B1 中級 其他腔 216 分類 收藏
開始影片後,點擊或框選字幕可以立即查詢單字
字庫載入中…
回報字幕錯誤
[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