very clear.

Speaker 3 (28:01):
We went to people who have these hallucination in real life,
people with Parkinson's disease, people with another condition you mentioned,
Charles Bonne syndrome, people who've had psychedelic experiences, not that
we're having them then and.

Speaker 4 (28:16):
There, but have had them.

Speaker 3 (28:18):
And we ask them to pick examples from our models
that were most similar to the experiences that they had,
and that way we can test our hypotheses about the
computational basis of these different kinds of hallucinations. So eventually,
and we're not there yet, but eventually the idea is

(28:41):
by doing this, we'll be able to make some predictions
about what we might see if we put these people
in brain imaging scanners and image what's happening while they
have their hallucinations. That's the next step, And overall the
goal is, I think, to put it in this larger frame,
you know, when you want to understand something like how
we experience the world, the nature of perception, it's often

(29:05):
a very good idea to look at those situations where
things are a little bit strange, you know, where people
are experiencing things differently. You poke around in it and
see what happens when things a little bit out of whack.
So the utility of studying hallucinations for me, it's firstly
for the people that have them. We can help them
understand their lived experience better. But it also reflects back

(29:29):
on our understanding of perception in general, because, as we've
been saying, fundamentally, it's the same process.

Speaker 1 (29:38):
Yes, now, let me drill in on this for a minute,
because you and I are both fascinated by individual differences
in the reality inside different heads, and I've made many
episodes on this topic. For example, the spectrum from a
fantasia to hyperfantasia, or the internal voice, or what happens
with synesthesia of different types. All these of things indicate

(30:01):
that we're experiencing different realities and ways that we can study.

Speaker 4 (30:04):
You.

Speaker 1 (30:05):
With your colleague Fiona mcpheerson, you launched the perception census.
Tell us about that and tell us what you've learned.

Speaker 4 (30:12):
Oh, thank you for asking this.

Speaker 3 (30:13):
I mean, I'm glad we're talking about this because I
think it is probably one of our strongest overlaps. And
I have to say a lot of my interest in
this area was inspired by conversations with you dating back scarily,
I think well over twenty years now when we first
started talking about these things, which but certainly the work
in synesthesia individual differences, it falls out as a consequence

(30:35):
of this way of thinking, and I think this is
worth saying first. If we start with a textbook view
of visual perception, it's kind of easy to think that
we will all see the world hear the world in
roughly the same way. And that's also how perceptual experience
feels like. It feels like I just see the world

(30:56):
as it is. It doesn't seem to me as though
it depends all that much on my brain, or certainly
not on the specific because of my brain compared to yours.
But how it seems is never a particularly good guide
to how things are and in this view of perception,
as this prediction, this controlled hallucination, this generative process, then

(31:21):
it's going to be different for each and every one
of us. You know, we all differ on the outside
in skin color and height and body shape, and we
all have slightly different brains too, and so we should
all experience the world to some extent differently. But the
key difference here is it's easy to tell whether people
are different heights, even if the difference is quite small.
You know, I can just look at two people standing

(31:42):
next to each other and I'll see if they're one's
taller than the other. But if you experience red slightly
differently from me, or time slightly differently from me, how
will we ever know? Because your experience is private, subjective
to you will probably use the same words. You know,
it's read that lasted about a second. It's so much

(32:06):
harder to tell. And so we end up assuming, I think,
overestimating the similarity of our inner world. And so this
project of the Perception Census, with Fiona Macpherson and many
other colleagues, Reny by Kovira, a postdoc who really drove
it here at Sussex, we wanted to study these individual differences,

(32:27):
but we wanted to do it at scale. So this
is not a new idea, David. You've done a lot
of this work, contributed a lot to this literature already.
But many of these studies focus on one or two
or three aspects of perception. Maybe synesthesia, which I'm sure
your listeners will know about because you wrote the book

(32:47):
on this, literally several books, but you know, or maybe
something else like time or ability to discriminate different musical notes.
We wanted to look at lot, lots of things together,
and we wanted to look at a lot of people,
and people from many places and of many ages, so

(33:07):
we got quite ambitious. We put together over fifty different
tasks rather than just two or three, fifty different experiments
that people could do, lots of visual illusions, sound had
to be things people could do at home, so we
couldn't do things like smell whatever, But fifty different things,

(33:28):
and overall we were able to get around forty thousand
people engaged in the census from ages of eighteen to
well over seventy and from one hundred and twenty seven
different countries.

Speaker 1 (33:43):
Wow.

Speaker 3 (33:43):
So it's been a huge data gathering effort and I
really see this as a resource, and I hope it's
going to be an important resource for the whole community
because we're going to make the data entirely open and
it can be a bit of a sandpit for testing
ideas or hypotheses that people might have. You might want
to ask, oh, to somebody with more vivid mental imagery,

(34:07):
you know, do they see more different shades of color?
What things tend to go together? And as part of
the census, we also have some data on people whether
where they reside on some of the more clinical dimensions too, autism, ADHD,

(34:27):
things like that, so we can start to understand how
these conditions relate to the normal spectrum of variability. So
I've been using the term here perceptual diversity rather than
what many people have heard neurodiversity, And I want to
just dwell on that for a second, because this idea

(34:49):
of neurodiversity has been very important and it's led to
a lot of recognition that people with conditions like autism
is the one that's most commonly used here as others
as well ADHD, that they experience things differently and that
can cause some problems, can give some benefits, but it's different.

(35:09):
But ironically, in my mind, the idea of neurodiversity has
kind of reinforced the idea that if you're not neurodivergent
in some way, then you're neurotypical and you see the
world as it is, and it's underplayed I think the reality,

(35:30):
or certainly the reality we're exploring with the census.

Speaker 4 (35:33):
Let's see if it's true that.

Speaker 3 (35:35):
There's just variation, and maybe when you get somewhere towards
the extreme, if a distribution, a label is slapped on
it and it becomes a neurodivergent condition. But I think
that if we all understood that each of us experiences
the world in our own unique individual way, it will

(35:57):
This is not at all to diminish or minimize neurodiversion conditions.
I just want to understand them as part of the
spectrum in which there's variation among all of us, just
as there is in high body shape anything else. That's
what the perception census is empirically trying to look at,
because we just don't have this deita yet. How much

(36:20):
variation is out there, what does it look like, how
does it correlate?

Speaker 1 (36:40):
By the way, just for final I'll tell you about
my idiothesis. Idiothesis is the term we use in my
lab for an idiotic hypothesis. But it's what I've been
interested in is, as you know, I'm a lover of literature,
and I've noticed that authors like Ernest Hemingway and let's say,
Thomas Hardy have very different ways of writing. And I

(37:00):
think this is a I'm calling this retrospective brain scanning,
because I think we can tell that Hemingway was probably
a fantasic, meaning he didn't picture details in his head
and didn't care about them. But somebody like Thomas Hardy
or you know, Fenimore Cooper or anybody like that, put

(37:22):
so many details on the page of the red curtains billowed,
and the flowers were these flowers in this arrangement and
so on. And I happen to be on the a
fantasic end of things, and so I can't stand those
authors who give tons and tons of detail.

Speaker 2 (37:37):
That don't matter.

Speaker 1 (37:38):
Now, the reason this is an idiothesis is because I
can't ever approve this is true, and there may be
other reasons why they wrote in the styles they did,
but I like the idea that this is yet another
correlation in the world is by looking at people's outputs,
the way they talk, the way they described the world,
and that might give us some even very rough insight
about what's happening internally.

Speaker 3 (37:59):
I think that there's something too that I think. One
thing that's well known, I mean, you'll correct me if
I'm wrong, is that some authors were clearly synesthetic. So
I think Vladimir Nabokov was well known as a synaesthete.
And that's interesting not only for the perspective of how
he might have described the world around him, whether it

(38:19):
was detailed, but whether it affected other things, whether there
was sort of just more cross fertilization, more associativity in
the way he was writing compared to other people. Because synthesia,
which again is something that well you actually did. I
think it was the first large scale survey of synesthesia
in the synesthesia Battery. So we're looking at cynesesia again,

(38:43):
but of course in the same group of people, we're
looking at all kinds of other things too, So I'm
really excited to see what synesthesia is associated with. Unfortunately,
we didn't ask people whether they'd written any great works
of literature.

Speaker 1 (38:57):
You know, the truth is always struggled with this question
about weather. Synesthesia is associated with higher creativity because in
a sense, it's not creative. If I always see M
as purple and B is orange and so on, that's
I'm particularly creative.

Speaker 2 (39:12):
It's just an association that I have.

Speaker 1 (39:15):
In the case of letting me in a book off
he you know, there are little clues that we have. Ever,
just as one example, he was a lepidopterist, which means,
you know, he studied butterflies, and for him, his favorite
butterfly had a particular pattern of yellow black yellow, and
so his novel Aida a Da happens to map onto

(39:38):
those colors of those letters, where you know A is
yellow and D is black.

Speaker 2 (39:44):
And A is yellow.

Speaker 1 (39:45):
So we see little clues like that, But I don't
know if it makes a person more creative or not.

Speaker 3 (39:49):
Yeah, there's another example of that. I actually had that
writing a piece for a gallery catalog about and an
artist ya Yu Kusana.

Speaker 4 (39:56):
She's still alive.

Speaker 3 (39:57):
She's in a nineties in Japan, and she's very very
well known for these artworks that have things like red
polka dots everywhere, and that there's this sort of patterns
over everything, representations of landscapes and so on and it's
a it's a very distinctive style. She also does these

(40:19):
mirror infinity rooms. But it seems that she has this
way of experiencing the world in which she literally does
see red dots everywhere. At some times there's I forget
the technical name for it, but there's there's a you know,
a sort of sexual condition where where this happens. And
so exactly to your point, maybe her artistic innovation was

(40:44):
to some extent a direct transcription of what she was
just seeing. Now, I think that is not the full story.
You know, you can you have to. I think it
provides the raw materials that people can use to generate
pieces of art, whether the paintings, music, novels that have
a creative and artistic impact. So I don't think it

(41:05):
reduces But also I always agree with you that that disassociation.
We have to be careful because if it's automatic and normal,
you know, where is the creativity. The creativity is and
what you do with it. It's not the thing itself.

Speaker 2 (41:20):
That's right.

Speaker 1 (41:21):
And by the way, Kandinsky, the visual painter, he had
a very rich synessiege that was triggered by sound, and
so what he would do is crank up his phonograph
and stand in front of the canvas and paint what
he was seeing. And what's interesting about that, that's an
example where he's just transcribing what his perception is. What's

(41:41):
interesting is, given that we all have different internal worlds,
it's nice to find ways to share like that. And
I do think in let's say, literature and novels, you
are getting to step into the shoes of someone who
sees the world differently than you do, and that's why
we love to share stories with one another.

Speaker 3 (42:00):
Yeah, I think again, something that you and I no
doubt agree on is that, you know, when when we
face the challenge of understanding consciousness, you know, in the
large in the sort of most expansive way, we want
to understand what it is like to be me or
to be you. And sure we can study visual perception

(42:21):
or auditory perception or something like that, but for a
lot of people, the experience of being who they are
is tied up with the self, that's tied up with
some kind of internal narrative, distinctive way of being in
the world. And literature has done, i think, more than
science to explore examine that aspect of consciousness.

Speaker 1 (42:42):
I'm so glad you brought this up because I was
going to ask you about this. So in your book
Being You, you talked about the way that we build
a model of the outside world by minimizing the prediction aris,
but also we build models of the inside world.

Speaker 2 (42:56):
So tell us about that.

Speaker 3 (42:57):
So this is another INVERTI if you like a conceptual
upside down move, We've already had one, which is the
idea that instead of experiencing the world in this kind
of outside in direction where the world just pours into
the mind, it's this act of construction, this controlled hallucination. Now,
another common assumption or sort of it might seem an

(43:20):
obvious way of thinking about things, is when we think
about the self, and it might seem just intuitive to
say that the self what it is to be me. Well,
that's the thing that does the perceiving. There's something, there's
some essence of me inside my skull. That is the
recipients of all these perceptions, however they're constructed, that takes

(43:44):
them in, figures out what to do next, does something,
and we sense, we think, we act.

Speaker 4 (43:50):
And go round and round and round.

Speaker 3 (43:53):
Now, I think this is not the way to think
about things, I think is very different. Rather than the
self being that which does the perceiving, I think it's
more correct to say that the self itself is a
kind of perception. So there's just a same process going on.

(44:14):
The brain is making inferences about sensory signals from different
places and over different time scales. Some of those inferences
underlie our experiences of the world, and some of those inferences,
some of those controlled hallucinations, are what the self actually is.
The self is this collection of perceptions that have and

(44:40):
I think this is the key, you know, So why
is the self different from the world. What's special about
the self? Well, one of the things that's special about
the self is the body. So perceptions of self, for
me anyway, are rooted in the brain's perception predictions about
the body, and all our aspects itself are built on that.

Speaker 1 (45:05):
Great So your brain is monitoring the outside world, and
it's monitoring the inside world inside the body.

Speaker 2 (45:12):
And he made the argument that.

Speaker 1 (45:15):
It not only tries to predict, but eventually becomes good
at saying Okay, look, this is my model is that
let's say the body temperature should be at this, so
if it fluctuates, this is what gives you the ability
to do homeostasis to keep things in order because you
have a well established model at this point.

Speaker 4 (45:34):
That's absolutely right.

Speaker 3 (45:35):
And I know you recently had Lisa Felman Barrett on
the podcast, and she and I have a pretty similar
view about this, but I think we came to it
from very different directions. I mean, Lisa has always been
interested specifically in emotion and pushing back against these Diarwinian
ideas of hard coded emotion circuits in the brain. I

(45:57):
came to these kinds of ideas by just saying thinking about, well,
how does the brain perceive the outside world, Well, maybe
there's something similar happening about emotion. Building actually on some
very early ideas of William James and Carl Langer, who
talked about emotion as a process of the brain's appraisal
or interpretation of the physiological condition of the body. But

(46:21):
where we end up is somewhere. It's not exactly the same.
There are differences between how Lisa and I see things,
But where we end up is one fundamental realization about
what brains are for that really I think casts everything
we think about perception consciousness in a different light, and

(46:41):
this is that brains are not for creating art, or
writing poetry, or doing complex math, or even having conversations.
If you think about what the primary duty of any
brain is, it's to keep the body and itself alive.
If you don't do that, you don't do anything. And
to keep it an organism alive is a difficult thing.

(47:03):
I mean, that's why evolution has shaped all these weird
and wonderful creatures. But it usually well. In fact, it
always requires keeping certain physiological variables like heart rate, blood pressure,
blood oxygenation within very tight ranges. You've got to regulate
your physiology. If your blood oxygen drops, you won't last

(47:25):
very long if it drops too much. And so how
does the brain do this well? Any control engineer will
tell you that a good way to regulate something, to
keep it where it needs to be, is to have
a predictive model about it, because if you have a
predictive model, you can anticipate deviations. When we both finish

(47:46):
recording and we stand up, the brain is anticipating that
our blood pressure will drop a bit, so it transiently
increases the blood pressure. It constricts our vessels, so our
blood pressure in fact remains pretty much exactly the same.
We don't faint, So the ability to control and regulate
is done best through prediction. And so for me that

(48:12):
that's the fundamental reason why brains work this way, why
perception works this way. It's all built on this fundamental
imperative of the brain to regulate homeostatically, allostatically the physiology
of the body. And I think the way I like
to put it in my book is we experience the
world and the self with through and because of our

(48:34):
living bodies.

Speaker 2 (48:35):
And so impact that just a little bit more.

Speaker 3 (48:37):
So, there's I mean, this whole question of what emotions are,
how they come about with their force is super super interesting.
But they kind of polarize two extremes, and one extreme
you have Darwin, or at least a kind of caricature
of Darwin, which says that there are certain there's a

(48:57):
certain repertoire of emotions happiness, anger, sadness, discussed things like this.
They're pretty much hard wired into our brains. They're kind
of different from cognition, from thinking, you know, they're more
physiological like survival reflexes that feel particular ways, and they're

(49:20):
pretty fixed and conserved. The ideas across time across cultures,
and this has been kind of the mainstream view, I think,
in one way or another for a long time. And
then on the other hand, you've got the idea of
emotions as being somewhat constructed. Now this has also got
a long history. William James, we mentioned a minute ago,

(49:41):
he realized over one hundred years ago that emotions were very,
very deeply embodied. That you know, he proposed the idea
that if say, a bear comes charging into the room,
we will feel afraid and then we might run away,
and it might be normal to think that, you know,

(50:05):
it's the sight of the bear that causes the feeling
of fear, and then the feeling of the fear causes
me to run away, and James is becoming a common
theme to our conversation. Now flips this around, right, So,
for William James and also Carl Ager, the bear comes
charging into the room, my brain registers the presence of

(50:27):
the bear. This puts my body into a particular physiological state,
and adrenaline shoots up, courts all starts racing around. And
then my brain perceives this change happening in the body
in the context of a bear being there, and that
is the emotion of fear. So fear in this case

(50:49):
follows or is at least part of the change in
the body. It's not that fear then causes the change
in the body. The experience of fear is the perception
of what's happening in the body in this context of
something dangerous happening. So that's that's the way I think
of that. I've come to think about emotions as grounded
in this this imperative for regulation. And you know, just

(51:13):
as with the perception census, there will be variation, but
there will be also a lot of similarity. There's a
lot of similarities in the way we see things. So
you know, I tend to always land in this this
sort of happy or unhappy medium where there may be
some amount of biological conservation going on. There's sort of

(51:34):
good reasons for that, but there might also be more
variation than we might think.

Speaker 1 (51:41):
Given the way that you are thinking about consciousness, what
do you think about other animals having consciousness?

Speaker 2 (51:45):
And what to take on AI becoming conscious.

Speaker 3 (51:48):
Two super important and increasingly timely questions. I mean, there's
so much excitement, height and some amount of anxiety and
fear about AI and.

Speaker 4 (52:01):
Things are changing.

Speaker 3 (52:02):
Animals, of course, have been around for a very long time,
and we've had over history varying views about their status
as conscious creatures or not, and I think they pose
us very different challenges. So we humans, we tend to
see the world through the lens of being human. We're

(52:25):
very anthropocentric, and not only that, we're very anthropomorphic. We
tend to project human like things into other systems on
the basis of what might be superficial similarities. So this
is to say that when it comes to other animals,
if they're different from us, we tend to withhold from

(52:48):
them things that we think are distinctively human or matter
to our humanity, like intelligence and consciousness. And I think
it's this combination of biases that can get us into
trouble here. Because we see things through a human lens.
We've tended over history to associate something like consciousness with
other things that we think of as distinctively human, like

(53:10):
intelligence and language. So if we do that and we
look at non human animals, we tend to reserve consciousness
for those animals that seem the smartest. And in fact,
you know, we've done worse than that. It wasn't that
long ago that people didn't give babies anesthesia. I mean,

(53:30):
this seems crazy now that it was not common practice
until a few decades ago, there was this sort of assumptions.
I didn't really feel paid, and I think that just
shows how deeply our assumptions like this can affect our practice.
And we have exactly the opposite situation now with artificial intelligence.

(53:53):
So AI systems like chat, GPT or Claude can speak
to us fluently. Whether it count as conversations, I'm much
less sure. I mean, Sherry Turkle has talked about this
beautifully and said, when we converse with a machine, we
unthinkably simplify what we mean by a conversation. It's a
different form of human machine interaction. But because words are

(54:14):
being exchanged and some of the things that language models
can say, it really are quite surprisingly articulate and informative.
We tend to project qualities like consciousness into these machines
because if it was a human being talking to us
like that, well that human being would clearly be conscious.

Speaker 4 (54:35):
But it's not.

Speaker 3 (54:36):
It's something very, very, very different. So I think the
first thing we have to do is recognize how much
our intuitions about the circle of consciousness, how far these
intuitions are shaped by our biases, and that we can't
just crawl out from under them, We can't get away
from them. These biases might be what we might call

(54:59):
cognitively imped like some visual illusions. You know, there are
some illusions that even when you know two lines are
the same length, they will always look different. This is
the Mullaalaya illusion. So even if we know we're biased
in these anthropocentric ways, we will still be unable to
resist these intuitions. So we need to just surface that

(55:22):
and then ask the question, so what's actually most likely
the case? And here I think there's extremely good reason
to believe that consciousness is pretty widespread in non human animals.
If you look through all mammals, whether it's a tree,
shrew or orangutang, they have the same basic neural architecture

(55:46):
that we know is important in human beings for consciouness.
I think things get really tricky when we look at birds,
when we look at fish, when we look at insects.
And here I just think we have to we can't
be determined, as we don't know one way or the other.
But we just need to keep updating our credence in
consciousness in these things. And I think the key thing

(56:08):
is we also have to ask, well, what kind of
consciousness matters. It may well be the case that not
many non human animals have fully developed reflexive, reflective experiences
of being particular individuals. I mean, we don't know yet,
but it seems unlikely. But ethically what matters is whether
they have the capacity to suffer, to feel pain. This

(56:32):
is a very utilitarian perspective, but I think it's a
sensible one. So I think we underestimate probably the extent
of animal consciousness, and I think we overestimate the likelihood
of AI being conscious. And there are many reasons why
I'm very skeptical of this, but fundamentally, my great basis

(56:55):
for the skepticism is I think we've just overused the
metaphor of the brain as a computer. It's a beautiful, brilliant,
powerful metaphor, but it's a metaphor. We've always used a
dominant technology of the day as a metaphor for the brain,
and we always get into trouble, or we usually get
into trouble when we start confusing a metaphor with the

(57:17):
thing itself.

Speaker 1 (57:19):
What do you see, is the biggest unanswered question in
consciousness research and what excites you the most about where
the field is headed?

Speaker 3 (57:27):
Oh wow, I mean the biggest unanswered question is it's
still the old question. How does it happen? We still
don't really know. I don't think. I mean, there's progress,
and to be honest, and I don't know. I'd be
interested if you feel the same way that id David.
We've been doing this more or less for the same
amount of time, long time now decades. Some days I

(57:52):
feel like, ah, it's still a complete mystery. You know,
we have neurons, chemicals, electrical signals. Why do I experience anything?
Why is there experience in the world, in the universe
at all? Those days, you know, we'll have a cup
of coffee and get on with things. But then on
other days I think back to where we were in
the You know, when I started studying and in the

(58:14):
early nineteen nineties, consciousness wasn't even on the menu if
you wanted to look at psychology and neuroscience.

Speaker 2 (58:21):
It was.

Speaker 3 (58:21):
It was ostracized completely. I mean we met because I
came to San Diego in the Lake in the early
two thousands, which was at the time one of the
only places where you could study consciousness legitimately with the
approval of a PI in a lab, and was still
a pretty rare thing to do. And so looking back
through that lens and looking at the theories that we

(58:43):
now have and the kinds of experiments that have been done,
and how we've learned more about different kinds of consciousness,
you know, I then worry less that this big metaphysical
existential question is still waiting in the wings.

Speaker 4 (58:58):
And so I think that.

Speaker 3 (59:01):
Maybe the way to put it, I haven't thought about
it in quite this way before, But maybe the biggest
unanswered question in consciousness research is whether there will turn
out to be a big unanswered question in consciousness research
or not. In other words, as we just understand more
and more about different kinds of experience, what happens in anesthesia,

(59:24):
maybe the question of consciousness will turn out to be
a little bit like the question of life.

Speaker 1 (59:30):
You know.

Speaker 3 (59:30):
One hundred and fifty years ago, many people thought that
life was something beyond the reach of science, that there
had to be something supernatural, some elan vital, some spark
of life to explain the difference between the living and
the non living. And of course We don't think that anymore.
I mean, life is still not a completely written book.
We don't understand everything, but there's no conceptual mystery. That

(59:54):
life is part of nature, and even its origin seems
increasingly within reach to explain and understand. And what happened
that wasn't that anybody proved that life didn't exist or
found the spark of life in some Eureka moment. Noe
biologists started just to make progress, explaining this property of

(01:00:14):
living systems homeostas is this property reproduction, this property metabolism,
And little by little we learned about the nature of
life and we stopped worrying that there had there was
something fundamentally inexplicable about it. And progress in science is
often like this. You know, sometimes it's not only the

(01:00:37):
answers that change, but it's the questions that change too.
So that's what I've got my own. That's what gives
me hope. I think we understand more when the questions
we ask start to look different.

Speaker 2 (01:00:49):
I totally agree with you. And what's interesting.

Speaker 1 (01:00:51):
What's been interesting for me is I also feel the
way you knew many mornings where I think, gosh, yeah,
when you and I were in San Diego and the
early thousands, the questions that were being asked were mostly
the same, but things have changed slowly. And just as
one example, at that time, we all sort of every

(01:01:11):
all neuroscientists were sort of snarky about artificial intelligence because
they felt like, Okay, we've been at this for a
long time, it's.

Speaker 2 (01:01:19):
Not really happening.

Speaker 1 (01:01:20):
There were artificial neural networks with the same principles that
we have now, but it seemed like it's never gonna happen.
And now we look at you at GPT every day
and do experiments on it, and we find, wow, it's
it's actually working in a way.

Speaker 2 (01:01:37):
Now. Who knows if it's conscious.

Speaker 1 (01:01:39):
I think we share the view that it's probably not conscious,
but boy is it impressive. And so because of improvements
in our technology, in our ability to measure things in biology,
in you know, in just data gathering and ways of
doing things bigger and better, we we find different views

(01:02:00):
now than we did one quarter century ago.

Speaker 4 (01:02:02):
I think that that's right.

Speaker 3 (01:02:03):
By the way, I have to just one other aspect
of the whole AI think that I think is really
useful to think about. So there's this whole debate about
whether chat GPT or language models might be conscious. People
ask the question, right, and some people think that they are.
But just yesterday I was lucky enough to visit deep
mines in London and that's where alpha fold was developed,

(01:02:27):
which is another AI system that's able to predict protein
structure from sequence I mean acid sequences. The protein folding
problem has been one of the biggest challenges in biology
for a long time. Alpha fold basically sorts that out. Now,
isn't it interesting that no one thinks that alpha fold
is conscious. I've not heard anybody suggests to me that

(01:02:49):
alpha fold might have experience.

Speaker 2 (01:02:52):
Yet.

Speaker 3 (01:02:53):
You know, there's some differences, but they're very, very similar
to the architectures. I mean they're made out of that.
They're both computer algorithms. Then you're on network based with
some other stuff that they even have transformer architectures. They're
really not that different. Yet our intuitions about the two
are so different. I think that really, to me highlights
how much of what we think is driven by our

(01:03:17):
psychological bias. I think the other thing is is that, yeah,
there can be this interesting nonlinearity here.

Speaker 4 (01:03:24):
Right.

Speaker 3 (01:03:24):
AI seemed to flatline for a long time, and there's
this sudden rush of discovery and invention and increase incompetence.
Maybe this is going to happen in the neuroscience of
consciousness too, And there are some super exciting developments over
where you're part of the world in Stanford, there's so

(01:03:44):
much exciting work going on in optogenetics and in synthetic biology.
People developing brain organoids, these these collections of brain cells
in dishes that are derived from human embryonic stem cells,
things that really, as I think you said, that give
us different perspectives, different views, different tools, and science often

(01:04:08):
needs that. Yeah, you can have ideas, you can have theories,
but a lot of advance historically in science is when
people acquire optit invent new tools, and I think we're
seeing a lot of that in neuroscience now, So I
am actually pretty optimistic for the future.

Speaker 1 (01:04:29):
That was my interview with Analseth, professor of cognitive and
Computational neuroscience at Sussex and the author of Being You.
We explored here how our brains create our reality, from
control hallucinations to the rich diversity of individual perceptions.

Speaker 2 (01:04:47):
What I want to.

Speaker 1 (01:04:47):
Leave you with is an idea that you've heard me
talk about many times before on this podcast, which is
that consciousness is not just about directly experiencing the world,
but instead it's about actively constructing it. While consciousness remains
one of the central mysteries and neuroscience, today's conversation, I
hope gives us a lens through which to view it

(01:05:09):
not as something mystical or otherworldly, but as a grounded
biological phenomenon rooted in the brain's continuous dance of sensory
input and its own predictions. Now, thinking about our perception
of the world not as a direct reflection of reality,
but a construction of the brain, the inner world meeting

(01:05:30):
the outer world. This is really the single reasonable way
to understand it, and in a sense, it's a little
strange that this is still rare in textbooks or in
common knowledge. And note that this process of trying to
predict signals applies not just to our perception of the
outside world, but to the swirling world inside our bodies

(01:05:51):
as well, which sheds light on how we experience emotions
and maintain homeostasis. In this way, the self is not
a fixed entity, but an ever changing construct. And one
of the other themes that emerge today is that each
of us carries around a slightly different version of reality,

(01:06:12):
shaped by our biology and our experiences. This is a
reminder of how subjective and varied human experience can be,
and how much we still have to learn about the
minds of others. That includes other humans and animals, and
possibly at some point ai are we overestimating the sentience

(01:06:35):
of machines or underestimating that of animals. So, in closing,
as Annel mentioned, just as we once demystified the life itself,
step by step through the patient work of science, we
may one day come to understand consciousness in a similar manner.
The key, as it is so often in science, is

(01:06:56):
not just about answering the big question, but can continually
asking smaller, better ones. Go to Eagleman dot com slash
podcast for more information and to find further reading.

Speaker 2 (01:07:11):
Send me an.

Speaker 5 (01:07:12):
Email at podcasts at eagleman dot com with questions or
discussion and check out Subscribe to Inner Cosmos on YouTube
for videos of each episode and to leave comments.

Speaker 2 (01:07:24):
Until next time.

Speaker 1 (01:07:25):
I'm David Eagleman and this is Inner Cosmos.