Hi I'm Michael Corayer and this is Psych Exam Review. In this video we're going to
look at another example of a heuristic identified by the work of Amos Tversky
and Daniel Kahneman. So for this I'd like you to imagine a hypothetical situation.
Here I want you to imagine that I have conducted 100 interviews with 70
engineers and 30 lawyers and what I'm going to do is I'm going to mix up all
of these assessments and I'm going to randomly pull out a profile and read
this person's profile to you. Your job is to guess what are the odds you
think this person is a lawyer or an engineer. Ok, so we'll start with, let's
imagine I pull out the profile of a man named Adam and I tell you that Adam
seemed to be outgoing, he was interested in politics, and he displayed particular
skill in argument. So what do you think about Adam? What are the odds
Adam is a lawyer or an engineer? Ok, let's look at another profile. Let's
imagine I pull out a profile of Dick here. It says Dick is a thirty year
old man. He's married with no children, he's shown high ability and motivation,
and has been quite successful in his field, and he's well-liked by his
colleagues. So what do you think about Dick? What are the odds Dick is a lawyer
or an engineer? So if you were thinking about these people here and you
thought Adam was probably a lawyer and you thought Dick was a little harder, you
might have said, you know, "I couldn't really tell, 50/50 chance. I mean he could
be a lawyer, he could be an engineer, I really don't know". Well if you thought
this way, which is how most of the participants in Tversky and Kahneman's
study thought, then you've demonstrated this representativeness heuristic. So why
is that? Well what you're doing if you thought this way is you're comparing to
your prototypes of what you think an engineer or a lawyer is, right? You're
trying to decide which category to put this person in and so you compare them
to sort of a mental prototype of what is a lawyer or what is an engineer. If
you were thinking this way when trying to figure out whether Adam or Dick was a
lawyer or an engineer, you were doing it wrong. So there's a
much better way to determine this because what you were ignoring is the
base rate information. All right, I actually told you at the start the odds
that either one of them is a lawyer or an engineer, right? I told you there were
70 engineers and 30 lawyers I wrote it down right here. You probably didn't
include that in your analysis of each profile right? If you include that, you
would just say engineer every time, right? 70 to 30 odds. You certainly wouldn't say
for Dick, "well 50/50 chance" and that's actually what many
participants said. It's an equal chance of lawyer or engineer. Well, no 70/30, I mean
he's probably an engineer and even with the case for Adam, you know, I told
you he's interested in politics and skilled in argument. That was to sort of
prime your prototype of what you might associate with lawyers. But of course
there are engineers who are interested in politics and there are engineers who are
good at argument and so, you know, the odds are still very much in favor of
engineer even though he sounds like a lawyer.
Ok, so what's happening here? In the availability heuristic, remember, we said
that our mind substituted. It substitutes a different question. We're not good at
estimating frequency and so what we do is we just see how easily does it come
to mind, right? And we answer that question instead. So what's the question
here that triggers this representativeness heuristic and what
question do we do we replace it with? Well the idea is we're not good at
calculating odds. We don't like doing probability; our minds are really not
equipped to do this. So whenever we ask this question, as I was doing here, which
is "what are the odds of something, what are the odds of X?", you know, in this case
it was "what are the odds this person is a lawyer?". Well as soon as we start thinking
about this our mind says, "you know what I'm not doing odds. There's probability,
these calculations and statistics, it's very very complicated, let's not try to
actually do all of that. That's going to take a long time and, you know, I don't
have the time to do that. Instead let's take a shortcut. Let's
answer a different question." So the shortcut is to say "how well does this
match my prototype?".
Now here's a question that we can answer. How well does this match my prototype of
that. So instead of "what are the odds of this, this person is a lawyer" say well
"how well does this person match my prototype of a lawyer?". There's a
question that we can answer. We can say "Oh Adam, he sounds like a lawyer, you know,
he fits my profile, my prototype fits, my sort of stereotypes about what a
lawyer is or what an engineer is". So the idea is well if it matches my prototype
then I'll say that the odds are high and if it doesn't match my prototype I'll
say that the odds are low. Of course we can think about other situations beyond
this hypothetical here where this is a real problem. We don't want to just be
making decisions based on how well things match our prototype. I mean
imagine that you were interviewing for a job and maybe what's really happening is
it's just a matter of seeing how well you match the prototype of the
interviewer for this particular position. Well what's my prototype of this
position and does this person match? Ok, they seem to match, give them the job,
right? We don't want that sort of thing happening and it's even worse if you
think about other situations. I mean, what are the odds this person is a
criminal? What are the odds this person is a terrorist? Here we can see why
things like racial profiling occur because what happens is that's a really
hard question to answer. What are the odds that this person is a terrorist? How
do we go about doing that? How well does this person match my
prototype of a terrorist, in other words, how well do they match the stereotypes
that I have about what, you know, who terrorists are? Of course that's not a
very fair way of trying to answer this question, right? We're going to ignore
other potentially relevant information when we're comparing to a prototype so
there's a danger to this representativeness heuristic and how it
can lead us to make errors that can have important consequences. Ok, so that's this
representativeness heuristic; the idea that we make comparisons to prototypes
rather than actually looking at the base rates of things and actually trying to
figure out the odds of something occurring. I hope you found this helpful,
if so, please like the video and subscribe to the channel for more. Thanks
for watching!