Why specializing early doesnt always mean career success David Epstein

Transcriber: Leslie Gauthier
Reviewer: Camille Martínez

So, I’d like to talk about
the development of human potential,

and I’d like to start with maybe the most
impactful modern story of development.

Many of you here have probably heard
of the 10,000 hours rule.

Maybe you even model
your own life after it.

Basically, it’s the idea
that to become great in anything,

it takes 10,000 hours
of focused practice,

so you’d better get started
as early as possible.

The poster child for this story
is Tiger Woods.

His father famously gave him a putter
when he was seven months old.

At 10 months, he started imitating
his father’s swing.

At two, you can go on YouTube
and see him on national television.

Fast-forward to the age of 21,

he’s the greatest golfer in the world.

Quintessential 10,000 hours story.

Another that features
in a number of bestselling books

is that of the three Polgar sisters,

whose father decided to teach them chess
in a very technical manner

from a very early age.

And, really, he wanted to show

that with a head start
in focused practice,

any child could become
a genius in anything.

And in fact,

two of his daughters went on to become
Grandmaster chess players.

So when I became the science writer
at “Sports Illustrated” magazine,

I got curious.

If this 10,000 hours rule is correct,

then we should see
that elite athletes get a head start

in so-called “deliberate practice.”

This is coached,
error-correction-focused practice,

not just playing around.

And in fact, when scientists
study elite athletes,

they see that they spend more time
in deliberate practice –

not a big surprise.

When they actually track athletes
over the course of their development,

the pattern looks like this:

the future elites actually spend
less time early on

in deliberate practice
in their eventual sport.

They tend to have what scientists
call a “sampling period,”

where they try a variety
of physical activities,

they gain broad, general skills,

they learn about
their interests and abilities

and delay specializing until later
than peers who plateau at lower levels.

And so when I saw that, I said,

“Gosh, that doesn’t really comport
with the 10,000 hours rule, does it?”

So I started to wonder about other domains

that we associate with obligatory,
early specialization,

like music.

Turns out the pattern’s often similar.

This is research
from a world-class music academy,

and what I want to draw
your attention to is this:

the exceptional musicians didn’t start
spending more time in deliberate practice

than the average musicians

until their third instrument.

They, too, tended to have
a sampling period,

even musicians we think of
as famously precocious,

like Yo-Yo Ma.

He had a sampling period,

he just went through it more rapidly
than most musicians do.

Nonetheless, this research
is almost entirely ignored,

and much more impactful

is the first page of the book
“Battle Hymn of the Tiger Mother,”

where the author recounts
assigning her daughter violin.

Nobody seems to remember
the part later in the book

where her daughter turns to her
and says, “You picked it, not me,”

and largely quits.

So having seen this sort of surprising
pattern in sports and music,

I started to wonder about domains
that affect even more people,

like education.

An economist found a natural experiment

in the higher-ed systems
of England and Scotland.

In the period he studied,
the systems were very similar,

except in England, students had
to specialize in their mid-teen years

to pick a specific course
of study to apply to,

whereas in Scotland, they could
keep trying things in the university

if they wanted to.

And his question was:

Who wins the trade-off,
the early or the late specializers?

And what he saw was that the early
specializers jump out to an income lead

because they have more
domain-specific skills.

The late specializers get to try
more different things,

and when they do pick,
they have better fit,

or what economists call “match quality.”

And so their growth rates are faster.

By six years out,

they erase that income gap.

Meanwhile, the early specializers
start quitting their career tracks

in much higher numbers,

essentially because they were
made to choose so early

that they more often made poor choices.

So the late specializers
lose in the short term

and win in the long run.

I think if we thought about
career choice like dating,

we might not pressure people
to settle down quite so quickly.

So this got me interested,
seeing this pattern again,

in exploring the developmental backgrounds
of people whose work I had long admired,

like Duke Ellington, who shunned
music lessons as a kid

to focus on baseball
and painting and drawing.

Or Maryam Mirzakhani, who wasn’t
interested in math as a girl –

dreamed of becoming a novelist –

and went on to become
the first and so far only woman

to win the Fields Medal,

the most prestigious prize
in the world in math.

Or Vincent Van Gogh
had five different careers,

each of which he deemed his true calling
before flaming out spectacularly,

and in his late 20s, picked up a book
called “The Guide to the ABCs of Drawing.”

That worked out OK.

Claude Shannon was an electrical engineer
at the University of Michigan

who took a philosophy course
just to fulfill a requirement,

and in it, he learned about
a near-century-old system of logic

by which true and false statements
could be coded as ones and zeros

and solved like math problems.

This led to the development
of binary code,

which underlies all
of our digital computers today.

Finally, my own sort of role model,
Frances Hesselbein –

this is me with her –

she took her first professional
job at the age of 54

and went on to become
the CEO of the Girl Scouts,

which she saved.

She tripled minority membership,

added 130,000 volunteers,

and this is one of the proficiency badges
that came out of her tenure –

it’s binary code for girls
learning about computers.

Today, Frances runs a leadership institute

where she works
every weekday, in Manhattan.

And she’s only 104,

so who knows what’s next.

(Laughter)

We never really hear developmental
stories like this, do we?

We don’t hear about the research

that found that Nobel laureate scientists
are 22 times more likely

to have a hobby outside of work

as are typical scientists.

We never hear that.

Even when the performers
or the work is very famous,

we don’t hear these
developmental stories.

For example, here’s
an athlete I’ve followed.

Here he is at age six,
wearing a Scottish rugby kit.

He tried some tennis,
some skiing, wrestling.

His mother was actually a tennis coach
but she declined to coach him

because he wouldn’t return balls normally.

He did some basketball,
table tennis, swimming.

When his coaches wanted
to move him up a level

to play with older boys,

he declined, because he just wanted
to talk about pro wrestling

after practice with his friends.

And he kept trying more sports:

handball, volleyball, soccer,
badminton, skateboarding …

So, who is this dabbler?

This is Roger Federer.

Every bit as famous
as an adult as Tiger Woods,

and yet even tennis enthusiasts
don’t usually know anything

about his developmental story.

Why is that, even though it’s the norm?

I think it’s partly because
the Tiger story is very dramatic,

but also because it seems like
this tidy narrative

that we can extrapolate to anything
that we want to be good at

in our own lives.

But that, I think, is a problem,

because it turns out that in many ways,
golf is a uniquely horrible model

of almost everything
that humans want to learn.

(Laughter)

Golf is the epitome of

what the psychologist Robin Hogarth
called a “kind learning environment.”

Kind learning environments
have next steps and goals that are clear,

rules that are clear and never change,

when you do something, you get feedback
that is quick and accurate,

work next year will look like
work last year.

Chess: also a kind learning environment.

The grand master’s advantage

is largely based on
knowledge of recurring patterns,

which is also why
it’s so easy to automate.

On the other end of the spectrum
are “wicked learning environments,”

where next steps and goals
may not be clear.

Rules may change.

You may or may not get feedback
when you do something.

It may be delayed, it may be inaccurate,

and work next year
may not look like work last year.

So which one of these sounds like
the world we’re increasingly living in?

In fact, our need to think
in an adaptable manner

and to keep track of interconnecting parts

has fundamentally changed our perception,

so that when you look at this diagram,

the central circle on the right
probably looks larger to you

because your brain is drawn to

the relationship
of the parts in the whole,

whereas someone who hasn’t been
exposed to modern work

with its requirement for adaptable,
conceptual thought,

will see correctly that
the central circles are the same size.

So here we are in the wicked work world,

and there, sometimes
hyperspecialization can backfire badly.

For example, in research
in a dozen countries

that matched people
for their parents' years of education,

their test scores,

their own years of education,

the difference was
some got career-focused education

and some got broader, general education.

The pattern was those who got
the career-focused education

are more likely to be hired
right out of training,

more likely to make more money right away,

but so much less adaptable
in a changing work world

that they spend so much less time
in the workforce overall

that they win in the short term
and lose in the long run.

Or consider a famous,
20-year study of experts

making geopolitical
and economic predictions.

The worst forecasters
were the most specialized experts,

those who’d spent their entire careers
studying one or two problems

and came to see the whole world
through one lens or mental model.

Some of them actually got worse

as they accumulated
experience and credentials.

The best forecasters were simply
bright people with wide-ranging interests.

Now in some domains, like medicine,

increasing specialization has been
both inevitable and beneficial,

no question about it.

And yet, it’s been a double-edged sword.

A few years ago, one of the most popular
surgeries in the world for knee pain

was tested in a placebo-controlled trial.

Some of the patients got “sham surgery.”

That means the surgeons make an incision,

they bang around like
they’re doing something,

then they sew the patient back up.

That performed just as a well.

And yet surgeons who specialize
in the procedure continue to do it

by the millions.

So if hyperspecialization isn’t always
the trick in a wicked world, what is?

That can be difficult to talk about,

because it doesn’t always
look like this path.

Sometimes it looks like
meandering or zigzagging

or keeping a broader view.

It can look like getting behind.

But I want to talk about what
some of those tricks might be.

If we look at research on technological
innovation, it shows that increasingly,

the most impactful patents
are not authored by individuals

who drill deeper, deeper, deeper
into one area of technology

as classified by the US Patent Office,

but rather by teams
that include individuals

who have worked across a large number
of different technology classes

and often merge things
from different domains.

Someone whose work I’ve admired
who was sort of on the forefront of this

is a Japanese man named Gunpei Yokoi.

Yokoi didn’t score well
on his electronics exams at school,

so he had to settle for a low-tier job
as a machine maintenance worker

at a playing card company in Kyoto.

He realized he wasn’t equipped
to work on the cutting edge,

but that there was so much
information easily available

that maybe he could combine things
that were already well-known

in ways that specialists
were too narrow to see.

So he combined some well-known technology
from the calculator industry

with some well-known technology
from the credit card industry

and made handheld games.

And they were a hit.

And it turned this playing card company,

which was founded in a wooden
storefront in the 19th century,

into a toy and game operation.

You may have heard of it;
it’s called Nintendo.

Yokoi’s creative philosophy

translated to “lateral thinking
with withered technology,”

taking well-known technology
and using it in new ways.

And his magnum opus was this:

the Game Boy.

Technological joke in every way.

And it came out at the same time
as color competitors from Saga and Atari,

and it blew them away,

because Yokoi knew
what his customers cared about

wasn’t color.

It was durability, portability,
affordability, battery life,

game selection.

This is mine that I found
in my parents' basement.

(Laughter)

It’s seen better days.

But you can see the red light is on.

I flipped it on and played some Tetris,

which I thought was especially impressive

because the batteries had expired
in 2007 and 2013.

(Laughter)

So this breadth advantage holds
in more subjective realms as well.

In a fascinating study of what leads
some comic book creators

to be more likely to make
blockbuster comics,

a pair of researchers found

that it was neither the number of years
of experience in the field

nor the resources of the publisher

nor the number of previous comics made.

It was the number of different genres
that a creator had worked across.

And interestingly,

a broad individual
could not be entirely replaced

by a team of specialists.

We probably don’t make as many
of those people as we could

because early on,
they just look like they’re behind

and we don’t tend to incentivize anything
that doesn’t look like a head start

or specialization.

In fact, I think in the well-meaning
drive for a head start,

we often even counterproductively
short-circuit even the way

we learn new material,

at a fundamental level.

In a study last year,
seventh-grade math classrooms in the US

were randomly assigned
to different types of learning.

Some got what’s called “blocked practice.”

That’s like, you get problem type A,

AAAAA, BBBBB, and so on.

Progress is fast,

kids are happy,

everything’s great.

Other classrooms got assigned
to what’s called “interleaved practice.”

That’s like if you took all the problem
types and threw them in a hat

and drew them out at random.

Progress is slower,
kids are more frustrated.

But instead of learning
how to execute procedures,

they’re learning how to match
a strategy to a type of problem.

And when the test comes around,

the interleaved group blew
the block practice group away.

It wasn’t even close.

Now, I found a lot of this research
deeply counterintuitive,

the idea that a head start,

whether in picking a career
or a course of study

or just in learning new material,

can sometimes undermine
long-term development.

And naturally, I think there are
as many ways to succeed

as there are people.

But I think we tend only to incentivize
and encourage the Tiger path,

when increasingly, in a wicked world,

we need people who travel
the Roger path as well.

Or as the eminent physicist
and mathematician

and wonderful writer,
Freeman Dyson, put it –

and Dyson passed away yesterday,

so I hope I’m doing
his words honor here –

as he said: for a healthy ecosystem,
we need both birds and frogs.

Frogs are down in the mud,

seeing all the granular details.

The birds are soaring up above
not seeing those details

but integrating
the knowledge of the frogs.

And we need both.

The problem, Dyson said,

is that we’re telling everyone
to become frogs.

And I think,

in a wicked world,

that’s increasingly shortsighted.

Thank you very much.

(Applause)

抄写员:Leslie Gauthier
审稿人:Camille Martínez

所以,我想谈谈
人类潜能的开发

,我想从可能最具
影响力的现代发展故事开始。

在座的许多人可能
听说过 10,000 小时规则。

也许你甚至模仿
你自己的生活。

基本上,
想要在任何事情上变得出色,

都需要 10,000 小时
的集中练习,

所以你最好
尽早开始。

这个故事的典型代表
是老虎伍兹。

在他七个月大的时候,他的父亲给了他一个著名的推杆。

10 个月大时,他开始模仿
父亲的挥杆动作。

两点钟,你可以上 YouTube
并在国家电视台上看到他。

快进到 21 岁,

他是世界上最伟大的高尔夫球手。

典型的 10,000 小时故事。

许多畅销书中的另一个特色

是波尔加三姐妹的故事,

她们的父亲从很小的时候就决定
以非常技术的方式教她们下棋

而且,真的,他想

表明,只要
在集中练习中领先一步,

任何孩子都可以
在任何事情上成为天才。

事实上,

他的两个女儿后来成为了
象棋大师。

所以当我成为
《体育画报》杂志的科普作家时,

我很好奇。

如果这个 10,000 小时规则是正确的,

那么我们应该
看到精英运动员

在所谓的“刻意练习”中领先一步。

这是有指导的、
以纠错为重点的练习,

而不仅仅是玩耍。

事实上,当科学家们
研究精英运动员时,

他们发现他们会花更多时间
在刻意练习上——这

并不奇怪。

当他们在运动员
的发展过程中实际跟踪运动员时

,模式看起来是这样的

:未来的精英们实际上在他们最终的运动中花
更少的时间在早期

的刻意练习
上。

他们往往会有科学家
所谓的“采样期”

,在此期间,他们尝试
各种体育活动

,获得广泛的通用技能

,了解
自己的兴趣和能力,

并将专攻推迟到
比处于较低水平的同龄人更晚的时间。

所以当我看到它时,我说,

“天哪,这
不符合 10,000 小时规则,是吗?”

所以我开始想

知道我们与强制性的
早期专业化相关的其他领域,

比如音乐。

事实证明,这种模式通常很相似。

这是
来自世界级音乐学院的研究

,我想提请
您注意的是

:杰出的音乐家直到他们的第三件乐器才开始比普通音乐家
花更多的时间在刻意练习上

他们也倾向于有
一个采样期,

即使是我们
认为众所周知的早熟的音乐家,

比如马友友。

他有一个采样期,

他只是比大多数音乐家更快地完成了它

尽管如此,这项
研究几乎完全被忽略了

,更具影响力的


《虎妈战歌》一书的第一页

,作者讲述了
分配她女儿小提琴的故事。

似乎没有人记得
书后面的部分

,她的女儿转向
她说:“是你选的,不是我的”,

然后基本上退出了。

因此,在看到
体育和音乐中这种令人惊讶的模式后,

我开始想
知道影响更多人的领域,

比如教育。

一位经济学家


英格兰和苏格兰的高等教育系统中发现了一个自然实验。

在他学习期间
,系统非常相似,

除了在英格兰,学生
必须在他们的青少年时期专攻

选择
要申请的特定课程,

而在苏格兰,他们可以
在大学里继续尝试,

如果 他们想要。

他的问题是:

谁赢得了权衡
,早期的专家还是晚期的专家?

他所看到的是,早期的
专业人士一跃成为收入领先者,

因为他们拥有更多
特定领域的技能。

后期的专家可以尝试
更多不同的东西

,当他们选择时,
他们有更好的契合度,

也就是经济学家所说的“匹配质量”。

所以他们的增长速度更快。

六年后,

他们消除了收入差距。

与此同时,早期的专业人士
开始退出职业生涯

的人数要多得多,

主要是因为他们
太早做出选择,

以至于他们更经常做出错误的选择。

所以后期的专
精在短期内是输,从长远来看是

赢的。

我认为,如果我们考虑
像约会这样的职业选择,

我们可能不会迫使
人们这么快安定下来。

所以这让我感兴趣,
再次看到这种模式

,探索
我一直钦佩的人的发展背景,

比如艾灵顿公爵,他从小就避开
音乐课

,专注于棒球
和绘画。

或者玛丽亚姆·米尔扎哈尼(Maryam Mirzakhani),她小时候
对数学不感兴趣——

梦想成为一名小说家

——后来成为
第一位也是迄今为止唯一

一位获得菲尔兹奖的女性,菲尔兹奖

是世界上最负盛名的数学奖 .

或者文森特梵高
有五个不同的职业,

每一个他都认为自己真正的使命,
然后才大放异彩

,在他 20 多岁的时候,拿起了一本
名为“绘画基础知识指南”的书。

结果很好。

Claude Shannon 是
密歇根大学的一名电气工程师,

他参加哲学课程
只是为了满足一个要求

,在其中,他了解了
一个近百年历史的逻辑系统,

通过该系统
可以将真假陈述编码为 零

并像数学问题一样解决。

这导致
了二进制代码的发展,


是我们今天所有数字计算机的基础。

最后,我自己的榜样,
Frances Hesselbein——

这是我和她在一起的——

她在 54 岁时开始了她的第一份职业
工作,

并继续
成为女童子军的首席执行官

,她拯救了这个职位。

她将少数族裔会员人数增加了两倍,

增加了 130,000 名志愿者

,这是她任职期间获得的熟练程度徽章之一
——

这是女孩学习计算机的二进制代码

如今,弗朗西斯在曼哈顿经营着一个领导力学院

,她
每个工作日都在那里工作。

她只有 104

岁,所以谁知道接下来会发生什么。

(笑声)

我们从来没有真正听到过这样的发展
故事,不是吗?

我们没有听说过一项研究

发现,诺贝尔奖获得者科学家

在工作之外拥有爱好的可能性

是典型科学家的 22 倍。

我们从来没有听说过。

即使表演者
或作品非常有名,

我们也听不到这些
发展故事。

例如,这是
我关注的一名运动员。

在这里,他六岁,
穿着苏格兰橄榄球队服。

他尝试了一些网球,
一些滑雪,摔跤。

他的母亲实际上是一名网球教练,
但她拒绝指导他,

因为他不会正常回球。

他会打篮球、
乒乓球、游泳。

当他的教练
想让他提升一个级别

以与年龄较大的男孩一起比赛时,

他拒绝了,因为他只想

在练习后与朋友谈论职业摔跤。

而且他不断尝试更多的运动:

手球、排球、足球、
羽毛球、滑板……

那么,这个涉足者是谁?

这是罗杰·费德勒。 和

老虎伍兹一样有名

,但即使是网球爱好者
通常也不

知道他的成长故事。

为什么会这样,即使这是常态?

我认为这部分是
因为老虎的故事非常具有戏剧性,

但也因为它看起来
像是一个整洁的叙事

,我们可以推断
出我们想要

在自己的生活中擅长的任何事情。

但是,我认为,这是一个问题,

因为事实证明,在许多方面,
高尔夫是人类想要学习的几乎所有东西的独特可怕模型

(笑声)

高尔夫是

心理学家罗宾·霍加斯(Robin Hogarth)
所说的“友善的学习环境”的缩影。

友善的学习环境
有明确的下一步和目标

,明确且永不改变的规则,

当你做某事时,你会
得到快速准确的反馈,

明年的工作看起来就像
去年的工作。

国际象棋:也是一种友善的学习环境。

大师的

优势主要是基于
对重复模式的了解,

这也是
它如此容易自动化的原因。

另一方面
是“邪恶的学习环境”

,下一步和目标
可能不清楚。

规则可能会改变。 当你做某事时,

你可能会也可能不会得到反馈

它可能会延迟,可能不准确,

明年的工作
可能看起来不像去年的工作。

那么其中哪一个听起来像
我们越来越生活的世界?

事实上,我们需要
以一种适应性强的方式思考

并跟踪相互关联的部分,

这从根本上改变了我们的看法,

所以当你看这张图时,

右边的中心圆圈
对你来说可能看起来更大,

因为你的大脑被吸引到

整体中各部分的关系,

而没有
接触过现代

作品的人需要适应的
概念性思维,

会正确地
看到中心圆圈的大小相同。

因此,我们处于邪恶的工作世界中,

而在那里,有时超
专业化会适得其反。

例如,
在十几个国家的研究中

,将
人们与父母的教育年限、

考试成绩、

自己的教育年限进行匹配

,差异在于
有些人接受了以职业为中心的教育

,有些人接受了更广泛的普通教育。

模式是那些接受
了以职业为中心的教育的

人更有可能在
培训结束后立即被聘用,

更有可能立即赚更多的钱,


在不断变化的工作世界中的适应能力却大大降低,

以至于他们在职场上花费的时间更少
。 总体

而言,他们在短期内获胜,
在长期内失败。

或者考虑
一项对

地缘政治
和经济预测的专家进行了 20 年的著名研究。

最糟糕的预测
者是最专业的专家

,他们将整个职业生涯都花在
研究一两个问题上,

然后
通过一个镜头或心理模型来看待整个世界。 随着经验和资历的积累

,他们中的一些人实际上变得更糟了

最好的预测者只是
兴趣广泛的聪明人。

现在,在某些领域,比如医学,

日益专业化
既是不可避免的,也是有益的,这是

毫无疑问的。

然而,这是一把双刃剑。

几年前,世界上最受欢迎
的膝关节疼痛手术之一

在安慰剂对照试验中进行了测试。

一些患者接受了“假手术”。

这意味着外科医生会做一个切口,

他们像
在做某事一样四处乱撞,

然后将病人缝合起来。

这表现得一样好。

然而,专门
从事该手术的外科医生仍在继续

数以百万计地进行手术。

因此,如果
在一个邪恶的世界里,超专业化并不总是把戏,那是什么?

这可能很难谈论,

因为它并不总是
看起来像这条路。

有时它看起来像
曲折或曲折

或保持更广阔的视野。

它看起来像是落后了。

但我想谈谈
其中一些技巧可能是什么。

如果我们看一下关于技术
创新的研究,就会发现,越来越多

的最具影响力的专利
不是由对美国专利局分类的某一技术领域进行

更深入、更深入、更深入研究的个人撰写

而是由
包括个人在内的团队撰写

他们曾在
大量不同的技术类别中工作,

并且经常合并
来自不同领域的事物。


钦佩他的工作在这方面处于领先地位的

人是一个名叫横井军平的日本人。

横井
在学校的电子考试成绩不佳,

所以他不得不

在京都一家扑克牌公司做一份机器维修工的低级工作。

他意识到他没有能力
在最前沿工作,

但有太多
信息很容易获得

,也许他可以
将已经众所周知的东西结合起来,以

专家
们太狭隘看不到的方式。

于是他
把计算器行业

的一些知名技术
和信用卡行业的一些知名技术结合起来

,制作了掌上游戏。

他们很受欢迎。

并将这家 19 世纪

成立于木制
店面的扑克牌公司

变成了一家玩具和游戏公司。

您可能听说过;
它叫任天堂。

横井的创作哲学

转化为“
用枯萎的技术进行横向思考”,

采用众所周知的技术
并以新的方式使用它。

他的代表作是

:Game Boy。

各方面的技术笑话。


与 Saga 和 Atari 的颜色竞争对手同时问世,

让他们大吃一惊,

因为横井
知道他的客户关心

的不是颜色。

它是耐用性、便携性、
可负担性、电池寿命、

游戏选择。

这是
我在父母地下室找到的。

(笑声

) 已经看到了更好的日子。

但是你可以看到红灯亮了。

我打开它,玩了一些俄罗斯方块

,我认为这特别令人印象深刻,

因为电池已经
在 2007 年和 2013 年到期。

(笑声)

所以这种广度优势也适用
于更主观的领域。

在一项关于是什么导致
一些漫画书创作者

更有可能制作
大片漫画的有趣研究中,

一对研究人员发现

,这既不
是该领域的多年经验,

也不是出版商的资源,

也不是以前漫画的数量 制成。


是一个创作者所创作的不同类型的数量。

有趣的是,

一个广泛的个人
不能完全

被一个专家团队所取代。

我们可能没有尽可能多
地培养这些人,

因为在早期,
他们只是看起来落后了,

而且我们不倾向于激励任何
看起来不像是领先

或专业化的事情。

事实上,我认为在善意的
推动下,

我们甚至经常会适得其反地
短路,即使是

我们学习新材料的方式,

从根本上来说也是如此。

在去年的一项研究
中,美国七年级的数学教室

被随机分配
到不同类型的学习中。

有些人得到了所谓的“封锁练习”。

这就像,你遇到问题类型 A、

AAAAA、BBBBB 等等。

进步很快,

孩子们很开心,

一切都很好。

其他教室被分配
到所谓的“交错练习”。

这就像你把所有的问题
类型都扔进帽子里

,然后随机抽出来。

进步更慢,
孩子们更沮丧。

但是,他们不是在学习
如何执行程序,

而是在学习如何将
策略与一类问题相匹配。

而当考试来临时

,交错组
把街区练习组吹走了。

它甚至没有接近。

现在,我发现很多这项研究
非常违反

直觉,即

无论是在选择职业
、学习课程

还是仅仅学习新材料时,抢占先机

有时会破坏
长期发展。

当然,我认为有
多少人就有多少成功的方法

但我认为我们往往只会激励
和鼓励老虎之路

,而在一个邪恶的世界中,

我们也越来越需要
走罗杰之路的人。

或者正如杰出的物理学家
、数学家

和出色的作家
弗里曼·戴森所说

——戴森昨天去世了,

所以我希望我在
这里兑现他的话——

正如他所说:为了一个健康的生态系统,
我们需要两只鸟 和青蛙。

青蛙倒在泥里,

看到了所有的颗粒状细节。

鸟儿翱翔在上面,
看不到那些细节,

而是整合
了青蛙的知识。

我们两者都需要。

戴森说,问题

在于我们告诉每个人
都变成青蛙。

而且我认为,

在一个邪恶的世界里,

这越来越短视了。

非常感谢你。

(掌声)