The Neuroscience of Consciousness – with Anil Seth

[APPLAUSE] - Two out of the three fundamental mysteries about our place in the universe have already been resolved. The first is literally about our place in the universe. Many years ago Copernicus told us that we were not at its centre, that we were just a tiny dot suspended in the abyss. This is an image of the earth taken from the probe Voyager 1 as it was leaving the solar system from about six billion kilometres away. All of human history, all of the history of life on Earth, has taken place on that pale blue dot. The second mystery, Darwin then revealed that we humans are just one branch, or one twig, of a beautifully rich and delicate evolutionary tree. And that much of the machinery of life is shared even with the lowliest of our fellow creatures. The third mystery is that of consciousness, our inner universe. Now earlier this year, for the third time in my life, I ceased to exist. As the propofol anaesthetic flowed from the cannula in my wrist into my bloodstream and then into my brain, there was a falling apart. A blackness. An absence. And then, I was back. Drowsy and disoriented, but definitely there. And when you wake from a deep sleep, you might be confused what time it is, especially in flying somewhere, but you'll know that some time has passed. There seems to be some basic continuity between your consciousness then, and your consciousness now. But coming around from a general anaesthetic, it could have been five minutes. It could have been five hours. It could have been five days, or five years. I was simply not there. A premonition of the oblivion of death. And general anaesthesia doesn't just work on your brain. It doesn't just work on your mind. It works on your consciousness. By altering the delicate electrochemical circuitry inside your head, the basic ground state of what it is to be is temporarily abolished. And in this process lies one of the greatest remaining mysteries in science and philosophy. How does consciousness happen? Why is life in the first person? It is going away, and coming back. The modern incarnation of this problem is usually traced to Descartes, who in the 17th century distinguished between matter stuff, res extensa, the stuff that these desks are made of, that clothes are made of. But also the brains and bodies and made of, material stuff. And res cogitans, the stuff of thought, of feelings. The stuff of consciousness. And in making this distinction, he gave rise to the now infamous mind/body problem, and life has never been simple ever since. But Descartes actually generated even more mischief with his doctrine of the beast machine, which I'm going to mention now, because it anticipates where I'm going to end up as the bell rings when I finish in an hour. Before Descartes, people commonly believed in something called the great chain of being, with rocks and plants at one end, and other non-human animals, a bit higher up than humans, and then angels and gods at the very top. And this great scale of being was also a scale of moral virtue, so that humans had more moral virtues than animals and plants, and then rocks and so on. Now Descartes, in making this division between mind and matter, argued that only humans had minds, and therefore moral status, while other animals didn't have minds. They were merely physiological machines, or beast machines, morally equivalent to plants, and to rocks. And in this view, the physiological mechanisms that give rise to the property of being alive were not relevant to the presence of mind or consciousness. Now I'm going to propose, at the end of this talk, the opposite. That our conscious sense of self arises because of, and not in spite of, the fact that we, too, are beast machines. So to get there, let's return to the apparent mystery of consciousness. Now as recently as 1989, which is quite a while ago, but not that long ago, Stuart Sutherland, who was founding professor of experimental psychology at my university of Sussex, had this to say. "Consciousness is a fascinating but elusive phenomenon. It is impossible to specify what it is, what it does, or why it evolved. Nothing worth reading has been written on it." [LAUGHTER] It's quite a pessimistic point of view. And that may have been true then. I don't think it was true then, but in any case things have changed a lot since. And more or less, about the time that Sutherland made these remarks, we can see the birth, or the rebirth, of the study of consciousness within the neurosciences. And a good landmark is this paper by Francis Crick and Christof Koch, published in 1990. And they start their paper by saying that it is remarkable that most of the work in cognitive sciences, and the neurosciences, makes no reference to consciousness or awareness at all. And then they go on to propose their own theory of what the neural correlates of consciousness are. What it is in the brain that is responsible for being conscious. And since then, over the last 25 years, there's been first a trickle, and now a deluge of research on the brain basis of conscious experience. Some of this work is being carried out in my laboratory, the Sackler Centre, the consciousness science, which was founded six years ago with Hugo Critchley, my co-director. And there are now even specialised academic journals, The Neuroscience of Consciousness, which I started last year with Oxford University Press. And this is a real change of the tide. When I started out more than 20 years ago, it was thought to be a very-- it was thought to be career suicide to want to study consciousness, scientifically. And it may still be, we don't know. Let's see. So while the brain basis of consciousness is still a mystery, it is, in some sense, an accessible mystery. And the author, Mark Haddon, put this very nicely, I think. He said the raw material of consciousness is not on the other side of the universe. It didn't happen 14 billion years ago. And it's not squirrelled away deep inside an atom. The raw material of consciousness is right here, inside your head, and you can hold the brain in your hands. But the brain won't deliver its secrets very easily. What's extraordinary about the brain is not so much the number of neurons, though there are about 90 billion. It's not even the number of connections, though there are so many that if you counted one every second, it would take you about three million years to finish counting. What's truly extraordinary are the patterns of connectivity, which to a large extent, are still not known, but within which are inscribed everything that makes you, you. The challenge is then this, at least the the way I see it. How can the structure and dynamics of the brain, in connection with the body and the environment, account for the subjective phenomenological properties of consciousness? And considering things this way, we come up against what the philosopher David Chalmers has often called the hard problem of consciousness. And the idea is this. There is an easy problem. The easy problem is to understand how the combined operations of the brain and the body give rise to perception, to cognition, to thinking, to learning, to behaviour. How the brain works, in other words. The hard problem is to understand why and how any of this should have anything to do with consciousness at all. Why aren't we just robots, or philosophical zombies, without any in a universe? Now there's a tempting intuition here, which is that, even if we solve the hard problem, even if we solve the easy problem, the hard problem would still remain as mysterious as it seems now. But this just seems wrong-headed to me. It may not be necessary to explain why consciousness exists at all, in order to make progress in understanding its material basis. And this for me, is the real problem of consciousness; how to account for its various properties in terms of biological mechanisms without pretending that it doesn't exist at all, as you do if you solve the easy problem, and without trying to account for why it's parts of the universe in the first place, which is the hard problem. And in the history of science, we've been somewhere similar before. It's hard to say if it's exactly the same situation. But in our understanding of life, eminent biochemists of the time found it entirely mysterious how biological mechanisms could give rise to the property of being alive. And there were proposed of things, like elan vital and essence vital, and all sorts of other stuff. And although we don't yet understand everything about life, this initial sense of mystery about life has, to a large extent, dissolved as biologists have just got on with the business of understanding the properties of living systems in terms of mechanisms. An important part of this story was the realisation that life is not just one thing, but rather a constellation of partially dependent, partially separable, processes, like metabolism, homeostasis, and reproduction. In the same way, to make progress on the real problem of consciousness, it can be useful to distinguish different aspects or dimensions of what it is to be conscious. The space of possible minds, if you like. And one simple classification is into conscious level, which is the property of being conscious at all. For example, the difference between being in a dreamless sleep, or under general anaesthesia, and being awake and conscious as you are now. And the conscious content, when you are conscious, you're conscious of something. The myriad of sights, sounds, smells, emotions, feelings, and beliefs that populate your inner universe at any one time. And one thing you are conscious of when you are conscious, is the experience, the specific experience, of being you, and this is conscious self. And it's the third dimension of consciousness. Now I don't claim these distinctions mark completely independent aspects of what it is to be conscious, but they're a pragmatically useful way of breaking down the problem a bit. So let's start with conscious level. What are the fundamental brain mechanisms that underlie our ability to be conscious at all? And we can think of this, at least a first approximation, as a scale from being completely unconscious, as if you were in a coma, or under general anaesthesia, to being awake, alert, and fully conscious as you are now. And there's various states in between being drowsy, being mildly sedated and so on. What's important is that, while being conscious and being awake often go together, this is not always the case. For instance, when you are dreaming you are asleep, but you are having conscious experiences. The conscious experience of your dreams. And on the other side of this diagram, there are pathological states, like the vegetative state, where physiologically you will go through sleep/wake cycles, but there is nobody at home. There is no consciousness happening. So what are the specific mechanisms that underlie being conscious and not simply being physiologically awake? Well there are a number of possibilities. Is it the number of neurons? Well actually, probably not. There are more neurons in your cerebellum, this bit at the back of your brain, than in the rest of your brain put together. In fact are about four times more neurons in your cerebellum than in the rest of your cortex. But if you have damage to your cerebellum, yeah, you'll have some problems with coordination and other things, some cognitive problems, but you won't lose consciousness. It's not just the number of neurons. Doesn't seem to be any particular region. In fact, there are regions that, if you suffer damage, you will permanently lose consciousness; in thalamina, the nuclei, and the thalamus deep inside the brain. But these seems to be more like on/off switches than actual generators of conscious experience. It's not even neural activity, at least not simple kinds of neural activity. Your brain is still highly active doing unconscious states, during the sleep. And even if your brain is highly synchronised, one of the first theories of consciousness was it depended on neurons firing in synchrony with each other. If your brain is too synchronised, you will lose consciousness, and this happens in states of absence epilepsy. What seems to be the case is that, being conscious it all, depends on how different brain regions talk to each other in specific ways. And this was some groundbreaking work by Marcello Massimini in Milan about 10 years ago. And what he did here, was he stimulated the cortex of the brain with a brief pulse of electromagnetic energy, using a technique called transcranial magnetic stimulation or TMS. And then he used EEG electroencephalography to listen to the brain's echos. A little bit like banging on the brain and listening to its electrical response. And what he noticed when you do this, and you can see on the left is asleep, and on the right is awake. And this is very much slowed down. When you stimulate the brain in a sleep condition, there is still a response. There's still an echo, but the echo stays very localised to the point of stimulation. It doesn't travel around very much, and it doesn't last very long. But when you stimulate a conscious brain, there's a spatial temporally complex response. This echo bounces around all over the cortex in very interesting ways. What's more, the complexity of this echo can be quantified. You can apply some simple algorithms to describe how complex, how rich, this pattern of interactivity is. This is also from the Milan group. And what they've done here is, they basically look at the echo as it moves around the brain. And they see the extent to which you could describe it, the minimum description length. How much can you compress the image of that echo? Much the same way that algorithms make compressed files from digital images in your phone. And they came up with an index called the perturbational complexity index. And what you find is, you now have a number that you can attach to how conscious you are. This is, I think, really intriguing, because it's a first step towards having an actual measurement of conscious level. This graph on the bottom shows this measure applied to a variety of conscious states, ranging from pathological conscious states, like the vegetative state, where there is no consciousness at all, all the way through locked in syndrome, and then healthy waking. And you can immediately see that techniques like this might already have clinical value in diagnosing potential for consciousness patients might have after severe brain injury. Now at Sussex, we are continuing work along these lines. We actually look, instead of bashing on the brain with a sharp pulse of energy, we want to see whether we can get something similar just by recording the spontaneous activity of the brain. So we look at spontaneous dynamics from, in this case, waking states and anaesthesia. This is work with my PhD students Michael Schartner and Adam Barrett. We measure its complexity, and indeed, we find that we can distinguish different levels of consciousness just by the spontaneous activity of the brain. In a way this isn't that surprising, because we know various things change. The balance of different frequencies of your brain activity changes when you lose consciousness. But this doesn't have to do with that. This is independent of that. There's something specific that is being detected by these changes in complexity. More recently we've applied the same measures to sleep, in this case taking advantage with colleagues in Milan of recordings taken from directly within the human cerebral cortex. These are implanted electrodes. And we see much the same story. If you compare where the two areas are, you compare the complexity of wakeful rest, and early non-rem sleep, where you are not dreaming very much. You see that complexity fools a great deal. What's interesting here is, if you compare wakeful rest to REM sleep, where people will often report dreams if you wake them up, the level of complexity is very much as it is during the wakeful state. There's something else going on here, which is that the complexity in the frontal part of the brain seems to be higher than in other parts of the brain. And that's something we still don't understand fully, yet. I just wanted to give you something hot off the press, so to speak, which is where you've also been applying these measures now to data taken from people under the influence of psychedelic drugs; psilocybin, ketamine, and LSD. And what we find, at least in our hands to start with here, is that the level of complexity actually increases as compared to the baseline state, which is not something we've seen before in any other application of these measures. So what's important about this way of looking at things is that, it's grounded in a theory that tries to explain why certain sorts of brain dynamics go along with being conscious. And put very simply, the idea is this-- and it goes back to Guilio Tononi and Gerald Edelman, people that I went to work with in America about 18 years ago-- the idea is very simple. Consciousness is extremely informative. Every conscious experience you have, or have had, or will have, is different from every other conscious experience you have had, are having, or will have. Even the experience of pure darkness rules out a vast repertoire of alternative possible experiences that you could have, or might have in the future. There's a huge amount of information for the organism in any conscious experience. At the same time every experience that you have is highly integrated. Every conscious scene is experienced, as all of a piece, is bound together. We don't experience colours and shapes separately in any way. It's conscious experiences at the level of phenomenology combine these properties. They are the one hand highly informative, composed of many different parts. On the other, bound together in a unified whole. And this motivates us to search for mathematical measures which have the same property, which are neither lacking in information. On the left, you see a system which is all connected together, so it can't enter very many different states. On the right is a system which is completely dissociated, so they can enter states, but it's not a single system. We want measures that track this middle ground of systems, that combine both integration and differentiation. And a number of these measures now exist. There are some equations here, which we can talk about later if you like, that try to target this middle ground. And time will tell whether, by applying these more phenomenologically grounded measures, we come up with even more precise practical measures of consciousness. Now why is this business of measurement important? And I want to make a general point here, which is that, if you're trying to naturalise a phenomenon which seems mysterious, the ability to measure it is usually one of the most important steps you can take. And we've seen numerous examples of this. The history of our understanding of heat and temperature is one very good example Here's an early thermometer from the 18th century, which used the expansion of air. But of course there are many problems in generating a reliable thermometer and a scale of temperature, if you don't already have fixed points. And if you don't know what heat is you get trapped in a kind of vicious circle that took a long time to break out of. But people did break out of this, and the development of the thermometers catalysed our understanding of heat from being something that flowed in and out of objects, to being something that was identical to a physical property. The mean molecular kinetic energy of molecules in a substance. And having that concept of heat now allows us to talk about temperature far beyond the realms of human experience. We can talk about the temperature on the surface of the sun, in a sense, the way we can talk about the temperature of interstellar space, close to absolute zero. None of these things make any sense and in our phenomenological experience of hot and cold. So this brings me to my first take-home message. Measurement is important, and consciousness, conscious level, depends on a complex balance of differentiation and integration in brain dynamics, reflecting the fact that conscious experiences themselves are both highly informative and always integrated. Now when we are conscious, we are conscious of something. So what are the brain mechanisms that determine the content of consciousness? And the hero for this part of the story is the German physicist and physiologist Hermann Von Helmholtz. And he proposed the idea that the brain is a kind of prediction machine. That what we see, hear, and feel are nothing other than the brain's best guess about the causes of sensory inputs. And the basic idea is, again, quite simple. The brain is locked inside its bony skull home, and has very indirect access to the external world. All it receives are ambiguous and noisy sensory signals, which are highly and directly related to this external world of objects, and so on, if there is an external world of objects out there at all. They know about that. Perception in this view is, by necessity, a process of inference in which the brain interprets these ambiguous and noisy sensory signals with respect to some prior expectations or beliefs about the way the world is. And this forms the brain's best guess of the causes of the sensory signals that are impacting our sensory surfaces all the time. What we see is the brain's best guess of what's out there. I want to give you a couple of examples that illustrate this process. It's quite easy to do, in a way. This first example is a well-known visual illusion called Edelstein's Checkerboard. Now here, you'll see two patches. You'll see patches A and B. And I hope to you they look to be different shades of grey. Do they? They look to be different shades of grey. Of course they are exactly the same shade of grey. I can illustrate that by putting an alternative image, and joining up those two patches. You'll see that's it's the same shade of grey. You may not believe me, so what I'll do is, I'll shift it along, and you'll see even more clearly. There are no sharp edges. It's the same shade of grey. What's going on here, of course, is that the brain is unconsciously applying its prior knowledge that a cast shadow dims the appearance of surfaces that it casts onto. So we therefore see the patch B as being lighter than it really is, in order to account for that effect. And this is of course an illustration of the success of the visual system, not its failure. The visual system is a very bad physical light metre, but that's not what it's supposed to do. It's supposed to, or one thing it's supposed to do, is to interpret the causes of sensory signals in terms of meaningful objects in the world. It's also an example of what we sometimes call cognitive impenetrability. Even if you know the patches are the same shade of grey, when I take that bar away, they again look different. Can't do much about that. The second example just shows you how quickly the brain can take in new prior information to change the nature of conscious perception. This is a so-called Mooney image. And if you haven't seen it before, hopefully what you will see here is just a passing of black and white splotches. Does everybody kind of get that? Black and white splotches? Some of you might have seen this before. And now what I'm going to do is fill it in, and you see something very different. What you'll see is a very meaningful scene here involving at least two objects, a woman, a hat and a horse. Now if you stare at this for a while, I won't leave it up for too long-- but if you just look at it for a little bit, and then I take that image away again, you should still be able to see the objects within that image. Now for me this is quite remarkable, because the sensory information hasn't changed at all. All that's changed are your brain's prior expectations about what that sensory data signifies. And this changes what you consciously see. Now this also works in the auditory domain. Here are two spectrograms. This is something called sine wave speech, and what you see here are two time frequency representations of speech sounds. The one on the top has all the sharp acoustical features that provide normal speech removed. A little bit like thresholding an image. And the bottom is something else. So I'm going to play the top first, and let's see what it sounds like. [STRANGE BEEPS AND NOISES] And now I'll play you something else. (BOTTOM SOUND - A MAN'S VOICE): Jazz and swing fans like fast music. - So I hope you all understood that piece of sage advice. And now if I play the original sound again-- [BEEPS AND WHISTLES THAT SOUND LIKE THE SENTENCE] - Yeah? This is exactly the same. Again, all this change is what we expect that sound to signify. [SAME SOUND PLAYED AGAIN] - One more time, just for luck. It's not just a bunch of noisy whistles, it's speech. Now this typical framework for thinking about these kinds of effects is Bayesian Inference. And this is a form of probabilistic reasoning, which is applicable in all sorts of domains, not just in neuroscience, in medical diagnosis, and all sorts of things, like finding lost submarines. But in neuroscience, we talk about the Bayesian brain. And it's a way of formalising Helmholtz's idea that perception is a form of best guessing. And the idea is that sensory signals and prior beliefs can be represented as probability distribution. So for instance, this yellow curve is the probability of something being the case, maybe that you've got a brief glimpse of an object moving to the right. The sensory data may say something different. It may have a probability that peaks at a different angle of movement. Maybe it's drifting in a different direction. And the optimal combination of the prior, and the likelihood, the yellow curve and the red curve, is this green curve, which we will call the posterior distribution. And that represents the best optimal combination of these two sorts of evidence. And the idea is, well that's what we perceive. Thinking about perception in this way does something rather strange to the way, classically in neuroscience, people have thought about perception. The classical view is that the brain processes sensory information in a bottom-up, or feed-forward direction. This is a picture of the visual system of the monkey, and the idea is that information comes in through the retina, then goes through the thalamus. It then goes to the back of the brain. And as the sensory signals percolate deeper and deeper and deeper into the brain, they encode or represent progressively more sophisticated features of objects. So you start out at early levels the visual cortex with response to luminance, and edges, and then higher up to objects, including other monkeys. What's important here is the perceptual heavy lifting is done by information flowing in this bottom-up or feed-forward direction. Now the Bayesian brain idea says something very different. It says that what's really important are the top-down or inside-out connections that flow from the centre of the brain back out. And we've known for a long time there's a large number, a very large number of these connections, and some descriptions more than flow the other way around. But the function has been rather mysterious. Thinking about the Bayesian brain gives us a nice way to interpret this. Which is that it's exactly these top-down or inside-out connections that convey predictions from high levels of the brain to lower levels, to lower levels, back out to the sensory surfaces. So these blue arrows convey the brain's predictions about the causes of sensory signals. And then what flows in the feed-forward or bottom-up direction, from the outside-in, that's just the prediction area, the difference between what the brain expects and what it gets at each level of description. So this is often called predictive coding, or predictive processing, in some formal frameworks. And the idea is that minimization of prediction error occurs across all levels of this hierarchy at the same time, and what we then perceive is the consequence of this joint minimization of prediction error. So you can think of perception as a sort of controlled hallucination, in which our perceptual predictions are being reined in at all points by sensory information from the world and the body. Now there's quite a lot of experiments that show that something like this is probably going on in the brain. These are a couple of examples. And since they're-- I was looking for the best example so they don't come from my lab at all. [LAUGHTER] This is from Lars Muckli in Glasgow. He's shown using advanced brain reading techniques, which I won't describe, that you can decode the context of what a person is seeing from parts of the visual cortex that isn't even receiving any input. And what's more, you can decode better when you decode from the top part of the cortex, which is supposed to receive predictions from higher levels. So that suggests there are predictions being fed back. And another study by Andre Bastos and Pascal Fries in Germany, they used a method called Granger causality, which is sensitive to information flow in systems. And they find that top-down signals and bottom-up signals are conveyed in different frequency bands in the cortex, which is what you'd predict from predictive coding. One last experiment which I find particularly interesting is an experiment from a Japanese group of Masanori Murayama. And they used to optigenetics, which is a way of using lights to selectively turn on or off neural circuits. And in this experiment they showed that by just deactivating top superficial levels of somatosensory cortex in a mouse brain, the part of the mouse brain that's sensitive to touch, they could affect how well that mouse was able to do tactile discriminations. Those top-down connections were coming from a motor cortex. So there's a lot of evidence that top-down connections in the brain are important for perception, is the basic message there. But what's rather strange, and what I'm going to tell you next is that all this stuff is all very good, but predictive processing is not a theory of consciousness. Nothing I've said has anything to do with consciousness, at all. It has to do-- it's a very general theory of how brains do what they do. How they do perception, how they do cognition, how they do action. So somewhat counter-intuitively, I think this is exactly why it's a great theory of consciousness. And the reason I think this is because it allows us to ask all sorts of questions about the real problem. About what it is, what happens in brains that underlies what you happen to be conscious of right now, without getting sucked into the metaphysical pluckhole of why you are conscious in the first place. In other words, it provides a powerful approach to looking for neural correlates of consciousness, those things in the brain that go along with being conscious. Because we can now take advantage of a very general theory of how brains do what they do, rather than just looking at this region or that region. So what does predictive processing, or the Bayesian brain say about consciousness, specifically? Well many years ago, some influential experiments revealed a very strong connection between top-down signalling and conscious contents. In this example by Alvaro Pascual-Leone and Vincent Walsh, what they did was they had people look at visual motion, examples of visual motion. And they used TMS, this interventional technique where you can zap the brain very briefly. I mentioned it before. But they used it here, specifically to interrupt the top-M down signalling that was evoked by this perception of visual motion. And the result was that, if you interrupted specifically the top-down feedback, you would abolish the conscious perception of visual motion, even if you left the bottom-up signalling intact. So that was an early key. Now, more recently, in our lab and in many other labs all over the place, we've been asking some other questions about the relationship between what you expect and what you consciously experience. One of the most basic questions you can ask is, do we consciously see what we expect to see? Or do we see what violates our expectations of what we expect? And a recent study from our group, led by Yair Pinto, used a method called continuous flash suppression to address this question. It's illustrated here. You see different images in the different eyes. In one eye you see this rapidly changing Mondrian pattern of squares. And in the other eye, you either see a face or a house. And they change contrast like this. So initially, the person would just see this random pattern, and then they'll see either a house or a face. And simply, you just ask them to expect to see-- you just tell them a face is more likely or a house is more likely. And what we find over a number of studies is that we see faces more quickly when that's what we're expecting to see. It may seem obvious, but it could be the other way around. At least in these studies, we see what we expect to see, not what violates our expectations. That's the data. And the same goes for houses. These kinds of studies support the idea that it's the top-down predictions that are really important for determining what we're conscious of. There's another experiment which I will just mention. We did pretty much the same thing. This is called motion induced blindness. If you are in a lab rather than in a lecture theatre, and you stare at this central point here, then this red dot might disappear from time to time. And what we did was after it disappeared, we changed its colour and we led people to expect the colour change to be one thing or another. And again, it reappeared more quickly if it changed colour in the way you were expecting. Again, I am confirming that once your expectations were validated, then that accelerated your conscious awareness of something in the world. Now that's just behavioural evidence. That's just asking people what they see and when they see it. We've also been interested in the brain mechanisms that underlie and shape how our expectations change, what we consciously see. And we've been particularly interested in something called the alpha rhythm. Now the alpha rhythm is an oscillation of about 10 hertz or 10 cycles per second. That's especially prominent in the visual cortex, across the back of the brain. In one study, led by in this case a PhD student, Maxine Sherman, with Ryota Kanai, in Sussex. What we did here, we manipulated people's expectations of what they we're likely to see. And it's a very boring experiment, this was. The only thing they could see was what we call Gabor patches. They're just very dim patches of lines that are blurry around the edges. But the visual system loves these kinds of things. They activate early visual cortex very, very well. And people were expecting either that a patch was there, or that it wasn't there, in different conditions. And while doing this we measured brain activity. And to cut a long story short, what we found was that there were certain phases, certain parts of the cycle, this 10 Hertz cycle, at which your expectation had a greater effect on what you said that you saw. So there was part of this cycle, as the alpha rhythm, there was part of it where your expectations dominated your perception. And there was another part of it which was the opposite, where your sensory signals were more important in determining what you saw at that point. So this suggests that this oscillation in the back of the brain is orchestrating this exchange of predictions and prediction errors. And that's the sort of cycle that might be the neural mechanism for conscious vision. And other theories about what the alpha rhythm is doing, there are many. One is that it's doing nothing, it's just the brain idling away, and I think this is at least a more interesting way to think about it. Another experiment we've done with another PhD student Asa-Chang, we showed people these very fast changing luminance sequences. And it turns out that your brains will learn to predict the specific changes in these sequences that change you very quickly. And the signature of this learning, again, happens to be in the alpha rhythm, and suggests that this oscillation has something important to do with how the brain learns and updates predictions about sensory signals. But we do not go around the world looking at Gabor patches or rapidly changing things like this. We go around the world looking at people and objects. And that's what our visual world is composed of. So can any of these ideas say anything about our everyday visual experience? And I think that's a very important challenge in neuroscience to cross. Get out of the lab and think about real world experiences. So we've been using virtual reality over the last few years to try to get at some of these ideas. This is an Oculus Rift, which is now available to buy, I think. And we've been using these to address some of these real world aspects of visual perception. And one of these real world aspects is called perceptual presence. And this is the observation that, in normal perception, objects really seem to be there, as opposed to being images of objects. And this is, of course, what Magritte plays with in his famous painting, The Treachery of Images. For instance, this is an object. I think it's there, and in some sense I can perceive the back of it, even though I can't see the back of it, even though the back of it's not giving me any sensory data, I perceive it as an object with a back. How does one explain that? Well one idea you can come up with within this Bayesian brain framework is that, the brain is not only predicting the possible causes of the sensory signals getting right here, right now. But it's also predicting how sensory signals would change were I to make particular actions. Were I to pick this object up and move it around, or just move my eyes from one place to another. There's a long paper. I wrote about that which I-- please don't read it. [LAUGHTER] - But that's the basic idea. And how do you test an idea like that? So we've been using some innovative virtual reality methods, or augmented reality methods, with my post-doc, Keisuke Suzuki. And what we do is, we have virtual objects, and these virtual objects, they either behave as a normal object would. They're all weird, unfamiliar objects. But they can either behave as a normal object would behave, so you can learn to predict what would happen. This one is weird. It always shows you the same face, a little bit like having the moon on a plate in front of you. And then there are other conditions where objects respond to your movements, but they do so in unreliable and strange ways. So the question is, what does the brain learn about these objects, and how do we experience them? Do we experience them as objects in different ways when they behave differently? And we're still doing those experiments. Another way we can use VR, is to investigate what happens in visual hallucinations of the kind experienced in psychosis, and in certain other more pharmacologically induced conditions. What we're doing here is, we're coupling immersive virtual reality, imagine you've got a headset strapped to your head again, with clever image processing that models the effects of overactive priors on visual perception to generate a highly immersive experience. This is Sussex campus, actually, but now it seems quite different than it did at lunchtime today. I'll tell you that. What we've done, we've recorded this panoramic video which we can feedback through VR headset. And we've processed this video through one of these Google deep dream algorithms you might have seen, that can generate sort of bowls of pasta that looks like animals. And this might seem like a lot of fun. It is fun, but there is a serious purpose here, because it allows us to model the effects, model very unusual forms of perception and how they might play out in different ways in the visual hierarchy. And understanding how visual hallucinations might happen, and how the wider effects they have on the mind, I think, is a very important part of studying visual perception. So that brings us to the second take-home message, which is that what we consciously see is the brain's best guess of the causes of its sensory input. Normal perception is a fantasy that is constrained by reality. Now before I move on to the last section, I want to pay tribute to an unlikely character in a talk about neuroscience, which is Ernst Gombrich. Ernst Gombrich was one of the foremost historians of art of the 20th century. And it turns out that Gombrich's approach to understanding art had a lot in common with ideas and the Bayesian brain. And more specifically, with the idea that perception is largely an act of imagination, or construction, on the part of the perceiver. And this is most apparent in his concept of the beholder's share, which emphasises that the viewer brings an awful lot to the table in the act of experiencing an artwork. So he had this to say in his 1960 book, Art and Illusion, "the artist gives the beholder 'more to do', he draws them into the magic circle of creation and allows him to experience something of the thrill of making which had once been the privilege of the artist." I think for me this is very powerful when looking at, especially, things like Impressionist art. And here, one way to think about this is, that the artist has reverse engineered the whole perceptual process, so that what's there are not the objects, the end points of perception, but rather the raw materials; the patterns of light that engage our perceptual machinery in doing its work. And for me this might be why paintings like this are particularly powerful. Now the final dimension of consciousness I want to talk about is conscious self. The fundamental experience of being someone. Being someone like you. There are many aspects to our experience of being a conscious self. There is the bodily self, the experience of being and identifying with a particular body. A bit of the world goes around with you in the world all the time. There's the perspectival self, the experience of seeing the world, or experiencing the world, from a particular first person perspective, usually somewhere in the body, but not always. There is the volitional self, the experience of intending to do things, and of making things happen in the world of agency. And these ideas are, of course, often associated with concepts of will. Then there's the narrative self. This is where-- only until now, we don't have to worry about the concept of I, but when we get to the narrative self, there is now and I. There is a continuity of self experience from hour to hour, from day to day, from month to month, and from year to year, that you associate a name with, and a particular set of autobiographical memories. And finally, there's a social self. The way I experience being me is partly dependent on the way I perceive you as perceiving me. I'm just going to talk, in the minutes remain, about the bodily self. This is something we're working on quite a lot in Sussex. The experience of identifying with, and owning, a particular body. And the basic idea I want to convey, is again, very simple. It's just that we should think of our experience of body ownership in the same way that we think about our experience of other things, as well. That is, it's the brain's best guess of the causes of body-related signals. And the brain is always making this inference. It's making its inference about what in the world is part of the body, and what is not part of the body. But it has access, in this case, to other sorts of sensory signals, not just visual signals, or tactile signals, but also proprioceptive signals. The orange arrows here. These inform the brain about the body's configuration and position in space. And then also, and often overlooked, are interoceptive signals. These are signals that originate from within the body, that tell the brain about the physiological state or condition of the inside of the internal physiological milieu. And you can think the idea is that our experience of embodied self-hood is the brain's best guess of the causes of all the signals put together. Yeah, that's just to emphasise interoception. An important part of this idea is that interoception, the sense of the body from within, should work along the same principles, the same Bayesian principles that we've been thinking about, vision and audition, previously. That is, our experience of the inside of our bodies is the brain's best guess of the causes of the signals that come from the inside of our bodies. So we can think of, again, top-down predictions carrying predictions about what the bodily state is like, and bottom-M up prediction errors that report the differences between what's going on and what the brain expects. So what is our experience of the inside of our bodies? Well, way back at the beginning of psychology, William James and Carl Langer proposed that emotions, emotional experience, was really about the brain's perception of changes in its physiological state, rather than perception of the outside world. So, in this classic example, seeing a bear does not in itself generate the experience of fear. Rather seeing the bear sets in train, a load of physiological changes preparing for fight and flight responses. And it's the perception of those bodily changes in the context with the bear being around that leads to our experience of fear. So the Bayesian perspective just generalises that idea, and says that emotional experience is the brain's best guess of the causes of interoceptive signals. And this fits very nicely with a lot of evidence. And this is just one study done by a Finnish group. And all they did here was, they had people report where on their bodies they felt various emotions to take place. And so you feel anxiety in one part of your body. You feel fear in another, and so on. So our experience of emotion does seem to be intrinsically embodied. Now another part of our experience of being a body is the body as a physical object in the world. And this might seem quite easy to take for granted, since our physical body is just always there. It goes around with us, it changes over the years, in unfortunate ways. But it's always there. But it would be a mistake to take our experience of body ownership for granted. And there are some classic experiments that demonstrate how malleable our experience of body ownership is. This is the famous rubber hand experiment. Probably some of you have seen this. What happens here is that a volunteer has their hand hidden under a table, and the fake hand is put on top of the table, and then both hands are simultaneously stroked with a paintbrush. And it turns out that just seeing a hand-like object where a hand might be, feeling touch, and then seeing the object being touched, is enough evidence that the brain's best guess becomes that fake hand is, in fact, part of my body. Sort of part of my body. This is what it looks like in practise. Here you can see the fake hands, focusing on it. There's the real hands, not focusing on it, simultaneous stroking-- and there are various ways you can test it. [AUDIENCE LAUGHTER] - I found in doing this, it works even better on children, by the way, if you do that. So that's interesting, because that's using visual and tactile signals to convince the brain that this object is part of its body. In my lab, we've been interested in whether these signals that come from inside the body also play a role. So we set up a virtual reality version of this rubber hand illusion, where people wear these goggles, and they see a virtual fake hand. And we also record their heartbeats. And now what we can do is, we can make the virtual hand flash either in time or out of time with their heart beat. And we asked the question, do people experience this virtual hand as more belonging to them when it's flashing in time, rather than out of time, with their heartbeat? And the answer is that it does. And this is just some data, basically that, bigger than that, which means that, indeed, they experience the hand as more their own. The way we measure that actually, is that first we can ask them. That's the easiest way. Then we can also ask them to point to where they think their hand really is, and we can see how far they drift from where their hand really is to where the virtual hand is. And that provides a more objective way of measuring the strength of the effect. Here's what it looks like in practise. Again if you can see this, that's the real hand. That's somebody's virtual hand. Again, imagine you're wearing a headset so you'll see this in 3-D. And you can just about see it pulsing to read and back, I hope. And you can also do some other things with these virtual reality rubber hands that you couldn't do with real rubber hands. For instance, you can map movements of the real hand to the virtual hand, so you can start to ask questions about the extent to which the virtual hand moves as I predict it to move. How much does that affect the extent to which I feel it to be part of my body? You can make it change colour. So you can have somebody embody a skin colour associated with a cultural out-group, and see if they become less racist as a result. And then my favourite is where you can change, actually, the size of the body. And that's coming up here. So here what we do is, we can have the hand telescope up and down in size. And again, this might seem like fun, and it is fun, but there is a serious purpose. There are various conditions. There's in fact, a condition called Alice in Wonderland Syndrome, where people report that all parts of their body are, indeed, telescoping up and down in size. And in a more subtle way, there are lots of body dysmorphias, of subtle misperceptions of body shape, which might be associated with eating disorders. And so these sorts of techniques allow us to approach, in a very fine grained way, how people might mis-perceive their own bodies. That brings me to the third take-home message about self. And with apologies to Descartes, the take home message is that, I predict myself, therefore I am. In the last nine minutes, before the bell rings, I want to go full circle and return to this Cartesian idea of the beast machine. To try to convince you that our experience of being a conscious self is intimately tied up with our beast machine nature. And to do this, I need to mention one final aspects of perceptual inference, which has a lot to do with Karl Friston, who's done a lot of work in the Bayesian brain UCL here in London. And if we think of the brain as being in the business of minimising prediction errors, this can be done either by updating our perceptual predictions, which is what I've been talking about so far. And this what Helmholtz said. Or we can minimise prediction errors by making actions. We can change what we predict, or we can make an action so that our predictions come true. You can change with sensory input, or you can change what you believe about your sensory input. One point of doing this is, that you can make actions, then, to find out more about the world that surrounds you. And this is what Helmholtz has in mind when he says that each movement we make, by which we alter the appearance of objects, should be thought of as an experiment designed to test whether we've understood correctly the invariant relations of the phenomena before us. Which Gregory, much later, said something similar when he talked about perception as hypothesis testing. The point of this is, that we make eye movements, and other kinds of movements, to understand what the world is like. That, in fact, there was is tomato there, for instance. But there's another way to think about active inference, which is that, when we minimise prediction error, what we're actually doing is controlling a sensory variable. We're preventing it from changing, because we're making our prediction about what it is come true. And this is the use of active inference, to control or regulate something, rather than to understand what the causes of that something are. And this brings a very different tradition to mind, which is 20th century cybernetics. And this is Ross Ashby, who was a pioneer of this way of thinking. And he, with Roger Conan right at the end of his life, wrote a paper. The title of the paper, was "Every good regulator of a system must be a model of that system". The idea here is, if you want to regulate something very precisely, then you need a good model of what effects that system. Now you could apply this idea to the external world, as well. When you try to catch a cricket ball, you are actually trying to control the level of the angle above the horizon. But it applies more naturally, I think, to the internal state of our body. So really, what matters about my internal physiological condition, I don't really need to know exactly what it's like inside my body, and care about it. But I need to control it, and my brain needs to regulate it. So this way of thinking about active inference applies more naturally to interoception. Think about it this way that. Having good predictive models are always useful, but we can have a pendulum that swings, on the one hand, towards control. We can use these predictive models for control, and that's more applied to the state of our internal body. Or we can swing the other way, and think about perception, understanding. You could think of the instrumental, epistemic ways of thinking about the role of action and perception. And this brings to mind-- I mentioned Karl Friston. He's come up with this thing called the free energy principle. And I can only nod to the vast body of work he's done here on this. With the slogan, which is that organisms, over the long run, maintain themselves in states in which they expect to be in, in virtue of having good predictive models about their own internal condition. So this takes us right back to Descartes, but in a very different way. As I said right at the beginning of this lecture, for Descartes our physiological reality was rather irrelevant to our minds, our rationality, our consciousness. This is a quote from a 1968 paper on his beast machine argument, "without minds to direct their bodily movements, animals must be regarded as unthinking, unfeeling machines that move like clockwork." Now I think if you try to think how this idea of our predictive models controlling our internal physiological states, and the resulting experiences that perceptual content that might give rise to, you can make the opposite case. And the opposite case would be that conscious self-hood emerges because of, and not in spite of, the fact that we are beast machines. And I think this is a deeply embodied view of consciousness and self, and it speaks to this fundamental link in continuity between what it is to be alive, what it is to have a mind, and what it is to be a conscious self. So I repeat, the third take-home message should make even more sense now. That I predict myself, therefore I am. And I am a conscious self because I'm a bag of self-fulfilling predictions about my own physiological persistence over time. Now why does any of this matter? It's a lot of interesting ideas, but why should we be interested in studying consciousness? Well it's a very interesting thing, I hope I've convinced you. But there are lots of practical reasons to be interested as well. There are between 20 and 60,000 patients in the UK alone, who are in disorders of consciousness. You are in the vegetative state, or in coma, or in some other severely abnormal state of consciousness. Having better measures of conscious level, as I described at the beginning, is going to really change again, and how we treat people like this. And of course, in psychiatry. Psychiatric disorders are increasing that prevalence across all modern societies, and it's estimated one in six of us, at any one time, are suffering from a psychiatric condition. And understanding the mechanisms that underlie conscious content and conscious self, because a lot of psychiatric disorders include disturbances of the way we experience our body, even though that might not be the most obvious symptom, can help us understand the mechanisms involved in psychiatric disorders, not just the symptoms. There are also some more general reasons for studying consciousness, which bring up some ethical questions. When does consciousness emerge in development on newborn babies conscious? Or does consciousness start even in the womb? Maybe different dimensions of consciousness emerge at different times. Are other animals conscious? Well I think it can make a very good case for mammals and primates, but what about the octopus? The octopus has more neurons in its arms than in its central brain. They're very smart creatures. Here, you have to ask the question not only, what is it like to be an octopus, but what is it like to be an octopus arm? And finally, with the rise of artificial intelligence, we should begin to ask questions about what would it take for a machine to have some kind of subjective experience. I don't think we're anywhere near that yet, but we should consider what science can tell us about its possibility, because that would raise some very, very tricky ethical questions. But, fundamentally, consciousness remains fascinating for me for the same reason that it's motivated people throughout the ages. I mean, Hippocrates, the founder of modern medicine, put it one way. He said, from the brain and from the brain alone arise our sorrows, our joys. And he also had his first view of psychiatry that madness comes from its moistness. And then Francis Crick, in the 1990s, who I mentioned in the beginning. He gave birth, if you like, to the modern neuroscience of consciousness. He said much the same thing in his astonishing hypothesis. But there is this mystery and wonder still, about how the biological machinery inside our heads gives rise to the rich inner universe that makes life worth living. And despite this mystery, modern science is making progress. I hope I've given you a flavour, even though we don't understand how consciousness happens, we can begin to understand its mechanisms. So we should not be afraid of naturalising consciousness. It's not a bad thing to understand its basis in the material world. As so often in science, with greater understanding comes a larger sense of wonder, and a greater realisation that we are part of, and not apart from, the rest of nature. [AUDIENCE APPLAUSE]