Name: 【TED】勞拉-舒爾茨:寶寶們驚人的邏輯思維》（勞拉-舒爾茨：寶寶們驚人的邏輯思維）。 (【TED】Laura Schulz: The surprisingly logical minds of babies (Laura Schulz: The surprisingly logical minds of babies))
Uploaded: 2021-01-14T07:29:31.000Z
Duration: 20 min 19 s

譯者: Kitty Lau 審譯者: Jack Ricardo

one of the fundamental problems of cognitive science

馬克．吐溫用一句妙語概括了， 我認為是認知科學的一個最根本問題。

He said, "There's something fascinating about science.

一個人可從微不足道的事得出了偉大的猜想。」

One gets such wholesale returns of conjecture

out of such a trifling investment in fact."

馬克．吐溫當然只是開玩笑，但他是對的。

Twain meant it as a joke, of course, but he's right:

從幾塊骨頭，我們推測了恐龍的存在;

There's something fascinating about science.

From a few bones, we infer the existence of dinosuars.

From spectral lines, the composition of nebulae.

以及從血液流入大腦的重建影像，

在我的研究則是從幼兒的行為中，

我們嘗試解釋人類認知的基本機制。

and from reconstructed images of blood flowing through the brain,

我在麻省理工大腦及認知科學系實驗室中，

or in my case, from the behavior of very young children,

we try to say something about the fundamental mechanisms

就是兒童如何從零開始， 快速地學到那麼多的東西。

In particular, in my lab in the Department of Brain and Cognitive Sciences at MIT,

I have spent the past decade trying to understand the mystery

of how children learn so much from so little so quickly.

那就是孩子從零碎和離亂的訊息中， 能夠得出豐富而抽象的推論的能力。

Because, it turns out that the fascinating thing about science

is also a fascinating thing about children,

which, to put a gentler spin on Mark Twain,

is precisely their ability to draw rich, abstract inferences

雖然我將會談及我實驗室的研究，

rapidly and accurately from sparse, noisy data.

但這個研究的靈感是來自一個領域，

I'm going to give you just two examples today.

一個我要感謝世界各地的導師、 同事和工作夥伴付出的領域。

One is about a problem of generalization,

and the other is about a problem of causal reasoning.

歸納數據樣本在科學上是不可或缺的，

And although I'm going to talk about work in my lab,

this work is inspired by and indebted to a field.

I'm grateful to mentors, colleagues, and collaborators around the world.

我們觀察一小撮病人在臨床試驗中的反應，

Let me start with the problem of generalization.

Generalizing from small samples of data is the bread and butter of science.

但只有在整個人口中隨機抽樣才可行。

We poll a tiny fraction of the electorate

and we predict the outcome of national elections.

We see how a handful of patients responds to treatment in a clinical trial,

又或在治療心臟病的臨床試驗中，

But this only works if our sample is randomly drawn from the population.

這樣的結果便不能代表整個人口。

If our sample is cherry-picked in some way --

因此科學家著緊抽樣的方法是否隨機。

or say, in our clinical trials for treatments for heart disease,

嬰兒在任何時候都要歸納數據樣本，

當他們看到幾隻橡皮鴨， 並知道它們浮在水面。

the results may not generalize to the broader population.

又或見到幾個皮球， 並知道它們能彈跳。

So scientists care whether evidence is randomly sampled or not,

從中他們建立對橡膠鴨和皮球的概念，

but what does that have to do with babies?

並將這概念延伸至日後會見到的 所有橡膠鴨和皮球。

Well, babies have to generalize from small samples of data all the time.

嬰兒對橡膠鴨和皮球的這種概括，

They see a few rubber ducks and learn that they float,

or a few balls and learn that they bounce.

鞋子、船、封蠟、捲心菜和皇帝。

And they develop expectations about ducks and balls

因此嬰兒留意這些細節能否代表整體。

that they're going to extend to rubber ducks and balls

And the kinds of generalizations babies have to make about ducks and balls

這兩段短片分別代表實驗的兩個情況。

they have to make about almost everything:

shoes and ships and sealing wax and cabbages and kings.

So do babies care whether the tiny bit of evidence they see

而這兩個嬰兒在很多地方都是不同的。

is plausibly representative of a larger population?

但這兩個嬰兒將代表更大的群組，

你將看到的不同之處則代表 嬰兒行為中的平均差異。

在每一段短片中你會見到嬰兒 在做些他們正常會做的事。

one from each of two conditions of an experiment,

and because you're going to see just two movies,

and any two babies differ from each other in innumerable ways.

But these babies, of course, here stand in for groups of babies,

因為這兩段短片唯一不同的地方，

represent average group differences in babies' behavior across conditions.

我們把一些藍色和黃色的球給嬰兒看。

In each movie, you're going to see a baby doing maybe

權孝媛當時是我的學生， 現在則是史丹佛大學的同事。

just exactly what you might expect a baby to do,

and we can hardly make babies more magical than they already are.

而當她拿出這些球時， 她會把球擠一下，

is the contrast between these two conditions,

because the only thing that differs between these two movies

is the statistical evidence the babies are going to observe.

從一個裝滿藍色球的箱中， 抽出三個藍色球是件很容易的事。

We're going to show babies a box of blue and yellow balls,

and my then-graduate student, now colleague at Stanford, Hyowon Gweon,

is going to pull three blue balls in a row out of this box,

因此當你可以在箱中隨機地抽出 能吱吱叫的物件時，

and when she pulls those balls out, she's going to squeeze them,

也許箱中所有物件都能吱吱叫，

所以嬰兒可能會假設黃色球也能吱吱叫。

And if you're a baby, that's like a TED Talk.

所以嬰兒可用它們做些不同的事，

But the important point is it's really easy to pull three blue balls in a row

就讓我們看看這嬰兒會做甚麼。

(影片) 權孝媛: 看看這個。 (球發出吱吱聲)

It's plausibly a random sample from this population.

And if you can reach into a box at random and pull out things that squeak,

then maybe everything in the box squeaks.

So maybe babies should expect those yellow balls to squeak as well.

Now, those yellow balls have funny sticks on the end,

羅拉·舒爾茨: 早就說了。 (笑聲)

so babies could do other things with them if they wanted to.

(影片) 孝媛: 看到這個嗎? (球發出吱吱聲)

克拉拉, 這個是給你的, 你拿去玩吧。

(Video) Hyowon Gweon: See this? (Ball squeaks)

嬰兒把藍色球的特性套用到黃色球上。

嬰兒從模仿我們中學習，這是很神奇的，

但我們早就知道嬰兒能這樣做。

有趣的地方是當把一樣的東西給嬰兒看時， 甚麼事會發生。

我們能肯定這是完全一樣的， 因我們有個秘密的空間，

但這次我們改變了抽樣的母體。

【TED】勞拉-舒爾茨:寶寶們驚人的邏輯思維》（勞拉-舒爾茨：寶寶們驚人的邏輯思維）。 (【TED】Laura Schulz: The surprisingly logical minds of babies (Laura Schulz: The surprisingly logical minds of babies))

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【TED】勞拉-舒爾茨:寶寶們驚人的邏輯思維》（勞拉-舒爾茨：寶寶們驚人的邏輯思維）。 (【TED】Laura Schulz: The surprisingly logical minds of babies (Laura Schulz: The surprisingly logical minds of babies))

cognitive

evidence

structure

random