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ABBY NOYCE: First, a little bit about how
we make decision making.
So, one model of how we make decisions
is what's called the expected utility model.
And the expected utility model basically
says that you look at--
if you have two possibilities, you
look at what you think the likely outcomes of each
of these possibilities are, how probable those outcomes are,
and how you value those outcomes.
So, if you think that if I make choice A,
then I'm very likely to have a good thing happen but there's
a small possibility of having something really bad happen,
or choice B is kind of neutral, then
depending on just how you value these different outcomes,
you're going to make different choices.
AUDIENCE: Like expected value?
ABBY NOYCE: It's all about-- yeah-- expected value.
So, you can think of this as like--
if you're trying to make decisions like what to do--
It's Friday night.
You've got an exam on Monday.
What are the things you could do?
So, you've got a decision.
So, plan A is stay home and study, right?
You could stay home.
You could be a diligent student.
And let's suppose that if you stay home and study,
then it's almost certain that you'll do well on the exam.
AUDIENCE: There's a 1% chance you'll waste your whole time
playing computer games.
ABBY NOYCE: Right, let's keep this a little bit simpler
for a minute.
Your other option is to do something else.
What might you do on a Friday night
if you don't want to stay home and study?
AUDIENCE: Go to a party.
ABBY NOYCE: Go to a party.
Go to a party.
What are some possible outcomes of going to a party?
Good things that might happen, bad things that might happen.
AUDIENCE: You meet new people.
AUDIENCE: You might have fun.
ABBY NOYCE: You might have fun.
Or it might be a party where you don't know anybody and nobody
seems to want to talk to you.
It might be a party where you have no fun.
So, what do you think--
give me a relative likelihood of these two things.
Are these equally likely outcomes?
Is one more likely than the other?
AUDIENCE: Well, it depends whose party.
ABBY NOYCE: Depends whose party.
Think of a party.
Pick one at random, some kid you know from school
but you don't know really well.
But they're having a party and you heard about it.
AUDIENCE: Were you invited?
ABBY NOYCE: Were you invited?
I don't know.
AUDIENCE: We just go to the party anyway.
ABBY NOYCE: I've done that.
So, all right.
Let's say that there's a 75% chance
that you'll have fun and a 25% chance
that you'll be miserable.
And we'll say that this is a certainty, that there's
a 100% chance that you'll do well on your exam
if you stay home and study.
How good or bad are these outcomes
on a scale from like minus 10 to 10, where 0 is kind of neutral
and minus 10 is the worst thing that could possibly happen
to you and 10 is amazing?
Where is having fun at a party on this value scale?
Is it a 2?
Is it an 8?
Is it a minus 3?
AUDIENCE: 6
ABBY NOYCE: Probably not.
A 6?
Sure.
AUDIENCE: 7.5.
ABBY NOYCE: How bad is it to go to a party and be miserable?
AUDIENCE: 0.
ABBY NOYCE: It's just neutral?
It's not bad?
AUDIENCE: That's a negative.
AUDIENCE: Minus 5.
ABBY NOYCE: Give me a negative.
Someone give me a negative number.
Minus--
AUDIENCE: Negative 7.
ABBY NOYCE: Negative 7?
All right.
How much of a good--
how important is it to you to do well on an exam?
AUDIENCE: 20.
ABBY NOYCE: Scale of minus 10 to 10.
AUDIENCE: 10.
ABBY NOYCE: It's a 10?
This is super important?
So, we probably want something that makes it more valuable
than having fun at a party, maybe.
Let's call it an 8.
OK.
So, expected utility model would look at this and say,
this option tree has a 100% chance of getting a value of 8.
So, the value of this branch of my decision tree is 8.
The expected value is 8.
The value of this branch of my decision tree
is going to be 3/4 but--
remember we assign probabilities to these.
This outcome is a lot more likely.
So, it would be 6 plus a quarter of minus 7, which is--
AUDIENCE: That's negative 7.
ABBY NOYCE: So, what is it?
It's 3/4 of 6, which is 4 and 1/2, plus a quarter of minus 7,
which is minus-- so, this is 4 and 1/2.
This is minus 1.75, right?
So 4 and 1/2, 3 and 1/2, 2 and--
AUDIENCE: .75
ABBY NOYCE: Thank you.
3/4, yes.
So, according to an expected utility model,
if this is how you weight all of these things--
the goodness here, the badness over here
and the probabilities of each of these--
then which of these is going to be the more rational outcome
to pick?
AUDIENCE: Study.
ABBY NOYCE: Stay home and study.
So, on the other hand, the fact that people probably--
that may not be the outcome that everybody would
choose in this situation--
probably shows that people either assess
these probabilities different or weight the outcomes
differently.
But this is a model of how you can expect people to behave.
AUDIENCE: I think that we forgot to take into account the
being bored while we study.
ABBY NOYCE: So, you think that studying should be--
stay home and study--
AUDIENCE: It has a short-term outcome
and a long-term outcome.
ABBY NOYCE: So, the total outcome should be maybe lower.
You think our assigned values are off.
AUDIENCE: Yes.
AUDIENCE: What are we considering "well" on the exam?
ABBY NOYCE: Up to you.
AUDIENCE: 105%.
ABBY NOYCE: Sure.
Anyway, this is a model.
So, in cases where you think that people
are going to make rational decisions,
this is a model that gets used.
So, like, economists think about decision-making
in terms of maximizing your expected outcome
and things like that.
So, the thing is that decisions that are actually
made by real live human beings, who are not
perfectly rational creatures, as you've probably
noticed at some point--
people make decisions that are different.
So, here's an example, kind of a hypothetical question
that is a good example of how this works.
Suppose the nation is preparing for the outbreak of a disease.
We know that there is an epidemic coming.
And it's expected to kill 200 people.
And the government and the national health people
and the CDC are like, OK, there are two options that we could--
kind of like 24-- there are two options
that we could do to deal with this.
In one option, we are certain to save 200 of these 600 people.
And in the other option, there's a one third probability
that we'll save all of them, but there's a 2/3 probability
that all 600 people will die from this disease.
Which of these options would you support?
AUDIENCE: The first one.
[INTERPOSING VOICES]
ABBY NOYCE: You may not even be one of the 600 people
who's expected to die in this.
You may be safely ensconced in your, I don't know,
your apocalyptic little hideaway.
But who thinks that option one looks like a more appealing
option here?
AUDIENCE: I'd run away.
ABBY NOYCE: Who thinks option two looks
like a more appealing option?
You are forced to choose.
Pick one or the other.
Option one?
Option one.
Put your hands up where I can see them so I can count you.
Make a decision.
One, two, three, four, five, six.
Option two?
And three.
Now, look at our expected utility model.
These come out the same if you count.
So, OK, one third of 600, if you multiply
the probability times the number, is 200 people.
So, according to this model, the expected utility of these
is the same.
And yet, a majority of people will like option one better.
Why?
Well, let's get to that.
Here's another scenario, same idea.
We're preparing for the outbreak of a disease.
There's been two options presented.
The first option will result in 400 of these people
dying, no matter what we do.
The second option-- well, there's
a 33% chance that nobody will die at all,
that everyone will be saved.
And a 2/3 probability that 600 people are going to die.
Which of these options do you like better?
AUDIENCE: Isn't that the same thing?
ABBY NOYCE: Who likes option one better?
Raise your hands.
Who likes option two better?
Raise your hands.
So, that's it.
All right, so by seeing them both together,
you guys notice that these are exactly the same scenario,
right?
The numbers are identical.
But if you present them to people separately,
what we'll find is that in the first case, when
this is phrased as how many people are going to be saved,
people like certainty.
People are risk-averse.
They don't want to pick a risky situation when
the question is couched in terms of how much we're
going to gain.
When the question is couched in terms
of how much you might lose, when we're looking at deaths,
people start being more attracted
to the riskier possibility.
When you have a chance of something bad happening,
but a chance that it might not, people will go for risk.
Whereas if we phrase it as a chance of something
good happening or a chance that it might not,
people will go for certainty.
People like certainty for good things and risk for bad things.
We're risk-averse when things are
discussed in terms of losses.
Does this make sense?
Think of it as, like, gambling.
If I tell you, you can either win $50 for sure
or you have a 75% chance of winning
$150 and a 25% chance of winning nothing, which of these
are you going to pick?
50 certain dollars or a gamble on getting more than that?
AUDIENCE: More.
ABBY NOYCE: Who would pick 50 certain dollars?
Who would pick the gamble?
AUDIENCE: A 75% chance of what?
ABBY NOYCE: $150.
Expected utility model says that these are equal, right?
But when it's couched as things you can get,
people tend to be--
and there's going to be variations in this for amounts
and for personality-- people tend to be more attracted
to the certain thing.
If I spin that the other way around and I said, well you
could either lose $50 for certain
or you could take a gamble where there's
a 25% chance of losing nothing or a 75% chance of losing $150,
which one are you going to prefer?
AUDIENCE: The second one.
ABBY NOYCE: The gamble.
Who thinks they prefer the gamble?
Who thinks they prefer to lose $50 for certain?
Again, the expected utility math says that they're the same.
But people like the risk.
AUDIENCE: Because there's a difference between losing $50
and not gaining $50.
Losing $50 means you lose money.
Not gaining $50 means you just don't-- nothing happens.
ABBY NOYCE: That's part of it.
So, somebody put together something
that's similar with a study, that they said, so,
imagine you're at a casino and you either--
and they set it up as two possible scenarios.
You win $200 and you have a chance
of gaining some more beyond that, or you win $400
and you have a chance of losing some beyond that,
so that all the scenarios kind of average out.
So, you ended up with $250 or something.
And I don't remember the exact numbers
and I could probably work it out but it
would take me 10 minutes.
And what they found is that even when your net outcome would
have been the same across either the win $200 and then win some
more versus win $400 and then lose some, people are, again,
more attracted-- they're--
a certain thing, when they have a chance of gaining something,
they'd rather take a smaller, certain gain,
than a riskier, larger gain.
And the opposite is true when they stand to lose something.
They'd rather take a riskier possible larger loss
but possible not losing anything, versus a smaller
certain loss.
So, even when the amounts are--
so, our perception of the change is different.
It's like we feel like gaining $50 is less bad than losing
$50, that one of these is a bigger change than the other.
AUDIENCE: Is it like the [INAUDIBLE]
when you have nothing to lose, like kind of go for it
type thing?
ABBY NOYCE: It's probably related to that,
although I'd say that that might more come from this risk
attraction to risky--
when we're losing something.
So, you're like, eh, what am I going to lose?
Actually I suspect that it's more like
if you feel like you have nothing to lose,
then you're not risking as much.
So, what might seem like a risky possibility
to someone who's got a vested interest just isn't as much
of a risk.
Like the bad things that could happen
are less bad, in that case.
But it's probably related.
There's a lot of studies looking at how people assess risk.
And the outcome is that in a lot of ways we're not--
you can set up a lot of scenarios in which we're not
very good at it.
Probably because the kinds of risks
that we evolved to deal with, the risks
you have to deal with as a primate running around
on the Savannah trying to not get eaten by a lion,
are very different from the kinds of risks
you have to assess as a human being living in a city
and dealing with cars and finances and all
of these other things that we try and make
sense out of every day.
So, our intuitive math for these things
doesn't deal well with a lot of situations
that we find ourselves in.
Yeah?
AUDIENCE: If you start off with $200,
don't people feel that, Oh, I already have $200.
So, I can only gain.
I can't lose.
Like, they have something could push off and go forward.
Because if you have $400, they're like,
oh, I can lose more than $200 or something.
ABBY NOYCE: Maybe, although I suspect
you'd see the same outcomes no matter
what your base amounts were.
I suspect you'd see the same thing with $10 and $20
and values ending up somewhere in the middle.
And I suspect you'd see it--
I think it's a factor of how the questions are phrased,
whether it's set up as a situation of loss
or a situation of gain, versus where the baselines actually
are.
All right.
So, humans are kind of generally bad at numbers.
We're good at some other things too.
So, the rules of thumb that people
use when they're trying to decide--
and this is less decision making per se--
but when we're trying to figure out how to make
sense out of the world.
Who here has taken some amount of CS, done some programming?
One-- some amount of computer science,
some amount of programming in school or for a hobby.
A few, OK.
So, when you're writing software,
you're interested in algorithms, right?
So, an algorithm is a series of steps that will always
get you a right answer.
People, humans, human brains, don't seem
to be so much on algorithms.
We tend to take a lot of shortcuts.
We tend to use what are called heuristics.
And your heuristic is like a general strategy
that will usually get you the right answer.
It's good enough.
But if you're interested in how decision-making works,
how people make some kinds of judgment calls or things like
that, then-- just like how, in vision,
it's interesting to look at the places where your visual system
makes mistakes, looking at things like optical illusions
in order to understand how it works--
people who study decision-making look at places
where these heuristics seem to lead us to making bad decisions
or decisions that are wrong relative to the facts or we
will come to a conclusion that is not actually true.
So, I want to talk about three major heuristics
that we seem to use in order to understand how our world works.
First up is what's called the representative heuristic.
So, suppose you toss a coin six times.
Which of these potential outcomes is most likely?
Heads, heads, heads, tails, tails, tails, or tails, heads,
tails, heads, tails, heads, or heads, tails, tails, heads,
tails, heads, or heads, heads, heads, heads, heads, heads?
Which of these is most likely?
AUDIENCE: It's the same, no matter what?
ABBY NOYCE: It is the same no matter what.
How many people have kind of an intuitive perception
that, for example, the last of these
is less likely if you looked at that?
Who would kind of, even if you know the math,
know better from a math perspective.
But it feels less likely, right?
If you flipped a coin six times and got
all heads you'd be like, whoa.
And yet, it's no less likely than
any other specific pattern.
But what happens is that because we
know that we're taking a random sample from a pretty
random decision space, we think that it's more likely
if the sample is similar to that population from which it's
selected.
So, this is kind of the, if it looks like a duck
and it walks like a duck and it quacks like a duck,
it's probably a duck, heuristic.
If the sample that we're looking at
seems to match the population that it's drawn from,
then this is probably true.
So, representativeness heuristics, for all
that we're going to talk about the places where they break,
heuristics are generally useful.
I mean, think about it.
They've got to be.
We wouldn't hang on to strategies
for getting around in the world, if they
weren't reasonably good at it.
You'd get wiped out of the gene pool.
It just wouldn't stick around.
So, representativeness heuristics
are good for some things.
They fail when dealing with randomness-- people
are generally bad with randomness.
They also fail when you're thinking
about complicated categories-- categories
that human beings tend to fall into.
So, If I was to describe for you my friend, Marie, and Marie
is a sophomore in college.
And she tends to wear a lot of black,
like a black t-shirt with a long sleeve fishnet
shirt underneath it, baggy black skater pants
with some D-rings and straps on them.
You guys know the look, right?
Which of these two things do you think
is more likely to be true about Marie?
Marie is an economics major or Marie is an economics
major and one of the folks who puts
on Rocky Horror at our university every Halloween.
Option A or option B, which is more likely?
So, who thinks it's option A?
Raise your hands.
Who thinks it's option B?
Raise your hands.
Which is odd because the math says
that if I've got a conjunction of two things, where Marie
is both an economics major and helps put on the Rocky Horror
Show, that can't be more likely than simply the fact
that she's an economics major.
So, this is a case where the fact that Marie,
this small sample of one, matches
our perception of people who do Rocky Horror better
than our perception of people who are economics majors.
That makes it seem like one of these things is more likely.
Something to watch out for.
So, this is, in part--
oh, one more one more kind of--
here, think about some stats questions.
So, picture a town that has two hospitals, a big one
and a little one.
They both have an OBGYN unit.
They both have babies.
About 45 babies a day are born in the big hospital
and about 15 in the little one.
And you guys probably know that pretty much half of all babies
are boys, right?
But there's going to be some day to day fluctuation in this.
So, you might have one day there's more boys than girls,
one day there's more girls than boys.
But over time, it would average out to 50%.
So, suppose that these hospitals are keeping
a record of how often there were more than 60%
of the babies born were boys.
Which hospital do you think is going
to have more days in a given year
where there are more than 60% boys?
More than 60% of the babies born that day were boys.
Larger or smaller or about the same?
Who thinks it's larger?
Smaller?
About the same?
That's what most people tend to say.
But this is one of those things where you have
what's called the small sample fallacy.
So, the sample size is--
the fact that the smaller hospital has a smaller sample
size, it's going to see skews from the average
more frequently.
If I toss a coin three times, you probably
wouldn't be shocked if they turned up
all heads or all tails, right?
If I tossed the coin 20 times, and it
was all heads or all tails, you'd be a little bit more
taken aback.
If I tossed it 100 times and got all hands,
it would be really weird, right?
Getting three heads for--
there's, what, like a 12.5% chance
of getting three heads and something much smaller--
much, much much smaller than that-- chance of getting 20
heads, 1 over 2 to the 20.
So, when you take a small sample,
the smaller your sample is relative to the population
you're drawing from, then the more likely
your sample is to be really different.
Suppose I went to your high school and just closed my eyes,
had the list of all the kids in your grade, closed my eyes,
and picked three--
what are the chances they'd all be girls?
Not huge but it wouldn't be shocking
if I picked out three kids at random and they were all girls.
What if I picked out 50?
It's a lot less likely.
The larger the sample that we're taking, the more likely
it is to be representative of the population
we're drawing from.
So, one of the places where people make mistakes
with this representative heuristic,
is when they're looking at small sample sizes
but not taking that into account when they try and consider what
the larger population would be.
This is the same mistake people make
if they know three people who fall into a category
and therefore make some kind of broad generalization,
like, I know three guys who are engineers,
therefore all engineers are guys.
No, it doesn't work that way.
But you've got to watch out for it.
All right.
Another pattern people use is what's called the availability
heuristic.
So, read this list of names.
And look at me when you're done so I know.
Just read them.
I'm not going to quiz you on them or anything, I promise.
All right.
Notice anything about them?
Anything that calls itself to your attention?
Yeah, some of them are authors.
Good.
AUDIENCE: I only recognize the author names.
ABBY NOYCE: Here's a question.
Were there more women's names or men's names?
AUDIENCE: Men's.
AUDIENCE: I couldn't tell.
ABBY NOYCE: Who thinks there were more guys' names than
women's names?
Who thinks there were more women's names than men's names?
Got to pick one.
I'm going to make you pick one.
AUDIENCE: [INAUDIBLE]
ABBY NOYCE: Nope.
Forced choice, you've got to pick one.
Who thinks there were more women than men listed there?
AUDIENCE: [INAUDIBLE]
ABBY NOYCE: Who thinks there were more men than women
listed?
So, there's more guys than women.
On the other hand, you guys probably
noticed that the women's names are
names you recognized, right?
At least some of them.
The guys' names, probably not.
The guys' names are just kind of pulled at random.
So, the availability heuristic is a guideline we use,
where we judge frequency of an occurrence
based on how easy it is for us to pull up an example,
pull up relevant examples of it.
This is kind of the mistake that's being made--
who here knows somebody who is afraid of flying,
doesn't like to fly, possibly because they
think that they're going to die in some kind of terrible plane
crash?
Who here knows somebody who's afraid to fly,
who drives or rides in cars that people drive?
What's with that?
We all know that you're way more likely to die terribly
in a car accident than a plane accident,
by like a factor of 100, right?
But a lot of people feel like dying in a plane crash
is more likely.
And one of the reasons for this is that car accidents
don't generally make the news.
Car accidents happen.
They might show up on page four of the newspaper or something.
Whereas plane crashes are a week-long media extravaganza.
They're on the news 24/7 if a plane goes down.
You've probably encountered this.
So, one of the things that happens
is if you're trying to assess the relative frequency of car
accidents versus plane crashes, then
you might have an easier time retrieving the plane crash
incidents from your memory.
You heard more about them.
You thought more about them.
They were big news when they happened.
And so, you perceive plane crashes
as being more frequent than car crashes.
So, this availability heuristic is good for lots of things.
If I ask you, do you think there are
more guys or girls in your high school class,
or is it about the same?
It's probably pretty close to about the same.
But one of the ways that we answer this kind of question
is by thinking through the members of our high school
class that we know, and saying, how many of them are girls?
How many of them are guys?
And so, if the number of girls and guys that you can recall
is roughly equal, you're going to guess
that they're about the same.
So, two things that affect how available something is
in memory, are what's called--
one of them is recency, so, how recently we
thought about something.
So, if you ask people, what do you
think your odds are of dying in a plane crash,
and ask people to estimate this--
and you ask them to do it and then--
well probably if there was a big plane crash
and it's in the news, and a week later you
ask people to estimate what their odds are
of dying in a plane crash, they'll give you one number.
And suppose six months later, you find the same people
and ask them to give you another estimate, which
of these numbers do you think is going to be bigger?
AUDIENCE: The first one?
ABBY NOYCE: Probably the first one, the one immediately
after whatever it is.
Because the recency of the event makes
it more available to them.
It's easier to remember.
It's right there on top of your memory.
And so, your perception of how frequently the stuff happens
is skewed by that.
AUDIENCE: I heard that's how people choose
the numbers that they gamble on when
they're playing, say, roulette.
They say, hey, this number has appeared frequently.
I'm going to bet on this one because it's a "hot" number.
ABBY NOYCE: Yeah, you'll see that.
One of these things--
it's called the gambler's fallacy
in something that's entirely random like roulette,
like flipping a coin.
Where-- but there's this kind of idea
because it really feels like whatever has just happened
is going to affect what happens next.
Probably not true.
AUDIENCE: OK, is there a game where
you have a bullet in the gun but it's only one bullet and then
you spin the barrel and then you try to kill yourself?
AUDIENCE: I think that's Russian Roulette.
ABBY NOYCE: Russian Roulette.
AUDIENCE: It is a game, though.
ABBY NOYCE: I'm not sure I'd call it a game, but yes.
More often you'll hear it in the context
of being used as a metaphor, like such and so
is equivalent to playing--
[SIDE CONVERSATION]
ABBY NOYCE: So, the other thing that
affects this availability idea is how familiar you
are with the outcome.
So, again, and this is the thing where the level of--
how many people think that more kids
get kidnapped in the United States today than 50 years ago?
AUDIENCE: I don't know.
I've never really thought about it.
ABBY NOYCE: This is a more interesting question
to ask grownups, people who actually were paying attention
to the news 20, 30 years ago.
Because the thing is that it's actually pretty stable--
kidnapping rates in this country are pretty stable.
And yet, over time, you'll see things
like parents get more concerned.
They don't let their kids play on their own as much.
They don't let their kids get places on their own as much.
You'll see these patterns, where if you ask your parents,
were they allowed to go around town alone?
Were they allowed to take the T alone?
Were they allowed to ride their bike to school?
You'll see things where 30 years ago, people
were allowed to do this stuff.
And today, kids aren't.
And one of the things I think that affects this--
is and this is probably--
I'm going to try not to let this devolve into my "the news
media is out to get you" rant, which is separate.
But I think one of the things that
affects this is that there's been this transition,
so that things like, for example, a missing
kid goes from being local news in the city
where it happened in the immediate surroundings,
to national news.
So, our perception of how many of these
are going on increases.
And because we know more about them,
we are more familiar with the incidents.
And so, this increased familiarity, again,
makes it easier for us to think of them.
We judge them as being more risky, more frequent
occurrences.
[INAUDIBLE]
One more heuristic that we tend to use,
that I wanted to bring up--
this is what's called the anchoring and adjustment
heuristic.
So, when we're trying to estimate the size of something
or figure out, I don't know, how much should it
cost me to fly to Denver, we'll make an approximation.
And then as we get new information,
we'll adjust that approximation.
But what often tends to happen, is
that that first number, that anchor number, we
kind of treat it too much.
Even if it was just a random guess,
even if it had nothing to do, whatsoever, with whatever we're
trying to estimate, it tends to kind of affect the changes
that we make.
We won't make big enough changes away from that original anchor.
So, one example of this would be,
suppose I want to fly to Denver for this weekend-- which
I do, but I'm not going to because I'm poor
and Denver is expensive to get to because it
takes a plane to it.
Colorado.
And suppose I said, OK, I think flying to Denver--
Suppose I set aside some money to fly to Denver,
and I start looking for airplane tickets.
And the first airplane tickets I look at are like $1,200.
And I go, oh, boy, no not so much.
And I start doing some hunting, some looking for cheaper fares.
And, eventually, I hunt and I find smaller fares
and smaller fares.
And by the time I find fares that are $700,
it looks like a really good deal.
So, I buy them because $700 is a lot less than what I originally
saw.
On the other hand, let's still say
if I budgeted $500 to do this, then $700
is more than I had anticipated to spend.
But because I had one idea set as this anchor of $1,200,
then adjustments from that feel significant,
even when they're not.
Even if I hadn't still gotten down
to what I'd planned to spend on tickets,
it felt like it was a much better deal
than what I'd originally seen.
So, this is the anchoring an adjustment heuristic.
And so, what's interesting with this,
is that people do this even when the information that
seems to being set as an anchor has nothing, whatsoever,
to do with what they're trying to estimate.
So, Tversky and Kahneman asked people to estimate percentages.
What percentage of UN delegates are from Africa?
What percentage of states in the United States
have more than one state university?
What percentage of-- probably a lot higher now than it was
in 1974--
what percentage of, I don't know,
people in the United States live in a city?
Various percentages.
And they would ask the participant this question,
and they had a wheel, like a mini Wheel of Fortune wheel,
with just the numbers from 0 to 100 arranged
in order around the rim.
And so they would ask the question,
they would spin the wheel, and then the wheel
would stop on something.
And then they'd have the participants indicate
their answer by moving the wheel so
that the pointer was on the percentage
that they thought was true.
We got this?
So, they've got a wheel.
They spin the wheel.
So, if I ask you, what percentage of UN delegates
are from African nations.
I spin the wheel, the wheel hits 10,
you think it's probably higher than 10-- more than 10%
of countries are in Africa.
So, you're going to move it up from there.
And what's interesting-- what they found,
is that this entirely randomly chosen number--
I spun this wheel right in front of you,
you saw me do it you know it's random--
affects the outcomes that people give.
You know, it's if wheel stops on a low number, most
of the people are moving it up from that low number.
The estimates that they give are lower than
if it lands on a high number and people are moving it down,
consistently.
AUDIENCE: Is there a set interval?
ABBY NOYCE: I don't know if they compared--
the way I would probably do this,
is I would compare a group where I
didn't have any wheel-spinning nonsense
and just had them guess a percentage,
and took the average of those, so that I
could see, what does the general population believe
the number of African nations in the UN
to be, and compare from there.
But I couldn't find any data that they had done this.
The example that the book in which I was reading
about this experiment gave--
they gave an example of the wheel landing on 10.
Participants would give an answer of 25%.
The wheel landing on 60, people had to move it down.
They'd give an answer of more like 45%.
So, a reasonably big range between these two but I
don't have a good solid answer for it.
AUDIENCE: Could it be because they
wouldn't want to move it so much from where it was randomly--
ABBY NOYCE: --because it was so heavy.
Well, yeah.
That's pretty much what we're saying, is
that once a number has been set on, where--
not even consciously, but we're reluctant to move too far
away from it.
So, we tend to want to make small changes rather
than big changes.
It's like, suppose your favorite ice cream place
kept the cost of an ice cream cone
at $2 for years and years and years.
And then all of a sudden, it went up to $4.
You'd be like, what?
The price of my ice cream just doubled, I don't think so.
AUDIENCE: Ridiculous!
ABBY NOYCE: You'd be ticked off, right?
You'd probably buy less ice cream.
But if it went up gradually over the same amount of time,
like, instead of being the same for so long
and then going up suddenly, if it went up by $0.25
a year or something, you'd be a lot less averse that.
So, that's another example where big changes in some kind
of numbers are disconcerting.
We don't like them.
Little changes we're pretty OK with.
All right.
So, kind of take-away things from this.
So, decisions that we make, judgments that we make,
all of this are influenced by a lot of things
that we aren't directly aware of.
One of the things that's interesting in all
of this heuristic study, is when you point out
to people that people tend to use these heuristics,
that they tend to make these kinds of statistical decisions,
that this is a piece of information you should
take into account, they get better at it.
So, there's at least--
so, we use these heuristics and kind of naively
we don't think about what the flaws in them are,
but people can learn to take that into account
and make better decisions and more accurate
estimates by being aware of how their heuristic systems work.
OK.
Shifting gears a little bit, I want
to talk about one particular study.
So, we were talking a minute ago--
for the first part of this-- about the strategies
that people use to make decisions in their life.
These guys are looking at what some
of the underlying neuroscience is in this.
And they had people do a very simple task.
They said, we're going to have you
have a button in each hand and whenever you feel like it,
push one of them.
So, they had people do this.
And they had them sit-in an FMRI tube.
And they wanted to know, is there
any way that we can measure what kind of decision people
are going to take before they're aware that they've decided it?
So, here you are.
You're sitting in the FMRI tube and you're
seeing a screen which is showing a stream of letters
for 500 milliseconds, for half a second, each.
So, it'd show a letter, show another letter,
show another letter.
And you're just hanging out there.
And whenever you feel like it, you
are asked to do two things--
to press either the left or the right button
and also to make a note of which letter
was being shown at the moment that you made the decision.
So, you were asked to decide and press right away--
not to decide and then wait for a minute, just make a decision,
push the button, and notice which letter was being shown.
So, what they did is after you pushed the button,
you got a screen and you were asked
to indicate which letter had been
displayed when you had made the decision to press the button.
So, if I'm sitting here watching the screens
and in this case, when the moment when the Q is shown,
is when I decide to press the button.
And no matter how fast I go from decision to button press--
it might be a little bit slow--
so what's actually being displayed
when the computer detects the button press, might be the V.
And after I press the button, the computer's going to ask me,
of these three previous slides, which one was showing when you
decided to press the button?
And if the Q was showing when I decided to press the button,
I'd indicate that.
I'd say it was the Q and move on and do it again.
So, is what they're asking subjects to do make sense?
Decide to push a button, remember which letter it was,
tell us which letter it was.
And what these guys wanted to find out
was a couple of things.
There's been research in the literature for a while,
for about 20 years now, showing that when
people decide to make a motor movement,
like pressing a button--
which is a motor response; it's a very small one compared
to a lot of other motor things--
then at least some period in advance of that,
maybe half a second in advance of that,
there's activity in the brain that
seems to indicate that a decision has been
made before the participant is aware
that they've made a decision.
Before you say, I've decided, before you can think that,
your brain apparently already knows that you've decided.
Creepy, yeah.
So, these guys said, OK, the previous rounds of this
had only asked people to push a single button.
They didn't have a choice.
So, we want to see if we can predict the choice that people
make.
Here they've got two options.
You can push either the button in your left hand
or the button in your right hand.
And they want to see if they can predict which
choice people make before the people are
aware that they've done it.
So, what did they find?
Look, pretty brain pictures.
OK.
So, they had people do this in an FMRI.
They looked at their patterns of brain activity
in particular regions over a temporal scale.
So, each of these-- as you can see,
there's different particular brain regions
that are being graphed here.
each of these graphs has time along the bottom axis
in seconds.
This red line is the point at which
subjects reported that they had decided
to make a decision using that--
which letter was showing when you decided--
that method.
And then this is showing how accurately the vertical axis
is showing.
So, they looked at the particular patterns
of activation in each of these regions.
And they said, can we find a pattern here that predicts--
Using the information in this brain region,
at this point in time, can we predict which choice you made?
So, this is the 50% line here.
So, that's chance.
And then anything up above that-- so, this
would be like 75% accuracy.
That's pretty good.
So, the filled-in black dots are places
where they're significantly better than chance
at determining which choice was made.
The white ones are places where they're not,
where it's just the same as chance.
So, what did they find out?
Well, they found out that after subjects
made this conscious decision to push the button, then
all of these motor cortex areas are really good at predicting
whether you're going to press it with the left hand
or the right hand, which isn't too surprising.
Motor cortex is involved in all of these kinds of decisions.
Left motor cortex, right motor cortex, the supplemental motor
area and the pre-supplemental motor area,
which are, again, as you guys might remember from last week,
are areas that seem to deal with planning
activities and goal-directed motion and stuff.
And so, all of these are areas that
can identify what choice you're making after you've consciously
made it.
What's more interesting is these ones,
which show some amount of ability
to predict what choice you make before you're consciously
aware that you made it.
So, even way out here-- look at this.
This is frontopolar cortex, lateral and medial.
So, way out here.
Eight seconds before participants
report that they've made a decision, just looking
at activity in the frontopolar cortex,
these researchers could predict with 60% accuracy
whether you were going to push the right button
or the left button.
Eight seconds-- that's like forever in neuroscience terms--
before you know that you made that decision.
I can tell you which decision you're going to make.
AUDIENCE: That's creepy.
ABBY NOYCE: Yeah.
Slightly sooner in posterior cingulate cortex.
It's not quite as good in medial frontopolar.
But they said if they combined the information from all three
of these areas then their accuracy actually went up.
It went up above that 60% to like 75%,
3/4 accuracy at predicting whether subjects
were going to push the left or the right button
before they thought they'd made that decision, which is cool.
AUDIENCE: Well, couldn't they have a 50% chance of
[INAUDIBLE]?
ABBY NOYCE: Right, so it's getting things
above 50% that are relevant.
So, again, the filled- incircles on the graphs are time points
at which the difference between their accuracy and chance is
statistically significant, which--
it's not just random that they got this,
that it actually means something.
Whereas the white ones, where it's very close to chance,
are not significant.
Questions, comments, creepy?
AUDIENCE: I think there was an article in, like, The Wall
Street Journal about that.
ABBY NOYCE: Yeah, there's been results
like this for quite a while.
It's kind of an ongoing topic of research.
Like I said, the first people doing this
were doing it in the '80s, in the mid '80s.
But as the methods get better and as new techniques
get looked at--
so I think this is the first one like this where they
have the two-choice option.
This is a paper from like May of this year, I want to say.
This is new.
This is shiny new.
AUDIENCE: What happens if you visualize
pressing with your left hand as you do it with your right?
ABBY NOYCE: I have no idea.
You'd probably mess up their data.
Only do this to researchers with whom you are not friends,
at least without telling them that's what you're doing.
AUDIENCE: That's a good test to do [INAUDIBLE]
ABBY NOYCE: Maybe.
Yeah.
And on the other hand, we know that when
you visualize doing something, at least to some extent,
the pieces of cortex that would actually be involved
in doing it, are active.
So, if I ask you to visualize a banana, the same parts
of your cortex that would be active
if you were actually looking at a, banana are active.
Or if I ask you to imagine standing up and doing
a jumping jack, then the pieces of motor cortex
that would be involved in that, are active.
All right.
So, this brings us around to some
of the big philosophical questions in this field.
So, we've been talking pretty much all
through this course with this underlying
assumption that our subjective experience of the world--
our perceptions, our understandings, our memories,
all this--
can be traced directly to physical stuff
going on in the brain.
And we're pretty sure that physical stuff has to be
caused by other physical stuff.
You can't have something that is only
mental without a physiological basis that
can then cause physical stuff.
So, physical changes in the brain
have to be caused by other physical changes.
Some of the stuff where we've looked at the chemistry
or the cellular-level changes underlying things--
that's this idea that physical changes are caused
by other physical events.
And this starts bringing us into all of these fun
questions about free will.
So, if you decide to do something
based on the state your brain is in right now
and the state your brain is in right now can be traced back
to every state your brain has always been in then
do you get to make a free decision
or are all of your decisions predetermined
by the experiences you've previously had?
AUDIENCE: This class has made me stop trusting everything.
AUDIENCE: That's a logical argument.
ABBY NOYCE: I'm OK with that as an outcome.
AUDIENCE: I would say no.
But I would say both answers could be arguments
because if you said yes, you could think that it's--
I don't know.
ABBY NOYCE: So here's a question.
Suppose you are on the jury in a criminal case for a guy who's
been accused of beating up a bunch of people,
like a serial assault, like assault on a number of people,
has injured people.
And what his attorney says is that this guy--
you get lots of witnesses saying that up until five years ago
this guy was gentle as a lamb, never hurt
anybody in his entire life.
And his attorney says that he got a brain tumor
and he now has a brain tumor that has caused
this change in his behavior.
Is this guy responsible for what he has done?
Should he be convicted?
AUDIENCE: Yes.
AUDIENCE: No.
AUDIENCE: Yes.
AUDIENCE: No.
AUDIENCE: Yes.
AUDIENCE: --because he got a brain tumor,
then it's not his fault. He didn't
choose to get a brain tumor.
AUDIENCE: Yeah, but he still hurt other people.
ABBY NOYCE: OK.
What happens if we say, OK, you have to be treated for this.
We remove the brain tumor, and this guy goes back to his
previous exemplary, gentle-as-a-lamb lifestyle?
We postponed the trial for a year for him
to get this treatment.
Then what?
You going to throw this guy in jail?
He's got kids.
He's got family.
He's got a good job.
He's a staple of his community.
But he beat up 10 people and put them in the hospital.
These aren't easy questions.
And this is kind of one of the more clear-cut cases.
But we're also starting to see--
and I think we're going to see more of this--
for example, OK.
So, that's an extreme case.
What about
AUDIENCE: People who plead insanity
after doing something really stupid?
ABBY NOYCE: Insanity plea is an interesting thing.
Legally it's got to be based on a professional's judgment
that the person did not know that what they were doing
was wrong at the time they did it.
So, the classic good case of this
is, for example, somebody who is severely mentally retarded
and doesn't know that it's wrong to take a candy bar,
for an example of this.
AUDIENCE: But what if somebody's a sociopath,
who doesn't realize that it's wrong to kill people?
ABBY NOYCE: So, that's where you don't send this person
to the death penalty.
You send them to involuntary committal.
You move them-- so, the state has
the right to-- if somebody is a danger to others,
the state has the right to get a judge to say,
this person can be locked up.
This person can be confined to a treatment center,
until they are no longer such a danger, which
may be indefinite.
So, what about something that's kind
of like the brain tumor issue.
So, we know that, for example, in a lot of ways,
experiences in early childhood really
strongly influence a lot of how your brain develops.
For example, we know that kids who
are abused as little kids tend to grow up
to be more aggressive, to have a hard time forming
relationships, to have, in general,
have more issues, all around, than kids who grew up
in stable, healthy families.
So, what happens when you have some young adult who
does something violent, who hurt somebody
and the case that their attorney makes is he couldn't help it.
His brain was wired to do this by his past.
Where people start saying that you lose-- that because
of how our brains are shaped by experience that happens
to them, and then our future behavior is defined
by these experiences, how much control
does one really have over what one does?
It starts getting kind of iffy.
And it's kind of dark to think about, in a lot of ways.
AUDIENCE: But then, wouldn't that
mean that no one's responsible for anything that they do?
ABBY NOYCE: Yes.
AUDIENCE: They can go around killing everybody, knowing--
ABBY NOYCE: This is the slippery slope argument for this,
is that it's definitely possible to start out thinking about
this and come to the conclusion that there is no such thing
as personal responsibility, that you cannot be held responsible
for anything you do because it's all just your brain doing it
and it's all defined by personal experience,
previous experience.
It's all mechanical, that there isn't a choice option involved.
AUDIENCE: That's why I think that, legally, they should not
take into account--
they should assume that everyone does have free will.
Because otherwise, it wouldn't work.
Nothing would actually work.
ABBY NOYCE: Yes, this is an excellent point.
That your assumption of free will-- not
just for the legal system, but for, like,
getting out of bed in the morning, dude.
You've got to believe that you have free will
or it all falls apart.
I don't know.
AUDIENCE: --slave to your brain.
AUDIENCE: --robots.
ABBY NOYCE: So, what's the difference
between you and your brain?
AUDIENCE: I don't know.
ABBY NOYCE: Is there a difference
between you and your brain?
AUDIENCE: It's kind of squishy.
ABBY NOYCE: It's a good thing it's
inside that nice solid skull.
Is there a difference?
Part of the big question that comes down here--
is there a difference between your self and your brain?
Is that a distinction that we can make validly?