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PROFESSOR: Good afternoon.

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For the past couple of lectures,
what I've been doing

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is using love and romance as
the way to talk about broad

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issues like evolutionary
psychology that could be

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talked about with a wide range
of other examples.

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Love and romance just happen to
provide a particularly good

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set of examples for that
particular topic.

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I'm going to do the same thing
now with attitude formation

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and the links between attitudes
and behavior.

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But I'm going to switch from
the love in relationships

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topic, to the topic of
racist or prejudicial

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behavior and attitudes.

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Again, not because that's the
only set of attitudes that are

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interesting or important, but
because it makes for an

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interesting path through
this material.

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So, when you read the book, get
the topics discussed and

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rather more general terms.

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And, here, I'll discuss them in
the specific terms of this

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particular problem.

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I should note at the outset
what it says about halfway

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down, I see the first page of
the handout, which is, I'm

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going to do my best to give
an explanation about why

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prejudiced attitudes are easy
to come by and, are readily

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comprehensible in terms of
psychological processes that

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we actually know something
about.

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But explaining these things
is not the same

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thing as excusing them.

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You can have a society that
says, look, all else being

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equal, racial prejudice and
particularly behaviors based

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on prejudices, based on gender
race, national origin,

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religion, that those sort
of biases are bad.

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And when they lead to behavior,
we want to change

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that behavior.

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That's quite separate, not
unrelated to, but it's

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separate from the question of
how you would explain it

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psychologically.

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It's important to remember that
explanation is not the

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same thing as excuse.

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Because I don't want people
going out of the lecture

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saying, my psych professor
explained that it's really,

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really easy to develop
prejudicial

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attitudes, and that's OK.

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It's the that's OK part --

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I also put on the handout a pair
of quotes from the early

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days of the civil rights
movement; one from Eisenhower

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saying that you can't legislate
morality, and a

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response from Martin Luther King
saying, well maybe not,

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but you can legislate
the moral behavior.

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That's really the social
policy point.

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If you decide you don't like
something that biology,

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psychology or whatever is
pushing people towards, you

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simply have to do something to
make it harder for them to go

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where that tendency
might push them.

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Well, in any case, what I'm
going to do, is work a story

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about the development of
prejudicial attitudes that's

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got four factors here
listed at the top.

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And, I work through
each of them.

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I hope you can see how they're
tied together to make

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prejudice a very available
option to us.

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Why this sort of thing happens
to us with some regularity.

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The first factor that I list
there is ethinocentrism.

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That's the tendency
to think that your

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group is the best group.

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We're number one
kind of thing.

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If you want to come up with,
say, an evolutionary psych

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argument for why this might
happen, it's kind of trivial.

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If you've got some notion that
you want to get your genes

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into the next generation, then
you might as well favor the

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people who are more closely
related to you.

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So you should be more
favorable to

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humans than to mice.

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You should be likely to be
more favorable to people

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within your group, and even more
so to people within your

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family, out of this sort of
fairly straightforward

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application of evolutionary
theory for example.

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The more interesting aspect
psychologically is how easy it

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is to get ethinocentric
effects.

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So to get the we're
number one effect.

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Interesting evidence for this
comes from these minimal

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attachment experiments that
called, minimal group

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affiliation experiments.

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There's a lot of them.

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Let me describe a
couple to you.

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So here's an experiment.

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You come into the lab, and
we're going to do an

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assessment of your taste
in abstract art.

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And, we're going to show you a
bunch of abstract pictures.

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And, you're going to say how
much you like it on a scale of

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one to seven or something.

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And, then the feedback you're
going to get from this at the

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end is that you like the work
of Paul Clay, one abstract

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artist, better than you like the
work of Wassily Kandinsky,

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another abstract artist.

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Or it might be the
other way around.

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I don't even remember, by the
way, whether they were

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actually using real Clay
and Kandinsky pictures.

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But you're going to get told
that you're in the Clay group

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or Kandinsky group.

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This is not a group assignment
where there is a lot at stake.

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Let us suppose that you're
in the Clay group.

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You've been assigned
to the Clay group.

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In the second part of the
experiment, you're playing

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some sort of a game.

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I don't quite remember
what the story was.

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And, it ends up with you being
able to operate under one of

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two pay- off rules.

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In one rule, you're going to
give the other group one buck,

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and your going to
get two bucks.

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That's one possibility.

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The other possibility is a rule
that gets you three bucks

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and give them four bucks.

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So, this is you.

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This is them.

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You've got a choice between
option A, and option B.

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Clearly, the rational choice,
from your vantage point is

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option B, because you get three

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bucks, and not two bucks.

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But, in fact, there's a bias
towards picking this option.

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Why is that?

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Well, if you get two bucks, you
getting more then them.

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Here, I'm going to get more than
I would have got there,

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but these guys are going to
get a whole bunch more.

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Why should I give
those Kandinsky

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lovers a bunch of stuff?

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Maybe it turns out that after
you figure that these

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Kandinsky sorts really are a
different sort of scummy kind

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of person, who you really
ought to stick it to.

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Well, in fact, there's
no difference

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between these groups.

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The group assignment is
completely random.

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So any such distinction that you
had made in your own mind

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is meaningless but maybe
you didn't know that.

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So we can re-run this whole
experiment by saying get rid

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of this silly cover story.

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We're going to flip a coin.

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You're in group B. B.
A. A. B. B. B. B.

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Look guys, it's random.

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There is nothing differentiating
your group

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from the other group.

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Guess what?

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You get the same result.

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I think it does get a little
bit weaker, but not much.

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So, just the act of being in
a group, causes you to be

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inclined to favor that group.

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You are also inclined to think
that group membership is

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diagnostic.

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Let me describe a different
experiment actually, as the

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easiest way to describe this.

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Suppose I put up a bunch of
dots, and we're above the

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subitizing range here,
obviously.

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OK.

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Quickly.

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How many dots are they there?

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I don't know.

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You could guess.

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You get lucky, and
be right on.

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But you'd either be above or
below, so you're going to do

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this for awhile.

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Guess how many dots there are.

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And, we are going to declare
that you are an overestimater

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or an underestimater.

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So, that's the group assignment
in this case.

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Now the interesting thing in
this experiment is that you've

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done it with another person.

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And, the possibilities are that
it could be two guys,

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both of them are
overestimaters.

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It could be two guys, one of
them an overestimater, one of

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them an underestimater.

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Similarly for females right?

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They could both be
underestimaters and the

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critical conditions are.

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It could be a male, and a,
female who, are both

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overestimaters both labelled
as overestimaters or both

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labelled as underestimaters.

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And the critical condition is
male overestimater, female

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underestimater or the
other way around.

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But the important point here is
that the male is identified

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as one, the female is identified
as the other.

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So you've got people in all
these various groups.

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I don't know.

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Somebody can figure out how many
groups there must be for

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the full design here.

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Got people in all
those groups.

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And, now you ask a question,
at the end of the whole

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experiment, do you think there's
a sex difference, a

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systematic sex difference
between men and

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women on this task?

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What do you think the sex
difference is, if there's a

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sex difference?

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All these groups, all the same
sex groups, on average, I

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don't think there's
a difference.

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Some people say, I think
women are better.

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Some people think men are
overestimaters or whatever.

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But, there's no systematic
bias here.

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Nothing systematic
happens here.

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Here something systematic
happens, in this particular

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version of it.

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You would declare that males as
a group are overestimaters

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and females has a group
are underestimaters.

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What's the evidence for that?

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One of each.

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Clearly not meaningful.

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There's no statistical
reliability that you could

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glean from this.

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00:13:26,670 --> 00:13:34,200
It could be that brown haired
people or blond haired people

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or something are different.

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But you know that there's a
group difference here, because

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males and females are definitely
different groups.

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As soon as you've got evidence
that there's a difference

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across this group, you are
willing to start making

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assumptions that difference
applies to

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the group as a whole.

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You see how that works?

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More or less?

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00:13:58,380 --> 00:13:59,350
Somebody's nodding their head.

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That's encouraging.

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00:14:00,080 --> 00:14:02,080
Ok.

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00:14:02,080 --> 00:14:07,060
So that's factor one--

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that you're inclined to see
your group as number one.

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And this is a point we'll come
back to, which is that you're

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inclined to see groups as having
properties that pertain

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to that group.

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00:14:23,490 --> 00:14:26,300
And, you'll jump to
that quickly.

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Now the tendency to think that
group identification tells you

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something about properties of
the group, that's known as

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00:14:39,480 --> 00:14:40,560
stereotyping.

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If I know that you are part of
this group, I believe I know

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something about you.

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00:14:46,510 --> 00:14:47,760
Period.

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When we talk about stereotyping,
we tend to think

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00:14:54,340 --> 00:14:56,680
of it in negative terms.

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00:14:56,680 --> 00:15:02,700
That is a fairly self evident
consequence of factor one

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mixed with factor two.

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00:15:04,210 --> 00:15:09,480
If you are inclined to think
that your group is number one,

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00:15:09,480 --> 00:15:14,200
and you're inclined to think
that group identity tells you

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00:15:14,200 --> 00:15:19,140
something, it follows that
being a member of another

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00:15:19,140 --> 00:15:23,470
group, means you're of a group
that is, at best number two.

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00:15:23,470 --> 00:15:25,470
Ain't number one,
that's my group.

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00:15:25,470 --> 00:15:30,180
So you in this group that I've
identified, are in the some

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00:15:30,180 --> 00:15:34,570
other lesser group
on this scale.

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00:15:34,570 --> 00:15:35,800
That is fairly obvious.

246
00:15:35,800 --> 00:15:38,010
What's a little less obvious,
and is, at least worth

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00:15:38,010 --> 00:15:43,300
mentioning, is that stereotypes
are not just

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descriptions of things that
are common that to that

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population.

250
00:15:49,000 --> 00:15:50,990
Here's the silly example.

251
00:15:50,990 --> 00:15:57,680
Let us consider the Asian
women stereotype.

252
00:15:57,680 --> 00:16:03,150
Is it part of the Asian woman,
is bipedality the Asian woman

253
00:16:03,150 --> 00:16:06,390
stereotype?

254
00:16:06,390 --> 00:16:07,180
No.

255
00:16:07,180 --> 00:16:09,610
Nobody sits around and
says all those Asian

256
00:16:09,610 --> 00:16:12,300
women have two feet.

257
00:16:12,300 --> 00:16:13,290
That's stupid.

258
00:16:13,290 --> 00:16:15,630
Nobody's going to say anything
like that, because everybody's

259
00:16:15,630 --> 00:16:17,080
got two feet.

260
00:16:17,080 --> 00:16:22,620
What's important in a stereotype
is, stereotypes are

261
00:16:22,620 --> 00:16:24,610
different scores in the sense.

262
00:16:24,610 --> 00:16:27,530
It's not necessarily accurate
ones you understand.

263
00:16:27,530 --> 00:16:28,880
They can be completely bogus.

264
00:16:28,880 --> 00:16:33,780
But what defines a stereotype,
is what somebody thinks

265
00:16:33,780 --> 00:16:40,950
differentiates one group from
the population as a whole.

266
00:16:40,950 --> 00:16:48,450
So I put some data down here
from one big study of

267
00:16:48,450 --> 00:16:51,380
stereotypes.

268
00:16:51,380 --> 00:16:54,340
Please note that these
are not facts.

269
00:16:54,340 --> 00:16:56,250
I mean they are facts in the
sense that they are data.

270
00:16:56,250 --> 00:16:58,940
They're not true facts about,
in this case, the German

271
00:16:58,940 --> 00:16:59,610
population.

272
00:16:59,610 --> 00:17:05,830
What they are is what this
particular group of subjects

273
00:17:05,830 --> 00:17:08,460
reported believing about
different groups.

274
00:17:08,460 --> 00:17:10,740
In this case, the Germans.

275
00:17:10,740 --> 00:17:16,300
I excerpted it from
a huge study.

276
00:17:16,300 --> 00:17:18,880
The factors, efficient,
extremely nationalistic,

277
00:17:18,880 --> 00:17:21,690
scientific- minded, and
pleasure- loving.

278
00:17:21,690 --> 00:17:28,040
You will note that the largest
single category, the highest

279
00:17:28,040 --> 00:17:32,190
value for the German population,
in this collection

280
00:17:32,190 --> 00:17:38,290
of data points, is pleasure-
loving, but that's not part of

281
00:17:38,290 --> 00:17:41,590
what would be considered the
stereotypes here, because it's

282
00:17:41,590 --> 00:17:45,770
not higher then the population
as a whole.

283
00:17:45,770 --> 00:17:49,420
This particular group of
subjects asserted that 82% of

284
00:17:49,420 --> 00:17:53,580
people in the world would be
pleasure- loving, and a mere

285
00:17:53,580 --> 00:17:55,820
72% of the Germans would be.

286
00:17:55,820 --> 00:17:58,430
Therefore, pleasure- lovingness
would not be

287
00:17:58,430 --> 00:18:02,710
considered part of that
stereotype because it it's not

288
00:18:02,710 --> 00:18:11,050
something that distinguishes
this view of Germans from the

289
00:18:11,050 --> 00:18:14,530
view of people as a whole.

290
00:18:14,530 --> 00:18:17,160
And, so, efficient, yes.

291
00:18:17,160 --> 00:18:18,910
Extremely nationalistic yes.

292
00:18:18,910 --> 00:18:22,040
And interesting is something
like scientifically- minded

293
00:18:22,040 --> 00:18:25,200
would be considered part of the
stereotype, even though

294
00:18:25,200 --> 00:18:27,000
it's not even held
to be a majority.

295
00:18:29,570 --> 00:18:32,020
This perception doesn't say that
a majority of Germans are

296
00:18:32,020 --> 00:18:34,870
scientifically- minded,
but more Germans are

297
00:18:34,870 --> 00:18:40,290
scientifically- minded according
to this view then

298
00:18:40,290 --> 00:18:41,800
the population as a whole.

299
00:18:41,800 --> 00:18:44,630
Again, I have no idea what the
true data would be for the

300
00:18:44,630 --> 00:18:45,660
German population.

301
00:18:45,660 --> 00:18:48,350
I don't know if they think
about science at all.

302
00:18:48,350 --> 00:18:54,100
But, the perception is that the
stereotype would include

303
00:18:54,100 --> 00:18:57,450
efficient, nationalistic, and
scientific, and not pleasure-

304
00:18:57,450 --> 00:19:03,820
minded, because of this issue of
a differential relationship

305
00:19:03,820 --> 00:19:06,630
to the perceived baseline.

306
00:19:06,630 --> 00:19:11,470
To the population as a whole.

307
00:19:11,470 --> 00:19:16,120
One of the factors contributing
to the power of

308
00:19:16,120 --> 00:19:19,840
stereotyping is, what's
known as the out group

309
00:19:19,840 --> 00:19:21,670
homogeneaity effect.

310
00:19:21,670 --> 00:19:24,540
So, the in group is you.

311
00:19:24,540 --> 00:19:26,130
You're in some group.

312
00:19:26,130 --> 00:19:29,160
The out group are
other people.

313
00:19:29,160 --> 00:19:33,870
The out group homogeneaity
effect is the tendency to

314
00:19:33,870 --> 00:19:40,790
think that all of them
are kind of alike.

315
00:19:40,790 --> 00:19:43,080
You don't think that about
you in the same way.

316
00:19:46,950 --> 00:19:50,260
From the last lecture,
the nerdy high

317
00:19:50,260 --> 00:19:53,730
school freshman group.

318
00:19:53,730 --> 00:19:57,430
If that was my in group, I would
know that they aren't

319
00:19:57,430 --> 00:20:00,590
all alike, because I'm one of
them, and I know that I'm

320
00:20:00,590 --> 00:20:05,260
different from all those other
nerdy high school freshman.

321
00:20:05,260 --> 00:20:10,780
But, the jocks, man,
they're all alike.

322
00:20:10,780 --> 00:20:14,090
It's an effect, an
ignorance effect.

323
00:20:14,090 --> 00:20:19,290
Now, how does this
play into bias?

324
00:20:19,290 --> 00:20:24,230
How does this ignorance factor
play into not just thinking

325
00:20:24,230 --> 00:20:27,610
that all those people are alike,
but the thinking less

326
00:20:27,610 --> 00:20:28,640
of those people?

327
00:20:28,640 --> 00:20:34,900
If you ask about what you know
about groups that you don't

328
00:20:34,900 --> 00:20:39,300
interact with much, where
do you got your

329
00:20:39,300 --> 00:20:41,370
information about them?

330
00:20:41,370 --> 00:20:43,390
You get your information
from the news.

331
00:20:43,390 --> 00:20:46,340
What gets you on to the news?

332
00:20:46,340 --> 00:20:49,780
The fact that you're
a good student, and

333
00:20:49,780 --> 00:20:51,940
you love your mother?

334
00:20:51,940 --> 00:20:53,580
No.

335
00:20:53,580 --> 00:20:58,670
But, if you decide to go off and
commit an armed robbery,

336
00:20:58,670 --> 00:21:01,780
or something like that, that
might get you on the news.

337
00:21:01,780 --> 00:21:06,160
And, if you are a member of a
distinctive group, of some

338
00:21:06,160 --> 00:21:10,450
sort, that's not my group, I'm
going to say, hey, look, I've

339
00:21:10,450 --> 00:21:14,020
got a data point about, --
we can keep picking on

340
00:21:14,020 --> 00:21:15,370
the Asian women --

341
00:21:15,370 --> 00:21:18,210
I've got a data point
about Asian women.

342
00:21:18,210 --> 00:21:21,370
That one committed
an armed robbery.

343
00:21:21,370 --> 00:21:22,970
I know about Asian women now.

344
00:21:22,970 --> 00:21:24,390
They all commit armed robbery.

345
00:21:24,390 --> 00:21:27,090
Well, you don't do anything
quite that bald and stupid.

346
00:21:27,090 --> 00:21:31,080
But that's the sense in which
you are willing to color the

347
00:21:31,080 --> 00:21:33,790
entire group on the basis of
whatever information you have

348
00:21:33,790 --> 00:21:35,320
about one member of it.

349
00:21:35,320 --> 00:21:39,050
That's another version
of this effect.

350
00:21:39,050 --> 00:21:44,210
Is likely to lead to negative
assessments of the out group,

351
00:21:44,210 --> 00:21:46,510
because the information you
get about groups that you

352
00:21:46,510 --> 00:21:50,140
don't interact with is skewed
towards the negative.

353
00:21:50,140 --> 00:21:53,100
The stuff that's going to make
the news about a group is

354
00:21:53,100 --> 00:21:57,930
going to be typically
the negative effect.

355
00:22:02,140 --> 00:22:06,330
So where are the strongest
stereotypes in the American

356
00:22:06,330 --> 00:22:11,650
population -- well actually this
is a somewhat old study

357
00:22:11,650 --> 00:22:17,950
at this point -- but when you
assess, you can use various

358
00:22:17,950 --> 00:22:23,850
questionnaire assessments
to assess how strongly a

359
00:22:23,850 --> 00:22:28,210
population holds stereotypic
views of another population.

360
00:22:28,210 --> 00:22:33,045
The heavily stereotyped views
held by an American population

361
00:22:33,045 --> 00:22:37,970
-- this is now about 15 years
ago-- were held of Turks,

362
00:22:37,970 --> 00:22:43,480
Arabs to a lesser degree of the
Japanese, groups that were

363
00:22:43,480 --> 00:22:50,460
not heavily represented in the
general Americans mix.

364
00:22:50,460 --> 00:22:54,650
Less heavy stereotypes for
groups with large immigrant

365
00:22:54,650 --> 00:22:58,590
populations in this country,
because you were more likely

366
00:22:58,590 --> 00:23:03,250
to know some people in that
group, and that makes the

367
00:23:03,250 --> 00:23:10,050
stereotypes less firm,
less strong.

368
00:23:10,050 --> 00:23:18,060
One of the reasons that these
stereotypes matter is because

369
00:23:18,060 --> 00:23:23,510
along with being willing to
build them quickly and easily,

370
00:23:23,510 --> 00:23:31,050
we are also inclined to think
that the attributes that we

371
00:23:31,050 --> 00:23:36,150
put on other groups
are causal.

372
00:23:36,150 --> 00:23:37,040
At least in others.

373
00:23:37,040 --> 00:23:39,690
This is what's known as the
fundamental attribution error.

374
00:23:39,690 --> 00:23:41,760
Let me explain a little.

375
00:23:41,760 --> 00:23:48,480
But I sent a note to a couple of
my social psych colleagues

376
00:23:48,480 --> 00:23:51,630
yesterday, as I was thinking
about this lecture, asking who

377
00:23:51,630 --> 00:23:55,450
was it who named the fundamental
attribution error,

378
00:23:55,450 --> 00:23:58,320
because that's a great thing
to be able to do.

379
00:23:58,320 --> 00:24:03,410
To be able to call what you
work on fundamental.

380
00:24:03,410 --> 00:24:07,380
And, it turns out to be Nisbett,
N I S B E T T, from

381
00:24:07,380 --> 00:24:10,720
the University of Michigan if
you want to track that down.

382
00:24:10,720 --> 00:24:15,060
But, in any case, let me
explain what this is.

383
00:24:15,060 --> 00:24:22,110
There are two broad ways of
thinking about personality.

384
00:24:22,110 --> 00:24:26,960
We all have some notion that
we've got a personality.

385
00:24:26,960 --> 00:24:31,070
And, thinking about the
attributes that make up that

386
00:24:31,070 --> 00:24:34,880
personality can be divided into
two broad categories that

387
00:24:34,880 --> 00:24:37,900
map onto the usual nature/
nurture kind of

388
00:24:37,900 --> 00:24:39,850
arguments in the field.

389
00:24:39,850 --> 00:24:48,610
There are trait theories that
are typically on the more

390
00:24:48,610 --> 00:24:50,850
nature side of it.

391
00:24:50,850 --> 00:24:56,280
There are fundamental attributes
of personality

392
00:24:56,280 --> 00:24:59,460
maybe coming from a
genetic origin.

393
00:25:03,190 --> 00:25:06,830
You are who you are because
you have these traits.

394
00:25:06,830 --> 00:25:11,180
The alternative from the more
nurtured side of things, the

395
00:25:11,180 --> 00:25:24,050
environmental side of it is,
is a situationalist account

396
00:25:24,050 --> 00:25:29,320
that says you are who you are
because of where you are.

397
00:25:29,320 --> 00:25:37,650
Trait theory, you are here now
because you where born smart,

398
00:25:37,650 --> 00:25:42,280
and hard working, and studious,
or you basically

399
00:25:42,280 --> 00:25:47,000
have consistent over time,
hardworking, studious

400
00:25:47,000 --> 00:25:49,610
attributes to you.

401
00:25:49,610 --> 00:25:52,870
The situationlist account says
you're sitting here right now

402
00:25:52,870 --> 00:25:57,820
emitting student behavior
because you're in a student

403
00:25:57,820 --> 00:25:59,250
kind of environment.

404
00:25:59,250 --> 00:26:06,470
If we put you on a farm, you
would not be sitting there in

405
00:26:06,470 --> 00:26:08,480
the middle of the field
with a notebook taking

406
00:26:08,480 --> 00:26:09,294
notes about the cow.

407
00:26:09,294 --> 00:26:11,720
That's not what you'd
be doing.

408
00:26:11,720 --> 00:26:15,270
In that situation, you'd be
doing farm kind of stuff.

409
00:26:15,270 --> 00:26:19,870
Like all such debates in the
field, the truth is going to

410
00:26:19,870 --> 00:26:22,400
lie somewhere in between.

411
00:26:22,400 --> 00:26:23,910
There's going to be
bits of both.

412
00:26:23,910 --> 00:26:25,900
You're not going to get any
mileage out of arguing

413
00:26:25,900 --> 00:26:28,790
strictly one or strictly
the other.

414
00:26:28,790 --> 00:26:30,920
What you think about this,
what you think about the

415
00:26:30,920 --> 00:26:35,390
balance here, is important
for the policy

416
00:26:35,390 --> 00:26:36,640
purposes, for instance.

417
00:26:36,640 --> 00:26:41,800
Why did this guy
commit a crime?

418
00:26:41,800 --> 00:26:44,890
We know he committed a crime,
because we just convicted him

419
00:26:44,890 --> 00:26:47,500
of committing this crime.

420
00:26:47,500 --> 00:26:49,460
Why did he commit a crime?

421
00:26:49,460 --> 00:26:55,310
Is it because he's a criminal
sort of person?

422
00:26:55,310 --> 00:26:58,650
Fundamentally, dishonest,
nasty, kind of person.

423
00:26:58,650 --> 00:27:03,970
Or, is it because he was in
a situation that promoted

424
00:27:03,970 --> 00:27:06,000
criminal behavior?

425
00:27:06,000 --> 00:27:07,080
Why does that matter?

426
00:27:07,080 --> 00:27:12,420
Well if you're in this mode, you
may send him off to prison

427
00:27:12,420 --> 00:27:13,550
in both cases.

428
00:27:13,550 --> 00:27:16,750
In this case, you're likely to
think of prison as a place

429
00:27:16,750 --> 00:27:20,650
where you put bad people to keep
them out of the way for a

430
00:27:20,650 --> 00:27:22,970
length of time that's
appropriate to whatever bad

431
00:27:22,970 --> 00:27:24,670
thing they did.

432
00:27:24,670 --> 00:27:27,780
But, it's basically
as a punishment.

433
00:27:27,780 --> 00:27:31,260
This is the personality theory,
that would lead you to

434
00:27:31,260 --> 00:27:34,670
call your prison a correctional
institution.

435
00:27:34,670 --> 00:27:38,960
Because you think that you could
correct this person.

436
00:27:38,960 --> 00:27:43,470
If you think he's fundamentally
a bad person,

437
00:27:43,470 --> 00:27:46,460
the trick is to make sure he
can't do that anymore.

438
00:27:46,460 --> 00:27:49,970
If you think it's because of
the situation, that somehow

439
00:27:49,970 --> 00:27:53,060
forced to him into or pushed
him into criminal behavior,

440
00:27:53,060 --> 00:27:54,310
you want to fix that.

441
00:27:58,800 --> 00:28:04,070
Modern prison philosophy is
neither all the way here, or

442
00:28:04,070 --> 00:28:05,390
all the way there.

443
00:28:05,390 --> 00:28:10,250
But, the balance is really a
personality theory question.

444
00:28:10,250 --> 00:28:16,820
The fundamental attribution
error is a tendency to hold to

445
00:28:16,820 --> 00:28:20,600
a more trait theoretic position
when you're talking

446
00:28:20,600 --> 00:28:24,910
about other people than when
you're talking about yourself.

447
00:28:24,910 --> 00:28:25,930
Why is it in error?

448
00:28:25,930 --> 00:28:32,090
Well it logically can't be the
case that you are the product

449
00:28:32,090 --> 00:28:36,250
largely of the situation and
that they are a product of

450
00:28:36,250 --> 00:28:39,390
their invariant parts.

451
00:28:39,390 --> 00:28:42,850
That over the population as a
whole isn't going to isn't

452
00:28:42,850 --> 00:28:44,100
going to hold up.

453
00:28:50,490 --> 00:28:54,230
Why did this guy rob the bank?

454
00:28:54,230 --> 00:28:57,720
He robbed the bank, because
he's a criminal.

455
00:28:57,720 --> 00:28:59,200
Why did I rob the bank?

456
00:28:59,200 --> 00:29:02,930
I robbed the bank, because I was
hungry, and the door was

457
00:29:02,930 --> 00:29:05,830
unlocked, and I didn't really
rob the bank, I just kind of

458
00:29:05,830 --> 00:29:08,770
picked up the money that was
lying on the floor, and

459
00:29:08,770 --> 00:29:11,830
anyway, it was other guy.

460
00:29:11,830 --> 00:29:16,070
Much more likely to give
us situational account.

461
00:29:16,070 --> 00:29:21,610
Why did you get a bad grade
on the midterms.

462
00:29:21,610 --> 00:29:23,030
Suppose you got a bad grade
on the midterm.

463
00:29:23,030 --> 00:29:24,510
Why did you get a bad grade
on the midterm.

464
00:29:24,510 --> 00:29:27,330
Well, the story was really lame,
and it distracted me.

465
00:29:27,330 --> 00:29:28,880
And I didn't get enough sleep.

466
00:29:28,880 --> 00:29:34,080
And the course wasn't a
big priority for me.

467
00:29:34,080 --> 00:29:38,760
That's why I got it bad grade.

468
00:29:38,760 --> 00:29:42,720
Your TA says-- well your TA is a
good person, and doesn't say

469
00:29:42,720 --> 00:29:45,940
this-- but your TA looks at
the exam and says, why did

470
00:29:45,940 --> 00:29:47,510
they get a bad grade
on the exam?

471
00:29:47,510 --> 00:29:49,300
They're stupid!

472
00:29:49,300 --> 00:29:52,900
That would be the fundamental
attribution error.

473
00:29:52,900 --> 00:29:56,520
Why did your TA get
a bad grade?

474
00:29:56,520 --> 00:29:59,170
I didn't get enough sleep
and stuff like that.

475
00:29:59,170 --> 00:30:02,170
We're more inclined to give
situationalist accounts of our

476
00:30:02,170 --> 00:30:07,890
own behavior and more inclined
to get trait accounts of other

477
00:30:07,890 --> 00:30:08,780
people's behavior.

478
00:30:08,780 --> 00:30:12,240
All right, so let's see where
this has gotten us to.

479
00:30:12,240 --> 00:30:14,700
We're inclined to put
people into groups.

480
00:30:14,700 --> 00:30:20,230
We're inclined to assign
attributes to those groups.

481
00:30:20,230 --> 00:30:23,980
We're likely to assign more
negative attributes to groups

482
00:30:23,980 --> 00:30:26,970
than we ought to because our
information about groups that

483
00:30:26,970 --> 00:30:31,540
we don't know is being skewed
in that direction, and we're

484
00:30:31,540 --> 00:30:36,390
likely to attribute behavior
to what we now perceive

485
00:30:36,390 --> 00:30:39,570
correctly or incorrectly as
the traits of the group.

486
00:30:39,570 --> 00:30:43,800
And, so you can see how you're
going to end up with a story

487
00:30:43,800 --> 00:30:47,460
that is a pretty negative
account of some

488
00:30:47,460 --> 00:30:49,600
out group or other.

489
00:30:49,600 --> 00:30:53,080
Oh by the way, there's a very
interesting wrinkle on the

490
00:30:53,080 --> 00:30:56,050
fundamental attribution error,
or at least there's certainly

491
00:30:56,050 --> 00:30:58,960
used to be, I do not know
whether this is still the case

492
00:30:58,960 --> 00:31:02,220
in the population as a whole,
and I certainly don't know,

493
00:31:02,220 --> 00:31:04,950
though I would be very
interested to know, whether

494
00:31:04,950 --> 00:31:07,370
it's true at MIT.

495
00:31:10,130 --> 00:31:13,030
Let's try this as an experiment,
and see how your

496
00:31:13,030 --> 00:31:15,870
intuition goes --

497
00:31:15,870 --> 00:31:20,200
OK we can do the trait versus
situational thing, and we're

498
00:31:20,200 --> 00:31:29,220
going to have a math test.

499
00:31:36,190 --> 00:31:39,930
You're interpreting your own
score on a math test.

500
00:31:39,930 --> 00:31:49,090
That score can be good or
bad, as you may know.

501
00:31:49,090 --> 00:31:56,150
There are explanations of why
the test for was good.

502
00:31:56,150 --> 00:32:00,810
A trait theory would
be, I'm a genius.

503
00:32:00,810 --> 00:32:04,140
A situationalist theory
would be, I'm lucky.

504
00:32:12,800 --> 00:32:20,320
And, if the test is bad, you can
have an assessment that it

505
00:32:20,320 --> 00:32:30,050
says I'm dumb, or you going to
have an assessment that said,

506
00:32:30,050 --> 00:32:33,150
that the test is unfair.

507
00:32:33,150 --> 00:32:38,360
The interesting bit is that
one gender is more

508
00:32:38,360 --> 00:32:39,890
likely to say this.

509
00:32:39,890 --> 00:32:42,990
And the other gender is more
likely to say this.

510
00:32:42,990 --> 00:32:45,790
One gender is more likely
to say this.

511
00:32:45,790 --> 00:32:48,250
And the other gender's more
likely to say that.

512
00:32:48,250 --> 00:32:51,270
So each of these cells can be
associated with a gender.

513
00:32:51,270 --> 00:32:53,930
So I got a good grade
on the test.

514
00:32:53,930 --> 00:32:54,710
I'm a genius.

515
00:32:54,710 --> 00:32:56,020
Who are we talking about?

516
00:32:56,020 --> 00:32:57,270
AUDIENCE: Men.

517
00:32:57,270 --> 00:33:00,820
PROFESSOR: All right.

518
00:33:00,820 --> 00:33:06,760
And, it follows that this
must be the female cell.

519
00:33:06,760 --> 00:33:10,480
I got a bad grade on the test.

520
00:33:10,480 --> 00:33:11,360
I'm dumb.

521
00:33:11,360 --> 00:33:13,390
AUDIENCE: Female.

522
00:33:13,390 --> 00:33:16,590
PROFESSOR: And, that is the
historical finding.

523
00:33:16,590 --> 00:33:19,390
These are studies that came out
in the early days of the

524
00:33:19,390 --> 00:33:22,060
women's movement.

525
00:33:22,060 --> 00:33:23,580
And, I don't know
about whether or

526
00:33:23,580 --> 00:33:27,170
not it's still pertains.

527
00:33:27,170 --> 00:33:35,270
But the disturbing finding at
the time was that males were

528
00:33:35,270 --> 00:33:40,010
inclined to give trait theoretic
answers for the good

529
00:33:40,010 --> 00:33:46,260
stuff, I'm good, I'm bright,
I'm gorgeous.

530
00:33:46,260 --> 00:33:51,640
And, females were likely to
say I'm lucky and it's all

531
00:33:51,640 --> 00:33:53,760
makeup, or something
like that.

532
00:33:53,760 --> 00:33:57,200
And on the other side, on the
bad side, the females were

533
00:33:57,200 --> 00:34:00,990
likely to give trait
theoretic answers.

534
00:34:00,990 --> 00:34:04,900
I'm dumb and ugly and depressed
and its terrible.

535
00:34:04,900 --> 00:34:09,520
And the guys were likely to
say I'm brilliant, I'm

536
00:34:09,520 --> 00:34:13,080
gorgeous, et cetera, and the
test was really kind unfair

537
00:34:13,080 --> 00:34:19,250
and my teacher hated
me and stuff.

538
00:34:19,250 --> 00:34:21,050
It would be interesting
to know whether

539
00:34:21,050 --> 00:34:24,850
that was still true.

540
00:34:24,850 --> 00:34:25,980
We might as well take a poll.

541
00:34:25,980 --> 00:34:28,510
How many people think that if
we actually collected data,

542
00:34:28,510 --> 00:34:32,890
we'd find that something like
that was still true?

543
00:34:32,890 --> 00:34:35,530
How many think that we would
find it has gone away?

544
00:34:39,840 --> 00:34:45,590
I have no idea if there's
new data on that.

545
00:34:45,590 --> 00:34:52,150
The basic point is that with
that modulation possibly we

546
00:34:52,150 --> 00:34:54,370
tend to see situational
explanations of our own

547
00:34:54,370 --> 00:34:57,570
behavior, and trait
explanations of

548
00:34:57,570 --> 00:34:59,440
other people's behavior.

549
00:34:59,440 --> 00:35:05,530
Now let's take a look at this
last factor, which I'm calling

550
00:35:05,530 --> 00:35:14,160
the role of the ignorance
in person perception.

551
00:35:14,160 --> 00:35:26,330
And see how that can lead to
what looks like a biased

552
00:35:26,330 --> 00:35:30,280
outcome, perhaps even
if you didn't

553
00:35:30,280 --> 00:35:33,970
have these other factors.

554
00:35:33,970 --> 00:35:36,600
This ignorance factor's now
going to interact with these

555
00:35:36,600 --> 00:35:42,340
other factors to make
biased outcomes.

556
00:35:42,340 --> 00:35:45,470
It's quite easy to come by.

557
00:35:45,470 --> 00:35:51,980
So, let's do a version of the
classic physics joke, about

558
00:35:51,980 --> 00:35:53,650
assume the horse is a sphere.

559
00:35:57,210 --> 00:36:00,160
We're going to over simplify
the situation.

560
00:36:00,160 --> 00:36:11,640
The issue here is who are you
going to be friends with?

561
00:36:11,640 --> 00:36:15,720
Well, first of all, we have to
go back to the good, ernest

562
00:36:15,720 --> 00:36:19,880
high school discussion about
making friends with people.

563
00:36:22,440 --> 00:36:24,440
And, does it matter if
they're wearing the

564
00:36:24,440 --> 00:36:26,690
latest designer whatever?

565
00:36:26,690 --> 00:36:30,590
And, the answer, of course, is
no, because you shouldn't

566
00:36:30,590 --> 00:36:34,120
judge a book by its cover.

567
00:36:34,120 --> 00:36:34,620
Good.

568
00:36:34,620 --> 00:36:36,380
Nice cliche.

569
00:36:36,380 --> 00:36:38,110
And, it is, of course, true.

570
00:36:41,200 --> 00:36:43,890
In the first assume the
horse is a sphere over

571
00:36:43,890 --> 00:36:48,590
simplification, the problem, let
us assume that the set of

572
00:36:48,590 --> 00:36:51,240
people with whom you might be
friends in the world, is the

573
00:36:51,240 --> 00:36:56,480
set of let's just make it
all MIT undergraduates.

574
00:36:56,480 --> 00:37:00,810
And we know we don't want to
judge books by covers, so,

575
00:37:00,810 --> 00:37:05,790
therefore, what you're going
to do is, set up in depth

576
00:37:05,790 --> 00:37:08,780
interviews with everybody.

577
00:37:08,780 --> 00:37:10,570
And decide who's going
to be your friend on

578
00:37:10,570 --> 00:37:12,790
the basis of that.

579
00:37:12,790 --> 00:37:13,940
No.

580
00:37:13,940 --> 00:37:15,830
That's not going to work.

581
00:37:15,830 --> 00:37:18,890
Well, all right, the other
alternative is, don't want to

582
00:37:18,890 --> 00:37:22,170
judge books by their cover,
therefore I won't talk to

583
00:37:22,170 --> 00:37:23,520
anybody ever again.

584
00:37:23,520 --> 00:37:24,920
That's not going
to work either.

585
00:37:24,920 --> 00:37:29,810
So it is self- evident again,
various bits of this are self-

586
00:37:29,810 --> 00:37:33,830
evident, you've gotta make snap
decisions on the basis of

587
00:37:33,830 --> 00:37:35,380
imperfect information.

588
00:37:35,380 --> 00:37:37,620
It doesn't mean that you have to
make your decisions on the

589
00:37:37,620 --> 00:37:43,050
basis of whether or not they're
wearing designer

590
00:37:43,050 --> 00:37:44,370
whatevers, of course.

591
00:37:44,370 --> 00:37:48,000
But you're necessarily going
to have to make a first cut

592
00:37:48,000 --> 00:37:52,170
through the population on
the basis of essentially

593
00:37:52,170 --> 00:37:54,130
superficial information.

594
00:37:54,130 --> 00:37:55,970
Well, what's that going to do?

595
00:37:55,970 --> 00:38:08,440
Let us assume that in the world,
in an act of massive,

596
00:38:08,440 --> 00:38:15,610
further over simplification,
there are bad people and are

597
00:38:15,610 --> 00:38:17,700
good people.

598
00:38:17,700 --> 00:38:21,790
And, your job is to divide
the world into those two

599
00:38:21,790 --> 00:38:22,480
categories.

600
00:38:22,480 --> 00:38:24,710
So you're going to make
an assessment.

601
00:38:27,360 --> 00:38:31,140
You're going to perform an act
of person perception, and

602
00:38:31,140 --> 00:38:41,300
divide the world into bad
people, and good people.

603
00:38:41,300 --> 00:38:44,450
We've got a nice simple two
buy two design here.

604
00:38:44,450 --> 00:38:49,550
Ideally you want everybody to
be in those two populations,

605
00:38:49,550 --> 00:38:50,990
and in those two cells.

606
00:38:50,990 --> 00:38:55,760
Even if you could do in depth
interviews with everybody,

607
00:38:55,760 --> 00:38:58,880
it's not clear you'd never make
a mistake, but clearly,

608
00:38:58,880 --> 00:39:01,250
if you're just going to be
basing your decisions on

609
00:39:01,250 --> 00:39:04,570
relatively superficial
information, there are going

610
00:39:04,570 --> 00:39:06,910
to be errors.

611
00:39:06,910 --> 00:39:09,450
I labeled these Type 1 and
Type 2, which is actually

612
00:39:09,450 --> 00:39:12,340
jargon from signal detection
land, but

613
00:39:12,340 --> 00:39:14,460
don't worry about that.

614
00:39:14,460 --> 00:39:22,950
It just, in this case, give us
a chance to ask the question.

615
00:39:22,950 --> 00:39:27,720
Which of these type of
errors is worse?

616
00:39:27,720 --> 00:39:34,850
If you had a choice about which
error to make, how many

617
00:39:34,850 --> 00:39:40,760
people, given the choice
between, let's be clear about

618
00:39:40,760 --> 00:39:43,920
this, you've got a good person,
and you declare that

619
00:39:43,920 --> 00:39:47,290
good person to be bad, or you've
got a bad person, and

620
00:39:47,290 --> 00:39:48,380
you declare him to be good.

621
00:39:48,380 --> 00:39:50,240
You got a choice between
which of those errors

622
00:39:50,240 --> 00:39:50,940
you're going to make.

623
00:39:50,940 --> 00:39:54,080
How many people vote
for Type 1?

624
00:39:54,080 --> 00:39:56,280
How many people vote
for Type 2?

625
00:39:56,280 --> 00:39:56,780
OK.

626
00:39:56,780 --> 00:39:58,820
So a Type 2 person.

627
00:39:58,820 --> 00:40:03,260
Why do you you prefer
the Type 2 error?

628
00:40:07,390 --> 00:40:08,810
Any Type 2 person?

629
00:40:08,810 --> 00:40:10,060
AUDIENCE: [INAUDIBLE]

630
00:40:12,510 --> 00:40:14,190
PROFESSOR: You're missing
out on good people.

631
00:40:14,190 --> 00:40:17,090
That's the nice person answer.

632
00:40:17,090 --> 00:40:19,710
You don't want to inadvertently
tar some nice,

633
00:40:19,710 --> 00:40:25,060
good person with the
label of being bad.

634
00:40:25,060 --> 00:40:28,800
How about a Type 1 person?

635
00:40:28,800 --> 00:40:34,950
AUDIENCE: [INAUDIBLE]

636
00:40:34,950 --> 00:40:35,430
PROFESSOR: There's
a lot of people

637
00:40:35,430 --> 00:40:36,890
who can be your friends.

638
00:40:36,890 --> 00:40:37,840
You don't need all of them.

639
00:40:37,840 --> 00:40:39,320
And you want to get to
those bad people.

640
00:40:39,320 --> 00:40:39,970
How come?

641
00:40:39,970 --> 00:40:40,870
Anybody else?

642
00:40:40,870 --> 00:40:44,770
AUDIENCE: It could be harmful.

643
00:40:44,770 --> 00:40:45,220
PROFESSOR: Yeah.

644
00:40:45,220 --> 00:40:47,990
it could be dangerous.

645
00:40:47,990 --> 00:40:52,580
If we dichotomise this into good
and really bad, nasty,

646
00:40:52,580 --> 00:40:56,990
dangerous people, then that
intuition becomes a little

647
00:40:56,990 --> 00:41:00,140
clearer that you whatever you
else you do, you want to keep

648
00:41:00,140 --> 00:41:01,390
these people away.

649
00:41:04,820 --> 00:41:07,650
This is applied signal
detection theory.

650
00:41:07,650 --> 00:41:13,090
Usually you do signal detection
theory in visual

651
00:41:13,090 --> 00:41:14,880
perception land or something.

652
00:41:14,880 --> 00:41:21,290
But this is what the next page
of the handout has on it.

653
00:41:21,290 --> 00:41:24,720
But here's where this
is coming from.

654
00:41:24,720 --> 00:41:27,840
Let us suppose, again, for
the sake of vast over-

655
00:41:27,840 --> 00:41:42,250
simplification, that there are
on a scale of goodness, that

656
00:41:42,250 --> 00:41:44,620
the only two types of
people in the world.

657
00:41:44,620 --> 00:41:53,570
There are good people
and bad people.

658
00:41:53,570 --> 00:41:59,420
And, you want to pick
the good people and

659
00:41:59,420 --> 00:42:00,750
reject the bad people.

660
00:42:00,750 --> 00:42:07,230
But the difficulty is that you
can't successfully see this,

661
00:42:07,230 --> 00:42:09,060
because your information
is lousy.

662
00:42:09,060 --> 00:42:13,790
The effect of your information
being lousy is that what you

663
00:42:13,790 --> 00:42:23,070
see is a distribution of
goodness and badness,

664
00:42:23,070 --> 00:42:24,320
something like that.

665
00:42:27,390 --> 00:42:29,960
By the way, if you were doing
this in vision land, this

666
00:42:29,960 --> 00:42:34,110
would be one light and
another light.

667
00:42:34,110 --> 00:42:36,040
Can you tell the difference
between a dim light and a

668
00:42:36,040 --> 00:42:38,090
bright light or something
like that.

669
00:42:38,090 --> 00:42:40,920
By the time it goes through your
nervous system, rather

670
00:42:40,920 --> 00:42:43,830
than being always looking
exactly like this, or always

671
00:42:43,830 --> 00:42:45,990
looking exactly like this,
sometimes the bright light

672
00:42:45,990 --> 00:42:48,440
looks a little dim, sometimes
the dim light

673
00:42:48,440 --> 00:42:49,280
looks a little bright.

674
00:42:49,280 --> 00:42:51,540
And how do you decide which
one you've seen?

675
00:42:51,540 --> 00:42:55,010
So, you got a person
in front of you.

676
00:42:55,010 --> 00:42:57,230
How can you decide whether
they're good or bad?

677
00:42:57,230 --> 00:42:59,840
Well, the best you
can do, is put a

678
00:42:59,840 --> 00:43:01,490
criterion in there somewhere.

679
00:43:01,490 --> 00:43:06,010
So let's just divide it.

680
00:43:06,010 --> 00:43:09,110
If I do that, that's OK.

681
00:43:09,110 --> 00:43:14,000
That means I'm going to declare
everybody on this side

682
00:43:14,000 --> 00:43:21,330
to be good, and everybody
on this side to be bad.

683
00:43:21,330 --> 00:43:25,320
And, OK, so I'm declaring all
of these people, who are, in

684
00:43:25,320 --> 00:43:29,550
fact, good to be good.

685
00:43:29,550 --> 00:43:37,280
The difficulty, the sad thing,
is the good people who I'm

686
00:43:37,280 --> 00:43:41,010
declaring to be bad, so the
Type 1 errors are here.

687
00:43:45,570 --> 00:43:47,710
These are good people who
I declared to be bad.

688
00:43:47,710 --> 00:43:49,710
That's too bad.

689
00:43:49,710 --> 00:43:51,410
OK.

690
00:43:51,410 --> 00:43:55,300
Here, on this side are all
the bad people who I

691
00:43:55,300 --> 00:43:56,290
declared to be bad.

692
00:43:56,290 --> 00:43:57,910
That's exactly what
I wanted to do.

693
00:43:57,910 --> 00:44:06,080
But these guys, these are
the Type 2 errors.

694
00:44:06,080 --> 00:44:10,750
These are bad people who beat my
criterion level, and I said

695
00:44:10,750 --> 00:44:12,640
they're good.

696
00:44:12,640 --> 00:44:16,240
Now, you should be able to
figure out, looking at a

697
00:44:16,240 --> 00:44:20,790
picture like this, there's no
way to eliminate error.

698
00:44:20,790 --> 00:44:25,400
If this is the situation of
the stimuli I have to deal

699
00:44:25,400 --> 00:44:26,890
with, there's no way
to eliminate error.

700
00:44:26,890 --> 00:44:29,740
All I can do is apportion
error.

701
00:44:29,740 --> 00:44:34,350
So, if I decide that the Type
2 errors are the dangerous

702
00:44:34,350 --> 00:44:38,410
errors that I need to avoid,
then I'm going to move my

703
00:44:38,410 --> 00:44:40,440
criterion over.

704
00:44:40,440 --> 00:44:45,330
This is the second picture
on the handout.

705
00:44:45,330 --> 00:44:53,810
If I move my criterion over so
that I reduce my Type 2 errors

706
00:44:53,810 --> 00:44:59,760
to just these few, let's say,
now the result is that I've

707
00:44:59,760 --> 00:45:03,790
massively increased
my Type 1 errors.

708
00:45:03,790 --> 00:45:07,750
I'm now declaring all these
lovely people to be people I

709
00:45:07,750 --> 00:45:09,890
don't want to make
friends with.

710
00:45:09,890 --> 00:45:13,320
It's sad, but that's
the way it go.

711
00:45:13,320 --> 00:45:16,680
Because, as the gentleman
back there said, yeah.

712
00:45:16,680 --> 00:45:18,650
There are a lot of people here,
so I've got plenty of

713
00:45:18,650 --> 00:45:19,670
people to be friends with.

714
00:45:19,670 --> 00:45:22,430
And these guys will just have
to deal with the fact that

715
00:45:22,430 --> 00:45:23,190
they're not my friend.

716
00:45:23,190 --> 00:45:24,540
That's just the way it is.

717
00:45:24,540 --> 00:45:28,550
But, I'm not letting any of
these mean, nasty, rotten

718
00:45:28,550 --> 00:45:32,130
people in, except
for these guys.

719
00:45:32,130 --> 00:45:34,490
Most of these guys, I'm
going to avoid.

720
00:45:34,490 --> 00:45:34,900
OK.

721
00:45:34,900 --> 00:45:40,730
Now, look what happens when you
deal with an out group.

722
00:45:40,730 --> 00:45:43,180
When you deal with a group
other than your own.

723
00:45:43,180 --> 00:45:50,800
If the argument is that part of
what makes an out group the

724
00:45:50,800 --> 00:45:56,110
out group is the fact that
you know less about them.

725
00:45:56,110 --> 00:46:02,160
The way to express that in these
sort of signal detection

726
00:46:02,160 --> 00:46:04,810
terms, is as an increase
in the noise.

727
00:46:04,810 --> 00:46:07,280
An increase in the spread
of these distributions.

728
00:46:07,280 --> 00:46:10,660
So what that's going to end up
looking like is you still have

729
00:46:10,660 --> 00:46:12,760
the good people and
the bad people.

730
00:46:12,760 --> 00:46:23,100
But now your perception
is less accurate.

731
00:46:44,480 --> 00:46:44,960
OK.

732
00:46:44,960 --> 00:46:46,170
That'll do.

733
00:46:46,170 --> 00:46:50,310
So, they now just overlap more,
because we just don't

734
00:46:50,310 --> 00:46:51,760
know as much about
these people.

735
00:46:51,760 --> 00:46:54,310
You're not as good at --

736
00:46:54,310 --> 00:46:56,060
you want a trivial
example of this?

737
00:46:59,770 --> 00:47:09,240
Let's take wolves, there's an
out group you know, the ones

738
00:47:09,240 --> 00:47:11,880
with the big sharp, you know,
grandma what big teeth you've

739
00:47:11,880 --> 00:47:14,410
got, kind of wolves.

740
00:47:14,410 --> 00:47:16,970
Maybe there's a good wolf
out there somewhere.

741
00:47:16,970 --> 00:47:17,790
A nice wolf.

742
00:47:17,790 --> 00:47:19,850
You know the kind of wolf that
we're supposed to have adopted

743
00:47:19,850 --> 00:47:27,390
back in antiquity to make
into dogs eventually.

744
00:47:27,390 --> 00:47:32,280
But you meet a wolf on the
street, and you don't know

745
00:47:32,280 --> 00:47:34,340
much about him.

746
00:47:34,340 --> 00:47:37,230
Where should you draw your
threshold before bringing him

747
00:47:37,230 --> 00:47:40,480
home to play with your
six year old?

748
00:47:40,480 --> 00:47:42,020
You're going to draw
your threshold out

749
00:47:42,020 --> 00:47:43,940
here somewhere, right?

750
00:47:43,940 --> 00:47:47,510
I don't care if I reject
the one nice wolf.

751
00:47:47,510 --> 00:47:52,410
It's just really risky
to bring wolves home.

752
00:47:52,410 --> 00:47:54,160
And, that's because you're just
really, really ignorant

753
00:47:54,160 --> 00:47:55,830
about wolves, you just don't
know much about them.

754
00:47:55,830 --> 00:47:57,800
Maybe if you knew wolves better,
you'd know who the

755
00:47:57,800 --> 00:47:58,560
nice ones were.

756
00:47:58,560 --> 00:47:59,300
All right.

757
00:47:59,300 --> 00:48:01,710
With human populations it's
obviously much less

758
00:48:01,710 --> 00:48:02,530
dramatic than that.

759
00:48:02,530 --> 00:48:06,910
But you don't know much about
these other people, so the

760
00:48:06,910 --> 00:48:10,100
distributions theoretically
overlap more.

761
00:48:10,100 --> 00:48:13,300
You still only want to make very
few errors where you let

762
00:48:13,300 --> 00:48:15,100
bad people in next you.

763
00:48:15,100 --> 00:48:18,740
So that's going to cause you to
move that threshold still

764
00:48:18,740 --> 00:48:20,950
further over in this
direction.

765
00:48:20,950 --> 00:48:23,430
Not because you don't
like these people.

766
00:48:23,430 --> 00:48:27,110
Understand that there's no
explicit bias going on here.

767
00:48:27,110 --> 00:48:32,300
You're just being cautious
in this in this story.

768
00:48:32,300 --> 00:48:33,870
You can get explicit
bias out of those

769
00:48:33,870 --> 00:48:34,940
the first three factors.

770
00:48:34,940 --> 00:48:37,740
But here, this factor has no
explicit bias in it at all.

771
00:48:37,740 --> 00:48:38,930
Just ignorance.

772
00:48:38,930 --> 00:48:42,540
So now you say oh good I'm only
letting this percentage

773
00:48:42,540 --> 00:48:47,010
of really bad people and now
obviously there's a little

774
00:48:47,010 --> 00:48:48,230
problem here.

775
00:48:48,230 --> 00:48:53,580
Your Type 1 where you
reject good people.

776
00:48:57,570 --> 00:49:02,530
Now you have rejected almost the
entire population of this

777
00:49:02,530 --> 00:49:04,050
other group.

778
00:49:04,050 --> 00:49:08,060
You know that you're not
biased in your heart of

779
00:49:08,060 --> 00:49:11,760
hearts, because you can still
say, as it says on the

780
00:49:11,760 --> 00:49:16,790
handouts, this little tale of
the distribution some of my

781
00:49:16,790 --> 00:49:22,860
best friends are X. Whatever
that out group is.

782
00:49:22,860 --> 00:49:27,070
Some of my best friends are
white, black, Christian,

783
00:49:27,070 --> 00:49:30,710
Jewish, whatever the other out
group is you're dealing with.

784
00:49:30,710 --> 00:49:34,860
This signal detection story will
get you there with some

785
00:49:34,860 --> 00:49:37,460
people in the group who are
fine, because they beat your

786
00:49:37,460 --> 00:49:39,560
threshold, and the vast bulk
of the rest of it, who

787
00:49:39,560 --> 00:49:48,150
disappear because you're
applying the same caution to

788
00:49:48,150 --> 00:49:50,360
an out group, that you were
applying to the group that you

789
00:49:50,360 --> 00:49:51,820
knew something about.

790
00:49:51,820 --> 00:50:01,150
So, that's how ignorance can
end up being a factor in

791
00:50:01,150 --> 00:50:04,390
producing what looks like
biased behavior.

792
00:50:04,390 --> 00:50:08,070
If you compare these two, you'd
have to say I'm biased

793
00:50:08,070 --> 00:50:08,780
against this group.

794
00:50:08,780 --> 00:50:12,820
Because 100 of these people, I'm
only letting five of them

795
00:50:12,820 --> 00:50:13,760
be my friend.

796
00:50:13,760 --> 00:50:15,260
100 of these people
I'm letting 60

797
00:50:15,260 --> 00:50:16,850
of them be my friend.

798
00:50:16,850 --> 00:50:21,690
That's a biased outcome
from no explicit bias.

799
00:50:21,690 --> 00:50:27,530
Now this question of explicit
versus either no bias or

800
00:50:27,530 --> 00:50:31,040
implicit bias is an
interesting one.

801
00:50:31,040 --> 00:50:40,780
You don't necessarily have a
clear idea of the biases that

802
00:50:40,780 --> 00:50:41,600
you may have.

803
00:50:41,600 --> 00:50:50,970
One of the more interesting and
more disturbing bits --

804
00:50:50,970 --> 00:50:53,930
there's a thing called
an implicit

805
00:50:53,930 --> 00:50:58,460
attitude test, the IAT.

806
00:50:58,460 --> 00:51:03,600
If you want to try this
out on yourself, go to

807
00:51:03,600 --> 00:51:07,990
www.prejudice.com I think
is the right site.

808
00:51:07,990 --> 00:51:09,860
That is one site.

809
00:51:09,860 --> 00:51:18,150
But if that fails go and find
your way to the website

810
00:51:18,150 --> 00:51:19,400
mosuronbanashi at Harvard.

811
00:51:23,220 --> 00:51:25,550
She is one of the leading
practitioners of this, and her

812
00:51:25,550 --> 00:51:28,430
website will link you to a place
where you can try this

813
00:51:28,430 --> 00:51:30,360
out on yourself.

814
00:51:30,360 --> 00:51:34,520
As the website will tell
you, before forewarned.

815
00:51:34,520 --> 00:51:37,510
You may find the results of
this experiment to be

816
00:51:37,510 --> 00:51:39,510
disturbing to you.

817
00:51:39,510 --> 00:51:42,010
But, it's well worth trying
out yourself.

818
00:51:42,010 --> 00:51:44,240
Now what is this experiment
about?

819
00:51:44,240 --> 00:51:49,600
This experiment is, in effect,
a version of a Stroop

820
00:51:49,600 --> 00:51:51,450
interference test.

821
00:51:51,450 --> 00:51:55,580
The classic stroop interference
experiment is an

822
00:51:55,580 --> 00:51:58,500
experiment where what you do
is you see a collection of

823
00:51:58,500 --> 00:52:03,610
words and your job is, whatever,
the word says, it's

824
00:52:03,610 --> 00:52:07,770
just to tell me what color the
ink is that the word is

825
00:52:07,770 --> 00:52:08,590
written in.

826
00:52:08,590 --> 00:52:15,880
So if I write cat in red
ink, you say red.

827
00:52:15,880 --> 00:52:21,000
And, if I write dog, in blue
ink, you say, dog.

828
00:52:21,000 --> 00:52:21,620
No.

829
00:52:21,620 --> 00:52:23,330
You say blue.

830
00:52:23,330 --> 00:52:24,680
That's a different
interference.

831
00:52:24,680 --> 00:52:33,710
The problem is, that if I write
red in blue ink, some

832
00:52:33,710 --> 00:52:37,260
people will simply make the
mistake of saying red, and

833
00:52:37,260 --> 00:52:42,380
everybody on average, will be
substantially slowed down,

834
00:52:42,380 --> 00:52:48,100
because of an inability to
suppress that response.

835
00:52:48,100 --> 00:52:52,110
And if I do red in red ink
they'll be speeded up.

836
00:52:52,110 --> 00:52:54,930
So, if the two sources conflict
with each other,

837
00:52:54,930 --> 00:52:55,720
you're slowed.

838
00:52:55,720 --> 00:52:59,120
If the two sources agree with
each other you're speeded.

839
00:52:59,120 --> 00:52:59,570
OK.

840
00:52:59,570 --> 00:53:07,660
So, here's what you do
in an IAT experiment.

841
00:53:07,660 --> 00:53:13,770
What you do, is you tell people,
I'm going to show you

842
00:53:13,770 --> 00:53:15,720
some words.

843
00:53:15,720 --> 00:53:20,550
And, if they're good words,
they're nice words, you push

844
00:53:20,550 --> 00:53:21,800
this button.

845
00:53:25,400 --> 00:53:32,290
And, if they're nasty words,
you push this button.

846
00:53:32,290 --> 00:53:33,730
OK.

847
00:53:33,730 --> 00:53:34,410
No problem.

848
00:53:34,410 --> 00:53:38,040
So nice boink.

849
00:53:38,040 --> 00:53:39,620
Evil.

850
00:53:39,620 --> 00:53:40,010
Boink.

851
00:53:40,010 --> 00:53:40,650
Pain.

852
00:53:40,650 --> 00:53:41,410
Boink.

853
00:53:41,410 --> 00:53:42,570
And so on.

854
00:53:42,570 --> 00:53:43,920
Not very tough.

855
00:53:43,920 --> 00:53:44,640
OK.

856
00:53:44,640 --> 00:53:46,020
Second task.

857
00:53:46,020 --> 00:53:53,680
I'm going to show you some
pictures of people, if it's an

858
00:53:53,680 --> 00:53:59,890
old person, I want you to push
one button, if it's a young

859
00:53:59,890 --> 00:54:05,350
person, I want you to
push another button.

860
00:54:05,350 --> 00:54:09,330
So we put me up there, boink.

861
00:54:09,330 --> 00:54:12,720
Put you up there, boink.

862
00:54:12,720 --> 00:54:17,400
Now what we do, is we
do mixed blocks.

863
00:54:17,400 --> 00:54:20,910
Where you're going to see words
and pictures together.

864
00:54:20,910 --> 00:54:28,140
And, I'm going to tell you,
OK, now if you see a nice

865
00:54:28,140 --> 00:54:33,070
word, or an old face, push
this button, if you see a

866
00:54:33,070 --> 00:54:37,200
nasty word, or young face,
push this button.

867
00:54:37,200 --> 00:54:40,980
It's just the tasks on
top of each other.

868
00:54:40,980 --> 00:54:45,280
Whatever we do in this regard,
you'll be slower, because now

869
00:54:45,280 --> 00:54:47,880
you have to keep two rules in
mind at once, but what's

870
00:54:47,880 --> 00:54:53,690
striking, is it if you do nice
and old, you're significantly

871
00:54:53,690 --> 00:54:57,450
slower than if you do
nice and young.

872
00:54:57,450 --> 00:55:02,170
Its as if nice and old maps
the same response.

873
00:55:02,170 --> 00:55:06,160
It causes a conflict that
doesn't work for you.

874
00:55:06,160 --> 00:55:08,240
And nice and young does.

875
00:55:08,240 --> 00:55:14,820
As if you've got a biased in
favor of young over old.

876
00:55:18,340 --> 00:55:21,230
If we ask you to why is this an
implicit attitude test if

877
00:55:21,230 --> 00:55:25,210
we give you an explicit attitude
test that says what's

878
00:55:25,210 --> 00:55:27,330
your attitude towards young
and old people.

879
00:55:27,330 --> 00:55:30,040
You may perfectly well report
I love old people.

880
00:55:30,040 --> 00:55:30,660
I love young people.

881
00:55:30,660 --> 00:55:32,610
I love everybody.

882
00:55:32,610 --> 00:55:36,010
But, you'll still come
up with this result.

883
00:55:36,010 --> 00:55:38,970
I deliberately did this one,
because it doesn't tend to

884
00:55:38,970 --> 00:55:43,850
carry an awful lot emotional
loading for people.

885
00:55:43,850 --> 00:55:54,310
But the reason this may be a
disturbing test for people to

886
00:55:54,310 --> 00:55:58,520
take -- you should still go off
and do it-- is that if I

887
00:55:58,520 --> 00:56:05,270
do nice and black, African
American pictures and nasty

888
00:56:05,270 --> 00:56:15,230
and white, regardless of your
explicit report, of what you

889
00:56:15,230 --> 00:56:19,100
consider your bias to be, the
white population in this

890
00:56:19,100 --> 00:56:23,000
country, will, on average, have
slower reaction time to

891
00:56:23,000 --> 00:56:29,040
nice, black pairings then to a
pairing of nice and white.

892
00:56:29,040 --> 00:56:31,780
Actually, one of the more
interesting and depressing

893
00:56:31,780 --> 00:56:36,730
findings, is that doesn't even
completely reverse with an

894
00:56:36,730 --> 00:56:39,180
African American population
of subjects.

895
00:56:39,180 --> 00:56:41,570
In the African American
population, the last time I

896
00:56:41,570 --> 00:56:45,480
checked on the data, the
pairings were roughly equal.

897
00:56:45,480 --> 00:56:49,970
So, the African American
population has presumably some

898
00:56:49,970 --> 00:56:55,840
ethnocentric biased in its own
favor, an implicit bias in its

899
00:56:55,840 --> 00:56:59,250
own favor, but that's counter
balanced, by some

900
00:56:59,250 --> 00:57:03,460
incorporation of overall bias
against, and so they come out

901
00:57:03,460 --> 00:57:06,640
is roughly equal.

902
00:57:06,640 --> 00:57:11,600
The debates and the literature
about this line of work is

903
00:57:11,600 --> 00:57:16,380
this talking about implicit
attitudes which is a notion

904
00:57:16,380 --> 00:57:18,830
that regardless of what we
think, we're all racists or

905
00:57:18,830 --> 00:57:19,780
something like that.

906
00:57:19,780 --> 00:57:26,020
Or, is this just say that we
have somehow incorporated that

907
00:57:26,020 --> 00:57:29,470
we know, at some level, the
biases of the culture, as a

908
00:57:29,470 --> 00:57:32,630
whole, even though, we,
ourselves, may not be biased.

909
00:57:32,630 --> 00:57:35,390
It is an interesting question
beyond the scope of what I can

910
00:57:35,390 --> 00:57:38,470
talk about today, whether there
is a serious difference

911
00:57:38,470 --> 00:57:40,660
between those two.

912
00:57:40,660 --> 00:57:44,880
But, the disturbing finding is,
-- and you can do this --

913
00:57:44,880 --> 00:57:48,440
it's not black, white, old,
young, by no means is the

914
00:57:48,440 --> 00:57:49,660
limit on this game.

915
00:57:49,660 --> 00:57:51,530
Once you've got the
methodology, you

916
00:57:51,530 --> 00:57:53,550
can do it on anything.

917
00:57:53,550 --> 00:58:00,430
So, shortly after September 11,
they started doing these

918
00:58:00,430 --> 00:58:04,680
tests on opinions
about nice --

919
00:58:04,680 --> 00:58:07,470
and you don't need to do with
faces either, so if you want

920
00:58:07,470 --> 00:58:13,180
to do Arab versus non- Arab, you
can just do it with names.

921
00:58:13,180 --> 00:58:18,670
So, you do Abdul, and Mohammed
and so on, and then you do

922
00:58:18,670 --> 00:58:20,370
Chris, and Jane.

923
00:58:20,370 --> 00:58:22,980
And, you find that in the
American population, at the

924
00:58:22,980 --> 00:58:28,190
moment, nice and Mohammed is
slower then nice and Robert.

925
00:58:28,190 --> 00:58:34,430
It's not surprising, but, it is,
nevertheless, disturbing

926
00:58:34,430 --> 00:58:37,790
how easy it is to show
these effects.

927
00:58:37,790 --> 00:58:38,590
They're robust.

928
00:58:38,590 --> 00:58:44,670
They show up across populations,
and they show up

929
00:58:44,670 --> 00:58:48,790
fairly independent of
what people report.

930
00:58:48,790 --> 00:58:54,820
It doesn't matter if you
explicitly, and let's assume

931
00:58:54,820 --> 00:58:56,050
that people are reporting
honestly.

932
00:58:56,050 --> 00:59:01,430
It doesn't matter if you
explicitly have the bias.

933
00:59:01,430 --> 00:59:07,480
You can show something that
looks like a bias with a test

934
00:59:07,480 --> 00:59:07,840
of this sort.

935
00:59:07,840 --> 00:59:09,130
Anyway, give it a try.

936
00:59:09,130 --> 00:59:12,530
It's interesting
and disturbing.

937
00:59:12,530 --> 00:59:16,750
And the interesting and
disturbing department, I will

938
00:59:16,750 --> 00:59:20,380
continue in a minute or two
talking about the link between

939
00:59:20,380 --> 00:59:24,000
attitudes and actual behavior.

940
00:59:24,000 --> 00:59:26,650
So, this is a good place
for a short break.

941
01:01:31,240 --> 01:01:41,280
So, look.

942
01:01:41,280 --> 01:01:42,970
Bias.

943
01:01:42,970 --> 01:01:49,060
We can presumably agree that
bias isn't a good thing, but

944
01:01:49,060 --> 01:01:53,060
if it stays in the realm of
private opinion, or if it

945
01:01:53,060 --> 01:01:56,490
stays in the realm of implicit
opinion that you don't even

946
01:01:56,490 --> 01:02:02,720
know what you have yourself,
it's not exactly front page

947
01:02:02,720 --> 01:02:06,860
news, but we also all know from
reading the front page,

948
01:02:06,860 --> 01:02:13,010
that there are regrettably
frequent occcasions where one

949
01:02:13,010 --> 01:02:19,080
group is willing to slaughter
another group based on very

950
01:02:19,080 --> 01:02:24,700
little more, if anything more,
than group identity.

951
01:02:24,700 --> 01:02:34,880
So, an absolutely critical
question, is how can people be

952
01:02:34,880 --> 01:02:39,130
moved from attitude to action?

953
01:02:39,130 --> 01:02:44,690
And, the disturbing aspect of
what we know from experimental

954
01:02:44,690 --> 01:02:50,920
psych about this, is that it is
surprisingly easy to have

955
01:02:50,920 --> 01:02:56,330
your behavior controlled
by outside forces.

956
01:02:56,330 --> 01:03:00,550
The experimental work, of
course, cannot get people to

957
01:03:00,550 --> 01:03:04,200
go out and slaughter
each other.

958
01:03:04,200 --> 01:03:07,340
Nothing like that would be even
faintly moral, but rather

959
01:03:07,340 --> 01:03:10,760
like the Clay and Kandinsky
experiments.

960
01:03:10,760 --> 01:03:14,220
You can do experiments that show
that it's surprisingly

961
01:03:14,220 --> 01:03:21,260
easy to manipulate the situation
in ways that changes

962
01:03:21,260 --> 01:03:25,440
behavior in ways, that changes
behavior in ways that look at

963
01:03:25,440 --> 01:03:26,920
least a little bit disturbing.

964
01:03:26,920 --> 01:03:31,100
One of the classics, back in
the '50's, that gets this

965
01:03:31,100 --> 01:03:36,720
literature going, was done by
Ash at Columbia at the time.

966
01:03:36,720 --> 01:03:43,680
I think that the picture
is still in the book.

967
01:03:43,680 --> 01:03:46,280
A marvelous collection
of male, Columbian

968
01:03:46,280 --> 01:03:49,740
nerds from the '50's.

969
01:03:49,740 --> 01:03:51,500
Anybody read the chapter
yet, and happen to

970
01:03:51,500 --> 01:03:52,750
know if it's there?

971
01:03:55,870 --> 01:03:58,660
Here's the basic experiment,
here you come into this

972
01:03:58,660 --> 01:04:04,850
experiment, and you're doing
an experiment on visual

973
01:04:04,850 --> 01:04:07,280
perception.

974
01:04:07,280 --> 01:04:14,100
Ash shows you a card with
three lines on it.

975
01:04:18,970 --> 01:04:23,310
Your job is to say which
line is longer.

976
01:04:26,100 --> 01:04:28,480
Now, the odd thing about this,
well, you're not an

977
01:04:28,480 --> 01:04:30,350
experimentalist, so why
should you care?

978
01:04:30,350 --> 01:04:32,870
But it's a little odd, as
an experiment to be

979
01:04:32,870 --> 01:04:34,010
doing this in a group.

980
01:04:34,010 --> 01:04:36,770
But it turns out, you're
doing this in a group.

981
01:04:36,770 --> 01:04:43,060
And, so Ash holds up the card,
and you say B, and you say B,

982
01:04:43,060 --> 01:04:47,260
and you say B, and everybody
said B. Next card.

983
01:04:47,260 --> 01:04:49,910
I'm not going to bother changing
my cards, but you get

984
01:04:49,910 --> 01:04:50,930
the basic idea.

985
01:04:50,930 --> 01:04:58,600
On the critical trial,
up comes this card.

986
01:04:58,600 --> 01:05:11,990
He says, C. He says C. He says
C. She says C. C. C. Now,

987
01:05:11,990 --> 01:05:15,720
we're up to --

988
01:05:15,720 --> 01:05:17,750
I stuck with her because,
she's got glasses.

989
01:05:17,750 --> 01:05:22,860
Because the nerdy Columbia
guy has glasses on too.

990
01:05:22,860 --> 01:05:24,870
What does she do?

991
01:05:24,870 --> 01:05:26,370
Well, why did all these
guys say C?

992
01:05:26,370 --> 01:05:28,330
Are some kind of morons?

993
01:05:28,330 --> 01:05:30,200
They're all confederates
of the experimenter.

994
01:05:30,200 --> 01:05:34,110
The only real subject
is this person.

995
01:05:34,110 --> 01:05:41,340
And, the question is,
does she say C?

996
01:05:41,340 --> 01:05:50,850
The answer is about a third of
the time in the original Ash

997
01:05:50,850 --> 01:05:55,790
experiment, the answer's yes,
she says C. It is completely

998
01:05:55,790 --> 01:05:59,970
clear that even when she
doesn't say C, she's

999
01:05:59,970 --> 01:06:02,290
uncomfortable.

1000
01:06:02,290 --> 01:06:06,090
This is an experiment
on peer pressure.

1001
01:06:06,090 --> 01:06:11,490
And, it's perfectly clear that
when everybody else is saying

1002
01:06:11,490 --> 01:06:15,260
C, she's busy taking off her
glasses, and checking them,

1003
01:06:15,260 --> 01:06:18,630
and stuff like that, to see
what's going on here.

1004
01:06:18,630 --> 01:06:20,340
There's something wrong.

1005
01:06:20,340 --> 01:06:23,950
What do you think
manipulates --

1006
01:06:23,950 --> 01:06:26,140
the standard result is you got
about a third of the people

1007
01:06:26,140 --> 01:06:27,640
complying with the pressure.

1008
01:06:27,640 --> 01:06:30,250
What reduces that compliance?

1009
01:06:36,420 --> 01:06:37,620
AUDIENCE: How [INAUDIBLE]

1010
01:06:37,620 --> 01:06:38,870
PROFESSOR: Yeah, sure.

1011
01:06:42,470 --> 01:06:44,010
Presumably it's hard
to get the result.

1012
01:06:46,560 --> 01:06:49,220
But, OK if we keep the physical
stimuli the same.

1013
01:06:49,220 --> 01:06:50,470
Yes.

1014
01:06:52,120 --> 01:06:53,970
AUDIENCE: [INAUDIBLE]

1015
01:06:53,970 --> 01:06:58,920
PROFESSOR: If somebody picks
A, and it just looks noisy.

1016
01:06:58,920 --> 01:07:01,160
I don't know if they ever did
that particular manipulation.

1017
01:07:01,160 --> 01:07:03,640
That's an interesting
question.

1018
01:07:03,640 --> 01:07:05,040
That might change things.

1019
01:07:05,040 --> 01:07:06,529
AUDIENCE: If they have six or
seven people, [INAUDIBLE]

1020
01:07:10,780 --> 01:07:14,770
PROFESSOR: It doesn't
take any support.

1021
01:07:14,770 --> 01:07:17,700
You probably know this from
arguments with groups of

1022
01:07:17,700 --> 01:07:19,610
people or something.

1023
01:07:19,610 --> 01:07:21,770
It's hard to be the first
person to voice

1024
01:07:21,770 --> 01:07:22,590
the minority view.

1025
01:07:22,590 --> 01:07:24,200
It's much easier to be
the second person.

1026
01:07:24,200 --> 01:07:25,880
I think I actually have
the data from that.

1027
01:07:25,880 --> 01:07:26,380
Yeah.

1028
01:07:26,380 --> 01:07:27,880
One supporter.

1029
01:07:27,880 --> 01:07:32,000
One supporter in the group
drops compliance from one

1030
01:07:32,000 --> 01:07:34,130
third to 1/12.

1031
01:07:34,130 --> 01:07:40,190
So, big drop in the amount
of compliance.

1032
01:07:40,190 --> 01:07:45,240
The more people who say C, the
more likely you are to comply.

1033
01:07:45,240 --> 01:07:48,890
The smaller the group, the less
likely you are to comply.

1034
01:07:48,890 --> 01:07:55,070
But, the point is, that even
in a matter as seemingly

1035
01:07:55,070 --> 01:08:01,060
straight forward as which line
is longer, you can feel that

1036
01:08:01,060 --> 01:08:04,280
pressure from others.

1037
01:08:04,280 --> 01:08:08,270
The most famous experiment in
this canon is an experiment

1038
01:08:08,270 --> 01:08:11,560
done by Stanley Milgram.

1039
01:08:11,560 --> 01:08:15,160
And, in that experiment,
here's the set up.

1040
01:08:15,160 --> 01:08:23,660
You come into the lab for
a study on learning.

1041
01:08:23,660 --> 01:08:26,440
The effects of punishment
on learning.

1042
01:08:26,440 --> 01:08:28,250
And, there are two of you.

1043
01:08:28,250 --> 01:08:33,450
And, one of you is going to be
the learner and one of you is

1044
01:08:33,450 --> 01:08:36,640
going to be the teacher.

1045
01:08:36,640 --> 01:08:37,040
OK.

1046
01:08:37,040 --> 01:08:39,180
And we're going to decide
this randomly.

1047
01:08:39,180 --> 01:08:41,990
Flip a coin.

1048
01:08:41,990 --> 01:08:44,910
You're going to be the
teacher today.

1049
01:08:44,910 --> 01:08:47,090
Now, in fact, this
is not random.

1050
01:08:47,090 --> 01:08:48,950
The subject is always
the teacher.

1051
01:08:48,950 --> 01:08:53,870
The learner is always a stooge
of the experiementer and

1052
01:08:53,870 --> 01:08:55,570
here's what happens.

1053
01:08:55,570 --> 01:08:58,640
You're told you going to do some
sort of a task, and your

1054
01:08:58,640 --> 01:09:05,000
job, as the teacher, is to give
the learner a shock every

1055
01:09:05,000 --> 01:09:07,590
time they make a mistake.

1056
01:09:07,590 --> 01:09:11,075
This was done back in the '60's
with this great big hunk

1057
01:09:11,075 --> 01:09:18,200
of electrical equipment, with a
gazillion switches on there,

1058
01:09:18,200 --> 01:09:25,770
running from 15 volts to, I
think 450 volts, in 15 volt

1059
01:09:25,770 --> 01:09:32,190
increments, with instructive
little labels like mild, and

1060
01:09:32,190 --> 01:09:36,850
then, up here somewhere is
severe, and by the time, you

1061
01:09:36,850 --> 01:09:41,220
get up here, it's labeled
something like XXX.

1062
01:09:44,460 --> 01:09:47,300
The rule is every time you make
a mistake, you increase

1063
01:09:47,300 --> 01:09:48,970
the voltage.

1064
01:09:48,970 --> 01:09:53,880
Now, we will give you
a 45 volt shock.

1065
01:09:53,880 --> 01:09:57,190
You, the teacher, gets a
45 volt shock, just to

1066
01:09:57,190 --> 01:09:58,430
see what it's like.

1067
01:09:58,430 --> 01:10:02,410
And, a 45 volt shock from
this apparatus is mildly

1068
01:10:02,410 --> 01:10:03,110
unpleasant.

1069
01:10:03,110 --> 01:10:05,150
It's nothing you'd want
to sign up for.

1070
01:10:05,150 --> 01:10:07,910
It's not going to kill ya,
though the suggestion is that

1071
01:10:07,910 --> 01:10:10,500
this might.

1072
01:10:10,500 --> 01:10:16,060
And, that's the rule
of the game.

1073
01:10:16,060 --> 01:10:22,010
Now, before doing the experiment
Milgram went and

1074
01:10:22,010 --> 01:10:26,620
asked everybody under the sun,
what the result would be.

1075
01:10:32,090 --> 01:10:34,480
Well, the answer is that
everybody under the sun will

1076
01:10:34,480 --> 01:10:37,030
bail out pretty early here.

1077
01:10:37,030 --> 01:10:39,690
Nobody's going to get them
very massive shocks.

1078
01:10:47,860 --> 01:10:52,160
He asked theologians, he asked
psychologists, he ask people

1079
01:10:52,160 --> 01:10:57,380
off the street, and everybody
agreed this is not going to

1080
01:10:57,380 --> 01:11:01,230
lead to much in the
way of shocks.

1081
01:11:01,230 --> 01:11:06,106
And, this made the result of
the first experimental a

1082
01:11:06,106 --> 01:11:07,720
little surprising.

1083
01:11:07,720 --> 01:11:11,870
Everybody went all the way
through to 450 volts.

1084
01:11:11,870 --> 01:11:15,930
The entire population of
subjects in the first study

1085
01:11:15,930 --> 01:11:19,300
went through to 450 volts.

1086
01:11:19,300 --> 01:11:23,320
Now, were they with a
thrilled about this?

1087
01:11:23,320 --> 01:11:24,180
No.

1088
01:11:24,180 --> 01:11:26,590
It was also absolutely clear
that the subjects were very

1089
01:11:26,590 --> 01:11:28,240
uncomfortable about this.

1090
01:11:28,240 --> 01:11:32,640
And, that they questioned
whether they should do this.

1091
01:11:32,640 --> 01:11:36,950
And, Milgram had an absolutely
stereotypical, he'd prepared,

1092
01:11:36,950 --> 01:11:39,220
in advance, the response.

1093
01:11:39,220 --> 01:11:42,570
Please continue, the experiment
must go on.

1094
01:11:42,570 --> 01:11:43,820
And, that was it.

1095
01:11:43,820 --> 01:11:45,630
You were free to leave.

1096
01:11:45,630 --> 01:11:47,980
Though he didn't explain this
to you in great detail.

1097
01:11:47,980 --> 01:11:50,080
But if you said, should
I do this?

1098
01:11:50,080 --> 01:11:52,130
He said, please continue.

1099
01:11:52,130 --> 01:11:53,500
The experiment must go on.

1100
01:11:53,500 --> 01:11:55,960
And people did.

1101
01:11:55,960 --> 01:12:00,640
Now, he was a little
surprised.

1102
01:12:00,640 --> 01:12:06,060
In the original version, the
alleged learner had been taken

1103
01:12:06,060 --> 01:12:07,950
out and put in a
different room.

1104
01:12:07,950 --> 01:12:09,510
And Milgram figured,
well, look.

1105
01:12:09,510 --> 01:12:13,060
Maybe these people just didn't
believe the set up story here.

1106
01:12:13,060 --> 01:12:20,050
So, they moved the alleged
learner to a position where he

1107
01:12:20,050 --> 01:12:26,110
was visible and making
noises about this.

1108
01:12:29,850 --> 01:12:34,210
He's vigorously protesting as
the voltage gets larger.

1109
01:12:34,210 --> 01:12:40,050
At some point, up here,
he says he's not

1110
01:12:40,050 --> 01:12:41,440
going to respond anymore.

1111
01:12:48,170 --> 01:12:50,040
So, the experiment's rigged,
so that he keeps making

1112
01:12:50,040 --> 01:12:51,080
mistakes as a result.

1113
01:12:51,080 --> 01:12:52,830
No response is considered
an error.

1114
01:12:52,830 --> 01:12:54,830
And Milgram is there saying
please continue, the

1115
01:12:54,830 --> 01:12:56,400
experiment must go on.

1116
01:12:56,400 --> 01:12:56,560
Oh.

1117
01:12:56,560 --> 01:12:59,320
He also makes useful comments
like, I have a heart

1118
01:12:59,320 --> 01:13:01,740
condition, and stuff
like that.

1119
01:13:01,740 --> 01:13:04,010
It's pretty vivid stuff.

1120
01:13:04,010 --> 01:13:05,600
So, what happens in that case?

1121
01:13:05,600 --> 01:13:05,750
Well.

1122
01:13:05,750 --> 01:13:06,040
Great.

1123
01:13:06,040 --> 01:13:08,690
No longer do 100% of the
subjects go all the way

1124
01:13:08,690 --> 01:13:10,150
through to the end.

1125
01:13:10,150 --> 01:13:14,880
Only 2/3 of them go all the way
through with a subject who

1126
01:13:14,880 --> 01:13:20,650
has stopped responding, and has
announced for all you know

1127
01:13:20,650 --> 01:13:24,130
you're killing this
guy, perhaps.

1128
01:13:24,130 --> 01:13:29,390
This is pretty disturbing
kind of stuff.

1129
01:13:29,390 --> 01:13:34,060
It was very disturbing
to the the subjects.

1130
01:13:34,060 --> 01:13:38,400
Actually, this is one of the
experiments that produces the

1131
01:13:38,400 --> 01:13:43,830
need for informed consent in
experimental psychology.

1132
01:13:43,830 --> 01:13:46,990
You didn't just go and hijack
people off the street and say,

1133
01:13:46,990 --> 01:13:50,120
you have to be in
my experiment.

1134
01:13:50,120 --> 01:13:52,080
These people were volunteers.

1135
01:13:52,080 --> 01:13:55,390
But, there was no sort of
consent process the way we

1136
01:13:55,390 --> 01:13:56,760
would have today.

1137
01:13:56,760 --> 01:14:00,020
And, the level of distress
produced in these subjects was

1138
01:14:00,020 --> 01:14:08,260
part of what drove the field to
require informed consent.

1139
01:14:08,260 --> 01:14:10,190
Why was the experiment
done at all?

1140
01:14:10,190 --> 01:14:13,870
This is an experiment done
in the '60's, less than a

1141
01:14:13,870 --> 01:14:17,120
generation after World War II.

1142
01:14:17,120 --> 01:14:19,990
And, a question that had
obsessed social psychology,

1143
01:14:19,990 --> 01:14:23,450
since World War II, was
how could the Nazi

1144
01:14:23,450 --> 01:14:25,460
atrocities have happened?

1145
01:14:25,460 --> 01:14:29,250
Who were the people who
went and killed

1146
01:14:29,250 --> 01:14:31,010
millions of other people?

1147
01:14:31,010 --> 01:14:34,470
Not the soldiers, but the people
who killed six million

1148
01:14:34,470 --> 01:14:38,500
Jews, and I don't remember, a
million and a half gypsies,

1149
01:14:38,500 --> 01:14:41,810
and some large number
of gays and so on.

1150
01:14:41,810 --> 01:14:43,910
Who were these people?

1151
01:14:43,910 --> 01:14:49,000
There was a theory out there
that encapsulated in a book

1152
01:14:49,000 --> 01:14:51,490
called The Authoritarian
Personality which among other

1153
01:14:51,490 --> 01:14:56,720
things, fed into stereotypes
about the Germans which was

1154
01:14:56,720 --> 01:15:00,770
that there was a certain type of
person, who was willing --

1155
01:15:00,770 --> 01:15:03,850
the Nuremberg trials, the war
crime trials after the war,

1156
01:15:03,850 --> 01:15:07,170
had produced over and over
again, the line, "I was just

1157
01:15:07,170 --> 01:15:10,710
following orders".

1158
01:15:10,710 --> 01:15:12,880
And, the notion was, well there
are some people who are

1159
01:15:12,880 --> 01:15:13,700
just good it that.

1160
01:15:13,700 --> 01:15:16,500
They just follow orders.

1161
01:15:16,500 --> 01:15:19,420
And, the rest of us were
not like that.

1162
01:15:19,420 --> 01:15:23,180
Milgram suspected that
was not the case.

1163
01:15:23,180 --> 01:15:26,200
Milgram suspected that the
answer was that under the

1164
01:15:26,200 --> 01:15:31,220
right situation, many people
could be pushed into acts,

1165
01:15:31,220 --> 01:15:36,140
that they would objectively
think were impermissible.

1166
01:15:36,140 --> 01:15:43,970
This is his effort to get
at that question.

1167
01:15:47,020 --> 01:15:51,660
If this sounds like current
events to you, every social

1168
01:15:51,660 --> 01:15:54,990
psychologist with half a
credential was on the news

1169
01:15:54,990 --> 01:16:01,090
after the Abu Ghraib Prison
Scandals earlier in the year,

1170
01:16:01,090 --> 01:16:04,320
because it just sounded
so much like this

1171
01:16:04,320 --> 01:16:05,800
sort of problem again.

1172
01:16:05,800 --> 01:16:11,760
The people who were charged,
many of them responded with

1173
01:16:11,760 --> 01:16:16,580
the response, that they were
simply if not following

1174
01:16:16,580 --> 01:16:20,720
orders, following the implicit
instructions that they felt

1175
01:16:20,720 --> 01:16:22,220
around them.

1176
01:16:22,220 --> 01:16:25,360
You got to endless articles in
the papers, saying, Oh, you

1177
01:16:25,360 --> 01:16:28,670
know, so and so was just like
everybody else back home.

1178
01:16:28,670 --> 01:16:31,420
I don't understand how he, I
don't understand how she,

1179
01:16:31,420 --> 01:16:35,850
could end up in these pictures
doing things that any

1180
01:16:35,850 --> 01:16:39,860
reasonable person would say
are unacceptable in a in a

1181
01:16:39,860 --> 01:16:42,030
military prison situation.

1182
01:16:42,030 --> 01:16:45,290
It's the same kind of question,
different scale of

1183
01:16:45,290 --> 01:16:47,980
magnitude, of course, to
the Nazi atrocities.

1184
01:16:47,980 --> 01:16:49,570
But it's the same question.

1185
01:16:49,570 --> 01:16:56,540
How do people end up doing
things like this?

1186
01:16:56,540 --> 01:16:58,950
Before attempting to answer,
which I can see is going to

1187
01:16:58,950 --> 01:17:02,470
run into my next lecture, let me
tell you about a different

1188
01:17:02,470 --> 01:17:05,700
experiment designed to get
at the same question.

1189
01:17:05,700 --> 01:17:14,310
This is an experiment where you
think you're in a study,

1190
01:17:14,310 --> 01:17:17,510
sort of a consumer relations
kind of study, that you might

1191
01:17:17,510 --> 01:17:19,560
run into it the shopping mall.

1192
01:17:19,560 --> 01:17:23,350
They've set up the experiment
at a shopping mall.

1193
01:17:23,350 --> 01:17:26,100
And, the cover story is this.

1194
01:17:26,100 --> 01:17:30,650
We're doing some research
on community values.

1195
01:17:30,650 --> 01:17:32,320
Because we're basically doing

1196
01:17:32,320 --> 01:17:35,380
investigations for a legal case.

1197
01:17:35,380 --> 01:17:38,010
Here's the situation.

1198
01:17:38,010 --> 01:17:40,510
This guy was living
with a woman to

1199
01:17:40,510 --> 01:17:42,910
whom he's not married.

1200
01:17:42,910 --> 01:17:48,160
His employer found out about
it, and fired him.

1201
01:17:48,160 --> 01:17:55,720
This has gone to court, and
the court case hinges on

1202
01:17:55,720 --> 01:17:57,240
community standards.

1203
01:17:57,240 --> 01:18:01,470
If community standards are that
living in sin, out of

1204
01:18:01,470 --> 01:18:04,580
wedlock, is a bad, bad thing,
then it's OK to fire him.

1205
01:18:04,580 --> 01:18:05,040
Otherwise not.

1206
01:18:05,040 --> 01:18:07,540
So we've gotta find out what the
community standards are.

1207
01:18:07,540 --> 01:18:09,310
Let's all have a discussion.

1208
01:18:09,310 --> 01:18:11,650
So, I've told you the story.

1209
01:18:11,650 --> 01:18:13,530
I've got the cameras
rolling here.

1210
01:18:13,530 --> 01:18:16,330
I, the experimenter, I'm
going to step out

1211
01:18:16,330 --> 01:18:17,310
and you guys discuss.

1212
01:18:17,310 --> 01:18:17,780
OK.

1213
01:18:17,780 --> 01:18:18,500
You guys discuss.

1214
01:18:18,500 --> 01:18:19,490
That's great.

1215
01:18:19,490 --> 01:18:22,840
Now, I come back and there's
a group of ten of you or

1216
01:18:22,840 --> 01:18:24,040
something discussing this.

1217
01:18:24,040 --> 01:18:26,670
I come back in, and I
say, that was great.

1218
01:18:26,670 --> 01:18:29,230
Thank you very much.

1219
01:18:29,230 --> 01:18:31,840
I know what you believe,
because I've

1220
01:18:31,840 --> 01:18:32,670
been watching this.

1221
01:18:32,670 --> 01:18:37,370
But, I want you, you and you,
please to argue from the point

1222
01:18:37,370 --> 01:18:39,770
of view, that the guy
should be fired.

1223
01:18:39,770 --> 01:18:41,190
I know it's not what
you really believe.

1224
01:18:41,190 --> 01:18:42,620
But just argue for that.

1225
01:18:42,620 --> 01:18:43,900
OK.

1226
01:18:43,900 --> 01:18:46,370
Comes back in.

1227
01:18:46,370 --> 01:18:46,580
OK.

1228
01:18:46,580 --> 01:18:49,950
Now I want you, you, and you to
join that argument arguing

1229
01:18:49,950 --> 01:18:51,130
that the guy should be fired.

1230
01:18:51,130 --> 01:18:52,150
Fine.

1231
01:18:52,150 --> 01:18:56,820
And then in the penultimate
step, is I want everybody to

1232
01:18:56,820 --> 01:19:02,470
have a chance to look into the
camera, and say why the guy

1233
01:19:02,470 --> 01:19:03,120
should be fired.

1234
01:19:03,120 --> 01:19:03,920
I know you don't believe.

1235
01:19:03,920 --> 01:19:07,990
But, just give me a little
speech as if you believed it.

1236
01:19:07,990 --> 01:19:08,850
OK.

1237
01:19:08,850 --> 01:19:09,290
Cool.

1238
01:19:09,290 --> 01:19:09,890
OK.

1239
01:19:09,890 --> 01:19:10,900
Last step.

1240
01:19:10,900 --> 01:19:15,030
Here's this statement that
says that I can use my

1241
01:19:15,030 --> 01:19:20,790
videotape in any fashion I wish
including submitting it

1242
01:19:20,790 --> 01:19:22,950
as evidence in court.

1243
01:19:22,950 --> 01:19:24,200
Will you please sign.

1244
01:19:27,060 --> 01:19:28,070
I'll be back in a minute.

1245
01:19:28,070 --> 01:19:29,560
I gotta go rinse a
few things out.

1246
01:19:29,560 --> 01:19:31,670
But you guys decide.

1247
01:19:31,670 --> 01:19:37,440
The question is do
people sign?

1248
01:19:37,440 --> 01:19:41,050
I mean this is obviously
basically asking you to

1249
01:19:41,050 --> 01:19:47,310
perjure yourself if that wasn't
what you believed.

1250
01:19:47,310 --> 01:19:49,940
Now this was a huge experimental
design

1251
01:19:49,940 --> 01:19:50,390
originally.

1252
01:19:50,390 --> 01:19:55,170
They were crossing a number of
people with gender and they

1253
01:19:55,170 --> 01:19:58,410
originally had a plan for,
well I can't remember, a

1254
01:19:58,410 --> 01:19:59,530
gazillion groups.

1255
01:19:59,530 --> 01:20:04,350
They called off the experiment
early, because like the Ash

1256
01:20:04,350 --> 01:20:06,610
experiment and like the Milgram
experiment, this

1257
01:20:06,610 --> 01:20:09,200
experiment produced
extremely strong

1258
01:20:09,200 --> 01:20:12,750
feelings in their subjects.

1259
01:20:12,750 --> 01:20:15,680
They had gotten a form of
informed consent, which I

1260
01:20:15,680 --> 01:20:21,320
can't go into now, but even with
that, it felt unethical

1261
01:20:21,320 --> 01:20:22,770
to them to keep going.

1262
01:20:22,770 --> 01:20:24,440
So, they had 33 groups.

1263
01:20:24,440 --> 01:20:26,100
It was a busted design.

1264
01:20:26,100 --> 01:20:27,710
But they had 33 groups.

1265
01:20:27,710 --> 01:20:32,010
Of those 33 groups, of those
33 groups, how many did the

1266
01:20:32,010 --> 01:20:35,000
Milgram thing, went all the
way and everybody sign?

1267
01:20:35,000 --> 01:20:36,250
Do you think?

1268
01:20:38,040 --> 01:20:40,150
The answer is one.

1269
01:20:40,150 --> 01:20:40,840
I think.

1270
01:20:40,840 --> 01:20:42,790
One group got total obedience.

1271
01:20:42,790 --> 01:20:46,860
16 of the groups unanimous
refusal to sign.

1272
01:20:46,860 --> 01:20:52,240
Nine groups got majority
refusal to sign.

1273
01:20:52,240 --> 01:20:55,330
So, in this experiment,
compliance

1274
01:20:55,330 --> 01:20:58,810
was much, much lower.

1275
01:20:58,810 --> 01:21:03,110
And, the question that I'll
take up next time, for the

1276
01:21:03,110 --> 01:21:06,610
start of the next lecture, is
what's the difference between

1277
01:21:06,610 --> 01:21:08,030
the Milgram experiment --

1278
01:21:08,030 --> 01:21:10,690
let me say one last thing about
the Milgram experiment.

1279
01:21:10,690 --> 01:21:13,790
This is not an isolated
result --

1280
01:21:13,790 --> 01:21:15,210
Milgram's at Yale.

1281
01:21:15,210 --> 01:21:17,830
It's not just an isolated
experiment that happens in New

1282
01:21:17,830 --> 01:21:19,580
Haven in 1960.

1283
01:21:19,580 --> 01:21:22,260
Replicated all over the place.

1284
01:21:22,260 --> 01:21:23,640
Doesn't matter in America.

1285
01:21:23,640 --> 01:21:25,760
Doesn't matter age, sex
of the subjects.

1286
01:21:25,760 --> 01:21:27,550
It replicates beautifully.

1287
01:21:27,550 --> 01:21:29,960
Why does this work, and the
other experiment didn't?