ABOUT THE SPEAKER
Laura Schulz - Cognitive scientist
Developmental behavior studies spearheaded by Laura Schulz are changing our notions of how children learn.

Why you should listen

MIT Early Childhood Cognition Lab lead investigator Laura Schulz studies learning in early childhood. Her research bridges computational models of cognitive development and behavioral studies in order to understand the origins of inquiry and discovery.

Working in play labs, children’s museums, and a recently-launched citizen science website, Schultz is reshaping how we view young children’s perceptions of the world around them. Some of the surprising results of her research: before the age of four, children expect hidden causes when events happen probabilistically, use simple experiments to distinguish causal hypotheses, and trade off learning from instruction and exploration.

More profile about the speaker
Laura Schulz | Speaker | TED.com
TED2015

Laura Schulz: The surprisingly logical minds of babies

Laura Schulz: As sorprendentemente lóxicas mentes dos bebés

Filmed:
1,888,975 views

Como aprenden os bebés tanto de tan pouco, e tan rápido? Nunha charla divertida e chea de experimentos, a científica cognitiva Laura Schulz amosa como os nosos cativos toman decisións cun sentido da lóxica sorprendentemente forte, xa moito antes de que dean falado.
- Cognitive scientist
Developmental behavior studies spearheaded by Laura Schulz are changing our notions of how children learn. Full bio

Double-click the English transcript below to play the video.

00:12
Mark Twain summed up
what I take to be
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Mark Twain resumiu
o que eu considero que é
00:14
one of the fundamental problems
of cognitive science
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un dos problemas fundamentais
da ciencia cognitiva
00:18
with a single witticism.
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cunha sinxela ocorrencia.
00:20
He said, "There's something
fascinating about science.
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Dixo, "A ciencia é fascinante.
00:23
One gets such wholesale
returns of conjecture
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Conséguense cantidades
masivas de conxecturas
00:26
out of such a trifling
investment in fact."
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a partir dun investimento
tan insignificante en feitos.”
00:29
(Laughter)
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(Risas)
00:32
Twain meant it as a joke,
of course, but he's right:
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Twain quería facer unha broma, claro,
pero ten razón:
00:34
There's something
fascinating about science.
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A ciencia é fascinante.
00:37
From a few bones, we infer
the existence of dinosuars.
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A partir duns cantos ósos, inferimos
a existencia dos dinosauros.
00:42
From spectral lines,
the composition of nebulae.
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Das liñas espectrais,
a composición das nebulosas.
00:47
From fruit flies,
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A partir das moscas da froita,
00:50
the mechanisms of heredity,
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os mecanismos da herdanza,
00:53
and from reconstructed images
of blood flowing through the brain,
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e de imaxes reconstruídas de sangue
fluíndo a través do cerebro,
00:57
or in my case, from the behavior
of very young children,
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ou no meu caso, do comportamento
de nenos moi pequenos,
01:02
we try to say something about
the fundamental mechanisms
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intentamos dicir algo
sobre os mecanismos fundamentais
01:05
of human cognition.
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da cognición humana.
01:07
In particular, in my lab in the Department
of Brain and Cognitive Sciences at MIT,
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En concreto, no meu laboratorio no Dpto.
de Cerebro e Ciencias Cognitivas, no MIT,
01:12
I have spent the past decade
trying to understand the mystery
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pasei a última década
intentando entender o misterio
de por que os nenos aprenden tanto,
a partir de tan pouco, e tan rápido.
01:16
of how children learn so much
from so little so quickly.
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01:20
Because, it turns out that
the fascinating thing about science
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Porque resulta que o que a ciencia
ten de fascinante
01:23
is also a fascinating
thing about children,
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téñeno tamén de fascinante os nenos,
01:27
which, to put a gentler
spin on Mark Twain,
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e é, dicíndoo de forma máis suave
ca Mark Twain,
01:29
is precisely their ability
to draw rich, abstract inferences
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precisamente a súa capacidade
de extraer inferencias ricas e abstractas
01:34
rapidly and accurately
from sparse, noisy data.
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de forma rápida e precisa a partir
de datos dispersos e confusos.
01:40
I'm going to give you
just two examples today.
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Vou dar só dous exemplos hoxe.
01:42
One is about a problem of generalization,
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Un deles aborda
un problema de xeneralización,
01:45
and the other is about a problem
of causal reasoning.
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e o outro un de razoamento causal.
01:47
And although I'm going to talk
about work in my lab,
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E aínda que vou falar
do que facemos no meu laboratorio,
01:50
this work is inspired by
and indebted to a field.
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este traballo está inspirado por un campo
e en débeda con el.
01:53
I'm grateful to mentors, colleagues,
and collaborators around the world.
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Estoulles agradecida a mentores,
colegas e colaboradores de todo o mundo.
01:59
Let me start with the problem
of generalization.
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Quero comezar
co problema de xeneralización.
02:02
Generalizing from small samples of data
is the bread and butter of science.
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Xeneralizar a partir de pequenas mostras
de datos é o pan de cada día da ciencia.
02:06
We poll a tiny fraction of the electorate
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Entrevistamos unha fracción
mínima do electorado
02:09
and we predict the outcome
of national elections.
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e predicimos o resultado
das eleccións nacionais.
02:12
We see how a handful of patients
responds to treatment in a clinical trial,
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Vemos como un puñado de pacientes
responde a tratamento nun ensaio clínico,
02:16
and we bring drugs to a national market.
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e incorporamos fármacos
ao mercado nacional.
02:19
But this only works if our sample
is randomly drawn from the population.
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Pero isto soamente funciona se a mostra
se extrae aleatoriamente da poboación.
02:23
If our sample is cherry-picked
in some way --
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Se a nosa mostra ten algunha manipulación
--por exemplo,
entrevistamos só votantes urbanos,
02:26
say, we poll only urban voters,
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02:28
or say, in our clinical trials
for treatments for heart disease,
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ou nos nosos ensaios clínicos
de tratamentos para doenzas cardíacas
02:32
we include only men --
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incluímos só homes--
02:34
the results may not generalize
to the broader population.
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os resultados poden
non ser xeneralizables a toda a poboación.
Por tanto aos científicos impórtalles
se a mostra se recolleu ou non ao chou,
02:38
So scientists care whether evidence
is randomly sampled or not,
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02:42
but what does that have to do with babies?
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pero que ten iso que ver cos bebés?
02:44
Well, babies have to generalize
from small samples of data all the time.
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Os bebés teñen que xeneralizar seguido
a partir de pequenas mostras de datos.
02:49
They see a few rubber ducks
and learn that they float,
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Ven uns poucos parrulos de goma
e aprenden que flotan,
02:52
or a few balls and learn that they bounce.
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ou algunhas pelotas e aprenden que botan.
02:55
And they develop expectations
about ducks and balls
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E desenvolven expectativas
sobre os parrulos e as pelotas
02:58
that they're going to extend
to rubber ducks and balls
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que aplicarán a uns e outras
03:01
for the rest of their lives.
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o resto das súas vidas.
03:03
And the kinds of generalizations
babies have to make about ducks and balls
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E os tipos de xeneralizacións
que deben facer sobre parrulos e pelotas,
03:07
they have to make about almost everything:
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deben facelos para case todo:
03:09
shoes and ships and sealing wax
and cabbages and kings.
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zapatos e barcos e lacre e verzas e reis.
03:14
So do babies care whether
the tiny bit of evidence they see
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Entón aos bebés impórtalles
se o pequeno anaco de proba que ven
03:17
is plausibly representative
of a larger population?
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representa de forma plausíbel
unha poboación maior?
03:21
Let's find out.
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Descubrámolo.
03:23
I'm going to show you two movies,
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Vou amosar dous vídeos,
03:25
one from each of two conditions
of an experiment,
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un por cada suposto dun experimento,
03:27
and because you're going to see
just two movies,
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e como só se verán dous vídeos,
03:30
you're going to see just two babies,
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só se verán dous bebés,
03:32
and any two babies differ from each other
in innumerable ways.
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e un par calquera de bebés difire
de calquera outro de innumerábeis formas.
03:36
But these babies, of course,
here stand in for groups of babies,
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Pero estes bebés, por suposto,
representan aquí a grupos de bebés,
03:39
and the differences you're going to see
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e as diferenzas que se van ver
03:41
represent average group differences
in babies' behavior across conditions.
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representan as diferenzas grupais medias
no comportamento dos bebés
en cada suposto.
03:47
In each movie, you're going to see
a baby doing maybe
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En cada vídeo verase
un bebé facendo tal vez
03:49
just exactly what you might
expect a baby to do,
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xusto o que se agardaría que fixese,
03:53
and we can hardly make babies
more magical than they already are.
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e dificilmente podemos volver
os bebés máis máxicos do que xa son.
03:58
But to my mind the magical thing,
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Pero para min o máxico,
04:00
and what I want you to pay attention to,
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e ao que quero que se lle preste atención,
04:02
is the contrast between
these two conditions,
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é o contraste entre estes dous supostos,
04:05
because the only thing
that differs between these two movies
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porque o único que difire
entre os dous vídeos
04:08
is the statistical evidence
the babies are going to observe.
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son os datos estatísticos
que os bebés van observar.
04:13
We're going to show babies
a box of blue and yellow balls,
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Imos ensinarlles unha caixa
de bólas azuis e amarelas,
04:16
and my then-graduate student,
now colleague at Stanford, Hyowon Gweon,
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e a que era a miña estudante graduada,
hoxe compañeira en Stanford, Hyowon Gweon,
04:21
is going to pull three blue balls
in a row out of this box,
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vai sacar tres bólas azuis
seguidas desta caixa,
04:24
and when she pulls those balls out,
she's going to squeeze them,
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e despois de sacalas, vainas apertar,
04:27
and the balls are going to squeak.
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e as bólas van chiar.
04:29
And if you're a baby,
that's like a TED Talk.
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E se es un bebé,
iso é como un charla TED.
Non pode haber nada mellor.
04:32
It doesn't get better than that.
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(Risas)
04:34
(Laughter)
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04:38
But the important point is it's really
easy to pull three blue balls in a row
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Pero o importante é que é moi sinxelo
sacar tres bólas azuis seguidas
04:42
out of a box of mostly blue balls.
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dunha caixa que ten
sobre todo bólas azuis.
04:44
You could do that with your eyes closed.
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Poderíase facer cos ollos pechados.
04:46
It's plausibly a random sample
from this population.
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Pódese admitir que é unha
mostra aleatoria desta poboación.
04:49
And if you can reach into a box at random
and pull out things that squeak,
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E se podes meter a man aleatoriamente
nunha caixa e sacar cousas que chían,
04:53
then maybe everything in the box squeaks.
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ao mellor todo o que hai na caixa chía.
04:56
So maybe babies should expect
those yellow balls to squeak as well.
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Así que tal vez os bebés deberían esperar
que as bólas amarelas chíen tamén.
05:00
Now, those yellow balls
have funny sticks on the end,
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As bólas amarelas teñen
divertidos paus nun extremo,
05:02
so babies could do other things
with them if they wanted to.
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que permiten facer con elas
outras cousas se se quere.
05:05
They could pound them or whack them.
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Poderían axitalas ou bater con elas.
05:07
But let's see what the baby does.
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Pero vexamos qué fai o bebé.
05:12
(Video) Hyowon Gweon: See this?
(Ball squeaks)
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(Vídeo) Ves isto? (A bóla chía)
05:16
Did you see that?
(Ball squeaks)
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Viches iso? (A bóla chía)
05:20
Cool.
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Xenial.
05:24
See this one?
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Ves estoutra?
05:26
(Ball squeaks)
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(A bóla chía)
Uaau.
05:28
Wow.
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05:33
Laura Schulz: Told you. (Laughs)
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Díxenvolo. (Ri)
05:35
(Video) HG: See this one?
(Ball squeaks)
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Viches esta? (A bóla chía)
05:39
Hey Clara, this one's for you.
You can go ahead and play.
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Clara, agora esta é para ti.
Veña, podes collela e xogar.
(Barullo) (Risas)
05:51
(Laughter)
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05:56
LS: I don't even have to talk, right?
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LS: Non teño nin que dicir nada, verdade?
05:59
All right, it's nice that babies
will generalize properties
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Vale, está ben que os bebés
xeneralicen propiedades
das bólas azuis ás bolas amarelas.
06:02
of blue balls to yellow balls,
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E é impresionante que poidan
aprender imitándonos.
06:03
and it's impressive that babies
can learn from imitating us,
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06:06
but we've known those things about babies
for a very long time.
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Pero sabemos iso dos bebés
dende hai moito tempo.
06:10
The really interesting question
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A pregunta realmente interesante é
que ocorre cando lles amosamos
aos bebés exactamente a mesma cousa,
06:12
is what happens when we show babies
exactly the same thing,
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06:15
and we can ensure it's exactly the same
because we have a secret compartment
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podemos asegurar que é a mesma
porque temos un compartimento secreto
06:18
and we actually pull the balls from there,
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e en realidade sacamos as bólas del,
06:20
but this time, all we change
is the apparent population
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pero esta vez o que cambiamos
foi a poboación aparente
06:24
from which that evidence was drawn.
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da que extraemos as mostras.
06:27
This time, we're going to show babies
three blue balls
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Esta vez amosarémoslles
aos bebés tres bólas azuis
06:30
pulled out of a box
of mostly yellow balls,
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sacadas dunha caixa que ten sobre todo
bólas amarelas,
06:34
and guess what?
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e saben que?
06:35
You [probably won't] randomly draw
three blue balls in a row
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Non se poden sacar aleatoriamente
tres bólas azuis seguidas
06:38
out of a box of mostly yellow balls.
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dunha caixa que ten sobre todo
bólas amarelas.
06:40
That is not plausibly
randomly sampled evidence.
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Esa non é unha mostra aleatoria.
06:44
That evidence suggests that maybe Hyowon
was deliberately sampling the blue balls.
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Esa proba suxire que ao mellor Hyowon
estivo amosando deliberadamente as azuis.
06:49
Maybe there's something special
about the blue balls.
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Tal vez as bólas azuis teñen algo especial
06:52
Maybe only the blue balls squeak.
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Tal vez soamente as bólas azuis chían.
06:55
Let's see what the baby does.
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Vexamos o que fai o bebé.
06:57
(Video) HG: See this?
(Ball squeaks)
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(Vídeo) Ves isto?
(A bóla chía)
07:02
See this toy?
(Ball squeaks)
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Ves este xoguete?
(A bóla chía)
07:05
Oh, that was cool. See?
(Ball squeaks)
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Oh, que xenial. Ves?
(A bóla chía)
07:10
Now this one's for you to play.
You can go ahead and play.
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Agora esta é para que xogues ti.
Veña, podes xogar.
07:18
(Fussing)
(Laughter)
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(Barullo) (Risas)
07:26
LS: So you just saw
two 15-month-old babies
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LS: Acabades de ver dous
bebés de 15 meses
07:29
do entirely different things
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facendo dúas cousas totalmente diferentes
07:31
based only on the probability
of the sample they observed.
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baseadas só na probabilidade
da mostra que observaron.
07:35
Let me show you the experimental results.
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Quero ensinar os resultados experimentais.
07:37
On the vertical axis, you'll see
the percentage of babies
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No eixe vertical, pódese ver
a porcentaxe de bebés
07:40
who squeezed the ball in each condition,
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que apertaron a bóla en cada suposto,
07:42
and as you'll see, babies are much
more likely to generalize the evidence
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e como se ve, os bebés tenden
moito máis a xeneralizar a mostra
07:46
when it's plausibly representative
of the population
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cando é representativa da poboación
07:49
than when the evidence
is clearly cherry-picked.
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ca cando está claramente manipulada.
07:53
And this leads to a fun prediction:
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E isto lévanos a unha predición curiosa:
07:55
Suppose you pulled just one blue ball
out of the mostly yellow box.
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supoñamos que sacamos só unha bóla azul
da caixa que ten sobre todo
bólas amarelas.
08:00
You [probably won't] pull three blue balls
in a row at random out of a yellow box,
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Non se poderían sacar aleatoriamente
3 bólas azuis seguidas dunha caixa amarela
08:04
but you could randomly sample
just one blue ball.
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pero poderíase sacar soamente unha.
08:07
That's not an improbable sample.
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Non é unha mostra improbable.
08:09
And if you could reach into
a box at random
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E se se puidese meter a man
ao chou nunha caixa
08:11
and pull out something that squeaks,
maybe everything in the box squeaks.
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e sacar algo que chía,
tal vez todo o da caixa chíe.
08:15
So even though babies are going to see
much less evidence for squeaking,
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Entón, aínda que os bebés van observar
moita menos probas para chíos,
08:20
and have many fewer actions to imitate
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e contan con moitas menos
accións que imitar
08:22
in this one ball condition than in
the condition you just saw,
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neste suposto dunha única bóla
ca no que vimos antes,
08:25
we predicted that babies themselves
would squeeze more,
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predicimos que os bebés por si sós
apertarían a bóla máis veces,
08:29
and that's exactly what we found.
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e iso é exactamente o que atopamos.
08:32
So 15-month-old babies,
in this respect, like scientists,
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Así que aos bebés de 15 meses,
neste sentido, como científicos,
08:37
care whether evidence
is randomly sampled or not,
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impórtalles se a proba é
unha mostra representativa ou non,
08:40
and they use this to develop
expectations about the world:
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e usan isto para desenvolver
expectativas sobre o mundo:
08:43
what squeaks and what doesn't,
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qué chía e qué non,
08:45
what to explore and what to ignore.
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qué explorar e qué ignorar.
08:50
Let me show you another example now,
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Agora quero amosar outro exemplo,
08:52
this time about a problem
of causal reasoning.
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esta vez sobre un problema
de razoamento causal.
E comeza cun problema de proba confusa
08:55
And it starts with a problem
of confounded evidence
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08:57
that all of us have,
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que todos temos:
08:59
which is that we are part of the world.
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o feito de que formamos parte do mundo.
09:01
And this might not seem like a problem
to you, but like most problems,
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Isto pode non parecer un problema,
pero como a maior parte deles,
09:04
it's only a problem when things go wrong.
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maniféstase só cando as cousas van mal.
09:07
Take this baby, for instance.
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Velaquí este bebé, por exemplo.
09:09
Things are going wrong for him.
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As cousas están indo mal para el.
09:10
He would like to make
this toy go, and he can't.
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Gustaríalle facer funcionar
o seu xoguete, e non pode.
09:13
I'll show you a few-second clip.
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Amosarei un vídeo duns poucos segundos.
09:21
And there's two possibilities, broadly:
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En xeral, hai dúas posibilidades:
09:23
Maybe he's doing something wrong,
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ou el está facendo algo mal,
09:25
or maybe there's something
wrong with the toy.
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ou algo non funciona no xoguete.
09:30
So in this next experiment,
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Así que no seguinte experimento,
darémoslles aos bebés só
unha mínima porción de datos estatísticos
09:32
we're going to give babies
just a tiny bit of statistical data
169
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3297
09:35
supporting one hypothesis over the other,
170
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2582
que apoian unha das hipóteses
sobre a outra,
09:38
and we're going to see if babies
can use that to make different decisions
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e veremos se os bebés poden usar iso
para tomar decisións diferentes
09:41
about what to do.
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sobre qué facer.
09:43
Here's the setup.
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2022
Velaquí o plan.
09:46
Hyowon is going to try to make
the toy go and succeed.
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Hyowon vai intentar que o xoguete
funcione, e conségueo.
09:49
I am then going to try twice
and fail both times,
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3320
Entón eu vou intentalo dúas veces
e fracasar as dúas,
09:52
and then Hyowon is going
to try again and succeed,
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3112
despois Hyowon vai intentalo
outra vez e conseguilo,
09:55
and this roughly sums up my relationship
to my graduate students
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3172
o que resume en xeral a miña relación
cos meus estudantes de posgrao
09:58
in technology across the board.
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no que ten que ver coa tecnoloxía.
10:02
But the important point here is
it provides a little bit of evidence
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3292
Pero o importante aquí é
que proporciona algunha proba
10:05
that the problem isn't with the toy,
it's with the person.
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de que o problema non é o xoguete,
senón a persoa.
10:08
Some people can make this toy go,
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2350
Algunhas poden facer
que o xoguete funcione,
10:11
and some can't.
182
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959
e outras non.
10:12
Now, when the baby gets the toy,
he's going to have a choice.
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3413
Agora, cando o bebé consegue o xoguete,
vai ter unha elección.
10:16
His mom is right there,
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2188
Súa nai está xusto alí,
polo que pode ir e darlle o xoguete
e cambiar a persoa,
10:18
so he can go ahead and hand off the toy
and change the person,
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3315
10:21
but there's also going to be
another toy at the end of that cloth,
186
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3158
pero tamén vai haber outro xoguete
no bordo desa tea,
10:24
and he can pull the cloth towards him
and change the toy.
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3552
así que pode tirar da tea cara a el
e cambiar o xoguete.
10:28
So let's see what the baby does.
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Vexamos logo qué fai o bebé.
10:30
(Video) HG: Two, three. Go!
(Music)
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(Vídeo) HG: Dous, tres. Xa!
(Música)
10:34
LS: One, two, three, go!
190
622698
3131
LS: Un, dous, tres. Xa!
10:37
Arthur, I'm going to try again.
One, two, three, go!
191
625829
7382
Arthur, vou intentalo outra vez.
Un, dous, tres. Xa!
10:45
YG: Arthur, let me try again, okay?
192
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2600
HG: Arthur, déixame probar outra vez, si?
10:48
One, two, three, go!
(Music)
193
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4550
Un, dous, tres. Xa! (Música)
10:53
Look at that. Remember these toys?
194
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1883
Mira. Acórdaste destes xoguetes?
10:55
See these toys? Yeah, I'm going
to put this one over here,
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3264
Ves estes xoguetes?
Si, vou poñer este por aquí,
e a ti vouche dar este.
10:58
and I'm going to give this one to you.
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2062
11:00
You can go ahead and play.
197
648792
2335
Veña, xa podes xogar.
11:23
LS: Okay, Laura, but of course,
babies love their mommies.
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671213
4737
LS: Vale, Laura, pero claro,
os bebés quérenlles ás súas mamás.
11:27
Of course babies give toys
to their mommies
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675950
2182
Normal que lles dean os xoguetes a ela
11:30
when they can't make them work.
200
678132
2030
cando non conseguen que funcionen.
11:32
So again, the really important question
is what happens when we change
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3593
De novo, a pregunta realmente importante
é que ocorre cando cambiamos
11:35
the statistical data ever so slightly.
202
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3154
os datos estatísticos só levemente.
11:38
This time, babies are going to see the toy
work and fail in exactly the same order,
203
686909
4087
Agora, os bebés van ver o xoguete
funcionar e fallar xusto na mesma orde,
11:42
but we're changing
the distribution of evidence.
204
690996
2415
pero imos cambiar a distribución da proba.
11:45
This time, Hyowon is going to succeed
once and fail once, and so am I.
205
693411
4411
Agora, Hyowon vai conseguilo unha vez
e fracasar outra, e eu tamén.
11:49
And this suggests it doesn't matter
who tries this toy, the toy is broken.
206
697822
5637
O que suxire que non importa
quen proba este xoguete, está roto.
Non funciona nunca.
11:55
It doesn't work all the time.
207
703459
1886
De novo, o bebé
vai ter que tomar unha decisión.
11:57
Again, the baby's going to have a choice.
208
705345
1965
A súa nai está xusto ao lado,
así que pode cambiar a persoa,
11:59
Her mom is right next to her,
so she can change the person,
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707310
3396
12:02
and there's going to be another toy
at the end of the cloth.
210
710706
3204
e haberá outro xoguete ao final da tea.
Vexamos que fai.
12:05
Let's watch what she does.
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1378
12:07
(Video) HG: Two, three, go!
(Music)
212
715288
4348
HG: Dous, tres, xa!
(Música)
12:11
Let me try one more time.
One, two, three, go!
213
719636
4984
Déixame probar outra vez.
Un, dous, tres, xa!
12:17
Hmm.
214
725460
1697
Umm.
12:19
LS: Let me try, Clara.
215
727950
2692
LS: Déixame probar a min, Clara.
12:22
One, two, three, go!
216
730642
3945
Un, dous, tres, xa!
Umm, déixame probar outra vez.
12:27
Hmm, let me try again.
217
735265
1935
12:29
One, two, three, go!
(Music)
218
737200
5670
Un, dos, tres, xa!
(Música)
12:35
HG: I'm going
to put this one over here,
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743009
2233
HG: Vou poñer este por aquí,
12:37
and I'm going to give this one to you.
220
745242
2001
e vouche dar este a ti.
12:39
You can go ahead and play.
221
747243
3597
Veña, xa podes xogar.
12:58
(Applause)
222
766376
4897
(Aplausos)
13:04
LS: Let me show you
the experimental results.
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2392
LS: Amosarei agora
os resultados experimentais.
13:07
On the vertical axis,
you'll see the distribution
224
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2475
No eixe vertical, vese a distribución
13:09
of children's choices in each condition,
225
777860
2577
das eleccións dos nenos
baixo cada suposto,
13:12
and you'll see that the distribution
of the choices children make
226
780437
4551
e vese que a distribución
das eleccións que fan
13:16
depends on the evidence they observe.
227
784988
2787
depende da proba que observan.
13:19
So in the second year of life,
228
787775
1857
No segundo ano de idade,
os bebés poden usar unha fracción
mínima de datos estatísticos
13:21
babies can use a tiny bit
of statistical data
229
789632
2577
13:24
to decide between two
fundamentally different strategies
230
792209
3367
para decidir entre dúas estratexias
fundamentalmente diferentes
13:27
for acting in the world:
231
795576
1881
para actuar no mundo:
13:29
asking for help and exploring.
232
797457
2743
pedir axuda e explorar.
13:33
I've just shown you
two laboratory experiments
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3434
Acabo de amosar
dous experimentos de laboratorio
13:37
out of literally hundreds in the field
that make similar points,
234
805134
3691
dos literalmente centos neste campo
que chegan a conclusións similares,
13:40
because the really critical point
235
808825
2392
porque o auténtico punto clave
13:43
is that children's ability
to make rich inferences from sparse data
236
811217
5108
é que a capacidade dos nenos
para facer ricas inferencias
partindo de datos dispersos
13:48
underlies all the species-specific
cultural learning that we do.
237
816325
5341
serve de base a toda a nosa aprendizaxe
cultural específica como especie.
13:53
Children learn about new tools
from just a few examples.
238
821666
4597
Os nenos aprenden sobre novas ferramentas
a partir duns poucos exemplos.
13:58
They learn new causal relationships
from just a few examples.
239
826263
4717
Aprenden novas relacións causais
a partir duns poucos exemplos.
14:03
They even learn new words,
in this case in American Sign Language.
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831928
4871
Incluso aprenden palabras novas ,
neste caso en lingua de signos americana.
14:08
I want to close with just two points.
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836799
2311
Quero concluír con só dúas cousas.
14:12
If you've been following my world,
the field of brain and cognitive sciences,
242
840050
3688
A quen seguise o meu campo
(o do cerebro e as ciencias cognitivas)
14:15
for the past few years,
243
843738
1927
durante os últimos anos,
chamaríanlle a atención
tres grandes ideas.
14:17
three big ideas will have come
to your attention.
244
845665
2415
14:20
The first is that this is
the era of the brain.
245
848080
3436
A primeira é que esta é a era do cerebro.
14:23
And indeed, there have been
staggering discoveries in neuroscience:
246
851516
3669
E por suposto, houbo descubrimentos
impresionantes en neurociencia:
14:27
localizing functionally specialized
regions of cortex,
247
855185
3436
localizar rexións do córtex
funcionalmente especializadas,
14:30
turning mouse brains transparent,
248
858621
2601
facer transparentes os cerebros de ratos,
14:33
activating neurons with light.
249
861222
3776
activar neuronas con luz.
14:36
A second big idea
250
864998
1996
Unha segunda grande idea
14:38
is that this is the era of big data
and machine learning,
251
866994
4104
é que esta é a era dos datos masivos
e da aprendizaxe automática,
14:43
and machine learning promises
to revolutionize our understanding
252
871098
3141
e a aprendizaxe automática promete
revolucionar a nosa comprensión
14:46
of everything from social networks
to epidemiology.
253
874239
4667
de todo, dende as redes sociais
ata a epidemioloxía.
E tal vez, á vez que afronta problemas
de comprensión do contexto
14:50
And maybe, as it tackles problems
of scene understanding
254
878906
2693
14:53
and natural language processing,
255
881599
1993
e de procesamento da linguaxe natural,
14:55
to tell us something
about human cognition.
256
883592
3324
poida desvelarnos algo
sobre a cognición humana.
14:59
And the final big idea you'll have heard
257
887756
1937
E a gran idea final que escoitarían
15:01
is that maybe it's a good idea we're going
to know so much about brains
258
889693
3387
é que pode ser boa idea
saber tanto sobre os cerebros
15:05
and have so much access to big data,
259
893080
1917
e ter tanto acceso a datos masivos,
15:06
because left to our own devices,
260
894997
2507
porque pola nosa conta,
15:09
humans are fallible, we take shortcuts,
261
897504
3831
os humanos somos falíbeis,
buscamos atallos,
15:13
we err, we make mistakes,
262
901335
3437
erramos, temos fallos,
15:16
we're biased, and in innumerable ways,
263
904772
3684
non somos neutrais,
e de formas innumerables,
15:20
we get the world wrong.
264
908456
2969
chegamos a ideas falsas sobre o mundo.
15:24
I think these are all important stories,
265
912843
2949
Eu creo que todas estas
son noticias importantes,
15:27
and they have a lot to tell us
about what it means to be human,
266
915792
3785
e que teñen moito que contarnos
sobre qué significa ser humano,
15:31
but I want you to note that today
I told you a very different story.
267
919577
3529
pero gustaríame destacar
que hoxe tratei unha noticia moi distinta.
15:35
It's a story about minds and not brains,
268
923966
3807
Unha noticia sobre mentes,
non sobre cerebros,
15:39
and in particular, it's a story
about the kinds of computations
269
927773
3006
e en particular,
sobre o tipo de computación
que só as mentes humanas poden realizar,
15:42
that uniquely human minds can perform,
270
930779
2590
15:45
which involve rich, structured knowledge
and the ability to learn
271
933369
3944
que implican coñecementos ricos
e estruturados e capacidade de aprender
15:49
from small amounts of data,
the evidence of just a few examples.
272
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5268
a partir de pequenas cantidades de datos,
coa proba de só uns poucos exemplos.
15:56
And fundamentally, it's a story
about how starting as very small children
273
944301
4299
E fundamentalmente, é unha noticia
sobre como dende meniños
16:00
and continuing out all the way
to the greatest accomplishments
274
948600
4180
e continuando todo o camiño
ata os máis grandes logros
16:04
of our culture,
275
952780
3843
da nosa cultura,
16:08
we get the world right.
276
956623
1997
conseguimos entender ben o mundo.
16:12
Folks, human minds do not only learn
from small amounts of data.
277
960433
5267
Amigos, as mentes humanas non aprenden só
a partir de pequenas cantidades de datos
16:18
Human minds think
of altogether new ideas.
278
966285
2101
As mentes humanas pensan
ideas totalmente novas.
16:20
Human minds generate
research and discovery,
279
968746
3041
As mentes humanas xeran
investigación e descubrimento,
16:23
and human minds generate
art and literature and poetry and theater,
280
971787
5273
e as mentes humanas xeran
arte e literatura e poesía e teatro,
16:29
and human minds take care of other humans:
281
977070
3760
e as mentes humanas
coidan doutros seres humanos:
16:32
our old, our young, our sick.
282
980830
3427
os nosos maiores, a nosa mocidade,
os nosos enfermos.
16:36
We even heal them.
283
984517
2367
Incluso os curamos.
16:39
In the years to come, we're going
to see technological innovations
284
987564
3103
Nos próximos anos,
imos ver innovacións tecnolóxicas
16:42
beyond anything I can even envision,
285
990667
3797
máis alá do que podo concibir,
16:46
but we are very unlikely
286
994464
2150
pero hai moi poucas probabilidades
16:48
to see anything even approximating
the computational power of a human child
287
996614
5709
de que vexamos algo
que se aproxime sequera
ao poder computacional dun neno humano,
16:54
in my lifetime or in yours.
288
1002323
4298
no resto da miña vida ou da vosa.
16:58
If we invest in these most powerful
learners and their development,
289
1006621
5047
Se investimos nestes potentísimos
aprendices e no seu desenvolvemento,
17:03
in babies and children
290
1011668
2917
en bebés e cativos,
17:06
and mothers and fathers
291
1014585
1826
e nais e pais
17:08
and caregivers and teachers
292
1016411
2699
e coidadores e profesores
17:11
the ways we invest in our other
most powerful and elegant forms
293
1019110
4170
do xeito que investimos nas nosas
outras poderosísimas e elegantes formas
17:15
of technology, engineering and design,
294
1023280
3218
de tecnoloxía, enxeñaría e deseño,
17:18
we will not just be dreaming
of a better future,
295
1026498
2939
non estaremos simplemente
soñando cun mellor futuro,
estaremos planificándoo.
17:21
we will be planning for one.
296
1029437
2127
17:23
Thank you very much.
297
1031564
2345
Moitísimas grazas.
(Aplausos)
17:25
(Applause)
298
1033909
3421
17:29
Chris Anderson: Laura, thank you.
I do actually have a question for you.
299
1037810
4426
Chris Anderson: Grazas, Laura.
Quería facerche unha pregunta.
17:34
First of all, the research is insane.
300
1042236
2359
Antes de nada,
esta investigación é de tolos.
17:36
I mean, who would design
an experiment like that? (Laughter)
301
1044595
3725
Quen deseñaría
un experimento coma ese? (Risas)
17:41
I've seen that a couple of times,
302
1049150
1790
Vino unhas cantas veces,
17:42
and I still don't honestly believe
that that can truly be happening,
303
1050940
3222
e sigo sen acabar de crer
que poida estar ocorrendo de verdade,
17:46
but other people have done
similar experiments; it checks out.
304
1054162
3158
pero outras persoas fixeron
experimentos similares; está comprobado.
17:49
The babies really are that genius.
305
1057320
1633
Os bebés son realmente xenios.
17:50
LS: You know, they look really impressive
in our experiments,
306
1058953
3007
LS: Parecen realmente impresionantes
nos nosos experimentos,
17:53
but think about what they
look like in real life, right?
307
1061960
2652
pero pensa no que fan na vida real, non?
Todo comeza cun bebé.
17:56
It starts out as a baby.
308
1064612
1150
Dezaoito meses despois, estache falando,
17:57
Eighteen months later,
it's talking to you,
309
1065762
2007
e as primeiras palabras dos bebés
non van de pelotas e parrulos,
17:59
and babies' first words aren't just
things like balls and ducks,
310
1067769
3041
18:02
they're things like "all gone,"
which refer to disappearance,
311
1070810
2881
son cousas como “non ta”
que se refire á desaparición,
ou “uh oh”, para referirse
a accións involuntarias.
18:05
or "uh-oh," which refer
to unintentional actions.
312
1073691
2283
18:07
It has to be that powerful.
313
1075974
1562
Ten que ser así de poderoso.
Ten que ser moito máis poderoso
que o que ensinei.
18:09
It has to be much more powerful
than anything I showed you.
314
1077536
2775
Están descifrando o mundo enteiro.
18:12
They're figuring out the entire world.
315
1080311
1974
Un neno de catro anos
pode falarche sobre case todo.
18:14
A four-year-old can talk to you
about almost anything.
316
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3144
(Aplausos)
18:17
(Applause)
317
1085429
1601
18:19
CA: And if I understand you right,
the other key point you're making is,
318
1087030
3414
CA: E se entendo ben,
o outro punto clave que destacas é
18:22
we've been through these years
where there's all this talk
319
1090444
2754
que durante estes anos
tivemos todo este debate
18:25
of how quirky and buggy our minds are,
320
1093198
1932
sobre o peculiares e confusas
que son as nosas mentes,
18:27
that behavioral economics
and the whole theories behind that
321
1095130
2867
coa economía condutual
e teorías enteiras detrás
18:29
that we're not rational agents.
322
1097997
1603
de que non somos axentes racionais.
18:31
You're really saying that the bigger
story is how extraordinary,
323
1099600
4216
E ti estás a dicir que este fenómeno
é extraordinario,
18:35
and there really is genius there
that is underappreciated.
324
1103816
4944
e que en realidade hai xenialidade
que está subestimada.
18:40
LS: One of my favorite
quotes in psychology
325
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2070
Unha das miñas citas favoritas
en psicoloxía
18:42
comes from the social
psychologist Solomon Asch,
326
1110830
2290
é do psicólogo social Solomon Asch,
18:45
and he said the fundamental task
of psychology is to remove
327
1113120
2807
que dixo que
“o cometido fundamental da psicoloxía
é eliminar
o veo de autoevidencia das cousas”.
18:47
the veil of self-evidence from things.
328
1115927
2626
18:50
There are orders of magnitude
more decisions you make every day
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Hai millóns de decisións
que se toman a diario
18:55
that get the world right.
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que interpretan ben o mundo.
18:56
You know about objects
and their properties.
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Coñecemos os obxectos
e as súas propiedades.
18:58
You know them when they're occluded.
You know them in the dark.
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Recoñecémolos cando están ocultos.
Recoñecémolos na escuridade.
19:01
You can walk through rooms.
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Camiñamos por cuartos.
Podemos percibir o que pensan outros.
Podemos falarlles.
19:02
You can figure out what other people
are thinking. You can talk to them.
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Podemos navegar no espazo.
Coñecemos os números.
19:06
You can navigate space.
You know about numbers.
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Entendemos as relacións causais.
Entendemos o razoamento moral.
19:08
You know causal relationships.
You know about moral reasoning.
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E todo isto sen esforzo ningún,
por iso non nos decatamos,
19:11
You do this effortlessly,
so we don't see it,
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pero así interpretamos ben o mundo,
19:14
but that is how we get the world right,
and it's a remarkable
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e moi difícil de entender.
19:16
and very difficult-to-understand
accomplishment.
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CA: Imaxino que hai persoas no público
que comparten
19:19
CA: I suspect there are people
in the audience who have
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esa visión do crecente poder tecnolóxico
19:21
this view of accelerating
technological power
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que poderían cuestionar a túa afirmación
de que nunca nas nosas vidas
19:24
who might dispute your statement
that never in our lifetimes
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un ordenador fará
o que un neno de tres anos pode facer,
19:27
will a computer do what
a three-year-old child can do,
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pero está claro que en calquera situación,
19:29
but what's clear is that in any scenario,
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as nosas máquinas teñen
moito que aprender dos nosos cativos.
19:32
our machines have so much to learn
from our toddlers.
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19:38
LS: I think so. You'll have some
machine learning folks up here.
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LS: Eu tamén o creo. Aquí haberá
partidarios da aprendizaxe automática.
19:41
I mean, you should never bet
against babies or chimpanzees
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Nunca deberías apostar
contra os bebés ou os chimpancés
19:45
or technology as a matter of practice,
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ou da tecnoloxía, en principio.
19:49
but it's not just
a difference in quantity,
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pero non se trata só
dunha diferenza de cantidade,
19:53
it's a difference in kind.
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é unha diferenza cualitativa.
19:55
We have incredibly powerful computers,
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Temos ordenadores incriblemente potentes,
19:57
and they do do amazingly
sophisticated things,
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que fan cousas incriblemente sofisticadas,
20:00
often with very big amounts of data.
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por veces con enormes cantidades de datos.
As mentes humanas fan, para min,
algo bastante diferente,
20:03
Human minds do, I think,
something quite different,
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20:05
and I think it's the structured,
hierarchical nature of human knowledge
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e creo que é a natureza estruturada
e xerarquizada do coñecemento humano
20:09
that remains a real challenge.
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o que permanece como
un verdadeiro desafío.
20:11
CA: Laura Schulz, wonderful
food for thought. Thank you so much.
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CA: Laura Schulz, un gran tema
para reflexionar. Moitas grazas.
20:14
LS: Thank you.
(Applause)
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Grazas
(Aplausos)

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ABOUT THE SPEAKER
Laura Schulz - Cognitive scientist
Developmental behavior studies spearheaded by Laura Schulz are changing our notions of how children learn.

Why you should listen

MIT Early Childhood Cognition Lab lead investigator Laura Schulz studies learning in early childhood. Her research bridges computational models of cognitive development and behavioral studies in order to understand the origins of inquiry and discovery.

Working in play labs, children’s museums, and a recently-launched citizen science website, Schultz is reshaping how we view young children’s perceptions of the world around them. Some of the surprising results of her research: before the age of four, children expect hidden causes when events happen probabilistically, use simple experiments to distinguish causal hypotheses, and trade off learning from instruction and exploration.

More profile about the speaker
Laura Schulz | Speaker | TED.com