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We like to believe we're in control of everything we do, | 0:00:02 | 0:00:06 | |
everything we think and everything we feel. | 0:00:06 | 0:00:09 | |
But scientists are discovering that at every moment of our lives, | 0:00:11 | 0:00:17 | |
an unseen presence is guiding us all. | 0:00:17 | 0:00:20 | |
Now, they're exploring the secret world of your unconscious mind. | 0:00:25 | 0:00:29 | |
It's why we feel a certain way, why we think a certain way, | 0:00:29 | 0:00:34 | |
it's why we are the way we are. | 0:00:34 | 0:00:38 | |
Long associated with dark desires, | 0:00:38 | 0:00:41 | |
the real nature of the unconscious is becoming clear. | 0:00:41 | 0:00:45 | |
The unconscious, it's not a primal, unruly, animal thing. | 0:00:45 | 0:00:49 | |
It's, in fact, one of the most sophisticated things we have. | 0:00:49 | 0:00:53 | |
New experiments are now revealing that what you think you do | 0:00:55 | 0:01:01 | |
and what you really do can be very different. | 0:01:01 | 0:01:06 | |
Most of the time we're on cruise control. | 0:01:06 | 0:01:09 | |
From what you eat to who you love, | 0:01:09 | 0:01:12 | |
your unconscious can actually call the shots. | 0:01:12 | 0:01:17 | |
And because it is so powerful, | 0:01:17 | 0:01:20 | |
scientists are finding ways to harness its hidden potential. | 0:01:20 | 0:01:24 | |
If you think that the internet and Facebook have caused a revolution, | 0:01:24 | 0:01:27 | |
wait until you see what happens when we really understand the human brain. | 0:01:27 | 0:01:31 | |
If you think you're really in control of your life, | 0:01:31 | 0:01:36 | |
you may have to think again. | 0:01:36 | 0:01:39 | |
A normal street in a normal town. | 0:01:49 | 0:01:52 | |
But there's more here than meets the eye. | 0:01:54 | 0:01:57 | |
Each day, life whirls around you in a hectic blur. | 0:02:04 | 0:02:09 | |
So just stop. | 0:02:11 | 0:02:12 | |
Take a moment. | 0:02:15 | 0:02:17 | |
Have a proper look around. | 0:02:18 | 0:02:20 | |
It really is a busy, cluttered world out there. | 0:02:22 | 0:02:25 | |
How much of all this are you actually aware of? | 0:02:26 | 0:02:31 | |
Scientists are trying to find out. | 0:02:34 | 0:02:37 | |
They're investigating the limits of how much anyone can consciously take in at once. | 0:02:39 | 0:02:44 | |
We have a sense of seeing this continuous world | 0:02:48 | 0:02:51 | |
that's unravelling continuously around us. | 0:02:51 | 0:02:54 | |
And that's probably not what we're picking up from the world at all. | 0:02:54 | 0:02:58 | |
For example, we move our eyes about five times a second - | 0:02:58 | 0:03:01 | |
incredibly rapid eye movements. | 0:03:01 | 0:03:03 | |
It's probably the fastest movements that our bodies can make, | 0:03:03 | 0:03:06 | |
these ballistic eye movements. | 0:03:06 | 0:03:08 | |
What we're really doing is taking snapshots every time we glance at something | 0:03:08 | 0:03:13 | |
and in between, where the world would be whizzing by our retinas, we're blind. | 0:03:13 | 0:03:17 | |
And then if we look within a given snapshot, | 0:03:17 | 0:03:20 | |
you think, at least within a given snapshot this is the world and I sense it, | 0:03:20 | 0:03:24 | |
but when we try to get down and measure what a person actually takes in in any given glance, | 0:03:24 | 0:03:29 | |
it's hard to estimate. | 0:03:29 | 0:03:31 | |
Finding out how much someone can take in from the snapshot | 0:03:34 | 0:03:36 | |
of a single glance can reveal their brain's ability. | 0:03:36 | 0:03:42 | |
-OK, George, do you want to take a seat? -Yeah. | 0:03:42 | 0:03:45 | |
It's a subject being studied in the University of Oxford's Brain and Cognition Laboratory. | 0:03:45 | 0:03:50 | |
We're going to try not to squish your head | 0:03:50 | 0:03:52 | |
so let me know when you touch. | 0:03:52 | 0:03:54 | |
Graduate student George is today's guinea pig. | 0:03:54 | 0:03:58 | |
I think you're there, yeah. | 0:03:58 | 0:04:00 | |
I'm also going to give you this fibre optic response pad. | 0:04:00 | 0:04:04 | |
OK. | 0:04:04 | 0:04:07 | |
Using these four shapes on screen, | 0:04:07 | 0:04:10 | |
today's test will find out how much George can consciously take in. | 0:04:10 | 0:04:14 | |
He'll have 200 milliseconds, the duration of single glance, | 0:04:14 | 0:04:19 | |
to remember the shapes' positions. | 0:04:19 | 0:04:22 | |
Just one will then reappear | 0:04:23 | 0:04:27 | |
but its orientation has changed. | 0:04:27 | 0:04:30 | |
George's task, based on that brief glimpse, | 0:04:31 | 0:04:36 | |
is to choose whether it's been rotated left or right. | 0:04:36 | 0:04:39 | |
This is how it first appeared. | 0:04:40 | 0:04:44 | |
In this case, it was rotated to the right. | 0:04:44 | 0:04:47 | |
By doing this, we'll be able to compute how many of these four objects | 0:04:48 | 0:04:54 | |
he was actually able to hold in his mind. | 0:04:54 | 0:04:56 | |
Try it yourself. | 0:04:56 | 0:04:58 | |
Remember, you have to decide whether the object that reappears | 0:04:58 | 0:05:04 | |
has rotated left or right from its original position. | 0:05:04 | 0:05:08 | |
Left. | 0:05:15 | 0:05:16 | |
Try again. | 0:05:16 | 0:05:18 | |
Left again. | 0:05:22 | 0:05:24 | |
Not easy, is it? | 0:05:24 | 0:05:27 | |
Most of us would probably think before an experiment like that | 0:05:28 | 0:05:32 | |
that any one of us can hold four simple coloured shapes in mind | 0:05:32 | 0:05:35 | |
and be able to respond about them after a second. | 0:05:35 | 0:05:38 | |
In fact, we see that not to be the case. | 0:05:38 | 0:05:41 | |
George had to hold in mind the position of just four shapes. | 0:05:41 | 0:05:46 | |
He couldn't do it. He couldn't even manage three. | 0:05:46 | 0:05:50 | |
Averaged over the test, he remembered 2.8. | 0:05:51 | 0:05:55 | |
This figure represents the approximate number of things | 0:05:55 | 0:05:58 | |
that George's brain can consciously deal with at any one time. | 0:05:58 | 0:06:02 | |
And this result is typical for everyone. | 0:06:02 | 0:06:07 | |
This simple screen contains more things | 0:06:07 | 0:06:09 | |
than you can consciously handle. | 0:06:09 | 0:06:12 | |
Your conscious mind can cope with no more than two or three tasks at once. | 0:06:12 | 0:06:17 | |
So it just shows us how amazingly limited our perceptual awareness is | 0:06:18 | 0:06:23 | |
even once we've stripped down the world to, you know, an absurd level. | 0:06:23 | 0:06:28 | |
So take another look around. | 0:06:31 | 0:06:33 | |
Even now, there's more going on than you can consciously take in. | 0:06:35 | 0:06:39 | |
The sense that you're aware of everything occurring around you | 0:06:40 | 0:06:44 | |
is nothing more than one of life's greatest illusions. | 0:06:44 | 0:06:47 | |
But if your conscious mind can deal with only a fraction of the things that happen to you each day, | 0:06:50 | 0:06:55 | |
something else must be responsible for all the rest. | 0:06:55 | 0:07:00 | |
And this is where the hidden processes | 0:07:03 | 0:07:06 | |
of your unconscious mind come in. | 0:07:06 | 0:07:09 | |
Often associated with dreams and repressed desires, | 0:07:11 | 0:07:15 | |
the unconscious is now starting to reveal its true power. | 0:07:15 | 0:07:20 | |
But how big a role do scientists think it plays in your life? | 0:07:24 | 0:07:29 | |
Imagine that sheet of paper represents everything the brain can do, | 0:07:34 | 0:07:38 | |
how much do you think is conscious and how much is unconscious? | 0:07:38 | 0:07:42 | |
Wow. That's an interesting question. | 0:07:43 | 0:07:46 | |
How much is conscious and how much is not conscious? | 0:07:46 | 0:07:50 | |
You're not serious. | 0:07:52 | 0:07:55 | |
You are? | 0:07:55 | 0:07:57 | |
Now that's a very tricky thing to do. | 0:07:57 | 0:07:59 | |
That is very interesting. | 0:07:59 | 0:08:02 | |
I guess, if I had to guess... | 0:08:03 | 0:08:06 | |
..I would say that if this is everything the brain can do... | 0:08:07 | 0:08:11 | |
..about this much... | 0:08:12 | 0:08:16 | |
is conscious. | 0:08:16 | 0:08:17 | |
Erm, I would say maybe something like that. | 0:08:17 | 0:08:24 | |
Out of the whole bit of paper. | 0:08:25 | 0:08:27 | |
I would say about this much is conscious. | 0:08:27 | 0:08:32 | |
So if this whole sheet of paper was...? OK. | 0:08:33 | 0:08:37 | |
I will probably draw something small in the middle like that | 0:08:42 | 0:08:47 | |
to represent the conscious bit. | 0:08:47 | 0:08:49 | |
That's my...guess. | 0:08:49 | 0:08:52 | |
I have no idea. | 0:08:53 | 0:08:55 | |
Scientists agree that the role played by your conscious mind | 0:09:01 | 0:09:05 | |
is much smaller than previously thought... | 0:09:05 | 0:09:08 | |
..which raises a puzzling question. | 0:09:09 | 0:09:12 | |
Are you in control of your unconscious | 0:09:12 | 0:09:15 | |
or is it in control of you? | 0:09:15 | 0:09:19 | |
To find out, scientists need to reveal the strategies it uses to guide you. | 0:09:23 | 0:09:29 | |
The problem is, unconscious strategies are shrouded in secrecy. | 0:09:30 | 0:09:34 | |
Here in Ohio, Dr Dennis Shaffer is attempting to reveal them | 0:09:37 | 0:09:42 | |
in an unusual experiment. | 0:09:42 | 0:09:44 | |
He has spent his career investigating the hidden workings of the unconscious mind. | 0:09:47 | 0:09:52 | |
The strategies that are used by the brain, | 0:09:52 | 0:09:54 | |
we're typically not consciously aware of. | 0:09:54 | 0:09:57 | |
There's a huge discrepancy between the strategies that we use | 0:09:57 | 0:10:01 | |
and, kind of, our conscious expectations of those. | 0:10:01 | 0:10:04 | |
These volunteers don't realise it, | 0:10:07 | 0:10:09 | |
but Dennis will be comparing what they consciously think they do | 0:10:09 | 0:10:13 | |
with the real strategies at work in their unconscious minds. | 0:10:13 | 0:10:17 | |
And, to do it, he'll be using... | 0:10:17 | 0:10:20 | |
..this. | 0:10:21 | 0:10:23 | |
What you're going to be doing today is chasing this toy helicopter. | 0:10:23 | 0:10:27 | |
We're going to put this video camera over your head | 0:10:27 | 0:10:30 | |
so we get the perspective of what you're seeing. | 0:10:30 | 0:10:33 | |
Each participant believes they have their own personal strategy for catching the helicopter. | 0:10:36 | 0:10:41 | |
The question is, is this what's really going on in their heads? | 0:10:42 | 0:10:48 | |
First up, Trish. Her strategy is speed. | 0:10:48 | 0:10:52 | |
As far as key strategy, I'd say you've got to focus on | 0:10:52 | 0:10:57 | |
keeping your eye on the helicopter and keeping a steady speed. | 0:10:57 | 0:11:02 | |
Next up, Sid. His strategy is all about positioning. | 0:11:08 | 0:11:13 | |
My strategy's to make sure each time it moves, | 0:11:13 | 0:11:17 | |
I move just as quickly to stay below the helicopter. | 0:11:17 | 0:11:22 | |
For Keith, it's all about angles. | 0:11:26 | 0:11:31 | |
You've got to get it right on its angle of approach towards the ground, | 0:11:31 | 0:11:35 | |
looking at the whole line of the arc. | 0:11:35 | 0:11:39 | |
So do these personal strategies represent what's actually happening in their unconscious minds? | 0:11:41 | 0:11:48 | |
Dennis now has enough head camera footage to find out. | 0:11:48 | 0:11:53 | |
So what we're doing is identifying where the helicopter is positioned | 0:11:53 | 0:11:58 | |
relative to the background scenery from the pursuer's perspective. | 0:11:58 | 0:12:03 | |
Having chosen a background point for reference, | 0:12:05 | 0:12:08 | |
Dennis marks the helicopter's position | 0:12:08 | 0:12:10 | |
and then advances the video a few frames. | 0:12:10 | 0:12:13 | |
The helicopter's new position is now recorded. | 0:12:16 | 0:12:19 | |
The process is repeated, | 0:12:21 | 0:12:23 | |
gradually mapping what the flight path of the helicopter | 0:12:23 | 0:12:26 | |
looked like to the pursuer throughout the entire pursuit. | 0:12:26 | 0:12:30 | |
Despite the random path taken by the helicopter, | 0:12:31 | 0:12:33 | |
a pattern soon begins to emerge. | 0:12:33 | 0:12:36 | |
What this shows is that what the pursuers are doing is moving in such a way | 0:12:39 | 0:12:44 | |
so as to keep the toy helicopter appearing to move | 0:12:44 | 0:12:48 | |
relative to the background scenery in a straight line. | 0:12:48 | 0:12:52 | |
Remarkably, the exact same results are seen in every single person, | 0:12:55 | 0:12:59 | |
regardless of the apparent chaos of their pursuit. | 0:12:59 | 0:13:03 | |
As the helicopter moved, each of them adjusted their position | 0:13:04 | 0:13:08 | |
so that, to them, it appeared to fly in a straight line | 0:13:08 | 0:13:11 | |
against the background scenery. | 0:13:11 | 0:13:14 | |
'From an outside appearance they may be running in different paths, | 0:13:14 | 0:13:19 | |
'but the one constant is that they keep the toy helicopter | 0:13:19 | 0:13:22 | |
'appearing to move in a straight line.' | 0:13:22 | 0:13:25 | |
A beautifully simple, unconscious algorithm | 0:13:26 | 0:13:29 | |
hardwired into the head of every pursuer | 0:13:29 | 0:13:31 | |
is responsible for getting them to the right spot. | 0:13:31 | 0:13:36 | |
THEY APPLAUD AND WHISTLE | 0:13:36 | 0:13:37 | |
This is not something that they're consciously aware that they're doing. | 0:13:37 | 0:13:41 | |
It's all about patience. | 0:13:41 | 0:13:42 | |
And you can demonstrate that just by asking them how they do it. | 0:13:42 | 0:13:47 | |
Keeping a steady speed. | 0:13:47 | 0:13:48 | |
And it's not going to match up to this at all. | 0:13:48 | 0:13:50 | |
Go criss-cross, feet over feet. | 0:13:50 | 0:13:53 | |
So, although you might think you're conscious of everything you do, | 0:13:54 | 0:13:59 | |
this experiment reveals that your unconscious is often in control... | 0:13:59 | 0:14:04 | |
employing its own rapid, efficient strategies | 0:14:04 | 0:14:07 | |
to guide your every step through life. | 0:14:07 | 0:14:09 | |
But how does your unconscious make these split-second decisions? | 0:14:16 | 0:14:21 | |
For scientists, it's a complex question. | 0:14:22 | 0:14:25 | |
So, for help, they're turning to creatures | 0:14:27 | 0:14:30 | |
that might display the same characteristics | 0:14:30 | 0:14:32 | |
as the neurons which make up the brain. | 0:14:32 | 0:14:35 | |
Rock ants. | 0:14:35 | 0:14:37 | |
At little over 2mm long, rock ants don't amount to much on their own | 0:14:39 | 0:14:45 | |
but their collective decision-making behaviour | 0:14:45 | 0:14:48 | |
is providing insights into the sophisticated way | 0:14:48 | 0:14:51 | |
that your unconscious mind might work. | 0:14:51 | 0:14:55 | |
What we can do with these ants is... | 0:14:55 | 0:14:59 | |
we can hold an entire ant colony in a small Petri dish, like this, | 0:14:59 | 0:15:05 | |
and we can think of each individual worker as an excitable, activatable unit | 0:15:05 | 0:15:10 | |
and that has a parallel with neurons in our brains, | 0:15:10 | 0:15:13 | |
that are units that are wired together | 0:15:13 | 0:15:15 | |
that get more and more excited and can excite one another. | 0:15:15 | 0:15:19 | |
But studying the similarities between ant decision-making | 0:15:22 | 0:15:25 | |
and the workings of the brain is no easy job. | 0:15:25 | 0:15:29 | |
To do this, ants need to be identifiable... | 0:15:31 | 0:15:34 | |
..and that means each one needs to be given | 0:15:36 | 0:15:38 | |
a microscopic radio tag "rucksack". | 0:15:38 | 0:15:41 | |
It's is an intricate task. | 0:15:43 | 0:15:45 | |
Each ant is anaesthetised before the radio tag is glued to its back. | 0:15:47 | 0:15:51 | |
These transmitters, just half a millimetre across, | 0:15:54 | 0:15:58 | |
will allow each ant to be tracked. | 0:15:58 | 0:16:00 | |
Tagging complete, the entire colony is now presented with a momentous decision - | 0:16:01 | 0:16:07 | |
choosing a new home. | 0:16:07 | 0:16:09 | |
Right, so what we have to do is bring in the colony | 0:16:10 | 0:16:15 | |
that is going to have to make the decision | 0:16:15 | 0:16:18 | |
and they've been living very nicely in this microscope slide nest | 0:16:18 | 0:16:21 | |
and essentially what we're going to do is be a little bit beastly to them | 0:16:21 | 0:16:26 | |
but not too much, we're going to actually have to | 0:16:26 | 0:16:29 | |
destroy this nest by taking off the roof. | 0:16:29 | 0:16:31 | |
So, in a flash, this colony will be homeless | 0:16:31 | 0:16:34 | |
and they'll have to find a new nest. | 0:16:34 | 0:16:37 | |
So, there I go... | 0:16:38 | 0:16:39 | |
..and all of a sudden there are draughts racing in there | 0:16:40 | 0:16:44 | |
and they, you know, howling gales | 0:16:44 | 0:16:46 | |
from the perspective of an individual ant, | 0:16:46 | 0:16:49 | |
and they're spilling out in all directions, | 0:16:49 | 0:16:51 | |
looking for a new place to live. | 0:16:51 | 0:16:53 | |
The ants' search will take them to the other end of the arena, | 0:16:53 | 0:16:57 | |
where Professor Franks has placed two alternative new homes. | 0:16:57 | 0:17:01 | |
Each has a laser radio tag reader over the door | 0:17:01 | 0:17:05 | |
to monitor which ants visit. | 0:17:05 | 0:17:08 | |
But the similarities end here. | 0:17:08 | 0:17:11 | |
The left-hand nest is darker - a more likely choice for the ants. | 0:17:11 | 0:17:16 | |
So, we're trying to give them a very obvious and simple choice | 0:17:17 | 0:17:20 | |
between a really good nest and a rather mediocre one | 0:17:20 | 0:17:23 | |
and we'll see how they perform. | 0:17:23 | 0:17:25 | |
It doesn't take long for individual ants to discover particular nests, | 0:17:27 | 0:17:33 | |
but how do they collectively decide which is best? | 0:17:33 | 0:17:37 | |
The colony's dilemma represents the instinctive, split-second choices | 0:17:37 | 0:17:41 | |
which your unconscious faces each day. | 0:17:41 | 0:17:44 | |
To solve the problem, | 0:17:47 | 0:17:48 | |
the ants now start working together democratically, | 0:17:48 | 0:17:52 | |
just like neurons, | 0:17:52 | 0:17:53 | |
to reach a consensus on the best possible decision. | 0:17:53 | 0:17:56 | |
If an ant likes what it finds, | 0:17:58 | 0:18:00 | |
it returns to the old nest to recruit a follower, | 0:18:00 | 0:18:03 | |
which it leads back to the new site. | 0:18:03 | 0:18:05 | |
Here, the second ant will conduct its own independent survey. | 0:18:08 | 0:18:13 | |
So, basically, you've got two populations that are being recruited, | 0:18:13 | 0:18:16 | |
one to this particular nest | 0:18:16 | 0:18:19 | |
and the other population to the alternative. | 0:18:19 | 0:18:23 | |
As the experiment progresses, | 0:18:23 | 0:18:25 | |
the population of ants in favour of the darker nest snowballs. | 0:18:25 | 0:18:29 | |
By sharing information, the ants are building up a group picture | 0:18:31 | 0:18:35 | |
of their surrounding environment. | 0:18:35 | 0:18:38 | |
Soon they're finding so many other ants in the darker nest | 0:18:38 | 0:18:43 | |
that they pass a threshold, the quorum threshold, | 0:18:43 | 0:18:46 | |
and the group decides that this must be the best choice. | 0:18:46 | 0:18:50 | |
This will be their new home. | 0:18:50 | 0:18:52 | |
When it comes to decision-making, the wisdom of the crowd prevails. | 0:18:56 | 0:19:01 | |
In ant colony and brain, it's a wonderfully efficient system. | 0:19:01 | 0:19:05 | |
In both systems, you can have these populations | 0:19:08 | 0:19:10 | |
growing up to a particular threshold, | 0:19:10 | 0:19:12 | |
a sort of quorum threshold, if you will, where it's a tipping point, | 0:19:12 | 0:19:16 | |
where the whole system will change from one behaviour to another. | 0:19:16 | 0:19:20 | |
Most remarkably of all, both systems can vary the threshold | 0:19:23 | 0:19:27 | |
based on the urgency of the decision. | 0:19:27 | 0:19:29 | |
'The quorum isn't fixed, it's beautifully flexible, | 0:19:32 | 0:19:35 | |
'they can lower the threshold in an emergency' | 0:19:35 | 0:19:38 | |
or they can raise the threshold when they've got all the time in the world, | 0:19:38 | 0:19:41 | |
it's a beautiful decision-making system. | 0:19:41 | 0:19:44 | |
This ability to weigh up the pros and cons | 0:19:47 | 0:19:49 | |
as everything changes around you | 0:19:49 | 0:19:51 | |
is one of your unconscious mind's most vital skills. | 0:19:51 | 0:19:55 | |
Yet even this only scratches the surface of how it shapes your life. | 0:19:56 | 0:20:01 | |
Because every day your unconscious can resort to the slyest of tricks. | 0:20:04 | 0:20:09 | |
Wherever life takes you, | 0:20:18 | 0:20:19 | |
your unconscious will be subtly shaping the illusion that you call reality. | 0:20:19 | 0:20:24 | |
Take a place like this, a world of temptation. | 0:20:26 | 0:20:30 | |
Now you know that life's little luxuries come with a health warning | 0:20:31 | 0:20:36 | |
but chances are you indulge anyway, | 0:20:36 | 0:20:40 | |
all the while remaining optimistic about your future well-being. | 0:20:40 | 0:20:43 | |
Dr Tali Sharot wants to know why. | 0:20:45 | 0:20:47 | |
'Think, for example, about eating food that's not good for you,' | 0:20:50 | 0:20:54 | |
like these lovely cupcakes, or smoking, or unprotected sex. | 0:20:54 | 0:21:00 | |
All of these examples are examples in which people act in a way | 0:21:00 | 0:21:05 | |
that's maybe rewarding for them at present | 0:21:05 | 0:21:07 | |
but can be very harmful in the future. | 0:21:07 | 0:21:10 | |
It seems we're all optimists. | 0:21:11 | 0:21:13 | |
Despite the risks, we just carry on anyway. | 0:21:13 | 0:21:17 | |
From health to finance, to how we drive, | 0:21:18 | 0:21:22 | |
negative information doesn't really sink in. | 0:21:22 | 0:21:25 | |
'We go through life experiencing heartache and failure' | 0:21:25 | 0:21:28 | |
but still we remain optimistic and that's a great puzzle. | 0:21:28 | 0:21:31 | |
How is it that we remain optimistic in the face of reality? | 0:21:31 | 0:21:35 | |
To find out why takes scientists deep into the machinery of the mind. | 0:21:43 | 0:21:49 | |
And finally, I'm going to put this on top... | 0:21:49 | 0:21:53 | |
Today, Tali is using a brain scanner | 0:21:53 | 0:21:56 | |
to find out why we ignore so much of the negative information that comes our way. | 0:21:56 | 0:22:01 | |
OK, Tom, so, we're about to start the experiment now. | 0:22:08 | 0:22:11 | |
To do this, she'll be asking volunteer Tom | 0:22:12 | 0:22:14 | |
to predict his chances of experiencing | 0:22:14 | 0:22:17 | |
a selection of 80 different negative events in the future. | 0:22:17 | 0:22:21 | |
So, we're recording Tom's brain activity and what you can see here | 0:22:24 | 0:22:27 | |
is actually what Tom is looking at in the scanner, through his mirror. | 0:22:27 | 0:22:32 | |
For example, he will see the word "cancer", | 0:22:32 | 0:22:34 | |
and then he will have to estimate how likely it is | 0:22:34 | 0:22:36 | |
that he will suffer from cancer in his lifetime. | 0:22:36 | 0:22:40 | |
Tom reckons his chance of cancer is 18% and types it in. | 0:22:40 | 0:22:45 | |
OK, so, now, we're going to show him the average likelihood | 0:22:47 | 0:22:50 | |
of suffering from cancer, which is about 30% in the Western world. | 0:22:50 | 0:22:54 | |
Tom has a moment to realise that he's underestimated his chance of cancer - | 0:22:55 | 0:23:00 | |
he's been too optimistic. | 0:23:00 | 0:23:02 | |
He's then presented with the next of the 80 negative events. | 0:23:04 | 0:23:08 | |
With each one, he again gives his prediction | 0:23:09 | 0:23:14 | |
before finding out the real statistic. | 0:23:14 | 0:23:17 | |
When he reaches the last of the 80 events, | 0:23:22 | 0:23:26 | |
the same list is repeated and he has to predict his chances again. | 0:23:26 | 0:23:30 | |
'And what we're interested in,' | 0:23:32 | 0:23:33 | |
is whether Tom is going to use information that we gave him, | 0:23:33 | 0:23:36 | |
in order to change his beliefs. | 0:23:36 | 0:23:38 | |
Each time this experiment is performed, | 0:23:42 | 0:23:45 | |
the results are most surprising. | 0:23:45 | 0:23:48 | |
So what we found was that when you give people positive information about the future, | 0:23:49 | 0:23:54 | |
for example, you tell them | 0:23:54 | 0:23:55 | |
that their likelihood of suffering from Alzheimer's is lower | 0:23:55 | 0:23:58 | |
than what they thought, they take on board the information. | 0:23:58 | 0:24:01 | |
We all tend to update our views about the future | 0:24:01 | 0:24:05 | |
when we receive new information suggesting things will turn out better for us than we thought. | 0:24:05 | 0:24:10 | |
'However, when you give people' | 0:24:10 | 0:24:11 | |
negative information about the future, | 0:24:11 | 0:24:14 | |
for example, if they believe that their chances of suffering from Alzheimer's is only two percent | 0:24:14 | 0:24:19 | |
and we tell them, well, the average is much higher than that, | 0:24:19 | 0:24:23 | |
for example, it's ten percent, so this is negative information, | 0:24:23 | 0:24:26 | |
they don't change their beliefs | 0:24:26 | 0:24:27 | |
and they stick to this very optimistic view of the world. | 0:24:27 | 0:24:31 | |
The scans show that the part of the brain | 0:24:34 | 0:24:37 | |
that considers negative information about the future | 0:24:37 | 0:24:40 | |
seems to malfunction. | 0:24:40 | 0:24:42 | |
The part that deals with positive information | 0:24:42 | 0:24:44 | |
appears much more active. | 0:24:44 | 0:24:47 | |
It suggests that your brain wilfully ignores negative things | 0:24:48 | 0:24:52 | |
and maintains a rose-tinted and inaccurate view of the world instead. | 0:24:52 | 0:24:56 | |
It looks like the brain is not doing what it's supposed to be doing | 0:25:00 | 0:25:05 | |
but the reason that our brain tricks us | 0:25:05 | 0:25:07 | |
is because if we expect positive events in our future, | 0:25:07 | 0:25:10 | |
stress and anxiety is reduced and that's good for our health. | 0:25:10 | 0:25:14 | |
And there's another reason too. | 0:25:15 | 0:25:19 | |
'I think if we expect to get ahead, if you expect the gold medal,' | 0:25:19 | 0:25:23 | |
that motivates you to put in the effort to train, | 0:25:23 | 0:25:27 | |
'you know, for four years before the Olympics, for example. | 0:25:27 | 0:25:30 | |
'So, you might, at the end, not get the golden medal' | 0:25:30 | 0:25:32 | |
but the idea is that you need to expect the gold medal | 0:25:32 | 0:25:34 | |
in order to get the silver. | 0:25:34 | 0:25:36 | |
'And so it acts as a motivation. | 0:25:36 | 0:25:39 | |
'And that's why, I think, the brain has evolved to become optimistic.' | 0:25:40 | 0:25:45 | |
This in-built tendency to optimistically ignore starkly obvious risks | 0:25:48 | 0:25:53 | |
has been essential to our success as a species. | 0:25:53 | 0:25:56 | |
If you think about things such as our ancestors deciding to go | 0:25:59 | 0:26:02 | |
out of Africa and exploring the rest of the world, | 0:26:02 | 0:26:05 | |
in order to explore something new, | 0:26:05 | 0:26:07 | |
you have to imagine that there is something out there for you to find. | 0:26:07 | 0:26:11 | |
Something novel, and something better than what you have now | 0:26:11 | 0:26:14 | |
because otherwise there is no need to go and discover other parts of the world, | 0:26:14 | 0:26:19 | |
or even other parts of the universe. | 0:26:19 | 0:26:21 | |
This is one of the most ingenious tricks of the unconscious. | 0:26:29 | 0:26:32 | |
By making you view the world through rose-tinted glasses, | 0:26:32 | 0:26:36 | |
it keeps you striving for a better future. | 0:26:36 | 0:26:39 | |
Taken together, the latest discoveries are starting to reveal | 0:26:51 | 0:26:54 | |
that the sense you're consciously in control of everything you do | 0:26:54 | 0:26:58 | |
is just an illusion. | 0:26:58 | 0:27:00 | |
It's a sophisticated and intricate one but it's no luxury, | 0:27:00 | 0:27:05 | |
it's a necessity | 0:27:05 | 0:27:08 | |
because your very survival has long depended upon everything | 0:27:08 | 0:27:11 | |
that your unconscious does for you behind the scenes. | 0:27:11 | 0:27:15 | |
It's something that scientists are investigating in Oxford. | 0:27:20 | 0:27:25 | |
Taking part is GY, a volunteer who wishes to stay anonymous. | 0:27:26 | 0:27:30 | |
When he was young, his visual cortex, | 0:27:33 | 0:27:35 | |
the part of the brain that deals with vision, | 0:27:35 | 0:27:38 | |
was damaged in an accident. | 0:27:38 | 0:27:40 | |
In both eyes he's partly blind. | 0:27:40 | 0:27:43 | |
He's able to see only to the left, not the right. | 0:27:43 | 0:27:46 | |
'I don't actually see anything in my blind field. | 0:27:47 | 0:27:51 | |
'It's a very strange phenomena.' | 0:27:51 | 0:27:53 | |
Yet today's experiment will attempt to show something remarkable | 0:27:53 | 0:27:58 | |
that, in the areas where he's blind, | 0:27:58 | 0:28:01 | |
GY is somehow, instinctively, able to see. | 0:28:01 | 0:28:04 | |
What I'm going to do is to present a stimulus | 0:28:07 | 0:28:10 | |
moving upwards or downwards in GY's blind field | 0:28:10 | 0:28:14 | |
and I'm simply going to ask him to indicate | 0:28:14 | 0:28:17 | |
whether the stimulus moves up or down. | 0:28:17 | 0:28:19 | |
'Now, this is a stimulus which he's unable to see.' | 0:28:19 | 0:28:23 | |
-Ready? -Yep. | 0:28:23 | 0:28:25 | |
Up. | 0:28:27 | 0:28:28 | |
Down. | 0:28:30 | 0:28:32 | |
Although GY can't consciously see the moving shape, | 0:28:32 | 0:28:34 | |
he is required to guess which way it moves. | 0:28:34 | 0:28:38 | |
Up. | 0:28:38 | 0:28:39 | |
Down. | 0:28:41 | 0:28:42 | |
After a number of trials, some compelling results come through. | 0:28:43 | 0:28:47 | |
He was right on 37 out of 40 trials in that run, | 0:28:50 | 0:28:54 | |
which is an extremely significant result. | 0:28:54 | 0:28:57 | |
Erm, so what this shows is that, | 0:28:57 | 0:28:59 | |
despite the fact that he's clinically blind, | 0:28:59 | 0:29:02 | |
he's capable of discriminating the direction of motion | 0:29:02 | 0:29:05 | |
of something that's moving in his blind field. | 0:29:05 | 0:29:07 | |
Remarkable. | 0:29:07 | 0:29:09 | |
This ability is known as blindsight. | 0:29:11 | 0:29:14 | |
I don't actually see anything move at all, | 0:29:17 | 0:29:20 | |
it's just an awareness of movement | 0:29:20 | 0:29:22 | |
and I can detect the direction it goes in. | 0:29:22 | 0:29:25 | |
That sounds really weird, doesn't it? | 0:29:25 | 0:29:27 | |
Somehow, GY experiences movement, | 0:29:30 | 0:29:33 | |
even though he can't properly see it himself. | 0:29:33 | 0:29:36 | |
I always refer to it as a "visual experience," | 0:29:38 | 0:29:41 | |
but I don't actually see anything. | 0:29:41 | 0:29:43 | |
Just, I know something and I don't know what, has gone up or down. | 0:29:43 | 0:29:47 | |
So where does GY's blindsight stem from | 0:29:51 | 0:29:54 | |
and why does this ability exist? | 0:29:54 | 0:29:56 | |
It all comes down to the remarkable construction of the brain itself. | 0:30:01 | 0:30:06 | |
With one hundred billion neurons connected by over | 0:30:08 | 0:30:12 | |
one hundred trillion synapses, | 0:30:12 | 0:30:14 | |
the human brain is immensely complicated. | 0:30:14 | 0:30:17 | |
So this is what a human brain looks like. | 0:30:21 | 0:30:23 | |
This is the front, this is the back, two cerebral hemispheres. | 0:30:23 | 0:30:28 | |
And if we want to understand what's going on in blindsight, | 0:30:28 | 0:30:31 | |
I need to show you a specimen that's been dissected already. | 0:30:31 | 0:30:36 | |
And this is the inner surface of the hemisphere. | 0:30:36 | 0:30:39 | |
And this region here is the primary visual cortex, | 0:30:43 | 0:30:46 | |
which is the area that's damaged in blindsight. | 0:30:46 | 0:30:50 | |
By interpreting signals flowing from the eyes, | 0:30:50 | 0:30:53 | |
the visual cortex allows us to see the outside world. | 0:30:53 | 0:30:57 | |
If it's damaged, like in GY, these signals aren't registered, | 0:30:58 | 0:31:02 | |
even if the eyes are still working. | 0:31:02 | 0:31:05 | |
But there is another, older, visual pathway from eyes to brain. | 0:31:07 | 0:31:11 | |
As it turns out, only about 90 percent of the fibres | 0:31:12 | 0:31:15 | |
leaving the eye terminate in the primary visual cortex. | 0:31:15 | 0:31:19 | |
The remainder of the fibres | 0:31:19 | 0:31:21 | |
head off to other centres in the brain. | 0:31:21 | 0:31:24 | |
Most important of these is the superior colliculus, | 0:31:24 | 0:31:26 | |
which you can see just here. | 0:31:26 | 0:31:29 | |
Its name belies its size. | 0:31:29 | 0:31:32 | |
In humans, the superior colliculus might be tiny. | 0:31:33 | 0:31:37 | |
But in evolutionary terms, it's always been vital. | 0:31:40 | 0:31:44 | |
In many other creatures it's one of the brain's biggest structures, | 0:31:44 | 0:31:49 | |
geared to rapidly orienting the eyes toward sudden movements. | 0:31:49 | 0:31:53 | |
This evolutionary remnant | 0:31:57 | 0:31:59 | |
is where GY's blindsight is thought to come from. | 0:31:59 | 0:32:03 | |
Despite not being able to properly see, | 0:32:05 | 0:32:08 | |
he retains a primal awareness of sudden movements, | 0:32:08 | 0:32:11 | |
a sense that something is there. | 0:32:11 | 0:32:14 | |
We need to not only be able to identify | 0:32:14 | 0:32:19 | |
what's out there in the visual scene, but where they are. | 0:32:19 | 0:32:21 | |
Because in the case of a predator, | 0:32:21 | 0:32:23 | |
ultimately we need to take evasive action. | 0:32:23 | 0:32:26 | |
GY's blindsight helps to show | 0:32:30 | 0:32:31 | |
that in the human brain's long history, | 0:32:31 | 0:32:34 | |
the unconscious preceded the conscious mind, | 0:32:34 | 0:32:37 | |
but it wasn't replaced by it. | 0:32:37 | 0:32:39 | |
It's still there today, hidden from view, | 0:32:40 | 0:32:45 | |
but still on the lookout for danger. | 0:32:45 | 0:32:48 | |
But there's another thing that the unconscious does for you each day. | 0:32:54 | 0:32:59 | |
Take all those complex skills you've perfected in life. | 0:33:02 | 0:33:06 | |
The truth is that once you've got the hang of them, | 0:33:08 | 0:33:13 | |
you barely have to concentrate on them at all. | 0:33:13 | 0:33:16 | |
They've become automatic, and unconsciously controlled. | 0:33:16 | 0:33:20 | |
How this happens is one of neuroscience's biggest mysteries. | 0:33:23 | 0:33:27 | |
And the place to solve it is here. | 0:33:32 | 0:33:34 | |
The problem for Professor Julien Doyon | 0:33:36 | 0:33:38 | |
is that little of what you learn in life can be done in a brain scanner. | 0:33:38 | 0:33:43 | |
Obviously you have only 60 centimetres in the scanner, | 0:33:43 | 0:33:47 | |
and so it's very difficult to study motor movements, | 0:33:47 | 0:33:52 | |
for example, movements like in golf or a tennis movement, | 0:33:52 | 0:33:55 | |
one cannot do that in the scanner. | 0:33:55 | 0:33:57 | |
The changes that happen inside your brain as you learn new, | 0:33:59 | 0:34:02 | |
automatic skills, are clearly not easy to study. | 0:34:02 | 0:34:06 | |
But a chance conversation with an old friend led Julien | 0:34:09 | 0:34:13 | |
to a most unusual solution - | 0:34:13 | 0:34:16 | |
knitting. | 0:34:16 | 0:34:18 | |
At the time, we were actually carrying on a conversation | 0:34:28 | 0:34:33 | |
like this, and he saw me knitting. | 0:34:33 | 0:34:36 | |
'I said to her,' | 0:34:36 | 0:34:37 | |
"It looks like this movement is completely automatic for you. | 0:34:37 | 0:34:42 | |
"You basically do your movements and you're able to talk." | 0:34:42 | 0:34:46 | |
And then he said, "Oh, this would be a great activity to use, | 0:34:49 | 0:34:52 | |
"but if you were in the scanner, you'd have to lie there, | 0:34:52 | 0:34:56 | |
"very, very still, not move your shoulders and knit | 0:34:56 | 0:34:59 | |
"lying on your back. Can people do that?" | 0:34:59 | 0:35:02 | |
And I said, "Well, any knitter who's automatic can do that!" | 0:35:02 | 0:35:06 | |
But simply seeing into the mind of an experienced knitter wasn't | 0:35:07 | 0:35:12 | |
enough to reveal how the process of learning a new skill occurs. | 0:35:12 | 0:35:16 | |
What Julien needed was a way of comparing how the brain | 0:35:16 | 0:35:20 | |
performs automatically with how it works when it's starting to learn. | 0:35:20 | 0:35:25 | |
It was a rather tricky task. | 0:35:25 | 0:35:28 | |
'But then Rhonda told me something very important, she said, | 0:35:28 | 0:35:31 | |
'"There are two approaches to knit,' | 0:35:31 | 0:35:35 | |
"and if I try to knit with this other approach, | 0:35:35 | 0:35:39 | |
"this other technique, that would be like starting again, | 0:35:39 | 0:35:43 | |
"I would need to think about the movements that I have to make, | 0:35:43 | 0:35:46 | |
"and learn from scratch." | 0:35:46 | 0:35:48 | |
For Julien, this was a revelation. | 0:35:51 | 0:35:53 | |
And so began one of the most | 0:35:55 | 0:35:56 | |
colourful experiments in neuroscience history. | 0:35:56 | 0:35:59 | |
Today, Julien will be scanning Rhonda's brain as she knits. | 0:36:08 | 0:36:12 | |
So Julien's going to give you the needles... | 0:36:13 | 0:36:17 | |
She will start with the style of knitting she's been doing | 0:36:17 | 0:36:20 | |
since she was a child, | 0:36:20 | 0:36:21 | |
and which is now completely automated in her unconscious mind. | 0:36:21 | 0:36:25 | |
We're all ready to start, we're going to go to the other side. | 0:36:26 | 0:36:29 | |
OK, Rhonda. How are you? | 0:36:32 | 0:36:34 | |
'I'm fine, very relaxed.' | 0:36:34 | 0:36:36 | |
OK, I'm going to ask you to produce | 0:36:36 | 0:36:39 | |
knitting movements for about 30 seconds. | 0:36:39 | 0:36:42 | |
OK, here we go... | 0:36:42 | 0:36:44 | |
So, here Rhonda is producing movements which she has been | 0:36:51 | 0:36:54 | |
practising for years that are completely automatic for her. | 0:36:54 | 0:36:58 | |
Data soon starts appearing on screen. | 0:36:59 | 0:37:01 | |
And we can see that there is a lot of activity in the striatum. | 0:37:03 | 0:37:07 | |
As Rhonda knits, the striatum, deep in the brain, | 0:37:09 | 0:37:12 | |
coordinates her complex automated movements. | 0:37:12 | 0:37:15 | |
It's a wonderfully streamlined process. | 0:37:16 | 0:37:19 | |
But what the team wants to see is what happens | 0:37:19 | 0:37:23 | |
when the learning process begins. | 0:37:23 | 0:37:25 | |
If we were then asking her to do the knitting | 0:37:26 | 0:37:29 | |
with a technique that she's not familiar with then we'd see | 0:37:29 | 0:37:32 | |
perhaps a very different pattern of activity. | 0:37:32 | 0:37:36 | |
The team now runs the test one more time. | 0:37:36 | 0:37:40 | |
-All right? -'All right.' -Here we go. | 0:37:40 | 0:37:43 | |
OK, so we're starting to see some activity | 0:37:50 | 0:37:53 | |
in the primary motor cortex. | 0:37:53 | 0:37:56 | |
And you're starting to see some activity | 0:37:56 | 0:37:59 | |
in both sides of the cerebellum, as well. | 0:37:59 | 0:38:03 | |
We think that those regions at the beginning are important | 0:38:05 | 0:38:09 | |
to try to figure out what's the best way to produce movements. | 0:38:09 | 0:38:13 | |
When you learn a skill, from knitting to juggling, | 0:38:13 | 0:38:18 | |
multiple parts of your brain, especially the cerebellum, | 0:38:18 | 0:38:22 | |
work hard to coordinate your new movements. | 0:38:22 | 0:38:25 | |
But as you practise, something profound occurs. | 0:38:28 | 0:38:32 | |
The architecture of your brain starts to change. | 0:38:35 | 0:38:38 | |
New, efficient neural networks form, a process known as plasticity. | 0:38:38 | 0:38:44 | |
It's one of neuroscience's biggest discoveries. | 0:38:45 | 0:38:49 | |
So, while you might find the process of learning hard, with perseverance, | 0:38:49 | 0:38:54 | |
your unconscious mind will rewire itself to share the load. | 0:38:54 | 0:38:59 | |
When the movements are completely automatic, it allows us | 0:38:59 | 0:39:05 | |
to free up our attentional demands for other activities. | 0:39:05 | 0:39:09 | |
And so now we can pay attention to other things that we want | 0:39:09 | 0:39:12 | |
to do in life. | 0:39:12 | 0:39:14 | |
By automating complex actions like this, your unconscious | 0:39:14 | 0:39:19 | |
frees your conscious mind, and makes you who you are. | 0:39:19 | 0:39:24 | |
The discovery of plasticity represents a new era | 0:39:33 | 0:39:36 | |
in our understanding of the human brain. | 0:39:36 | 0:39:39 | |
It reveals the power of the unconscious to adapt | 0:39:41 | 0:39:44 | |
and form new connections. | 0:39:44 | 0:39:46 | |
But scientists are wondering whether this power can get out of control. | 0:39:48 | 0:39:53 | |
It's something that doctors are researching | 0:39:57 | 0:40:00 | |
in a rather pleasant and exclusive laboratory. | 0:40:00 | 0:40:03 | |
One of America's finest golf courses, here in Arizona. | 0:40:08 | 0:40:12 | |
They're studying the curse of many experienced golfers... | 0:40:19 | 0:40:24 | |
the yips. | 0:40:24 | 0:40:26 | |
The yips is a symptom which golfers describe in which | 0:40:35 | 0:40:39 | |
they get a twisting, a twitching, a jerking movement during | 0:40:39 | 0:40:43 | |
the time of putting, and less than a second before actually | 0:40:43 | 0:40:48 | |
striking the ball, the involuntary movement occurs. | 0:40:48 | 0:40:51 | |
This uncontrollable twitch can stop the most experienced players | 0:40:53 | 0:40:57 | |
sinking the simplest putts. | 0:40:57 | 0:40:58 | |
Expert golfer Tom Wilcox knows this only too well. | 0:41:03 | 0:41:07 | |
I've been a golf professional for 40 years | 0:41:07 | 0:41:10 | |
and it certainly has been a problem in competitive situations | 0:41:10 | 0:41:14 | |
because when you get the yips, you literally | 0:41:14 | 0:41:16 | |
can feel a little jerk in your hands, | 0:41:16 | 0:41:18 | |
and you can feel it affect the blade of the putter, | 0:41:18 | 0:41:21 | |
and the ball goes off line, or goes too far, | 0:41:21 | 0:41:23 | |
or doesn't go far enough, so obviously that's more strokes, | 0:41:23 | 0:41:27 | |
and they pay money for low scores, not for high scores in golf. | 0:41:27 | 0:41:31 | |
For years, the yips has been thought of simply | 0:41:33 | 0:41:36 | |
as golfers crumbling under pressure. | 0:41:36 | 0:41:39 | |
But Dr Adler suspects that there might be more to it | 0:41:41 | 0:41:44 | |
than choking in the heat of competition. | 0:41:44 | 0:41:47 | |
His research here has taken him | 0:41:48 | 0:41:50 | |
deep into the mysterious workings of the brain itself. | 0:41:50 | 0:41:55 | |
-Make a muscle. -OK. | 0:42:00 | 0:42:04 | |
Today, Dr Adler and his assistant, Luann, | 0:42:04 | 0:42:06 | |
are attempting to see if the yips might be caused | 0:42:06 | 0:42:09 | |
by the brain getting out of control. | 0:42:09 | 0:42:12 | |
We're going to record from wrist flexor, extensor, bicep, | 0:42:14 | 0:42:19 | |
tricep and deltoid. | 0:42:19 | 0:42:20 | |
First they wire up Tom, to monitor the messages his muscles | 0:42:20 | 0:42:24 | |
receive from his brain. | 0:42:24 | 0:42:27 | |
I'll be a bionic golfer, right? | 0:42:27 | 0:42:30 | |
They're looking for a tell-tale signal | 0:42:32 | 0:42:35 | |
which might reveal the problem. | 0:42:35 | 0:42:39 | |
What I would like you to do is slip on this CyberGlove. | 0:42:39 | 0:42:44 | |
Last on is a sophisticated glove which will record | 0:42:44 | 0:42:46 | |
Tom's exact wrist movements as he putts. | 0:42:46 | 0:42:49 | |
And what it does is, it allows us to measure movement | 0:42:52 | 0:42:55 | |
at all of the different joints, to look at what happens | 0:42:55 | 0:42:59 | |
to finger movements and hand movements during the putting stroke. | 0:42:59 | 0:43:04 | |
It's, er, not exactly how I normally dress for golf, | 0:43:04 | 0:43:07 | |
so I suspect that this is going to be an interesting | 0:43:07 | 0:43:11 | |
feeling when I get to putting. | 0:43:11 | 0:43:13 | |
The glove shows Tom's exact hand position, | 0:43:14 | 0:43:17 | |
it will reveal any twitches as he putts. | 0:43:17 | 0:43:21 | |
Good, and move the wrist. | 0:43:21 | 0:43:23 | |
Perfect. | 0:43:23 | 0:43:24 | |
For the next half-hour, Tom putts repetitively, | 0:43:27 | 0:43:31 | |
to build a picture of the signals flowing from his brain. | 0:43:31 | 0:43:35 | |
It's an elusive little thing, isn't it? | 0:43:39 | 0:43:42 | |
Oh, God! | 0:43:45 | 0:43:47 | |
-Do you feel anything? -I did that one. | 0:43:48 | 0:43:51 | |
Often, as Tom attempts to make a putt, | 0:43:51 | 0:43:53 | |
there is a distinct twitch in his wrist. | 0:43:53 | 0:43:57 | |
That was nasty. | 0:44:00 | 0:44:02 | |
This research is only new, but the hypothesis, | 0:44:18 | 0:44:21 | |
based on evidence from other studies, is that in some golfers, | 0:44:21 | 0:44:26 | |
the yips may be caused by faulty wiring in the brain. | 0:44:26 | 0:44:29 | |
The suggestion is that the neural networks which form during | 0:44:29 | 0:44:33 | |
the initial process of learning new skills can start to go wrong. | 0:44:33 | 0:44:38 | |
The result? | 0:44:38 | 0:44:40 | |
A condition known as a focal dystonia, | 0:44:40 | 0:44:42 | |
in which the rogue brain connections cause involuntary movements, | 0:44:42 | 0:44:46 | |
a bit like Tom's twitch. | 0:44:46 | 0:44:48 | |
There may be some abnormal wiring within the brain, | 0:44:51 | 0:44:54 | |
in which the brain is perceiving things differently | 0:44:54 | 0:44:57 | |
than one would normally perceive, and causing muscles | 0:44:57 | 0:45:00 | |
to contract involuntarily. | 0:45:00 | 0:45:03 | |
The unconscious, it seems, doesn't always behave itself. | 0:45:03 | 0:45:07 | |
But as scientists begin to understand how it works, | 0:45:10 | 0:45:12 | |
they're starting to wonder whether it's possible to rewire it | 0:45:12 | 0:45:16 | |
and solve the problem. | 0:45:16 | 0:45:17 | |
Guitarist Douglas Rogers hopes so. | 0:45:27 | 0:45:30 | |
In the 1970s, he was one of Britain's top classical guitarists, | 0:45:31 | 0:45:36 | |
playing concerts worldwide. | 0:45:36 | 0:45:38 | |
But like golfers with the yips, | 0:45:41 | 0:45:42 | |
he began experiencing involuntary, unconsciously-controlled | 0:45:42 | 0:45:46 | |
hand movements which derailed his career. | 0:45:46 | 0:45:49 | |
I missed the first finger, there... | 0:45:54 | 0:45:57 | |
To solve this problem, | 0:45:57 | 0:45:58 | |
he's come to University College London | 0:45:58 | 0:46:01 | |
to try a radical new treatment. | 0:46:01 | 0:46:03 | |
It seems to get more and more unreliable... | 0:46:03 | 0:46:06 | |
Dr Mark Edwards, an expert in movement disorders, | 0:46:06 | 0:46:08 | |
is going to try treating Douglas, by attempting to access | 0:46:08 | 0:46:14 | |
the hidden depths of Douglas's brain. | 0:46:14 | 0:46:16 | |
So everything's a mess... | 0:46:16 | 0:46:18 | |
When you make a movement, | 0:46:18 | 0:46:20 | |
the brain usually activates one muscle | 0:46:20 | 0:46:22 | |
and actively turns off other muscles, | 0:46:22 | 0:46:25 | |
that's why we can make very precise movements, | 0:46:25 | 0:46:27 | |
that's something called surround inhibition, | 0:46:27 | 0:46:30 | |
it's a very useful thing for everybody, | 0:46:30 | 0:46:32 | |
but particularly for playing a musical instrument. | 0:46:32 | 0:46:34 | |
And we know that that process seems to go wrong | 0:46:34 | 0:46:37 | |
in people with hand dystonia. | 0:46:37 | 0:46:39 | |
So what we're going to try and do | 0:46:39 | 0:46:40 | |
is to deliberately turn up this mechanism in the brain, | 0:46:40 | 0:46:43 | |
that should inhibit movements that you don't want. | 0:46:43 | 0:46:46 | |
To do this, the team attempts to teach Douglas's brain | 0:46:50 | 0:46:54 | |
how to increase the inhibition signal it sends to his hand | 0:46:54 | 0:46:57 | |
as he performs a simple task just pushing a button. | 0:46:57 | 0:47:02 | |
When he moves, a device resting against his hand vibrates. | 0:47:04 | 0:47:09 | |
So what we're doing now is giving some vibration | 0:47:12 | 0:47:15 | |
to a surround muscle, so it's like boosting | 0:47:15 | 0:47:18 | |
the error signal to the brain, saying, | 0:47:18 | 0:47:20 | |
"Look, this muscle is contracting and it shouldn't be, | 0:47:20 | 0:47:24 | |
"so try and suppress it." | 0:47:24 | 0:47:25 | |
So we're trying to train the brain to turn on the muscles that should | 0:47:25 | 0:47:29 | |
be turned on, and actively turn off the muscles that should be | 0:47:29 | 0:47:33 | |
turned off, and that way we're letting | 0:47:33 | 0:47:36 | |
better control happen in the hand. | 0:47:36 | 0:47:38 | |
Over the coming weeks, they'll be doing this multiple times | 0:47:40 | 0:47:43 | |
to help build up Douglas's surround inhibition response. | 0:47:43 | 0:47:46 | |
But today, the team plans to try an even more cutting-edge treatment. | 0:47:49 | 0:47:54 | |
Transcranial direct current stimulation. | 0:47:56 | 0:47:59 | |
So what we're doing is stimulating the cerebellum, back here. | 0:47:59 | 0:48:03 | |
So that's the bit of the brain that's involved | 0:48:03 | 0:48:06 | |
in motor learning and motor function in general. | 0:48:06 | 0:48:10 | |
The theory is that motor memories normally remain | 0:48:10 | 0:48:15 | |
securely locked in the brain. | 0:48:15 | 0:48:16 | |
But by recalling these memories, | 0:48:16 | 0:48:19 | |
by having Douglas play the guitar, they will become vulnerable. | 0:48:19 | 0:48:23 | |
So this is a transcranial direct current stimulator. | 0:48:24 | 0:48:28 | |
And these are the wires that are attached to | 0:48:28 | 0:48:31 | |
the pads on Douglas's scalp, and I'm just going | 0:48:31 | 0:48:34 | |
to plug those into the box, to get the stimulation going. | 0:48:34 | 0:48:38 | |
Direct electrical current is now flowing through Douglas's cerebellum | 0:48:41 | 0:48:46 | |
to try to disable the rogue neural networks causing his dystonia. | 0:48:46 | 0:48:51 | |
The aim is to induce plasticity in his brain | 0:48:51 | 0:48:54 | |
returning it to a similar state it was in | 0:48:54 | 0:48:57 | |
when he first learnt to play. | 0:48:57 | 0:48:59 | |
We know from recent research that memories | 0:49:01 | 0:49:04 | |
when they're stored in memory are fairly solid, | 0:49:04 | 0:49:06 | |
they're fairly secure, but when they're recalled | 0:49:06 | 0:49:09 | |
they go into quite a vulnerable state, actually quite similar | 0:49:09 | 0:49:12 | |
to what happens when you're originally laying down the memory. | 0:49:12 | 0:49:15 | |
So if we get Douglas to play in the way that produces | 0:49:15 | 0:49:18 | |
the abnormal movement he has with his thumb, | 0:49:18 | 0:49:20 | |
maybe if we're giving some suppressive | 0:49:20 | 0:49:22 | |
brain stimulation at that time, it might suppress the memory. | 0:49:22 | 0:49:26 | |
This is the first time this technique has been used | 0:49:29 | 0:49:33 | |
to treat a musician with dystonia. | 0:49:33 | 0:49:36 | |
It's a dramatic show of just how far our understanding | 0:49:36 | 0:49:39 | |
of the unconscious brain has come. | 0:49:39 | 0:49:42 | |
We're now at this very exciting stage where we're | 0:49:44 | 0:49:47 | |
not just bystanders, just looking at what the brain is doing, | 0:49:47 | 0:49:50 | |
we can actually interact with it, we can stimulate bits, | 0:49:50 | 0:49:53 | |
we can turn bits up, we can turn bits down. | 0:49:53 | 0:49:55 | |
And it's starting to yield results, | 0:49:55 | 0:49:58 | |
and it's starting to give us real insights | 0:49:58 | 0:50:00 | |
into how we might try to fix some things in some quite precise ways. | 0:50:00 | 0:50:04 | |
But if you think that none of this affects you, think again. | 0:50:20 | 0:50:25 | |
Because the unconscious mind holds such potential | 0:50:25 | 0:50:28 | |
that scientists are now asking if they can harness its immense power. | 0:50:28 | 0:50:32 | |
Every hour of every day, your brain is flooded with images. | 0:50:43 | 0:50:48 | |
You can only concentrate on a few at once, but all the while, | 0:50:50 | 0:50:53 | |
your unconscious will be automatically filtering | 0:50:53 | 0:50:56 | |
this visual deluge. | 0:50:56 | 0:50:58 | |
The human brain is really an amazing machine, | 0:51:00 | 0:51:02 | |
it's an amazing system. | 0:51:02 | 0:51:04 | |
I mean, one of the really intriguing things about the brain | 0:51:04 | 0:51:08 | |
is that we're able to take this visual chaos and clutter | 0:51:08 | 0:51:10 | |
and then find salient information in that scene that matters to us, | 0:51:10 | 0:51:15 | |
that generates this, "Ah-ha, wait a minute, I should look over there." | 0:51:15 | 0:51:20 | |
Using its own powerful internal code, your unconscious decides | 0:51:20 | 0:51:24 | |
which information is worthy of your conscious attention. | 0:51:24 | 0:51:28 | |
There are essentially these signals that are labelling the world, | 0:51:28 | 0:51:34 | |
what we like to call neural signatures | 0:51:34 | 0:51:36 | |
or neural markers that are saying, "That might be worth exploring, | 0:51:36 | 0:51:39 | |
"that might be interesting." | 0:51:39 | 0:51:42 | |
Harnessing these signals could change how we cope | 0:51:44 | 0:51:48 | |
with the data overload we all face in the 21st century, | 0:51:48 | 0:51:52 | |
a prospect raising the interest of the US military. | 0:51:52 | 0:51:57 | |
Afghanistan. | 0:52:07 | 0:52:10 | |
In war zones, enemy bases can be hard to spot. | 0:52:10 | 0:52:13 | |
To find them, the military rely on satellite images. | 0:52:16 | 0:52:20 | |
Hunting through these is a slow | 0:52:24 | 0:52:25 | |
and monotonous task that can't be done automatically by computer. | 0:52:25 | 0:52:30 | |
An image analyst might have to look at a very large aerial image, | 0:52:32 | 0:52:36 | |
for instance here, an image that's | 0:52:36 | 0:52:38 | |
tens to hundreds of square kilometres. | 0:52:38 | 0:52:40 | |
The question is, "Where do I look in this image to find buildings, | 0:52:40 | 0:52:44 | |
"to find objects of interest?" | 0:52:44 | 0:52:46 | |
But by tapping into the power of the brain, | 0:52:46 | 0:52:48 | |
Professor Sajda thinks this process can be dramatically shortened. | 0:52:48 | 0:52:54 | |
One thing we might want to do is, | 0:52:54 | 0:52:56 | |
instead of scanning this image from the upper corner down, | 0:52:56 | 0:53:00 | |
have a more intelligent search that's based on little regions | 0:53:00 | 0:53:04 | |
that grab our attention. | 0:53:04 | 0:53:07 | |
To do this, the satellite image is randomly separated | 0:53:07 | 0:53:09 | |
into hundreds of sub-images. | 0:53:09 | 0:53:13 | |
A few show buildings, which is what Professor Sajda hopes to find. | 0:53:15 | 0:53:20 | |
He will rapidly view all the sub-images | 0:53:21 | 0:53:24 | |
while his brain response to each one is recorded with this EEG cap. | 0:53:24 | 0:53:28 | |
This is an electroencephalography cap, it allows us to detect signals | 0:53:30 | 0:53:35 | |
that would be related to what we would call an "ah-ha" moment, | 0:53:35 | 0:53:38 | |
we see something of interest, it catches our attention, | 0:53:38 | 0:53:41 | |
it generates, "Ah-ha, that's important to me," | 0:53:41 | 0:53:44 | |
and that information is transmitted from this cap | 0:53:44 | 0:53:47 | |
to a computer which analyses it to label imagery. | 0:53:47 | 0:53:50 | |
This is mind-reading, 21st-century style. | 0:53:58 | 0:54:02 | |
To begin with, Professor Sajda looks at a sample image | 0:54:04 | 0:54:06 | |
containing a building. | 0:54:06 | 0:54:08 | |
The computer registers his resulting neural "ah-ha" signal. | 0:54:09 | 0:54:13 | |
What we're interested in doing is finding the patterns | 0:54:13 | 0:54:16 | |
that are related to this "ah-ha" signal, and then use that | 0:54:16 | 0:54:20 | |
pattern of activity to rank all the images that I'm going to see. | 0:54:20 | 0:54:24 | |
With brain and computer now linked, the sub-images from | 0:54:24 | 0:54:28 | |
the large satellite picture start flashing up on screen. | 0:54:28 | 0:54:31 | |
So what I'm doing now is looking at a whole barrage of images, | 0:54:37 | 0:54:40 | |
five or ten a second, and while I'm doing that my brain | 0:54:40 | 0:54:44 | |
is decoding that information and using it to label the images. | 0:54:44 | 0:54:47 | |
So when you're looking at these images, the best thing to do | 0:54:49 | 0:54:53 | |
is actually relax. | 0:54:53 | 0:54:54 | |
You get in to a zone where your brain just does the work. | 0:54:54 | 0:54:59 | |
Professor Sajda is not immediately aware of any images of buildings, | 0:54:59 | 0:55:03 | |
but his brain activity suggests something very different. | 0:55:03 | 0:55:07 | |
Back in the main lab, the results appear on screen. | 0:55:09 | 0:55:14 | |
What you see actually is a tiling of the entire image, | 0:55:14 | 0:55:18 | |
where each of these little squares is actually | 0:55:18 | 0:55:21 | |
one of the images as it was flashed. | 0:55:21 | 0:55:24 | |
They're colour-coded based on the ranking | 0:55:24 | 0:55:26 | |
that was computed from my brain activity. | 0:55:26 | 0:55:28 | |
So, essentially, how strong was the "ah-ha" | 0:55:28 | 0:55:31 | |
when you saw that particular image? | 0:55:31 | 0:55:33 | |
So, regions that are marked in red are very strong, | 0:55:33 | 0:55:36 | |
they grabbed my attention, in dark blue are less engaging. | 0:55:36 | 0:55:41 | |
This little tile here is actually the most highly ranked image. | 0:55:44 | 0:55:48 | |
This is a close-up of that particular region. | 0:55:48 | 0:55:50 | |
What you can see here is this is basically a compound. | 0:55:50 | 0:55:54 | |
There are roadways, there's obviously a building, | 0:55:54 | 0:55:57 | |
some man-made structure. | 0:55:57 | 0:55:59 | |
So, the real gain here is that instead of moving through | 0:55:59 | 0:56:03 | |
this large image very laboriously, I can now jump | 0:56:03 | 0:56:06 | |
from image to image, or location to location, | 0:56:06 | 0:56:10 | |
based on what grabbed my attention. | 0:56:10 | 0:56:12 | |
By tapping into his own brain, | 0:56:15 | 0:56:18 | |
Professor Sajda has increased his image-spotting efficiency | 0:56:18 | 0:56:21 | |
by 300 percent. | 0:56:21 | 0:56:23 | |
It's a breakthrough, not just for military image analysts, | 0:56:23 | 0:56:28 | |
but for everyone. | 0:56:28 | 0:56:30 | |
From interacting with computer games, to advertising, | 0:56:31 | 0:56:35 | |
to revolutionising the analysis of medical images, | 0:56:35 | 0:56:38 | |
the ability to harness the power of the unconscious | 0:56:38 | 0:56:41 | |
heralds a bold new future. | 0:56:41 | 0:56:43 | |
The true nature of your unconscious mind is now becoming clear. | 0:56:55 | 0:57:00 | |
Far from being the lowly, primal thing of popular imagination, | 0:57:01 | 0:57:05 | |
your unconscious turns out to be | 0:57:05 | 0:57:07 | |
the sophisticated centre of everything you ever do. | 0:57:07 | 0:57:11 | |
When it comes down to it, | 0:57:12 | 0:57:14 | |
your brain runs mostly unconsciously, on autopilot. | 0:57:14 | 0:57:18 | |
And by tapping its immense power, | 0:57:20 | 0:57:22 | |
you might one day change your life for ever. | 0:57:22 | 0:57:25 | |
The human brain is really an awesome thing, | 0:57:27 | 0:57:29 | |
I mean, from the engineering and technology point of view, | 0:57:29 | 0:57:33 | |
understanding the brain will ultimately lead us | 0:57:33 | 0:57:37 | |
to areas that we can't imagine. | 0:57:37 | 0:57:39 | |
I mean, if you think that the internet | 0:57:39 | 0:57:41 | |
and networking and Facebook have caused a revolution, | 0:57:41 | 0:57:44 | |
wait until you see what happens when we really understand the human brain. | 0:57:44 | 0:57:48 | |
Subtitles by Red Bee Media Ltd | 0:58:13 | 0:58:16 |