Daniel Wolpert: The real reason for brains

342,203 views ・ 2011-11-03

TED


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譯者: kane tan 審譯者: Ana Choi
00:15
I'm a neuroscientist.
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我是一個神經學家。
00:17
And in neuroscience,
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在神經學中,
00:19
we have to deal with many difficult questions about the brain.
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我們必須處理許多關於大腦的艱深問題。
00:22
But I want to start with the easiest question
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但是我想從最簡單的問題開始談起,
00:24
and the question you really should have all asked yourselves at some point in your life,
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而每個人在人生中都該問過自己這個問題,
00:27
because it's a fundamental question
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因為想了解大腦的運作,
00:29
if we want to understand brain function.
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是最根本的問題。
00:31
And that is, why do we and other animals
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這問題就是,為什麼我們和其他動物
00:33
have brains?
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會有大腦呢?
00:35
Not all species on our planet have brains,
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並非地球上所有的生物都有大腦,
00:38
so if we want to know what the brain is for,
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所以如果我們想知道大腦的作用,
00:40
let's think about why we evolved one.
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就得想想我們為何會進化出一個大腦。
00:42
Now you may reason that we have one
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你現在可能認為大腦的存在
00:44
to perceive the world or to think,
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是為了感覺這個世界或是思考,
00:46
and that's completely wrong.
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這是完全錯誤的。
00:48
If you think about this question for any length of time,
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如果你花了很多時間去思考這個問題,
00:51
it's blindingly obvious why we have a brain.
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這將會使你誤判為什麼我們會有大腦。
00:53
We have a brain for one reason and one reason only,
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我們擁有大腦的唯一原因,
00:56
and that's to produce adaptable and complex movements.
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是為了產生適合且複雜的動作。
00:59
There is no other reason to have a brain.
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這是大腦存在的唯一理由。
01:01
Think about it.
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想想看。
01:03
Movement is the only way you have
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動作是你感覺這個世界
01:05
of affecting the world around you.
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唯一的反應方式。
01:07
Now that's not quite true. There's one other way, and that's through sweating.
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這並非完全正確。還有一個方式,就是透過冒汗。
01:10
But apart from that,
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但是除了那個以外,
01:12
everything else goes through contractions of muscles.
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每件事都必須藉由肌肉的收縮。
01:14
So think about communication --
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所以, 看看溝通 --
01:16
speech, gestures, writing, sign language --
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說話、姿勢、寫字、手語 --
01:19
they're all mediated through contractions of your muscles.
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這些都藉由收縮你的肌肉來達成。
01:22
So it's really important to remember
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記住事情是很重要的,
01:24
that sensory, memory and cognitive processes are all important,
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感覺、記憶和理解程序都很重要,
01:28
but they're only important
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但這些是為了能夠做到
01:30
to either drive or suppress future movements.
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開始或結束後續的動作, 所以才會重要。
01:32
There can be no evolutionary advantage
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小時候的回憶累積、
01:34
to laying down memories of childhood
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或是對於玫瑰顏色的認知,
01:36
or perceiving the color of a rose
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對於進化並沒有什麼影響,
01:38
if it doesn't affect the way you're going to move later in life.
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如果它對你未來生活的行為沒有幫助。
01:41
Now for those who don't believe this argument,
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對於那些不相信這種說法的人們,
01:43
we have trees and grass on our planet without the brain,
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雖然地球上的樹和草都沒有大腦,
01:45
but the clinching evidence is this animal here --
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但這動物就是確切的證據 --
01:47
the humble sea squirt.
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這渺小的海鞘。
01:49
Rudimentary animal, has a nervous system,
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這種未進化的動物,擁有神經系統,
01:52
swims around in the ocean in its juvenile life.
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幼年時會在海洋中游盪著。
01:54
And at some point of its life,
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等到長大之後,
01:56
it implants on a rock.
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它便會攀附在岩石上。
01:58
And the first thing it does in implanting on that rock, which it never leaves,
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當它攀附上這永遠居住的岩石之後,
02:01
is to digest its own brain and nervous system
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它所作的第一件事,
02:04
for food.
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就是將它的大腦和神經系統當食物吃掉。
02:06
So once you don't need to move,
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所以一旦你不再需要移動,
02:08
you don't need the luxury of that brain.
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你就不需要大腦這種奢侈品了。
02:11
And this animal is often taken
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這種動物常被拿來
02:13
as an analogy to what happens at universities
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當作一種比喻,當大學教授
02:15
when professors get tenure,
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獲得終身職位之後會發生的事情,
02:17
but that's a different subject.
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不過那是另外一個話題了。
02:19
(Applause)
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(掌聲)
02:21
So I am a movement chauvinist.
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我是一個活動主義者。
02:24
I believe movement is the most important function of the brain --
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我認為大腦最重要的功能就是控制動作,
02:26
don't let anyone tell you that it's not true.
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別讓任何人告訴你這不是真的。
02:28
Now if movement is so important,
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如果動作那麼重要,
02:30
how well are we doing
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我們對於了解大腦如何控制動作
02:32
understanding how the brain controls movement?
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這方面的研究進展如何呢?
02:34
And the answer is we're doing extremely poorly; it's a very hard problem.
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答案是,少得可憐;這是很艱深的難題。
02:36
But we can look at how well we're doing
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但我們可以換個方向來思考,
02:38
by thinking about how well we're doing building machines
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看看我們對於建造能做出和人類一樣動作的機器
02:40
which can do what humans can do.
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這種研究進展如何。
02:42
Think about the game of chess.
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想想看西洋棋這種遊戲。
02:44
How well are we doing determining what piece to move where?
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我們決定該將哪個棋子移到哪個位置這種研究做得怎麼樣?
02:47
If you pit Garry Kasparov here, when he's not in jail,
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如果你在 Gary Kasparov 還沒去坐牢之前,
02:50
against IBM's Deep Blue,
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讓他跟 IBM 的深藍電腦進行比賽,
02:52
well the answer is IBM's Deep Blue will occasionally win.
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IBM 的深藍電腦有時候可以獲勝。
02:55
And I think if IBM's Deep Blue played anyone in this room, it would win every time.
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我想如果讓 IBM 的深藍電腦跟在座任何一位下棋,它每次都會獲勝。
02:58
That problem is solved.
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這個問題就被解決了。
03:00
What about the problem
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但如果這個問題是
03:02
of picking up a chess piece,
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拿起一個棋子,
03:04
dexterously manipulating it and putting it back down on the board?
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靈巧地拿起它,再放回棋盤上呢?
03:07
If you put a five year-old child's dexterity against the best robots of today,
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如果你讓一個五歲的小孩跟現今最棒的機器人進行比賽,
03:10
the answer is simple:
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答案很簡單:
03:12
the child wins easily.
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那個小孩可以輕易獲勝。
03:14
There's no competition at all.
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機器人完全不是對手。
03:16
Now why is that top problem so easy
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那麼,為什麼之前的問題很容易做到?
03:18
and the bottom problem so hard?
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而接著的問題卻很困難呢?
03:20
One reason is a very smart five year-old
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原因之一是,一個很聰明的五歲小孩
03:22
could tell you the algorithm for that top problem --
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可以告訴你上面問題的演算法則 --
03:24
look at all possible moves to the end of the game
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找出直到遊戲結束的所有可能移動步法,
03:26
and choose the one that makes you win.
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然後選擇可以讓你獲勝的步法。
03:28
So it's a very simple algorithm.
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所以這是很簡單的演算法則。
03:30
Now of course there are other moves,
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當然有其他的步法,
03:32
but with vast computers we approximate
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但是利用龐大的電腦系統,我們估算
03:34
and come close to the optimal solution.
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並且找出最佳解答。
03:36
When it comes to being dexterous,
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當討論到靈巧時,
03:38
it's not even clear what the algorithm is you have to solve to be dexterous.
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甚至沒有明確的演算法則告訴你什麼叫做靈巧。
03:40
And we'll see you have to both perceive and act on the world,
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於是你必須感覺同時去做出反應,
03:42
which has a lot of problems.
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這就會遇到很多問題。
03:44
But let me show you cutting-edge robotics.
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讓我介紹一些先進的機器人。
03:46
Now a lot of robotics is very impressive,
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現在有許多優秀的機器人,
03:48
but manipulation robotics is really just in the dark ages.
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但是操控機器人仍舊處於黑暗的時代。
03:51
So this is the end of a Ph.D. project
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這是在某一個很棒的機器人學院中,
03:53
from one of the best robotics institutes.
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一個博士研究項目的成果。
03:55
And the student has trained this robot
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這位學生訓練這個機器人
03:57
to pour this water into a glass.
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將水倒進杯子裡面。
03:59
It's a hard problem because the water sloshes about, but it can do it.
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這是很困難的題目,因為水會濺出來,但是它可以辦到。
04:02
But it doesn't do it with anything like the agility of a human.
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但是它無法像人類做得那麼靈巧。
04:05
Now if you want this robot to do a different task,
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如果你希望這個機器人進行另一項任務,
04:08
that's another three-year Ph.D. program.
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那將是另一個三年期的博士研究計畫。
04:11
There is no generalization at all
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在機器人工程學裡,
04:13
from one task to another in robotics.
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一項任務和另一項任務是沒有共通性的。
04:15
Now we can compare this
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我們可以將這個
04:17
to cutting-edge human performance.
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和人類優異的表現做比較。
04:19
So what I'm going to show you is Emily Fox
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我要給大家看的是 Emily Fox,
04:21
winning the world record for cup stacking.
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她是贏得堆疊杯子世界冠軍的人。
04:24
Now the Americans in the audience will know all about cup stacking.
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觀眾席中如果有美國人,應該知道這個堆疊杯子的比賽。
04:26
It's a high school sport
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這是一項高中常見的運動,
04:28
where you have 12 cups you have to stack and unstack
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你得把 12 個杯子依據指定的順序
04:30
against the clock in a prescribed order.
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快速的堆疊再分開。
04:32
And this is her getting the world record in real time.
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這是她創下世界紀錄的畫面,以正常速度播放。
04:39
(Laughter)
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(笑聲)
04:47
(Applause)
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(掌聲)
04:52
And she's pretty happy.
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她非常開心。
04:54
We have no idea what is going on inside her brain when she does that,
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我們不知道當她做這件事情時,腦子裡發生了什麼事情,
04:56
and that's what we'd like to know.
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那是我們很想知道。
04:58
So in my group, what we try to do
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所以我的團隊,我們想要做的是
05:00
is reverse engineer how humans control movement.
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針對人類如何控制動作這件事去進行逆向工程。
05:03
And it sounds like an easy problem.
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這聽起來是很簡單的問題。
05:05
You send a command down, it causes muscles to contract.
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你送出一個指令,這會讓肌肉收縮。
05:07
Your arm or body moves,
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你的手臂或身體移動,
05:09
and you get sensory feedback from vision, from skin, from muscles and so on.
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然後你得到來自於視覺、皮膚、肌肉等處的感覺回饋。
05:12
The trouble is
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問題是,
05:14
these signals are not the beautiful signals you want them to be.
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這些訊息不如你預期的那樣完美。
05:16
So one thing that makes controlling movement difficult
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讓控制動作變得困難的其中一個因素是,
05:18
is, for example, sensory feedback is extremely noisy.
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舉例來說,感覺回饋是充滿雜訊的。
05:21
Now by noise, I do not mean sound.
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關於雜訊,我指的不是聲音。
05:24
We use it in the engineering and neuroscience sense
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雜訊一般用在工程學與神經科學的檢測中,
05:26
meaning a random noise corrupting a signal.
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是指干擾主要訊號的不規律且雜亂的訊號。
05:28
So the old days before digital radio when you were tuning in your radio
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所以在數位收音機出現之前,當你轉動舊式收音機,
05:31
and you heard "crrcckkk" on the station you wanted to hear,
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你會在你想聽得電台中聽見「嘎啦嘎啦」的聲音,
05:33
that was the noise.
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那就是雜訊。
05:35
But more generally, this noise is something that corrupts the signal.
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講白話一點,雜訊就是干擾訊號的東西。
05:38
So for example, if you put your hand under a table
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例如,當你將手放在桌子底下,
05:40
and try to localize it with your other hand,
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試著用另一隻手去找到它的位置,
05:42
you can be off by several centimeters
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你可能會誤差好幾公分,
05:44
due to the noise in sensory feedback.
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因為感知回饋中有雜訊。
05:46
Similarly, when you put motor output on movement output,
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同樣地,當你將動力源的力量變成動作的力量時,
05:48
it's extremely noisy.
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訊號將是非常雜亂的。
05:50
Forget about trying to hit the bull's eye in darts,
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先不談射飛鏢時能射中靶心,
05:52
just aim for the same spot over and over again.
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只要試著去重複瞄準同一個點看看。
05:54
You have a huge spread due to movement variability.
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因為動作的差異性,你會丟到許多不同的點上去。
05:57
And more than that, the outside world, or task,
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更別提在外在世界,或是執行任務時,
05:59
is both ambiguous and variable.
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充滿著不確定性和變異性。
06:01
The teapot could be full, it could be empty.
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茶壺可能是滿的,也可能是空的。
06:03
It changes over time.
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每次都不一樣。
06:05
So we work in a whole sensory movement task soup of noise.
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所以我們是在充滿雜訊的環境中進行動作。
06:09
Now this noise is so great
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因為這個雜訊非常巨大,
06:11
that society places a huge premium
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所以我們的社會給予那些
06:13
on those of us who can reduce the consequences of noise.
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能夠抵抗雜訊的人鉅額獎賞。
06:16
So if you're lucky enough to be able to knock a small white ball
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所以如果你能將一顆小白球
06:19
into a hole several hundred yards away using a long metal stick,
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用一根金屬長棍打進幾百碼外的洞裡,
06:22
our society will be willing to reward you
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人們願意給你
06:24
with hundreds of millions of dollars.
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好幾億的獎金。
06:27
Now what I want to convince you of
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而我想要讓你知道的是
06:29
is the brain also goes through a lot of effort
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大腦做了許多的努力
06:31
to reduce the negative consequences
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去減少這些雜訊以及變異性
06:33
of this sort of noise and variability.
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所造成的負面效應。
06:35
And to do that, I'm going to tell you about a framework
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為此,我將會介紹一個
06:37
which is very popular in statistics and machine learning of the last 50 years
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在過去五十年間,常被用在統計與機械學習方面的架構,
06:40
called Bayesian decision theory.
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叫做貝葉斯決策理論。
06:42
And it's more recently a unifying way
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近來它已經逐漸變成用來解釋
06:45
to think about how the brain deals with uncertainty.
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大腦如何處理不確定性的主要方法。
06:48
And the fundamental idea is you want to make inferences and then take actions.
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它的基本概念是,你先做出假設,然後去行動。
06:51
So let's think about the inference.
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我們先來看看假設。
06:53
You want to generate beliefs about the world.
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你要產生出對事物的信念。
06:55
So what are beliefs?
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什麼是信念呢?
06:57
Beliefs could be: where are my arms in space?
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信念可以是:我的手臂在空間中的哪個位置?
06:59
Am I looking at a cat or a fox?
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我看見的是一隻貓還是一隻狐狸?
07:01
But we're going to represent beliefs with probabilities.
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而我們必須用可能性來表示信念。
07:04
So we're going to represent a belief
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我們要將信念表達為
07:06
with a number between zero and one --
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介於 0 到 1 之間的數字 --
07:08
zero meaning I don't believe it at all, one means I'm absolutely certain.
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0 代表我完全不相信,1 則表示我絕對相信。
07:11
And numbers in between give you the gray levels of uncertainty.
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而介於期間的數字則是代表不確定性的灰色地帶。
07:14
And the key idea to Bayesian inference
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貝葉斯假設的關鍵在於
07:16
is you have two sources of information
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你有兩種不同的資訊來源
07:18
from which to make your inference.
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用來建立起你的假設。
07:20
You have data,
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你會有資訊,
07:22
and data in neuroscience is sensory input.
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在神經科學中,這資訊就是你的感覺。
07:24
So I have sensory input, which I can take in to make beliefs.
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我有感覺,所以我可以將它用來建立信念。
07:27
But there's another source of information, and that's effectively prior knowledge.
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但還有另一種資訊的來源,就是已經擁有的知識。
07:30
You accumulate knowledge throughout your life in memories.
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藉由生命中的回憶,知識會被累積下來。
07:33
And the point about Bayesian decision theory
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而貝葉斯決策理論的重點在於
07:35
is it gives you the mathematics
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它提供你一種
07:37
of the optimal way to combine
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數學的最佳化方式
07:39
your prior knowledge with your sensory evidence
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來合併你原有的知識和你的感覺
07:41
to generate new beliefs.
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以產生出新的信念。
07:43
And I've put the formula up there.
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它的公式在這裡。
07:45
I'm not going to explain what that formula is, but it's very beautiful.
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我不會解釋公式是什麼,但是它很漂亮。
07:47
And it has real beauty and real explanatory power.
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它擁有真實的美感,和真實的說服力。
07:50
And what it really says, and what you want to estimate,
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它真正表達的,以及你想要估計出的,
07:52
is the probability of different beliefs
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是由你的感覺所產生出
07:54
given your sensory input.
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不同信念的可能性。
07:56
So let me give you an intuitive example.
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我舉一個很直接的例子。
07:58
Imagine you're learning to play tennis
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想像你正在學習打網球,
08:01
and you want to decide where the ball is going to bounce
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當球飛過網子朝你過來時,
08:03
as it comes over the net towards you.
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你要決定球會掉在哪個位置。
08:05
There are two sources of information
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依據貝葉斯的理論,
08:07
Bayes' rule tells you.
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你有兩個資訊來源。
08:09
There's sensory evidence -- you can use visual information auditory information,
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一個是感覺證據 -- 你可以藉由視覺和聽覺的資訊,
08:12
and that might tell you it's going to land in that red spot.
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那可能會讓你判斷在紅點處。
08:15
But you know that your senses are not perfect,
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而你知道你的感覺並不完美,
08:18
and therefore there's some variability of where it's going to land
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所以它的落點會有誤差,
08:20
shown by that cloud of red,
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這就是紅色區域,
08:22
representing numbers between 0.5 and maybe 0.1.
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而可能性大概是在 0.5 到 0.1 之間。
08:26
That information is available in the current shot,
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這資訊來自於這一次的發球,
08:28
but there's another source of information
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還有另外的資訊
08:30
not available on the current shot,
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並非由這次發球而來,
08:32
but only available by repeated experience in the game of tennis,
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而是來自於反覆進行網球比賽的經驗,
08:35
and that's that the ball doesn't bounce
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經驗告訴你,在這場比賽中,
08:37
with equal probability over the court during the match.
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球落在球場上每個位置的可能性並不相等。
08:39
If you're playing against a very good opponent,
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如果你的對手技術很棒,
08:41
they may distribute it in that green area,
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他們會讓球落在綠色區域,
08:43
which is the prior distribution,
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就是所謂的先驗分布,
08:45
making it hard for you to return.
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這會讓你難以回擊。
08:47
Now both these sources of information carry important information.
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這些訊息來源都帶有重要的訊息。
08:49
And what Bayes' rule says
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依據貝葉斯理論所說,
08:51
is that I should multiply the numbers on the red by the numbers on the green
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我應該將紅色區域的機率和綠色區域的機率相乘,
08:54
to get the numbers of the yellow, which have the ellipses,
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就會得到橢圓形黃色區域的機率,
08:57
and that's my belief.
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而這就是我的信念。
08:59
So it's the optimal way of combining information.
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這是合併訊息的最佳方式。
09:02
Now I wouldn't tell you all this if it wasn't that a few years ago,
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幾年前我們的研究發現,
09:04
we showed this is exactly what people do
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人們在學習新的動作技巧時,
09:06
when they learn new movement skills.
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確實有同樣的現象。
09:08
And what it means
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也就是說,
09:10
is we really are Bayesian inference machines.
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我們就像是使用貝葉斯假設的機器。
09:12
As we go around, we learn about statistics of the world and lay that down,
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在生活中,我們學習並累積了關於世界的許多統計資料,
09:16
but we also learn
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但我們也學習了
09:18
about how noisy our own sensory apparatus is,
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我們自身感知器官產生的雜訊有多少,
09:20
and then combine those
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然後將這些合併在一起,
09:22
in a real Bayesian way.
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這些正是貝葉斯法則。
09:24
Now a key part to the Bayesian is this part of the formula.
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貝葉斯法則的一個關鍵部份就是這個公式的這個部份。
09:27
And what this part really says
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這部份是在說
09:29
is I have to predict the probability
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我必須利用不同的感知回饋
09:31
of different sensory feedbacks
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去預測各種可能性
09:33
given my beliefs.
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來創造出我的信念。
09:35
So that really means I have to make predictions of the future.
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意思是說,我必須要去預測未來。
09:38
And I want to convince you the brain does make predictions
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我要讓大家了解的是,
09:40
of the sensory feedback it's going to get.
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大腦真的能夠預測即將獲得的感知回饋。
09:42
And moreover, it profoundly changes your perceptions
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並且,它會因為你的行為
09:44
by what you do.
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而深深改變你的感覺。
09:46
And to do that, I'll tell you
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為此,我將會告訴你,
09:48
about how the brain deals with sensory input.
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大腦是怎麼處理獲得的感知訊號。
09:50
So you send a command out,
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於是你送出一個指令,
09:53
you get sensory feedback back,
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你得到一個感知的回饋,
09:55
and that transformation is governed
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而這個轉換是由
09:57
by the physics of your body and your sensory apparatus.
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你的身體和感知器官的物理層面所管理。
10:00
But you can imagine looking inside the brain.
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但是你可以想像一下大腦的內部狀況。
10:02
And here's inside the brain.
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這是大腦的內側。
10:04
You might have a little predictor, a neural simulator,
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有一個小小的預測器具,一種神經模擬器,
10:06
of the physics of your body and your senses.
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可以模擬出你的身體和感覺的物理現象。
10:08
So as you send a movement command down,
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於是當你送出動作的指令,
10:10
you tap a copy of that off
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你順便複製了一份指令,
10:12
and run it into your neural simulator
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然後將它送進你的神經模擬器
10:14
to anticipate the sensory consequences of your actions.
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去預測動作造成的感知結果。
10:18
So as I shake this ketchup bottle,
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所以當我搖動這瓶蕃茄醬時,
10:20
I get some true sensory feedback as the function of time in the bottom row.
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我得到真正的感知回饋,就是那個底下那個時間函數。
10:23
And if I've got a good predictor, it predicts the same thing.
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如果我有一個很好的預測器具,它可以預測出同樣的東西。
10:26
Well why would I bother doing that?
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為什麼我要這麼做呢?
10:28
I'm going to get the same feedback anyway.
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反正我會得到同樣的回饋啊。
10:30
Well there's good reasons.
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這可是有很好的解釋的。
10:32
Imagine, as I shake the ketchup bottle,
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想像一下,當我搖動這瓶蕃茄醬時,
10:34
someone very kindly comes up to me and taps it on the back for me.
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某人很好心隨著我的動作,將它拍回來給我。
10:37
Now I get an extra source of sensory information
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現在我因為這個額外的動作
10:39
due to that external act.
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而有了額外的感知訊息。
10:41
So I get two sources.
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於是我有了兩個感知來源。
10:43
I get you tapping on it, and I get me shaking it,
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一個是你拍它,一個是我搖它,
10:46
but from my senses' point of view,
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但是對我的感覺來說,
10:48
that is combined together into one source of information.
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它被合併在一起,變成了一種感知來源。
10:51
Now there's good reason to believe
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現在有了很好的理由去相信
10:53
that you would want to be able to distinguish external events from internal events.
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你會希望能夠將外在和內在的事件給分開來。
10:56
Because external events are actually much more behaviorally relevant
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因為相對於在我體內進行事物的感覺,
10:59
than feeling everything that's going on inside my body.
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外在的事件跟行為事件更具有相關性。
11:02
So one way to reconstruct that
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要將感覺重新建立的方法是
11:04
is to compare the prediction --
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去針對基於你的動作指令做的預測
11:06
which is only based on your movement commands --
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以及真實的狀況
11:08
with the reality.
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去進行比較。
11:10
Any discrepancy should hopefully be external.
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幸運的話,出現的差異都屬於外在的影響。
11:13
So as I go around the world,
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所以當我在生活中,
11:15
I'm making predictions of what I should get, subtracting them off.
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我預測將會遇到的狀況,然後將這些預期剔除。
11:18
Everything left over is external to me.
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剩下的就是外在對我的影響。
11:20
What evidence is there for this?
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有什麼證據能證明嗎?
11:22
Well there's one very clear example
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嗯,有一個很明確的例子,
11:24
where a sensation generated by myself feels very different
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當我自己本身產生的感覺
11:26
then if generated by another person.
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和別人的感覺很不一樣的時候。
11:28
And so we decided the most obvious place to start
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於是我們決定開始測試最常見的狀況,
11:30
was with tickling.
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那就是搔癢。
11:32
It's been known for a long time, you can't tickle yourself
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大家都知道,你自己搔自己癢的感覺
11:34
as well as other people can.
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不會像別人搔你癢那麼強烈。
11:36
But it hasn't really been shown, it's because you have a neural simulator,
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但是大家都還不知道,那是因為你擁有神經模擬器,
11:39
simulating your own body
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模擬著你自己的身體,
11:41
and subtracting off that sense.
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並且將這個感覺給剔除掉。
11:43
So we can bring the experiments of the 21st century
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而我們在這21世紀進行實驗時,
11:46
by applying robotic technologies to this problem.
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可以藉由機器人技術來解決這個問題。
11:49
And in effect, what we have is some sort of stick in one hand attached to a robot,
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我們的作法是,在一個機器人的手中裝置一根棍子,
11:52
and they're going to move that back and forward.
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然後讓它前後移動。
11:54
And then we're going to track that with a computer
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接著我們會用電腦來追蹤這個動作,
11:56
and use it to control another robot,
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藉以控制另一個機器人,
11:58
which is going to tickle their palm with another stick.
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它會用另一根棍子來搔對方手掌心的癢。
12:00
And then we're going to ask them to rate a bunch of things
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接著我們會要求它們針對一些事情進行評分,
12:02
including ticklishness.
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也包含了癢的程度。
12:04
I'll show you just one part of our study.
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我所展示的只是我們研究中的一部分。
12:06
And here I've taken away the robots,
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這邊我沒有放入機器人,
12:08
but basically people move with their right arm sinusoidally back and forward.
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只是用人依據正弦波的方式去前後移動右手。
12:11
And we replay that to the other hand with a time delay.
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接著我們用另一隻手稍微慢一點再做一次。
12:14
Either no time delay,
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或是以同樣速度,
12:16
in which case light would just tickle your palm,
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輕輕搔癢你的手心,
12:18
or with a time delay of two-tenths of three-tenths of a second.
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或是有個 0.2 或 0.3 秒的時間差。
12:22
So the important point here
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有一個重點是,
12:24
is the right hand always does the same things -- sinusoidal movement.
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右手總是進行正弦波的動作。
12:27
The left hand always is the same and puts sinusoidal tickle.
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而左手總是進行同樣的動作,並且進行搔癢。
12:30
All we're playing with is a tempo causality.
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我們穩定的增加時間差。
12:32
And as we go from naught to 0.1 second,
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當我們將時間差從 0 增大到 0.1 秒時,
12:34
it becomes more ticklish.
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會覺得稍微癢一點。
12:36
As you go from 0.1 to 0.2,
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當時間差由 0.1 增大到 0.2 秒時,
12:38
it becomes more ticklish at the end.
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最後會覺得更癢。
12:40
And by 0.2 of a second,
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當時間差到達 0.2 秒時,
12:42
it's equivalently ticklish
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那個癢度將等同於
12:44
to the robot that just tickled you without you doing anything.
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在你什麼都不做時,機器人搔你癢的感覺。
12:46
So whatever is responsible for this cancellation
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不論消弭感覺的原因是什麼,
12:48
is extremely tightly coupled with tempo causality.
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它和這個時間差有密切的關聯性。
12:51
And based on this illustration, we really convinced ourselves in the field
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基於這個例子,我們可以確信
12:54
that the brain's making precise predictions
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大腦會做出精確的預測
12:56
and subtracting them off from the sensations.
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並且將預測內容從感覺中剔除。
12:59
Now I have to admit, these are the worst studies my lab has ever run.
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我必須承認,這些是我的實驗室所做過的案子中最糟的研究。
13:02
Because the tickle sensation on the palm comes and goes,
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因為這些在手掌上搔癢的感覺一下就沒了,
13:04
you need large numbers of subjects
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你需要大量的測試者來獲得數據,
13:06
with these stars making them significant.
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才能使測試結果比較有意義。
13:08
So we were looking for a much more objective way
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所以我們找尋了比較客觀的方法
13:10
to assess this phenomena.
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來評估這個現象。
13:12
And in the intervening years I had two daughters.
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在中間這幾年,我有了兩個女兒。
13:14
And one thing you notice about children in backseats of cars on long journeys,
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在長途旅程中,你會發現在車子後座上的小孩們,
13:17
they get into fights --
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她們會吵架 --
13:19
which started with one of them doing something to the other, the other retaliating.
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當某個小孩對另一個人動了手之後,另一個也會還手。
13:22
It quickly escalates.
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很快就會越來越嚴重。
13:24
And children tend to get into fights which escalate in terms of force.
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小孩們打架時通常會越打越大力。
13:27
Now when I screamed at my children to stop,
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當我叫她們住手時,
13:29
sometimes they would both say to me
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有時候她們都會跟我說
13:31
the other person hit them harder.
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另一個人打得比較大力。
13:34
Now I happen to know my children don't lie,
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我剛好知道我的小孩不會說謊的,
13:36
so I thought, as a neuroscientist,
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所以身為一個神經科學家,我開始思考,
13:38
it was important how I could explain
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要怎麼解釋,她們兩人口中的真相會相互矛盾
13:40
how they were telling inconsistent truths.
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是很重要的一件事。
13:42
And we hypothesize based on the tickling study
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我們假設,依據這個搔癢的研究,
13:44
that when one child hits another,
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當一個小孩打另一個人時,
13:46
they generate the movement command.
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她們產生了動作指令。
13:48
They predict the sensory consequences and subtract it off.
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她們預測了感知結果,並且剔除了它。
13:51
So they actually think they've hit the person less hard than they have --
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所以她們認為自己打對方的力量比較小 --
13:53
rather like the tickling.
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就像搔癢一樣。
13:55
Whereas the passive recipient
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然而,在被打的時候,
13:57
doesn't make the prediction, feels the full blow.
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並不會產生這種預測,所以感覺到了全力。
13:59
So if they retaliate with the same force,
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所以如果她們還手的力道一樣,
14:01
the first person will think it's been escalated.
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第一個出手的人會覺得力量變大了。
14:03
So we decided to test this in the lab.
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所以我們決定在實驗室做這個實驗。
14:05
(Laughter)
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(笑聲)
14:08
Now we don't work with children, we don't work with hitting,
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我們不是用小孩做測試,我們也不是測試打架,
14:10
but the concept is identical.
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不過概念上是完全一樣的。
14:12
We bring in two adults. We tell them they're going to play a game.
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我們找了兩個大人。我們告訴他們將會進行一場遊戲。
14:15
And so here's player one and player two sitting opposite to each other.
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於是一個玩家和另一個人面對面的坐著。
14:17
And the game is very simple.
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這個遊戲很簡單。
14:19
We started with a motor
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我們用了一個馬達
14:21
with a little lever, a little force transfuser.
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上面裝著一根小槓桿,一個小型力量傳輸器。
14:23
And we use this motor to apply force down to player one's fingers
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我們用這個馬達來對下面的玩家手指施力
14:25
for three seconds and then it stops.
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持續三秒鐘,然後停止。
14:28
And that player's been told, remember the experience of that force
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這個玩家被告知要記住這個力量的感覺,
14:31
and use your other finger
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然後用你另一隻手指
14:33
to apply the same force
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藉由力量傳輸器,去施加相同的力量
14:35
down to the other subject's finger through a force transfuser -- and they do that.
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到下面受測試者的手指上 -- 於是他們這麼做了。
14:38
And player two's been told, remember the experience of that force.
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第二個玩家被告知要記住這個力量的感覺。
14:41
Use your other hand to apply the force back down.
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用你的另一隻手去施加相同的力量回去。
14:44
And so they take it in turns
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於是他們輪流來回地
14:46
to apply the force they've just experienced back and forward.
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去施加他們所感受到的力量。
14:48
But critically,
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不過關鍵點是,
14:50
they're briefed about the rules of the game in separate rooms.
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他們是在不同的房間內被告知遊戲的規則。
14:53
So they don't know the rules the other person's playing by.
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所以他們不知道彼此的遊戲規則。
14:55
And what we've measured
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而我們所量測到的力量
14:57
is the force as a function of terms.
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是一個次數的函式。
14:59
And if we look at what we start with,
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如果我們看看初始的力量,
15:01
a quarter of a Newton there, a number of turns,
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這是牛頓 (單位)的¼ ,次數是第一次,
15:03
perfect would be that red line.
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完美的情況應該是這條紅線。
15:05
And what we see in all pairs of subjects is this --
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而我們看見所有被測試者的結果是這樣 --
15:08
a 70 percent escalation in force
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在每一次進行時
15:10
on each go.
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會上升 70% 的力量。
15:12
So it really suggests, when you're doing this --
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所以依據這個研究以及先前的研究結果,
15:14
based on this study and others we've done --
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可以清楚的告訴你,
15:16
that the brain is canceling the sensory consequences
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大腦會將感知的結果給剔除掉,
15:18
and underestimating the force it's producing.
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因而低估自身產生的力量。
15:20
So it re-shows the brain makes predictions
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再次說明了,大腦會進行預測
15:22
and fundamentally changes the precepts.
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因而從根本上改變了遊戲規則。
15:25
So we've made inferences, we've done predictions,
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於是我們做出判斷,做出預測,
15:28
now we have to generate actions.
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然後我們必須產生行動。
15:30
And what Bayes' rule says is, given my beliefs,
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貝葉斯法則所說的,基於我的信念,
15:32
the action should in some sense be optimal.
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在某種意義上而言,這個行動應該是最好的選擇。
15:34
But we've got a problem.
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但是我們卻遇到了問題。
15:36
Tasks are symbolic -- I want to drink, I want to dance --
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任務只具有象徵性的意義 -- 我想要喝水,我想要跳舞 --
15:39
but the movement system has to contract 600 muscles
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但是動作系統卻必須依據特定順序
15:41
in a particular sequence.
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去收縮 600 條肌肉。
15:43
And there's a big gap
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在任務和運動系統之間
15:45
between the task and the movement system.
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有著很大的差距。
15:47
So it could be bridged in infinitely many different ways.
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所以其中可能有無限可能的方式去進行橋接溝通。
15:49
So think about just a point to point movement.
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想想看就單一點對點的移動。
15:51
I could choose these two paths
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我可以從這無限可能中
15:53
out of an infinite number of paths.
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找出兩條路徑。
15:55
Having chosen a particular path,
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一旦選定一條路徑後,
15:57
I can hold my hand on that path
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我可以將我的手保持在這路徑上,
15:59
as infinitely many different joint configurations.
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但仍然有許多不同的連接結構可選。
16:01
And I can hold my arm in a particular joint configuration
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我可以將我的手臂保持在特定的連接結構上,
16:03
either very stiff or very relaxed.
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不管是很僵硬或是很放鬆。
16:05
So I have a huge amount of choice to make.
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所以我可以有許多的選擇。
16:08
Now it turns out, we are extremely stereotypical.
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結果就是,我們總是一成不變。
16:11
We all move the same way pretty much.
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我們總是用同樣的方式進行動作。
16:14
And so it turns out we're so stereotypical,
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所以導致我們總是一成不變,
16:16
our brains have got dedicated neural circuitry
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我們的大腦產生了特定的神經迴路
16:18
to decode this stereotyping.
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來處理這個老套的動作。
16:20
So if I take some dots
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如果我使用一些點
16:22
and set them in motion with biological motion,
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將他們設定進行生態動作,
16:25
your brain's circuitry would understand instantly what's going on.
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你的大腦迴路會立刻了解那是在做什麼。
16:28
Now this is a bunch of dots moving.
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這是許多點在移動。
16:30
You will know what this person is doing,
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你可以了解這個人在做什麼,
16:33
whether happy, sad, old, young -- a huge amount of information.
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是否開心、難過、年老、年輕 -- 許多的資訊。
16:36
If these dots were cars going on a racing circuit,
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如果這些點是正在賽車場上奔馳的車子,
16:38
you would have absolutely no idea what's going on.
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你就一定不知道它們在做什麼了。
16:41
So why is it
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那麼,這是為什麼
16:43
that we move the particular ways we do?
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我們會用特定的方式去做動作呢?
16:45
Well let's think about what really happens.
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我們來想想到底發生了什麼事吧。
16:47
Maybe we don't all quite move the same way.
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也許我們做動作的方式並不完全相同。
16:50
Maybe there's variation in the population.
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也許人與人之間有些差異。
16:52
And maybe those who move better than others
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也許比較擅長進行動作的人
16:54
have got more chance of getting their children into the next generation.
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擁有比較高的機會讓他們的下一代繼續生存。
16:56
So in evolutionary scales, movements get better.
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於是在演化的過程中,動作越做越好。
16:59
And perhaps in life, movements get better through learning.
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也許在人生中,藉由學習可以讓動作變得更好。
17:02
So what is it about a movement which is good or bad?
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那麼,怎樣的動作算是好或不好呢?
17:04
Imagine I want to intercept this ball.
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想像一下,我要攔截這個球。
17:06
Here are two possible paths to that ball.
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這個有兩種可能的行進路線。
17:09
Well if I choose the left-hand path,
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如果我選了左手邊的路徑,
17:11
I can work out the forces required
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我可以以時間函數的方式
17:13
in one of my muscles as a function of time.
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去算出我的肌肉所需要的力氣。
17:15
But there's noise added to this.
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但其中有個雜訊。
17:17
So what I actually get, based on this lovely, smooth, desired force,
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依據這個可愛的、平滑的、令人期待的力量,
17:20
is a very noisy version.
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事實上我得到的是一個有著雜訊的版本。
17:22
So if I pick the same command through many times,
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所以如果我在不同時間下了同樣的指令,
17:25
I will get a different noisy version each time, because noise changes each time.
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我每次都會得到不同的雜訊版本,因為雜訊每次都不一樣。
17:28
So what I can show you here
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所以這兒我可以給你看的是,
17:30
is how the variability of the movement will evolve
447
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如果我用這種方式去選擇,
17:32
if I choose that way.
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我可以得到的運動方式有多少變異性。
17:34
If I choose a different way of moving -- on the right for example --
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如果我選了一種不一樣的運動方式 -- 例如右邊這個 --
17:37
then I'll have a different command, different noise,
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於是我會有不同的指令、不同的雜訊,
17:39
playing through a noisy system, very complicated.
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透過雜訊系統,是相當複雜的。
17:42
All we can be sure of is the variability will be different.
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我們能確定的是,變異性會是不同的。
17:45
If I move in this particular way,
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如果我以特定的方式去動作,
17:47
I end up with a smaller variability across many movements.
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在許多動作後,我會獲得類似的變異性。
17:50
So if I have to choose between those two,
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所以如果我必須在這兩者間作選擇,
17:52
I would choose the right one because it's less variable.
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我會選擇右邊這個,因為它的變化比較小。
17:54
And the fundamental idea
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基本的概念是,
17:56
is you want to plan your movements
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在你計畫你的行動時,
17:58
so as to minimize the negative consequence of the noise.
459
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你就是在設法降低負面的雜訊干擾。
18:01
And one intuition to get
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從直覺上可得知,
18:03
is actually the amount of noise or variability I show here
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這裡的雜訊或變異性,
18:05
gets bigger as the force gets bigger.
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會隨著力量上升而變大。
18:07
So you want to avoid big forces as one principle.
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所以原則上你會避免用太大的力量。
18:10
So we've shown that using this,
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藉由這個,
18:12
we can explain a huge amount of data --
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我們可以解釋許多的資訊 --
18:14
that exactly people are going about their lives planning movements
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人們在計畫進行行動的時候,
18:17
so as to minimize negative consequences of noise.
467
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確實就是在設法降低負面雜訊的干擾。
18:20
So I hope I've convinced you the brain is there
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我希望我能讓你了解,
18:22
and evolved to control movement.
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大腦的存在就是為了控制動作的。
18:24
And it's an intellectual challenge to understand how we do that.
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想要了解這件事,是很具有挑戰性的。
18:27
But it's also relevant
471
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但這同時也跟
18:29
for disease and rehabilitation.
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疾病和康復有相關連性。
18:31
There are many diseases which effect movement.
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有許多疾病會影響行動能力。
18:34
And hopefully if we understand how we control movement,
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如果我們能了解大腦如何控制動作,
18:36
we can apply that to robotic technology.
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我們就能將它運用在機器人技術上。
18:38
And finally, I want to remind you,
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最後,我要提醒大家,
18:40
when you see animals do what look like very simple tasks,
477
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當你看見動物做著看似簡單的動作時,
18:42
the actual complexity of what is going on inside their brain
478
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它們腦中正在進行的事情,
18:44
is really quite dramatic.
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可是非常令人驚訝的複雜。
18:46
Thank you very much.
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非常謝謝大家。
18:48
(Applause)
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(掌聲)
18:56
Chris Anderson: Quick question for you, Dan.
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Chris Anderson: Dan, 我想問個簡短的問題。
18:58
So you're a movement -- (DW: Chauvinist.) -- chauvinist.
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你是一個動作 -- (DW:沙文主義者。) -- 沙文主義者。
19:02
Does that mean that you think that the other things we think our brains are about --
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是不是表示,你認為我們腦中在想的其他事情 --
19:05
the dreaming, the yearning, the falling in love and all these things --
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夢想、渴望、戀愛和這些事情 --
19:08
are a kind of side show, an accident?
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只是一些附帶結果或是意外產生的呢?
19:11
DW: No, no, actually I think they're all important
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DW: 不,事實上我認為這些事情
19:13
to drive the right movement behavior to get reproduction in the end.
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在促使正確的動作行為以達到繁衍目的是很重要的。
19:16
So I think people who study sensation or memory
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所以我認為人們在研究感覺或記憶時,
19:19
without realizing why you're laying down memories of childhood.
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忽略了去理解為什麼人們要累積兒時的記憶。
19:21
The fact that we forget most of our childhood, for example,
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舉例來說,事實上我們忘了大部分兒時的回憶,
19:24
is probably fine, because it doesn't effect our movements later in life.
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這也許是沒關係的,因為這不會影響往後人生的動作。
19:27
You only need to store things which are really going to effect movement.
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你只需要去記住那些真正會影響動作的事情。
19:30
CA: So you think that people thinking about the brain, and consciousness generally,
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CA: 所以你認為人們思考大腦的功用時,
19:33
could get real insight
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一般而言,可以藉由思考動作所扮演的意義,
19:35
by saying, where does movement play in this game?
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來找到真正的答案?
19:37
DW: So people have found out for example
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DW: 舉例來說,人們已經發現,
19:39
that studying vision in the absence of realizing why you have vision
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不了解為何擁有視覺,就去研究視覺,
19:41
is a mistake.
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是一項錯誤。
19:43
You have to study vision with the realization
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在研究視覺的時候,
19:45
of how the movement system is going to use vision.
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你必須了解動作系統是如何運用視覺的。
19:47
And it uses it very differently once you think about it that way.
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一旦你這樣想,運用的方式將會相當不同。
19:49
CA: Well that was quite fascinating. Thank you very much indeed.
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CA:這真的很有意思。非常感謝你。
19:52
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