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PROFESSOR: Good afternoon.

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Good afternoon.

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I want to remind you that
a week from today

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is the second exam.

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The format is identical to
what you had, 50 multiple

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choice questions; 30 from the
book, Chapters 5 to 7; 20 from

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Sacks and the lectures;
short answers.

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So talking about tests,
why do we have tests?

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You live in a world of tests.

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Everything from SATs to, on your
horizons, maybe GREs or

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MCATs or LSATs.

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So why do people have tests,
besides making it miserable to

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be under 30?

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It's the revenge of
the older people.

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Why do people have tests?

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Yeah?

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AUDIENCE: [INAUDIBLE].

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PROFESSOR: One of them
is just verify

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that you know something.

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And people will call it sort
of have you learned stuff?

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And the test is meant
to be an estimate of

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what have you learned?

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But a second form of testing,
what people sometimes will

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call high-stakes tests in a
slightly different way, are

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ones about selection and
prediction, which sometimes

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involve achievement or
aptitude, things

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like SATs or GREs.

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So achievements are meant to be
estimates of what you know,

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advanced placement tests, how
much math you know or physics.

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Content in a given area.

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And sort of a broader sense
of aptitude, SATs or GREs.

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Now, one thing that's different
about today's

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lecture on intelligence, it'll
be a little similar when we do

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personalities, up until now, to
a first approximation, most

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of the time we've talked about
what we view as general

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principles of the human mind
and brain across everybody.

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How vision works across
everybody, memory across

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everybody, thinking
across everybody.

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And now, we're switching
to a domain.

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And it doesn't have to be this
way, but this is the way the

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world has done it, where
we emphasized

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differences between people.

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So when people take tests, it's
not to see that everybody

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took the test.

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What they're going to do is, you
get an A, or a B, or a C,

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or you get a high SAT or a
medium SAT, and schools use

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the differences to
think about you.

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And so one question is, from an
empirical sense, are these

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tests pretty good at
predicting things?

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And the answer is
they're decent.

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They're not terrific.

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So the tests survive, like SATs
or GREs, because there's

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correlations between these kinds
of tests before you get

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to graduate school for example,
and how faculty rates

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students or how productive
people are in terms of

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research or as a graduate
student in this case, or the

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GPAs, or things like that.

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And the correlations are
about 0.3 to 0.4.

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To give you an intuitive
sense, the correlations

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between sex and height, whether
you're a man or a

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woman and height,
is about 0.4.

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So of course lots of women are
taller than lots of men.

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On average, men are
taller than women.

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It's a 0.4 correlation.

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So this is in that range.

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So tests are pretty
good predictors.

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Everybody says that for things
like predicting college

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performance or graduate school
performance, a combination of

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tests and grades are better.

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The difference is grades take
a lot of years to produce.

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Tests are one traumatic
outing.

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So tests are just a
part of our life

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in selection processes.

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So now what is intelligence?

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And it's a loaded word.

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I mean memory is kind
of a neutral word.

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Attention is a neutral word.

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Intelligence is a loaded word.

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I mean nobody wants to be
low on intelligence.

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And so psychologists have
come out of this way.

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But it's kind of funny, because
there's not going to

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be a really deep definition.

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Maybe it's a lot of different
things and how they average up

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or something like that.

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But people have said well,
it's something about the

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ability to solve problems, to
understand and learn complex

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material, to adapt to the
environment, mental quickness.

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Those are all senses you might
have of something that you

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think of as intelligence
in a person.

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But whether they're one thing,
many things, it's very hard to

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pull that out.

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And we don't know too much
about that really.

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And also I want remind you of
something we talked about, but

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it's especially important
in this field.

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We talked about an experiment
versus correlations.

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An experiment, you have to
have a dependent measure,

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something you measure.

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But critically and uniquely,
you have an independent

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variable, something
that you vary.

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That's what makes a study an
experiment, as opposed to a

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correlation.

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So studies of intelligence of
every kind are almost always

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correlational and almost
never experimental.

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That means they're always
subject to lots of

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interpretation and many
factors in the world.

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And that's very important
to keep in mind.

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And again, they tend to focus on
variation in people, rather

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than what is intelligence
itself.

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And again, here's another
correlation

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of height and weight.

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That's a stronger correlation.

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So where did intelligence
start with?

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It's a fascinating story.

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And by the way, let me tell
you the SAT story, on the

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sidebar, for a moment.

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So SATs, nowadays do people,
some people get ready a lot

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for SATs by courses you take
and things like that?

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I read about that.

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When I took it, people weren't
that organized.

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But getting into college wasn't
that competitive.

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And the original goals of SATs
was what, do you know?

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It's really interesting.

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Because it ends up being the
thing, they say well, because

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people who are maybe under more
supported circumstances,

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more financially advantageous
circumstances, get more help

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to get ready for the SAT.

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Those things might amplify SAT
differences in a way that's

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not really about the person
to start with.

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The original goal of the SATs
was to have a national test

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that was fair for everybody, so
that schools like Harvard,

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for example, that tended to
admit the same kind of

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students from the same
small set of

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schools in the Northeast.

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So now, we have a test for
everybody across the country.

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And we can admit people on their
merit, rather than on

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small, social connectivity
networks.

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Does that make sense?

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And yet, SATs are now viewed
as a suspicious

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tool in that regard.

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It was originally meant
to be democratizing.

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It's like everybody in the
country takes the same test.

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It's not whether you have a
connection through a network,

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to our university.

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IQs have the same story.

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For good reasons, and we'll talk
about that, IQ tests and

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interpretations of IQ don't
have a good history.

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But the original goal was
pretty reasonable.

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So in France in 1904, they began
universal elementary

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education, that every
child goes to class.

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And Alfred Binet, a physician,
wanted an objective way to

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identify children who
needed extra help.

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A perfectly good idea.

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A child comes to school.

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Some children are ready to go.

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Some children need a lot of
extra help because they have a

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difficulty.

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So he developed a set of tests
where he probed many different

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abilities, like copying a
drawing, repeating digits,

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recognizing coins, explaining
why a statement

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did not make sense.

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They wanted to probe different
little areas of abilities, so

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if a child is terrible at
something, or two things, the

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teacher knows this child needs
some extra help to get going.

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So that's a good use of
testing, I think by

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everybody's criterion.

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But then testing sort of
took off, IQ testing.

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IQ stands for intelligence
quotient.

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It's a test to give
typical children.

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They give it often for the most
commonly used IQ tests,

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at many different ages.

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They'll test a large number
of individuals.

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And they'll compare what
nowadays what they think of as

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your mental age to your
chronological age.

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So your chronological age is
how long you've lived.

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And your mental age is how
well you score on a test.

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The most famous IQ test, the
most widely used, takes about

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two hours of testing.

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It comes from David Wechsler,
the Wechsler Adult

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Intelligence Scale.

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One for children.

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And within that test, what's
within that test?

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Because when people talk about
tests I think, whatever kind

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of test it is, it can only
about as good as the test

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design itself, to the extent
that it's any good.

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A ridiculous test is a
ridiculous test, even if

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society gives it to you.

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But tests can be better
than that.

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So what is inside the most
widely used IQ tests,

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especially in clinical
environments?

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So let's talk about
one of them.

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It has a bunch of
little tests.

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One is vocabulary.

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They ask you to define
words, similarities.

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They'll ask you a series of
questions, like how are an

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airplane and a car alike?

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Very simple arithmetic
operations.

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Digit spans, how many digits
can you remember.

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Information, they'll give you
a series of questions, like

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who was Martin Luther
King, Jr.?

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Comprehension, they'll ask you
things about the world, like

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why are there taxes and
things like that.

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And you give an answer.

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00:09:24,030 --> 00:09:25,830
Somebody scribbles down
when you say.

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And they score you.

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You get two points
or one point.

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We'll talk about that.

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And then they add up these
scores in the subtests.

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So in the nonverbal verbal
tests or the performance

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tests, they'll show you a
picture with a thing missing.

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Have you did that a kid?

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I love those things.

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What's missing in the picture
and stuff like that.

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Blocks, they'll have you
rearrange them into various

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geometric design.

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Picture arrangement, you'll
get cartoon panels and you

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have to put them in the correct
order to tell a story.

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And they measure how accurately
and quickly you do

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these things.

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There's nothing amazing about
any of these things.

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They're probing a variety
of little

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00:09:59,100 --> 00:10:02,060
abilities that you have.

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00:10:02,060 --> 00:10:04,720
And again, they will take the
mental age and chronological

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00:10:04,720 --> 00:10:06,580
age, divide it, multiply
it by 100.

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00:10:06,580 --> 00:10:09,750
And they'll standardize it, so
that 100 is the population

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mean in the United States.

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00:10:11,040 --> 00:10:13,150
Other countries do these
things, as well.

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00:10:13,150 --> 00:10:15,050
Is that OK?

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00:10:15,050 --> 00:10:17,490
So I mean a test can only be
as possibly good as the

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content of it.

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And what it measures or doesn't
measure depends on the

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00:10:20,490 --> 00:10:24,320
specific conflict of it.

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00:10:24,320 --> 00:10:26,450
They'll scale the scores out,
so you get a normal

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00:10:26,450 --> 00:10:29,220
distribution, with a
peak in the middle.

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00:10:29,220 --> 00:10:30,800
The raw score is the
number you get.

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00:10:30,800 --> 00:10:33,030
The standardized one is
adjusted for your age.

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00:10:33,030 --> 00:10:34,820
And it's a pretty important
concept, because we'll come

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00:10:34,820 --> 00:10:36,010
back to this in a moment.

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00:10:36,010 --> 00:10:37,810
The raw score is your
actual score.

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00:10:37,810 --> 00:10:39,970
But then they adjust
it for your age.

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Because you don't expect a
five-year-old to do as well as

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00:10:43,080 --> 00:10:49,540
a 15-year-old, to do as well
as peak 18 to 23-year-olds.

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And then bad things happen
as you get older.

250
00:10:51,870 --> 00:10:54,460
I'll tell you a bit of
them about that too.

251
00:10:54,460 --> 00:10:56,950
2/3 of the people are at plus or
minus 1 standard deviation.

252
00:10:56,950 --> 00:10:58,600
They set it for that.

253
00:10:58,600 --> 00:11:01,260
More than two standard
deviations up or down, are

254
00:11:01,260 --> 00:11:04,600
about 5% on either end of the
distribution of scores.

255
00:11:04,600 --> 00:11:06,020
And they norm it by age.

256
00:11:06,020 --> 00:11:09,690
Again, you don't expect a
six-year-old to do something.

257
00:11:09,690 --> 00:11:10,860
But you get some funny things.

258
00:11:10,860 --> 00:11:12,460
Not that you have to worry
about this in detail.

259
00:11:12,460 --> 00:11:14,380
But it gives you a sense.

260
00:11:14,380 --> 00:11:18,820
So imagine you're a
five-year-old and you take the

261
00:11:18,820 --> 00:11:21,140
test, on the last day you're
a five-year-old.

262
00:11:21,140 --> 00:11:23,045
Or imagine you have your
birthday today and

263
00:11:23,045 --> 00:11:24,400
you're six years old.

264
00:11:24,400 --> 00:11:28,120
By moving from five to six, from
Wednesday to Thursday,

265
00:11:28,120 --> 00:11:31,410
you've moved up a year
for the scoring.

266
00:11:31,410 --> 00:11:33,410
Because they go by
year, not by day.

267
00:11:33,410 --> 00:11:36,940
So if you would have scored 120
on the IQ test yesterday,

268
00:11:36,940 --> 00:11:38,890
when they say what should
you have scored for a

269
00:11:38,890 --> 00:11:41,630
five-year-old, the very same
test, the next day,

270
00:11:41,630 --> 00:11:43,590
you would score 100.

271
00:11:43,590 --> 00:11:45,910
Now, that's the extreme case.

272
00:11:45,910 --> 00:11:47,290
But you understand what
happens, right?

273
00:11:47,290 --> 00:11:49,130
Because here's how
well you did.

274
00:11:49,130 --> 00:11:50,440
That might have been
pretty good.

275
00:11:50,440 --> 00:11:53,950
120 is above the average
for a five-year-old.

276
00:11:53,950 --> 00:11:57,190
But the next day, if you're
a six-year-old, now for a

277
00:11:57,190 --> 00:11:59,140
six-year-old, it's
just average.

278
00:11:59,140 --> 00:12:02,850
So age has a big effect
on these things.

279
00:12:02,850 --> 00:12:06,810
Here's something sad for aging,
but good for you.

280
00:12:06,810 --> 00:12:08,850
Be smart now.

281
00:12:08,850 --> 00:12:11,920
A 40-year-old who
scores average

282
00:12:11,920 --> 00:12:16,280
would be a real undergrad.

283
00:12:16,280 --> 00:12:18,560
If I score like you--

284
00:12:18,560 --> 00:12:19,560
no, let me rephrase this.

285
00:12:19,560 --> 00:12:22,830
If you were compared to a
40-year-old, you would have

286
00:12:22,830 --> 00:12:25,320
100 IQ, you would
have a 230 IQ.

287
00:12:25,320 --> 00:12:28,310
Because age knocks down
the score as well.

288
00:12:28,310 --> 00:12:30,230
So things go up and
then things go

289
00:12:30,230 --> 00:12:33,210
down, in this problem.

290
00:12:33,210 --> 00:12:36,670
And usually they think of a
score below 70 as clinically

291
00:12:36,670 --> 00:12:38,850
concerning.

292
00:12:38,850 --> 00:12:41,400
And the top 2%, you get to join
Mensa and do whatever

293
00:12:41,400 --> 00:12:42,130
Mensa people do.

294
00:12:42,130 --> 00:12:44,310
Which I don't know exactly,
but it sounds interesting.

295
00:12:46,950 --> 00:12:49,710
An important thing about tests
of any kind, are two huge

296
00:12:49,710 --> 00:12:51,220
concepts, reliability
and validity.

297
00:12:51,220 --> 00:12:53,690
So reliability refers to the
idea that if you were tested

298
00:12:53,690 --> 00:12:56,900
more than once, you would
get the same score.

299
00:12:56,900 --> 00:12:58,650
The better IQ tests
work a fair bit to

300
00:12:58,650 --> 00:13:00,360
try to achieve that.

301
00:13:00,360 --> 00:13:03,190
Validity is the deeper
question.

302
00:13:03,190 --> 00:13:05,360
Does the test validly measure
what it's supposed to measure?

303
00:13:05,360 --> 00:13:08,180
So if we had you flip a coin and
said that's your IQ, you'd

304
00:13:08,180 --> 00:13:09,830
say, that's ridiculous.

305
00:13:09,830 --> 00:13:11,210
That's not a valid test.

306
00:13:11,210 --> 00:13:13,090
We could measure your height.

307
00:13:13,090 --> 00:13:14,830
That would be extremely
reliable measure.

308
00:13:14,830 --> 00:13:16,390
You'll have the same height
today as tomorrow, if we did a

309
00:13:16,390 --> 00:13:17,340
decent job.

310
00:13:17,340 --> 00:13:20,350
That wouldn't be a valid measure
of anything about any

311
00:13:20,350 --> 00:13:22,580
mental ability, never mind
something like intelligence.

312
00:13:22,580 --> 00:13:25,050
So those are two important
concepts in

313
00:13:25,050 --> 00:13:27,620
testing, but very different.

314
00:13:27,620 --> 00:13:30,300
Now, why do people think IQ
tests are interesting, besides

315
00:13:30,300 --> 00:13:33,330
just this weird curiosity
about intelligence?

316
00:13:33,330 --> 00:13:36,450
Because IQ tests correlate with
GPA in high school and

317
00:13:36,450 --> 00:13:39,910
college, job success, salary,
stable marriages, staying out

318
00:13:39,910 --> 00:13:42,340
of jail, and how
long you live.

319
00:13:42,340 --> 00:13:43,380
Again, it's a correlation.

320
00:13:43,380 --> 00:13:45,645
It could be many different
things.

321
00:13:45,645 --> 00:13:48,250
The thing I want to talk you out
of is the idea that if you

322
00:13:48,250 --> 00:13:52,985
have a 200 IQ today, you've got
something stored up that

323
00:13:52,985 --> 00:13:54,590
will fend off disease.

324
00:13:54,590 --> 00:13:56,850
It's not that, necessarily.

325
00:13:56,850 --> 00:13:59,290
It's that there's lots of things
in your life that got

326
00:13:59,290 --> 00:14:00,600
you to that score.

327
00:14:00,600 --> 00:14:02,710
And all those things are likely
to persist in your

328
00:14:02,710 --> 00:14:07,250
life: good health, support of
education, interest maybe in

329
00:14:07,250 --> 00:14:09,230
taking on challenging tasks.

330
00:14:09,230 --> 00:14:10,110
You had those before.

331
00:14:10,110 --> 00:14:11,400
You had them while you
took the test.

332
00:14:11,400 --> 00:14:14,440
You continue to have
them after.

333
00:14:14,440 --> 00:14:16,660
Another way to think about it
though, which is both a plus

334
00:14:16,660 --> 00:14:19,390
and minus, depending on how
you look at it, IQ scores

335
00:14:19,390 --> 00:14:21,670
account for approximately
25% of the

336
00:14:21,670 --> 00:14:23,260
variation on these things.

337
00:14:23,260 --> 00:14:24,220
So that's not huge.

338
00:14:24,220 --> 00:14:27,020
That means there's many, many,
many other factors, besides

339
00:14:27,020 --> 00:14:28,890
whatever IQ is capturing.

340
00:14:28,890 --> 00:14:31,250
But it's about as big a chunk
as you can get for a couple

341
00:14:31,250 --> 00:14:32,300
hours of testing.

342
00:14:32,300 --> 00:14:35,290
And we know that other things
like personality, variation,

343
00:14:35,290 --> 00:14:38,210
education, culture, all those
things blend in on how a

344
00:14:38,210 --> 00:14:40,775
person will do on all
these outcomes.

345
00:14:43,630 --> 00:14:44,720
So I'm going to go
back and forth.

346
00:14:44,720 --> 00:14:45,850
Because on the one hand, we
say there's a lot of stuff

347
00:14:45,850 --> 00:14:46,210
besides IQ.

348
00:14:46,210 --> 00:14:48,010
But IQ has some interestingness,
otherwise

349
00:14:48,010 --> 00:14:49,040
nobody would talk about it.

350
00:14:49,040 --> 00:14:50,260
And it would just disappear
from the world.

351
00:14:50,260 --> 00:14:55,790
So on average, on average, what
does 15 IQ points mean?

352
00:14:55,790 --> 00:14:58,640
So on average, a person with
100 IQ in the US will go to

353
00:14:58,640 --> 00:15:00,660
high school and a year or two
in a community college.

354
00:15:00,660 --> 00:15:04,560
On average, these are averages
over huge variety of people.

355
00:15:04,560 --> 00:15:07,120
You get 15 more IQ points, and
on average, you're likely to

356
00:15:07,120 --> 00:15:09,630
finish college and have
a white collar job.

357
00:15:09,630 --> 00:15:11,890
Have 15 fewer points, and you're
not likely to complete

358
00:15:11,890 --> 00:15:12,700
high school.

359
00:15:12,700 --> 00:15:14,570
And you'd have to have a job
that doesn't require

360
00:15:14,570 --> 00:15:16,270
educational background.

361
00:15:16,270 --> 00:15:20,870
So 15 point on averages
correlate with things.

362
00:15:20,870 --> 00:15:24,450
Here's another one, because
everybody in the end is a

363
00:15:24,450 --> 00:15:25,930
little bit curious.

364
00:15:25,930 --> 00:15:28,680
In your own life, how much of
what you're good at or not

365
00:15:28,680 --> 00:15:32,210
good at, of any of us, comes
from our genes, from our

366
00:15:32,210 --> 00:15:32,970
environment?

367
00:15:32,970 --> 00:15:34,790
Where does that come from?

368
00:15:34,790 --> 00:15:37,930
What are the things that
support us or limit us?

369
00:15:37,930 --> 00:15:42,970
So here's one study that took
women from the same family.

370
00:15:42,970 --> 00:15:45,800
So they're all sisters
from the same family.

371
00:15:45,800 --> 00:15:48,690
They're trying to control a
little bit of the genetics,

372
00:15:48,690 --> 00:15:50,220
because they share genes
within the family.

373
00:15:50,220 --> 00:15:51,280
They're not twins.

374
00:15:51,280 --> 00:15:53,360
And definitely shared
environment.

375
00:15:53,360 --> 00:15:55,760
They're in the same household.

376
00:15:55,760 --> 00:15:59,320
And within those families, they
look at the IQ scores.

377
00:15:59,320 --> 00:16:02,580
So here's the average group,
above average, well above

378
00:16:02,580 --> 00:16:05,760
average, a little below average,
a lot below average.

379
00:16:05,760 --> 00:16:09,330
And they say that in these
women, all coming from the

380
00:16:09,330 --> 00:16:13,120
same household, here's their
average income and here's the

381
00:16:13,120 --> 00:16:17,500
average rate of illegitimate
births, which is one measure

382
00:16:17,500 --> 00:16:21,540
that people think is kind of
a social, on average not a

383
00:16:21,540 --> 00:16:23,500
desired outcome, to have a
child without a family

384
00:16:23,500 --> 00:16:23,990
supporting.

385
00:16:23,990 --> 00:16:26,180
It's not going to help a person
succeed in the world to

386
00:16:26,180 --> 00:16:28,570
be a single mother.

387
00:16:28,570 --> 00:16:32,370
And so you can see, the salary
pretty much moves with the IQ

388
00:16:32,370 --> 00:16:33,320
on average.

389
00:16:33,320 --> 00:16:36,890
And the rate of illegitimate
births pretty much

390
00:16:36,890 --> 00:16:38,620
moves with that too.

391
00:16:38,620 --> 00:16:41,730
So again, it's an average.

392
00:16:41,730 --> 00:16:44,510
But there's some relationship
between these things.

393
00:16:44,510 --> 00:16:49,980
Now, if you pick up the
newspaper today and read about

394
00:16:49,980 --> 00:16:55,110
nuclear disasters in Japan and
problems in the Middle East

395
00:16:55,110 --> 00:16:57,750
and other things like that, you
would say, it's hard to

396
00:16:57,750 --> 00:16:59,880
say the world is getting
smarter.

397
00:16:59,880 --> 00:17:02,010
But you may or may not be
surprised to hear of the

398
00:17:02,010 --> 00:17:02,940
so-called Flynn effect.

399
00:17:02,940 --> 00:17:05,450
This was kind of a surprise when
it was discovered, which

400
00:17:05,450 --> 00:17:06,430
is basically this.

401
00:17:06,430 --> 00:17:09,369
IQ scores, where they're
measured, go up around the

402
00:17:09,369 --> 00:17:10,400
world all the time.

403
00:17:10,400 --> 00:17:14,089
By IQ test scores, it's as if
the world is getting smarter

404
00:17:14,089 --> 00:17:17,859
all the time, which is kind
of an optimistic thing.

405
00:17:17,859 --> 00:17:20,240
Whether it's getting wiser
is a good question.

406
00:17:20,240 --> 00:17:23,030
But by IQ score, smarter
all the time.

407
00:17:23,030 --> 00:17:26,630
So that's hidden for many years,
because every few years

408
00:17:26,630 --> 00:17:29,320
these tests get renormed
for the groups.

409
00:17:29,320 --> 00:17:30,670
Does that makes sense?

410
00:17:30,670 --> 00:17:33,210
So they're given again and
100 is the average.

411
00:17:33,210 --> 00:17:35,310
And they're given again and
100 is the average.

412
00:17:35,310 --> 00:17:38,040
But if they ignore this
averaging and just say, across

413
00:17:38,040 --> 00:17:39,970
these tests that only change a
little bit, some of them only

414
00:17:39,970 --> 00:17:42,760
change a little bit, if we just
take the scores, look at

415
00:17:42,760 --> 00:17:45,606
1930, 1940.

416
00:17:45,606 --> 00:17:48,770
And it just keeps going up.

417
00:17:48,770 --> 00:17:51,340
So Flynn is the person who
discovered that the world is

418
00:17:51,340 --> 00:17:54,320
getting smarter by
raw IQ tests.

419
00:17:56,970 --> 00:17:59,640
And one of tests they use, I'll
show you an example of

420
00:17:59,640 --> 00:18:01,420
this, it's another widely
used test called Raven's

421
00:18:01,420 --> 00:18:03,510
Progressive Matrices, IQs
go up about three

422
00:18:03,510 --> 00:18:05,770
points every 10 years.

423
00:18:05,770 --> 00:18:10,160
So that if you were born in
1930, a person would have an

424
00:18:10,160 --> 00:18:15,490
IQ of 100, their child 108,
and their grandchild, 120,

425
00:18:15,490 --> 00:18:16,810
with a standard deviation
higher.

426
00:18:16,810 --> 00:18:20,590
So it's like smarter,
smarter, smarter.

427
00:18:20,590 --> 00:18:24,860
I don't recommend to go home and
tell your parents that any

428
00:18:24,860 --> 00:18:26,610
debate you have with them is
pointless, because you're

429
00:18:26,610 --> 00:18:29,390
smarter than they are.

430
00:18:29,390 --> 00:18:32,180
But if push comes to shove,
you can look up the Flynn

431
00:18:32,180 --> 00:18:34,760
effect and have a discussion
about that.

432
00:18:34,760 --> 00:18:39,240
So somebody who has an IQ today,
just flip it around.

433
00:18:39,240 --> 00:18:41,360
If you have a 100, your
grandparent would

434
00:18:41,360 --> 00:18:44,176
have a IQ of 82.

435
00:18:44,176 --> 00:18:46,400
By this reasoning, people
in 1900 would have

436
00:18:46,400 --> 00:18:47,650
a mean IQ of 70.

437
00:18:50,330 --> 00:18:52,580
And nobody knows why it's going
up all over the world.

438
00:18:52,580 --> 00:18:54,010
There's all kinds of
ideas about maybe

439
00:18:54,010 --> 00:18:55,650
nutrition is going up.

440
00:18:55,650 --> 00:18:57,950
Maybe reasoning abilities
are being pushed.

441
00:18:57,950 --> 00:18:59,350
Maybe educational things.

442
00:18:59,350 --> 00:19:01,730
I mean it's impossible
scientifically to know why,

443
00:19:01,730 --> 00:19:05,450
because it's happening in such
a global, giant scale.

444
00:19:05,450 --> 00:19:08,410
But some people have said, the
tests don't go up uniformly

445
00:19:08,410 --> 00:19:09,120
across the tests.

446
00:19:09,120 --> 00:19:12,320
Which gives us a hint about the
way the world is changing

447
00:19:12,320 --> 00:19:15,840
and how intelligence tests in
some way become a barometer of

448
00:19:15,840 --> 00:19:17,200
what mental abilities are

449
00:19:17,200 --> 00:19:20,710
increasingly prized by societies.

450
00:19:20,710 --> 00:19:23,670
So there's relatively small
gains across years in

451
00:19:23,670 --> 00:19:26,950
vocabulary or general knowledge
or arithmetic.

452
00:19:26,950 --> 00:19:29,530
Where there are large gains
are what you might call

453
00:19:29,530 --> 00:19:32,850
abstract thinking tasks,
like similarities.

454
00:19:32,850 --> 00:19:34,730
So let's focus on that
for a moment.

455
00:19:34,730 --> 00:19:36,670
So a similarities question
might be like this.

456
00:19:36,670 --> 00:19:39,810
In what way are dogs
and rabbits alike?

457
00:19:39,810 --> 00:19:43,870
If you answer both mammals,
you get two points.

458
00:19:43,870 --> 00:19:46,090
That's the best you can do.

459
00:19:46,090 --> 00:19:48,870
And that's what we would call an
abstract or taxonomic one.

460
00:19:48,870 --> 00:19:51,140
You're abstracting across
the two things.

461
00:19:51,140 --> 00:19:53,190
Or you could give an answer
like, you use

462
00:19:53,190 --> 00:19:55,660
dogs to hunt rabbits.

463
00:19:55,660 --> 00:19:57,150
That's not a wrong answer.

464
00:19:57,150 --> 00:20:00,090
That's considered a
one-point answer.

465
00:20:00,090 --> 00:20:01,920
You kind of seem to know what a
dog is, you kind of seem to

466
00:20:01,920 --> 00:20:02,410
know a rabbit.

467
00:20:02,410 --> 00:20:05,120
But you didn't give the best
answer by this test criterion.

468
00:20:05,120 --> 00:20:07,250
Because it's a functional
description of these things,

469
00:20:07,250 --> 00:20:09,980
not a abstract, conceptual
one.

470
00:20:09,980 --> 00:20:13,210
So let's think about different
societies, besides the Western

471
00:20:13,210 --> 00:20:16,500
industrialized one that
we represent mostly.

472
00:20:16,500 --> 00:20:22,250
In Liberia, they ask people to
sort baskets of food, tools,

473
00:20:22,250 --> 00:20:24,710
containers, and clothing.

474
00:20:24,710 --> 00:20:28,330
So most of us, Western educated
in a very broad

475
00:20:28,330 --> 00:20:31,350
sense, would put the things of
food together, the things of

476
00:20:31,350 --> 00:20:32,000
tools together.

477
00:20:32,000 --> 00:20:34,100
Those are the abstract
categories.

478
00:20:34,100 --> 00:20:36,370
The people from this tribe
put them in terms

479
00:20:36,370 --> 00:20:37,300
of functional pairings.

480
00:20:37,300 --> 00:20:39,870
They put the potato next to the
knife they use to peel the

481
00:20:39,870 --> 00:20:42,250
potato, right?

482
00:20:42,250 --> 00:20:44,250
And here's the fun thing
in this study.

483
00:20:44,250 --> 00:20:47,040
They said, what would
a fool do with this?

484
00:20:47,040 --> 00:20:49,720
And they said oh, well, a fool
would put all the food things

485
00:20:49,720 --> 00:20:51,610
together and all the
utensils together.

486
00:20:51,610 --> 00:20:54,060
But that's pointless to do that,
because what are you

487
00:20:54,060 --> 00:20:55,310
going to do with all the food
things over here, when the

488
00:20:55,310 --> 00:20:56,500
utensils are over here.

489
00:20:56,500 --> 00:20:57,330
You understand?

490
00:20:57,330 --> 00:20:59,500
It's what our society
emphasizes as a

491
00:20:59,500 --> 00:21:01,830
useful way of thinking.

492
00:21:01,830 --> 00:21:05,540
So it could be arbitrary.

493
00:21:05,540 --> 00:21:08,460
But it's not arbitrarily
rewarded and prized in the

494
00:21:08,460 --> 00:21:14,000
world of education, that
we live in here.

495
00:21:14,000 --> 00:21:17,970
So intelligence tests attach
value at a specific time and

496
00:21:17,970 --> 00:21:18,700
specific test.

497
00:21:18,700 --> 00:21:21,180
Scores are changing over time.

498
00:21:21,180 --> 00:21:24,230
Different cultures are valuing
different things.

499
00:21:24,230 --> 00:21:25,480
It's constantly renormed.

500
00:21:28,200 --> 00:21:30,340
And because of that, here's
another example of how the

501
00:21:30,340 --> 00:21:31,110
renorming is.

502
00:21:31,110 --> 00:21:34,360
It used to be for some number of
years, that 70 was used as

503
00:21:34,360 --> 00:21:37,080
a cut-off for estimates of,
broadly speaking, mental

504
00:21:37,080 --> 00:21:38,120
retardation.

505
00:21:38,120 --> 00:21:41,040
And why that matters is that a
person for example would get

506
00:21:41,040 --> 00:21:45,140
certain kinds of medical
services provided to them.

507
00:21:45,140 --> 00:21:48,530
When a new version of the WISC,
of the Wechsler test,

508
00:21:48,530 --> 00:21:51,980
came out in 1991, so a renormed
version, so that

509
00:21:51,980 --> 00:21:56,360
means the scores are going up,
if every state in the country

510
00:21:56,360 --> 00:21:58,970
had simultaneously used that
new test when it came out,

511
00:21:58,970 --> 00:22:01,110
which they didn't quite, but
if they had, the number of

512
00:22:01,110 --> 00:22:04,440
people rated as retarded,
diagnosed as retarded, would

513
00:22:04,440 --> 00:22:06,630
have doubled in a single day.

514
00:22:06,630 --> 00:22:09,560
Because the norms aren't
pushing the numbers up.

515
00:22:09,560 --> 00:22:10,220
OK?

516
00:22:10,220 --> 00:22:13,620
So the number of people scoring
very poorly would have

517
00:22:13,620 --> 00:22:16,710
doubled in a single day because
of the renorming.

518
00:22:19,320 --> 00:22:21,730
It's very important to realize
that all these things, what's

519
00:22:21,730 --> 00:22:25,270
really in the test, what's
valued by a sort of society or

520
00:22:25,270 --> 00:22:28,030
culture, and the norming things
are really influencing

521
00:22:28,030 --> 00:22:29,855
a lot of how you get
to these numbers.

522
00:22:33,110 --> 00:22:37,540
So ideas about intelligence,
roughly in this field, have

523
00:22:37,540 --> 00:22:39,040
come out of what people call

524
00:22:39,040 --> 00:22:40,900
psychometric analysis of subtests.

525
00:22:40,900 --> 00:22:42,850
They give you the scatter
of tests.

526
00:22:42,850 --> 00:22:45,780
And the thing that struck many
people is how for a given

527
00:22:45,780 --> 00:22:48,570
individual, there's correlations
among the tests.

528
00:22:48,570 --> 00:22:52,280
A person who does well on one
test, tends to do well on

529
00:22:52,280 --> 00:22:55,270
another test, and tends to
do well on a third test.

530
00:22:55,270 --> 00:23:00,590
And a researcher named Spearman
gave this correlation

531
00:23:00,590 --> 00:23:03,730
among tests, the letter "g,"
the name g, to stand for

532
00:23:03,730 --> 00:23:07,660
general intelligence, and "s"
for specific intelligence.

533
00:23:07,660 --> 00:23:10,445
And since then, many people have
said well really is there

534
00:23:10,445 --> 00:23:12,680
one broad sense of intelligence,
are there

535
00:23:12,680 --> 00:23:15,060
multiple separate kinds
of intelligence?

536
00:23:15,060 --> 00:23:16,370
And people debate that
in the field.

537
00:23:16,370 --> 00:23:19,240
And probably somewhat the answer
is it depends what

538
00:23:19,240 --> 00:23:21,630
you're measuring for and the
purpose of the measurement.

539
00:23:21,630 --> 00:23:24,130
But a good one to look at,
because it's very clear, is

540
00:23:24,130 --> 00:23:27,600
the difference between fluid and
crystallized intelligence.

541
00:23:27,600 --> 00:23:29,810
So fluid intelligence is
what we think of as

542
00:23:29,810 --> 00:23:31,290
novel problem solving.

543
00:23:31,290 --> 00:23:32,440
You have a situation
you were never in

544
00:23:32,440 --> 00:23:34,640
before, what do you do?

545
00:23:34,640 --> 00:23:36,930
Crystallized intelligence
are facts you know,

546
00:23:36,930 --> 00:23:40,250
information you know.

547
00:23:40,250 --> 00:23:43,120
So here's the conceptual
idea that maybe general

548
00:23:43,120 --> 00:23:45,470
intelligence is a thing.

549
00:23:45,470 --> 00:23:48,250
And then it shows up on
different tests, with the

550
00:23:48,250 --> 00:23:49,680
different specific
intelligences.

551
00:23:49,680 --> 00:23:51,090
That's like maybe verbal
or arithmetic.

552
00:23:54,230 --> 00:23:58,710
But let me show you the fluid
and crystallized one.

553
00:23:58,710 --> 00:24:01,060
So the fluid one we said were
first mental processes that

554
00:24:01,060 --> 00:24:02,840
apply for a novel situation.

555
00:24:02,840 --> 00:24:06,110
And that declines a lot with
age and adulthood.

556
00:24:06,110 --> 00:24:10,090
So these lines going down
here, I'm about here and

557
00:24:10,090 --> 00:24:11,150
you're about here.

558
00:24:11,150 --> 00:24:14,930
Who should be grading who in
this course, you might ask?

559
00:24:14,930 --> 00:24:17,980
The only reason that I get to
grade you is because these

560
00:24:17,980 --> 00:24:19,190
lines keep going up here.

561
00:24:19,190 --> 00:24:21,970
This is how many
facts you know.

562
00:24:21,970 --> 00:24:25,150
So the plus of living longer
is that you get

563
00:24:25,150 --> 00:24:27,510
to know more stuff.

564
00:24:27,510 --> 00:24:30,170
The risk of living longer is you
don't use it as flexibly

565
00:24:30,170 --> 00:24:32,410
under novel situations.

566
00:24:32,410 --> 00:24:34,100
So a pretty strong distinction,
at least with

567
00:24:34,100 --> 00:24:37,040
aging, between so-called fluid
intelligence and crystallized

568
00:24:37,040 --> 00:24:38,140
intelligence.

569
00:24:38,140 --> 00:24:40,990
Here's another study of exactly
the same thing.

570
00:24:40,990 --> 00:24:44,960
It's a striking ski slope of
decline in fluid intelligence.

571
00:24:44,960 --> 00:24:50,840
Look at this, ernt, from 20
to 80, it just goes down

572
00:24:50,840 --> 00:24:52,650
relentlessly.

573
00:24:52,650 --> 00:24:54,680
But when it comes to
crystallized intelligence,

574
00:24:54,680 --> 00:24:57,810
things like vocabulary, that
kind of hangs in there, until

575
00:24:57,810 --> 00:25:00,530
you have some big
health problem.

576
00:25:00,530 --> 00:25:01,740
So at least those two
kinds seem to

577
00:25:01,740 --> 00:25:05,210
have a different basis.

578
00:25:05,210 --> 00:25:08,110
And people have thought about
different ways to think about

579
00:25:08,110 --> 00:25:08,770
intelligence.

580
00:25:08,770 --> 00:25:11,030
And I'll just tell you that in
general you get positive

581
00:25:11,030 --> 00:25:12,340
correlations among the tests.

582
00:25:12,340 --> 00:25:14,650
And so often people think
about this general

583
00:25:14,650 --> 00:25:15,280
intelligence.

584
00:25:15,280 --> 00:25:16,990
The correlation among
the tests is

585
00:25:16,990 --> 00:25:18,240
the best single predictor.

586
00:25:20,670 --> 00:25:23,560
Now, there's lots of responses
to ideas about intelligence.

587
00:25:23,560 --> 00:25:26,980
One of them is the study of
emotional intelligence.

588
00:25:26,980 --> 00:25:28,410
Peter Salovey is the researcher

589
00:25:28,410 --> 00:25:30,240
most involved in that.

590
00:25:30,240 --> 00:25:31,850
And we know this in
everyday life.

591
00:25:31,850 --> 00:25:36,730
We know smart people, what we
might call book smart people,

592
00:25:36,730 --> 00:25:40,070
who seem unwise in how they
relate to people, how they

593
00:25:40,070 --> 00:25:43,360
perceive emotions, how they
facilitate thought with

594
00:25:43,360 --> 00:25:45,930
emotion, how they understand or
manage their emotions, how

595
00:25:45,930 --> 00:25:47,470
they relate to other people.

596
00:25:47,470 --> 00:25:52,530
And so it's common sense to
think of that as something

597
00:25:52,530 --> 00:25:53,950
that varies across people.

598
00:25:53,950 --> 00:25:56,210
It's very hard to
measure that.

599
00:25:56,210 --> 00:25:59,400
I mean, part of the tyranny of
tests is what can be measured,

600
00:25:59,400 --> 00:26:00,120
gets measured.

601
00:26:00,120 --> 00:26:01,900
What's hard to measure,
is hard to measure.

602
00:26:01,900 --> 00:26:05,640
It's very hard to measure, in
a laboratory, in an hour, a

603
00:26:05,640 --> 00:26:06,900
person's emotional
intelligence.

604
00:26:06,900 --> 00:26:07,770
People have tried.

605
00:26:07,770 --> 00:26:10,020
It's just hard to do.

606
00:26:10,020 --> 00:26:12,310
Here's another area that's
gotten a lot of attention,

607
00:26:12,310 --> 00:26:14,560
from Howard Gardner
at Harvard.

608
00:26:14,560 --> 00:26:17,320
Kind of fighting back against
this idea of a single form of

609
00:26:17,320 --> 00:26:19,880
intelligence as captured by
g, the idea of multiple

610
00:26:19,880 --> 00:26:21,050
intelligences.

611
00:26:21,050 --> 00:26:23,760
And looking at things, the
associations in patients and

612
00:26:23,760 --> 00:26:24,840
development.

613
00:26:24,840 --> 00:26:27,380
That we can talk about
linguistic intelligence,

614
00:26:27,380 --> 00:26:30,540
spatial intelligence, music
intelligence, bodily

615
00:26:30,540 --> 00:26:31,140
intelligence.

616
00:26:31,140 --> 00:26:33,180
Great athletes have great
bodily intelligence.

617
00:26:33,180 --> 00:26:35,430
Intrapersonal or interpersonal,
it's kind of

618
00:26:35,430 --> 00:26:36,710
like emotional.

619
00:26:36,710 --> 00:26:38,020
Existential.

620
00:26:38,020 --> 00:26:39,250
So he said, there's all these
different kinds of

621
00:26:39,250 --> 00:26:40,720
intelligence.

622
00:26:40,720 --> 00:26:43,030
And each person is a
profile of these.

623
00:26:43,030 --> 00:26:45,340
You can be really strong in one,
and medium in another,

624
00:26:45,340 --> 00:26:47,210
and not so strong in another.

625
00:26:47,210 --> 00:26:49,700
The problem with this whole
approach is, yes, it's true

626
00:26:49,700 --> 00:26:51,520
that people have a
huge variety of

627
00:26:51,520 --> 00:26:52,640
facets of their humanity.

628
00:26:52,640 --> 00:26:55,480
This whole course, I feel like
every lecture we do, we just

629
00:26:55,480 --> 00:26:57,070
turn a person a little
bit to study another

630
00:26:57,070 --> 00:26:59,440
facet of their humanity.

631
00:26:59,440 --> 00:27:03,600
But it's hard as heck to
measure interpersonal

632
00:27:03,600 --> 00:27:05,880
intelligence or naturalistic
intelligence.

633
00:27:05,880 --> 00:27:07,670
Now, maybe somebody will figure
out how to do that.

634
00:27:07,670 --> 00:27:10,300
It's just hard to measure those
things, even if it feels

635
00:27:10,300 --> 00:27:13,430
like something conceptually
that's there.

636
00:27:13,430 --> 00:27:17,130
So this idea, which has had a
lot of resonance to people not

637
00:27:17,130 --> 00:27:20,580
liking very simple-minded ideas
of intelligence, very

638
00:27:20,580 --> 00:27:23,610
unitary ones, it's still not
gained much traction in

639
00:27:23,610 --> 00:27:24,860
practical use.

640
00:27:27,060 --> 00:27:29,165
So let me talk a bit about some
of the things that people

641
00:27:29,165 --> 00:27:31,240
have said, well, if we talk
about this intelligence, what

642
00:27:31,240 --> 00:27:33,590
might be some mental mechanisms
or neural

643
00:27:33,590 --> 00:27:36,400
mechanisms that support
or underlie it.

644
00:27:36,400 --> 00:27:39,110
So some things that go with
it are mental speed.

645
00:27:39,110 --> 00:27:42,410
And you know this, kind of
quickness of thought.

646
00:27:42,410 --> 00:27:44,910
OK, not even big thoughts, just
quickness feels like it

647
00:27:44,910 --> 00:27:46,860
helps a person solve problems.

648
00:27:46,860 --> 00:27:49,000
So for example there's a
moderately high correlation

649
00:27:49,000 --> 00:27:52,250
between IQ, complicated tests,
of all those tests, and how

650
00:27:52,250 --> 00:27:55,390
quickly you push a button
when the light comes on.

651
00:27:55,390 --> 00:27:59,360
Now, we never say, wow, Sally
or Fred is really smart.

652
00:27:59,360 --> 00:28:00,950
They can push a button
when the light

653
00:28:00,950 --> 00:28:03,090
comes on really fast.

654
00:28:03,090 --> 00:28:04,890
Let's go to them and get the
answers for the problem set.

655
00:28:07,490 --> 00:28:10,540
But that would go with the idea
that simply being fast is

656
00:28:10,540 --> 00:28:13,560
kind of a useful thing for
acting on the world.

657
00:28:13,560 --> 00:28:16,090
Or how quickly people decide
whether two lengths, two

658
00:28:16,090 --> 00:28:18,040
lines, are the same length
or a different length.

659
00:28:18,040 --> 00:28:20,810
Very simple thing, just speed.

660
00:28:20,810 --> 00:28:23,110
And another idea is working
memory capacity.

661
00:28:23,110 --> 00:28:25,790
How much information you can
hold in your head at any one

662
00:28:25,790 --> 00:28:28,000
time that's relevant
to the problem or

663
00:28:28,000 --> 00:28:29,510
task in front of you.

664
00:28:29,510 --> 00:28:31,220
So people have tried to explore
these in certain ways.

665
00:28:31,220 --> 00:28:32,950
And I'm going to talk to you
about a couple of brain

666
00:28:32,950 --> 00:28:34,970
studies that have tried
to look at this.

667
00:28:34,970 --> 00:28:38,730
So again, we talked last time
about frontal cortex and

668
00:28:38,730 --> 00:28:39,430
different parts of it.

669
00:28:39,430 --> 00:28:41,340
And especially the
dorsal-lateral prefrontal

670
00:28:41,340 --> 00:28:44,680
cortex is involved
in thinking.

671
00:28:44,680 --> 00:28:48,670
And so people have taken tasks
and said, what happens when

672
00:28:48,670 --> 00:28:49,720
you think about them?

673
00:28:49,720 --> 00:28:53,190
So here's one, an easy item
on the so-called Cattell

674
00:28:53,190 --> 00:28:54,710
Culture-Fair Test.

675
00:28:54,710 --> 00:28:57,920
So which of these things
goes into here.

676
00:28:57,920 --> 00:28:59,170
There's harder ones.

677
00:29:01,910 --> 00:29:06,350
Or here's a slightly adapted
version of the Raven's

678
00:29:06,350 --> 00:29:07,160
Progressive Matrices.

679
00:29:07,160 --> 00:29:08,290
I'll let you think about
that for a moment.

680
00:29:08,290 --> 00:29:09,980
I don't remember which it is.

681
00:29:09,980 --> 00:29:13,170
So you're told here's some
rows and columns.

682
00:29:13,170 --> 00:29:17,380
One of these eight answers
logically fits in here.

683
00:29:17,380 --> 00:29:21,100
And it's your job to figure
out which one.

684
00:29:21,100 --> 00:29:22,630
Let's see.

685
00:29:22,630 --> 00:29:24,890
How do we like--

686
00:29:24,890 --> 00:29:25,170
three.

687
00:29:25,170 --> 00:29:27,935
I like three too.

688
00:29:27,935 --> 00:29:29,330
Does that three seem OK?

689
00:29:32,110 --> 00:29:35,560
This is a medium-hard item
on this kind of a test.

690
00:29:35,560 --> 00:29:39,290
And then they do these kind
of map of results.

691
00:29:39,290 --> 00:29:41,060
This is what's fun about
the Raven's test, the

692
00:29:41,060 --> 00:29:41,630
item you just saw.

693
00:29:41,630 --> 00:29:43,950
Which is they sort of looked
at lots of other tests, so

694
00:29:43,950 --> 00:29:47,710
specifically spatial tests,
specifically verbal tests,

695
00:29:47,710 --> 00:29:49,720
specifically math tests.

696
00:29:49,720 --> 00:29:53,530
And they said if we have to give
you one test only, one

697
00:29:53,530 --> 00:29:57,400
test only, what test would best
predict your performance

698
00:29:57,400 --> 00:29:59,410
on all the other tests?

699
00:29:59,410 --> 00:30:03,130
And the answer is Raven's
or IQ tests like that.

700
00:30:03,130 --> 00:30:05,500
So of course if I give you
a math test, that better

701
00:30:05,500 --> 00:30:06,810
predicts your math
performance.

702
00:30:06,810 --> 00:30:08,760
If I give you a verbal test,
that better predicts your

703
00:30:08,760 --> 00:30:09,680
verbal performance.

704
00:30:09,680 --> 00:30:11,890
If I gave you a spatial test,
that better predicts your

705
00:30:11,890 --> 00:30:13,520
special performance.

706
00:30:13,520 --> 00:30:15,550
But if I want to give you one
test that gives me the best

707
00:30:15,550 --> 00:30:18,820
prediction of your performance
on other tests, it's things

708
00:30:18,820 --> 00:30:21,530
like these IQ tests
that do that.

709
00:30:21,530 --> 00:30:24,380
And that's just empirical.

710
00:30:24,380 --> 00:30:26,240
That's not arguing about
values or what

711
00:30:26,240 --> 00:30:27,460
intelligence is.

712
00:30:27,460 --> 00:30:29,760
A lot of people who work in this
field, they don't even

713
00:30:29,760 --> 00:30:31,080
care about these arguments.

714
00:30:31,080 --> 00:30:34,370
They just say, I just want
a test that predicts.

715
00:30:34,370 --> 00:30:37,020
It's your job, if you wanted
to figure out why it is.

716
00:30:37,020 --> 00:30:39,330
It's the genes, environment,
education, whatever.

717
00:30:39,330 --> 00:30:42,100
I just want a test
that predicts.

718
00:30:42,100 --> 00:30:44,290
So from that perspective,
these tests

719
00:30:44,290 --> 00:30:47,540
have a strong history.

720
00:30:47,540 --> 00:30:48,830
And what turns on in the
brain when you solve

721
00:30:48,830 --> 00:30:49,700
this kind of a task.

722
00:30:49,700 --> 00:30:52,245
Well, the frontal
cortex a lot.

723
00:30:52,245 --> 00:30:52,830
The parietal cortex.

724
00:30:52,830 --> 00:30:54,200
But especially, frontal
cortex.

725
00:30:54,200 --> 00:30:57,280
So it goes with our idea that
thinking is very much tied to

726
00:30:57,280 --> 00:30:58,990
prefrontal cortical function.

727
00:30:58,990 --> 00:31:02,440
Here's another example of a
study that said, let's compare

728
00:31:02,440 --> 00:31:04,680
what happens in the brain when
you have test items that are

729
00:31:04,680 --> 00:31:09,190
hard, which is the outlier one
here, that require a lot of g,

730
00:31:09,190 --> 00:31:12,060
or items that are easy, that
require very little g.

731
00:31:12,060 --> 00:31:14,990
Will up here in the brain, what
happens when you turn on

732
00:31:14,990 --> 00:31:16,610
your g, so to speak?

733
00:31:16,610 --> 00:31:19,730
And again, the prominent areas
tend to be, for both spatial

734
00:31:19,730 --> 00:31:21,365
and verbal, prefrontal cortex.

735
00:31:24,610 --> 00:31:26,350
One more.

736
00:31:26,350 --> 00:31:28,500
the n-back task, we
use that a lot.

737
00:31:28,500 --> 00:31:30,520
Where your job is,
letters come on.

738
00:31:30,520 --> 00:31:33,180
And in this one, one-back, you
say, what do I see a letter

739
00:31:33,180 --> 00:31:35,380
that's the same letter
as the one before?

740
00:31:35,380 --> 00:31:36,810
So, for example--

741
00:31:36,810 --> 00:31:37,910
two-back, sorry.

742
00:31:37,910 --> 00:31:40,970
So you see, R, R, M, X, this
is two-back, because an M

743
00:31:40,970 --> 00:31:42,210
occurred two items ago.

744
00:31:42,210 --> 00:31:43,370
Three-backs are really hard.

745
00:31:43,370 --> 00:31:44,970
Because the letters are going,
boom, boom, boom, boom.

746
00:31:44,970 --> 00:31:47,490
And what's hard about it is,
the letter in front of you,

747
00:31:47,490 --> 00:31:49,780
you have to say does it match
the one I saw three letters

748
00:31:49,780 --> 00:31:51,910
previously, plus remember that,
because it's going to be

749
00:31:51,910 --> 00:31:54,270
the target for the one that
comes three after.

750
00:31:54,270 --> 00:31:55,070
It's hard.

751
00:31:55,070 --> 00:31:55,910
I can tell you it's hard.

752
00:31:55,910 --> 00:31:56,720
You can be smart.

753
00:31:56,720 --> 00:31:58,500
It's pretty hard.

754
00:31:58,500 --> 00:32:02,660
And for this task in general,
the harder it gets, the more

755
00:32:02,660 --> 00:32:06,580
you turn on the dorsal-lateral
prefrontal cortex.

756
00:32:06,580 --> 00:32:08,965
And so they did this test with
students in Washington St.

757
00:32:08,965 --> 00:32:12,670
Louis, Louis, related it to
performance on the Raven's.

758
00:32:12,670 --> 00:32:16,790
And sure enough in the brain,
it's again prefrontal cortex.

759
00:32:16,790 --> 00:32:20,430
So a bunch of studies have found
all these tests involve

760
00:32:20,430 --> 00:32:21,290
a fair bit of the brain.

761
00:32:21,290 --> 00:32:24,623
But they seem to really draw
upon the capacities that you

762
00:32:24,623 --> 00:32:27,450
and I have in prefrontal
cortex.

763
00:32:27,450 --> 00:32:30,930
And one more note, in brain
imaging when we talk about

764
00:32:30,930 --> 00:32:33,100
things, sometimes it's better
to have more activation and

765
00:32:33,100 --> 00:32:34,570
sometimes less activation.

766
00:32:34,570 --> 00:32:36,830
And you have to know the
behavior to know that.

767
00:32:36,830 --> 00:32:39,660
So whenever you see somebody
saying oh, this person, this

768
00:32:39,660 --> 00:32:41,960
group, or whatever, had more
brain activity or less brain

769
00:32:41,960 --> 00:32:44,920
activity, you don't know until
you know the rest of the

770
00:32:44,920 --> 00:32:47,440
behavior, whether more
is good or bad.

771
00:32:47,440 --> 00:32:49,680
Sometimes when people get really
good at something, they

772
00:32:49,680 --> 00:32:52,880
use less of their brain, because
they can do it so

773
00:32:52,880 --> 00:32:54,240
efficiently.

774
00:32:54,240 --> 00:32:57,560
So here's an example of people
with lower and higher IQ doing

775
00:32:57,560 --> 00:32:58,370
an n-back task.

776
00:32:58,370 --> 00:33:01,440
And you can see that the people
with lower IQ have much

777
00:33:01,440 --> 00:33:04,460
more activation than the
people with higher IQ.

778
00:33:04,460 --> 00:33:07,150
It's as if these people have
to use all their brain

779
00:33:07,150 --> 00:33:08,950
resources to accomplish this.

780
00:33:08,950 --> 00:33:10,910
And these people only
have to use some.

781
00:33:10,910 --> 00:33:12,160
It's as if it were that.

782
00:33:14,600 --> 00:33:18,890
So here comes one of the
classic topics of

783
00:33:18,890 --> 00:33:20,140
the fate of a human.

784
00:33:20,140 --> 00:33:22,380
How much is your genes at birth
and how much is the

785
00:33:22,380 --> 00:33:24,230
environment that you're in.

786
00:33:24,230 --> 00:33:26,610
And if we were really to
understand that in a deep,

787
00:33:26,610 --> 00:33:29,120
deep scientific way, we would
know the genes are related to

788
00:33:29,120 --> 00:33:32,630
intelligence; the experiential
factors in the world, family,

789
00:33:32,630 --> 00:33:37,910
education, emotional support,
and so on; how they interact.

790
00:33:37,910 --> 00:33:39,860
All those things, we know
practically nothing about.

791
00:33:39,860 --> 00:33:41,230
I'll show you one
tiny example.

792
00:33:41,230 --> 00:33:42,750
We know practically nothing
about them in

793
00:33:42,750 --> 00:33:44,270
any depth at all.

794
00:33:44,270 --> 00:33:48,010
So we mostly end up using twin
studies to make the best

795
00:33:48,010 --> 00:33:49,710
estimates we can.

796
00:33:49,710 --> 00:33:51,410
Which doesn't tell you
what the factors are.

797
00:33:51,410 --> 00:33:54,710
It just tells you how much is
a twin who is identical more

798
00:33:54,710 --> 00:33:56,740
or less like a twin
who is fraternal.

799
00:33:56,740 --> 00:33:59,340
OK, we'll come back to that.

800
00:33:59,340 --> 00:34:01,750
But I do want to tell you sort
of an exciting direction

801
00:34:01,750 --> 00:34:04,950
that's slightly getting at the
genetic story of this.

802
00:34:04,950 --> 00:34:06,570
So you may know, many
of you more

803
00:34:06,570 --> 00:34:08,810
than I do, about genetics.

804
00:34:08,810 --> 00:34:11,350
But single nucleotide
polymorphisms are DNA

805
00:34:11,350 --> 00:34:12,800
variations.

806
00:34:12,800 --> 00:34:15,860
These occur relatively
commonly.

807
00:34:15,860 --> 00:34:17,699
They're estimated to make
up about 90% of

808
00:34:17,699 --> 00:34:19,260
human genetic variation.

809
00:34:19,260 --> 00:34:21,250
And they're constant from
generation to generation.

810
00:34:21,250 --> 00:34:24,250
So people have studied a few
of these variations among

811
00:34:24,250 --> 00:34:26,860
people and related them
to performance.

812
00:34:26,860 --> 00:34:29,290
So let me remind you of
something from last lecture,

813
00:34:29,290 --> 00:34:32,210
the Wisconsin Card
Sorting Task.

814
00:34:32,210 --> 00:34:36,250
Where you sorted these cards
mysteriously and we said that

815
00:34:36,250 --> 00:34:39,239
if you have a frontal lesion,
you do poorly on this task.

816
00:34:39,239 --> 00:34:43,460
You get stuck with one kind of
problem solving or habit,

817
00:34:43,460 --> 00:34:47,650
answer even when it's wrong,
in a new circumstance.

818
00:34:47,650 --> 00:34:51,270
So what people have done is look
at one particular gene

819
00:34:51,270 --> 00:34:56,830
that's related to this enzyme
that modulates dopamine

820
00:34:56,830 --> 00:34:59,290
function in the prefrontal
cortex.

821
00:34:59,290 --> 00:35:02,205
So it's a risk factor
for schizophrenia.

822
00:35:02,205 --> 00:35:05,710
It metabolizes dopamine
in prefrontal cortex.

823
00:35:05,710 --> 00:35:11,350
We vary with which version
of this gene we have.

824
00:35:11,350 --> 00:35:13,570
And here's the performance
on the card sorting task.

825
00:35:13,570 --> 00:35:14,730
And let me pick these.

826
00:35:14,730 --> 00:35:16,060
These are healthy people.

827
00:35:16,060 --> 00:35:19,550
If you have this version of the
gene or this pairing or

828
00:35:19,550 --> 00:35:23,240
this pairing, your card sorting
task is going up.

829
00:35:23,240 --> 00:35:25,620
So this is kind of amazing
that we're close to that,

830
00:35:25,620 --> 00:35:26,860
where a single gene
is varying.

831
00:35:26,860 --> 00:35:28,110
Now, the score doesn't
go up a lot.

832
00:35:28,110 --> 00:35:29,960
It accounts a small percent
of the variance.

833
00:35:29,960 --> 00:35:33,890
But it's one gene having an
identifiable correlation with

834
00:35:33,890 --> 00:35:37,580
performance on a
demanding task.

835
00:35:37,580 --> 00:35:39,160
And you can do this n-back
kind of task

836
00:35:39,160 --> 00:35:40,450
we just talked about.

837
00:35:40,450 --> 00:35:42,470
And then you can look in the
brain as people are performing

838
00:35:42,470 --> 00:35:46,360
this, divided by which version
of this gene they have.

839
00:35:46,360 --> 00:35:52,060
And what you get is more
activation in the people with

840
00:35:52,060 --> 00:35:56,750
this complement, than
this complement.

841
00:35:56,750 --> 00:35:58,690
Sort of consistent with the idea
they're working harder.

842
00:35:58,690 --> 00:36:01,200
There's the same group of people
who are performing less

843
00:36:01,200 --> 00:36:02,710
well in the card sorting task.

844
00:36:02,710 --> 00:36:05,210
So there are some genes that
have been identified that are

845
00:36:05,210 --> 00:36:07,930
associated with what we may call
broadly, intelligence.

846
00:36:07,930 --> 00:36:11,030
But that one gene only accounts
for a tiny bit of the

847
00:36:11,030 --> 00:36:13,430
overall score on these
kinds of things.

848
00:36:13,430 --> 00:36:15,840
And we don't know much more
about it than that.

849
00:36:15,840 --> 00:36:18,170
So how do people even
begin to think

850
00:36:18,170 --> 00:36:19,910
about nature and nurture?

851
00:36:19,910 --> 00:36:21,520
And I like to think about
this in two ways.

852
00:36:21,520 --> 00:36:23,830
One way is narrowly IQ tests,
because there's been so much

853
00:36:23,830 --> 00:36:24,840
work in that area.

854
00:36:24,840 --> 00:36:27,340
But it's the deep question
of how do we get

855
00:36:27,340 --> 00:36:29,260
to be who we are?

856
00:36:29,260 --> 00:36:31,900
So again, they mostly look at
twin studies, so-called

857
00:36:31,900 --> 00:36:35,100
behavioral genetics, and
estimated heritability, how

858
00:36:35,100 --> 00:36:38,302
much of variation is
due to genetics.

859
00:36:38,302 --> 00:36:39,810
And I'll come back to
this height thing.

860
00:36:39,810 --> 00:36:42,300
But to give you a sense, height
is thought to be 90%

861
00:36:42,300 --> 00:36:44,480
inheritable in the US.

862
00:36:44,480 --> 00:36:46,970
Tall parents on average will
average will have taller kids.

863
00:36:46,970 --> 00:36:49,030
Less tall parents,
less tall kids.

864
00:36:49,030 --> 00:36:51,860
And in twin studies, they
contrast monozygotic twins,

865
00:36:51,860 --> 00:36:54,210
twins that are identical or
have the same genes, with

866
00:36:54,210 --> 00:36:58,960
dizygotic ones, who share some
genes, but not nearly as many.

867
00:36:58,960 --> 00:37:01,250
Of course, they share the
same environment a lot.

868
00:37:01,250 --> 00:37:04,000
Have you guys seen the two twins
talking to each other

869
00:37:04,000 --> 00:37:06,220
video on YouTube?

870
00:37:06,220 --> 00:37:07,530
If you haven't seen it,
it's kind of fun.

871
00:37:07,530 --> 00:37:09,270
The two twins are babbling.

872
00:37:09,270 --> 00:37:11,930
But they're so demonstrative,
it's as if they were talking

873
00:37:11,930 --> 00:37:12,810
to each other in babble.

874
00:37:12,810 --> 00:37:13,913
It's kind of fun, if
you haven't see it.

875
00:37:13,913 --> 00:37:15,163
It's very cute.

876
00:37:18,390 --> 00:37:20,850
Here is it again.

877
00:37:20,850 --> 00:37:25,390
The fraternal twins and here's
the identical twins, before

878
00:37:25,390 --> 00:37:27,160
they're born.

879
00:37:27,160 --> 00:37:30,220
This has been studied
a fair bit.

880
00:37:30,220 --> 00:37:34,690
Here's the correlations between
IQ scores and people

881
00:37:34,690 --> 00:37:35,510
of varying relations.

882
00:37:35,510 --> 00:37:37,340
So let's work down that list.

883
00:37:37,340 --> 00:37:39,160
Unrelated persons,
reared together.

884
00:37:39,160 --> 00:37:41,350
So perhaps an adopted
person and his or

885
00:37:41,350 --> 00:37:42,750
her sibling or something.

886
00:37:42,750 --> 00:37:45,730
They're correlated at
about what, 0.2.

887
00:37:45,730 --> 00:37:48,030
Foster parent and child, it
goes up a little bit.

888
00:37:48,030 --> 00:37:49,830
Parent and child living
together, so that's a

889
00:37:49,830 --> 00:37:52,170
biological parent and child
living together, that goes up

890
00:37:52,170 --> 00:37:53,010
a fair bit.

891
00:37:53,010 --> 00:37:55,110
Brother and sister reared
apart, back down.

892
00:37:55,110 --> 00:37:56,400
Brother and sister
reared together.

893
00:37:56,400 --> 00:38:00,940
So you could look at this
as kind of shared genes.

894
00:38:00,940 --> 00:38:02,920
And this might be something
like environment.

895
00:38:02,920 --> 00:38:03,510
Does make sense?

896
00:38:03,510 --> 00:38:04,950
Intuitively.

897
00:38:04,950 --> 00:38:07,640
That's assuming there's no
interaction between genes and

898
00:38:07,640 --> 00:38:09,760
the environment, which
we know must happen.

899
00:38:09,760 --> 00:38:12,470
So when I do this I'm treating
them as if they were additive

900
00:38:12,470 --> 00:38:13,990
and independent.

901
00:38:13,990 --> 00:38:16,260
Everything we know about genes
and the environment are

902
00:38:16,260 --> 00:38:18,010
tremendously interactive.

903
00:38:18,010 --> 00:38:19,760
We just don't have insight
into that in any

904
00:38:19,760 --> 00:38:22,850
deep way, in humans.

905
00:38:22,850 --> 00:38:24,220
Here's the really
stunning one.

906
00:38:24,220 --> 00:38:28,430
Identical twins reared apart,
70.7 correlation.

907
00:38:28,430 --> 00:38:30,640
Identical twins reared apart.

908
00:38:30,640 --> 00:38:33,570
That suggests a huge
influence of genes.

909
00:38:36,170 --> 00:38:39,750
Identical twins reared
together, 90.9.

910
00:38:39,750 --> 00:38:41,830
So this is the finding
that stunned people.

911
00:38:41,830 --> 00:38:45,020
So look at identical
twins reared apart.

912
00:38:45,020 --> 00:38:48,670
They're much more like each
other than a brother and

913
00:38:48,670 --> 00:38:51,490
sister reared together, who
share all the same environment

914
00:38:51,490 --> 00:38:54,410
of education, parental support,
and other things.

915
00:38:54,410 --> 00:38:57,070
So I'm going to come
back to this.

916
00:38:57,070 --> 00:39:00,190
But as best people can estimate,
and these might be

917
00:39:00,190 --> 00:39:02,540
radically replaced as we deepen
our understanding of

918
00:39:02,540 --> 00:39:05,460
the actual genes and the actual
environmental factors,

919
00:39:05,460 --> 00:39:07,370
but people are estimating
it's about half

920
00:39:07,370 --> 00:39:10,150
and half for IQ tests.

921
00:39:10,150 --> 00:39:13,210
And we discussed the bit of
arbitrariness in that.

922
00:39:13,210 --> 00:39:14,600
So about half and half.

923
00:39:14,600 --> 00:39:16,370
Let me show you a couple of
environmental influences that

924
00:39:16,370 --> 00:39:17,960
have been identified.

925
00:39:17,960 --> 00:39:23,160
And the first two, one suspects
has to do on average

926
00:39:23,160 --> 00:39:25,470
with something about the
healthiness of the environment

927
00:39:25,470 --> 00:39:26,920
the child was born into.

928
00:39:26,920 --> 00:39:31,830
The third one is kind of fun
just to talk about because you

929
00:39:31,830 --> 00:39:34,950
may wonder if you have a sibling
or no sibling, what

930
00:39:34,950 --> 00:39:37,120
does it mean that you were first
born, or second born, or

931
00:39:37,120 --> 00:39:38,960
third born, at least
statistically.

932
00:39:38,960 --> 00:39:40,300
So let's do the first two.

933
00:39:40,300 --> 00:39:44,570
Here's breast feeding, 3,000
people were followed from

934
00:39:44,570 --> 00:39:46,720
birth to young adulthood, who
had breast feeding, for at

935
00:39:46,720 --> 00:39:48,670
least six months, correlated
with a five to

936
00:39:48,670 --> 00:39:50,590
seven point IQ gain.

937
00:39:50,590 --> 00:39:53,820
So we said, 15 points really
seems to mean something in

938
00:39:53,820 --> 00:39:54,740
educational outcome.

939
00:39:54,740 --> 00:39:56,610
So 5 to 7 is half of that.

940
00:39:56,610 --> 00:39:59,560
So that's something.

941
00:39:59,560 --> 00:40:00,580
So let's talk about
this for a minute.

942
00:40:00,580 --> 00:40:04,890
You could say, and I can tell
you as a parent who fairly

943
00:40:04,890 --> 00:40:09,130
recently had children, all the
mothers in the US that I know

944
00:40:09,130 --> 00:40:10,480
were going like, I've got
to breast feed and

945
00:40:10,480 --> 00:40:13,420
get my kid to MIT.

946
00:40:13,420 --> 00:40:15,250
Two more months of breast
feeding and this kid could go

947
00:40:15,250 --> 00:40:18,200
all the way to tenured
faculty.

948
00:40:18,200 --> 00:40:21,380
I can tell you there's a lot of
pressure on mothers, if you

949
00:40:21,380 --> 00:40:22,600
don't know this.

950
00:40:22,600 --> 00:40:23,900
Not my mother.

951
00:40:23,900 --> 00:40:27,110
But now mothers hear, do this
for your kid, do this for your

952
00:40:27,110 --> 00:40:30,570
kid, do this for your kid,
because of results like these.

953
00:40:30,570 --> 00:40:33,810
So one possibility is there's
something in breast milk.

954
00:40:33,810 --> 00:40:35,000
That would be the
direct mechanism

955
00:40:35,000 --> 00:40:36,620
that's making kids smarter.

956
00:40:36,620 --> 00:40:38,580
Give me another possibility that
has nothing to do with

957
00:40:38,580 --> 00:40:39,220
the breast milk?

958
00:40:39,220 --> 00:40:39,580
Yeah?

959
00:40:39,580 --> 00:40:42,940
AUDIENCE: Maybe mothers who
breast feed, also tend to be

960
00:40:42,940 --> 00:40:44,380
mothers who are more nurturing
to their children.

961
00:40:44,380 --> 00:40:45,640
PROFESSOR: It could be
more nurturing to

962
00:40:45,640 --> 00:40:46,780
children in many ways.

963
00:40:46,780 --> 00:40:50,050
They could be mothers who can
stay home and spend some time

964
00:40:50,050 --> 00:40:52,480
with the children versus being
under very pressured economic

965
00:40:52,480 --> 00:40:54,910
circumstances that
prohibit that.

966
00:40:54,910 --> 00:40:56,420
It could be a lot of things.

967
00:40:56,420 --> 00:40:57,300
Does that make sense?

968
00:40:57,300 --> 00:40:57,950
We don't know.

969
00:40:57,950 --> 00:41:00,720
Nobody has shown yet that it is
the breast milk that goes

970
00:41:00,720 --> 00:41:03,870
into your brain and pushes up
your brain, in any sense.

971
00:41:03,870 --> 00:41:05,330
It's just a correlation.

972
00:41:05,330 --> 00:41:08,750
And of course, if you're a
parent, I'll do the breast

973
00:41:08,750 --> 00:41:11,180
feeding and let the scientists
figure it out in the future.

974
00:41:11,180 --> 00:41:12,240
Here's another one.

975
00:41:12,240 --> 00:41:14,200
There's the birth weight.

976
00:41:14,200 --> 00:41:15,440
So again, you could say
the same thing.

977
00:41:15,440 --> 00:41:19,160
I mean certainly having very low
birth weight is a big risk

978
00:41:19,160 --> 00:41:20,680
factor for lots of things.

979
00:41:20,680 --> 00:41:24,240
But birth weight again goes
with many factors in the

980
00:41:24,240 --> 00:41:26,230
environment that are supportive
for a healthy

981
00:41:26,230 --> 00:41:28,070
pregnancy versus not.

982
00:41:28,070 --> 00:41:29,510
So it doesn't mean that
it's the birth weight.

983
00:41:29,510 --> 00:41:31,640
It could be all the other
things that were in the

984
00:41:31,640 --> 00:41:35,280
environment during the
pregnancy, at birth, and

985
00:41:35,280 --> 00:41:38,580
continue, on average, in the
environment of that child,

986
00:41:38,580 --> 00:41:40,740
throughout their childhood
and young education.

987
00:41:40,740 --> 00:41:41,840
Does that make sense?

988
00:41:41,840 --> 00:41:44,280
If they're born in a very
unsupportive medical

989
00:41:44,280 --> 00:41:47,370
environment, they might have
a low birth weight.

990
00:41:47,370 --> 00:41:49,380
And unless their parents win
the lottery or something,

991
00:41:49,380 --> 00:41:51,840
they're likely to be in a
difficult environment for all

992
00:41:51,840 --> 00:41:55,500
their educational
years to come.

993
00:41:55,500 --> 00:41:58,320
So people have tried to make
clear, because it's so easy to

994
00:41:58,320 --> 00:42:00,430
get into sort of little mental
traps on genes versus

995
00:42:00,430 --> 00:42:02,330
environment, how genetic
diversity affects

996
00:42:02,330 --> 00:42:03,670
heritability.

997
00:42:03,670 --> 00:42:05,000
So there's a couple different
ways to do it.

998
00:42:05,000 --> 00:42:05,890
Let's do this one.

999
00:42:05,890 --> 00:42:09,160
So imagine you had cloned
tomato plants.

1000
00:42:09,160 --> 00:42:12,520
That means they all have the
exact same genes in this

1001
00:42:12,520 --> 00:42:13,790
hypothetical example.

1002
00:42:13,790 --> 00:42:17,020
But somebody has poor soil,
somebody has good soil.

1003
00:42:17,020 --> 00:42:19,130
Well, you'll get big
differences.

1004
00:42:19,130 --> 00:42:21,470
And the heritability
will be zero.

1005
00:42:21,470 --> 00:42:23,880
Environment can have
huge influences.

1006
00:42:23,880 --> 00:42:28,160
But if you have genetically
diverse plants, then you're

1007
00:42:28,160 --> 00:42:29,210
going to have an interaction.

1008
00:42:29,210 --> 00:42:32,200
So some plants will do OK,
even in poor soil.

1009
00:42:32,200 --> 00:42:35,110
Some plants will do most
spectacularly in good soil and

1010
00:42:35,110 --> 00:42:36,090
less spectacularly alike.

1011
00:42:36,090 --> 00:42:38,120
Environment by genes'
interactions.

1012
00:42:38,120 --> 00:42:39,660
And you can see this in so
many different ways.

1013
00:42:39,660 --> 00:42:40,430
So here's one example.

1014
00:42:40,430 --> 00:42:43,730
So we said height is 90%
heritable within a society.

1015
00:42:43,730 --> 00:42:47,440
But Japanese men in the US, or
people with Japanese extract,

1016
00:42:47,440 --> 00:42:50,600
are three inches taller
than people in Japan.

1017
00:42:50,600 --> 00:42:53,630
So they still have whatever
Japanese genes are.

1018
00:42:53,630 --> 00:42:54,470
They have Japanese genes.

1019
00:42:54,470 --> 00:42:57,240
But they're in the US getting
the US diet of McDonald's.

1020
00:42:57,240 --> 00:43:00,000
And it's pushing them
up three inches.

1021
00:43:03,450 --> 00:43:04,660
Height is heritable.

1022
00:43:04,660 --> 00:43:06,980
But environment is a big
part of the story.

1023
00:43:06,980 --> 00:43:09,370
So it's just both.

1024
00:43:09,370 --> 00:43:12,560
Now, here's a fun study.

1025
00:43:12,560 --> 00:43:16,930
And a reason that if you're
a younger sibling and you

1026
00:43:16,930 --> 00:43:19,420
desperately want two IQ points,
you might knock off

1027
00:43:19,420 --> 00:43:20,570
your older sibling.

1028
00:43:20,570 --> 00:43:21,700
I don't recommend this.

1029
00:43:21,700 --> 00:43:23,310
This is tongue-in-cheek.

1030
00:43:23,310 --> 00:43:25,580
But it's kind of fun study,
just thinking about birth

1031
00:43:25,580 --> 00:43:27,160
order more in a fun way.

1032
00:43:27,160 --> 00:43:28,520
Well, look at the whole
thing, we're talking

1033
00:43:28,520 --> 00:43:29,950
about from 99 to 104.

1034
00:43:29,950 --> 00:43:32,520
So it's five points we're
talking there.

1035
00:43:32,520 --> 00:43:35,410
But in some countries, they sort
of test everybody going

1036
00:43:35,410 --> 00:43:37,180
into the military.

1037
00:43:37,180 --> 00:43:39,730
And I think this was
done in Denmark.

1038
00:43:39,730 --> 00:43:40,980
And what they said is.

1039
00:43:43,900 --> 00:43:45,510
These are all children.

1040
00:43:45,510 --> 00:43:46,910
These are the IQ scores.

1041
00:43:46,910 --> 00:43:50,130
And these are the IQ scores
that you would have.

1042
00:43:50,130 --> 00:43:52,040
But what happens if your
older sibling has

1043
00:43:52,040 --> 00:43:54,246
passed away, sadly.

1044
00:43:54,246 --> 00:43:56,250
You get those points.

1045
00:43:56,250 --> 00:43:58,770
And here's a person who
has an older sibling.

1046
00:43:58,770 --> 00:44:00,010
And the older sibling
passed away.

1047
00:44:00,010 --> 00:44:01,460
And they moved up
those points.

1048
00:44:01,460 --> 00:44:03,970
That is, if you're a younger
sibling, you have a few lower

1049
00:44:03,970 --> 00:44:07,230
IQ points, on average.

1050
00:44:07,230 --> 00:44:09,410
But kind of strikingly, if
your older sibling passes

1051
00:44:09,410 --> 00:44:13,110
away, sadly; you seem to get
those IQ points as you move up

1052
00:44:13,110 --> 00:44:14,580
the birth order.

1053
00:44:14,580 --> 00:44:15,840
Yeah?

1054
00:44:15,840 --> 00:44:17,620
AUDIENCE: Is this if they pass
away before birth, or --?

1055
00:44:17,620 --> 00:44:18,280
PROFESSOR: Yes.

1056
00:44:18,280 --> 00:44:19,700
Yeah, when they're young.

1057
00:44:19,700 --> 00:44:20,880
It's too late.

1058
00:44:20,880 --> 00:44:21,520
Sorry.

1059
00:44:21,520 --> 00:44:25,550
For all you in here, too late.

1060
00:44:25,550 --> 00:44:27,420
It's only a couple
points also.

1061
00:44:27,420 --> 00:44:29,060
But this got into Science,
because people are always

1062
00:44:29,060 --> 00:44:30,070
wondering about--

1063
00:44:30,070 --> 00:44:32,490
there's been a huge curiosity
about birth order

1064
00:44:32,490 --> 00:44:33,740
consequences.

1065
00:44:41,670 --> 00:44:44,200
But let's talk a little bit
about birth order again.

1066
00:44:44,200 --> 00:44:46,340
Because there's a second really
interesting point.

1067
00:44:46,340 --> 00:44:48,720
So here's Raven's score.

1068
00:44:48,720 --> 00:44:52,440
And here is if you're a lone
child, the second of two

1069
00:44:52,440 --> 00:44:55,590
children, the third of three,
the fourth of four, the eighth

1070
00:44:55,590 --> 00:44:56,860
of eight, to ninth of nine.

1071
00:44:56,860 --> 00:44:58,300
You can see the scores
keep going down.

1072
00:44:58,300 --> 00:45:00,920
It's not dramatic, but
larger families.

1073
00:45:00,920 --> 00:45:04,350
And you can have a million ideas
about why that would be.

1074
00:45:04,350 --> 00:45:06,770
About why it is the larger your
family and the further

1075
00:45:06,770 --> 00:45:09,810
you're down on the list, you
lose a few IQ points for every

1076
00:45:09,810 --> 00:45:11,560
sibling who's older.

1077
00:45:11,560 --> 00:45:17,190
So here's one idea from Robert
Zajonc, who's a very creative

1078
00:45:17,190 --> 00:45:17,820
psychologist.

1079
00:45:17,820 --> 00:45:18,960
We're not sure that
it's right.

1080
00:45:18,960 --> 00:45:21,390
But I think it's really
fun to think about.

1081
00:45:21,390 --> 00:45:22,550
So here is this idea.

1082
00:45:22,550 --> 00:45:25,370
He said, I'm going to make up
an idea, which is this.

1083
00:45:25,370 --> 00:45:28,216
The first born, maybe he is
exposed only to adults.

1084
00:45:28,216 --> 00:45:31,980
The adults are talking to them
about the views of the day and

1085
00:45:31,980 --> 00:45:32,570
all kinds of things.

1086
00:45:32,570 --> 00:45:35,330
The second born has two
adults, let's say

1087
00:45:35,330 --> 00:45:36,670
in a typical family.

1088
00:45:36,670 --> 00:45:39,650
But also has an older sibling.

1089
00:45:39,650 --> 00:45:41,600
So the intellectual environment
will be the

1090
00:45:41,600 --> 00:45:45,440
average of two adults
and a little child.

1091
00:45:45,440 --> 00:45:46,430
The next one, and so on.

1092
00:45:46,430 --> 00:45:48,160
So he said, you can do
a kind of a formula,

1093
00:45:48,160 --> 00:45:49,330
which is like this.

1094
00:45:49,330 --> 00:45:52,760
Pretend the two parents are
each 30 and you're born.

1095
00:45:52,760 --> 00:45:55,050
We'll call your family
intellectual stimulating

1096
00:45:55,050 --> 00:45:56,110
environment, 30.

1097
00:45:56,110 --> 00:45:57,890
Because we'll add up the ages
and divide by the number of

1098
00:45:57,890 --> 00:45:59,210
people in the family.

1099
00:45:59,210 --> 00:46:00,670
Does that make sense?

1100
00:46:00,670 --> 00:46:04,260
This is not meant to be
a real formula, just

1101
00:46:04,260 --> 00:46:06,470
a conceptual plan.

1102
00:46:06,470 --> 00:46:08,570
And then he said, now you have
the two parents and you have a

1103
00:46:08,570 --> 00:46:11,550
four-year-old sibling, because
you're the second born.

1104
00:46:11,550 --> 00:46:12,980
So now there's four of you.

1105
00:46:12,980 --> 00:46:13,840
We divide by 4.

1106
00:46:13,840 --> 00:46:15,930
And we'll say the average
intellectual thing in the

1107
00:46:15,930 --> 00:46:17,770
family is 16.

1108
00:46:17,770 --> 00:46:19,950
By the time the next child comes
along, a seven-year-old

1109
00:46:19,950 --> 00:46:22,810
sibling, three-year-old,
divide by five, and the

1110
00:46:22,810 --> 00:46:25,190
average intellectual
environment is 14.

1111
00:46:25,190 --> 00:46:27,060
Here's the idea.

1112
00:46:27,060 --> 00:46:30,730
On average, it's better for
a child to have lots of

1113
00:46:30,730 --> 00:46:33,210
interaction with a parent,
that with a little kid.

1114
00:46:33,210 --> 00:46:35,190
Because a parent will stimulate
you more than a

1115
00:46:35,190 --> 00:46:38,460
little kid, although a little
sibling might be a lot of fun.

1116
00:46:38,460 --> 00:46:42,430
But purely IQ land, two parents
talking to you about

1117
00:46:42,430 --> 00:46:47,150
stuff, is a more educational
environment than two parents

1118
00:46:47,150 --> 00:46:51,130
plus a sibling who is two
years older than you.

1119
00:46:51,130 --> 00:46:52,560
Because the two-year-old
can't help.

1120
00:46:52,560 --> 00:46:55,360
No matter how smart he or she
is, they can't talk with you

1121
00:46:55,360 --> 00:46:56,270
like an adult.

1122
00:46:56,270 --> 00:46:58,680
This is the thought.

1123
00:46:58,680 --> 00:47:02,460
And now, here's the really
fun observation.

1124
00:47:02,460 --> 00:47:04,860
And again, we don't really know
if this is exactly right.

1125
00:47:04,860 --> 00:47:08,990
But every year, if you're around
issues of education,

1126
00:47:08,990 --> 00:47:11,380
there's test scores that
different states have.

1127
00:47:11,380 --> 00:47:13,480
And the test scores go up.

1128
00:47:13,480 --> 00:47:17,690
And you have taken high-tests
tests right, in high schools

1129
00:47:17,690 --> 00:47:19,620
and grade schools and stuff.

1130
00:47:19,620 --> 00:47:23,140
If the test scores goes up,
everybody goes, oh, reelect us

1131
00:47:23,140 --> 00:47:24,860
because we're awesome
administration and our test

1132
00:47:24,860 --> 00:47:25,530
scores are going up.

1133
00:47:25,530 --> 00:47:26,830
We're doing everything right.

1134
00:47:26,830 --> 00:47:30,020
If the test scores go down, they
go oh, throw the bums out

1135
00:47:30,020 --> 00:47:30,950
of the administration.

1136
00:47:30,950 --> 00:47:33,410
Their kids are watching too
much MTV, turn off that

1137
00:47:33,410 --> 00:47:34,710
computer, and so on.

1138
00:47:34,710 --> 00:47:35,910
Everybody gets the
test scores.

1139
00:47:35,910 --> 00:47:39,100
And they figure out who's good
and who's bad, parents,

1140
00:47:39,100 --> 00:47:42,200
teachers, school systems,
politicians, whatever.

1141
00:47:42,200 --> 00:47:44,030
Zajonc says, and this is kind
of an interesting thought.

1142
00:47:44,030 --> 00:47:48,750
He says no, he can predict
whether scores will go up or

1143
00:47:48,750 --> 00:47:54,480
down simply by on average in a
given year, whether there are

1144
00:47:54,480 --> 00:47:56,950
more first borns, or second
borns, or third borns.

1145
00:47:56,950 --> 00:47:59,460
That if he just takes that
population average of every

1146
00:47:59,460 --> 00:48:04,260
kid, the moment they're born,
he can predict years later

1147
00:48:04,260 --> 00:48:05,800
where the scores go.

1148
00:48:05,800 --> 00:48:08,970
And so here's the
scores in Iowa.

1149
00:48:08,970 --> 00:48:10,860
Of the actual scores, they're
going up and down.

1150
00:48:10,860 --> 00:48:12,410
So you can imagine, scores
are going down.

1151
00:48:12,410 --> 00:48:15,810
Everybody's really upset about
kids who don't study, and

1152
00:48:15,810 --> 00:48:18,350
teachers who don't teach, and
parents who don't parent.

1153
00:48:18,350 --> 00:48:19,200
Scores are going up.

1154
00:48:19,200 --> 00:48:20,920
And the kids are awesome, the
teachers are awesome, the

1155
00:48:20,920 --> 00:48:21,920
parents are awesome.

1156
00:48:21,920 --> 00:48:23,670
Because that's who
gets the credit.

1157
00:48:23,670 --> 00:48:25,990
And he's saying, uh-uh.

1158
00:48:25,990 --> 00:48:32,370
This is my prediction here,
based simply on for a given

1159
00:48:32,370 --> 00:48:35,220
birth year, the average
number of older

1160
00:48:35,220 --> 00:48:37,800
children in your family.

1161
00:48:37,800 --> 00:48:38,550
That's it.

1162
00:48:38,550 --> 00:48:39,720
And look how well that tracks.

1163
00:48:39,720 --> 00:48:41,460
It's unbelievable.

1164
00:48:41,460 --> 00:48:44,680
So it just makes you rethink
that so often when we get

1165
00:48:44,680 --> 00:48:49,310
information about society of
what's going on, this may be

1166
00:48:49,310 --> 00:48:50,440
right or wrong.

1167
00:48:50,440 --> 00:48:52,460
But we don't really know
what's going on.

1168
00:48:52,460 --> 00:48:55,190
There's all kinds of huge things
out there in the world

1169
00:48:55,190 --> 00:49:00,220
that aren't the easiest things
to blame or give credit to.

1170
00:49:00,220 --> 00:49:03,160
So they're relatively small
differences, but they seem to

1171
00:49:03,160 --> 00:49:06,090
track interestingly in the
number of siblings you have

1172
00:49:06,090 --> 00:49:08,842
who are older than you.

1173
00:49:08,842 --> 00:49:11,490
And at the same time,
this idea of

1174
00:49:11,490 --> 00:49:13,650
parental exposure to language.

1175
00:49:13,650 --> 00:49:17,980
There's this widely cited study
from Hart and Risley.

1176
00:49:17,980 --> 00:49:21,890
So they recorded for each month,
for 2 and 1/2 years, a

1177
00:49:21,890 --> 00:49:25,240
heroic recording, one full hour
of every word spoken at

1178
00:49:25,240 --> 00:49:28,550
home between parents and
children in just 42 families.

1179
00:49:28,550 --> 00:49:32,220
But they categorized them
as high, medium, or low

1180
00:49:32,220 --> 00:49:33,130
socioeconomically.

1181
00:49:33,130 --> 00:49:34,830
So in terms of income
and education.

1182
00:49:34,830 --> 00:49:35,570
OK.

1183
00:49:35,570 --> 00:49:37,730
And they're recording
lots and lots of

1184
00:49:37,730 --> 00:49:39,420
conversations verbatim.

1185
00:49:39,420 --> 00:49:42,870
And they code and analyzed
every utterance in 1,300

1186
00:49:42,870 --> 00:49:46,140
transcripts of 30,000 pages.

1187
00:49:46,140 --> 00:49:48,680
The reason why this is not
done a lot, it's not very

1188
00:49:48,680 --> 00:49:50,350
glamorous work to do.

1189
00:49:50,350 --> 00:49:51,440
You can imagine sitting
down there.

1190
00:49:51,440 --> 00:49:55,010
But they're recording lots of
conversations at home in

1191
00:49:55,010 --> 00:49:58,880
families who are better off,
medium off, or poor.

1192
00:49:58,880 --> 00:50:02,280
And they're looking at what is
the conversation like, for an

1193
00:50:02,280 --> 00:50:03,640
hour a day.

1194
00:50:03,640 --> 00:50:05,120
And here's what they find.

1195
00:50:05,120 --> 00:50:07,460
That variation in children's
IQ and language abilities

1196
00:50:07,460 --> 00:50:09,020
related to the amount
that the parents

1197
00:50:09,020 --> 00:50:10,780
speak to their children.

1198
00:50:10,780 --> 00:50:14,000
And that the academic success by
age 8 or 9 or 10 is related

1199
00:50:14,000 --> 00:50:16,860
a lot to speech at 0 to 3.

1200
00:50:16,860 --> 00:50:19,080
Now again, you think causally.

1201
00:50:19,080 --> 00:50:21,780
Because you think, parents are
talking to you at 0 and 3.

1202
00:50:21,780 --> 00:50:22,840
And 9 and 10, you're doing

1203
00:50:22,840 --> 00:50:25,330
differential equations, causally.

1204
00:50:25,330 --> 00:50:25,630
No.

1205
00:50:25,630 --> 00:50:28,030
The parents that are talking
to you a lot, 0 to 3, are

1206
00:50:28,030 --> 00:50:30,870
talking to you at 4, 5, and
6 a lot, most of the time.

1207
00:50:30,870 --> 00:50:33,420
So if these things are not like,
you get an injection,

1208
00:50:33,420 --> 00:50:34,740
you're done.

1209
00:50:34,740 --> 00:50:36,960
Many of the factors that we
can measure, continue on

1210
00:50:36,960 --> 00:50:40,570
average, for most people
in their environment.

1211
00:50:40,570 --> 00:50:43,960
But it's very impressive how
the environment altogether

1212
00:50:43,960 --> 00:50:44,990
influences things.

1213
00:50:44,990 --> 00:50:47,880
By age 3, the cumulative
vocabulary is 1,100 words in a

1214
00:50:47,880 --> 00:50:51,480
professional family as a child,
750 in a working class,

1215
00:50:51,480 --> 00:50:53,400
and 500 in a welfare family.

1216
00:50:53,400 --> 00:50:56,870
And welfare families, 600 words
per hour, working class

1217
00:50:56,870 --> 00:50:59,800
750, professionals 2,153.

1218
00:50:59,800 --> 00:51:03,580
By age 3, the vocabulary used
by children in the high SES

1219
00:51:03,580 --> 00:51:08,270
homes, is larger than the
vocabulary used by parents in

1220
00:51:08,270 --> 00:51:09,080
the low SES.

1221
00:51:09,080 --> 00:51:11,210
By age 3, larger vocabulary.

1222
00:51:11,210 --> 00:51:15,600
This is just telling you the
incredible cumulative power of

1223
00:51:15,600 --> 00:51:18,120
strong environmental
influences.

1224
00:51:18,120 --> 00:51:20,160
Because it's unbelievably
multiplicative.

1225
00:51:20,160 --> 00:51:21,635
It's every day, all day long.

1226
00:51:21,635 --> 00:51:24,170
And if you're in an environment
that's supportive

1227
00:51:24,170 --> 00:51:26,420
or an environment that's not.

1228
00:51:26,420 --> 00:51:27,630
It's not one shot.

1229
00:51:27,630 --> 00:51:31,840
It's every day, multiply,
multiply, multiply.

1230
00:51:31,840 --> 00:51:33,940
300 words more per hour
for professional,

1231
00:51:33,940 --> 00:51:34,770
than the welfare family.

1232
00:51:34,770 --> 00:51:35,460
Extrapolate to a year.

1233
00:51:35,460 --> 00:51:37,870
A child in a professional family
hears 11 million words,

1234
00:51:37,870 --> 00:51:40,250
versus 3 million in
a welfare family.

1235
00:51:40,250 --> 00:51:43,110
And strong correlation with
IQ scores by age 9.

1236
00:51:43,110 --> 00:51:46,360
So very huge differences
in what children are

1237
00:51:46,360 --> 00:51:47,480
exposed to at home.

1238
00:51:47,480 --> 00:51:49,610
And they correlate highly
with these IQ things.

1239
00:51:52,210 --> 00:51:56,440
So the literature is that on
average, on average, the IQ

1240
00:51:56,440 --> 00:51:58,830
scores that children have around
9 or 10, correlate

1241
00:51:58,830 --> 00:52:00,160
pretty well with ones
they'll have for the

1242
00:52:00,160 --> 00:52:01,750
rest of their lives.

1243
00:52:01,750 --> 00:52:04,560
Before that, it extremely
jumps around.

1244
00:52:04,560 --> 00:52:06,090
We don't know why.

1245
00:52:06,090 --> 00:52:07,720
But that's they're finding.

1246
00:52:07,720 --> 00:52:11,360
By the time in 9 or 10, whatever
you have, on average

1247
00:52:11,360 --> 00:52:12,640
tends to hang in there.

1248
00:52:12,640 --> 00:52:15,950
If you're 100 at 10, you'll
tend to be 100 at 20.

1249
00:52:15,950 --> 00:52:18,090
If you're 120 at 10, you'll
tend to be 120 at 20.

1250
00:52:18,090 --> 00:52:20,860
Does that make sense?

1251
00:52:20,860 --> 00:52:22,620
So there's been a lot of
interest in evidence about

1252
00:52:22,620 --> 00:52:26,850
whether you can change
your IQ as an adult.

1253
00:52:26,850 --> 00:52:29,230
So here's one study that got a
lot of attention, because it

1254
00:52:29,230 --> 00:52:31,130
was kind of interesting
in that way.

1255
00:52:31,130 --> 00:52:34,530
So they did a complicated
task like this.

1256
00:52:34,530 --> 00:52:35,750
This was an n-back task.

1257
00:52:35,750 --> 00:52:38,560
Simultaneously you hear letters
and you see little

1258
00:52:38,560 --> 00:52:40,220
spatial locations like this.

1259
00:52:40,220 --> 00:52:44,040
And every time something matched
two items ago, you

1260
00:52:44,040 --> 00:52:44,730
would push a button.

1261
00:52:44,730 --> 00:52:45,920
So you're hearing them
at the same time.

1262
00:52:45,920 --> 00:52:47,170
And if you hear a letter
that was two letters

1263
00:52:47,170 --> 00:52:47,920
ago, you push a button.

1264
00:52:47,920 --> 00:52:49,480
Or same spatial location,
you push one.

1265
00:52:49,480 --> 00:52:50,710
It's pretty hard.

1266
00:52:50,710 --> 00:52:52,610
They took 34 adults.

1267
00:52:52,610 --> 00:52:55,590
They trained them from
eight to 19 days,

1268
00:52:55,590 --> 00:52:56,480
so less than a month.

1269
00:52:56,480 --> 00:52:57,550
Skipped weekends.

1270
00:52:57,550 --> 00:52:58,450
25 minutes a day.

1271
00:52:58,450 --> 00:53:00,470
It's only half an hour a day.

1272
00:53:00,470 --> 00:53:01,780
They train on this.

1273
00:53:01,780 --> 00:53:05,280
And then they tested them on
some of these widely used IQ

1274
00:53:05,280 --> 00:53:07,080
types of measures.

1275
00:53:07,080 --> 00:53:09,940
Here's their performance on
the task itself, getting

1276
00:53:09,940 --> 00:53:10,770
better and better.

1277
00:53:10,770 --> 00:53:12,280
OK, that's not surprising.

1278
00:53:12,280 --> 00:53:14,120
And the more they practiced,
the higher they got.

1279
00:53:14,120 --> 00:53:16,305
So if you practice really hard
at something, you get better

1280
00:53:16,305 --> 00:53:17,980
at doing it.

1281
00:53:17,980 --> 00:53:20,690
That's not the thing they
were interested in.

1282
00:53:20,690 --> 00:53:22,380
They were interested in this.

1283
00:53:22,380 --> 00:53:24,170
Here's the IQ scores before
they were trained.

1284
00:53:24,170 --> 00:53:25,020
The IQ scores.

1285
00:53:25,020 --> 00:53:26,180
They didn't practice
the IQ tests.

1286
00:53:26,180 --> 00:53:27,930
And here's the IQ
ones afterwards.

1287
00:53:27,930 --> 00:53:29,670
This is the control group,
who did nothing.

1288
00:53:29,670 --> 00:53:31,320
This is the group that did
the mental practice.

1289
00:53:31,320 --> 00:53:32,960
And you can see, they pushed
up their IQ scores.

1290
00:53:32,960 --> 00:53:33,850
Not a lot.

1291
00:53:33,850 --> 00:53:38,010
But it was only 20 minutes a
day for less than a month.

1292
00:53:38,010 --> 00:53:40,010
So these kinds of studies
really intrigued

1293
00:53:40,010 --> 00:53:41,810
people in two ways.

1294
00:53:41,810 --> 00:53:43,920
And the more training you
got, the more your

1295
00:53:43,920 --> 00:53:44,596
IQ score went up.

1296
00:53:44,596 --> 00:53:46,410
The more weeks you did this.

1297
00:53:46,410 --> 00:53:49,330
Because a), maybe we could push
up IQ scores, if that's

1298
00:53:49,330 --> 00:53:50,480
important to push up.

1299
00:53:50,480 --> 00:53:52,240
This is a big decision.

1300
00:53:52,240 --> 00:53:53,790
Even when you're an adult.

1301
00:53:53,790 --> 00:53:56,650
What if you did more than 20
minutes a day for three weeks.

1302
00:53:56,650 --> 00:54:00,400
And b), maybe we could push up
everybody's IQ score, who

1303
00:54:00,400 --> 00:54:02,090
needed help in that, with
the right kind of

1304
00:54:02,090 --> 00:54:04,070
training, at any age.

1305
00:54:04,070 --> 00:54:06,880
So a lot of interest in that.

1306
00:54:06,880 --> 00:54:10,720
So let me take a step now to
sort of harder topics.

1307
00:54:10,720 --> 00:54:14,730
Now, one of the reasons that
intelligence and IQ topics

1308
00:54:14,730 --> 00:54:18,860
have gotten appropriately lots
of concern is a terrible

1309
00:54:18,860 --> 00:54:22,930
history of racism and sexism
associated with IQ tests and

1310
00:54:22,930 --> 00:54:26,790
the way people use that to argue
basically inferiority of

1311
00:54:26,790 --> 00:54:29,250
groups, other than themselves.

1312
00:54:29,250 --> 00:54:32,490
So the US Immigration Act of
1924, the official government

1313
00:54:32,490 --> 00:54:34,800
talked about biologically
weak stocks.

1314
00:54:34,800 --> 00:54:38,060
At that time, they were focused
or prejudiced against

1315
00:54:38,060 --> 00:54:40,070
Italians and Jews from Europe.

1316
00:54:40,070 --> 00:54:42,540
Different societies, different
times, as you know,

1317
00:54:42,540 --> 00:54:47,020
tragically, had biases against
different groups in the world.

1318
00:54:47,020 --> 00:54:49,520
Unfortunately, it's part of
the world we live in.

1319
00:54:49,520 --> 00:54:52,930
And even though we know now with
certainty that we're all

1320
00:54:52,930 --> 00:54:55,660
almost identical genetically in
any practical sense across

1321
00:54:55,660 --> 00:55:01,720
racial groups, race remains a
powerful social category.

1322
00:55:01,720 --> 00:55:03,645
It's a very complicated topic.

1323
00:55:03,645 --> 00:55:06,530
The experience you've had, is
the experience you've had.

1324
00:55:06,530 --> 00:55:09,680
But we know it's a
social category.

1325
00:55:09,680 --> 00:55:12,380
People identify to a certain
extent with that.

1326
00:55:12,380 --> 00:55:15,060
They recognize other people
in that context.

1327
00:55:15,060 --> 00:55:16,780
We'll come back to that
later in the course.

1328
00:55:16,780 --> 00:55:19,320
But it's a sort of fact of life
that we often think of

1329
00:55:19,320 --> 00:55:21,980
ourselves as belonging to one
racial group or another and

1330
00:55:21,980 --> 00:55:26,070
somebody else as belonging to
one racial group or another.

1331
00:55:26,070 --> 00:55:28,240
So I'm going to focus on one
thing that's both troubled

1332
00:55:28,240 --> 00:55:31,910
people and led to bad things,
but has some interesting,

1333
00:55:31,910 --> 00:55:34,070
important aspects
to think about.

1334
00:55:34,070 --> 00:55:38,210
So in the US, when people
compare African American and

1335
00:55:38,210 --> 00:55:41,840
European American scores,
there's a gap of scores that

1336
00:55:41,840 --> 00:55:42,630
grows over time.

1337
00:55:42,630 --> 00:55:45,130
So in infancy, to the extent
you can test these kinds of

1338
00:55:45,130 --> 00:55:46,020
things, there's no difference.

1339
00:55:46,020 --> 00:55:50,740
By age 4, on average, people who
are European American will

1340
00:55:50,740 --> 00:55:52,160
tend to score about four
or five points

1341
00:55:52,160 --> 00:55:53,540
higher on IQ tests.

1342
00:55:53,540 --> 00:55:55,930
And as the goes up, it
continues to go up.

1343
00:55:55,930 --> 00:55:57,530
There's a gap on average.

1344
00:55:57,530 --> 00:55:58,320
This is a huge average.

1345
00:55:58,320 --> 00:56:04,370
It's across many people across
time, as people get

1346
00:56:04,370 --> 00:56:05,560
older in the US.

1347
00:56:05,560 --> 00:56:10,140
Now, we've strongly believed
for many, many reasons

1348
00:56:10,140 --> 00:56:12,400
empirically that this is an
environmental influence.

1349
00:56:12,400 --> 00:56:15,040
And here's one example of
many pieces of evidence.

1350
00:56:15,040 --> 00:56:18,340
So that children fathered, for
example, by Black America GIs

1351
00:56:18,340 --> 00:56:21,260
in Germany, brought up by German
mothers, have exactly

1352
00:56:21,260 --> 00:56:23,870
the same IQs as white
American GI

1353
00:56:23,870 --> 00:56:24,740
children and German mothers.

1354
00:56:24,740 --> 00:56:28,620
That is, in countries where
stereotypes were applied in

1355
00:56:28,620 --> 00:56:32,120
general, partly because there
wasn't a history of African

1356
00:56:32,120 --> 00:56:34,650
Americans in Germany at
that time, the IQ

1357
00:56:34,650 --> 00:56:35,370
scores are the same.

1358
00:56:35,370 --> 00:56:38,300
So we think it's overwhelmingly
evident that

1359
00:56:38,300 --> 00:56:39,480
it's an environmental effect.

1360
00:56:39,480 --> 00:56:43,610
But in the US, there's a lot
of relationship between, on

1361
00:56:43,610 --> 00:56:46,630
average, across the entire
country, on average, between

1362
00:56:46,630 --> 00:56:48,710
what group you're in
and average income.

1363
00:56:48,710 --> 00:56:51,160
So here's the average income a
few years ago, depending on

1364
00:56:51,160 --> 00:56:54,160
which group you identified
yourself as belonging to.

1365
00:56:54,160 --> 00:56:59,130
So we talked earlier about the
relationship between economic

1366
00:56:59,130 --> 00:57:01,880
comfort or stability or support
and things like the

1367
00:57:01,880 --> 00:57:05,600
number of words a parent can
speak to or it's time to speak

1368
00:57:05,600 --> 00:57:06,310
to their child.

1369
00:57:06,310 --> 00:57:08,310
And the habit seems to correlate
with all kinds of

1370
00:57:08,310 --> 00:57:09,730
advantages.

1371
00:57:09,730 --> 00:57:14,270
So here's an incredibly powerful
and important idea.

1372
00:57:14,270 --> 00:57:16,865
Claude Steele is a researcher
at Stanford, who I knew, who

1373
00:57:16,865 --> 00:57:19,100
has played a big part in this.

1374
00:57:19,100 --> 00:57:20,310
And here's the idea.

1375
00:57:20,310 --> 00:57:22,390
So it's called stereotype
threat.

1376
00:57:22,390 --> 00:57:23,980
It applies to all of us.

1377
00:57:23,980 --> 00:57:27,270
But in some ways depending on
the world we live in, some of

1378
00:57:27,270 --> 00:57:29,970
us can be more vulnerable than
others in certain situations.

1379
00:57:29,970 --> 00:57:31,040
Here's the idea.

1380
00:57:31,040 --> 00:57:32,350
So let me tell you the
experiment and then the

1381
00:57:32,350 --> 00:57:33,070
interpretation.

1382
00:57:33,070 --> 00:57:35,490
The experiment is that he'll
bring people in and give,

1383
00:57:35,490 --> 00:57:39,390
Claude or his students, black
and white participants,

1384
00:57:39,390 --> 00:57:40,400
college students.

1385
00:57:40,400 --> 00:57:41,940
And they say, it's a laboratory
experiment.

1386
00:57:41,940 --> 00:57:44,760
And they give them something
like an SAT test.

1387
00:57:44,760 --> 00:57:48,140
And the groups perform
just the same.

1388
00:57:48,140 --> 00:57:50,920
Stanford students, black and
white, perform just the same.

1389
00:57:50,920 --> 00:57:52,330
That's the one group.

1390
00:57:52,330 --> 00:57:55,480
Here comes the next group of
black and white students, who

1391
00:57:55,480 --> 00:57:59,670
are told we're going to give
you a test of intelligence.

1392
00:57:59,670 --> 00:58:00,710
Now, look what happens.

1393
00:58:00,710 --> 00:58:03,800
The scores of African
Americans go down.

1394
00:58:03,800 --> 00:58:05,610
And it doesn't even have to
be the phrase "test of

1395
00:58:05,610 --> 00:58:08,530
intelligence," you can have
people just tick off, on a

1396
00:58:08,530 --> 00:58:12,480
front page, what racial group
they belong to and you get

1397
00:58:12,480 --> 00:58:14,790
almost the same effect.

1398
00:58:14,790 --> 00:58:15,530
So what is this?

1399
00:58:15,530 --> 00:58:18,380
This is understood as stereotype
vulnerability.

1400
00:58:18,380 --> 00:58:23,580
That is, tragically in the US
there's a stereotype that

1401
00:58:23,580 --> 00:58:26,680
African Americans
are not as good

1402
00:58:26,680 --> 00:58:29,890
academically as white Americans.

1403
00:58:29,890 --> 00:58:31,700
It's a pernicious stereotype.

1404
00:58:31,700 --> 00:58:34,400
It's one we can decry morally
and say it's a bad thing.

1405
00:58:34,400 --> 00:58:35,990
But it's out there.

1406
00:58:35,990 --> 00:58:38,360
There's another area where
African Americans are thought

1407
00:58:38,360 --> 00:58:42,100
of as phenomenal by stereotype,
which is sports,

1408
00:58:42,100 --> 00:58:42,760
in this country.

1409
00:58:42,760 --> 00:58:44,010
It's a weird thing.

1410
00:58:49,260 --> 00:58:53,130
The idea is that knowledge of
these things, even if this is

1411
00:58:53,130 --> 00:58:56,040
your real performance, once that
thought enters your mind

1412
00:58:56,040 --> 00:58:59,380
that I'm doing something that
some people say I'm not going

1413
00:58:59,380 --> 00:59:02,820
to do such a good job at,
is enough to make you

1414
00:59:02,820 --> 00:59:06,550
underperform compared to
your true potential.

1415
00:59:06,550 --> 00:59:09,410
So the other area where this
has been studied a lot is

1416
00:59:09,410 --> 00:59:11,040
women and mathematics.

1417
00:59:11,040 --> 00:59:11,980
I think that's disappearing.

1418
00:59:11,980 --> 00:59:16,110
When I grew up, everybody was
worried that women weren't

1419
00:59:16,110 --> 00:59:18,160
given as good an academic
support as men

1420
00:59:18,160 --> 00:59:19,290
or girls and boys.

1421
00:59:19,290 --> 00:59:21,530
OK, it's reversed.

1422
00:59:21,530 --> 00:59:22,810
In my lifetime, it's reversed.

1423
00:59:22,810 --> 00:59:24,490
Because now, by every
criteria, women are

1424
00:59:24,490 --> 00:59:27,040
outperforming men in the US by
every criteria, at least

1425
00:59:27,040 --> 00:59:29,470
societally, the number who
finish school, your beginning

1426
00:59:29,470 --> 00:59:30,700
salaries, everything.

1427
00:59:30,700 --> 00:59:32,950
So now they're worried
that us boys are

1428
00:59:32,950 --> 00:59:34,740
really being left behind.

1429
00:59:34,740 --> 00:59:38,280
So these things change because
they're cultural inventions.

1430
00:59:38,280 --> 00:59:39,650
They're cultural inventions.

1431
00:59:39,650 --> 00:59:42,980
And cultural morays change.

1432
00:59:42,980 --> 00:59:46,470
And sometimes in good
ways, hopefully.

1433
00:59:46,470 --> 00:59:50,680
So stereotype threat is that
other's judgments or one's own

1434
00:59:50,680 --> 00:59:53,930
actions will confirm negative
stereotypes about one's group.

1435
00:59:53,930 --> 00:59:56,130
Oh, you should do badly on
something or you should do

1436
00:59:56,130 --> 00:59:57,590
well on something.

1437
00:59:57,590 --> 01:00:00,390
So they studied on women
on that, old people.

1438
01:00:00,390 --> 01:00:02,380
Athletic ability of course
runs the opposite.

1439
01:00:02,380 --> 01:00:03,710
The experiment was
kind of fun.

1440
01:00:03,710 --> 01:00:06,330
They had a very athletic African
American instructor

1441
01:00:06,330 --> 01:00:08,560
come up and start
doing push-ups.

1442
01:00:08,560 --> 01:00:11,420
And all the white guys started
to lapse in their push-ups.

1443
01:00:11,420 --> 01:00:13,680
They got tired really fast,
compared to an equally

1444
01:00:13,680 --> 01:00:17,510
athletic white trainer who
came up to the front.

1445
01:00:17,510 --> 01:00:19,290
Does that make sense?

1446
01:00:19,290 --> 01:00:20,360
Does that make sense?

1447
01:00:20,360 --> 01:00:22,490
It's whatever is the stereotype
you have in your

1448
01:00:22,490 --> 01:00:25,793
head, if you say OK, oh, oh, I'm
not supposed to do so good

1449
01:00:25,793 --> 01:00:27,160
in this, you underperform.

1450
01:00:27,160 --> 01:00:28,850
Not everybody, all the
time of course.

1451
01:00:28,850 --> 01:00:31,260
But enough that we have to
be concerned about that.

1452
01:00:31,260 --> 01:00:34,050
More for academic performance
probably than push-ups.

1453
01:00:34,050 --> 01:00:36,020
But who knows.

1454
01:00:36,020 --> 01:00:38,730
So here's this achievement
gap.

1455
01:00:38,730 --> 01:00:45,540
And different scores between
again, at age 13, between

1456
01:00:45,540 --> 01:00:47,830
whites and blacks in the United
States, which we know

1457
01:00:47,830 --> 01:00:49,190
shouldn't be there.

1458
01:00:49,190 --> 01:00:50,565
Many factors are in it.

1459
01:00:50,565 --> 01:00:52,590
A huge variation among
people, of course.

1460
01:00:52,590 --> 01:00:54,220
These are huge averages.

1461
01:00:54,220 --> 01:00:57,320
But one that everybody wants
to get rid of, in a just

1462
01:00:57,320 --> 01:01:01,070
society where everybody has
an equal opportunity.

1463
01:01:01,070 --> 01:01:05,440
So here's a result that's so
unbelievable, that if it

1464
01:01:05,440 --> 01:01:07,410
weren't for some subsequent
results, I wouldn't even show

1465
01:01:07,410 --> 01:01:09,550
it to you, even though it's
a well-done experiment.

1466
01:01:09,550 --> 01:01:11,800
But the message of it
seems so incredible.

1467
01:01:11,800 --> 01:01:13,330
So here's what they did.

1468
01:01:13,330 --> 01:01:15,200
In this well-controlled study
in Science, and there's been

1469
01:01:15,200 --> 01:01:17,850
some follow-ups that have
exactly done this.

1470
01:01:17,850 --> 01:01:22,230
So they took African American
students and white students.

1471
01:01:22,230 --> 01:01:25,690
And they had them take
these various tests.

1472
01:01:25,690 --> 01:01:27,788
But some of them, at the
beginning of the year, wrote

1473
01:01:27,788 --> 01:01:28,540
an affirmation.

1474
01:01:28,540 --> 01:01:30,570
They had to choose a value
from a list and write an

1475
01:01:30,570 --> 01:01:33,050
in-class essay about why
the chosen value is

1476
01:01:33,050 --> 01:01:34,470
important to you.

1477
01:01:34,470 --> 01:01:36,790
The controls chose a value
from those same lists and

1478
01:01:36,790 --> 01:01:38,630
wrote an in-class essay about
why the value might be

1479
01:01:38,630 --> 01:01:40,340
important for somebody else.

1480
01:01:40,340 --> 01:01:42,710
You're writing a one-time essay
about why something

1481
01:01:42,710 --> 01:01:45,370
matters to you or matters
to somebody else.

1482
01:01:45,370 --> 01:01:49,380
So obviously what matters to you
is more powerful for you.

1483
01:01:49,380 --> 01:01:52,650
And by just doing that, there's
a 40% reduction in the

1484
01:01:52,650 --> 01:01:54,480
African American students
in the study in

1485
01:01:54,480 --> 01:01:55,370
their achievement gap.

1486
01:01:55,370 --> 01:02:00,570
Their scores went up 40% because
they wrote one essay

1487
01:02:00,570 --> 01:02:04,580
at the beginning of the year
about why values are important

1488
01:02:04,580 --> 01:02:06,430
to them and related
to education.

1489
01:02:06,430 --> 01:02:08,770
One essay did that.

1490
01:02:08,770 --> 01:02:09,970
Unbelievable.

1491
01:02:09,970 --> 01:02:13,870
And just a reminder about how
much one's attitude incredibly

1492
01:02:13,870 --> 01:02:16,220
influences your performance.

1493
01:02:16,220 --> 01:02:18,310
You have a range of things
you can do it.

1494
01:02:18,310 --> 01:02:21,040
And what gets you to the top of
your range that's in you,

1495
01:02:21,040 --> 01:02:23,060
or the bottom of your range,
has a lot to do with

1496
01:02:23,060 --> 01:02:25,940
attitudes, that can be moved
around by a lot of things.

1497
01:02:25,940 --> 01:02:29,450
One essay overcame a tremendous
amount of the

1498
01:02:29,450 --> 01:02:32,680
concerns that on average,
African American students have

1499
01:02:32,680 --> 01:02:35,520
about academic achievement.

1500
01:02:35,520 --> 01:02:36,980
And it's not just that.

1501
01:02:36,980 --> 01:02:39,630
So in this course, you only get
a couple of tips of things

1502
01:02:39,630 --> 01:02:40,880
that are useful to
you for courses.

1503
01:02:40,880 --> 01:02:41,550
Here's one.

1504
01:02:41,550 --> 01:02:43,260
It's the same principle,
but it seems

1505
01:02:43,260 --> 01:02:45,560
to apply for everybody.

1506
01:02:45,560 --> 01:02:47,600
So here's the experiment, also
published in Science.

1507
01:02:47,600 --> 01:02:49,050
It's really cool.

1508
01:02:49,050 --> 01:02:51,850
So they brought in students.

1509
01:02:51,850 --> 01:02:54,020
And they said, look students
often are worried about how

1510
01:02:54,020 --> 01:02:56,000
they're going to do in a test,
especially if it's an area you

1511
01:02:56,000 --> 01:02:58,440
think you're not so good in.

1512
01:02:58,440 --> 01:03:02,200
So they had them take a test on
Gauss's modular arithmetic.

1513
01:03:02,200 --> 01:03:03,030
Here's the pre-test.

1514
01:03:03,030 --> 01:03:04,800
Everybody did about the same.

1515
01:03:04,800 --> 01:03:07,580
Then they said, OK now, they
have to create pressure.

1516
01:03:07,580 --> 01:03:10,240
Because of course if you take
a test in an experiment,

1517
01:03:10,240 --> 01:03:12,190
that's not pressured, right?

1518
01:03:12,190 --> 01:03:14,240
Pressure is the grade you're
going to get, whether it lets

1519
01:03:14,240 --> 01:03:16,420
you into medical school, whether
you get to become the

1520
01:03:16,420 --> 01:03:19,400
head of the American Medical
Association.

1521
01:03:19,400 --> 01:03:20,590
That's pressure, right?

1522
01:03:20,590 --> 01:03:22,860
So they say oh, here's what
we're going to do now.

1523
01:03:22,860 --> 01:03:24,830
We're going to give you another
test, everybody

1524
01:03:24,830 --> 01:03:26,000
scoring about the same.

1525
01:03:26,000 --> 01:03:29,030
But you can make a fair bit of
money with a partner who's

1526
01:03:29,030 --> 01:03:29,770
down the hall.

1527
01:03:29,770 --> 01:03:31,420
We should tell you that partner
has already done a

1528
01:03:31,420 --> 01:03:34,230
really good job on the second
version of the test.

1529
01:03:34,230 --> 01:03:36,930
So if you don't do well, you
don't get money and the

1530
01:03:36,930 --> 01:03:39,490
partner down the hall doesn't
get your money.

1531
01:03:39,490 --> 01:03:41,985
If you do well in the second
test you're about to take now,

1532
01:03:41,985 --> 01:03:42,900
then everybody wins.

1533
01:03:42,900 --> 01:03:44,240
So they're trying to make
you feel pressured.

1534
01:03:44,240 --> 01:03:45,750
On man, I want to
win the money.

1535
01:03:45,750 --> 01:03:47,740
Plus there's a guy down the hall
who already did well and

1536
01:03:47,740 --> 01:03:49,430
I'm going to let us down.

1537
01:03:49,430 --> 01:03:50,750
They're trying to
make you feel as

1538
01:03:50,750 --> 01:03:51,610
pressured as you might.

1539
01:03:51,610 --> 01:03:54,370
As much as they can in an
experiment, like you do in a

1540
01:03:54,370 --> 01:03:56,330
regular test, in a
regular course.

1541
01:03:56,330 --> 01:03:58,750
So trying to create pressure
and anxiety.

1542
01:03:58,750 --> 01:04:01,130
And then for 10 minutes, the
controls just sit there and

1543
01:04:01,130 --> 01:04:01,850
think about it.

1544
01:04:01,850 --> 01:04:03,480
Oh, I hope I don't
mess up now.

1545
01:04:03,480 --> 01:04:04,270
The pressure is on.

1546
01:04:04,270 --> 01:04:06,120
I hope I don't meet the person,
if I don't do a good

1547
01:04:06,120 --> 01:04:07,845
job, down the hall.

1548
01:04:07,845 --> 01:04:10,530
An expressive group wrote an
essay for 10 minutes about

1549
01:04:10,530 --> 01:04:13,676
their thoughts and feelings.

1550
01:04:13,676 --> 01:04:15,030
Oh, I wrote this.

1551
01:04:15,030 --> 01:04:16,980
This is a huge mistake.

1552
01:04:16,980 --> 01:04:21,240
This should be "better."
I'll fix it up.

1553
01:04:21,240 --> 01:04:22,350
Better, better, better.

1554
01:04:22,350 --> 01:04:23,860
I did it unrelated,
in writing.

1555
01:04:23,860 --> 01:04:25,420
So here's what happens.

1556
01:04:25,420 --> 01:04:28,920
The group that just sat
there did nothing.

1557
01:04:28,920 --> 01:04:32,990
Their performance doesn't--
let me get this right.

1558
01:04:32,990 --> 01:04:34,380
The performers that wrote the
expressive writing, their

1559
01:04:34,380 --> 01:04:35,690
score goes up.

1560
01:04:35,690 --> 01:04:37,990
Everybody else's
scores go down.

1561
01:04:37,990 --> 01:04:40,330
Because here, here's where
they're beginning.

1562
01:04:40,330 --> 01:04:42,810
Now, they get pressured.

1563
01:04:42,810 --> 01:04:44,930
And they perform less well.

1564
01:04:44,930 --> 01:04:47,670
They're choking.

1565
01:04:47,670 --> 01:04:49,190
The group that wrote
the essay, their

1566
01:04:49,190 --> 01:04:52,040
performance stays up.

1567
01:04:52,040 --> 01:04:56,260
And it's kind of ironic, because
a theme of comedy is

1568
01:04:56,260 --> 01:04:59,610
frequently when they tell you,
whatever you do, don't tell

1569
01:04:59,610 --> 01:05:00,830
somebody x.

1570
01:05:00,830 --> 01:05:02,030
And then my mistake,
you tell x.

1571
01:05:02,030 --> 01:05:04,590
Have you seen that
a comedy show?

1572
01:05:04,590 --> 01:05:07,120
So you could have thought, well
writing an essay about my

1573
01:05:07,120 --> 01:05:09,880
thoughts and feelings about how
much anxiety and pressure

1574
01:05:09,880 --> 01:05:11,910
I feel, is like the worst
thing you can do.

1575
01:05:11,910 --> 01:05:13,610
It's like, I don't
want face it.

1576
01:05:13,610 --> 01:05:15,530
I'd rather clear my mind.

1577
01:05:15,530 --> 01:05:18,300
But it turns out that when
people write this little essay

1578
01:05:18,300 --> 01:05:21,030
about the thoughts and feelings
about their anxiety,

1579
01:05:21,030 --> 01:05:24,100
their scores rise.

1580
01:05:24,100 --> 01:05:26,920
And if they don't write that
kind of an essay, the scores

1581
01:05:26,920 --> 01:05:29,030
fall, because you've introduced
here the stress and

1582
01:05:29,030 --> 01:05:31,780
anxiety that we think is a
part of regular testing.

1583
01:05:31,780 --> 01:05:33,280
Does that makes sense?

1584
01:05:33,280 --> 01:05:33,990
I'll fix the slide.

1585
01:05:33,990 --> 01:05:35,480
I'm sorry about the typo.

1586
01:05:35,480 --> 01:05:36,730
Is that OK?

1587
01:05:39,460 --> 01:05:41,130
I'm almost a little afraid to
give you advice, because

1588
01:05:41,130 --> 01:05:43,200
somebody will write an essay,
do bad on the physics test,

1589
01:05:43,200 --> 01:05:44,690
and say oh, this is terrible.

1590
01:05:44,690 --> 01:05:46,090
I would have gotten a A.

1591
01:05:46,090 --> 01:05:48,790
But it seems like writing a
little essay about your

1592
01:05:48,790 --> 01:05:50,900
thoughts and feelings actually
pushes scores up.

1593
01:05:50,900 --> 01:05:52,860
I mean that's the scientific
evidence.

1594
01:05:52,860 --> 01:05:56,540
But again, it's just a reminder
of how powerful

1595
01:05:56,540 --> 01:05:59,440
feelings and attitudes are,
whether they're in us or

1596
01:05:59,440 --> 01:06:03,390
visited on us by the world, in
how well we perform, within

1597
01:06:03,390 --> 01:06:08,260
the range of performance that
are within us to start with.

1598
01:06:08,260 --> 01:06:09,890
I have two more points.

1599
01:06:09,890 --> 01:06:12,440
So we talked a little bit about
entity versus growth.

1600
01:06:12,440 --> 01:06:14,700
You know people who believe
they're like, I'm only so good

1601
01:06:14,700 --> 01:06:15,560
at something.

1602
01:06:15,560 --> 01:06:17,260
I'm only so good at basketball,
I'm only so good

1603
01:06:17,260 --> 01:06:19,220
at math, I'm only so good at
programming, I'm only so good

1604
01:06:19,220 --> 01:06:21,700
of writing, or whatever, versus
people who say, the

1605
01:06:21,700 --> 01:06:24,400
harder I work, the
better I'll get.

1606
01:06:24,400 --> 01:06:27,300
And that pretty much, this work
from Carol Dweck, that

1607
01:06:27,300 --> 01:06:30,090
many people tend to go for
one thing or the other.

1608
01:06:30,090 --> 01:06:31,720
Is it are we born with
talent and that's

1609
01:06:31,720 --> 01:06:33,040
pretty much the deal?

1610
01:06:33,040 --> 01:06:34,620
Or the harder we work,
do we get more

1611
01:06:34,620 --> 01:06:36,500
talented at doing something?

1612
01:06:36,500 --> 01:06:37,700
And so this is the kind
of questionnaire.

1613
01:06:37,700 --> 01:06:39,280
And these are literally
questions, so she gives

1614
01:06:39,280 --> 01:06:42,170
students, you have a certain
amount of intelligence and you

1615
01:06:42,170 --> 01:06:43,360
really can't change it.

1616
01:06:43,360 --> 01:06:44,840
I strongly agree.

1617
01:06:44,840 --> 01:06:47,210
That's this trait model.

1618
01:06:47,210 --> 01:06:48,140
Or I disagree.

1619
01:06:48,140 --> 01:06:51,480
The harder I work, the
smarter I can be.

1620
01:06:51,480 --> 01:06:54,170
And what she did for example, is
give that kind of question

1621
01:06:54,170 --> 01:06:55,800
to students.

1622
01:06:55,800 --> 01:06:58,450
And then follow them in their
performance on a math course.

1623
01:06:58,450 --> 01:06:59,950
Here's the ones who believed
they could get better.

1624
01:06:59,950 --> 01:07:02,140
Here's the ones who said they
only have so much math ability

1625
01:07:02,140 --> 01:07:03,570
and intelligence.

1626
01:07:03,570 --> 01:07:05,050
And look at the ones who thought
they could get better,

1627
01:07:05,050 --> 01:07:06,500
got better.

1628
01:07:06,500 --> 01:07:08,830
So that seems like a really
good thing to have, is not

1629
01:07:08,830 --> 01:07:10,500
only the positive attitude.

1630
01:07:10,500 --> 01:07:14,540
But a real belief that the
harder the work, the smarter

1631
01:07:14,540 --> 01:07:15,790
you'll get.

1632
01:07:18,890 --> 01:07:20,720
Last two slides.

1633
01:07:20,720 --> 01:07:22,300
So finally, finally, finally.

1634
01:07:22,300 --> 01:07:25,220
And we've had a couple of
them, but almost none, a

1635
01:07:25,220 --> 01:07:27,800
longitudinal, random
assignment study.

1636
01:07:27,800 --> 01:07:30,870
Which doesn't sound hugely
exciting, but that's the best

1637
01:07:30,870 --> 01:07:32,400
science we can ever do.

1638
01:07:32,400 --> 01:07:34,160
Randomly assign people
to things.

1639
01:07:34,160 --> 01:07:37,200
And then follow them over time
to understand how life works.

1640
01:07:37,200 --> 01:07:42,180
So one of the famous studies is,
this is Perry Preschool.

1641
01:07:42,180 --> 01:07:46,030
So it followed into adulthood,
123 children from low SES

1642
01:07:46,030 --> 01:07:49,200
environments in Michigan.

1643
01:07:49,200 --> 01:07:51,740
And they were randomly divided,
randomly divided.

1644
01:07:51,740 --> 01:07:54,540
And there's no randomness
right, in life, mostly.

1645
01:07:54,540 --> 01:07:56,470
You're not randomly
given genes.

1646
01:07:56,470 --> 01:07:58,650
You're not randomly put
into a household.

1647
01:07:58,650 --> 01:08:01,550
It's always things are super
correlated with each other.

1648
01:08:01,550 --> 01:08:02,890
And it's very hard to
tease them apart.

1649
01:08:02,890 --> 01:08:05,910
But here they took kids who were
from poor families and

1650
01:08:05,910 --> 01:08:07,990
put them randomly
into two groups.

1651
01:08:07,990 --> 01:08:10,830
One group is a program group,
who received a high-quality

1652
01:08:10,830 --> 01:08:14,080
active learning preschool
program and home visits, to be

1653
01:08:14,080 --> 01:08:14,900
supportive as well.

1654
01:08:14,900 --> 01:08:19,130
So they got help at home
and help in preschool.

1655
01:08:19,130 --> 01:08:22,010
And a no-program group, who
didn't get any of these.

1656
01:08:22,010 --> 01:08:24,960
I can tell you that when you do
random assignment studies

1657
01:08:24,960 --> 01:08:29,100
in real life like this, some
people argue, I don't agreed

1658
01:08:29,100 --> 01:08:30,399
with this, that it's unethical,
because you should

1659
01:08:30,399 --> 01:08:33,450
help everybody all the time.

1660
01:08:33,450 --> 01:08:34,810
But if you don't do this,
you never figure

1661
01:08:34,810 --> 01:08:35,630
out what helps people.

1662
01:08:35,630 --> 01:08:38,520
OK It's a little bit
of a circular--

1663
01:08:38,520 --> 01:08:41,520
you can't figure out what
really helps people.

1664
01:08:41,520 --> 01:08:43,439
And then in a really rare
thing-- so it's random

1665
01:08:43,439 --> 01:08:44,140
assignment.

1666
01:08:44,140 --> 01:08:48,760
They followed them ages 3 to 11,
14 to 15, 19, 27, and 40.

1667
01:08:48,760 --> 01:08:50,290
They follow you through
most of your

1668
01:08:50,290 --> 01:08:52,600
childhood and adult life.

1669
01:08:52,600 --> 01:08:55,250
So what happens?

1670
01:08:55,250 --> 01:08:57,600
Here's their IQ scores?

1671
01:08:57,600 --> 01:09:00,160
Here's the program people
in red and the

1672
01:09:00,160 --> 01:09:02,000
no-program people in blue.

1673
01:09:02,000 --> 01:09:05,290
And you can see that for a few
years, they separate by IQ.

1674
01:09:05,290 --> 01:09:08,340
But by age 7, they're pretty
much the same.

1675
01:09:08,340 --> 01:09:11,880
And when these kind of data came
out, because of course

1676
01:09:11,880 --> 01:09:13,630
after the preschool, they
go back to their

1677
01:09:13,630 --> 01:09:15,330
regular local schools.

1678
01:09:15,330 --> 01:09:17,350
They're pretty much
the same schools.

1679
01:09:17,350 --> 01:09:20,660
So people, after that they said,
well Head Start is a

1680
01:09:20,660 --> 01:09:24,210
kind of a program to help kids
who are poor, get a head start

1681
01:09:24,210 --> 01:09:25,500
in education and support.

1682
01:09:25,500 --> 01:09:26,810
It's no good.

1683
01:09:26,810 --> 01:09:28,770
It doesn't do anything.

1684
01:09:28,770 --> 01:09:31,000
Because once they get back into
schools that not very

1685
01:09:31,000 --> 01:09:33,740
supportive, the benefits of
the program disappear.

1686
01:09:33,740 --> 01:09:37,120
You didn't do anything for the
kid, because unless you fix

1687
01:09:37,120 --> 01:09:40,350
everything in their lives, that
one year just doesn't

1688
01:09:40,350 --> 01:09:42,470
make a difference
in the long run.

1689
01:09:42,470 --> 01:09:46,080
So everybody said, Head Start
programs are just, sadly, a

1690
01:09:46,080 --> 01:09:49,870
waste of resources, because
they don't do anything for

1691
01:09:49,870 --> 01:09:51,689
these children over
the longer term.

1692
01:09:51,689 --> 01:09:55,350
But here's the remarkable
thing.

1693
01:09:55,350 --> 01:09:57,100
They kept following
these kids.

1694
01:09:57,100 --> 01:09:58,040
And here's what they found.

1695
01:09:58,040 --> 01:10:02,640
For example, in the number of
arrests, 36 had five or more

1696
01:10:02,640 --> 01:10:06,120
arrests in the program group.

1697
01:10:06,120 --> 01:10:08,580
But all the way up to 55, for
the no-program group.

1698
01:10:08,580 --> 01:10:10,920
That's a big difference, how
often you're arrested.

1699
01:10:10,920 --> 01:10:13,410
That's a big outcome in life.

1700
01:10:13,410 --> 01:10:17,110
Earning $20,000 or more at age
40, program group 60%;

1701
01:10:17,110 --> 01:10:19,180
no-program group, 40%.

1702
01:10:19,180 --> 01:10:21,650
Completed high school,
45% to 65%.

1703
01:10:21,650 --> 01:10:26,850
20% increase in completion of
high school, from one year of

1704
01:10:26,850 --> 01:10:28,385
help before kindergarten.

1705
01:10:30,980 --> 01:10:34,230
Basic achievement at
age 14, way up.

1706
01:10:34,230 --> 01:10:37,680
Willingness to do homework,
way up.

1707
01:10:37,680 --> 01:10:39,940
So what's that's telling
us is, IQ is a

1708
01:10:39,940 --> 01:10:40,940
pretty interesting thing.

1709
01:10:40,940 --> 01:10:42,100
And we've talked
a lot about it.

1710
01:10:42,100 --> 01:10:44,410
It is an interesting thing.

1711
01:10:44,410 --> 01:10:47,890
But there's something really
deep in the ways you can help

1712
01:10:47,890 --> 01:10:51,360
people, that make them
succeed in life.

1713
01:10:51,360 --> 01:10:55,390
Not get in trouble, avoid bad
things, make money, finish

1714
01:10:55,390 --> 01:10:57,610
school, do your work.

1715
01:10:57,610 --> 01:11:01,750
That one year of school changed
these kids for the

1716
01:11:01,750 --> 01:11:04,390
rest of their lives,
on average.

1717
01:11:04,390 --> 01:11:08,260
And imagine if you had done
two years or five years?

1718
01:11:08,260 --> 01:11:10,730
And IQ in this case doesn't
capture it at all.

1719
01:11:10,730 --> 01:11:13,310
There's something else in them
that changed, that made them

1720
01:11:13,310 --> 01:11:16,730
more resilient in life and
successful in the long run.

1721
01:11:16,730 --> 01:11:20,380
So there's a lot of pieces to
who does well and not well in

1722
01:11:20,380 --> 01:11:21,850
ways we wish for everybody.

1723
01:11:21,850 --> 01:11:23,430
And IQ is a piece
of that story.

1724
01:11:23,430 --> 01:11:25,450
But there's a lot of pieces.

1725
01:11:25,450 --> 01:11:26,700
So thanks very much.