Daniel Wolpert: The real reason for brains

341,413 views ・ 2011-11-03

TED


请双击下面的英文字幕来播放视频。

翻译人员: Yina Jin 校对人员: Xiaoqiao Xie
00:15
I'm a neuroscientist.
0
15260
2000
我是个神经生物学家
00:17
And in neuroscience,
1
17260
2000
在神经生物学研究里
00:19
we have to deal with many difficult questions about the brain.
2
19260
3000
我们需要处理很多关于大脑的难题
00:22
But I want to start with the easiest question
3
22260
2000
不过我今天要从这个最简单的问题开始
00:24
and the question you really should have all asked yourselves at some point in your life,
4
24260
3000
这个问题是大家应该在以前问过自己的
00:27
because it's a fundamental question
5
27260
2000
因为如果大家想了解大脑功能的话
00:29
if we want to understand brain function.
6
29260
2000
这个问题是很基本的
00:31
And that is, why do we and other animals
7
31260
2000
那就是,为什么我们和其他动物
00:33
have brains?
8
33260
2000
都有大脑呢?
00:35
Not all species on our planet have brains,
9
35260
3000
地球上不是所有物种都有大脑
00:38
so if we want to know what the brain is for,
10
38260
2000
所以要搞清大脑有什么用
00:40
let's think about why we evolved one.
11
40260
2000
那我们应该先考虑为什么我们进化出了大脑
00:42
Now you may reason that we have one
12
42260
2000
大家也许觉得
00:44
to perceive the world or to think,
13
44260
2000
我们有大脑是为了感知世界或者思考
00:46
and that's completely wrong.
14
46260
2000
其实是完全错误的
00:48
If you think about this question for any length of time,
15
48260
3000
如果再好好思考一段时间的话
00:51
it's blindingly obvious why we have a brain.
16
51260
2000
其实我们有大脑的原因很明显
00:53
We have a brain for one reason and one reason only,
17
53260
3000
大脑的存在,有且仅有一个原因
00:56
and that's to produce adaptable and complex movements.
18
56260
3000
就是为了生成有适应性的,复杂的动作
00:59
There is no other reason to have a brain.
19
59260
2000
没有其他原因了
01:01
Think about it.
20
61260
2000
想想看
01:03
Movement is the only way you have
21
63260
2000
动作是你唯一
01:05
of affecting the world around you.
22
65260
2000
对世界产生影响的方法
01:07
Now that's not quite true. There's one other way, and that's through sweating.
23
67260
3000
当然也不尽然,另一个办法是通过排汗
01:10
But apart from that,
24
70260
2000
不过除了那个以外
01:12
everything else goes through contractions of muscles.
25
72260
2000
所有事情都是由肌肉的收缩来办到的
01:14
So think about communication --
26
74260
2000
想想我们的沟通方式--
01:16
speech, gestures, writing, sign language --
27
76260
3000
语言,手势,写作,手语--
01:19
they're all mediated through contractions of your muscles.
28
79260
3000
都是由肌肉的收缩来完成的
01:22
So it's really important to remember
29
82260
2000
所以重要的一点就是
01:24
that sensory, memory and cognitive processes are all important,
30
84260
4000
感知、记忆和认知过程虽然也很关键
01:28
but they're only important
31
88260
2000
但是它们的重要性都只是在于
01:30
to either drive or suppress future movements.
32
90260
2000
它们能够促使或者抑制动作的发生
01:32
There can be no evolutionary advantage
33
92260
2000
如果不是能够影响到大家未来生活中的动作的话
01:34
to laying down memories of childhood
34
94260
2000
能够记住小时候的回忆
01:36
or perceiving the color of a rose
35
96260
2000
或者感知到玫瑰花的颜色
01:38
if it doesn't affect the way you're going to move later in life.
36
98260
3000
在进化这一点上没有任何特别的优势
01:41
Now for those who don't believe this argument,
37
101260
2000
如果有谁不相信这个说法
01:43
we have trees and grass on our planet without the brain,
38
103260
2000
我可以举例说不会运动的树和草都没有大脑
01:45
but the clinching evidence is this animal here --
39
105260
2000
不过有一种动物提供了确凿的证据--
01:47
the humble sea squirt.
40
107260
2000
不起眼的海鞘
01:49
Rudimentary animal, has a nervous system,
41
109260
3000
海鞘是一种初等生物,有神经系统
01:52
swims around in the ocean in its juvenile life.
42
112260
2000
生命初期在海中游动
01:54
And at some point of its life,
43
114260
2000
然后在某一时刻
01:56
it implants on a rock.
44
116260
2000
海鞘会把自己移植到岩石上再也不移动
01:58
And the first thing it does in implanting on that rock, which it never leaves,
45
118260
3000
海鞘在岩石上固定后的第一件事
02:01
is to digest its own brain and nervous system
46
121260
3000
就是把自己的大脑和神经系统
02:04
for food.
47
124260
2000
作为食物消化掉
02:06
So once you don't need to move,
48
126260
2000
也就是说,一旦不用再移动
02:08
you don't need the luxury of that brain.
49
128260
3000
也就不需要大脑这样的奢侈品了
02:11
And this animal is often taken
50
131260
2000
并且,这种动物
02:13
as an analogy to what happens at universities
51
133260
2000
常常被类比到大学里面
02:15
when professors get tenure,
52
135260
2000
教授获得终身职位之后的情况
02:17
but that's a different subject.
53
137260
2000
不过那是另一个话题了
02:19
(Applause)
54
139260
2000
(掌声)
02:21
So I am a movement chauvinist.
55
141260
3000
所以说,我是个运动沙文主义者
02:24
I believe movement is the most important function of the brain --
56
144260
2000
我认为运动是大脑最重要的功能
02:26
don't let anyone tell you that it's not true.
57
146260
2000
不要让别人告诉你这个观点不对
02:28
Now if movement is so important,
58
148260
2000
那么,如果运动如此重要
02:30
how well are we doing
59
150260
2000
我们已经能在多大程度上
02:32
understanding how the brain controls movement?
60
152260
2000
了解大脑如何控制动作了呢?
02:34
And the answer is we're doing extremely poorly; it's a very hard problem.
61
154260
2000
答案其实是非常少,因为这是相当困难的
02:36
But we can look at how well we're doing
62
156260
2000
不过我们已经制造出一些模拟人类动作的机器人
02:38
by thinking about how well we're doing building machines
63
158260
2000
分析这些机器的动作水平
02:40
which can do what humans can do.
64
160260
2000
就知道我们了解大脑控制动作的研究进展如何了
02:42
Think about the game of chess.
65
162260
2000
来看国际象棋这个游戏
02:44
How well are we doing determining what piece to move where?
66
164260
3000
我们让机器人决定如何走棋时候的表现如何呢?
02:47
If you pit Garry Kasparov here, when he's not in jail,
67
167260
3000
如果大家趁国际象棋冠军加里·卡斯帕罗夫还没进监狱的时候
02:50
against IBM's Deep Blue,
68
170260
2000
把他请来与IBM的深蓝对战
02:52
well the answer is IBM's Deep Blue will occasionally win.
69
172260
3000
深蓝有时会赢
02:55
And I think if IBM's Deep Blue played anyone in this room, it would win every time.
70
175260
3000
而且我觉得IBM的深蓝和在座的任何一位对战,应该每次都会赢
02:58
That problem is solved.
71
178260
2000
所以这方面完全没有问题
03:00
What about the problem
72
180260
2000
但是如果让机器人
03:02
of picking up a chess piece,
73
182260
2000
灵巧地拿起棋子
03:04
dexterously manipulating it and putting it back down on the board?
74
184260
3000
再放回棋盘上去呢?
03:07
If you put a five year-old child's dexterity against the best robots of today,
75
187260
3000
如果大家让五岁小孩子与当今最厉害的机器人对决
03:10
the answer is simple:
76
190260
2000
结果很简单
03:12
the child wins easily.
77
192260
2000
小孩子会赢得轻而易举
03:14
There's no competition at all.
78
194260
2000
完全没有悬念
03:16
Now why is that top problem so easy
79
196260
2000
那么为什么前面那个问题这么简单
03:18
and the bottom problem so hard?
80
198260
2000
后面这个问题就这么难呢?
03:20
One reason is a very smart five year-old
81
200260
2000
一个原因是,一个聪明点的五岁小孩子
03:22
could tell you the algorithm for that top problem --
82
202260
2000
就已经能够告诉你上面那个问题的解决算法了--
03:24
look at all possible moves to the end of the game
83
204260
2000
找出游戏结束之前所有可能的下法
03:26
and choose the one that makes you win.
84
206260
2000
选择赢面最大的一步来下
03:28
So it's a very simple algorithm.
85
208260
2000
所以其实这是个很简单的算法
03:30
Now of course there are other moves,
86
210260
2000
当然也有其他的步法
03:32
but with vast computers we approximate
87
212260
2000
不过用强大的计算机做近似计算
03:34
and come close to the optimal solution.
88
214260
2000
就能很容易找到近似最优解
03:36
When it comes to being dexterous,
89
216260
2000
但在灵活性这个问题上
03:38
it's not even clear what the algorithm is you have to solve to be dexterous.
90
218260
2000
我们甚至连让机器人变灵活的算法都找不到
03:40
And we'll see you have to both perceive and act on the world,
91
220260
2000
可以看到,如果既感知世界,又作用于世界的话
03:42
which has a lot of problems.
92
222260
2000
其实是要面对很多问题的
03:44
But let me show you cutting-edge robotics.
93
224260
2000
现在我来介绍一下最高端的机器人技术
03:46
Now a lot of robotics is very impressive,
94
226260
2000
如今许多项机器人技术已经相当厉害
03:48
but manipulation robotics is really just in the dark ages.
95
228260
3000
可是在机器人动作控制方面我们遇到了瓶颈,看不到曙光
03:51
So this is the end of a Ph.D. project
96
231260
2000
这是在一个顶尖机器人控制学院里
03:53
from one of the best robotics institutes.
97
233260
2000
一个博士研究项目的成果
03:55
And the student has trained this robot
98
235260
2000
这位博士生训练这个机器人
03:57
to pour this water into a glass.
99
237260
2000
向这个玻璃杯里面倒水
03:59
It's a hard problem because the water sloshes about, but it can do it.
100
239260
3000
这很难实现,因为水会洒出来;不过机器人能做到
04:02
But it doesn't do it with anything like the agility of a human.
101
242260
3000
但是机器人无法达到任何接近于人类的灵活程度
04:05
Now if you want this robot to do a different task,
102
245260
3000
并且如果想让机器人完成另一个任务的话
04:08
that's another three-year Ph.D. program.
103
248260
3000
就得要另外一个历时三年的博士项目了
04:11
There is no generalization at all
104
251260
2000
在机器人控制领域
04:13
from one task to another in robotics.
105
253260
2000
从一项任务到另一项任务,根本没有共通性可言
04:15
Now we can compare this
106
255260
2000
现在我们再来与
04:17
to cutting-edge human performance.
107
257260
2000
最尖端的人类性能做比较
04:19
So what I'm going to show you is Emily Fox
108
259260
2000
我会播放艾米莉・福克斯叠杯子
04:21
winning the world record for cup stacking.
109
261260
3000
获得世界记录的场景
04:24
Now the Americans in the audience will know all about cup stacking.
110
264260
2000
观众里有美国人的话一定知道叠杯子这个游戏
04:26
It's a high school sport
111
266260
2000
这是个高中里的娱乐项目
04:28
where you have 12 cups you have to stack and unstack
112
268260
2000
要求把12只杯子按照一定顺序
04:30
against the clock in a prescribed order.
113
270260
2000
以最快速度摞起来再展开回来
04:32
And this is her getting the world record in real time.
114
272260
3000
下面就是她创下世界记录的实时情况
04:39
(Laughter)
115
279260
8000
(笑声)
04:47
(Applause)
116
287260
5000
(掌声)
04:52
And she's pretty happy.
117
292260
2000
看她多开心
04:54
We have no idea what is going on inside her brain when she does that,
118
294260
2000
我们不清楚她叠杯子的时候大脑里发生了什么
04:56
and that's what we'd like to know.
119
296260
2000
而且我们的确很想知道
04:58
So in my group, what we try to do
120
298260
2000
所以我和我的团队
05:00
is reverse engineer how humans control movement.
121
300260
3000
试图去逆向实现人类如何控制动作的过程
05:03
And it sounds like an easy problem.
122
303260
2000
看起来问题很简单
05:05
You send a command down, it causes muscles to contract.
123
305260
2000
你送出一个指令,令肌肉收缩
05:07
Your arm or body moves,
124
307260
2000
这样手臂或躯干就会运动
05:09
and you get sensory feedback from vision, from skin, from muscles and so on.
125
309260
3000
然后能够获得从视觉,皮肤和肌肉和其他地方获得感官回馈
05:12
The trouble is
126
312260
2000
但问题是
05:14
these signals are not the beautiful signals you want them to be.
127
314260
2000
这些信号远没有想象中的那样完美
05:16
So one thing that makes controlling movement difficult
128
316260
2000
比如说,其中的难点之一
05:18
is, for example, sensory feedback is extremely noisy.
129
318260
3000
就是感官回馈中的杂音非常大
05:21
Now by noise, I do not mean sound.
130
321260
3000
我这里所说的“杂音”不是指声音
05:24
We use it in the engineering and neuroscience sense
131
324260
2000
在工程学和神经学里面
05:26
meaning a random noise corrupting a signal.
132
326260
2000
我们用“杂音”来表示干扰正常信号的随机杂乱信号
05:28
So the old days before digital radio when you were tuning in your radio
133
328260
3000
所以以前还没有电子收音机的时候,如果调那种老式收音机
05:31
and you heard "crrcckkk" on the station you wanted to hear,
134
331260
2000
电台里面有时候有“喀喇喀喇”的声音
05:33
that was the noise.
135
333260
2000
那个就是杂音
05:35
But more generally, this noise is something that corrupts the signal.
136
335260
3000
推广来说,杂音就是相对于正常信号的干扰信号
05:38
So for example, if you put your hand under a table
137
338260
2000
比如说,如果把一只手放在桌子底下
05:40
and try to localize it with your other hand,
138
340260
2000
然后在桌子上面用另一只手去对准
05:42
you can be off by several centimeters
139
342260
2000
最后位置可能相差好几厘米
05:44
due to the noise in sensory feedback.
140
344260
2000
这就是因为感官回馈里面的杂音在起作用
05:46
Similarly, when you put motor output on movement output,
141
346260
2000
同样,运动神经输出的肌肉动作和实际输出之间
05:48
it's extremely noisy.
142
348260
2000
也是有很多杂音的
05:50
Forget about trying to hit the bull's eye in darts,
143
350260
2000
且不谈扔飞镖的时候瞄准靶心去扔
05:52
just aim for the same spot over and over again.
144
352260
2000
只看重复瞄准同一点的时候发生什么情况
05:54
You have a huge spread due to movement variability.
145
354260
3000
由于每次动作都有差异,最后瞄准的结果会形成一片散点
05:57
And more than that, the outside world, or task,
146
357260
2000
更何况外界环境和要执行的任务
05:59
is both ambiguous and variable.
147
359260
2000
常常模糊和变化着的
06:01
The teapot could be full, it could be empty.
148
361260
2000
看这个茶壶,可能是满的,也可能是空的
06:03
It changes over time.
149
363260
2000
每次都不一样
06:05
So we work in a whole sensory movement task soup of noise.
150
365260
4000
所以我们其实是随时处在一大堆感官动作杂音环绕之中做动作的
06:09
Now this noise is so great
151
369260
2000
这种杂音相当厉害
06:11
that society places a huge premium
152
371260
2000
以至于我们社会会给那些
06:13
on those of us who can reduce the consequences of noise.
153
373260
3000
能有效减少杂音带来的后果的人巨额奖赏
06:16
So if you're lucky enough to be able to knock a small white ball
154
376260
3000
所以在座哪位能做到像老虎伍兹那样,用一根长金属杆
06:19
into a hole several hundred yards away using a long metal stick,
155
379260
3000
把一个小白球打进几百米开外的洞里
06:22
our society will be willing to reward you
156
382260
2000
我们的社会愿意
06:24
with hundreds of millions of dollars.
157
384260
3000
奖励你百万千万的钱
06:27
Now what I want to convince you of
158
387260
2000
好,我接下来想说明的是
06:29
is the brain also goes through a lot of effort
159
389260
2000
其实我们的大脑
06:31
to reduce the negative consequences
160
391260
2000
为了减少噪音和变化性的负面影响
06:33
of this sort of noise and variability.
161
393260
2000
也做了很多工作
06:35
And to do that, I'm going to tell you about a framework
162
395260
2000
为此,我来介绍一个在过去50年里
06:37
which is very popular in statistics and machine learning of the last 50 years
163
397260
3000
统计学和机器学习方面都很常用到的架构
06:40
called Bayesian decision theory.
164
400260
2000
叫做贝叶斯决策论(Bayesian decision theory)
06:42
And it's more recently a unifying way
165
402260
3000
近来这个理论常被用来
06:45
to think about how the brain deals with uncertainty.
166
405260
3000
从整体上理解大脑如何处理这种不确定性
06:48
And the fundamental idea is you want to make inferences and then take actions.
167
408260
3000
基本思路是先做推断,然后做出动作
06:51
So let's think about the inference.
168
411260
2000
我们先来看推断
06:53
You want to generate beliefs about the world.
169
413260
2000
在推断中,我们要建立对于当前情景的“信念”
06:55
So what are beliefs?
170
415260
2000
那么什么是信念?
06:57
Beliefs could be: where are my arms in space?
171
417260
2000
信念可以是 “我的胳膊在空间里的什么位置”
06:59
Am I looking at a cat or a fox?
172
419260
2000
也可以是 “我在看一只猫还是一只狐狸”
07:01
But we're going to represent beliefs with probabilities.
173
421260
3000
不过我们要把信念用概率来表示
07:04
So we're going to represent a belief
174
424260
2000
所以在这里我们把所谓的信念
07:06
with a number between zero and one --
175
426260
2000
表示成0到1之间的一个数--称之为置信值
07:08
zero meaning I don't believe it at all, one means I'm absolutely certain.
176
428260
3000
0表示完全不相信,1表示完全确信
07:11
And numbers in between give you the gray levels of uncertainty.
177
431260
3000
0到1之间就表示不同灰度的不确定程度
07:14
And the key idea to Bayesian inference
178
434260
2000
然后注意,贝叶斯推断的重点是
07:16
is you have two sources of information
179
436260
2000
我们靠两个信息源
07:18
from which to make your inference.
180
438260
2000
来做出推断
07:20
You have data,
181
440260
2000
第一我们有数据--
07:22
and data in neuroscience is sensory input.
182
442260
2000
在神经科学里这个数据就是感官输入进来的内容
07:24
So I have sensory input, which I can take in to make beliefs.
183
444260
3000
所以感官输入是其中一个信息源,用于生成我刚才说的”置信值“
07:27
But there's another source of information, and that's effectively prior knowledge.
184
447260
3000
不过还有第二个信息源,事实上也就是先前的知识
07:30
You accumulate knowledge throughout your life in memories.
185
450260
3000
因为我们在一生中在过去的记忆中积累知识
07:33
And the point about Bayesian decision theory
186
453260
2000
好了,贝叶斯决策论的重点就在于
07:35
is it gives you the mathematics
187
455260
2000
这个理论提供了一种计算方法
07:37
of the optimal way to combine
188
457260
2000
能找到最优的办法来整合
07:39
your prior knowledge with your sensory evidence
189
459260
2000
知识积累和感官输入这两种信息源
07:41
to generate new beliefs.
190
461260
2000
以生成新的置信值
07:43
And I've put the formula up there.
191
463260
2000
我现在把公式放在这里
07:45
I'm not going to explain what that formula is, but it's very beautiful.
192
465260
2000
我不详细解释这个公式了,但是这个公式非常漂亮
07:47
And it has real beauty and real explanatory power.
193
467260
3000
不仅有和谐的内在美,还有实实在在的说服力
07:50
And what it really says, and what you want to estimate,
194
470260
2000
这个公式真正的用途,也就是我们要估测的结果
07:52
is the probability of different beliefs
195
472260
2000
是给出我们感官输入的情况下
07:54
given your sensory input.
196
474260
2000
不同置信值出现的概率
07:56
So let me give you an intuitive example.
197
476260
2000
现在我举一个直观的例子
07:58
Imagine you're learning to play tennis
198
478260
3000
想象你现在在网球场练网球
08:01
and you want to decide where the ball is going to bounce
199
481260
2000
当这个网球越过球网飞过来的时候
08:03
as it comes over the net towards you.
200
483260
2000
你要决定这个球落在哪里
08:05
There are two sources of information
201
485260
2000
根据贝叶斯的理论
08:07
Bayes' rule tells you.
202
487260
2000
你现在有两个信息源
08:09
There's sensory evidence -- you can use visual information auditory information,
203
489260
3000
一个是感官输入--你的视觉和听觉收到的信息
08:12
and that might tell you it's going to land in that red spot.
204
492260
3000
告诉你球应该会落在图上的红点处
08:15
But you know that your senses are not perfect,
205
495260
3000
但是,你也知道自己的感官并不完美
08:18
and therefore there's some variability of where it's going to land
206
498260
2000
所以球的落地点可能会有误差--
08:20
shown by that cloud of red,
207
500260
2000
在图上用这块红色区域来表示
08:22
representing numbers between 0.5 and maybe 0.1.
208
502260
3000
这个区域的概率在0.5和大概0.1附近浮动
08:26
That information is available in the current shot,
209
506260
2000
以上这些信息是实时获得的
08:28
but there's another source of information
210
508260
2000
但是另一个获得信息的渠道
08:30
not available on the current shot,
211
510260
2000
不是实时获得的
08:32
but only available by repeated experience in the game of tennis,
212
512260
3000
而是来自只有通过反复练习网球才得来的经验
08:35
and that's that the ball doesn't bounce
213
515260
2000
经验告诉你,这个网球在比赛过程中
08:37
with equal probability over the court during the match.
214
517260
2000
不会以均等的概率落在球场里
08:39
If you're playing against a very good opponent,
215
519260
2000
如果你的对手水平很高
08:41
they may distribute it in that green area,
216
521260
2000
可能会让球的落点分布在图上的绿色区域
08:43
which is the prior distribution,
217
523260
2000
也就是所谓的”先验分布“
08:45
making it hard for you to return.
218
525260
2000
这些位置很难接到球
08:47
Now both these sources of information carry important information.
219
527260
2000
那么现在,两个信息源都包含重要的信息
08:49
And what Bayes' rule says
220
529260
2000
根据贝叶斯的理论
08:51
is that I should multiply the numbers on the red by the numbers on the green
221
531260
3000
我们应该把红色区域和绿色区域的数据相乘
08:54
to get the numbers of the yellow, which have the ellipses,
222
534260
3000
得到椭圆形的黄色区域
08:57
and that's my belief.
223
537260
2000
这就是我们的置信值
08:59
So it's the optimal way of combining information.
224
539260
3000
并且这是整合信息的最佳方案
09:02
Now I wouldn't tell you all this if it wasn't that a few years ago,
225
542260
2000
几年前我们考察过,人们学习新动作新技巧的时候
09:04
we showed this is exactly what people do
226
544260
2000
真的在遵循这样的模式
09:06
when they learn new movement skills.
227
546260
2000
所以我刚才才举了这个例子
09:08
And what it means
228
548260
2000
这说明
09:10
is we really are Bayesian inference machines.
229
550260
2000
我们实际上都是天生的贝叶斯推断器
09:12
As we go around, we learn about statistics of the world and lay that down,
230
552260
4000
在成长的过程中,我们不但学会了并记下了生活中的统计数据
09:16
but we also learn
231
556260
2000
也掌握了
09:18
about how noisy our own sensory apparatus is,
232
558260
2000
我们自己感官的杂音可能有多大
09:20
and then combine those
233
560260
2000
然后我们用贝叶斯法
09:22
in a real Bayesian way.
234
562260
2000
来整合处理这些数据
09:24
Now a key part to the Bayesian is this part of the formula.
235
564260
3000
那么贝叶斯公式里很关键的是这一项--预测
09:27
And what this part really says
236
567260
2000
这一项的意思是
09:29
is I have to predict the probability
237
569260
2000
我们需要预测
09:31
of different sensory feedbacks
238
571260
2000
在原有置信值条件下
09:33
given my beliefs.
239
573260
2000
不同感官回馈的概率
09:35
So that really means I have to make predictions of the future.
240
575260
3000
也就是说我们要对未来的可能做出预测
09:38
And I want to convince you the brain does make predictions
241
578260
2000
我现在要说服各位相信一点,那就是
09:40
of the sensory feedback it's going to get.
242
580260
2000
大脑的确在对未来可能的感官回馈做出预测
09:42
And moreover, it profoundly changes your perceptions
243
582260
2000
并且你本身做了什么动作
09:44
by what you do.
244
584260
2000
在很大程度上影响了你感知到的东西
09:46
And to do that, I'll tell you
245
586260
2000
为了说明这点,我来解释
09:48
about how the brain deals with sensory input.
246
588260
2000
我们的大脑如何处理感官输入
09:50
So you send a command out,
247
590260
3000
我们先送出一个指令
09:53
you get sensory feedback back,
248
593260
2000
然后得到一个感官回馈输入回来
09:55
and that transformation is governed
249
595260
2000
之间的转换过程是由
09:57
by the physics of your body and your sensory apparatus.
250
597260
3000
身体和感觉器官的物理过程完成的
10:00
But you can imagine looking inside the brain.
251
600260
2000
不过我们能想象大脑内部发生了什么
10:02
And here's inside the brain.
252
602260
2000
图上就是大脑内部
10:04
You might have a little predictor, a neural simulator,
253
604260
2000
里面可能有一个预测结构,也就是神经组成的模拟器
10:06
of the physics of your body and your senses.
254
606260
2000
来模拟身体和感觉器官的物理过程
10:08
So as you send a movement command down,
255
608260
2000
当一个动作指令发出来的时候
10:10
you tap a copy of that off
256
610260
2000
大脑复制一份这个指令
10:12
and run it into your neural simulator
257
612260
2000
然后在这个神经模拟器上运行
10:14
to anticipate the sensory consequences of your actions.
258
614260
4000
来预测出这个动作带来的感官回馈结果
10:18
So as I shake this ketchup bottle,
259
618260
2000
所以我往下磕这个番茄酱瓶子的时候
10:20
I get some true sensory feedback as the function of time in the bottom row.
260
620260
3000
在下面那行里我得到真实的感官回馈,是个关于时间的函数
10:23
And if I've got a good predictor, it predicts the same thing.
261
623260
3000
同时大脑里的预测结构如果准确的话,也预测出了同样的结果
10:26
Well why would I bother doing that?
262
626260
2000
好了,那我为什么非要多此一举来预测呢?
10:28
I'm going to get the same feedback anyway.
263
628260
2000
毕竟最终我总能得到同样的回馈
10:30
Well there's good reasons.
264
630260
2000
不过这样确实是有原因的
10:32
Imagine, as I shake the ketchup bottle,
265
632260
2000
想象我磕这个番茄酱瓶子的时候
10:34
someone very kindly comes up to me and taps it on the back for me.
266
634260
3000
台下有位热心观众过来帮我拍了下瓶底
10:37
Now I get an extra source of sensory information
267
637260
2000
于是我多了一个感官信息的来源
10:39
due to that external act.
268
639260
2000
因为我多接受了这个外在的动作
10:41
So I get two sources.
269
641260
2000
所以感官回馈现在有两个来源
10:43
I get you tapping on it, and I get me shaking it,
270
643260
3000
一个是你拍瓶底的动作,另一个是我磕瓶子的动作
10:46
but from my senses' point of view,
271
646260
2000
但是从我的感觉来讲
10:48
that is combined together into one source of information.
272
648260
3000
这两个是合起来作为一个信息源的
10:51
Now there's good reason to believe
273
651260
2000
可是我们有理由相信
10:53
that you would want to be able to distinguish external events from internal events.
274
653260
3000
我们希望能够把外来事件和内在的事件区分开
10:56
Because external events are actually much more behaviorally relevant
275
656260
3000
因为实际上相对于我身体上发生的内在事件
10:59
than feeling everything that's going on inside my body.
276
659260
3000
外来事件在行为上才更有分析价值
11:02
So one way to reconstruct that
277
662260
2000
所以区分开来的一种办法
11:04
is to compare the prediction --
278
664260
2000
就是把我的预测结果--
11:06
which is only based on your movement commands --
279
666260
2000
因为这个结果只基于我的动作指令--
11:08
with the reality.
280
668260
2000
和真实的感官反馈做比较
11:10
Any discrepancy should hopefully be external.
281
670260
3000
希望两者的差值应该就是外力的结果
11:13
So as I go around the world,
282
673260
2000
所以我和外界接触的时候
11:15
I'm making predictions of what I should get, subtracting them off.
283
675260
3000
我预测出我自己的动作可能得到什么回馈,减掉这些
11:18
Everything left over is external to me.
284
678260
2000
剩下的其他部分就是外界对我的产生的作用
11:20
What evidence is there for this?
285
680260
2000
那有什么证据支持这点吗?
11:22
Well there's one very clear example
286
682260
2000
我们认为其中一个非常明了的例子就是
11:24
where a sensation generated by myself feels very different
287
684260
2000
自身产生的动作带来的感觉
11:26
then if generated by another person.
288
686260
2000
与他人做同样动作带来的感觉是十分不同的
11:28
And so we decided the most obvious place to start
289
688260
2000
我们认为最直接的着手点
11:30
was with tickling.
290
690260
2000
就是挠痒痒这件事
11:32
It's been known for a long time, you can't tickle yourself
291
692260
2000
众所周知,人们挠自己的时候
11:34
as well as other people can.
292
694260
2000
总是不如别人挠自己的时候痒
11:36
But it hasn't really been shown, it's because you have a neural simulator,
293
696260
3000
但是没有被证明为什么。其实是因为每个人自己有一个神经模拟器
11:39
simulating your own body
294
699260
2000
模拟出自己动作带来的效果
11:41
and subtracting off that sense.
295
701260
2000
然后在感觉里自动减去这个效果
11:43
So we can bring the experiments of the 21st century
296
703260
3000
所以我们可以用21世纪的实验条件
11:46
by applying robotic technologies to this problem.
297
706260
3000
把机器人技术应用到这个问题上
11:49
And in effect, what we have is some sort of stick in one hand attached to a robot,
298
709260
3000
具体办法是,我们让试验者一只手握着一根连接在机器人上的小棍
11:52
and they're going to move that back and forward.
299
712260
2000
由手带动一起前后移动
11:54
And then we're going to track that with a computer
300
714260
2000
这时候我们用计算机追踪这个移动模式
11:56
and use it to control another robot,
301
716260
2000
然后用同样的模式带动另一个机器人
11:58
which is going to tickle their palm with another stick.
302
718260
2000
去用另一根小棍挠试验者的另一只手的手心
12:00
And then we're going to ask them to rate a bunch of things
303
720260
2000
之后,我们会让试验者给一系列指标打分
12:02
including ticklishness.
304
722260
2000
也包括痒痒的程度
12:04
I'll show you just one part of our study.
305
724260
2000
在这里我展示我们实验的其中一部分
12:06
And here I've taken away the robots,
306
726260
2000
图上没有画那两个机器人
12:08
but basically people move with their right arm sinusoidally back and forward.
307
728260
3000
基本上人们的右臂是以类正弦的方式前后移动的
12:11
And we replay that to the other hand with a time delay.
308
731260
3000
这时候我们把这个移动加上某个时间延迟,在另一只手上重放
12:14
Either no time delay,
309
734260
2000
延迟可以是0
12:16
in which case light would just tickle your palm,
310
736260
2000
就像直接挠手心一样
12:18
or with a time delay of two-tenths of three-tenths of a second.
311
738260
4000
也可以是0.1秒,0.2秒,0.3秒这样的延迟
12:22
So the important point here
312
742260
2000
这里的重点是
12:24
is the right hand always does the same things -- sinusoidal movement.
313
744260
3000
试验者的右手一直做同样的类正弦动作
12:27
The left hand always is the same and puts sinusoidal tickle.
314
747260
3000
左手一直在被同样的类正弦动作挠着
12:30
All we're playing with is a tempo causality.
315
750260
2000
唯一我们改变的就是因与果之间的步调
12:32
And as we go from naught to 0.1 second,
316
752260
2000
当我们把延迟从0加到0.1秒的时候
12:34
it becomes more ticklish.
317
754260
2000
感觉上越来越痒
12:36
As you go from 0.1 to 0.2,
318
756260
2000
再从0.1到0.2秒
12:38
it becomes more ticklish at the end.
319
758260
2000
越到后来越痒
12:40
And by 0.2 of a second,
320
760260
2000
到了0.2秒的时候
12:42
it's equivalently ticklish
321
762260
2000
发痒的程度已经等同于
12:44
to the robot that just tickled you without you doing anything.
322
764260
2000
自己什么也不做,机器人直接来挠的效果了
12:46
So whatever is responsible for this cancellation
323
766260
2000
因此不管是什么造成了自身动作的抵消
12:48
is extremely tightly coupled with tempo causality.
324
768260
3000
一定与因果之间的步调大有联系
12:51
And based on this illustration, we really convinced ourselves in the field
325
771260
3000
受这个实验的启发,我们终于相信了那个论点
12:54
that the brain's making precise predictions
326
774260
2000
也就是大脑能够做出准确预测
12:56
and subtracting them off from the sensations.
327
776260
3000
并将预测结果从实体感觉里减去
12:59
Now I have to admit, these are the worst studies my lab has ever run.
328
779260
3000
现在我要承认,这个是我实验室里进行过的最糟糕的实验
13:02
Because the tickle sensation on the palm comes and goes,
329
782260
2000
因为痒痒的感觉就像昙花一现
13:04
you need large numbers of subjects
330
784260
2000
所以需要大量的试验者
13:06
with these stars making them significant.
331
786260
2000
还只能靠星级来给痒痒的程度打分
13:08
So we were looking for a much more objective way
332
788260
2000
所以我们需要一个更客观的办法
13:10
to assess this phenomena.
333
790260
2000
来评估这个现象
13:12
And in the intervening years I had two daughters.
334
792260
2000
其间几年中,我有了两个女儿
13:14
And one thing you notice about children in backseats of cars on long journeys,
335
794260
3000
于是关于长途旅行里小孩子坐在后座上,你注意到一件事
13:17
they get into fights --
336
797260
2000
那就是他们会互相掐架--
13:19
which started with one of them doing something to the other, the other retaliating.
337
799260
3000
开始于其中一个对另一个动了什么手脚,另一个就要还手
13:22
It quickly escalates.
338
802260
2000
然后冲突很快就会升级
13:24
And children tend to get into fights which escalate in terms of force.
339
804260
3000
而小孩子们之间的冲突升级一般都体现在用力大小上
13:27
Now when I screamed at my children to stop,
340
807260
2000
于是我嚷嚷着叫我的孩子们住手的时候
13:29
sometimes they would both say to me
341
809260
2000
有的时候她们俩都会告诉我
13:31
the other person hit them harder.
342
811260
3000
对方下手比自己更重
13:34
Now I happen to know my children don't lie,
343
814260
2000
我恰好知道我的孩子们不会说假话
13:36
so I thought, as a neuroscientist,
344
816260
2000
所以作为一个神经学家
13:38
it was important how I could explain
345
818260
2000
我想我如何解释两人自相矛盾的真话
13:40
how they were telling inconsistent truths.
346
820260
2000
应该是很重要的吧
13:42
And we hypothesize based on the tickling study
347
822260
2000
于是我们根据挠痒痒的实验做出了假设
13:44
that when one child hits another,
348
824260
2000
就是当一个孩子打另一个的时候
13:46
they generate the movement command.
349
826260
2000
会生成动作命令
13:48
They predict the sensory consequences and subtract it off.
350
828260
3000
他们预测出了感知到的结果,然后减掉了
13:51
So they actually think they've hit the person less hard than they have --
351
831260
2000
实际上他们以为他们下手比真正要轻
13:53
rather like the tickling.
352
833260
2000
和挠痒的道理类似
13:55
Whereas the passive recipient
353
835260
2000
然而那个被打的孩子
13:57
doesn't make the prediction, feels the full blow.
354
837260
2000
没有做预测,感觉到了全部的打击
13:59
So if they retaliate with the same force,
355
839260
2000
所以他们用同样这个打击的力气还手的话
14:01
the first person will think it's been escalated.
356
841260
2000
第一个人会觉得力气变大,冲突升级了
14:03
So we decided to test this in the lab.
357
843260
2000
所以我们决定在实验室里做这个测试,好了
14:05
(Laughter)
358
845260
3000
(笑声)
14:08
Now we don't work with children, we don't work with hitting,
359
848260
2000
我们不用小孩子,也不用打的
14:10
but the concept is identical.
360
850260
2000
不过概念是相同的
14:12
We bring in two adults. We tell them they're going to play a game.
361
852260
3000
我们找来两个成人,告诉他们来做个小游戏
14:15
And so here's player one and player two sitting opposite to each other.
362
855260
2000
这里显示了玩家一和玩家二面对面坐着
14:17
And the game is very simple.
363
857260
2000
游戏很简单
14:19
We started with a motor
364
859260
2000
开始的时候这里有个电机
14:21
with a little lever, a little force transfuser.
365
861260
2000
连着小杠杆,作为传力器
14:23
And we use this motor to apply force down to player one's fingers
366
863260
2000
小电机向下对玩家一的手指施力
14:25
for three seconds and then it stops.
367
865260
3000
持续3秒钟后停止
14:28
And that player's been told, remember the experience of that force
368
868260
3000
我们告诉这名玩家一,记住这个力的感觉
14:31
and use your other finger
369
871260
2000
再用另一个手指
14:33
to apply the same force
370
873260
2000
以同样的力,通过传力器
14:35
down to the other subject's finger through a force transfuser -- and they do that.
371
875260
3000
下压玩家二的手指--玩家一这么做了
14:38
And player two's been told, remember the experience of that force.
372
878260
3000
然后玩家二被告知记住这个力的感觉
14:41
Use your other hand to apply the force back down.
373
881260
3000
用另一只手把同样的力施加回来
14:44
And so they take it in turns
374
884260
2000
这两个玩家轮流来回
14:46
to apply the force they've just experienced back and forward.
375
886260
2000
施加方才所受的力
14:48
But critically,
376
888260
2000
不过关键的一点是
14:50
they're briefed about the rules of the game in separate rooms.
377
890260
3000
这两个玩家是在不同房间里被介绍游戏规则的
14:53
So they don't know the rules the other person's playing by.
378
893260
2000
所以他们互相不知道对方遵循的规则
14:55
And what we've measured
379
895260
2000
而我们测量的
14:57
is the force as a function of terms.
380
897260
2000
是力的大小关于回合次数的函数
14:59
And if we look at what we start with,
381
899260
2000
我们来看图,开始的时候
15:01
a quarter of a Newton there, a number of turns,
382
901260
2000
力是四分之一牛顿,横轴是回合的次数
15:03
perfect would be that red line.
383
903260
2000
完美的结果应该是这条红线
15:05
And what we see in all pairs of subjects is this --
384
905260
3000
但是我们在所有实验配对中看到的都是这个--
15:08
a 70 percent escalation in force
385
908260
2000
每一次施力中
15:10
on each go.
386
910260
2000
大小上扬70%
15:12
So it really suggests, when you're doing this --
387
912260
2000
所以确实表明,我们在运动的时候--
15:14
based on this study and others we've done --
388
914260
2000
根据这个实验和我们所做的其他实验--
15:16
that the brain is canceling the sensory consequences
389
916260
2000
大脑一直在抵消感官带来的结果
15:18
and underestimating the force it's producing.
390
918260
2000
而低估自己产生的力
15:20
So it re-shows the brain makes predictions
391
920260
2000
所以再次说明大脑会做出预测
15:22
and fundamentally changes the precepts.
392
922260
3000
并且在根本上改变感知到的内容
15:25
So we've made inferences, we've done predictions,
393
925260
3000
到现在为止,我们做了推断,也做了预测
15:28
now we have to generate actions.
394
928260
2000
现在我们要生成动作了
15:30
And what Bayes' rule says is, given my beliefs,
395
930260
2000
贝叶斯法则说的是,基于置信值
15:32
the action should in some sense be optimal.
396
932260
2000
动作应该在某种意义上是最优的
15:34
But we've got a problem.
397
934260
2000
但在这里出现问题了
15:36
Tasks are symbolic -- I want to drink, I want to dance --
398
936260
3000
动作任务是符号性质的--我要喝水,我要跳舞--
15:39
but the movement system has to contract 600 muscles
399
939260
2000
但是运动系统需要按特定顺序
15:41
in a particular sequence.
400
941260
2000
收缩600块肌肉
15:43
And there's a big gap
401
943260
2000
况且有一个巨大的鸿沟
15:45
between the task and the movement system.
402
945260
2000
横在动作任务和运动系统之间
15:47
So it could be bridged in infinitely many different ways.
403
947260
2000
意思是我们有无穷多种方式越过这个鸿沟
15:49
So think about just a point to point movement.
404
949260
2000
来考虑仅仅是点到点的移动
15:51
I could choose these two paths
405
951260
2000
我可以在无穷条可能的路径中
15:53
out of an infinite number of paths.
406
953260
2000
选出这两条
15:55
Having chosen a particular path,
407
955260
2000
选定一条特定路径之后
15:57
I can hold my hand on that path
408
957260
2000
我可以把手放在这条路径上
15:59
as infinitely many different joint configurations.
409
959260
2000
但是又有无穷多种关节构型
16:01
And I can hold my arm in a particular joint configuration
410
961260
2000
另外我把的胳膊固定在某一种构型之后
16:03
either very stiff or very relaxed.
411
963260
2000
我可以紧张,也可以松弛
16:05
So I have a huge amount of choice to make.
412
965260
3000
所以我有非常多的选择余地
16:08
Now it turns out, we are extremely stereotypical.
413
968260
3000
不过结果是,我们都特别典型
16:11
We all move the same way pretty much.
414
971260
3000
都基本上用同一种方式移动
16:14
And so it turns out we're so stereotypical,
415
974260
2000
我们典型到
16:16
our brains have got dedicated neural circuitry
416
976260
2000
我们的大脑已经开辟出特定的神经回路
16:18
to decode this stereotyping.
417
978260
2000
来解码这类典型
16:20
So if I take some dots
418
980260
2000
因此如果我拿一些点
16:22
and set them in motion with biological motion,
419
982260
3000
然后让它们按生物动作形式来运动
16:25
your brain's circuitry would understand instantly what's going on.
420
985260
3000
大家的大脑回路会立即知道怎么回事
16:28
Now this is a bunch of dots moving.
421
988260
2000
好了,这仅仅是一堆点在动
16:30
You will know what this person is doing,
422
990260
3000
我们就知道这个人在做什么
16:33
whether happy, sad, old, young -- a huge amount of information.
423
993260
3000
是喜还是悲,是老还是少--很大量的信息
16:36
If these dots were cars going on a racing circuit,
424
996260
2000
如果这些点是赛车在赛道上绕圈
16:38
you would have absolutely no idea what's going on.
425
998260
3000
我们对发生了什么就完全没有概念了
16:41
So why is it
426
1001260
2000
那我们为什么
16:43
that we move the particular ways we do?
427
1003260
2000
用这种特定的模式移动呢?
16:45
Well let's think about what really happens.
428
1005260
2000
来想想真实情况可能是什么
16:47
Maybe we don't all quite move the same way.
429
1007260
3000
假如我们并不都用同一种模式移动
16:50
Maybe there's variation in the population.
430
1010260
2000
假如人群中有差异
16:52
And maybe those who move better than others
431
1012260
2000
而且假如那些比其他人移动得更好的个体
16:54
have got more chance of getting their children into the next generation.
432
1014260
2000
有更多机会让他们的子代进入下一代的群体里面
16:56
So in evolutionary scales, movements get better.
433
1016260
3000
那么在进化级别上,动作越来越优化
16:59
And perhaps in life, movements get better through learning.
434
1019260
3000
另外,也许在一生当中,学习会让动作更优化
17:02
So what is it about a movement which is good or bad?
435
1022260
2000
那么,是什么决定了动作的好与坏呢?
17:04
Imagine I want to intercept this ball.
436
1024260
2000
来想象我要截下这个球
17:06
Here are two possible paths to that ball.
437
1026260
3000
这里有两种路径来做到
17:09
Well if I choose the left-hand path,
438
1029260
2000
假如我选了左手边这条路径
17:11
I can work out the forces required
439
1031260
2000
我可以算出某一块肌肉需要施多大力
17:13
in one of my muscles as a function of time.
440
1033260
2000
用关于时间的函数表示
17:15
But there's noise added to this.
441
1035260
2000
但是还有杂音附加在上面
17:17
So what I actually get, based on this lovely, smooth, desired force,
442
1037260
3000
所以根据这条美好、光滑、期望中的曲线
17:20
is a very noisy version.
443
1040260
2000
我事实上得到的是个杂音很大的版本
17:22
So if I pick the same command through many times,
444
1042260
3000
那如果我多次发布同样的动作指令
17:25
I will get a different noisy version each time, because noise changes each time.
445
1045260
3000
每次会得到不同的含杂音的版本,因为每次杂音不一样
17:28
So what I can show you here
446
1048260
2000
所以我现在在这儿展示的
17:30
is how the variability of the movement will evolve
447
1050260
2000
只是这个动作的可变性有多大
17:32
if I choose that way.
448
1052260
2000
前提是如果我选这种方式的话
17:34
If I choose a different way of moving -- on the right for example --
449
1054260
3000
如果我选另一种方式--比如右边这个--
17:37
then I'll have a different command, different noise,
450
1057260
2000
那我就会有不同的指令,不同的杂音
17:39
playing through a noisy system, very complicated.
451
1059260
3000
还要透过杂音系统,总之非常复杂
17:42
All we can be sure of is the variability will be different.
452
1062260
3000
我们唯一能确定的是,两个方式的可变性会不同
17:45
If I move in this particular way,
453
1065260
2000
要是我用这种方式运动的话
17:47
I end up with a smaller variability across many movements.
454
1067260
3000
最后得到的可变性在很多种动作里面是最小的
17:50
So if I have to choose between those two,
455
1070260
2000
所以要是我在这两者里面选其一
17:52
I would choose the right one because it's less variable.
456
1072260
2000
我就会选右边那个,因为可变性更小
17:54
And the fundamental idea
457
1074260
2000
话说回来,我们的基本思路
17:56
is you want to plan your movements
458
1076260
2000
是要找到一种动作方式
17:58
so as to minimize the negative consequence of the noise.
459
1078260
3000
能够把杂音带来的负面影响降到最低
18:01
And one intuition to get
460
1081260
2000
并且我们凭直觉就知道
18:03
is actually the amount of noise or variability I show here
461
1083260
2000
我在这里展示的杂音,或者说可变性
18:05
gets bigger as the force gets bigger.
462
1085260
2000
随力的大小的增加而增加
18:07
So you want to avoid big forces as one principle.
463
1087260
3000
所以我们的原则之一就是避免施加太大的力
18:10
So we've shown that using this,
464
1090260
2000
用这个原则
18:12
we can explain a huge amount of data --
465
1092260
2000
我们就能解释很多得到的数据--
18:14
that exactly people are going about their lives planning movements
466
1094260
3000
那就是人们在生活中有意安排动作方式
18:17
so as to minimize negative consequences of noise.
467
1097260
3000
来把杂音带来的负面影响降到最低
18:20
So I hope I've convinced you the brain is there
468
1100260
2000
现在,我想我已经让大家相信大脑之所以存在
18:22
and evolved to control movement.
469
1102260
2000
是为了控制动作
18:24
And it's an intellectual challenge to understand how we do that.
470
1104260
3000
而且了解怎样做到这点是需要费一番脑筋的
18:27
But it's also relevant
471
1107260
2000
不过同样具有相关价值的
18:29
for disease and rehabilitation.
472
1109260
2000
是在疾病和康复方面
18:31
There are many diseases which effect movement.
473
1111260
3000
有很多病症有对动作产生影响
18:34
And hopefully if we understand how we control movement,
474
1114260
2000
所以如果有朝一日掌握了人类控制动作的机制
18:36
we can apply that to robotic technology.
475
1116260
2000
我们就可以用到机器人技术中去
18:38
And finally, I want to remind you,
476
1118260
2000
最后我要提醒大家
18:40
when you see animals do what look like very simple tasks,
477
1120260
2000
当你们看见动物做些看似非常简单的动作的时候
18:42
the actual complexity of what is going on inside their brain
478
1122260
2000
它们运行在大脑内部的真正复杂度
18:44
is really quite dramatic.
479
1124260
2000
其实是相当令人叹为观止的
18:46
Thank you very much.
480
1126260
2000
谢谢大家
18:48
(Applause)
481
1128260
8000
(掌声)
18:56
Chris Anderson: Quick question for you, Dan.
482
1136260
2000
克里斯・安德森(以下简称“安”):丹尼尔(简称‘沃“),我问一个简短的问题
18:58
So you're a movement -- (DW: Chauvinist.) -- chauvinist.
483
1138260
4000
所以你是一个运动--(沃:沙文主义者。)--沙文主义者
19:02
Does that mean that you think that the other things we think our brains are about --
484
1142260
3000
是不是意味着你认为我们觉得让大脑有存在意义的其他事情
19:05
the dreaming, the yearning, the falling in love and all these things --
485
1145260
3000
那些梦想,渴望,恋爱等等一切东西
19:08
are a kind of side show, an accident?
486
1148260
3000
都是些余兴节目或者意外收获呢?
19:11
DW: No, no, actually I think they're all important
487
1151260
2000
沃:不不不,其实我认为这些也很重要
19:13
to drive the right movement behavior to get reproduction in the end.
488
1153260
3000
它们能促使合适的动作行为,以达到繁殖的最终目的
19:16
So I think people who study sensation or memory
489
1156260
3000
所以我觉得有些人在研究感觉或者记忆
19:19
without realizing why you're laying down memories of childhood.
490
1159260
2000
却没意识到为什么我们要记下童年的回忆
19:21
The fact that we forget most of our childhood, for example,
491
1161260
3000
我们会忘记大部分幼年的记忆这个事实,比如说
19:24
is probably fine, because it doesn't effect our movements later in life.
492
1164260
3000
其实应该无关紧要,因为那些不会影响我们长大以后的动作
19:27
You only need to store things which are really going to effect movement.
493
1167260
3000
我们只需要存储能对动作产生真正影响的事情就行了
19:30
CA: So you think that people thinking about the brain, and consciousness generally,
494
1170260
3000
安:那你是否认为人们研究大脑,以及更笼统的意识
19:33
could get real insight
495
1173260
2000
如果要获得真正的领悟,就要问
19:35
by saying, where does movement play in this game?
496
1175260
2000
动作在这里起了什么作用?
19:37
DW: So people have found out for example
497
1177260
2000
沃:对。人们已经发现,比如说
19:39
that studying vision in the absence of realizing why you have vision
498
1179260
2000
在不明确视觉何以存在的时候,就去研究视觉
19:41
is a mistake.
499
1181260
2000
是个失误
19:43
You have to study vision with the realization
500
1183260
2000
研究视觉的时候
19:45
of how the movement system is going to use vision.
501
1185260
2000
我们必须先意识到运动系统要怎样用这个视觉
19:47
And it uses it very differently once you think about it that way.
502
1187260
2000
这样想的时候用法就不一样了
19:49
CA: Well that was quite fascinating. Thank you very much indeed.
503
1189260
3000
安:噢的确很有趣。非常感谢你。
19:52
(Applause)
504
1192260
2000
(掌声)
关于本网站

这个网站将向你介绍对学习英语有用的YouTube视频。你将看到来自世界各地的一流教师教授的英语课程。双击每个视频页面上显示的英文字幕,即可从那里播放视频。字幕会随着视频的播放而同步滚动。如果你有任何意见或要求,请使用此联系表与我们联系。

https://forms.gle/WvT1wiN1qDtmnspy7