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PROFESSOR: The midterm marks
is sort of an interesting

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dividing point in the subject
material of the course.

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It's not an absolute division
by any stretch of the

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imagination, but
it's a division.

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Up to this point we've been
talking about things that

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we could measure of
various varieties like

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your memory span.
 

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You can remember 7 plus or
minus 2, color names, you can

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see these colors, you can do
this, you can do that-- in

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these cases the you has been a
sort of a plural you and the

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data point of interest has been
the average data point, the

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mean data point, what's
normal, what's standard.

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We measure people's ability
to memorize color names,

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the average of that we've
decided is going to be 7.

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They'll be some variation
around that, but it's the 7

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that's been interesting.
 

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That turns out not to be the
case in the same way when

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you're talking about
something like intelligence.

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Particularly, intelligence
testing-- announcing that on

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average people have average
intelligence is kind of boring.

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What's interesting about
intelligence and what is

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interesting about things like
personality is the variation

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around the mean and the
explanation of that variation.

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And that's what we need to
start talking about now.

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Oh by the way, while it is of
course, by definition the case

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that on average people
have average intelligence,

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they don't believe it.
 

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If you ask people, do you think
that you are more intelligent,

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less intelligent or about of
average intelligence compared

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to the rest of the population
you'll get some distribution

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around that, too.
 

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But you'll find out that
the average perception

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of intelligence is that
we're all above average.

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This goes for a variety of
other questions like, how

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good looking are you?
 

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Above average, below
average, or average?

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Well, we're all a little
above average there, too.

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It turns out that there is one
group that gets the answer

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correct, that if you ask this
group of people on average--

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you brighter or dumber than
average?-- they'll come

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out in the middle.
 

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You ask them, are you cuter
or uglier than average--

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they'll come out average.
 

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Anybody know who that group is?
 

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[UNINTELLIGIBLE]
 

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Hand, we need a hand.
 

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No hands, nobody who cares
to speculate-- yeah, yeah.

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Yes, you with the
computer there.

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AUDIENCE: Kids.
 

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PROFESSOR: No, I
don't think so.

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I don't know what
the answer is.

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Certainly it's not true for
parents of kids-- all of

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whom know their children
are above average.

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AUDIENCE: Depressed people?
 

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PROFESSOR: Yes, it's
the depressed.

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Depressed people have an
accurate assessment of

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their own intelligence
and good looks.

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In fact, it has been seriously
argued that part of what keeps

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us undepressed is an
unrealistic assessment

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of the world.
 

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We're smart, we're good
looking, we're going places.

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If you knew the truth
we'd all be depressed.

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But that's the topic
for another day.

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Like when you get
the midterm back.

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Oh, I shouldn't have said that.
 

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But the midterm doesn't depress
us actually, it provides a

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certain amount of lighthearted
merriment for us you'll be

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happy to know because people do
come up with some great

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answers to stuff.
 

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So, as I say, we've been
interested mostly in the

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mean value of measurements
to this point.

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Now we're interested in the
variation, the distribution

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of those points for a
wide range of measures.

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If you measure a whole
population of people and you

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count the number of people--
whoops-- who fall into each

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bin, you know, have a bunch of
bins here-- different scores

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on a test, let's say.
 

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You'll get one of these
bell shaped or normal

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curved distributions.
 

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The questions that we're
interested in here are

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questions about, where does
that variability come from?

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The variance of a distribution
is the sum of the squares of

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the difference between the
mean and the data points.

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So, got the mean, got a data
point-- take that distance,

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square it, sum all those
up and divide by the

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number of observations.
 

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That's the variance and the
square root of that is one

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standard deviation away from
the mean, so this would be--

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and those units of standard
deviation are sort of the yard

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stick for how far away you are
from the mean of

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a distribution.
 

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So if you take something like
the SAT, for example, the SAT

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is scaled in such a way that
the mean is intended

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to be at about 500.
 

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At least that's where they
started and each hundred points

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is one standard deviation away.
 

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What that means, for example,
is that if you've got 700 or

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above that the number of people
getting 700 or above on a

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properly normed SAT type test
is about 2.5% of the

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population-- actually, that
line would be at 1.96 as it

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shows on the handout if you
want the 2.5% point.

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But roughly speaking two
standard deviations above

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the mean gives you about
2.5% of the population.

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Three standard deviations above
the mean, so that 800 score

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gives you-- I can't remember--
it's a very small percentage of

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a normal population
above the mean.

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That's at least the ideal for
something like an SAT test.

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Works well for the SAT 1 Math
and Verbal kinds of things.

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They are more or less
normally distributed.

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The place where it's a disaster
is things like the SAT 2.

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How many of you took the SAT 2?
 

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Their are two versions
of it, right?

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There's the hard version,
the easy version.

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How many of you took the
hard version of the SAT 2?

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INTERPOSING VOICES]
 

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PROFESSOR: What?
 

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Oh, sorry, the math.
 

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There are two versions
of the math.

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I got my jargon wrong because
they used to be called

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achievement tests, they're
now SAT 2s, right?

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Is it still AB and
BC or something?

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[INTERPOSING VOICES]
 

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PROFESSOR: Well, whatever it
is-- I don't care what it is.

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This is-- whoa, I'm
losing my glasses now.

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Getting too excited here.
 

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The problem is I took
the easy version.

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I took the easy version of it
because I looked at this

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distribution and I realized
that the whatever it is-- the

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fancy version of the SAT 2
actually has a distribution

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that looks like this, which is
to say everybody who takes it

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gets an 800 on it and those of
us, mathematical incompetents

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who were going to score-- I
don't know, 760 or something,

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we were going to come out in
the fifth percentile and not

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from the good side of
the distribution.

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So I took what was then the
achievement test because I knew

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that-- the math 1 achievement
test-- because I knew that I

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could score in the upper
end of the distribution.

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The other people taking that
were people who couldn't

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add and subtract.
 

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So anyway, it worked for me.
 

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Where does variability on
tests like this come from?

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There are lots and lots
of potential sources

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of variability.
 

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One important point to make at
the moment is to note that

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things that cause variability
across groups are not

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necessarily the same things the
cause variation within groups.

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Let's give a silly example, if
you grow a bunch of tomato

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plants and you vary the amount
of fertilizer you put on

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them-- more fertilizer,
bigger plants, right?

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So you can have something that
shows you that you can account

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for some of the variability in
the size of the plants

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from the fertilizer.
 

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Now if you grow some tomato
plants and some redwood trees

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you get very different heights
too, but the source of

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variability between tomatoes
and redwoods is different than

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the source of variability
within tomato plants.

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This becomes relevant in more
subtle and interesting ways

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when you start talking about
variation within population

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groups and across population
groups on a measure like

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IQ or intelligence.
 

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So IQ is just a number on a
test, but it's supposed to be

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a measure of intelligence.
 

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What is it actually measuring?
 

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Well there's a certain
circularity that's popular in

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the field, which is to say that
intelligence is what IQ tests

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measure and what do IQ tests--
well, they measure

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intelligence, but it'd be nice
to be a little more

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interesting than that.
 

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A little more interesting than
that is to note that you can

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divide up this sort of
intuitive idea of intelligence

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in various ways.
 

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One of the interesting ways to
divide it up is into so-called

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fluid and crystallized
intelligence.

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With IQ tests being a punitive
measure of fluid intelligence,

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fluid intelligence is the sort
of set of reasoning

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abilities that let you deal
with something like

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abstract relations.
 

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It is said to have reached
its mature state, its adults

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state by about age 16 or so.
 

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So you guys have pretty
much leveled out on that.

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Crystallized intelligence is
more the application of

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knowledge to particular tasks
and can continue growing

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throughout the life span.
 

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A particular example would be
something like vocabulary.

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Your vocabulary is not fixed
and with luck will continue to

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grow as you age and the idea
that the IQ is picking

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up intelligence.
 

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Tests that are IQ like are more
tests of fluid intelligence

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than they are of this
crystallized intelligence, at

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least that's the intent.
 

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What is it that is this
fluid intelligence?

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One possibility is that when
you talk about somebody being

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mentally quick that that's
literally what you're

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talking about.
 

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That what differs between
people who score high on these

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sort of tests and people
who don't is simple

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speed of response.
 

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And it is in fact the case
that simple reaction time is

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related to measures of IQ.
 

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People who bang a response key
more quickly in a reaction time

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experiment are also people
who score higher on IQ tests.

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Not a perfect relationship,
not even a hugely

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strong relationship.
 

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And in fact, we probably want
to look for something a little

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more subtle than that
in understanding what

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intelligence might be.
 

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More interesting are claims
that it has something to do

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with the constellation of
operations we were talking

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about in the context of working
memory earlier in the course.

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These sort of executive
function-- the desktop

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of the computer of your
mind kind of functions.

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How much stuff can you have
up there and how effectively

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can you manipulate it?
 

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So one possible correlate would
be something as simple as

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digit span or in this
class, color span.

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How many color names
can you name?

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I go red, green, blue.
 

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You say, red, green, blue,
that sort of thing.

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Again, related.
 

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It tracks along with
intelligence, but

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not all that well.
 

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People like Randy Engle in
Georgia have worked on creating

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tasks that they think capture
this working memory executive

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function aspect of it better
and that's-- I think, I put on

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the handout this notion
of active span tasks.

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These are tasks that are like
the color name task, but a

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little more complicated.
 

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So the sort of thing that you
might do if you were in Randy

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Engle's lab would be something
like this-- what I'm going to

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get you to do is I'm going to
read you a list in words and

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you're going to spit them
back to me in order.

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And the measure that's going to
relate to something like an IQ

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test score is going to be how
many you can get back in order.

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But I'm not just going
to read you the names.

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What I'm going to do is give
you a pair-- an equation and

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a word on each presentation.
 

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Either on a computer screen
or I can do this orally.

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So I might ask you to verify,
is it true that 2 plus

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3 minus 1 equals 4?
 

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And at the same time you
see the word uncle.

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And so your job at the
time is store uncle and

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verify the equation.
 

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OK, next one would be 4
minus 3 plus 5 equals 6 and

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the word would be fish.
 

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So-- no, that one
doesn't sound right.

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00:17:14 --> 00:17:18
OK, now what were
the two words?

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Uncle and fish.
 

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That's good.
 

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But if I was to do this without
the long song and dance and go

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up to 4, 5, 6 of these, you
would discover that you

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started losing them.
 

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And the number that you can
hold while doing these

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calculations at the same
time turns out to be a

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more powerful predictor of
things like IQ scores.

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Again, not perfect, but getting
closer, perhaps, to what the

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underlying substrate of what we
mean by intelligence might be.

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That it might have something
to do with how well you

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move things around in that
mental space that we were

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calling working memory.
 

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Another possibility, not
necessarily unrelated, but

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another possibility is that it
has to do with the degree to

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which your brain is plastic--
not in the plastic kind of

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sense, but plastic in
the modifiable sense.

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That maybe the ability to
change and modify the structure

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of your brain is the neural
substrate of intelligence.

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In any case, whatever it is,
it is a useful predictor

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00:18:46 --> 00:18:48
of a variety of things.
 

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00:18:48 --> 00:18:52
It's a usable predictor of
performance in school, which

278
00:18:52 --> 00:18:54
is in fact where it started.
 

279
00:18:54 --> 00:18:59
Binet in France-- that's
B-I-N-E-T, but it's French, so

280
00:18:59 --> 00:19:04
it's Binet, in France, started
intelligence testing as a way

281
00:19:04 --> 00:19:08
of seeing which kids might
need help in school.

282
00:19:08 --> 00:19:11
It is a predictor of a
variety of a things.

283
00:19:11 --> 00:19:17
So more IQ points,
higher lifetime salary.

284
00:19:17 --> 00:19:22
More IQ points less of a chance
of a criminal conviction, less

285
00:19:22 --> 00:19:24
of a chance of teen pregnancy.
 

286
00:19:24 --> 00:19:27
So it's related to stuff that
you'd like to know something

287
00:19:27 --> 00:19:34
about and you'd like to know
as a result where it is that

288
00:19:34 --> 00:19:36
the variability comes from.
 

289
00:19:36 --> 00:19:40
 
 

290
00:19:40 --> 00:19:47
Part of the class of
statistical tests for where

291
00:19:47 --> 00:19:51
variances come from, the effort
to parcel it out you will have

292
00:19:51 --> 00:19:53
seen in a variety of the papers
that you read, probably--

293
00:19:53 --> 00:19:55
statistical tests called ANOVA.
 

294
00:19:55 --> 00:19:59
It stands for Analysis
of Variance.

295
00:19:59 --> 00:20:04
It's part of the statistical
armamentarium that allows you

296
00:20:04 --> 00:20:08
to take the variance apart and
say, some of due to this and

297
00:20:08 --> 00:20:09
some of it's due to that.
 

298
00:20:09 --> 00:20:12
Now this is made simpler by
the fact that variances

299
00:20:12 --> 00:20:14
are additive.
 

300
00:20:14 --> 00:20:17
So if you have two sources of
variability that they add

301
00:20:17 --> 00:20:19
in a nice direct way.
 

302
00:20:19 --> 00:20:22
So if we've got the total
variation and we are in a

303
00:20:22 --> 00:20:28
psychology course one way to
divide up the variance, at

304
00:20:28 --> 00:20:32
least theoretically, is into
variance that's due to genetic

305
00:20:32 --> 00:20:38
factors-- our nice nativist
component and variance that's

306
00:20:38 --> 00:20:42
due to environmental factors.
 

307
00:20:42 --> 00:20:46
 
 

308
00:20:46 --> 00:20:50
In principle, if this is a good
way to think about the variance

309
00:20:50 --> 00:20:53
we should be able to go in and
do experiments that allow us to

310
00:20:53 --> 00:20:57
see the genetic variance and
the environmental variance

311
00:20:57 --> 00:21:01
and see how they add up to
make the total variance.

312
00:21:01 --> 00:21:08
What you will often see in
papers on intelligence and

313
00:21:08 --> 00:21:14
actually, widely in the popular
literature on the genetic

314
00:21:14 --> 00:21:18
component of pick your
favorite function.

315
00:21:18 --> 00:21:22
Chance [UNINTELLIGIBLE], what
is the genetic component

316
00:21:22 --> 00:21:26
to male fidelity or
something like that.

317
00:21:26 --> 00:21:32
You'll often see discussions
of so-called heritability

318
00:21:32 --> 00:21:35
usually written with a big H.
 

319
00:21:35 --> 00:21:42
Heritability is simply the--
whoops-- the genetic variance

320
00:21:42 --> 00:21:48
over the total variance, which
in this story would be the

321
00:21:48 --> 00:21:51
environmental variance plus
the genetic variance.

322
00:21:51 --> 00:21:57
There are difficulties with
thinking about-- we're

323
00:21:57 --> 00:22:03
withdrawing conclusions from
that that we will come to

324
00:22:03 --> 00:22:08
shortly, but the first thing I
want to do before going on to

325
00:22:08 --> 00:22:13
that is to talk about this sort
of data that are brought to

326
00:22:13 --> 00:22:16
bear to understand variability.
 

327
00:22:16 --> 00:22:21
Because a lot of this is
so-called correlational data

328
00:22:21 --> 00:22:26
and it is important to turn you
into educated consumers of

329
00:22:26 --> 00:22:30
correlational data, so that's
why you have this lovely

330
00:22:30 --> 00:22:32
second page of the handout.
 

331
00:22:32 --> 00:22:35
 
 

332
00:22:35 --> 00:22:38
Correlation is simply a way of
describing the relationship

333
00:22:38 --> 00:22:45
between two variables measured
on the same subject.

334
00:22:45 --> 00:22:49
So let's take the silly example
in the upper left there.

335
00:22:49 --> 00:22:55
If I took everybody here and
measured your height in inches

336
00:22:55 --> 00:23:02
and measured your height in
centimeters and for each

337
00:23:02 --> 00:23:05
person-- now each person
generates a data point-- those

338
00:23:05 --> 00:23:11
data points had better lie
on a straight line, right?

339
00:23:11 --> 00:23:14
Those are two measures that are
really seriously correlated;

340
00:23:14 --> 00:23:16
one with the other.
 

341
00:23:16 --> 00:23:20
If I know inches, I
know centimeters.

342
00:23:20 --> 00:23:24
Correlation coefficients are
calculated, so you calculate

343
00:23:24 --> 00:23:28
the line that fits the data.
 

344
00:23:28 --> 00:23:32
And then you calculate how
strong your correlation is by

345
00:23:32 --> 00:23:36
looking at the distances of
data points away

346
00:23:36 --> 00:23:37
from that line.
 

347
00:23:37 --> 00:23:41
In this case, all the data
points lie on the line and that

348
00:23:41 --> 00:23:46
leads to a regression value,
regression coefficient--

349
00:23:46 --> 00:23:51
usually written as
little r of 1.

350
00:23:51 --> 00:23:55
A correlation coefficient
of 1 means if I know this

351
00:23:55 --> 00:23:58
variable then I know
this variable perfectly.

352
00:23:58 --> 00:24:01
 
 

353
00:24:01 --> 00:24:05
Now those are really
boring data.

354
00:24:05 --> 00:24:07
Nobody spends a lot of time
working out correlation

355
00:24:07 --> 00:24:08
coefficients for that.
 

356
00:24:08 --> 00:24:11
You work out correlation
coefficients for data where

357
00:24:11 --> 00:24:13
there's some variability.
 

358
00:24:13 --> 00:24:14
That's the whole point here.
 

359
00:24:14 --> 00:24:18
So the second one is height and
weight-- actually, taken from a

360
00:24:18 --> 00:24:23
subset of a 900 class
some years ago.

361
00:24:23 --> 00:24:27
If you measure height and
weight you now get data points

362
00:24:27 --> 00:24:36
that are clustered in a broken
hunk of chalk around some line.

363
00:24:36 --> 00:24:40
If you calculate the regression
coefficient now it's going to

364
00:24:40 --> 00:24:45
be something less than
1, but still positive.

365
00:24:45 --> 00:24:51
So greater than
zero, less than 1.

366
00:24:51 --> 00:24:52
What is it actually
on the handout?

367
00:24:52 --> 00:24:55
Like 0.78 or something?
 

368
00:24:55 --> 00:25:00
The relationship between height
and weight is pretty strong.

369
00:25:00 --> 00:25:08
So if I know that your 6 foot
5 I don't know your weight

370
00:25:08 --> 00:25:13
exactly, but I can make
a nonrandom guess

371
00:25:13 --> 00:25:16
about that weight.
 

372
00:25:16 --> 00:25:25
On the other hand, if I know
your height-- let's plot, OK,

373
00:25:25 --> 00:25:27
we still have height there.
 

374
00:25:27 --> 00:25:32
So let's plot last two
digits of social security

375
00:25:32 --> 00:25:35
number against height.
 

376
00:25:35 --> 00:25:39
It is my firm belief-- I don't
know this to be true not having

377
00:25:39 --> 00:25:45
collected the data, but it is
my firm belief that those data

378
00:25:45 --> 00:25:48
are just a random cloud
of spots, right?

379
00:25:48 --> 00:25:50
I don't think there's anything
about social security number

380
00:25:50 --> 00:25:53
that's related to your
height, I hope not.

381
00:25:53 --> 00:25:58
 
 

382
00:25:58 --> 00:26:01
So, if I know your height
I know squat about your

383
00:26:01 --> 00:26:03
social security number.
 

384
00:26:03 --> 00:26:05
That is a correlation of zero.
 

385
00:26:05 --> 00:26:08
 
 

386
00:26:08 --> 00:26:13
Correlation can go below zero,
but below zero is not worse

387
00:26:13 --> 00:26:17
than zero, it just tells you
the direction of

388
00:26:17 --> 00:26:18
the correlation.
 

389
00:26:18 --> 00:26:21
So let's go back to the
inches-- OK, we'll

390
00:26:21 --> 00:26:22
stick with height.
 

391
00:26:22 --> 00:26:26
So this time I'm going to
measure-- get everybody here--

392
00:26:26 --> 00:26:28
I'm going to measure their
height and I'm going to measure

393
00:26:28 --> 00:26:33
the distance from the top of
their head to the ceiling, very

394
00:26:33 --> 00:26:34
exciting data collection.
 

395
00:26:34 --> 00:26:37
I'm going to get orderly
looking data, but this

396
00:26:37 --> 00:26:39
time it's going to
look like this right?

397
00:26:39 --> 00:26:41
I've just changed the slope.
 

398
00:26:41 --> 00:26:44
That's going to give me a
correlation in this silly

399
00:26:44 --> 00:26:46
example of minus 1.
 

400
00:26:46 --> 00:26:50
It's a perfect correlation, but
the direction of relationship

401
00:26:50 --> 00:26:51
is the other way.
 

402
00:26:51 --> 00:26:55
If I know distance from the
floor I absolutely know

403
00:26:55 --> 00:26:57
distance from the ceiling.
 

404
00:26:57 --> 00:27:00
It's just that as one goes
up the other one goes down.

405
00:27:00 --> 00:27:03
Again, that's a really
boring example.

406
00:27:03 --> 00:27:08
A more interesting example is
on the handout, which is if you

407
00:27:08 --> 00:27:14
come into my lab and we do a
reaction time experiment and I

408
00:27:14 --> 00:27:22
plot your average reaction time
in whatever the task is and I

409
00:27:22 --> 00:27:28
plot your error rate, what I
will find across individuals is

410
00:27:28 --> 00:27:35
data that are noisy,
but look like this.

411
00:27:35 --> 00:27:40
The faster you go the
more errors you make.

412
00:27:40 --> 00:27:46
It's known as a speed accuracy
trade-off in dull literature,

413
00:27:46 --> 00:27:53
which you will notice has an
interesting set of initials, so

414
00:27:53 --> 00:27:56
SAT gets written about in my
trade all the time, but has

415
00:27:56 --> 00:27:58
nothing to do with standardized
tests, it's a speed

416
00:27:58 --> 00:27:59
accuracy trade-off.
 

417
00:27:59 --> 00:28:02
But it will produce a
negative correlation.

418
00:28:02 --> 00:28:04
I think I put one of those
on the handout, too.

419
00:28:04 --> 00:28:06
What's the correlation there?
 

420
00:28:06 --> 00:28:08
AUDIENCE: Negative 0.52.
 

421
00:28:08 --> 00:28:12
PROFESSOR: OK, so negative
0.52 meaning a pretty good

422
00:28:12 --> 00:28:17
relationship, but going in
this negative direction.

423
00:28:17 --> 00:28:24
You will often see in papers
r squared rather than r.

424
00:28:24 --> 00:28:28
r squared of course
is always positive.

425
00:28:28 --> 00:28:34
So r squared here would be
what, about 0.26 or something?

426
00:28:34 --> 00:28:37
The reason people use r squared
is it turns out that that gives

427
00:28:37 --> 00:28:39
you the percentage of
the variance that

428
00:28:39 --> 00:28:41
you're explaining.
 

429
00:28:41 --> 00:28:47
So if you've got a correlation
of 0.5 you can explain a

430
00:28:47 --> 00:28:49
quarter of the variability.
 

431
00:28:49 --> 00:28:53
 
 

432
00:28:53 --> 00:28:57
If you know one you know about
where a quarter of the

433
00:28:57 --> 00:28:59
variability would come from in
the other variable

434
00:28:59 --> 00:29:00
in this case.
 

435
00:29:00 --> 00:29:02
Now it should say on the
handout, oh actually I think

436
00:29:02 --> 00:29:05
I put the answer on the
handout this time too.

437
00:29:05 --> 00:29:08
 
 

438
00:29:08 --> 00:29:09
I'm not sure it's really the
most important thing to know

439
00:29:09 --> 00:29:13
about correlation, but the
thing that gets lost in

440
00:29:13 --> 00:29:17
discussions in psychology all
the time, certainly in

441
00:29:17 --> 00:29:21
discussions of intelligence all
the time about correlation is

442
00:29:21 --> 00:29:25
that correlation does not
tell you about causality.

443
00:29:25 --> 00:29:29
It is extremely tempting, it's
not extremely tempting in

444
00:29:29 --> 00:29:33
these examples, right?
 

445
00:29:33 --> 00:29:38
Does inches tells you
about centimeter?

446
00:29:38 --> 00:29:43
Yeah, but not because
inches cause centimeters--

447
00:29:43 --> 00:29:45
that's stupid.
 

448
00:29:45 --> 00:29:53
But it's really tempting to
infer directly from nothing

449
00:29:53 --> 00:29:58
but correlational data,
to infer causality.

450
00:29:58 --> 00:30:02
I put fertilizer on the field,
the tomato plants grow bigger,

451
00:30:02 --> 00:30:14
so it I plot amount of
fertilizer in yellow now--

452
00:30:14 --> 00:30:22
cool-- against height of tomato
plants I presumably get data

453
00:30:22 --> 00:30:26
that look like this, some
nice positive correlation.

454
00:30:26 --> 00:30:31
I may have a notion, I may even
have a correct notion that the

455
00:30:31 --> 00:30:37
fertilizer is the cause of this
change in the height of the

456
00:30:37 --> 00:30:40
tomato plants, but just these
data don't tell me that.

457
00:30:40 --> 00:30:42
I would need more.
 

458
00:30:42 --> 00:30:47
I'd get exactly the same data
if I flipped the axes, right?

459
00:30:47 --> 00:30:50
Height of tomato
plants, fertilizer.

460
00:30:50 --> 00:30:51
Get exactly the same data.
 

461
00:30:51 --> 00:30:54
The height of the tomato
plant causes how much

462
00:30:54 --> 00:30:56
fertilizer I put on it.
 

463
00:30:56 --> 00:30:59
No, that would be stupid.
 

464
00:30:59 --> 00:31:04
You have to impose a theory
on your correlational data.

465
00:31:04 --> 00:31:07
The correlation's just math.
 

466
00:31:07 --> 00:31:08
It's not by itself a theory.
 

467
00:31:08 --> 00:31:12
It can be used to support
causal theories, but it is not

468
00:31:12 --> 00:31:15
by itself a causal theory.
 

469
00:31:15 --> 00:31:21
I'll try to point out later
where this runs into trouble.

470
00:31:21 --> 00:31:26
OK, so we can get
correlational data.

471
00:31:26 --> 00:31:33
Correlational data are very
important data in the study of

472
00:31:33 --> 00:31:38
variability in intelligence, so
you might get data like the

473
00:31:38 --> 00:31:52
following: let's plot parent's
IQ against child's IQ.

474
00:31:52 --> 00:31:55
 
 

475
00:31:55 --> 00:32:02
What you'll get is another one
of these clouds-- I think it

476
00:32:02 --> 00:32:04
actually says on the handout
that the r value is

477
00:32:04 --> 00:32:06
about 0.5 for that.
 

478
00:32:06 --> 00:32:09
So it is the case that if you
know the parents IQ you know

479
00:32:09 --> 00:32:14
something about the child's IQ.
 

480
00:32:14 --> 00:32:24
And the goal is to figure
out why, where's that

481
00:32:24 --> 00:32:27
relationship coming from?
 

482
00:32:27 --> 00:32:30
The obvious temptation of
course is to think that the

483
00:32:30 --> 00:32:37
answer is, well, the parents
have good genes or bad genes--

484
00:32:37 --> 00:32:39
they have some amount of
intelligence coded into their

485
00:32:39 --> 00:32:42
genes, they pass it
onto their kids.

486
00:32:42 --> 00:32:46
To understand why it's not
completely trivial recognize

487
00:32:46 --> 00:32:52
that parental wealth is
correlated with child's wealth

488
00:32:52 --> 00:32:55
not because there's money
coded into the genes.

489
00:32:55 --> 00:32:58
It is true that the richer your
parents are the richer you're

490
00:32:58 --> 00:33:07
likely to be, but the causes--
you do inherit that, but

491
00:33:07 --> 00:33:11
in a different kind of
non-genetic sort of way.

492
00:33:11 --> 00:33:16
 
 

493
00:33:16 --> 00:33:19
As I say, the simple story has
been to try to partition the

494
00:33:19 --> 00:33:25
variance into a genetic
component and a

495
00:33:25 --> 00:33:29
environmental component.
 

496
00:33:29 --> 00:33:34
The simple version of that
leaves out an important piece

497
00:33:34 --> 00:33:39
of it, which is that if you do
an analysis of variance--

498
00:33:39 --> 00:33:41
everybody should do an analysis
of variance sometime because

499
00:33:41 --> 00:33:44
the computer will do it
for you on your data now.

500
00:33:44 --> 00:33:49
Everybody should really do one
by hand-- back when we were

501
00:33:49 --> 00:33:53
young we had to do them by
hand, which took a long time.

502
00:33:53 --> 00:33:56
But anyway, when you do
that, suppose you've got

503
00:33:56 --> 00:33:59
two variables, like a
genetic component, an

504
00:33:59 --> 00:34:01
environmental component.
 

505
00:34:01 --> 00:34:05
An analysis of variance will
tell you-- this statistical

506
00:34:05 --> 00:34:08
test, think this much of the
variance is due to this and

507
00:34:08 --> 00:34:09
this much is due to this.
 

508
00:34:09 --> 00:34:13
But it'll also give you an
interaction term to say

509
00:34:13 --> 00:34:19
that the genetics and the
environment might interact

510
00:34:19 --> 00:34:20
in some fashion.
 

511
00:34:20 --> 00:34:23
Interestingly that term never
gets introduced into these

512
00:34:23 --> 00:34:27
calculations-- doesn't quite
get introduced in these

513
00:34:27 --> 00:34:30
calculations of heritability,
but let me try to give you

514
00:34:30 --> 00:34:32
a feeling for why
that's important.

515
00:34:32 --> 00:34:36
Let's move off of intelligence
and ask about a sort of

516
00:34:36 --> 00:34:38
a personality variable.
 

517
00:34:38 --> 00:34:45
How anxious do tests make you?
 

518
00:34:45 --> 00:34:49
You've now taken a bunch of MIT
tests, how anxious do you get?

519
00:34:49 --> 00:34:54
Well, let's develop a little
variance partitioning

520
00:34:54 --> 00:34:56
theory here.
 

521
00:34:56 --> 00:34:59
Let's not develop that theory,
let's develop this theory.

522
00:34:59 --> 00:35:02
 
 

523
00:35:02 --> 00:35:06
Whoops-- oh this looks
like a great theory.

524
00:35:06 --> 00:35:12
Get rid of that theory, that's
got too much fancy stuff in it.

525
00:35:12 --> 00:35:15
 
 

526
00:35:15 --> 00:35:17
We will have a simpler theory.
 

527
00:35:17 --> 00:35:23
So the simple theory might be
that the total variance, the

528
00:35:23 --> 00:35:31
total anxiety is a function of
the variance due to you-- to

529
00:35:31 --> 00:35:35
your personality-- are you an
anxious person or an

530
00:35:35 --> 00:35:36
unanxious person?
 

531
00:35:36 --> 00:35:39
And the variance
due to the class.

532
00:35:39 --> 00:35:42
 
 

533
00:35:42 --> 00:35:51
You know, if you're taking
introduction to clay ashtrays--

534
00:35:51 --> 00:35:56
make you that anxious and if
you're taking advanced

535
00:35:56 --> 00:36:03
thermonuclear chemical integral
thermodynamical bio-something

536
00:36:03 --> 00:36:06
or other and you skipped all
the prereqs- yeah, it makes

537
00:36:06 --> 00:36:09
you a little anxious.
 

538
00:36:09 --> 00:36:14
And you could try partitioning
your variance into

539
00:36:14 --> 00:36:15
these two components.
 

540
00:36:15 --> 00:36:19
But it turns out that's
not where the action is.

541
00:36:19 --> 00:36:25
The action in anxiety about
tests is all in the interaction

542
00:36:25 --> 00:36:30
term, which we can call you
crossed with class

543
00:36:30 --> 00:36:31
interaction term.
 

544
00:36:31 --> 00:36:39
Because how anxious you are
depends on how you are

545
00:36:39 --> 00:36:42
doing in this class.
 

546
00:36:42 --> 00:36:45
You-- know I'm sure it matters
if you're basically a nervous

547
00:36:45 --> 00:36:50
person or not, but you're going
to be nervous in intro psych if

548
00:36:50 --> 00:36:53
the midterm's coming up and
you never cracked the book.

549
00:36:53 --> 00:37:01
You might be less nervous in
calculus because you're good.

550
00:37:01 --> 00:37:06
Your neighbor might have
memorized Gleitman and

551
00:37:06 --> 00:37:09
forgotten to show up in
calculus and have the flip

552
00:37:09 --> 00:37:13
anxiety, the anxiety is
dependant on the interaction.

553
00:37:13 --> 00:37:16
And those interaction term, I
mean, you will almost always

554
00:37:16 --> 00:37:21
get a nice nod to the notion
that of course we know in

555
00:37:21 --> 00:37:27
something like intelligent that
the environment and genetics

556
00:37:27 --> 00:37:32
interact, but we will
typically not get much

557
00:37:32 --> 00:37:34
more than a nod at that.
 

558
00:37:34 --> 00:37:37
You then get simple minded
statements about just how

559
00:37:37 --> 00:37:38
heritable something is.
 

560
00:37:38 --> 00:37:41
Let me give you an example from
the intelligence literature

561
00:37:41 --> 00:37:46
from a new and I gather
somewhat controversial study--

562
00:37:46 --> 00:37:48
controversial in the sense that
there are other people who

563
00:37:48 --> 00:37:54
claim that the methodology is
flawed and that they don't

564
00:37:54 --> 00:37:56
believe the results.
 

565
00:37:56 --> 00:37:59
But this is new enough that
we don't know how it shakes

566
00:37:59 --> 00:38:02
out, but it makes the point
about the importance of

567
00:38:02 --> 00:38:05
interaction terms here.
 

568
00:38:05 --> 00:38:13
Suppose you calculate how
heritable intelligence is, but

569
00:38:13 --> 00:38:17
we're going to do it separately
as a function of, oh

570
00:38:17 --> 00:38:19
here, let's introduce
a little more jargon.

571
00:38:19 --> 00:38:23
SES stands for
socioeconomic status.

572
00:38:23 --> 00:38:26
It's the fancy way of asking
how much money and other

573
00:38:26 --> 00:38:28
resources you've got.
 

574
00:38:28 --> 00:38:39
If you look at high SES kids
and ask, how much of a genetic

575
00:38:39 --> 00:38:43
component is there to IQ?
 

576
00:38:43 --> 00:38:48
You get estimates, as I recall,
it's something like 0.6,

577
00:38:48 --> 00:38:49
0.7 something like that.
 

578
00:38:49 --> 00:38:53
A relatively high number
for that H, that

579
00:38:53 --> 00:38:54
heritability number.
 

580
00:38:54 --> 00:39:02
If you look at low SES kids, in
this particular study it was

581
00:39:02 --> 00:39:05
dramatically lower, about 0.1.
 

582
00:39:05 --> 00:39:06
What's going on there?
 

583
00:39:06 --> 00:39:13
Could it possibly be the case
that the genetics are operating

584
00:39:13 --> 00:39:17
differently at the low end of
the economic scale than at the

585
00:39:17 --> 00:39:18
high end of the economic scale?
 

586
00:39:18 --> 00:39:23
I mean, this implies that if
you plot something like

587
00:39:23 --> 00:39:30
parent's IQ against kid's IQ--
one of the ways of looking for

588
00:39:30 --> 00:39:36
a genetic contribution-- that
at the low end of the economic

589
00:39:36 --> 00:39:43
scale that the data look like a
cloud and at the high end they

590
00:39:43 --> 00:39:48
look closer to the
line kind of data.

591
00:39:48 --> 00:39:50
That's very odd.
 

592
00:39:50 --> 00:39:53
What really seems to be going
on is an interaction between

593
00:39:53 --> 00:39:57
environment and genetics
influencing this

594
00:39:57 --> 00:40:00
heritability thing.
 

595
00:40:00 --> 00:40:02
What's going on?
 

596
00:40:02 --> 00:40:07
Well, here look at this
equation and ask yourself what

597
00:40:07 --> 00:40:14
happens if you drive the
environmental variance to zero?

598
00:40:14 --> 00:40:20
Well, you're going to
drive this up towards 1.

599
00:40:20 --> 00:40:21
It's just going to be the
genetic variance over

600
00:40:21 --> 00:40:24
the genetic variance.
 

601
00:40:24 --> 00:40:32
It may be that for the point of
view of intelligence or what's

602
00:40:32 --> 00:40:36
measured on IQ tests, that by
the time you get into the

603
00:40:36 --> 00:40:40
middle class the environment
is largely homogeneous.

604
00:40:40 --> 00:40:45
Everybody gets fed, everybody
goes to school, everybody has

605
00:40:45 --> 00:40:51
books, and everybody has
access to medical care.

606
00:40:51 --> 00:40:54
Most people come from family
situations that are at least

607
00:40:54 --> 00:40:57
not vastly problematic.
 

608
00:40:57 --> 00:41:01
At the lowest end of the
socioeconomic scale

609
00:41:01 --> 00:41:02
that's not true.
 

610
00:41:02 --> 00:41:05
It's not true that everybody
has the same access to

611
00:41:05 --> 00:41:09
resources and this
environmental factor makes it

612
00:41:09 --> 00:41:13
much larger with the result
that this measure of the

613
00:41:13 --> 00:41:17
apparent heritability, the
genetic component if you like,

614
00:41:17 --> 00:41:19
seems to get bigger.
 

615
00:41:19 --> 00:41:27
So you see how the interaction
can end up being important.

616
00:41:27 --> 00:41:32
 
 

617
00:41:32 --> 00:41:35
All right, let's go back to
this example of parents and

618
00:41:35 --> 00:41:39
kids across the population as
a whole that relationship

619
00:41:39 --> 00:41:42
gives you an r squared
value of about 0.5.

620
00:41:42 --> 00:41:44
What does that mean?
 

621
00:41:44 --> 00:41:50
Well, we don't know really
because while you share some

622
00:41:50 --> 00:41:56
genetic material with your
parents, assuming you're not an

623
00:41:56 --> 00:42:02
adopted child-- we'll come back
to that in a minute-- if you

624
00:42:02 --> 00:42:06
share genetic material with
your biological parents, if you

625
00:42:06 --> 00:42:07
were raised with your
biological parents you

626
00:42:07 --> 00:42:13
also share a lot of
environment with them.

627
00:42:13 --> 00:42:16
The two factors co-vary.
 

628
00:42:16 --> 00:42:19
In order to sort of separate
this out what you want to do

629
00:42:19 --> 00:42:22
is you want to get the two
factors to vary at least

630
00:42:22 --> 00:42:23
somewhat independently.
 

631
00:42:23 --> 00:42:26
Now the little table on page
3 of the handout gives you

632
00:42:26 --> 00:42:28
one effort to do that.
 

633
00:42:28 --> 00:42:33
Let's look at sibling pairs
who vary systematically in

634
00:42:33 --> 00:42:35
the amount of genetic
material they share.

635
00:42:35 --> 00:42:42
So identical twins are
genetically identical and they

636
00:42:42 --> 00:42:48
have very high correlations
between their IQs, a

637
00:42:48 --> 00:42:50
correlation of around 0.9.
 

638
00:42:50 --> 00:42:54
Fraternal twins-- same sex
fraternal twins I should've

639
00:42:54 --> 00:43:00
added-- have a correlation that
drops to about 0.6, which tells

640
00:43:00 --> 00:43:07
you that environment, well,
they are only as related

641
00:43:07 --> 00:43:11
as standard siblings.
 

642
00:43:11 --> 00:43:14
And so you reduce that genetic
component, you reduce

643
00:43:14 --> 00:43:16
the correlation.
 

644
00:43:16 --> 00:43:20
Siblings, age different
siblings- standard brother

645
00:43:20 --> 00:43:23
sister, brother brother, kind
of pairs have a correlation of

646
00:43:23 --> 00:43:29
0.5 and if you have unrelated
children raised in the same

647
00:43:29 --> 00:43:34
family that correlation
drops to about 0.2.

648
00:43:34 --> 00:43:43
So that's certainly indicates a
genetic contribution to IQ, but

649
00:43:43 --> 00:43:49
it's not entirely clear what's
going on because again,

650
00:43:49 --> 00:43:53
environment and genetics are
co-varying; one with the other.

651
00:43:53 --> 00:43:57
Identical twins get treated
more similarly, they have a

652
00:43:57 --> 00:43:59
more similar environment
than fraternal twins.

653
00:43:59 --> 00:44:06
Fraternal twins by virtue of
being the same age are treated

654
00:44:06 --> 00:44:11
more similarly than age
separated siblings.

655
00:44:11 --> 00:44:15
And typically, adopted
children are treated

656
00:44:15 --> 00:44:17
somewhat differently.
 

657
00:44:17 --> 00:44:23
Children who have been adopted
into a family have experiences

658
00:44:23 --> 00:44:27
that kids born within the same
family don't have

659
00:44:27 --> 00:44:28
and vice versa.
 

660
00:44:28 --> 00:44:30
So again, there's more
of a difference there.

661
00:44:30 --> 00:44:34
So environment and genetics
are co-varying, so it's

662
00:44:34 --> 00:44:37
not a clean experiment.
 

663
00:44:37 --> 00:44:41
One way to do the clean
experiment is to take a jumbo

664
00:44:41 --> 00:44:46
jet full of identical twins at
birth, put little parachutes on

665
00:44:46 --> 00:44:52
them, fly around the world and
push them out at random.

666
00:44:52 --> 00:44:56
Come back 18 years later after
fluid intelligence has reached

667
00:44:56 --> 00:45:00
its asymptotic level, collect
all your twins and see

668
00:45:00 --> 00:45:02
what the correlation is.
 

669
00:45:02 --> 00:45:05
This is, for a variety of
reasons, a difficult experiment

670
00:45:05 --> 00:45:09
to actually do and so we
don't have the data on that.

671
00:45:09 --> 00:45:16
But there is a literature on
identical twins reared apart.

672
00:45:16 --> 00:45:19
It's not common, but
it does happen.

673
00:45:19 --> 00:45:25
It happens when either both
twins or one of a pair of twins

674
00:45:25 --> 00:45:28
gets put up for adoption.
 

675
00:45:28 --> 00:45:31
The one of a pair of twins
thing may sound a little

676
00:45:31 --> 00:45:34
strange, but this happens for
instance, typically at the

677
00:45:34 --> 00:45:38
lower end of the socioeconomic
scale when you're

678
00:45:38 --> 00:45:39
thinking, oh my goodness.
 

679
00:45:39 --> 00:45:43
I think we can just barely
manage this kid when

680
00:45:43 --> 00:45:48
he or she is born and
there's two of them.

681
00:45:48 --> 00:45:51
And so one of them gets
put up for adoption.

682
00:45:51 --> 00:45:54
It's rare, these
things are rare.

683
00:45:54 --> 00:45:57
But it does happen, and a
variety of efforts have been

684
00:45:57 --> 00:46:03
made over the years to
collect data on such.

685
00:46:03 --> 00:46:07
Some of this has less to do
with IQ than with the sort of

686
00:46:07 --> 00:46:08
general personality variables.
 

687
00:46:08 --> 00:46:12
There's a whole lot of amusing,
though god knows what to make

688
00:46:12 --> 00:46:16
of it, literature on identical
twins who only meet their

689
00:46:16 --> 00:46:21
twin-- didn't realize they had
a twin until they're adults and

690
00:46:21 --> 00:46:24
then they meet and oh my god,
they both married women named

691
00:46:24 --> 00:46:29
Gladys and they both have dogs
named Gerald Ford or

692
00:46:29 --> 00:46:30
something like that.
 

693
00:46:30 --> 00:46:33
 
 

694
00:46:33 --> 00:46:34
That's a little strange.
 

695
00:46:34 --> 00:46:37
It's a little hard to believe
that [? Gladys-ness ?]

696
00:46:37 --> 00:46:41
was wired into the genes,
but weird coincidental

697
00:46:41 --> 00:46:42
things happen.
 

698
00:46:42 --> 00:46:44
 
 

699
00:46:44 --> 00:46:48
The identical twins reared
apart stuff is again,

700
00:46:48 --> 00:46:50
not perfect data.
 

701
00:46:50 --> 00:46:53
Because for instance, twins
that are separated at birth are

702
00:46:53 --> 00:46:59
sometimes separated by going
off to live with aunt so and

703
00:46:59 --> 00:47:01
so, who lives in the next
valley or something like that.

704
00:47:01 --> 00:47:05
Is that really separated?
 

705
00:47:05 --> 00:47:06
Hard hard to know.
 

706
00:47:06 --> 00:47:08
Oh, the place where you do
these studies-- well, there's

707
00:47:08 --> 00:47:12
one thing on the handout about
the Minnesota twins study

708
00:47:12 --> 00:47:14
because those guys are at
Minnesota, but the place you

709
00:47:14 --> 00:47:17
want to do these studies
is Scandinavia.

710
00:47:17 --> 00:47:19
Not because they take your
twins apart a lot in

711
00:47:19 --> 00:47:24
Scandinavia, but because boy,
do those guys keep records.

712
00:47:24 --> 00:47:28
You get yourself some idea that
you want to know where all the

713
00:47:28 --> 00:47:33
twins who were separated at
birth are in Denmark and

714
00:47:33 --> 00:47:35
there's somebody in the Danish
bureaucracy who could get

715
00:47:35 --> 00:47:37
that answer for you.
 

716
00:47:37 --> 00:47:40
I mean, in the U.S. bureaucracy
you're lucky if they can figure

717
00:47:40 --> 00:47:42
out-- oh, I don't know-- where
your 300 tons of the Iraqi

718
00:47:42 --> 00:47:46
explosives are or something
like that, but in Denmark

719
00:47:46 --> 00:47:47
they'll know where
your stuff is.

720
00:47:47 --> 00:47:50
 
 

721
00:47:50 --> 00:47:54
The previous remark should not
be construed as being paid

722
00:47:54 --> 00:47:57
for by any particular
political campaign.

723
00:47:57 --> 00:48:01
It's just what bubbled into
my fevered brain here.

724
00:48:01 --> 00:48:06
But in any case, when you do
the identical twin reared apart

725
00:48:06 --> 00:48:13
study, people yell and holler
about these, but the best of

726
00:48:13 --> 00:48:16
these data look like
a correlation of

727
00:48:16 --> 00:48:19
around 0.7 or so.
 

728
00:48:19 --> 00:48:23
Significantly lower than the
0.9 for identical twins reared

729
00:48:23 --> 00:48:29
in the same family, but clearly
indicating some sort of a

730
00:48:29 --> 00:48:39
genetic component to what
is being measured by IQ.

731
00:48:39 --> 00:48:47
So I think it's been a very hot
topic for a variety of reasons,

732
00:48:47 --> 00:48:53
many of them not well formed
to ask how much of a genetic

733
00:48:53 --> 00:48:59
contribution there is to IQ.
 

734
00:48:59 --> 00:49:03
And it's been an answer where
the answer that you're looking

735
00:49:03 --> 00:49:05
for is heavily driven by your
politics as much as

736
00:49:05 --> 00:49:08
by your science.
 

737
00:49:08 --> 00:49:12
In part, well look
on the handout.

738
00:49:12 --> 00:49:15
There are a bunch of pitfalls
to this idea of heritability.

739
00:49:15 --> 00:49:17
Jumps down to number 3.
 

740
00:49:17 --> 00:49:20
Number 3 is perhaps the
reason that it's been

741
00:49:20 --> 00:49:22
most politically loaded.
 

742
00:49:22 --> 00:49:28
There are people and there are
groups whose IQ, on average,

743
00:49:28 --> 00:49:34
are lower than other
groups or other people.

744
00:49:34 --> 00:49:41
If you believe that there's a
strong genetic component to IQ

745
00:49:41 --> 00:49:49
and you believe that this item
3 here, that things that have

746
00:49:49 --> 00:49:56
strong heritable components are
largely unmodifiable then what

747
00:49:56 --> 00:50:04
you're saying is that if you've
got low IQ that's sort

748
00:50:04 --> 00:50:05
of your tough luck.
 

749
00:50:05 --> 00:50:09
There's nothing much that we as
a society could do about it.

750
00:50:09 --> 00:50:13
This was the thesis of--
actually, it's been a thesis of

751
00:50:13 --> 00:50:14
a repeated series of books.
 

752
00:50:14 --> 00:50:20
The most famous recent one is a
book called The Bell Curve by

753
00:50:20 --> 00:50:26
Herrnstein and Murray and the
basic argument ran like this,

754
00:50:26 --> 00:50:31
there's clear evidence
for heritability of IQ.

755
00:50:31 --> 00:50:37
IQ is not very modifiable.
 

756
00:50:37 --> 00:50:42
IQ is correlated positively
with good stuff and correlated

757
00:50:42 --> 00:50:43
negatively with bad stuff.
 

758
00:50:43 --> 00:50:46
 
 

759
00:50:46 --> 00:50:52
And that this country is
an IQ stratified country.

760
00:50:52 --> 00:50:54
Another way of putting
it is a meritocracy.

761
00:50:54 --> 00:51:00
It's not that you get born the
Duke of Cambridge or something

762
00:51:00 --> 00:51:05
like that, you get to rise to
the top because you've got this

763
00:51:05 --> 00:51:10
great IQ that gets you to MIT,
that gets you the good job and

764
00:51:10 --> 00:51:12
then you become President of
the United States or

765
00:51:12 --> 00:51:13
something like that.
 

766
00:51:13 --> 00:51:17
 
 

767
00:51:17 --> 00:51:18
Interesting.
 

768
00:51:18 --> 00:51:22
 
 

769
00:51:22 --> 00:51:26
Or you get this great IQ and
you come to MIT and you hang

770
00:51:26 --> 00:51:27
around in the infinite
corridor making [? boingy ?]

771
00:51:27 --> 00:51:29
noises.
 

772
00:51:29 --> 00:51:31
That's different.
 

773
00:51:31 --> 00:51:35
Anyway, if you take-- I don't
know, that 4 points or

774
00:51:35 --> 00:51:38
something-- you take those 4
points and you add them up,

775
00:51:38 --> 00:51:45
what you get to-- somebody over
there take a little walk down

776
00:51:45 --> 00:51:47
the hall and ask [? boingy ?]
 

777
00:51:47 --> 00:51:48
to cool it.
 

778
00:51:48 --> 00:51:51
Thank you.
 

779
00:51:51 --> 00:51:55
What you get to is the notion
that there are some people who

780
00:51:55 --> 00:51:59
are going to be at the bottom
of the stack in America.

781
00:51:59 --> 00:52:02
And that's just the way it's
going to be, you know?

782
00:52:02 --> 00:52:04
They're going to be the
criminals, they're going to get

783
00:52:04 --> 00:52:06
pregnant, they're not going to
make any money and

784
00:52:06 --> 00:52:08
that's just tough.
 

785
00:52:08 --> 00:52:09
You know, there's nothing
much to be done about it.

786
00:52:09 --> 00:52:11
I'm oversimplifying the book.
 

787
00:52:11 --> 00:52:15
It ought to be fairly obvious
that I disagree strongly

788
00:52:15 --> 00:52:17
with that thesis.
 

789
00:52:17 --> 00:52:19
But I should say, it's
not a stupid book.

790
00:52:19 --> 00:52:23
If you're interested in these
issues it's worth reading the

791
00:52:23 --> 00:52:27
book in the way that you know,
if you're on the left

792
00:52:27 --> 00:52:30
politically it's worth
listening to right-wing talk

793
00:52:30 --> 00:52:33
radio to kind of sharpen your
brain and if you're on the

794
00:52:33 --> 00:52:35
right politically it's worth
listening to left-wing talk

795
00:52:35 --> 00:52:41
radio to sharpen your brain as
long as it-- well actually talk

796
00:52:41 --> 00:52:43
radio's probably a wrong
example because it really is

797
00:52:43 --> 00:52:44
pretty stupid; most of it.
 

798
00:52:44 --> 00:52:46
Herrnstein and Murray
aren't stupid.

799
00:52:46 --> 00:52:48
I think they're wrong,
but they're not stupid.

800
00:52:48 --> 00:52:58
In any case, let me give a
non-intelligence example for

801
00:52:58 --> 00:53:02
why this pitfall of thinking
that these things are fixed

802
00:53:02 --> 00:53:06
and unalterable just
because they're inherited.

803
00:53:06 --> 00:53:08
Did it work?
 

804
00:53:08 --> 00:53:11
Oh, you weren't the person
looking for-- it's

805
00:53:11 --> 00:53:12
not the same guy.
 

806
00:53:12 --> 00:53:15
Oh, this is a lovely change
blindness thing, right?

807
00:53:15 --> 00:53:17
It's perfect.
 

808
00:53:17 --> 00:53:20
A guy went out,
the [? boingy ?]

809
00:53:20 --> 00:53:21
stopped, the guy came back.
 

810
00:53:21 --> 00:53:24
 
 

811
00:53:24 --> 00:53:25
Unfortunately I guess,
the [? boingy ?]

812
00:53:25 --> 00:53:28
guy has killed the
guy who went out.

813
00:53:28 --> 00:53:29
This is really sad.
 

814
00:53:29 --> 00:53:36
We could send another one
out, but where was I?

815
00:53:36 --> 00:53:43
OK, so things can be inherited,
things can be inherited

816
00:53:43 --> 00:53:45
related to intelligence and
nevertheless quite changeable.

817
00:53:45 --> 00:53:55
PKU is a disorder where you are
unable to metabolize one of the

818
00:53:55 --> 00:54:01
basic amino acids and the
result is it produces

819
00:54:01 --> 00:54:02
zero toxins.
 

820
00:54:02 --> 00:54:04
It munches up your brain-- kids
with this disorder, it's a

821
00:54:04 --> 00:54:09
genetic birth defect type of
disorder-- kids with this

822
00:54:09 --> 00:54:13
disorder before it was
understood were condemned to

823
00:54:13 --> 00:54:15
severe mental retardation.
 

824
00:54:15 --> 00:54:19
Once it was figured out what
the problem was you could

825
00:54:19 --> 00:54:23
prevent the severe
mental retardation by

826
00:54:23 --> 00:54:24
controlling diet.
 

827
00:54:24 --> 00:54:27
Basically, by controlling
an environmental factor.

828
00:54:27 --> 00:54:30
You take the precursor out of
the diet, you don't produce the

829
00:54:30 --> 00:54:33
neurotoxins, you don't produce
the mental retardation.

830
00:54:33 --> 00:54:35
It doesn't mean that
the disorder was

831
00:54:35 --> 00:54:38
any less heritable.
 

832
00:54:38 --> 00:54:39
It's a birth defect.
 

833
00:54:39 --> 00:54:40
Are you the guy?
 

834
00:54:40 --> 00:54:41
Is he the guy?
 

835
00:54:41 --> 00:54:43
Thank you.
 

836
00:54:43 --> 00:54:44
What was it?
 

837
00:54:44 --> 00:54:47
AUDIENCE: It was just
someone walking through

838
00:54:47 --> 00:54:48
the hall making noise.
 

839
00:54:48 --> 00:54:49
He left.
 

840
00:54:49 --> 00:54:53
PROFESSOR: OK, he's not
going to need medical

841
00:54:53 --> 00:54:54
care or nothing, right?
 

842
00:54:54 --> 00:54:55
AUDIENCE: He's OK.
 

843
00:54:55 --> 00:54:56
PROFESSOR: OK, good.
 

844
00:54:56 --> 00:54:57
No, I was worried that you
might have worked him

845
00:54:57 --> 00:55:00
over or something.
 

846
00:55:00 --> 00:55:03
Thank you for taking care
of that in any case.

847
00:55:03 --> 00:55:06
 
 

848
00:55:06 --> 00:55:10
So you can have something
that's clearly genetic in

849
00:55:10 --> 00:55:16
origin, where an environmental
change might make a difference.

850
00:55:16 --> 00:55:19
PKU is simply a dramatic
example of that.

851
00:55:19 --> 00:55:20
Let me just take a look.
 

852
00:55:20 --> 00:55:27
OK, so I've already hit the
pitfall 2, the notion that

853
00:55:27 --> 00:55:30
the interaction term
might be important.

854
00:55:30 --> 00:55:34
The evidence for that might be
this low socioeconomic status,

855
00:55:34 --> 00:55:40
high socioeconomic status
influence on the apparent

856
00:55:40 --> 00:55:42
heritability of it.
 

857
00:55:42 --> 00:55:47
And I really also already hit
that first pitfall there.

858
00:55:47 --> 00:55:53
High heritability might just
mean that there wasn't much

859
00:55:53 --> 00:55:56
variation in the environment in
your particular experiment.

860
00:55:56 --> 00:56:00
If you grow all the tomato
plants in exactly the same

861
00:56:00 --> 00:56:02
field with the same water,
the same sun and the same

862
00:56:02 --> 00:56:05
fertilizer, there will still be
some variability of course.

863
00:56:05 --> 00:56:09
You'll get a heritability score
that will be near 1, but that's

864
00:56:09 --> 00:56:14
because you've driven this term
down to zero or near zero.

865
00:56:14 --> 00:56:20
And that doesn't prove that the
environment is unimportant.

866
00:56:20 --> 00:56:25
So the fact that there is a
significant genetic component

867
00:56:25 --> 00:56:30
doesn't mean there is not a
significant environmental

868
00:56:30 --> 00:56:32
component.
 

869
00:56:32 --> 00:56:36
OK, take a quick stretch,
make [? boingy ?]

870
00:56:36 --> 00:56:36
noises.
 

871
00:56:36 --> 00:56:38
AUDIENCE: [UNINTELLIGIBLE]
 

872
00:56:38 --> 00:56:38
PROFESSOR: That's good.
 

873
00:56:38 --> 00:56:39
Thank you.
 

874
00:56:39 --> 00:56:46
 
 

875
00:56:46 --> 00:56:50
AUDIENCE: Hi, we actually
decided that we were going to

876
00:56:50 --> 00:56:52
go over the midterms in class.
 

877
00:56:52 --> 00:56:52
PROFESSOR: Yes, of course.
 

878
00:56:52 --> 00:56:53
Go ahead.
 

879
00:56:53 --> 00:56:55
AUDIENCE: OK, but my class
is right after this.

880
00:56:55 --> 00:56:56
PROFESSOR: No, go ahead.
 

881
00:56:56 --> 00:56:57
Absolutely.
 

882
00:56:57 --> 00:56:59
No, this is in case there
are all those society

883
00:56:59 --> 00:57:02
for neuroscience TAs.
 

884
00:57:02 --> 00:57:03
No, by all means.
 

885
00:57:03 --> 00:57:14
I want to cover for them,
but not to discourage you.

886
00:57:14 --> 00:57:40
 
 

887
00:57:40 --> 00:57:44
Looking at the time and looking
at my notes I realize I'd

888
00:57:44 --> 00:57:46
better get cooking here.
 

889
00:57:46 --> 00:57:49
So let me cook here.
 

890
00:57:49 --> 00:57:56
What I want to do is to tell
you a little bit, well,

891
00:57:56 --> 00:57:59
let me frame this.
 

892
00:57:59 --> 00:58:04
Since I've already introduced
the Herrnstein and Murray book,

893
00:58:04 --> 00:58:12
the most controversial piece of
the Herrnstein and Murray kind

894
00:58:12 --> 00:58:15
of story is when you start
getting to group differences

895
00:58:15 --> 00:58:19
and you say, look, in America
at the present time the average

896
00:58:19 --> 00:58:24
African American IQ score is
about 10 points lower than the

897
00:58:24 --> 00:58:28
average white American score.
 

898
00:58:28 --> 00:58:30
That's data.
 

899
00:58:30 --> 00:58:35
And what you care to make of
those data is very important

900
00:58:35 --> 00:58:38
from a policy point of view.
 

901
00:58:38 --> 00:58:42
Herrnstein and Murray were
making the argument that since

902
00:58:42 --> 00:58:46
genetics has this big role and
these are things that are

903
00:58:46 --> 00:58:52
unalterable that the fact that
blacks in the U.S. also, on

904
00:58:52 --> 00:58:54
average, make less
money and so on.

905
00:58:54 --> 00:58:59
You know, hey, man, that's
just science and genetics.

906
00:58:59 --> 00:59:02
And the fact that we're a
meritocracy and good things

907
00:59:02 --> 00:59:05
like that, live with it.
 

908
00:59:05 --> 00:59:10
What I want to do first is to
describe a couple of what are

909
00:59:10 --> 00:59:13
really sort of amusing
historical anecdotes about the

910
00:59:13 --> 00:59:18
history of intelligence testing
drawn from Stephen Gould's book

911
00:59:18 --> 00:59:21
called The Mismeasure of Man, I
will answer the question

912
00:59:21 --> 00:59:25
on the handout already.
 

913
00:59:25 --> 00:59:27
What's the point of
these amusing stories?

914
00:59:27 --> 00:59:31
The point is not to make
fun of folks operating

915
00:59:31 --> 00:59:33
100 or 150 years ago.
 

916
00:59:33 --> 00:59:41
The point is to make us take
with caution our understanding

917
00:59:41 --> 00:59:45
of similar answers that
we're getting today.

918
00:59:45 --> 00:59:49
So for example, who
has a higher average

919
00:59:49 --> 00:59:52
IQ: men or women?
 

920
00:59:52 --> 00:59:55
How many vote that it's men?
 

921
00:59:55 --> 00:59:57
How many vote that it's women?
 

922
00:59:57 --> 01:00:00
How many vote that it's equal?
 

923
01:00:00 --> 01:00:03
How many vote that I ain't
touching this because I smell a

924
01:00:03 --> 01:00:09
political question when I can--
how many vote, I don't vote?

925
01:00:09 --> 01:00:10
That's the wrong answer.
 

926
01:00:10 --> 01:00:12
At least, next Tuesday.
 

927
01:00:12 --> 01:00:15
This ad paid for you by the
league of women voters.

928
01:00:15 --> 01:00:20
 
 

929
01:00:20 --> 01:00:25
Just stepping aside here, it's
sort of a pain often to vote if

930
01:00:25 --> 01:00:28
you're an undergraduate, right?
 

931
01:00:28 --> 01:00:30
Because your way away from
whatever district you supposed

932
01:00:30 --> 01:00:32
to vote in, but go and vote.
 

933
01:00:32 --> 01:00:36
If for no other reason than
nobody polled you and you can

934
01:00:36 --> 01:00:39
screw up all those polls that
claimed that they knew the

935
01:00:39 --> 01:00:42
answer-- which they don't
know-- to the election one way

936
01:00:42 --> 01:00:45
or the other by voting because
when they called your

937
01:00:45 --> 01:00:47
parents you weren't home.
 

938
01:00:47 --> 01:00:50
So anyway, but you should vote.
 

939
01:00:50 --> 01:00:54
Oh, yeah, so men and women--
the answer is that men and

940
01:00:54 --> 01:00:58
women have the same average IQ.
 

941
01:00:58 --> 01:01:03
But the interesting question
is why that's the case?

942
01:01:03 --> 01:01:03
Why is it the case?
 

943
01:01:03 --> 01:01:07
Well, back at the beginning of
the 20th century when IQ tests

944
01:01:07 --> 01:01:12
came from France to America,
how do you build an IQ test?

945
01:01:12 --> 01:01:16
What you do is you make up some
questions that you think might

946
01:01:16 --> 01:01:20
be reasonable measures of
intelligence, you give them to

947
01:01:20 --> 01:01:22
a bunch of kids because this
was originally a school testing

948
01:01:22 --> 01:01:27
kind of thing, and then you
sort of ask, are these

949
01:01:27 --> 01:01:28
sensible questions?
 

950
01:01:28 --> 01:01:30
Like, do the kids who
the teacher thinks is

951
01:01:30 --> 01:01:33
smart, do well on them?
 

952
01:01:33 --> 01:01:34
And you work from that.
 

953
01:01:34 --> 01:01:37
And so they're working on
standardizing the original

954
01:01:37 --> 01:01:41
tests and on the original
drafts of the test women,

955
01:01:41 --> 01:01:45
girls were scoring about
10 points higher than men.

956
01:01:45 --> 01:01:48
 
 

957
01:01:48 --> 01:01:51
The guys literally, who
made up the test knew

958
01:01:51 --> 01:01:52
that this was wrong.
 

959
01:01:52 --> 01:01:55
 
 

960
01:01:55 --> 01:01:59
Now it is to their credit in
the early 20th century that

961
01:01:59 --> 01:02:02
they knew a priori that the
correct answer was that

962
01:02:02 --> 01:02:04
men and women were equal.
 

963
01:02:04 --> 01:02:07
It would have been no big
surprise at that point to have

964
01:02:07 --> 01:02:10
them figure out that men should
have been scoring higher, but

965
01:02:10 --> 01:02:13
they knew that the answer was
that men and women were equal.

966
01:02:13 --> 01:02:17
And here's what they did, you
do what's known as an item

967
01:02:17 --> 01:02:18
analysis on your test.
 

968
01:02:18 --> 01:02:23
You look at all the questions,
you say, oh look, guys did much

969
01:02:23 --> 01:02:27
better on this question
than women did.

970
01:02:27 --> 01:02:31
Oh look, women did much better
on this question than guys did.

971
01:02:31 --> 01:02:32
You know what we're going
to do with this question?

972
01:02:32 --> 01:02:35
We're going to throw
it off the exam.

973
01:02:35 --> 01:02:39
You know, we're making up the
exam as we go along and so they

974
01:02:39 --> 01:02:44
tooled the exam to get rid of
that 10 point difference and

975
01:02:44 --> 01:02:46
subsequent tests are
standardized against

976
01:02:46 --> 01:02:47
the older tests.
 

977
01:02:47 --> 01:02:50
That's why men and women
have the same IQ.

978
01:02:50 --> 01:02:50
Yes?
 

979
01:02:50 --> 01:02:53
AUDIENCE: How do you factor
motivation into this?

980
01:02:53 --> 01:02:55
Aren't little girls more
focused in general--

981
01:02:55 --> 01:02:58
PROFESSOR: Oh, there's a whole
lot of-- you know, I don't know

982
01:02:58 --> 01:03:02
when it is that guys
finally get focused.

983
01:03:02 --> 01:03:07
 
 

984
01:03:07 --> 01:03:10
And I don't have much direct
experience with this having

985
01:03:10 --> 01:03:15
nearly had 3 unfocused
guys myself in my family.

986
01:03:15 --> 01:03:18
I always wanted one of
those focused girls

987
01:03:18 --> 01:03:20
who just-- anyway.
 

988
01:03:20 --> 01:03:24
There's all sorts that
stuff like that.

989
01:03:24 --> 01:03:27
 
 

990
01:03:27 --> 01:03:29
That's a whole other course.
 

991
01:03:29 --> 01:03:34
But what they did was they made
the difference go away by

992
01:03:34 --> 01:03:37
manipulating the test.
 

993
01:03:37 --> 01:03:42
Now these tests were hardly
the first efforts to

994
01:03:42 --> 01:03:43
study intelligence.
 

995
01:03:43 --> 01:03:49
Actually, in an interesting
circular movement, these days

996
01:03:49 --> 01:03:53
there's great interest again
in looking at the brain and

997
01:03:53 --> 01:03:55
saying, is there something
about smart brains that's

998
01:03:55 --> 01:03:59
different from less
smart brains?

999
01:03:59 --> 01:04:02
We can do that with the renewed
interest comes from the fact

1000
01:04:02 --> 01:04:07
that you can now go and look at
brains in walking,

1001
01:04:07 --> 01:04:09
talking people.
 

1002
01:04:09 --> 01:04:15
The previous boom in looking at
brains was in the 19th century.

1003
01:04:15 --> 01:04:19
The reason Binet developed his
test was that looking at brains

1004
01:04:19 --> 01:04:21
was only good at autopsy.
 

1005
01:04:21 --> 01:04:23
You're not going to figure out
if your kid needs help in

1006
01:04:23 --> 01:04:25
school by cutting his head open
and looking at his

1007
01:04:25 --> 01:04:26
brains any good.
 

1008
01:04:26 --> 01:04:28
In the 19th century it's just
not a really practical

1009
01:04:28 --> 01:04:33
solution, but people like Paul
Broca of Broca's area-- famous

1010
01:04:33 --> 01:04:38
French neurologist, spent a lot
of time looking at the brains

1011
01:04:38 --> 01:04:41
of their deceased colleagues
and others to see what

1012
01:04:41 --> 01:04:43
made people talented.
 

1013
01:04:43 --> 01:04:48
Broca's doctrine, translated
from the French, but quoting is

1014
01:04:48 --> 01:04:51
that all other things being
equal, there's a remarkable

1015
01:04:51 --> 01:04:54
relationship between the
development of intelligence and

1016
01:04:54 --> 01:04:55
the volume of the brain.
 

1017
01:04:55 --> 01:05:00
He was interested in
the size of the brain.

1018
01:05:00 --> 01:05:02
To a first approximation
he's got to be right.

1019
01:05:02 --> 01:05:07
One of the reasons there are
no chickens enrolled as MIT

1020
01:05:07 --> 01:05:11
undergraduates is because
little chicken brains just

1021
01:05:11 --> 01:05:18
don't cut it when it comes
to surviving at MIT.

1022
01:05:18 --> 01:05:22
So having quantity of
brain really does

1023
01:05:22 --> 01:05:26
make some difference.
 

1024
01:05:26 --> 01:05:33
So what he did was to look
at the sizes of brains.

1025
01:05:33 --> 01:05:40
And his conclusion was that in
general, the brain is larger in

1026
01:05:40 --> 01:05:44
mature adults than in the
elderly, in man than in women,

1027
01:05:44 --> 01:05:48
in eminent men than in men of
mediocre talent, and in

1028
01:05:48 --> 01:05:52
superior races than
in inferior races.

1029
01:05:52 --> 01:05:57
This reflects very much the
biases of 19th century

1030
01:05:57 --> 01:06:00
European, but in fact
that's sort of the point.

1031
01:06:00 --> 01:06:04
It's not to say ooh, Broca was
an evil, nasty man, but you

1032
01:06:04 --> 01:06:07
should be a little suspicious
when your science places you

1033
01:06:07 --> 01:06:10
at the pinnacle of creation.
 

1034
01:06:10 --> 01:06:14
That should be a little warning
bell that goes, oh, the

1035
01:06:14 --> 01:06:19
inferior races by the way were
for Broca, were the Chinese,

1036
01:06:19 --> 01:06:22
the Hindus, that's
subcontinental

1037
01:06:22 --> 01:06:25
India, and blacks.
 

1038
01:06:25 --> 01:06:28
I can't remember if Jews made
it onto his list or not.

1039
01:06:28 --> 01:06:32
But in any case, it was sort of
the 19th century collection

1040
01:06:32 --> 01:06:35
of not white males.
 

1041
01:06:35 --> 01:06:38
But how did Broca get
to this conclusion?

1042
01:06:38 --> 01:06:41
Well, he weighed a lot of
brains and what he discovered

1043
01:06:41 --> 01:06:43
was in fact, it's true.
 

1044
01:06:43 --> 01:06:48
That female brains are on
average a little lighter than

1045
01:06:48 --> 01:06:51
male brain, on average.
 

1046
01:06:51 --> 01:06:54
All right, so?
 

1047
01:06:54 --> 01:06:56
Chicken brains are lighter than
your brain, chickens don't go

1048
01:06:56 --> 01:07:02
to MIT; female brains are
lighter than male brains, but

1049
01:07:02 --> 01:07:07
he also found that French
brains were lighter

1050
01:07:07 --> 01:07:10
than German brains.
 

1051
01:07:10 --> 01:07:13
Broca was French.
 

1052
01:07:13 --> 01:07:15
So he wrote,
 

1053
01:07:15 --> 01:07:19
"Germans ingest a quantity
of solid food and drink

1054
01:07:19 --> 01:07:22
far greater than that
which satisfies us.

1055
01:07:22 --> 01:07:25
 
 

1056
01:07:25 --> 01:07:28
This, joined with his
consumption of beer makes the

1057
01:07:28 --> 01:07:32
German so much more fleshy
than the Frenchman.

1058
01:07:32 --> 01:07:34
So much more so that the
relation of brain size to total

1059
01:07:34 --> 01:07:38
mass far from being superior to
ours seems to me, on the

1060
01:07:38 --> 01:07:42
contrary, to be inferior."
 

1061
01:07:42 --> 01:07:47
Well, what he's doing
is not stupid.

1062
01:07:47 --> 01:07:51
He's correcting for body size
and there's a very interesting

1063
01:07:51 --> 01:07:56
graph that you may have
seen someplace or other.

1064
01:07:56 --> 01:08:07
If you plot body size weight
against brain weight-- I can't

1065
01:08:07 --> 01:08:10
remember if it works just for
mammals or for sort of

1066
01:08:10 --> 01:08:15
everybody, but anyway-- let's
claim it's mammals, you get a

1067
01:08:15 --> 01:08:17
very tight correlation.
 

1068
01:08:17 --> 01:08:22
All the way up from
mouse to whale.

1069
01:08:22 --> 01:08:25
 
 

1070
01:08:25 --> 01:08:30
Except that humans are
up here somewhere.

1071
01:08:30 --> 01:08:32
They're off the curve.
 

1072
01:08:32 --> 01:08:35
So the argument is people
always-- a lot of people

1073
01:08:35 --> 01:08:37
think-- whales, they must be
these philosophers of the

1074
01:08:37 --> 01:08:39
deep, they got a huge brain.
 

1075
01:08:39 --> 01:08:42
But nobody quite understands
what they're doing with that

1076
01:08:42 --> 01:08:46
great big brain, but they
do lie on this function.

1077
01:08:46 --> 01:08:50
Humans have a very big brain
relative to their body size.

1078
01:08:50 --> 01:08:54
So this is the point at which
some-- typically, woman--

1079
01:08:54 --> 01:08:57
in the class is supposed to
raise their hand and say?

1080
01:08:57 --> 01:08:59
AUDIENCE: Females are smaller.
 

1081
01:08:59 --> 01:09:00
PROFESSOR: Yeah.
 

1082
01:09:00 --> 01:09:04
Females are smaller than males,
so if we correct for body

1083
01:09:04 --> 01:09:07
weight-- well, Broca wasn't
stupid, he knew that.

1084
01:09:07 --> 01:09:11
And he wrote, "we might ask, if
the small size of the female

1085
01:09:11 --> 01:09:15
brain depends exclusively on
the small size of her body.

1086
01:09:15 --> 01:09:18
We must not forget, says
Broca, that women are on

1087
01:09:18 --> 01:09:20
average, a little less
intelligent than men.

1088
01:09:20 --> 01:09:23
We are therefore permitted to
suppose that the relatively

1089
01:09:23 --> 01:09:27
small size of the female brain
depends in part on her physical

1090
01:09:27 --> 01:09:31
inferiority and in part, on her
intellectual inferiority." Now

1091
01:09:31 --> 01:09:37
those 2 quotes from Broca
are taken from completely

1092
01:09:37 --> 01:09:38
different publications.
 

1093
01:09:38 --> 01:09:40
Even Broca might have noticed
if you put them right next to

1094
01:09:40 --> 01:09:44
each other that there's a
certain conflict there.

1095
01:09:44 --> 01:09:46
Again, the point isn't to
say, Broca was a mean,

1096
01:09:46 --> 01:09:48
nasty, stupid man.
 

1097
01:09:48 --> 01:09:50
There's no evidence for any of
that, but there's clear

1098
01:09:50 --> 01:09:54
evidence that what he thought
he knew ahead of time was

1099
01:09:54 --> 01:10:01
influencing how he was
reading his data.

1100
01:10:01 --> 01:10:04
The brain weighing endeavor
sort of fell apart because it

1101
01:10:04 --> 01:10:05
didn't work all that well.
 

1102
01:10:05 --> 01:10:09
You know, people like the
scientist who gives his name to

1103
01:10:09 --> 01:10:13
that unit of magnetic whatever
it is and Gaussian curves and

1104
01:10:13 --> 01:10:15
stuff like that turned out
to have a small brain.

1105
01:10:15 --> 01:10:18
Had a lot of wrinkles
in it though.

1106
01:10:18 --> 01:10:20
Lenin, the Communists were
always big on carving up the

1107
01:10:20 --> 01:10:22
brains of their ex-leaders.
 

1108
01:10:22 --> 01:10:27
Lenin was reputed to have a
cortex-- we all have a cortex,

1109
01:10:27 --> 01:10:31
its got 6 cell layers in it--
Lenin's allegedly had 7.

1110
01:10:31 --> 01:10:38
 
 

1111
01:10:38 --> 01:10:42
Again, the point of these
stories is not to say that

1112
01:10:42 --> 01:10:45
we're smart and they were all
stupid and silly people, the

1113
01:10:45 --> 01:10:49
point is to say that people
tend to impose their ideas on

1114
01:10:49 --> 01:10:53
their data as well as imposing
their data on their ideas and

1115
01:10:53 --> 01:10:56
you've got to keep an
eye open for that.

1116
01:10:56 --> 01:10:59
In the remaining time let's ask
a bit about this question

1117
01:10:59 --> 01:11:04
about, suppose we really do
think that more IQ is better.

1118
01:11:04 --> 01:11:09
Is there any evidence
that we can get more?

1119
01:11:09 --> 01:11:12
You know, is there a
way to get more IQ?

1120
01:11:12 --> 01:11:17
The answer appears to be yes,
though we're not-- some of

1121
01:11:17 --> 01:11:18
it's kind of mysterious.
 

1122
01:11:18 --> 01:11:23
The mysterious bit is on the
handout on the back page I see.

1123
01:11:23 --> 01:11:27
Well, on page 4 at least
as the Flynn effect.

1124
01:11:27 --> 01:11:30
Flynn is a political scientist,
James Flynn is a political

1125
01:11:30 --> 01:11:35
scientist sitting down at the
University of Otago at the

1126
01:11:35 --> 01:11:39
bottom of New Zealand, which
nobody had ever heard of until

1127
01:11:39 --> 01:11:43
the "Lord of the Rings" movies,
but you don't get further away

1128
01:11:43 --> 01:11:45
unless you go to Antarctica.
 

1129
01:11:45 --> 01:11:51
But what he did was he took a
look just at raw IQ statistics,

1130
01:11:51 --> 01:12:00
initially in the Pacific Rim
countries in the period shortly

1131
01:12:00 --> 01:12:02
before and since World War 2--
couple of generations worth.

1132
01:12:02 --> 01:12:05
And what he found-- well, it's
summed up in the title of his

1133
01:12:05 --> 01:12:09
paper-- massive IQ gains
in these countries.

1134
01:12:09 --> 01:12:15
So for example, back a decade
or two ago when the Japanese

1135
01:12:15 --> 01:12:20
economy was booming, people
liked to point out-- people

1136
01:12:20 --> 01:12:24
given to these group IQ
arguments liked to point out

1137
01:12:24 --> 01:12:27
that the average Japanese IQ
was about 10 points higher than

1138
01:12:27 --> 01:12:31
the average American IQ and the
reasons they're eating our

1139
01:12:31 --> 01:12:36
lunch is because they've been
breeding with each other

1140
01:12:36 --> 01:12:37
for years, you know?
 

1141
01:12:37 --> 01:12:42
And they're just smarter people
than we are and we're all

1142
01:12:42 --> 01:12:44
doomed to serve the
Japanese forever.

1143
01:12:44 --> 01:12:47
Well, that argument has kind of
gone away since they went into

1144
01:12:47 --> 01:12:49
a 10-year recession
or something.

1145
01:12:49 --> 01:12:55
But the more interesting point,
from our point of view, is that

1146
01:12:55 --> 01:12:59
apparently they got smart
really fast because before

1147
01:12:59 --> 01:13:02
World War 2 the average
Japanese IQ was about 10 points

1148
01:13:02 --> 01:13:04
lower than the U.S. IQ.
 

1149
01:13:04 --> 01:13:10
There is no genetic story
that makes that work.

1150
01:13:10 --> 01:13:13
And this happens in
Pacific Rim country after

1151
01:13:13 --> 01:13:16
Pacific Rim country.
 

1152
01:13:16 --> 01:13:21
That after World War 2 IQs
just go way up and then

1153
01:13:21 --> 01:13:23
subsequently, it turns out
what's happening all over the

1154
01:13:23 --> 01:13:29
world in fact, it had been
mapped in the U.S. because IQ

1155
01:13:29 --> 01:13:32
tests keep getting renormed.
 

1156
01:13:32 --> 01:13:37
The average IQ in the U.S.
keeps drifting higher and then

1157
01:13:37 --> 01:13:42
because average IQ is defined
as 100 you renorm the test so

1158
01:13:42 --> 01:13:46
that the average is 100 again.
 

1159
01:13:46 --> 01:13:49
Flynn wrote a wonderful paper,
which I didn't cite on here,

1160
01:13:49 --> 01:13:52
but if anybody wants send me an
e-mail, where he was having fun

1161
01:13:52 --> 01:13:57
with these statistics proving
that if you used these

1162
01:13:57 --> 01:14:01
statistics literally you would
conclude that our founding

1163
01:14:01 --> 01:14:06
fathers were all congenital
idiots and were probably too

1164
01:14:06 --> 01:14:10
dumb to walk across the street
if you just extrapolate back.

1165
01:14:10 --> 01:14:12
Something very odd is going on.
 

1166
01:14:12 --> 01:14:13
It's not clear what it is.
 

1167
01:14:13 --> 01:14:16
One of the thoughts had been
that it was simply nutrition.

1168
01:14:16 --> 01:14:18
Pacific Rim nutrition got a
lot better after World War

1169
01:14:18 --> 01:14:22
2 and maybe better food
makes better brains.

1170
01:14:22 --> 01:14:24
That seems to be true, but
there's counter evidence.

1171
01:14:24 --> 01:14:30
For example, there's no dip in
the Dutch average IQ for the

1172
01:14:30 --> 01:14:34
cohort that was in the
vulnerable part of early

1173
01:14:34 --> 01:14:38
childhood during World War 2
when the Germans systematically

1174
01:14:38 --> 01:14:40
starved the Netherlands.
 

1175
01:14:40 --> 01:14:43
There's no dip in the IQ,
so it's not clear that

1176
01:14:43 --> 01:14:44
the food story works.
 

1177
01:14:44 --> 01:14:49
There are other stories that
say that what's going on is

1178
01:14:49 --> 01:14:53
it's something about a
change in the culture.

1179
01:14:53 --> 01:14:55
That the world culture has
become this much more

1180
01:14:55 --> 01:15:01
information intensive culture
that supports and nourishes

1181
01:15:01 --> 01:15:04
the sorts of things that
are measured by IQ.

1182
01:15:04 --> 01:15:08
That the sorts of talents that
you needed when you were a

1183
01:15:08 --> 01:15:13
subsistence farmer, you needed
some smarts to make yourself

1184
01:15:13 --> 01:15:15
survive, but they weren't the
sort of thing that were getting

1185
01:15:15 --> 01:15:21
picked up on IQ tests and that
now your life builds IQ points

1186
01:15:21 --> 01:15:23
and something-- it's not
clear what's going on.

1187
01:15:23 --> 01:15:26
What is clear is
it's not genetic.

1188
01:15:26 --> 01:15:30
It's just too fast to
be a genetic change.

1189
01:15:30 --> 01:15:33
Now that's at the level of
groups, can you change

1190
01:15:33 --> 01:15:36
individual IQs?
 

1191
01:15:36 --> 01:15:39
And the evidence there is
yeah, you can do that, too.

1192
01:15:39 --> 01:15:46
And the classic experiments
come from the 60s.

1193
01:15:46 --> 01:15:47
Oh, what is he today?
 

1194
01:15:47 --> 01:15:50
He's a rat.
 

1195
01:15:50 --> 01:15:52
All right, so we're
going to take rats.

1196
01:15:52 --> 01:15:55
Sort of a homogeneous
population of rats and we're

1197
01:15:55 --> 01:15:58
going to randomized them
into 3 populations.

1198
01:15:58 --> 01:16:03
Population 1 goes into-- well,
let's stick with socioeconomic

1199
01:16:03 --> 01:16:06
status because that's what
they were trying to model.

1200
01:16:06 --> 01:16:11
This is the low SES group,
which meant in rat case that

1201
01:16:11 --> 01:16:17
they lived-- looking like ducks
or something-- they lived all

1202
01:16:17 --> 01:16:19
alone in an isolated cage
with nothing to play with.

1203
01:16:19 --> 01:16:22
 
 

1204
01:16:22 --> 01:16:29
The medium group lived in what
was sort of the standard lab

1205
01:16:29 --> 01:16:36
cage of a couple
of cockroaches.

1206
01:16:36 --> 01:16:38
A couple of rats,
not much action.

1207
01:16:38 --> 01:16:44
And then there was the high SES
rat group or the enriched

1208
01:16:44 --> 01:16:51
group, who lived in a sort of a
big group rat daycare center.

1209
01:16:51 --> 01:16:56
You know, with lots of cool
toys to play with and the

1210
01:16:56 --> 01:16:59
Habitrail thingy and stuff.
 

1211
01:16:59 --> 01:17:02
You know, a lot of good,
cool stuff there.

1212
01:17:02 --> 01:17:05
Let them grow up in these
environments, test them

1213
01:17:05 --> 01:17:09
on little rat IQ tests,
cognitive tests for rats.

1214
01:17:09 --> 01:17:12
These guys do not do as
well as these guys.

1215
01:17:12 --> 01:17:14
Look at them at the end
of the experiment.

1216
01:17:14 --> 01:17:16
I mean, the real end of the
experiment when the rat is now

1217
01:17:16 --> 01:17:20
dead and you discover these
guys have brains that on

1218
01:17:20 --> 01:17:23
all sorts of measures look
better than these brains.

1219
01:17:23 --> 01:17:26
Thicker cortex, more
synapses, bigger brain.

1220
01:17:26 --> 01:17:29
Clearly, the enriched
environment was having

1221
01:17:29 --> 01:17:31
some sort of affect.
 

1222
01:17:31 --> 01:17:35
These data, back in the
60s, were part of what

1223
01:17:35 --> 01:17:37
motivated Head Start.
 

1224
01:17:37 --> 01:17:44
The notion that kids in low
socioeconomic environments had

1225
01:17:44 --> 01:17:48
lower IQs than kids in high
socioeconomic environments,

1226
01:17:48 --> 01:17:52
they could be brought along
to the same higher level by

1227
01:17:52 --> 01:17:54
putting them in school earlier.
 

1228
01:17:54 --> 01:17:58
Putting them in a preschool
enrichment situation.

1229
01:17:58 --> 01:17:59
And it worked.
 

1230
01:17:59 --> 01:18:02
That if you went--
[UNINTELLIGIBLE]

1231
01:18:02 --> 01:18:17
I need a spot-- if you took low
SES kids who plateaued out at

1232
01:18:17 --> 01:18:23
this level and you took them
and put them in Head Start they

1233
01:18:23 --> 01:18:29
went up to this higher level
that was the same level

1234
01:18:29 --> 01:18:33
as the high SES kids.
 

1235
01:18:33 --> 01:18:35
Well, what are Herrnstein and
Murray talking about then when

1236
01:18:35 --> 01:18:36
they say you can't change it?
 

1237
01:18:36 --> 01:18:41
Well, Herrnstein and Murray
said, yeah, that was very nice.

1238
01:18:41 --> 01:18:43
Look what happened when these
kids hit middle school

1239
01:18:43 --> 01:18:45
and high school.
 

1240
01:18:45 --> 01:18:48
And the answer is
[UNINTELLIGIBLE], they went

1241
01:18:48 --> 01:18:52
right back down while the
high SES kids stayed up.

1242
01:18:52 --> 01:18:56
Herrnstein and Murray concluded
that what this is, it's

1243
01:18:56 --> 01:18:57
like a rubber band.
 

1244
01:18:57 --> 01:19:03
Yeah, you can stretch it a bit,
but when you let go it just

1245
01:19:03 --> 01:19:05
snaps back to where it was.
 

1246
01:19:05 --> 01:19:09
The alternative view is yeah,
it's exactly like a rubber

1247
01:19:09 --> 01:19:12
band, but why did you let go?
 

1248
01:19:12 --> 01:19:17
When you let go you drop these
kids back into a lousy inner

1249
01:19:17 --> 01:19:21
city school rather than this
enriched state and they fell

1250
01:19:21 --> 01:19:24
back to where they were.
 

1251
01:19:24 --> 01:19:26
The effects were transient.
 

1252
01:19:26 --> 01:19:31
You want to-- sort of a silly
example-- think about diabetes.

1253
01:19:31 --> 01:19:34
So Diabetes has a similar sort
of graph except it's a little

1254
01:19:34 --> 01:19:39
more stark, no insulin
you're dead by your 20s.

1255
01:19:39 --> 01:19:41
With insulin you're not dead.
 

1256
01:19:41 --> 01:19:43
OK, so here we're in
the not dead state.

1257
01:19:43 --> 01:19:45
 
 

1258
01:19:45 --> 01:19:49
Let's say, OK, well you're done
with that insulin stuff now, we

1259
01:19:49 --> 01:19:51
stopped giving you insulin.
 

1260
01:19:51 --> 01:19:55
Oh, look, they're dead now.
 

1261
01:19:55 --> 01:19:57
There wasn't any point
to giving insulin.

1262
01:19:57 --> 01:20:01
Well, that's a
stupid conclusion.

1263
01:20:01 --> 01:20:04
In Diabetes obviously, isn't
that the conclusion is you

1264
01:20:04 --> 01:20:07
better keep giving insulin.
 

1265
01:20:07 --> 01:20:11
And you could argue that the
same thing is going on here.

1266
01:20:11 --> 01:20:15
That we know that if you want
more IQ points, putting people

1267
01:20:15 --> 01:20:18
in better environments works,
but you gotta keep them there.

1268
01:20:18 --> 01:20:22
 
 

1269
01:20:22 --> 01:20:28
I will tell you one more
factoid and leave it at that.

1270
01:20:28 --> 01:20:31
 
 

1271
01:20:31 --> 01:20:38
In adoption studies, if you
adopt kids into typically, when

1272
01:20:38 --> 01:20:42
adoption moves you from a lower
to a higher socioeconomic

1273
01:20:42 --> 01:20:47
status because that's just the
typical reason why kids would

1274
01:20:47 --> 01:20:50
be put up for adoption,
it doesn't go the other

1275
01:20:50 --> 01:20:51
direction, typically.
 

1276
01:20:51 --> 01:20:55
So most kids are moving
from low to high when

1277
01:20:55 --> 01:20:57
they're adopted.
 

1278
01:20:57 --> 01:21:00
Who does their IQ
correlate with?

1279
01:21:00 --> 01:21:03
The answer is it correlates
with their biological

1280
01:21:03 --> 01:21:08
relatives because there is
a genetic component to IQ.

1281
01:21:08 --> 01:21:12
What is their average
IQ, however?

1282
01:21:12 --> 01:21:17
Their average IQ is the
IQ of the environment

1283
01:21:17 --> 01:21:18
that they are now in.
 

1284
01:21:18 --> 01:21:22
So their IQ is, on average,
indistinguishable from the

1285
01:21:22 --> 01:21:25
biological kids in
the same family.

1286
01:21:25 --> 01:21:26
How can that be?
 

1287
01:21:26 --> 01:21:30
That doesn't sound like
it ought to work.

1288
01:21:30 --> 01:21:33
But let me just draw you a
quick picture and then you can

1289
01:21:33 --> 01:21:34
go off and think about it.
 

1290
01:21:34 --> 01:21:40
So here are these
pairs of siblings.

1291
01:21:40 --> 01:21:44
And their IQs-- let's take
pairs of siblings-- their IQs

1292
01:21:44 --> 01:21:47
are correlated with each other.
 

1293
01:21:47 --> 01:21:51
Now we're going to take 1 of
each of those pairs and that

1294
01:21:51 --> 01:21:53
kid's going to get adopted out.
 

1295
01:21:53 --> 01:21:55
And that kids going to move,
as a result, to a higher

1296
01:21:55 --> 01:21:57
socioeconomic status.
 

1297
01:21:57 --> 01:22:00
That doesn't change the
kid's genetics any.

1298
01:22:00 --> 01:22:06
And so, the kid still has a IQ
that's related to his brother

1299
01:22:06 --> 01:22:14
or sister, but it shifts all
of-- so this is the kid who's

1300
01:22:14 --> 01:22:17
in a low socioeconomic
state and stays there.

1301
01:22:17 --> 01:22:20
This is a kid going
for low to high.

1302
01:22:20 --> 01:22:25
What happens is that the low to
high move pushes everybody up.

1303
01:22:25 --> 01:22:29
So the cloud of spots
just moves up.

1304
01:22:29 --> 01:22:33
The correlation is the same.
 

1305
01:22:33 --> 01:22:38
The average IQ here is higher
because going from a low to a

1306
01:22:38 --> 01:22:41
high socioeconomic status
gives you IQ points.

1307
01:22:41 --> 01:22:46
If you really thought that what
you wanted to do as a society,

1308
01:22:46 --> 01:22:49
if you really believed more IQ
points mean more good

1309
01:22:49 --> 01:22:54
stuff, there's a clear
enough way to do it.

1310
01:22:54 --> 01:22:57
It becomes a social policy
issue about how you end up

1311
01:22:57 --> 01:23:00
doing this, but it seems
perfectly clear that it

1312
01:23:00 --> 01:23:03
is possible to do that.
 

1313
01:23:03 --> 01:23:05
I'm covered in chalk.
 

1314
01:23:05 --> 01:23:13