Speaker 1: What is consciousness? Do you perceive the color red the
Speaker 1: same way that I do on the inside? What is
Speaker 1: wrong with the standard textbook version of vision? Why do
Speaker 1: you have so many feedback loops in the brain? And
Speaker 1: what does any of this have to do with Ernest
Speaker 1: Hemingway or Plato's cave or artificial neural networks that.
Speaker 2: See dogs everywhere?
Speaker 1: Welcome to inner Cosmos with me David Eagleman, I mean
Speaker 1: neuroscientists and author at Stanford.
Speaker 2: And in these episodes we sail.
Speaker 1: Deeply into our three pound universe to understand why and
Speaker 1: how our lives look the way they do. Today's episode
Speaker 1: is about the sense of being alive, and in a moment,
Speaker 1: I'm going to bring in my colleague annal Seth, who
Speaker 1: wrote a great book called Being You, which is all
Speaker 1: about this neuroscientific problem. Now, by way of setting the
Speaker 1: table for this, you may say, what is the neuroscientific
Speaker 1: problem of consciousness? Well, it's simply this. It feels like
Speaker 1: something to be you, and that feeling flickers to life
Speaker 1: when you wake up in the morning, and it's not
Speaker 1: there when you're in a deep sleep or under anesthesia,
Speaker 1: and we're not sure how that happens. Our brains are
Speaker 1: made up of tens of billions of very sophisticated processing
Speaker 1: units neurons that are all operating together in a giant network.
Speaker 1: But just because something has a lot of pieces and
Speaker 1: parts doesn't tell you anything about why it's conscious, why
Speaker 1: there's any subjective experience. If you get the right tools
Speaker 1: and take your cover off your iPhone, you'll find that
Speaker 1: it has a chip which has nineteen billion transistors. Now
Speaker 1: just think about the interactions and the almost speed of
Speaker 1: light signaling in that rich, sweeping electronic landscape.
Speaker 2: But we don't have any meaningful reason.
Speaker 1: To believe that your phone has consciousness or that it
Speaker 1: would be like something to be a phone. In other words,
Speaker 1: when your phone plays a funny video on the screen,
Speaker 1: do you think it feels amused or is it more
Speaker 1: likely that it's zero's and ones moving around in a
Speaker 1: deterministic way through these billions of pathways when it gets
Speaker 1: an email from your boss, does it feel stressed when
Speaker 1: it registers the receipt of a text message? Does your
Speaker 1: phone have the capacity to feel sad? Probably not? But
Speaker 1: how do we make a more rigorous assessment of the question.
Speaker 1: How could we know what is conscious and what is
Speaker 1: not until we have we have tighter constraints on what
Speaker 1: consciousness is. This is the problem that neuroscience faces. How
Speaker 1: is it that all our billions of cells hook up
Speaker 1: in just such a way that we have consciousness? In
Speaker 1: other words, it feels like something to be us now.
Speaker 1: Not so long ago. In neuroscience, like thirty years ago,
Speaker 1: this problem of subjective experience was essentially not talked about.
Speaker 1: People generally felt it was too squishy, and they talked
Speaker 1: about how the brain worked, but not about why we
Speaker 1: have subjective experience. But things started to change around the
Speaker 1: nineteen nineties when some great minds started devoting themselves to
Speaker 1: taking this problem seriously. And two of those minds happened
Speaker 1: to be Nobel laureates in San Diego. One was Francis
Speaker 1: Crick and one was Gerald Adelman, and they worked in
Speaker 1: neighboring institutions. And it happened that when I was a
Speaker 1: young post doc, I got to work with Krick, and
Speaker 1: just across the way was another young postdoc working with Adelman.
Speaker 1: His name was Annal Seth, and we both were reared
Speaker 1: in this environment where it made sense to tackle the
Speaker 1: problem of subjective experience. The mission was how can we
Speaker 1: work towards a scientific understanding of what consciousness is and
Speaker 1: how brains give rise to it.
Speaker 2: Annal Seth is now.
Speaker 1: A professor of cognitive and Computational neuroscience at the University
Speaker 1: of Sussex, where he also directs the Sussex Center for
Speaker 1: Consciousness Science. In twenty seventeen he gave a very popular
Speaker 1: Ted talk called Your Brain Hallucinates Your Conscious Reality, and
Speaker 1: in twenty twenty one he published a book called Being You.
Speaker 2: A New Science of Consciousness.
Speaker 1: So I called him up to share his views on perception,
Speaker 1: consciousness and reality.
Speaker 2: Here's my interview with annal Seth. What is consciousness?
Speaker 3: Well, of course we could yan you and I have
Speaker 3: just this for many, many hours, and philosophers for centuries,
Speaker 3: and so you've got to be pragmatic and I define consciousness.
Speaker 3: So I follow this a philosopher Thomas Nagel, who I
Speaker 3: think put it very beautifully and very simply, and his
Speaker 3: idea was that for a conscious organism, there is something
Speaker 3: it is like to be that organism. It feels like
Speaker 3: something to be me, and it feels like something to
Speaker 3: be you. It's a bit circular, right, There's there's experiencing happening.
Speaker 3: But I think it's I think there's something useful about
Speaker 3: that because it doesn't mix up consciousness with other things
Speaker 3: like intelligence or language or a particular sense of identity.
Speaker 3: It's just any kind of experience whatsoever. So at least
Speaker 3: we know what we're talking about. You know, it's also
Speaker 3: what goes away under something like general anesthesia and then
Speaker 3: comes back. That for me, is a nice starting point
Speaker 3: for what consciousness is. So we know roughly what we're
Speaker 3: talking about, and then it's the Then the approach is, well,
Speaker 3: how do we explain it in terms of neuroscience, biology, physics, whatever,
Speaker 3: How does it happen? Why is it the way it is?
Speaker 3: And here my approach has been again quite pragmatic. Instead
Speaker 3: of trying to find some sort of magic Eureka solution
Speaker 3: that magic's conscious experience out of neurons or atoms or
Speaker 3: quantum fields or whatever it might be, let's just take
Speaker 3: a different approach and accept that consciousness exists, because there'll
Speaker 3: be some philosophers that try and tell you it doesn't
Speaker 3: even really exist, and that it has certain properties. Different
Speaker 3: experiences feel different ways, and emotion feels different from a
Speaker 3: visual experience, So let's try to understand how and why
Speaker 3: these experiences are the way they are. And every experience
Speaker 3: has various things in common too, like every experience is
Speaker 3: unified more than more than the sum of its many parts.
Speaker 3: So my approach is to try to find ways of
Speaker 3: bridging between description of the brain in some way collection
Speaker 3: of neurons or areas or whatever, and descriptions of experience.
Speaker 3: And this is where this idea of controlled hallucination comes in.
Speaker 3: And I'm sure we'll dig more into it, but very
Speaker 3: very simply, the idea is that instead of our experience
Speaker 3: of the world and the body sort of pouring itself
Speaker 3: into the brain through the transparent windows of the senses,
Speaker 3: in fact perception works the other way around. It's not
Speaker 3: a new idea, it's it's very old that perception is
Speaker 3: a process of inference. The brain is locked inside this
Speaker 3: bony vault of a skull. It's dark and silent in there,
Speaker 3: and so it has to make sense of sensory signals
Speaker 3: which don't have colors or shapes or labels, and they're
Speaker 3: uncertain that they're ambiguous and noisy with respect to whatever's
Speaker 3: going on in the world and the body. So the
Speaker 3: brain's always casting out predictions about the causes of its
Speaker 3: sensory signals, and using sensory signals to calibrate to update
Speaker 3: these predictions. And the claim here is that that that's
Speaker 3: what we experience. The brain doesn't read out the world
Speaker 3: from the outside in. It's always actively constructing the world
Speaker 3: from the top down or the inside out. This is
Speaker 3: why I think the term controlled hallucination is useful, because
Speaker 3: we tend to think of hallucinations as things that are
Speaker 3: internally generated, and I think that that's true for all
Speaker 3: our experiences. It's just that our normal perceptual experiences are
Speaker 3: controlled by calibrated, by yoked to, geared to the world
Speaker 3: and the body in ways that are useful. So perception
Speaker 3: is a controlled hallucination, and consciousness is a collection of perceptions.
Speaker 1: So let's double click on a couple things there. So
Speaker 1: first let's go back to the history. Let's say Kan't
Speaker 1: and Helmholtz and think about what were the first clues
Speaker 1: that people got in thinking about this idea that we're
Speaker 1: not seeing reality as it is out there, but instead
Speaker 1: it's something of a construction.
Speaker 4: I think you're right.
Speaker 3: The history of this is super fascinating and it's I
Speaker 3: think not given enough credits sometimes and you can go
Speaker 3: right back to Plato, I think in his allegory of
Speaker 3: the cave, where you have all these prisoners in a cave.
Speaker 3: They're chained to the walls, and all they can see
Speaker 3: are shadows cast on the wall of the cave by
Speaker 3: the light of a fire, and they take the shadows
Speaker 3: to be real because that's all they have access to,
Speaker 3: and they don't really know that there's anything out there,
Speaker 3: you know, that is actually responsible for the shadows. So
Speaker 3: I think that's a great starting point because our brain
Speaker 3: is in a bit of a similar situation to the
Speaker 3: prisoners in the cave. You know, they don't The brain
Speaker 3: doesn't have any direct access to anything really, the body,
Speaker 3: the world, whatever, so it has to make its best
Speaker 3: guess of what's going on on the basis of things
Speaker 3: like shadows, and then can't I think for me is
Speaker 3: always the reference point. I always keep coming back to
Speaker 3: his idea of the newmnen. So there is a reality.
Speaker 3: I'm often sometimes misunderstood as denying that there's a real
Speaker 3: world because I've used this word hallucination.
Speaker 4: But no, not at all. Now there is a real world.
Speaker 3: I think there is objective reality at least to answer
Speaker 3: that question, you'd better ask a physicist rather than me.
Speaker 3: But the world that we experience is never identical to
Speaker 3: that world. It's always an interpretation. In Kant's way of thinking,
Speaker 3: the newmenon reality as it really is is always hidden
Speaker 3: behind a sensory veil or a kind of inferential curtain,
Speaker 3: as I might describe it now. And then, Yeah, there
Speaker 3: were many hints I think initially in vision. I think
Speaker 3: most of the early workin this was done by vision.
Speaker 3: There's Ibanel Heitm, the Arabian scientist and polymath, did a
Speaker 3: lot of work hundreds of years ago basically arguing that
Speaker 3: perception cannot be this direct readoubt of the world because
Speaker 3: the relation is so in direct and you know, things
Speaker 3: seen to obey regularities that can't be explained purely in
Speaker 3: terms of the sensory data. Like when you take a
Speaker 3: piece of white paper from insider room to outside room,
Speaker 3: it still seems white.
Speaker 4: You know, how does that happen?
Speaker 3: It's because the brain isn't just reading off the light
Speaker 3: that comes into the eyes. It's trying to figure out
Speaker 3: what's causing the light.
Speaker 2: Just to double click on that.
Speaker 1: So if you're under let's say fluorescent lights, and then
Speaker 1: you walk under the sunlight. What's actually bouncing off the
Speaker 1: paper and hitting your eyes. There's a different wavelength, and
Speaker 1: yet we see it as white, and well, this is
Speaker 1: color constancy.
Speaker 3: The brain is always after utility. You know, we perceive
Speaker 3: the world in a way that evolution has decided is
Speaker 3: useful for us. The novelist deny as Nin put it
Speaker 3: beautifully when she said, you know, we do not see
Speaker 3: things as they are. We see them as we are.
Speaker 3: That's what the brain is gunning for.
Speaker 4: That.
Speaker 3: And then there's this trade off between the brains prior
Speaker 3: expectations or beliefs about what's going on, and how much
Speaker 3: the brain decides to update its beliefs, expectations, predictions with
Speaker 3: new sensory data. Sometimes it can update quite quickly, pay
Speaker 3: a lot of attention to this data. Sometimes it can
Speaker 3: pay less attention. In fact, attention is exactly that process
Speaker 3: in my mind anyway. Attention is exactly the process of
Speaker 3: balancing how much incoming sensory information is able to update
Speaker 3: the brain's best guess controlled hallucination of what's actually happening.
Speaker 1: Right, So people like Can't and Helmholtz and others all
Speaker 1: the way back to platos who point out have thought about, hey,
Speaker 1: maybe we're not seeing reality as it is, but we're
Speaker 1: seeing something.
Speaker 2: That we are having.
Speaker 1: To construction the inside because the brain is isolated. So
Speaker 1: then by the nineteen sixties, the neuroscientist Donald McKay noticed something.
Speaker 1: I don't know if other people had noticed this before,
Speaker 1: but he noticed that the the you know, the amount
Speaker 1: of input to the visual cortex in the back of
Speaker 1: the brain that's coming from the eyes is actually really small.
Speaker 1: I think the estimates now or that it's five percent
Speaker 1: of the input to the visual cortex comes from the
Speaker 1: eyeballs and all the rest is this sort of feedback.
Speaker 1: So let's come back to this issue how you think
Speaker 1: about this with predictions and what the brain is doing.
Speaker 3: That's a great place to start, because that's such a paradox,
Speaker 3: isn't it. I Mean when I was starting out, and
Speaker 3: I think you and I started in neuroscience around the
Speaker 3: same time, and the textbooks I remember reading just describe
Speaker 3: perception as this bottom up, inside out process. You know,
Speaker 3: signals would come into the retina early parts of the
Speaker 3: visual cortex right at the back of the brain would
Speaker 3: fish out really simple features like lines and edges, and
Speaker 3: then you got this picture of information marching deeper and
Speaker 3: deeper into the brain, sort of more complex things being
Speaker 3: fished out. And then somehow the brain put all these
Speaker 3: pieces back together, and that led to this experience.
Speaker 4: Of the world.
Speaker 3: Probably if you open a textbook now, you will probably
Speaker 3: see something quite similar. I think this has been a very,
Speaker 3: very pervasive idea despite this long and rich history from
Speaker 3: Plato to Cant to Helmholt and so on.
Speaker 1: In sixty something years since Mackay published his paper.
Speaker 3: Is sixty years since Mackay's observation, which is hard to
Speaker 3: understand in this classical view, Right, if perception is done
Speaker 3: in this bottom up direction, why do you have so
Speaker 3: many connections mark going the other way from the brain
Speaker 3: back out to the census.
Speaker 4: It doesn't make any sense.
Speaker 3: But from the perspective of perception as this constructive process,
Speaker 3: it makes much more sense. So digging into it a
Speaker 3: bit more so far, you know, we've kind of outlined
Speaker 3: that the brain has to make this inference about what's
Speaker 3: out there on the basis of noisy and ambiguous sensory
Speaker 3: signals without labels. Now, in mathematics we call this Bayesian inference.
Speaker 3: It's a process of reasoning under conditions of uncertainty, or
Speaker 3: in general, how you should update what you believe when
Speaker 3: you get new data. And this is a very very
Speaker 3: general formulation. The Reverend Thomas Bays and Laplace figured out
Speaker 3: the maths hundreds of years ago, and it's been used
Speaker 3: to do all kinds of things like figure out where
Speaker 3: to look for missing nuclear submarines, or even figure out
Speaker 3: how likely you are to have a disease if you've
Speaker 3: got particular symptoms. It's always when things are uncertain that
Speaker 3: this mathematics is useful. It's the same deal with perception.
Speaker 3: The brain is trying to figure out what it should
Speaker 3: believe now about what's out there, given some new information
Speaker 3: from the census. Now, the problem is that actually doing
Speaker 3: Bayesian inference is really really hard. In fact, it's almost
Speaker 3: impossible to do it exactly. So the brain, any good
Speaker 3: biological evolutionary hack has figured out an approximation, a very
Speaker 3: general approximation. And this approximation is in the business we
Speaker 3: call it predictive coding or predictive processing. And what this
Speaker 3: means is that the brain effectively has some kind of
Speaker 3: model of the world and the body, and it uses
Speaker 3: that model to generate predictions about the sensory information that
Speaker 3: should be coming in. And these predictions they cascade in
Speaker 3: this top down direction and back out to the sensory
Speaker 3: surfaces through all these connections that Mackay identified, and then
Speaker 3: the sensory signals instead of being read out by the brain,
Speaker 3: they just serve as prediction errors. They report the difference
Speaker 3: between what the brain expects and what it gets you
Speaker 3: at every level from the retina to the early parts
Speaker 3: of the visual cortex all the way up. And if
Speaker 3: the brain then follows a very very simple rule, which
Speaker 3: is just either make an action or update its prediction
Speaker 3: to try and minimize these prediction errors. So it's just
Speaker 3: trying to minimize and trying to reduce these prediction era
Speaker 3: sensory signals, then collectively its predictions will be a very
Speaker 3: very very good approximation to Baysing inference. So we get
Speaker 3: a picture in which the brain is constantly casting out
Speaker 3: predictions into the world, the sensory signals update these predictions,
Speaker 3: and there's always this simple mechanism which the brain is
Speaker 3: just trying to minimize prediction error. And as a result,
Speaker 3: what happens is the brain is able to make a
Speaker 3: best guess about what's out there in the world or
Speaker 3: in the body, and then the claim is, well, that
Speaker 3: that's what we perceive.
Speaker 2: So let me just summarize.
Speaker 1: So the idea is the brain can't know exactly what's
Speaker 1: out there, so it's making its best guesses and then saying, oh, wow,
Speaker 1: that guess was really off, that guess was a little closer,
Speaker 1: and so on, and as it matches the incoming data,
Speaker 1: it gets to refine its model that way until it
Speaker 1: has a reasonably good model. And so one of the
Speaker 1: ideas that's been floating around in neuroscience for a while,
Speaker 1: but again I don't think this makes it to the
Speaker 1: textbooks is this idea that all of the data that
Speaker 1: we see in the century courtices is actually the error.
Speaker 1: It's the part that you didn't get right. And if
Speaker 1: you were actually getting everything one hundred percent right, you'd have,
Speaker 1: you know, golden silence going on in there. But the
Speaker 1: world is complicated and things are always happening, not in
Speaker 1: the way you can predict away. But this is the
Speaker 1: frame shift that's real required to see this sort of thing.
Speaker 1: So coming back to why you call this a controlled hallucination,
Speaker 1: let's do it this way. Which is we don't need
Speaker 1: our eyes at all to have rich visual experience, right.
Speaker 1: You have dreams inn when your eyes are closed, right,
Speaker 1: And so the idea is that maybe you are dreaming,
Speaker 1: you're producing all the stuff on the inside, but use
Speaker 1: the word controlled to indicate, Look, you're anchored to what's going.
Speaker 2: On in the outside.
Speaker 1: You've got this, you know, let's say five percent of
Speaker 1: data coming in from the central world, and that anchors
Speaker 1: you somewhat. And that's what you mean, my controlled hallucination
Speaker 1: control dreaming in a sense.
Speaker 3: That's exactly right. In fact, if you describe perception as
Speaker 3: a kind of controlled hallucination, then you can just flip
Speaker 3: it around and you can say when you're hallucinating as
Speaker 3: we would normally use it. You know, when someone sees
Speaker 3: something that's not there, indeed, when they're dreaming, well you
Speaker 3: can just call that uncontrolled perception. And I think the
Speaker 3: point here is that there's a continuity. They're not completely
Speaker 3: different categories. So, you know, we used to thinking of
Speaker 3: dreams and hallucinations as coming more from the inside than
Speaker 3: the outside. And it's true that in normal perception the
Speaker 3: outside world plays a bigger role. But fundamentally, I think
Speaker 3: it's the same kind of process. It's the same dance,
Speaker 3: this exchange of prediction and prediction error, but you change
Speaker 3: exactly how this dance plays out, and you can sort
Speaker 3: of sweep through all these different ways of experiencing, whether
Speaker 3: it's normal perception or dreaming or hallucination or something else.
Speaker 1: Now use the terms controlled and uncontrolled, but in fact
Speaker 1: it's more of a spectrum. I'd imagine right where a
Speaker 1: controlled hallucination is I'm looking for something on my desk,
Speaker 1: I have to really pay attention, and so in that sense,
Speaker 1: it's really controlled by what's in the outside world. Here
Speaker 1: it's more controlled, whereas other times it's less and less
Speaker 1: controlled when I'm you know, imagining something or just seeing whatever.
Speaker 1: And you can imagine in cases like Charles Bonet syndrome,
Speaker 1: as people are going blind, they have formed visual hallucinations.
Speaker 1: They think they see somebody walking in or doing a
Speaker 1: bunch of dancers in the street or something, even though
Speaker 1: that's not there. So it's sort of like a spectrum
Speaker 1: of how much control the outside world has.
Speaker 3: That's exactly right, And in fact, one of the powers
Speaker 3: of this approach is that you can use it to
Speaker 3: better understand what's going on in conditions like Charles Bonne,
Speaker 3: where people have hallucinations. One of the things we did
Speaker 3: in my research group last year was we built computational
Speaker 3: models of this process of prediction and prediction error and
Speaker 3: so on, and we tweak the model in different ways
Speaker 3: to try and simulate different kinds of hallucination. So Charles
Speaker 3: bona syndrome where indeed people often see patterns, but also
Speaker 3: people wandering around. Then there's in Parkinson's disease, people often
Speaker 3: have quite rich and complex hallucinations. And then in psychedelics
Speaker 3: you get all kinds of different hallucinations too that seem
Speaker 3: sometimes seem to emerge out of things that are already
Speaker 3: there in the environment. That clouds can become animals or people,
Speaker 3: things like that. So we've been able to use this
Speaker 3: approach to really drill down and understand not only how
Speaker 3: these hallucinations happen, but how they and why they differ
Speaker 3: from each other. And what we did was we actually
Speaker 3: went to people who have these kinds of hallucinations in
Speaker 3: real life and we asked them to judge the output
Speaker 3: of the model so that we could test whether our
Speaker 3: you know, our computational model of these hallucinations was right.
Speaker 3: You know, would somebody with Parkinson's disease pick the output
Speaker 3: that we generated when we were trying to simulate Parkinson's
Speaker 3: hallucinations and more or less that that works well. So
Speaker 3: we're beginning to be able to really characterize and at
Speaker 3: the level of what the brain is doing these different
Speaker 3: kinds of hallucinations.
Speaker 1: You know, one of the things that I talked about
Speaker 1: in my book Incognito was this possibility that all of
Speaker 1: us might be having hallucinations all the time, but we
Speaker 1: don't know it. So for example, you know, it's something
Speaker 1: on my desk here, or you know, I think my
Speaker 1: dog's over there when he's not, and so on, and
Speaker 1: we only notice it when there's a clear indication that
Speaker 1: it's not true, either because someone else tells us, or
Speaker 1: I go look for my dog and realize that was
Speaker 1: a bag on the floor, not my dog, or so on.
Speaker 1: But probably this is happening all the time. I want
Speaker 1: to drill in on what you said about how you
Speaker 1: go out to different people with psychedelics or with Parkinson's
Speaker 1: disease and ask them and find out the degree to
Speaker 1: which this match is. What you're doing is taking things like,
Speaker 1: for example, in your Ted talk a while ago.
Speaker 2: You used Google deep.
Speaker 1: Dream to take footage, and Google deep dream sort of
Speaker 1: has these psychedelical aist nations where it sees dogs, faces
Speaker 1: and everything, So tell us about that.
Speaker 4: That's exactly right.
Speaker 3: So this is I think it's a nice story because
Speaker 3: we basically did their own to start with, just because
Speaker 3: it was a lot of fun. You know, we had
Speaker 3: this virtual reality lab. One of my post docs is
Speaker 3: very good at coding these things up, and we just thought,
Speaker 3: why can't we try and make a situation where you know,
Speaker 3: at the time, people had been just taking photos of
Speaker 3: bowls of pasta and then putting them through Google deep Dream,
Speaker 3: and they'd all become you know, they'd grow, loads of
Speaker 3: puppy heads would be there in the in the bowl
Speaker 3: of pasta. And we just thought, should we just try
Speaker 3: and do this in virtual reality just because? And so
Speaker 3: we took a panoramic movie, so three hundred and sixty
Speaker 3: degree movie, and we put every frame through an adaptation
Speaker 3: of this deep dream algorithm so that you would get
Speaker 3: framed frame continuity. So it was it was not an
Speaker 3: easy to do.
Speaker 1: Can I for just one second just over on sclear
Speaker 1: The reason Google deep dreams sees puppy faces everywhere is
Speaker 1: because that's its expectation. It's busy and prior is that
Speaker 1: it's looking for puppy faces, and so that's why that's
Speaker 1: why it sees it everywhere.
Speaker 2: If you even vaguely have like two dots.
Speaker 1: In the line or something, it says, oh, there's a
Speaker 1: puppy face.
Speaker 2: Okay, keep doing that.
Speaker 3: That's exactly right. And of course it doesn't have to
Speaker 3: be puppies. You could you could give the network another
Speaker 3: expectation and basically fix another node or part of the network,
Speaker 3: and then it's expecting something else. And that's actually the key.
Speaker 3: So once we'd done this with Sussex campus, and we
Speaker 3: did it with the dog expectations, so suddenly people would
Speaker 3: have all these strange experiences of Sussex University campus, all
Speaker 3: these dogs coming out of the walls and the windows
Speaker 3: and the sky and and we tried it and it
Speaker 3: was fun, and it just struck us when we were
Speaker 3: thinking about this, what would there be any actual scientific
Speaker 3: utility in it? And what we realized was that in
Speaker 3: a lot of psychology experiments, people focus, for good reasons,
Speaker 3: on very constrained situations. You know, they ask people was
Speaker 3: the dot moving to the left or the right, or
Speaker 3: was the patch of light there or not there, or
Speaker 3: something very very simple, so that you can make very
Speaker 3: precise measurements. But these things are very far from the
Speaker 3: richness of everyday conscious experience. In the end, that's what
Speaker 3: we want to understand. So what we realized was what
Speaker 3: we built was not a model of any kind of
Speaker 3: cognition of what people think or any kind of behavior
Speaker 3: what they do. What we built was a model of experience.
Speaker 3: We built a model of particular way of encountering the world,
Speaker 3: and not many people have really been doing that. In fact,
Speaker 3: it was probably one of the first examples of doing
Speaker 3: something like this, and it was really just for fun.
Speaker 3: So from that starting point, then you're point is exactly right.
Speaker 3: So that initial network was set up to project expectations
Speaker 3: of dogs into everything. But so what we then did
Speaker 3: was we moved on from the Google Deep Dream algorithm
Speaker 3: to some more complicated neural network architecture that we could
Speaker 3: tweak in different ways so that we could begin to
Speaker 3: simulate different kinds of hallucinations. So some hallucinations are very
Speaker 3: rich and very complex, others are very simple and very geometric.
Speaker 3: Some hallucinations appear out of nowhere, you know, they just spontaneous.
Speaker 3: Theorize other hallucinations are transformations of things that are already there.
Speaker 3: So we were able to kind of create this space
Speaker 3: we could move around in to generate these different kinds
Speaker 3: of experience.
Speaker 2: So tell us what you learned from that.
Speaker 3: So what we were able to do this is with
Speaker 3: my colleagues David Schwartzman and ks Ksuzuki.
Speaker 4: They did all this work. By the way, I want
Speaker 4: to make that very clear.
Speaker 3: We went to people who have these hallucination in real life,
Speaker 3: people with Parkinson's disease, people with another condition you mentioned,
Speaker 3: Charles Bonne syndrome, people who've had psychedelic experiences, not that
Speaker 3: we're having them then and.
Speaker 4: There, but have had them.
Speaker 3: And we ask them to pick examples from our models
Speaker 3: that were most similar to the experiences that they had,
Speaker 3: and that way we can test our hypotheses about the
Speaker 3: computational basis of these different kinds of hallucinations. So eventually,
Speaker 3: and we're not there yet, but eventually the idea is
Speaker 3: by doing this, we'll be able to make some predictions
Speaker 3: about what we might see if we put these people
Speaker 3: in brain imaging scanners and image what's happening while they
Speaker 3: have their hallucinations. That's the next step, And overall the
Speaker 3: goal is, I think, to put it in this larger frame,
Speaker 3: you know, when you want to understand something like how
Speaker 3: we experience the world, the nature of perception, it's often
Speaker 3: a very good idea to look at those situations where
Speaker 3: things are a little bit strange, you know, where people
Speaker 3: are experiencing things differently. You poke around in it and
Speaker 3: see what happens when things a little bit out of whack.
Speaker 3: So the utility of studying hallucinations for me, it's firstly
Speaker 3: for the people that have them. We can help them
Speaker 3: understand their lived experience better. But it also reflects back
Speaker 3: on our understanding of perception in general, because, as we've
Speaker 3: been saying, fundamentally, it's the same process.
Speaker 1: Yes, now, let me drill in on this for a minute,
Speaker 1: because you and I are both fascinated by individual differences
Speaker 1: in the reality inside different heads, and I've made many
Speaker 1: episodes on this topic. For example, the spectrum from a
Speaker 1: fantasia to hyperfantasia, or the internal voice, or what happens
Speaker 1: with synesthesia of different types. All these of things indicate
Speaker 1: that we're experiencing different realities and ways that we can study.
Speaker 4: You.
Speaker 1: With your colleague Fiona mcpheerson, you launched the perception census.
Speaker 1: Tell us about that and tell us what you've learned.
Speaker 4: Oh, thank you for asking this.
Speaker 3: I mean, I'm glad we're talking about this because I
Speaker 3: think it is probably one of our strongest overlaps. And
Speaker 3: I have to say a lot of my interest in
Speaker 3: this area was inspired by conversations with you dating back scarily,
Speaker 3: I think well over twenty years now when we first
Speaker 3: started talking about these things, which but certainly the work
Speaker 3: in synesthesia individual differences, it falls out as a consequence
Speaker 3: of this way of thinking, and I think this is
Speaker 3: worth saying first. If we start with a textbook view
Speaker 3: of visual perception, it's kind of easy to think that
Speaker 3: we will all see the world hear the world in
Speaker 3: roughly the same way. And that's also how perceptual experience
Speaker 3: feels like. It feels like I just see the world
Speaker 3: as it is. It doesn't seem to me as though
Speaker 3: it depends all that much on my brain, or certainly
Speaker 3: not on the specific because of my brain compared to yours.
Speaker 3: But how it seems is never a particularly good guide
Speaker 3: to how things are and in this view of perception,
Speaker 3: as this prediction, this controlled hallucination, this generative process, then
Speaker 3: it's going to be different for each and every one
Speaker 3: of us. You know, we all differ on the outside
Speaker 3: in skin color and height and body shape, and we
Speaker 3: all have slightly different brains too, and so we should
Speaker 3: all experience the world to some extent differently. But the
Speaker 3: key difference here is it's easy to tell whether people
Speaker 3: are different heights, even if the difference is quite small.
Speaker 3: You know, I can just look at two people standing
Speaker 3: next to each other and I'll see if they're one's
Speaker 3: taller than the other. But if you experience red slightly
Speaker 3: differently from me, or time slightly differently from me, how
Speaker 3: will we ever know? Because your experience is private, subjective
Speaker 3: to you will probably use the same words. You know,
Speaker 3: it's read that lasted about a second. It's so much
Speaker 3: harder to tell. And so we end up assuming, I think,
Speaker 3: overestimating the similarity of our inner world. And so this
Speaker 3: project of the Perception Census, with Fiona Macpherson and many
Speaker 3: other colleagues, Reny by Kovira, a postdoc who really drove
Speaker 3: it here at Sussex, we wanted to study these individual differences,
Speaker 3: but we wanted to do it at scale. So this
Speaker 3: is not a new idea, David. You've done a lot
Speaker 3: of this work, contributed a lot to this literature already.
Speaker 3: But many of these studies focus on one or two
Speaker 3: or three aspects of perception. Maybe synesthesia, which I'm sure
Speaker 3: your listeners will know about because you wrote the book
Speaker 3: on this, literally several books, but you know, or maybe
Speaker 3: something else like time or ability to discriminate different musical notes.
Speaker 3: We wanted to look at lot, lots of things together,
Speaker 3: and we wanted to look at a lot of people,
Speaker 3: and people from many places and of many ages, so
Speaker 3: we got quite ambitious. We put together over fifty different
Speaker 3: tasks rather than just two or three, fifty different experiments
Speaker 3: that people could do, lots of visual illusions, sound had
Speaker 3: to be things people could do at home, so we
Speaker 3: couldn't do things like smell whatever, But fifty different things,
Speaker 3: and overall we were able to get around forty thousand
Speaker 3: people engaged in the census from ages of eighteen to
Speaker 3: well over seventy and from one hundred and twenty seven
Speaker 3: different countries.
Speaker 1: Wow.
Speaker 3: So it's been a huge data gathering effort and I
Speaker 3: really see this as a resource, and I hope it's
Speaker 3: going to be an important resource for the whole community
Speaker 3: because we're going to make the data entirely open and
Speaker 3: it can be a bit of a sandpit for testing
Speaker 3: ideas or hypotheses that people might have. You might want
Speaker 3: to ask, oh, to somebody with more vivid mental imagery,
Speaker 3: you know, do they see more different shades of color?
Speaker 3: What things tend to go together? And as part of
Speaker 3: the census, we also have some data on people whether
Speaker 3: where they reside on some of the more clinical dimensions too, autism, ADHD,
Speaker 3: things like that, so we can start to understand how
Speaker 3: these conditions relate to the normal spectrum of variability. So
Speaker 3: I've been using the term here perceptual diversity rather than
Speaker 3: what many people have heard neurodiversity, And I want to
Speaker 3: just dwell on that for a second, because this idea
Speaker 3: of neurodiversity has been very important and it's led to
Speaker 3: a lot of recognition that people with conditions like autism
Speaker 3: is the one that's most commonly used here as others
Speaker 3: as well ADHD, that they experience things differently and that
Speaker 3: can cause some problems, can give some benefits, but it's different.
Speaker 3: But ironically, in my mind, the idea of neurodiversity has
Speaker 3: kind of reinforced the idea that if you're not neurodivergent
Speaker 3: in some way, then you're neurotypical and you see the
Speaker 3: world as it is, and it's underplayed I think the reality,
Speaker 3: or certainly the reality we're exploring with the census.
Speaker 4: Let's see if it's true that.
Speaker 3: There's just variation, and maybe when you get somewhere towards
Speaker 3: the extreme, if a distribution, a label is slapped on
Speaker 3: it and it becomes a neurodivergent condition. But I think
Speaker 3: that if we all understood that each of us experiences
Speaker 3: the world in our own unique individual way, it will
Speaker 3: This is not at all to diminish or minimize neurodiversion conditions.
Speaker 3: I just want to understand them as part of the
Speaker 3: spectrum in which there's variation among all of us, just
Speaker 3: as there is in high body shape anything else. That's
Speaker 3: what the perception census is empirically trying to look at,
Speaker 3: because we just don't have this deita yet. How much
Speaker 3: variation is out there, what does it look like, how
Speaker 3: does it correlate?
Speaker 1: By the way, just for final I'll tell you about
Speaker 1: my idiothesis. Idiothesis is the term we use in my
Speaker 1: lab for an idiotic hypothesis. But it's what I've been
Speaker 1: interested in is, as you know, I'm a lover of literature,
Speaker 1: and I've noticed that authors like Ernest Hemingway and let's say,
Speaker 1: Thomas Hardy have very different ways of writing. And I
Speaker 1: think this is a I'm calling this retrospective brain scanning,
Speaker 1: because I think we can tell that Hemingway was probably
Speaker 1: a fantasic, meaning he didn't picture details in his head
Speaker 1: and didn't care about them. But somebody like Thomas Hardy
Speaker 1: or you know, Fenimore Cooper or anybody like that, put
Speaker 1: so many details on the page of the red curtains billowed,
Speaker 1: and the flowers were these flowers in this arrangement and
Speaker 1: so on. And I happen to be on the a
Speaker 1: fantasic end of things, and so I can't stand those
Speaker 1: authors who give tons and tons of detail.
Speaker 2: That don't matter.
Speaker 1: Now, the reason this is an idiothesis is because I
Speaker 1: can't ever approve this is true, and there may be
Speaker 1: other reasons why they wrote in the styles they did,
Speaker 1: but I like the idea that this is yet another
Speaker 1: correlation in the world is by looking at people's outputs,
Speaker 1: the way they talk, the way they described the world,
Speaker 1: and that might give us some even very rough insight
Speaker 1: about what's happening internally.
Speaker 3: I think that there's something too that I think. One
Speaker 3: thing that's well known, I mean, you'll correct me if
Speaker 3: I'm wrong, is that some authors were clearly synesthetic. So
Speaker 3: I think Vladimir Nabokov was well known as a synaesthete.
Speaker 3: And that's interesting not only for the perspective of how
Speaker 3: he might have described the world around him, whether it
Speaker 3: was detailed, but whether it affected other things, whether there
Speaker 3: was sort of just more cross fertilization, more associativity in
Speaker 3: the way he was writing compared to other people. Because synthesia,
Speaker 3: which again is something that well you actually did. I
Speaker 3: think it was the first large scale survey of synesthesia
Speaker 3: in the synesthesia Battery. So we're looking at cynesesia again,
Speaker 3: but of course in the same group of people, we're
Speaker 3: looking at all kinds of other things too, So I'm
Speaker 3: really excited to see what synesthesia is associated with. Unfortunately,
Speaker 3: we didn't ask people whether they'd written any great works
Speaker 3: of literature.
Speaker 1: You know, the truth is always struggled with this question
Speaker 1: about weather. Synesthesia is associated with higher creativity because in
Speaker 1: a sense, it's not creative. If I always see M
Speaker 1: as purple and B is orange and so on, that's
Speaker 1: I'm particularly creative.
Speaker 2: It's just an association that I have.
Speaker 1: In the case of letting me in a book off
Speaker 1: he you know, there are little clues that we have. Ever,
Speaker 1: just as one example, he was a lepidopterist, which means,
Speaker 1: you know, he studied butterflies, and for him, his favorite
Speaker 1: butterfly had a particular pattern of yellow black yellow, and
Speaker 1: so his novel Aida a Da happens to map onto
Speaker 1: those colors of those letters, where you know A is
Speaker 1: yellow and D is black.
Speaker 2: And A is yellow.
Speaker 1: So we see little clues like that, But I don't
Speaker 1: know if it makes a person more creative or not.
Speaker 3: Yeah, there's another example of that. I actually had that
Speaker 3: writing a piece for a gallery catalog about and an
Speaker 3: artist ya Yu Kusana.
Speaker 4: She's still alive.
Speaker 3: She's in a nineties in Japan, and she's very very
Speaker 3: well known for these artworks that have things like red
Speaker 3: polka dots everywhere, and that there's this sort of patterns
Speaker 3: over everything, representations of landscapes and so on and it's
Speaker 3: a it's a very distinctive style. She also does these
Speaker 3: mirror infinity rooms. But it seems that she has this
Speaker 3: way of experiencing the world in which she literally does
Speaker 3: see red dots everywhere. At some times there's I forget
Speaker 3: the technical name for it, but there's there's a you know,
Speaker 3: a sort of sexual condition where where this happens. And
Speaker 3: so exactly to your point, maybe her artistic innovation was
Speaker 3: to some extent a direct transcription of what she was
Speaker 3: just seeing. Now, I think that is not the full story.
Speaker 3: You know, you can you have to. I think it
Speaker 3: provides the raw materials that people can use to generate
Speaker 3: pieces of art, whether the paintings, music, novels that have
Speaker 3: a creative and artistic impact. So I don't think it
Speaker 3: reduces But also I always agree with you that that disassociation.
Speaker 3: We have to be careful because if it's automatic and normal,
Speaker 3: you know, where is the creativity. The creativity is and
Speaker 3: what you do with it. It's not the thing itself.
Speaker 2: That's right.
Speaker 1: And by the way, Kandinsky, the visual painter, he had
Speaker 1: a very rich synessiege that was triggered by sound, and
Speaker 1: so what he would do is crank up his phonograph
Speaker 1: and stand in front of the canvas and paint what
Speaker 1: he was seeing. And what's interesting about that, that's an
Speaker 1: example where he's just transcribing what his perception is. What's
Speaker 1: interesting is, given that we all have different internal worlds,
Speaker 1: it's nice to find ways to share like that. And
Speaker 1: I do think in let's say, literature and novels, you
Speaker 1: are getting to step into the shoes of someone who
Speaker 1: sees the world differently than you do, and that's why
Speaker 1: we love to share stories with one another.
Speaker 3: Yeah, I think again, something that you and I no
Speaker 3: doubt agree on is that, you know, when when we
Speaker 3: face the challenge of understanding consciousness, you know, in the
Speaker 3: large in the sort of most expansive way, we want
Speaker 3: to understand what it is like to be me or
Speaker 3: to be you. And sure we can study visual perception
Speaker 3: or auditory perception or something like that, but for a
Speaker 3: lot of people, the experience of being who they are
Speaker 3: is tied up with the self, that's tied up with
Speaker 3: some kind of internal narrative, distinctive way of being in
Speaker 3: the world. And literature has done, i think, more than
Speaker 3: science to explore examine that aspect of consciousness.
Speaker 1: I'm so glad you brought this up because I was
Speaker 1: going to ask you about this. So in your book
Speaker 1: Being You, you talked about the way that we build
Speaker 1: a model of the outside world by minimizing the prediction aris,
Speaker 1: but also we build models of the inside world.
Speaker 2: So tell us about that.
Speaker 3: So this is another INVERTI if you like a conceptual
Speaker 3: upside down move, We've already had one, which is the
Speaker 3: idea that instead of experiencing the world in this kind
Speaker 3: of outside in direction where the world just pours into
Speaker 3: the mind, it's this act of construction, this controlled hallucination. Now,
Speaker 3: another common assumption or sort of it might seem an
Speaker 3: obvious way of thinking about things, is when we think
Speaker 3: about the self, and it might seem just intuitive to
Speaker 3: say that the self what it is to be me. Well,
Speaker 3: that's the thing that does the perceiving. There's something, there's
Speaker 3: some essence of me inside my skull. That is the
Speaker 3: recipients of all these perceptions, however they're constructed, that takes
Speaker 3: them in, figures out what to do next, does something,
Speaker 3: and we sense, we think, we act.
Speaker 4: And go round and round and round.
Speaker 3: Now, I think this is not the way to think
Speaker 3: about things, I think is very different. Rather than the
Speaker 3: self being that which does the perceiving, I think it's
Speaker 3: more correct to say that the self itself is a
Speaker 3: kind of perception. So there's just a same process going on.
Speaker 3: The brain is making inferences about sensory signals from different
Speaker 3: places and over different time scales. Some of those inferences
Speaker 3: underlie our experiences of the world, and some of those inferences,
Speaker 3: some of those controlled hallucinations, are what the self actually is.
Speaker 3: The self is this collection of perceptions that have and
Speaker 3: I think this is the key, you know, So why
Speaker 3: is the self different from the world. What's special about
Speaker 3: the self? Well, one of the things that's special about
Speaker 3: the self is the body. So perceptions of self, for
Speaker 3: me anyway, are rooted in the brain's perception predictions about
Speaker 3: the body, and all our aspects itself are built on that.
Speaker 1: Great So your brain is monitoring the outside world, and
Speaker 1: it's monitoring the inside world inside the body.
Speaker 2: And he made the argument that.
Speaker 1: It not only tries to predict, but eventually becomes good
Speaker 1: at saying Okay, look, this is my model is that
Speaker 1: let's say the body temperature should be at this, so
Speaker 1: if it fluctuates, this is what gives you the ability
Speaker 1: to do homeostasis to keep things in order because you
Speaker 1: have a well established model at this point.
Speaker 4: That's absolutely right.
Speaker 3: And I know you recently had Lisa Felman Barrett on
Speaker 3: the podcast, and she and I have a pretty similar
Speaker 3: view about this, but I think we came to it
Speaker 3: from very different directions. I mean, Lisa has always been
Speaker 3: interested specifically in emotion and pushing back against these Diarwinian
Speaker 3: ideas of hard coded emotion circuits in the brain. I
Speaker 3: came to these kinds of ideas by just saying thinking about, well,
Speaker 3: how does the brain perceive the outside world, Well, maybe
Speaker 3: there's something similar happening about emotion. Building actually on some
Speaker 3: very early ideas of William James and Carl Langer, who
Speaker 3: talked about emotion as a process of the brain's appraisal
Speaker 3: or interpretation of the physiological condition of the body. But
Speaker 3: where we end up is somewhere. It's not exactly the same.
Speaker 3: There are differences between how Lisa and I see things,
Speaker 3: But where we end up is one fundamental realization about
Speaker 3: what brains are for that really I think casts everything
Speaker 3: we think about perception consciousness in a different light, and
Speaker 3: this is that brains are not for creating art, or
Speaker 3: writing poetry, or doing complex math, or even having conversations.
Speaker 3: If you think about what the primary duty of any
Speaker 3: brain is, it's to keep the body and itself alive.
Speaker 3: If you don't do that, you don't do anything. And
Speaker 3: to keep it an organism alive is a difficult thing.
Speaker 3: I mean, that's why evolution has shaped all these weird
Speaker 3: and wonderful creatures. But it usually well. In fact, it
Speaker 3: always requires keeping certain physiological variables like heart rate, blood pressure,
Speaker 3: blood oxygenation within very tight ranges. You've got to regulate
Speaker 3: your physiology. If your blood oxygen drops, you won't last
Speaker 3: very long if it drops too much. And so how
Speaker 3: does the brain do this well? Any control engineer will
Speaker 3: tell you that a good way to regulate something, to
Speaker 3: keep it where it needs to be, is to have
Speaker 3: a predictive model about it, because if you have a
Speaker 3: predictive model, you can anticipate deviations. When we both finish
Speaker 3: recording and we stand up, the brain is anticipating that
Speaker 3: our blood pressure will drop a bit, so it transiently
Speaker 3: increases the blood pressure. It constricts our vessels, so our
Speaker 3: blood pressure in fact remains pretty much exactly the same.
Speaker 3: We don't faint, So the ability to control and regulate
Speaker 3: is done best through prediction. And so for me that
Speaker 3: that's the fundamental reason why brains work this way, why
Speaker 3: perception works this way. It's all built on this fundamental
Speaker 3: imperative of the brain to regulate homeostatically, allostatically the physiology
Speaker 3: of the body. And I think the way I like
Speaker 3: to put it in my book is we experience the
Speaker 3: world and the self with through and because of our
Speaker 3: living bodies.
Speaker 2: And so impact that just a little bit more.
Speaker 3: So, there's I mean, this whole question of what emotions are,
Speaker 3: how they come about with their force is super super interesting.
Speaker 3: But they kind of polarize two extremes, and one extreme
Speaker 3: you have Darwin, or at least a kind of caricature
Speaker 3: of Darwin, which says that there are certain there's a
Speaker 3: certain repertoire of emotions happiness, anger, sadness, discussed things like this.
Speaker 3: They're pretty much hard wired into our brains. They're kind
Speaker 3: of different from cognition, from thinking, you know, they're more
Speaker 3: physiological like survival reflexes that feel particular ways, and they're
Speaker 3: pretty fixed and conserved. The ideas across time across cultures,
Speaker 3: and this has been kind of the mainstream view, I think,
Speaker 3: in one way or another for a long time. And
Speaker 3: then on the other hand, you've got the idea of
Speaker 3: emotions as being somewhat constructed. Now this has also got
Speaker 3: a long history. William James, we mentioned a minute ago,
Speaker 3: he realized over one hundred years ago that emotions were very,
Speaker 3: very deeply embodied. That you know, he proposed the idea
Speaker 3: that if say, a bear comes charging into the room,
Speaker 3: we will feel afraid and then we might run away,
Speaker 3: and it might be normal to think that, you know,
Speaker 3: it's the sight of the bear that causes the feeling
Speaker 3: of fear, and then the feeling of the fear causes
Speaker 3: me to run away, and James is becoming a common
Speaker 3: theme to our conversation. Now flips this around, right, So,
Speaker 3: for William James and also Carl Ager, the bear comes
Speaker 3: charging into the room, my brain registers the presence of
Speaker 3: the bear. This puts my body into a particular physiological state,
Speaker 3: and adrenaline shoots up, courts all starts racing around. And
Speaker 3: then my brain perceives this change happening in the body
Speaker 3: in the context of a bear being there, and that
Speaker 3: is the emotion of fear. So fear in this case
Speaker 3: follows or is at least part of the change in
Speaker 3: the body. It's not that fear then causes the change
Speaker 3: in the body. The experience of fear is the perception
Speaker 3: of what's happening in the body in this context of
Speaker 3: something dangerous happening. So that's that's the way I think
Speaker 3: of that. I've come to think about emotions as grounded
Speaker 3: in this this imperative for regulation. And you know, just
Speaker 3: as with the perception census, there will be variation, but
Speaker 3: there will be also a lot of similarity. There's a
Speaker 3: lot of similarities in the way we see things. So
Speaker 3: you know, I tend to always land in this this
Speaker 3: sort of happy or unhappy medium where there may be
Speaker 3: some amount of biological conservation going on. There's sort of
Speaker 3: good reasons for that, but there might also be more
Speaker 3: variation than we might think.
Speaker 1: Given the way that you are thinking about consciousness, what
Speaker 1: do you think about other animals having consciousness?
Speaker 2: And what to take on AI becoming conscious.
Speaker 3: Two super important and increasingly timely questions. I mean, there's
Speaker 3: so much excitement, height and some amount of anxiety and
Speaker 3: fear about AI and.
Speaker 4: Things are changing.
Speaker 3: Animals, of course, have been around for a very long time,
Speaker 3: and we've had over history varying views about their status
Speaker 3: as conscious creatures or not, and I think they pose
Speaker 3: us very different challenges. So we humans, we tend to
Speaker 3: see the world through the lens of being human. We're
Speaker 3: very anthropocentric, and not only that, we're very anthropomorphic. We
Speaker 3: tend to project human like things into other systems on
Speaker 3: the basis of what might be superficial similarities. So this
Speaker 3: is to say that when it comes to other animals,
Speaker 3: if they're different from us, we tend to withhold from
Speaker 3: them things that we think are distinctively human or matter
Speaker 3: to our humanity, like intelligence and consciousness. And I think
Speaker 3: it's this combination of biases that can get us into
Speaker 3: trouble here. Because we see things through a human lens.
Speaker 3: We've tended over history to associate something like consciousness with
Speaker 3: other things that we think of as distinctively human, like
Speaker 3: intelligence and language. So if we do that and we
Speaker 3: look at non human animals, we tend to reserve consciousness
Speaker 3: for those animals that seem the smartest. And in fact,
Speaker 3: you know, we've done worse than that. It wasn't that
Speaker 3: long ago that people didn't give babies anesthesia. I mean,
Speaker 3: this seems crazy now that it was not common practice
Speaker 3: until a few decades ago, there was this sort of assumptions.
Speaker 3: I didn't really feel paid, and I think that just
Speaker 3: shows how deeply our assumptions like this can affect our practice.
Speaker 3: And we have exactly the opposite situation now with artificial intelligence.
Speaker 3: So AI systems like chat, GPT or Claude can speak
Speaker 3: to us fluently. Whether it count as conversations, I'm much
Speaker 3: less sure. I mean, Sherry Turkle has talked about this
Speaker 3: beautifully and said, when we converse with a machine, we
Speaker 3: unthinkably simplify what we mean by a conversation. It's a
Speaker 3: different form of human machine interaction. But because words are
Speaker 3: being exchanged and some of the things that language models
Speaker 3: can say, it really are quite surprisingly articulate and informative.
Speaker 3: We tend to project qualities like consciousness into these machines
Speaker 3: because if it was a human being talking to us
Speaker 3: like that, well that human being would clearly be conscious.
Speaker 4: But it's not.
Speaker 3: It's something very, very, very different. So I think the
Speaker 3: first thing we have to do is recognize how much
Speaker 3: our intuitions about the circle of consciousness, how far these
Speaker 3: intuitions are shaped by our biases, and that we can't
Speaker 3: just crawl out from under them, We can't get away
Speaker 3: from them. These biases might be what we might call
Speaker 3: cognitively imped like some visual illusions. You know, there are
Speaker 3: some illusions that even when you know two lines are
Speaker 3: the same length, they will always look different. This is
Speaker 3: the Mullaalaya illusion. So even if we know we're biased
Speaker 3: in these anthropocentric ways, we will still be unable to
Speaker 3: resist these intuitions. So we need to just surface that
Speaker 3: and then ask the question, so what's actually most likely
Speaker 3: the case? And here I think there's extremely good reason
Speaker 3: to believe that consciousness is pretty widespread in non human animals.
Speaker 3: If you look through all mammals, whether it's a tree,
Speaker 3: shrew or orangutang, they have the same basic neural architecture
Speaker 3: that we know is important in human beings for consciouness.
Speaker 3: I think things get really tricky when we look at birds,
Speaker 3: when we look at fish, when we look at insects.
Speaker 3: And here I just think we have to we can't
Speaker 3: be determined, as we don't know one way or the other.
Speaker 3: But we just need to keep updating our credence in
Speaker 3: consciousness in these things. And I think the key thing
Speaker 3: is we also have to ask, well, what kind of
Speaker 3: consciousness matters. It may well be the case that not
Speaker 3: many non human animals have fully developed reflexive, reflective experiences
Speaker 3: of being particular individuals. I mean, we don't know yet,
Speaker 3: but it seems unlikely. But ethically what matters is whether
Speaker 3: they have the capacity to suffer, to feel pain. This
Speaker 3: is a very utilitarian perspective, but I think it's a
Speaker 3: sensible one. So I think we underestimate probably the extent
Speaker 3: of animal consciousness, and I think we overestimate the likelihood
Speaker 3: of AI being conscious. And there are many reasons why
Speaker 3: I'm very skeptical of this, but fundamentally, my great basis
Speaker 3: for the skepticism is I think we've just overused the
Speaker 3: metaphor of the brain as a computer. It's a beautiful, brilliant,
Speaker 3: powerful metaphor, but it's a metaphor. We've always used a
Speaker 3: dominant technology of the day as a metaphor for the brain,
Speaker 3: and we always get into trouble, or we usually get
Speaker 3: into trouble when we start confusing a metaphor with the
Speaker 3: thing itself.
Speaker 1: What do you see, is the biggest unanswered question in
Speaker 1: consciousness research and what excites you the most about where
Speaker 1: the field is headed?
Speaker 3: Oh wow, I mean the biggest unanswered question is it's
Speaker 3: still the old question. How does it happen? We still
Speaker 3: don't really know. I don't think. I mean, there's progress,
Speaker 3: and to be honest, and I don't know. I'd be
Speaker 3: interested if you feel the same way that id David.
Speaker 3: We've been doing this more or less for the same
Speaker 3: amount of time, long time now decades. Some days I
Speaker 3: feel like, ah, it's still a complete mystery. You know,
Speaker 3: we have neurons, chemicals, electrical signals. Why do I experience anything?
Speaker 3: Why is there experience in the world, in the universe
Speaker 3: at all? Those days, you know, we'll have a cup
Speaker 3: of coffee and get on with things. But then on
Speaker 3: other days I think back to where we were in
Speaker 3: the You know, when I started studying and in the
Speaker 3: early nineteen nineties, consciousness wasn't even on the menu if
Speaker 3: you wanted to look at psychology and neuroscience.
Speaker 2: It was.
Speaker 3: It was ostracized completely. I mean we met because I
Speaker 3: came to San Diego in the Lake in the early
Speaker 3: two thousands, which was at the time one of the
Speaker 3: only places where you could study consciousness legitimately with the
Speaker 3: approval of a PI in a lab, and was still
Speaker 3: a pretty rare thing to do. And so looking back
Speaker 3: through that lens and looking at the theories that we
Speaker 3: now have and the kinds of experiments that have been done,
Speaker 3: and how we've learned more about different kinds of consciousness,
Speaker 3: you know, I then worry less that this big metaphysical
Speaker 3: existential question is still waiting in the wings.
Speaker 4: And so I think that.
Speaker 3: Maybe the way to put it, I haven't thought about
Speaker 3: it in quite this way before, But maybe the biggest
Speaker 3: unanswered question in consciousness research is whether there will turn
Speaker 3: out to be a big unanswered question in consciousness research
Speaker 3: or not. In other words, as we just understand more
Speaker 3: and more about different kinds of experience, what happens in anesthesia,
Speaker 3: maybe the question of consciousness will turn out to be
Speaker 3: a little bit like the question of life.
Speaker 1: You know.
Speaker 3: One hundred and fifty years ago, many people thought that
Speaker 3: life was something beyond the reach of science, that there
Speaker 3: had to be something supernatural, some elan vital, some spark
Speaker 3: of life to explain the difference between the living and
Speaker 3: the non living. And of course We don't think that anymore.
Speaker 3: I mean, life is still not a completely written book.
Speaker 3: We don't understand everything, but there's no conceptual mystery. That
Speaker 3: life is part of nature, and even its origin seems
Speaker 3: increasingly within reach to explain and understand. And what happened
Speaker 3: that wasn't that anybody proved that life didn't exist or
Speaker 3: found the spark of life in some Eureka moment. Noe
Speaker 3: biologists started just to make progress, explaining this property of
Speaker 3: living systems homeostas is this property reproduction, this property metabolism,
Speaker 3: And little by little we learned about the nature of
Speaker 3: life and we stopped worrying that there had there was
Speaker 3: something fundamentally inexplicable about it. And progress in science is
Speaker 3: often like this. You know, sometimes it's not only the
Speaker 3: answers that change, but it's the questions that change too.
Speaker 3: So that's what I've got my own. That's what gives
Speaker 3: me hope. I think we understand more when the questions
Speaker 3: we ask start to look different.
Speaker 2: I totally agree with you. And what's interesting.
Speaker 1: What's been interesting for me is I also feel the
Speaker 1: way you knew many mornings where I think, gosh, yeah,
Speaker 1: when you and I were in San Diego and the
Speaker 1: early thousands, the questions that were being asked were mostly
Speaker 1: the same, but things have changed slowly. And just as
Speaker 1: one example, at that time, we all sort of every
Speaker 1: all neuroscientists were sort of snarky about artificial intelligence because
Speaker 1: they felt like, Okay, we've been at this for a
Speaker 1: long time, it's.
Speaker 2: Not really happening.
Speaker 1: There were artificial neural networks with the same principles that
Speaker 1: we have now, but it seemed like it's never gonna happen.
Speaker 1: And now we look at you at GPT every day
Speaker 1: and do experiments on it, and we find, wow, it's
Speaker 1: it's actually working in a way.
Speaker 2: Now. Who knows if it's conscious.
Speaker 1: I think we share the view that it's probably not conscious,
Speaker 1: but boy is it impressive. And so because of improvements
Speaker 1: in our technology, in our ability to measure things in biology,
Speaker 1: in you know, in just data gathering and ways of
Speaker 1: doing things bigger and better, we we find different views
Speaker 1: now than we did one quarter century ago.
Speaker 4: I think that that's right.
Speaker 3: By the way, I have to just one other aspect
Speaker 3: of the whole AI think that I think is really
Speaker 3: useful to think about. So there's this whole debate about
Speaker 3: whether chat GPT or language models might be conscious. People
Speaker 3: ask the question, right, and some people think that they are.
Speaker 3: But just yesterday I was lucky enough to visit deep
Speaker 3: mines in London and that's where alpha fold was developed,
Speaker 3: which is another AI system that's able to predict protein
Speaker 3: structure from sequence I mean acid sequences. The protein folding
Speaker 3: problem has been one of the biggest challenges in biology
Speaker 3: for a long time. Alpha fold basically sorts that out. Now,
Speaker 3: isn't it interesting that no one thinks that alpha fold
Speaker 3: is conscious. I've not heard anybody suggests to me that
Speaker 3: alpha fold might have experience.
Speaker 2: Yet.
Speaker 3: You know, there's some differences, but they're very, very similar
Speaker 3: to the architectures. I mean they're made out of that.
Speaker 3: They're both computer algorithms. Then you're on network based with
Speaker 3: some other stuff that they even have transformer architectures. They're
Speaker 3: really not that different. Yet our intuitions about the two
Speaker 3: are so different. I think that really, to me highlights
Speaker 3: how much of what we think is driven by our
Speaker 3: psychological bias. I think the other thing is is that, yeah,
Speaker 3: there can be this interesting nonlinearity here.
Speaker 4: Right.
Speaker 3: AI seemed to flatline for a long time, and there's
Speaker 3: this sudden rush of discovery and invention and increase incompetence.
Speaker 3: Maybe this is going to happen in the neuroscience of
Speaker 3: consciousness too, And there are some super exciting developments over
Speaker 3: where you're part of the world in Stanford, there's so
Speaker 3: much exciting work going on in optogenetics and in synthetic biology.
Speaker 3: People developing brain organoids, these these collections of brain cells
Speaker 3: in dishes that are derived from human embryonic stem cells,
Speaker 3: things that really, as I think you said, that give
Speaker 3: us different perspectives, different views, different tools, and science often
Speaker 3: needs that. Yeah, you can have ideas, you can have theories,
Speaker 3: but a lot of advance historically in science is when
Speaker 3: people acquire optit invent new tools, and I think we're
Speaker 3: seeing a lot of that in neuroscience now, So I
Speaker 3: am actually pretty optimistic for the future.
Speaker 1: That was my interview with Analseth, professor of cognitive and
Speaker 1: Computational neuroscience at Sussex and the author of Being You.
Speaker 1: We explored here how our brains create our reality, from
Speaker 1: control hallucinations to the rich diversity of individual perceptions.
Speaker 2: What I want to.
Speaker 1: Leave you with is an idea that you've heard me
Speaker 1: talk about many times before on this podcast, which is
Speaker 1: that consciousness is not just about directly experiencing the world,
Speaker 1: but instead it's about actively constructing it. While consciousness remains
Speaker 1: one of the central mysteries and neuroscience, today's conversation, I
Speaker 1: hope gives us a lens through which to view it
Speaker 1: not as something mystical or otherworldly, but as a grounded
Speaker 1: biological phenomenon rooted in the brain's continuous dance of sensory
Speaker 1: input and its own predictions. Now, thinking about our perception
Speaker 1: of the world not as a direct reflection of reality,
Speaker 1: but a construction of the brain, the inner world meeting
Speaker 1: the outer world. This is really the single reasonable way
Speaker 1: to understand it, and in a sense, it's a little
Speaker 1: strange that this is still rare in textbooks or in
Speaker 1: common knowledge. And note that this process of trying to
Speaker 1: predict signals applies not just to our perception of the
Speaker 1: outside world, but to the swirling world inside our bodies
Speaker 1: as well, which sheds light on how we experience emotions
Speaker 1: and maintain homeostasis. In this way, the self is not
Speaker 1: a fixed entity, but an ever changing construct. And one
Speaker 1: of the other themes that emerge today is that each
Speaker 1: of us carries around a slightly different version of reality,
Speaker 1: shaped by our biology and our experiences. This is a
Speaker 1: reminder of how subjective and varied human experience can be,
Speaker 1: and how much we still have to learn about the
Speaker 1: minds of others. That includes other humans and animals, and
Speaker 1: possibly at some point ai are we overestimating the sentience
Speaker 1: of machines or underestimating that of animals. So, in closing,
Speaker 1: as Annel mentioned, just as we once demystified the life itself,
Speaker 1: step by step through the patient work of science, we
Speaker 1: may one day come to understand consciousness in a similar manner.
Speaker 1: The key, as it is so often in science, is
Speaker 1: not just about answering the big question, but can continually
Speaker 1: asking smaller, better ones. Go to Eagleman dot com slash
Speaker 1: podcast for more information and to find further reading.
Speaker 2: Send me an.
Speaker 5: Email at podcasts at eagleman dot com with questions or
Speaker 5: discussion and check out Subscribe to Inner Cosmos on YouTube
Speaker 5: for videos of each episode and to leave comments.
Speaker 2: Until next time.
Speaker 1: I'm David Eagleman and this is Inner Cosmos.