Daniel Wolpert: The real reason for brains

譯者: kane tan 審譯者: Ana Choi

I'm a neuroscientist.

我是一個神經學家。

And in neuroscience,

在神經學中，

we have to deal with many difficult questions about the brain.

我們必須處理許多關於大腦的艱深問題。

But I want to start with the easiest question

但是我想從最簡單的問題開始談起，

and the question you really should have all asked yourselves at some point in your life,

而每個人在人生中都該問過自己這個問題，

because it's a fundamental question

因為想了解大腦的運作，

if we want to understand brain function.

是最根本的問題。

And that is, why do we and other animals

這問題就是，為什麼我們和其他動物

have brains?

會有大腦呢？

Not all species on our planet have brains,

並非地球上所有的生物都有大腦，

so if we want to know what the brain is for,

所以如果我們想知道大腦的作用，

let's think about why we evolved one.

就得想想我們為何會進化出一個大腦。

Now you may reason that we have one

你現在可能認為大腦的存在

to perceive the world or to think,

是為了感覺這個世界或是思考，

and that's completely wrong.

這是完全錯誤的。

If you think about this question for any length of time,

如果你花了很多時間去思考這個問題，

it's blindingly obvious why we have a brain.

這將會使你誤判為什麼我們會有大腦。

We have a brain for one reason and one reason only,

我們擁有大腦的唯一原因，

and that's to produce adaptable and complex movements.

是為了產生適合且複雜的動作。

There is no other reason to have a brain.

這是大腦存在的唯一理由。

Think about it.

想想看。

Movement is the only way you have

動作是你感覺這個世界

of affecting the world around you.

唯一的反應方式。

Now that's not quite true. There's one other way, and that's through sweating.

這並非完全正確。還有一個方式，就是透過冒汗。

But apart from that,

但是除了那個以外，

everything else goes through contractions of muscles.

每件事都必須藉由肌肉的收縮。

So think about communication --

所以, 看看溝通 --

speech, gestures, writing, sign language --

說話、姿勢、寫字、手語 --

they're all mediated through contractions of your muscles.

這些都藉由收縮你的肌肉來達成。

So it's really important to remember

記住事情是很重要的，

that sensory, memory and cognitive processes are all important,

感覺、記憶和理解程序都很重要，

but they're only important

但這些是為了能夠做到

to either drive or suppress future movements.

開始或結束後續的動作, 所以才會重要。

There can be no evolutionary advantage

小時候的回憶累積、

to laying down memories of childhood

或是對於玫瑰顏色的認知，

or perceiving the color of a rose

對於進化並沒有什麼影響，

if it doesn't affect the way you're going to move later in life.

如果它對你未來生活的行為沒有幫助。

Now for those who don't believe this argument,

對於那些不相信這種說法的人們，

we have trees and grass on our planet without the brain,

雖然地球上的樹和草都沒有大腦，

but the clinching evidence is this animal here --

但這動物就是確切的證據 --

the humble sea squirt.

這渺小的海鞘。

Rudimentary animal, has a nervous system,

這種未進化的動物，擁有神經系統，

swims around in the ocean in its juvenile life.

幼年時會在海洋中游盪著。

And at some point of its life,

等到長大之後，

it implants on a rock.

它便會攀附在岩石上。

And the first thing it does in implanting on that rock, which it never leaves,

當它攀附上這永遠居住的岩石之後，

is to digest its own brain and nervous system

它所作的第一件事，

for food.

就是將它的大腦和神經系統當食物吃掉。

So once you don't need to move,

所以一旦你不再需要移動，

you don't need the luxury of that brain.

你就不需要大腦這種奢侈品了。

And this animal is often taken

這種動物常被拿來

as an analogy to what happens at universities

當作一種比喻，當大學教授

when professors get tenure,

獲得終身職位之後會發生的事情，

but that's a different subject.

不過那是另外一個話題了。

(Applause)

(掌聲)

So I am a movement chauvinist.

我是一個活動主義者。

I believe movement is the most important function of the brain --

我認為大腦最重要的功能就是控制動作，

don't let anyone tell you that it's not true.

別讓任何人告訴你這不是真的。

Now if movement is so important,

如果動作那麼重要，

how well are we doing

我們對於了解大腦如何控制動作

understanding how the brain controls movement?

這方面的研究進展如何呢？

And the answer is we're doing extremely poorly; it's a very hard problem.

答案是，少得可憐；這是很艱深的難題。

But we can look at how well we're doing

但我們可以換個方向來思考，

by thinking about how well we're doing building machines

看看我們對於建造能做出和人類一樣動作的機器

which can do what humans can do.

這種研究進展如何。

Think about the game of chess.

想想看西洋棋這種遊戲。

How well are we doing determining what piece to move where?

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？

If you pit Garry Kasparov here, when he's not in jail,

如果你在 Gary Kasparov 還沒去坐牢之前，

against IBM's Deep Blue,

讓他跟 IBM 的深藍電腦進行比賽，

well the answer is IBM's Deep Blue will occasionally win.

IBM 的深藍電腦有時候可以獲勝。

And I think if IBM's Deep Blue played anyone in this room, it would win every time.

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。

That problem is solved.

這個問題就被解決了。

What about the problem

但如果這個問題是

of picking up a chess piece,

拿起一個棋子，

dexterously manipulating it and putting it back down on the board?

靈巧地拿起它，再放回棋盤上呢？

If you put a five year-old child's dexterity against the best robots of today,

如果你讓一個五歲的小孩跟現今最棒的機器人進行比賽，

the answer is simple:

答案很簡單：

the child wins easily.

那個小孩可以輕易獲勝。

There's no competition at all.

機器人完全不是對手。

Now why is that top problem so easy

那麼，為什麼之前的問題很容易做到？

and the bottom problem so hard?

而接著的問題卻很困難呢？

One reason is a very smart five year-old

原因之一是，一個很聰明的五歲小孩

could tell you the algorithm for that top problem --

可以告訴你上面問題的演算法則 --

look at all possible moves to the end of the game

找出直到遊戲結束的所有可能移動步法，

and choose the one that makes you win.

然後選擇可以讓你獲勝的步法。

So it's a very simple algorithm.

所以這是很簡單的演算法則。

Now of course there are other moves,

當然有其他的步法，

but with vast computers we approximate

但是利用龐大的電腦系統，我們估算

and come close to the optimal solution.

並且找出最佳解答。

When it comes to being dexterous,

當討論到靈巧時，

it's not even clear what the algorithm is you have to solve to be dexterous.

甚至沒有明確的演算法則告訴你什麼叫做靈巧。

And we'll see you have to both perceive and act on the world,

於是你必須感覺同時去做出反應，

which has a lot of problems.

這就會遇到很多問題。

But let me show you cutting-edge robotics.

讓我介紹一些先進的機器人。

Now a lot of robotics is very impressive,

現在有許多優秀的機器人，

but manipulation robotics is really just in the dark ages.

但是操控機器人仍舊處於黑暗的時代。

So this is the end of a Ph.D. project

這是在某一個很棒的機器人學院中，

from one of the best robotics institutes.

一個博士研究項目的成果。

And the student has trained this robot

這位學生訓練這個機器人

to pour this water into a glass.

將水倒進杯子裡面。

It's a hard problem because the water sloshes about, but it can do it.

這是很困難的題目，因為水會濺出來，但是它可以辦到。

But it doesn't do it with anything like the agility of a human.

但是它無法像人類做得那麼靈巧。

Now if you want this robot to do a different task,

如果你希望這個機器人進行另一項任務，

that's another three-year Ph.D. program.

那將是另一個三年期的博士研究計畫。

There is no generalization at all

在機器人工程學裡，

from one task to another in robotics.

一項任務和另一項任務是沒有共通性的。

Now we can compare this

我們可以將這個

to cutting-edge human performance.

和人類優異的表現做比較。

So what I'm going to show you is Emily Fox

我要給大家看的是 Emily Fox,

winning the world record for cup stacking.

她是贏得堆疊杯子世界冠軍的人。

Now the Americans in the audience will know all about cup stacking.

觀眾席中如果有美國人，應該知道這個堆疊杯子的比賽。

It's a high school sport

這是一項高中常見的運動，

where you have 12 cups you have to stack and unstack

你得把 12 個杯子依據指定的順序

against the clock in a prescribed order.

快速的堆疊再分開。

And this is her getting the world record in real time.

這是她創下世界紀錄的畫面，以正常速度播放。

(Laughter)

(笑聲)

(Applause)

(掌聲)

And she's pretty happy.

她非常開心。

We have no idea what is going on inside her brain when she does that,

我們不知道當她做這件事情時，腦子裡發生了什麼事情，

and that's what we'd like to know.

那是我們很想知道。

So in my group, what we try to do

所以我的團隊，我們想要做的是

is reverse engineer how humans control movement.

針對人類如何控制動作這件事去進行逆向工程。

And it sounds like an easy problem.

這聽起來是很簡單的問題。

You send a command down, it causes muscles to contract.

你送出一個指令，這會讓肌肉收縮。

Your arm or body moves,

你的手臂或身體移動，

and you get sensory feedback from vision, from skin, from muscles and so on.

然後你得到來自於視覺、皮膚、肌肉等處的感覺回饋。

The trouble is

問題是，

these signals are not the beautiful signals you want them to be.

這些訊息不如你預期的那樣完美。

So one thing that makes controlling movement difficult

讓控制動作變得困難的其中一個因素是，

is, for example, sensory feedback is extremely noisy.

舉例來說，感覺回饋是充滿雜訊的。

Now by noise, I do not mean sound.

關於雜訊，我指的不是聲音。

We use it in the engineering and neuroscience sense

雜訊一般用在工程學與神經科學的檢測中，

meaning a random noise corrupting a signal.

是指干擾主要訊號的不規律且雜亂的訊號。

So the old days before digital radio when you were tuning in your radio

所以在數位收音機出現之前，當你轉動舊式收音機，

and you heard "crrcckkk" on the station you wanted to hear,

你會在你想聽得電台中聽見「嘎啦嘎啦」的聲音，

that was the noise.

那就是雜訊。

But more generally, this noise is something that corrupts the signal.

講白話一點，雜訊就是干擾訊號的東西。

So for example, if you put your hand under a table

例如，當你將手放在桌子底下，

and try to localize it with your other hand,

試著用另一隻手去找到它的位置，

you can be off by several centimeters

你可能會誤差好幾公分，

due to the noise in sensory feedback.

因為感知回饋中有雜訊。

Similarly, when you put motor output on movement output,

同樣地，當你將動力源的力量變成動作的力量時，

it's extremely noisy.

訊號將是非常雜亂的。

Forget about trying to hit the bull's eye in darts,

先不談射飛鏢時能射中靶心，

just aim for the same spot over and over again.

只要試著去重複瞄準同一個點看看。

You have a huge spread due to movement variability.

因為動作的差異性，你會丟到許多不同的點上去。

And more than that, the outside world, or task,

更別提在外在世界，或是執行任務時，

is both ambiguous and variable.

充滿著不確定性和變異性。

The teapot could be full, it could be empty.

茶壺可能是滿的，也可能是空的。

It changes over time.

每次都不一樣。

So we work in a whole sensory movement task soup of noise.

所以我們是在充滿雜訊的環境中進行動作。

Now this noise is so great

因為這個雜訊非常巨大，

that society places a huge premium

所以我們的社會給予那些

on those of us who can reduce the consequences of noise.

能夠抵抗雜訊的人鉅額獎賞。

So if you're lucky enough to be able to knock a small white ball

所以如果你能將一顆小白球

into a hole several hundred yards away using a long metal stick,

用一根金屬長棍打進幾百碼外的洞裡，

our society will be willing to reward you

人們願意給你

with hundreds of millions of dollars.

好幾億的獎金。

Now what I want to convince you of

而我想要讓你知道的是

is the brain also goes through a lot of effort

大腦做了許多的努力

to reduce the negative consequences

去減少這些雜訊以及變異性

of this sort of noise and variability.

所造成的負面效應。

And to do that, I'm going to tell you about a framework

為此，我將會介紹一個

which is very popular in statistics and machine learning of the last 50 years

在過去五十年間，常被用在統計與機械學習方面的架構，

called Bayesian decision theory.

叫做貝葉斯決策理論。

And it's more recently a unifying way

近來它已經逐漸變成用來解釋

to think about how the brain deals with uncertainty.

大腦如何處理不確定性的主要方法。

And the fundamental idea is you want to make inferences and then take actions.

它的基本概念是，你先做出假設，然後去行動。

So let's think about the inference.

我們先來看看假設。

You want to generate beliefs about the world.

你要產生出對事物的信念。

So what are beliefs?

什麼是信念呢？

Beliefs could be: where are my arms in space?

信念可以是：我的手臂在空間中的哪個位置？

Am I looking at a cat or a fox?

我看見的是一隻貓還是一隻狐狸？

But we're going to represent beliefs with probabilities.

而我們必須用可能性來表示信念。

So we're going to represent a belief

我們要將信念表達為

with a number between zero and one --

介於 0 到 1 之間的數字 --

zero meaning I don't believe it at all, one means I'm absolutely certain.

0 代表我完全不相信，1 則表示我絕對相信。

And numbers in between give you the gray levels of uncertainty.

而介於期間的數字則是代表不確定性的灰色地帶。

And the key idea to Bayesian inference

貝葉斯假設的關鍵在於

is you have two sources of information

你有兩種不同的資訊來源

from which to make your inference.

用來建立起你的假設。

You have data,

你會有資訊，

and data in neuroscience is sensory input.

在神經科學中，這資訊就是你的感覺。

So I have sensory input, which I can take in to make beliefs.

我有感覺，所以我可以將它用來建立信念。

But there's another source of information, and that's effectively prior knowledge.

但還有另一種資訊的來源，就是已經擁有的知識。

You accumulate knowledge throughout your life in memories.

藉由生命中的回憶，知識會被累積下來。

And the point about Bayesian decision theory

而貝葉斯決策理論的重點在於

is it gives you the mathematics

它提供你一種

of the optimal way to combine

數學的最佳化方式

your prior knowledge with your sensory evidence

來合併你原有的知識和你的感覺

to generate new beliefs.

以產生出新的信念。

And I've put the formula up there.

它的公式在這裡。

I'm not going to explain what that formula is, but it's very beautiful.

我不會解釋公式是什麼，但是它很漂亮。

And it has real beauty and real explanatory power.

它擁有真實的美感，和真實的說服力。

And what it really says, and what you want to estimate,

它真正表達的，以及你想要估計出的，

is the probability of different beliefs

是由你的感覺所產生出

given your sensory input.

不同信念的可能性。

So let me give you an intuitive example.

我舉一個很直接的例子。

Imagine you're learning to play tennis

想像你正在學習打網球，

and you want to decide where the ball is going to bounce

當球飛過網子朝你過來時，

as it comes over the net towards you.

你要決定球會掉在哪個位置。

There are two sources of information

依據貝葉斯的理論，

Bayes' rule tells you.

你有兩個資訊來源。

There's sensory evidence -- you can use visual information auditory information,

一個是感覺證據 -- 你可以藉由視覺和聽覺的資訊，

and that might tell you it's going to land in that red spot.

那可能會讓你判斷在紅點處。

But you know that your senses are not perfect,

而你知道你的感覺並不完美，

and therefore there's some variability of where it's going to land

所以它的落點會有誤差，

shown by that cloud of red,

這就是紅色區域，

representing numbers between 0.5 and maybe 0.1.

而可能性大概是在 0.5 到 0.1 之間。

That information is available in the current shot,

這資訊來自於這一次的發球，

but there's another source of information

還有另外的資訊

not available on the current shot,

並非由這次發球而來，

but only available by repeated experience in the game of tennis,

而是來自於反覆進行網球比賽的經驗，

and that's that the ball doesn't bounce

經驗告訴你，在這場比賽中，

with equal probability over the court during the match.

球落在球場上每個位置的可能性並不相等。

If you're playing against a very good opponent,

如果你的對手技術很棒，

they may distribute it in that green area,

他們會讓球落在綠色區域，

which is the prior distribution,

就是所謂的先驗分布，

making it hard for you to return.

這會讓你難以回擊。

Now both these sources of information carry important information.

這些訊息來源都帶有重要的訊息。

And what Bayes' rule says

依據貝葉斯理論所說，

is that I should multiply the numbers on the red by the numbers on the green

我應該將紅色區域的機率和綠色區域的機率相乘，

to get the numbers of the yellow, which have the ellipses,

就會得到橢圓形黃色區域的機率，

and that's my belief.

而這就是我的信念。

So it's the optimal way of combining information.

這是合併訊息的最佳方式。

Now I wouldn't tell you all this if it wasn't that a few years ago,

幾年前我們的研究發現，

we showed this is exactly what people do

人們在學習新的動作技巧時，

when they learn new movement skills.

確實有同樣的現象。

And what it means

也就是說，

is we really are Bayesian inference machines.

我們就像是使用貝葉斯假設的機器。

As we go around, we learn about statistics of the world and lay that down,

在生活中，我們學習並累積了關於世界的許多統計資料，

but we also learn

但我們也學習了

about how noisy our own sensory apparatus is,

我們自身感知器官產生的雜訊有多少，

and then combine those

然後將這些合併在一起，

in a real Bayesian way.

這些正是貝葉斯法則。

Now a key part to the Bayesian is this part of the formula.

貝葉斯法則的一個關鍵部份就是這個公式的這個部份。

And what this part really says

這部份是在說

is I have to predict the probability

我必須利用不同的感知回饋

of different sensory feedbacks

去預測各種可能性

given my beliefs.

來創造出我的信念。

So that really means I have to make predictions of the future.

意思是說，我必須要去預測未來。

And I want to convince you the brain does make predictions

我要讓大家了解的是，

of the sensory feedback it's going to get.

大腦真的能夠預測即將獲得的感知回饋。

And moreover, it profoundly changes your perceptions

並且，它會因為你的行為

by what you do.

而深深改變你的感覺。

And to do that, I'll tell you

為此，我將會告訴你，

about how the brain deals with sensory input.

大腦是怎麼處理獲得的感知訊號。

So you send a command out,

於是你送出一個指令，

you get sensory feedback back,

你得到一個感知的回饋，

and that transformation is governed

而這個轉換是由

by the physics of your body and your sensory apparatus.

你的身體和感知器官的物理層面所管理。

But you can imagine looking inside the brain.

但是你可以想像一下大腦的內部狀況。

And here's inside the brain.

這是大腦的內側。

You might have a little predictor, a neural simulator,

有一個小小的預測器具，一種神經模擬器，

of the physics of your body and your senses.

可以模擬出你的身體和感覺的物理現象。

So as you send a movement command down,

於是當你送出動作的指令，

you tap a copy of that off

你順便複製了一份指令，

and run it into your neural simulator

然後將它送進你的神經模擬器

to anticipate the sensory consequences of your actions.

去預測動作造成的感知結果。

So as I shake this ketchup bottle,

所以當我搖動這瓶蕃茄醬時，

I get some true sensory feedback as the function of time in the bottom row.

我得到真正的感知回饋，就是那個底下那個時間函數。

And if I've got a good predictor, it predicts the same thing.

如果我有一個很好的預測器具，它可以預測出同樣的東西。

Well why would I bother doing that?

為什麼我要這麼做呢？

I'm going to get the same feedback anyway.

反正我會得到同樣的回饋啊。

Well there's good reasons.

這可是有很好的解釋的。

Imagine, as I shake the ketchup bottle,

想像一下，當我搖動這瓶蕃茄醬時，

someone very kindly comes up to me and taps it on the back for me.

某人很好心隨著我的動作，將它拍回來給我。

Now I get an extra source of sensory information

現在我因為這個額外的動作

due to that external act.

而有了額外的感知訊息。

So I get two sources.

於是我有了兩個感知來源。

I get you tapping on it, and I get me shaking it,

一個是你拍它，一個是我搖它，

but from my senses' point of view,

但是對我的感覺來說，

that is combined together into one source of information.

它被合併在一起，變成了一種感知來源。

Now there's good reason to believe

現在有了很好的理由去相信

that you would want to be able to distinguish external events from internal events.

你會希望能夠將外在和內在的事件給分開來。

Because external events are actually much more behaviorally relevant

因為相對於在我體內進行事物的感覺，

than feeling everything that's going on inside my body.

外在的事件跟行為事件更具有相關性。

So one way to reconstruct that

要將感覺重新建立的方法是

is to compare the prediction --

去針對基於你的動作指令做的預測

which is only based on your movement commands --

以及真實的狀況

with the reality.

去進行比較。

Any discrepancy should hopefully be external.

幸運的話，出現的差異都屬於外在的影響。

So as I go around the world,

所以當我在生活中，

I'm making predictions of what I should get, subtracting them off.

我預測將會遇到的狀況，然後將這些預期剔除。

Everything left over is external to me.

剩下的就是外在對我的影響。

What evidence is there for this?

有什麼證據能證明嗎？

Well there's one very clear example

嗯，有一個很明確的例子，

where a sensation generated by myself feels very different

當我自己本身產生的感覺

then if generated by another person.

和別人的感覺很不一樣的時候。

And so we decided the most obvious place to start

於是我們決定開始測試最常見的狀況，

was with tickling.

那就是搔癢。

It's been known for a long time, you can't tickle yourself

大家都知道，你自己搔自己癢的感覺

as well as other people can.

不會像別人搔你癢那麼強烈。

But it hasn't really been shown, it's because you have a neural simulator,

但是大家都還不知道，那是因為你擁有神經模擬器，

simulating your own body

模擬著你自己的身體，

and subtracting off that sense.

並且將這個感覺給剔除掉。

So we can bring the experiments of the 21st century

而我們在這21世紀進行實驗時，

by applying robotic technologies to this problem.

可以藉由機器人技術來解決這個問題。

And in effect, what we have is some sort of stick in one hand attached to a robot,

我們的作法是，在一個機器人的手中裝置一根棍子，

and they're going to move that back and forward.

然後讓它前後移動。

And then we're going to track that with a computer

接著我們會用電腦來追蹤這個動作，

and use it to control another robot,

藉以控制另一個機器人，

which is going to tickle their palm with another stick.

它會用另一根棍子來搔對方手掌心的癢。

And then we're going to ask them to rate a bunch of things

接著我們會要求它們針對一些事情進行評分，

including ticklishness.

也包含了癢的程度。

I'll show you just one part of our study.

我所展示的只是我們研究中的一部分。

And here I've taken away the robots,

這邊我沒有放入機器人，

but basically people move with their right arm sinusoidally back and forward.

只是用人依據正弦波的方式去前後移動右手。

And we replay that to the other hand with a time delay.

接著我們用另一隻手稍微慢一點再做一次。

Either no time delay,

或是以同樣速度，

in which case light would just tickle your palm,

輕輕搔癢你的手心，

or with a time delay of two-tenths of three-tenths of a second.

或是有個 0.2 或 0.3 秒的時間差。

So the important point here

有一個重點是，

is the right hand always does the same things -- sinusoidal movement.

右手總是進行正弦波的動作。

The left hand always is the same and puts sinusoidal tickle.

而左手總是進行同樣的動作，並且進行搔癢。

All we're playing with is a tempo causality.

我們穩定的增加時間差。

And as we go from naught to 0.1 second,

當我們將時間差從 0 增大到 0.1 秒時，

it becomes more ticklish.

會覺得稍微癢一點。

As you go from 0.1 to 0.2,

當時間差由 0.1 增大到 0.2 秒時，

it becomes more ticklish at the end.

最後會覺得更癢。

And by 0.2 of a second,

當時間差到達 0.2 秒時，

it's equivalently ticklish

那個癢度將等同於

to the robot that just tickled you without you doing anything.

在你什麼都不做時，機器人搔你癢的感覺。

So whatever is responsible for this cancellation

不論消弭感覺的原因是什麼，

is extremely tightly coupled with tempo causality.

它和這個時間差有密切的關聯性。

And based on this illustration, we really convinced ourselves in the field

基於這個例子，我們可以確信

that the brain's making precise predictions

大腦會做出精確的預測

and subtracting them off from the sensations.

並且將預測內容從感覺中剔除。

Now I have to admit, these are the worst studies my lab has ever run.

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。

Because the tickle sensation on the palm comes and goes,

因為這些在手掌上搔癢的感覺一下就沒了，

you need large numbers of subjects

你需要大量的測試者來獲得數據，

with these stars making them significant.

才能使測試結果比較有意義。

So we were looking for a much more objective way

所以我們找尋了比較客觀的方法

to assess this phenomena.

來評估這個現象。

And in the intervening years I had two daughters.

在中間這幾年，我有了兩個女兒。

And one thing you notice about children in backseats of cars on long journeys,

在長途旅程中，你會發現在車子後座上的小孩們，

they get into fights --

她們會吵架 --

which started with one of them doing something to the other, the other retaliating.

當某個小孩對另一個人動了手之後，另一個也會還手。

It quickly escalates.

很快就會越來越嚴重。

And children tend to get into fights which escalate in terms of force.

小孩們打架時通常會越打越大力。

Now when I screamed at my children to stop,

當我叫她們住手時，

sometimes they would both say to me

有時候她們都會跟我說

the other person hit them harder.

另一個人打得比較大力。

Now I happen to know my children don't lie,

我剛好知道我的小孩不會說謊的，

so I thought, as a neuroscientist,

所以身為一個神經科學家，我開始思考，

it was important how I could explain

要怎麼解釋，她們兩人口中的真相會相互矛盾

how they were telling inconsistent truths.

是很重要的一件事。

And we hypothesize based on the tickling study

我們假設，依據這個搔癢的研究，

that when one child hits another,

當一個小孩打另一個人時，

they generate the movement command.

她們產生了動作指令。

They predict the sensory consequences and subtract it off.

她們預測了感知結果，並且剔除了它。

So they actually think they've hit the person less hard than they have --

所以她們認為自己打對方的力量比較小 --

rather like the tickling.

就像搔癢一樣。

Whereas the passive recipient

然而，在被打的時候，

doesn't make the prediction, feels the full blow.

並不會產生這種預測，所以感覺到了全力。

So if they retaliate with the same force,

所以如果她們還手的力道一樣，

the first person will think it's been escalated.

第一個出手的人會覺得力量變大了。

So we decided to test this in the lab.

所以我們決定在實驗室做這個實驗。

(Laughter)

(笑聲)

Now we don't work with children, we don't work with hitting,

我們不是用小孩做測試，我們也不是測試打架，

but the concept is identical.

不過概念上是完全一樣的。

We bring in two adults. We tell them they're going to play a game.

我們找了兩個大人。我們告訴他們將會進行一場遊戲。

And so here's player one and player two sitting opposite to each other.

於是一個玩家和另一個人面對面的坐著。

And the game is very simple.

這個遊戲很簡單。

We started with a motor

我們用了一個馬達

with a little lever, a little force transfuser.

上面裝著一根小槓桿，一個小型力量傳輸器。

And we use this motor to apply force down to player one's fingers

我們用這個馬達來對下面的玩家手指施力

for three seconds and then it stops.

持續三秒鐘，然後停止。

And that player's been told, remember the experience of that force

這個玩家被告知要記住這個力量的感覺，

and use your other finger

然後用你另一隻手指

to apply the same force

藉由力量傳輸器，去施加相同的力量

down to the other subject's finger through a force transfuser -- and they do that.

到下面受測試者的手指上 -- 於是他們這麼做了。

And player two's been told, remember the experience of that force.

第二個玩家被告知要記住這個力量的感覺。

Use your other hand to apply the force back down.

用你的另一隻手去施加相同的力量回去。

And so they take it in turns

於是他們輪流來回地

to apply the force they've just experienced back and forward.

去施加他們所感受到的力量。

But critically,

不過關鍵點是，

they're briefed about the rules of the game in separate rooms.

他們是在不同的房間內被告知遊戲的規則。

So they don't know the rules the other person's playing by.

所以他們不知道彼此的遊戲規則。

And what we've measured

而我們所量測到的力量

is the force as a function of terms.

是一個次數的函式。

And if we look at what we start with,

如果我們看看初始的力量，

a quarter of a Newton there, a number of turns,

這是牛頓 (單位)的¼ ，次數是第一次，

perfect would be that red line.

完美的情況應該是這條紅線。

And what we see in all pairs of subjects is this --

而我們看見所有被測試者的結果是這樣 --

a 70 percent escalation in force

在每一次進行時

on each go.

會上升 70% 的力量。

So it really suggests, when you're doing this --

所以依據這個研究以及先前的研究結果，

based on this study and others we've done --

可以清楚的告訴你，

that the brain is canceling the sensory consequences

大腦會將感知的結果給剔除掉，

and underestimating the force it's producing.

因而低估自身產生的力量。

So it re-shows the brain makes predictions

再次說明了，大腦會進行預測

and fundamentally changes the precepts.

因而從根本上改變了遊戲規則。

So we've made inferences, we've done predictions,

於是我們做出判斷，做出預測，

now we have to generate actions.

然後我們必須產生行動。

And what Bayes' rule says is, given my beliefs,

貝葉斯法則所說的，基於我的信念，

the action should in some sense be optimal.

在某種意義上而言，這個行動應該是最好的選擇。

But we've got a problem.

但是我們卻遇到了問題。

Tasks are symbolic -- I want to drink, I want to dance --

任務只具有象徵性的意義 -- 我想要喝水，我想要跳舞 --

but the movement system has to contract 600 muscles

但是動作系統卻必須依據特定順序

in a particular sequence.

去收縮 600 條肌肉。

And there's a big gap

在任務和運動系統之間

between the task and the movement system.

有著很大的差距。

So it could be bridged in infinitely many different ways.

所以其中可能有無限可能的方式去進行橋接溝通。

So think about just a point to point movement.

想想看就單一點對點的移動。

I could choose these two paths

我可以從這無限可能中

out of an infinite number of paths.

找出兩條路徑。

Having chosen a particular path,

一旦選定一條路徑後，

I can hold my hand on that path

我可以將我的手保持在這路徑上，

as infinitely many different joint configurations.

但仍然有許多不同的連接結構可選。

And I can hold my arm in a particular joint configuration

我可以將我的手臂保持在特定的連接結構上，

either very stiff or very relaxed.

不管是很僵硬或是很放鬆。

So I have a huge amount of choice to make.

所以我可以有許多的選擇。

Now it turns out, we are extremely stereotypical.

結果就是，我們總是一成不變。

We all move the same way pretty much.

我們總是用同樣的方式進行動作。

And so it turns out we're so stereotypical,

所以導致我們總是一成不變，

our brains have got dedicated neural circuitry

我們的大腦產生了特定的神經迴路

to decode this stereotyping.

來處理這個老套的動作。

So if I take some dots

如果我使用一些點

and set them in motion with biological motion,

將他們設定進行生態動作，

your brain's circuitry would understand instantly what's going on.

你的大腦迴路會立刻了解那是在做什麼。

Now this is a bunch of dots moving.

這是許多點在移動。

You will know what this person is doing,

你可以了解這個人在做什麼，

whether happy, sad, old, young -- a huge amount of information.

是否開心、難過、年老、年輕 -- 許多的資訊。

If these dots were cars going on a racing circuit,

如果這些點是正在賽車場上奔馳的車子，

you would have absolutely no idea what's going on.

你就一定不知道它們在做什麼了。

So why is it

那麼，這是為什麼

that we move the particular ways we do?

我們會用特定的方式去做動作呢？

Well let's think about what really happens.

我們來想想到底發生了什麼事吧。

Maybe we don't all quite move the same way.

也許我們做動作的方式並不完全相同。

Maybe there's variation in the population.

也許人與人之間有些差異。

And maybe those who move better than others

也許比較擅長進行動作的人

have got more chance of getting their children into the next generation.

擁有比較高的機會讓他們的下一代繼續生存。

So in evolutionary scales, movements get better.

於是在演化的過程中，動作越做越好。

And perhaps in life, movements get better through learning.

也許在人生中，藉由學習可以讓動作變得更好。

So what is it about a movement which is good or bad?

那麼，怎樣的動作算是好或不好呢？

Imagine I want to intercept this ball.

想像一下，我要攔截這個球。

Here are two possible paths to that ball.

這個有兩種可能的行進路線。

Well if I choose the left-hand path,

如果我選了左手邊的路徑，

I can work out the forces required

我可以以時間函數的方式

in one of my muscles as a function of time.

去算出我的肌肉所需要的力氣。

But there's noise added to this.

但其中有個雜訊。

So what I actually get, based on this lovely, smooth, desired force,

依據這個可愛的、平滑的、令人期待的力量，

is a very noisy version.

事實上我得到的是一個有著雜訊的版本。

So if I pick the same command through many times,

所以如果我在不同時間下了同樣的指令，

I will get a different noisy version each time, because noise changes each time.

我每次都會得到不同的雜訊版本，因為雜訊每次都不一樣。

So what I can show you here

所以這兒我可以給你看的是，

is how the variability of the movement will evolve

如果我用這種方式去選擇，

if I choose that way.

我可以得到的運動方式有多少變異性。

If I choose a different way of moving -- on the right for example --

如果我選了一種不一樣的運動方式 -- 例如右邊這個 --

then I'll have a different command, different noise,

於是我會有不同的指令、不同的雜訊，

playing through a noisy system, very complicated.

透過雜訊系統，是相當複雜的。

All we can be sure of is the variability will be different.

我們能確定的是，變異性會是不同的。

If I move in this particular way,

如果我以特定的方式去動作，

I end up with a smaller variability across many movements.

在許多動作後，我會獲得類似的變異性。

So if I have to choose between those two,

所以如果我必須在這兩者間作選擇，

I would choose the right one because it's less variable.

我會選擇右邊這個，因為它的變化比較小。

And the fundamental idea

基本的概念是，

is you want to plan your movements

在你計畫你的行動時，

so as to minimize the negative consequence of the noise.

你就是在設法降低負面的雜訊干擾。

And one intuition to get

從直覺上可得知，

is actually the amount of noise or variability I show here

這裡的雜訊或變異性，

gets bigger as the force gets bigger.

會隨著力量上升而變大。

So you want to avoid big forces as one principle.

所以原則上你會避免用太大的力量。

So we've shown that using this,

藉由這個，

we can explain a huge amount of data --

我們可以解釋許多的資訊 --

that exactly people are going about their lives planning movements

人們在計畫進行行動的時候，

so as to minimize negative consequences of noise.

確實就是在設法降低負面雜訊的干擾。

So I hope I've convinced you the brain is there

我希望我能讓你了解，

and evolved to control movement.

大腦的存在就是為了控制動作的。

And it's an intellectual challenge to understand how we do that.

想要了解這件事，是很具有挑戰性的。

But it's also relevant

但這同時也跟

for disease and rehabilitation.

疾病和康復有相關連性。

There are many diseases which effect movement.

有許多疾病會影響行動能力。

And hopefully if we understand how we control movement,

如果我們能了解大腦如何控制動作，

we can apply that to robotic technology.

我們就能將它運用在機器人技術上。

And finally, I want to remind you,

最後，我要提醒大家，

when you see animals do what look like very simple tasks,

當你看見動物做著看似簡單的動作時，

the actual complexity of what is going on inside their brain

它們腦中正在進行的事情，

is really quite dramatic.

可是非常令人驚訝的複雜。

Thank you very much.

非常謝謝大家。

(Applause)

(掌聲)

Chris Anderson: Quick question for you, Dan.

Chris Anderson: Dan, 我想問個簡短的問題。

So you're a movement -- (DW: Chauvinist.) -- chauvinist.

你是一個動作 -- (DW:沙文主義者。) -- 沙文主義者。

Does that mean that you think that the other things we think our brains are about --

是不是表示，你認為我們腦中在想的其他事情 --

the dreaming, the yearning, the falling in love and all these things --

夢想、渴望、戀愛和這些事情 --

are a kind of side show, an accident?

只是一些附帶結果或是意外產生的呢？

DW: No, no, actually I think they're all important

DW: 不，事實上我認為這些事情

to drive the right movement behavior to get reproduction in the end.

在促使正確的動作行為以達到繁衍目的是很重要的。

So I think people who study sensation or memory

所以我認為人們在研究感覺或記憶時，

without realizing why you're laying down memories of childhood.

忽略了去理解為什麼人們要累積兒時的記憶。

The fact that we forget most of our childhood, for example,

舉例來說，事實上我們忘了大部分兒時的回憶，

is probably fine, because it doesn't effect our movements later in life.

這也許是沒關係的，因為這不會影響往後人生的動作。

You only need to store things which are really going to effect movement.

你只需要去記住那些真正會影響動作的事情。

CA: So you think that people thinking about the brain, and consciousness generally,

CA: 所以你認為人們思考大腦的功用時，

could get real insight

一般而言，可以藉由思考動作所扮演的意義，

by saying, where does movement play in this game?

來找到真正的答案？

DW: So people have found out for example

DW: 舉例來說，人們已經發現，

that studying vision in the absence of realizing why you have vision

不了解為何擁有視覺，就去研究視覺，

is a mistake.

是一項錯誤。

You have to study vision with the realization

在研究視覺的時候，

of how the movement system is going to use vision.

你必須了解動作系統是如何運用視覺的。

And it uses it very differently once you think about it that way.

一旦你這樣想，運用的方式將會相當不同。

CA: Well that was quite fascinating. Thank you very much indeed.

CA：這真的很有意思。非常感謝你。

(Applause)

(掌聲)