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Speaker 1: Welcome to Stuff you Should Know, a production of iHeartRadio.

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Speaker 2: Hey, and welcome to the podcast. I'm Josh, and there's

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Speaker 2: Chuck and Jerry's here too, and we are getting down

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Speaker 2: to business, getting right to it here on stuff you

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Speaker 2: should know, because we've got a lot to cover here.

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Speaker 2: That's right. So, Chuck, I got a little bit of

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Speaker 2: an intro.

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Speaker 1: Let's hear it was that it? That wasn't it?

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Speaker 2: Do you remember how homeostasis used to come up a lot? Yes, So,

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Speaker 2: for those of you who haven't been listening that long,

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Speaker 2: homeostasis is what your body, in your mind and your

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Speaker 2: brain wants to return to. Right. You just want everything

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Speaker 2: nice and even, keel and normal and without exerting too

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Speaker 2: much effort and energy. Right, that's homeostasis.

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Speaker 1: That's are you asking me? Sure?

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Speaker 2: Okay? So one of the ways that your brain returns

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Speaker 2: to homeostasis as fast as it can is to use

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Speaker 2: shortcuts in making decisions, right, Because if you're having to

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Speaker 2: decide something, you're actively being challenged, you have to You're

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Speaker 2: not in your homeostatic space. So if you use a shortcut,

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Speaker 2: you can say something like I've had the red apple

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Speaker 2: in the past and it was delicious, I've eaten the

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Speaker 2: brown mushy one before and it was awful. I'm going

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Speaker 2: to eat this red apple, right. Rather than going to

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Speaker 2: the trouble of pulling both apples out and like analyzing

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Speaker 2: them with a microscope and all that, you can just

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Speaker 2: kind of use a little shortcut. That's a heuristic, and

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Speaker 2: it makes a lot of sense because your brain is like, great,

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Speaker 2: I didn't use that much energy. I made the right decision,

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Speaker 2: and we're good to go. The problem that comes about, though,

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Speaker 2: is that with heuristics, you're not always right. You don't

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Speaker 2: always make the right decision, you're not always taking all

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Speaker 2: of the information into account, and when that happens, you

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Speaker 2: start stumbling into cognitive biases.

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Speaker 1: Yeah. Like, this is a frustrating episode because I feel

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Speaker 1: like the title could be cognitive bias is everything you

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Speaker 1: think you know is wrong.

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Speaker 2: Yeah, well that's a great title. Let's go with that.

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Speaker 1: It just it made me feel like a dummy the

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Speaker 1: whole time.

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Speaker 2: Oh don't it's You're not a dummy. All humans are

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Speaker 2: dummies as far as cognitive biases go. It's not just you,

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Speaker 2: and this stuff is hardwired into us because like I

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Speaker 2: just said, we take mental shortcuts and the problem, Chuck,

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Speaker 2: is what we're talking about mostly today are unconscious biases. Right,

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Speaker 2: there's conscious biases. We just usually call those biases, right,

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Speaker 2: those are the active challenges that you need to overcome

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Speaker 2: to be a better version of yourself. These are like unconscious.

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Speaker 2: So there's not a lot you can do about it.

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Speaker 2: Although at the end we're going to kind of give

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Speaker 2: you some tips and pointers, but it's a challenge for

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Speaker 2: absolutely everybody. Doesn't it doesn't make you dumb.

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Speaker 1: Yeah, I mean I think the tips and things can help,

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Speaker 1: for sure, But it's just part of being human, you know,

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Speaker 1: the unconscious bias, and there's not a lot we can

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Speaker 1: do to completely eradicate them. And if it's a you know,

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Speaker 1: if it's a real problem, then I'm sorry.

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Speaker 2: Well. One of the big problems that we all kind

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Speaker 2: of face is that we are were predictably irrational, as

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Speaker 2: was said by Dan Arielli, who was a behavioral economist,

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Speaker 2: And because of that, corporations, marketers, basically everybody who wants

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Speaker 2: to sell you something knows about these things, and they

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Speaker 2: can manipulate those things. They can trick you into making

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Speaker 2: decisions you wouldn't otherwise make.

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Speaker 1: Yeah, for sure, And we wouldn't even be here probably

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Speaker 1: talking about this, but hadn't been for two kind of

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Speaker 1: revolutionary thinkers who ended up being some of the more

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Speaker 1: off cited researchers in the history of research, as we'll learn,

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Speaker 1: especially when it comes to economics. And they were a

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Speaker 1: couple of psychologists, Israeli psychologists named Amos. And I looked

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Speaker 1: up different ways to pronounce this because we always get

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Speaker 1: guff and I've heard everything from hard Tbirski to his

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Speaker 1: colleague Daniel Kahneman doing more of one of those Verski.

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Speaker 2: Oh, I just heard him refer to him as Big T.

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Speaker 1: The Big T because it's TV. But those were the

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Speaker 1: two guys working together. They developed this concept in the

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Speaker 1: seventies at the Hebrew University of Jerusalem and really like

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Speaker 1: got down to it pretty quickly as a result of

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Speaker 1: Connoman I think taking some issue with the Big T's research,

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Speaker 1: and I guess they kind of bonded over that or something.

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Speaker 2: Yeah, it was pretty cool because Tski, basically he was

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Speaker 2: a mathematical psychologist, which anytime you hear mathematical and it's

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Speaker 2: to do with something other than math, what that means

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Speaker 2: is you've taken something and you've set it out in

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Speaker 2: a very standardized way, so you can explore it, you

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Speaker 2: can teach it based on certain facets and the upshot

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Speaker 2: of mathematical psychology as far as human behavior goes. These

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Speaker 2: are the people who came up with the kroc idea

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Speaker 2: that humans behave as rational actors. We're self interested, we

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Speaker 2: take all the best information available to make the best

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Speaker 2: decision for ourselves. And Daniel Kaneman was like, this is

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Speaker 2: not at all true, and he started challenging Amos Tawirski's

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Speaker 2: theories and Tavsky instead of saying like, no, you shut up,

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Speaker 2: he was like, all right, let's go figure out, let's

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Speaker 2: get to the bottom of this. And because of that, Yeah,

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Speaker 2: they formed this partnership that a huge impact on the world. Yeah.

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Speaker 1: I think it's kind of heartening that they, as academics,

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Speaker 1: you know, got together. There were no ruffled feathers or

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Speaker 1: at least it didn't end up that way, and they

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Speaker 1: work together. It's kind of a heartening thing I think

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Speaker 1: these days.

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Speaker 2: Yeah, there's got to be at least one.

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Speaker 1: Yeah, that's right. They came up with a program called

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Speaker 1: the Heroistics and Biases Program to basically, you know, study

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Speaker 1: how human beings make their decisions, how they go through

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Speaker 1: life making choices when they don't have like all the

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Speaker 1: information at hand, all the most perfect information to make

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Speaker 1: that choice, or if they don't have like all the

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Speaker 1: time in the world to look at the information that

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Speaker 1: they do have to make that choice. So, like, how

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Speaker 1: are people making decisions? How are they making mistakes and

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Speaker 1: their decision making? And they ended up coming up with

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Speaker 1: a couple of different systems, one which is super quick,

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Speaker 1: and one which is much more deliberate.

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Speaker 2: Yeah, Daniel Kahman came out with Thinking Fast and Slow,

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Speaker 2: which was one of those super popular airport books.

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Speaker 1: You know, Yeah, thinking Comma faster.

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Speaker 2: Yes, thank you, eat shoots and leaves, that's right. And

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Speaker 2: in it he basically lays out this kind of shorthand models.

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Speaker 2: He's very explicit to say, like this is not this

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Speaker 2: is not how like your brain is actually laid out,

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Speaker 2: but it's a good metaphor for it. And System one

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Speaker 2: is how you think quickly, You think almost unconsciously, you

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Speaker 2: make rapid decisions, and that is kind of how we

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Speaker 2: generally navigate life. System two is much more deliberate. It's

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Speaker 2: where we take into account like different ideas, It's where

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Speaker 2: we really stop and think about something before making a decision,

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Speaker 2: and they're essentially competing. There's something called interference, and system

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Speaker 2: one has a really great tendency to interfere with system two.

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Speaker 2: And there was a psychologist working all the way back

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Speaker 2: in nineteen thirty five named John Ridley's Stroop who basically

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Speaker 2: discovered the Stroop effect that is a way of demonstrating

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Speaker 2: how system one interferes with the slower, more deliberate system too.

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Speaker 1: Yeah, I bet a boy. I bet he patted himself

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Speaker 1: on the back after this one, because it's one of

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Speaker 1: those things that's so simple, But I bet he winked

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Speaker 1: at everyone like watch this. Yeah, this is going to

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Speaker 1: break your brain.

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Speaker 2: It's genius.

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Speaker 1: It really kind of is. So what they did was

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Speaker 1: they simply wrote down the names of colors, but they

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Speaker 1: would write down the name of that color in a

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Speaker 1: different color, and then he would just say, as to

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Speaker 1: the person to read out loud, the color of the

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Speaker 1: word that is written, not the color that it's written in.

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Speaker 1: And it is surprisingly difficult to do that. It's just

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Speaker 1: a little weird brain breaking thing.

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Speaker 2: Yeah. Yeah, so he's showing that your system one just

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Speaker 2: wants to hurry up and read it, yeah, and it's

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Speaker 2: getting it wrong and that's interference, right. So that kind

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Speaker 2: of like started to lay the groundwork for this idea

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Speaker 2: that we do have kind of competing ways of seeing

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Speaker 2: the world and making decisions. And what Knoman was saying

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Speaker 2: is that most of the decisions were walking around making

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Speaker 2: are actually the System one super fast shorthand decisions. But

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Speaker 2: we think that we're using our more rational mind because

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Speaker 2: we make post hoc explanations for why we decided that.

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Speaker 2: And that's not to say we're all walking around with

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Speaker 2: these creepy little secrets that we know. We're like fooling ourselves.

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Speaker 2: We don't realize we're doing this. That's why these biases

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Speaker 2: are unconscious. Even if you stop and think about what

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Speaker 2: you're doing, you may still not come up with the

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Speaker 2: answer like, oh, yeah, I was making up explanations after

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Speaker 2: the fact to explain why I actually used System two

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Speaker 2: when I didn't. It's really hard to do that.

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Speaker 1: Yeah, for sure, Livy gives a pretty good example of that.

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Speaker 1: As far as like hiring somebody, Someone may make an

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Speaker 1: impression in an interview that kind of locks it up

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Speaker 1: from the second they walk in. Maybe they look like

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Speaker 1: their mom or dad, or a relative, or maybe they

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Speaker 1: remind them of themselves, or maybe like who knows what

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Speaker 1: it could be. Then they end up getting that job,

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Speaker 1: and later if you ask the person who hired them,

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Speaker 1: you might say, oh, what it was because of this,

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Speaker 1: this and this and this, when in fact that's really

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Speaker 1: just system too kind of confirming, Like now it's because

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Speaker 1: the guy walked in wearing a New York Giants T shirt.

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Speaker 2: Yeah, and we'll get into some of the problems with

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Speaker 2: the stuff throughout, but this is a good example. Right

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Speaker 2: if the opposite happened, If like you didn't hire somebody

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Speaker 2: because they weren't quite like you, that's an example of

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Speaker 2: a bias too, even if you don't think that that's

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Speaker 2: why you did it. If you're looking at their CV

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Speaker 2: afterward and you're like, oh, they didn't graduate from college,

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Speaker 2: it's why. But really it was not because you're racist,

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Speaker 2: it's not because you're a woman hater. It was because

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Speaker 2: you're preserving your own level of comfort because other groups

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Speaker 2: that are different than you make you uncomfortable. And that's

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Speaker 2: how groups can become entrenched. Right You just once one

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Speaker 2: group kind of dominates an organization, they tend to continue

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Speaker 2: doing that because people hire other people who they're comfortable around,

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Speaker 2: rather than pushing themselves outside of their comfort zone and

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Speaker 2: probably improving their organization. And that's why diversity programs exist

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Speaker 2: in the first place, because of that human tendency.

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Speaker 1: Yeah, or maybe they were just a Jets fan.

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Speaker 2: That's possible. I mean, no Jets is going to hire

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Speaker 2: a Giants fan.

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Speaker 1: Yeah. Well, here's a tip. I don't know a lot

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Speaker 1: about interviewing other than be yourself and try and get

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Speaker 1: someone to like you. But don't go into any interview

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Speaker 1: wearing any sort of branded sports apparel.

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Speaker 2: Yeah, especially a jersey. Yeah, I think that says quite

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Speaker 2: a bit.

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Speaker 1: Yeah, you wear that Giants jersey in there. Well, I

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Speaker 1: guess you're rolling the dice. You've either got that job

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Speaker 1: right or there's no way you're going to get it.

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Speaker 1: So maybe it's not a bad idea. Then I don't

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Speaker 1: even know what I'm I might be wrong.

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Speaker 2: Yeah, I mean, I guess if you dressed it up

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Speaker 2: with a bow tie, maybe you could get away with it.

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Speaker 2: But yes, it is still a gamble regardless.

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Speaker 1: Well, not all jobs you have to wear a suit

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Speaker 1: and tie, you.

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Speaker 2: Realize, I know, But I'm saying, like you dress for

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Speaker 2: the part you want. If you're wearing a giant's jersey

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Speaker 2: with the bow tie, I think you're making a good

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Speaker 2: impression out of the gate.

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Speaker 1: All right, So I guess we can talk about We're

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Speaker 1: going to go through a list of about ten different

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Speaker 1: biases and they're all pretty interesting and I know everyone

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Speaker 1: can identify with probably each of these at some point.

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Speaker 1: But before we do that, we need to point out that, like,

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Speaker 1: these are all mental shortcuts or the result of a

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Speaker 1: mental shortcut, but not all of them work in the

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Speaker 1: same way. And how our brains work, a lot of

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Speaker 1: you know, there could be a lot of things at play.

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Speaker 1: Emotions can come into play, maybe, like we were just

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Speaker 1: talking about, like it's hard to reassess something after you've

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Speaker 1: gotten a first impression. People humans historically through their life

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Speaker 1: tend to make bad guesses at things because if you

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Speaker 1: make great guesses, then things like you know, gambling would

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Speaker 1: be super easy. So all of these things come into play.

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Speaker 1: There's not just like a single way that this it's

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Speaker 1: a broken system.

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Speaker 2: Right yeah, yeah, yeah, But people generally, it seems like

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Speaker 2: universally in a lot of these cases, behave in these

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Speaker 2: ways under the same circumstances.

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Speaker 1: Yeah, like some of their stuff wasn't replicatable, but that's

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Speaker 1: sort of standard for studies in psychology. Like a lot

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Speaker 1: of this stuff, as we'll see, has checked out across cultures.

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Speaker 2: Yeah, which is huge, you know, considering the whole weird

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Speaker 2: problem in psychology in.

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Speaker 1: Particular that weird people.

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Speaker 2: Are Western educated, industrial hich are Yeah, rich democratic, I think.

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Speaker 1: All right, so we'll start. We'll leave the biggest guy

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Speaker 1: for last, I think, which will be after a break probably,

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Speaker 1: but we'll start then with maybe hindsight bias. And this

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Speaker 1: is the idea that after something has occurred and we

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Speaker 1: talked about this one before here and there, that we

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Speaker 1: tend to think like, oh, well, of course that was

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Speaker 1: going to happen. In fact, not only was that should

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Speaker 1: I have seen that coming, It was probably inevitable that

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Speaker 1: that happened. And a lot of a time maybe because

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Speaker 1: you're misremembering your expectation before it even happened, right.

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Speaker 2: Like we can rearrange our memory of how we felt

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Speaker 2: about the event or the outcome of the event afterward

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Speaker 2: to basically match the outcome. Yeah, I guess because we

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Speaker 2: have this number ending need to be right.

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Speaker 1: Yeah, that probably had something to do with it.

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Speaker 2: I knew you were going to say that, so I've

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Speaker 2: got another one for you, Chuck all right, self serving

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Speaker 2: bias combined with a little fundamental attribution error on the side.

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Speaker 1: Yeah, that's a good one. It's a good side dish.

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Speaker 2: So these things basically go hand in hand. It's basically

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Speaker 2: how we see ourselves in a great light and how

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Speaker 2: we see other people in a more negative light. Self

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Speaker 2: serving bias is basically saying, if something good happens to you,

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Speaker 2: it's because you are good, like you earned it. It's

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Speaker 2: because of you doing something right. Something bad happens to you,

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Speaker 2: it's external forces that made that happen, right. Fundamental attribution

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Speaker 2: error is the exact opposite with other people, If they

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Speaker 2: do something right, it was just luck. If something bad

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Speaker 2: happens to them, it's their own fault. So good example

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Speaker 2: of this is like if a coworker comes in late

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Speaker 2: one day, you're like, they're just lazy and slack, but

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Speaker 2: then you come in late the next day and you're

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Speaker 2: like it was traffic.

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Speaker 1: Right.

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Speaker 2: That's basically the two things going hand in hand, and

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Speaker 2: those are those are both biases.

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Speaker 1: Yeah, and I hope people understand that, Like all of

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Speaker 1: those things can also be true. You know. Sure, so

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Speaker 1: if you're if you're thinking like, well, no, but some

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Speaker 1: you know, sometimes I did deserve the thing, and sometimes

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Speaker 1: it was someone's fault. Yeah, sure, that can't happen. That's

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Speaker 1: we're not These aren't absolutes.

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Speaker 2: No, it's more just yeah, your your your tendency to

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Speaker 2: think in certain ways. Yeah, sometimes you're going to be right.

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Speaker 2: Sometimes you're going to be wrong for.

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Speaker 1: Like humanities tendency. Yeah, you got to take a big

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Speaker 1: broad view here.

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Speaker 2: Yeah, but also you specifically right you James Kirkland listening

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Speaker 2: in Baltimore.

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Speaker 1: There's one. Oh, man, James Kirkland is going to pull

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Speaker 1: over to the side of the road right now and really

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Speaker 1: freak out.

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Speaker 2: I hope, man, I hope I nailed it.

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Speaker 1: Yeah, I think you picked a common enough name.

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Speaker 2: We'll see, all right.

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Speaker 1: So anchoring bias is another one. This one I've fallen

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Speaker 1: prey too. I'm gonna say that probably about all these,

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Speaker 1: but that is the first piece of info that you

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Speaker 1: get about something can really affect and even in a

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Speaker 1: very disproportional way, things that happen after that, Like, once

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Speaker 1: something is kind of locked in, it's hard to unwind that.

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Speaker 2: Yeah, that first piece of information, it's like, oh, okay,

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Speaker 2: this is going to basically prime you in your answer

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Speaker 2: your decision. Right. So a good example I saw is

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Speaker 2: there was a study that says, like, Okay, the Mississippi

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Speaker 2: River is less than two miles long, how long is it?

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Speaker 2: And those people would say something like fifteen hundred miles.

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Speaker 2: And then other people would say, okay, the Mississippi River

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Speaker 2: is less than five hundred miles long, and people would

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Speaker 2: say like, it's like three hundred miles. And then another

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Speaker 2: group was the Mississippi River is less than eighty miles long.

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Speaker 2: Those people would answer like sixty. It's the same thing,

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Speaker 2: the length of the Mississippi River. But they were presented

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Speaker 2: with this basically this priming number, a large one, a

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Speaker 2: middle number, or a smaller number, and their answers were

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Speaker 2: related to that first piece of information that they got,

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Speaker 2: and that's anchoring bias.

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Speaker 1: Yeah, And Livia pointed out another little side this year,

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Speaker 1: which is called the decoy effect, and that's when you

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Speaker 1: will go into a restaurant and they might and this

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Speaker 1: is how just kind of one way this can affect economics,

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Speaker 1: which will come up a lot. But you'll go into

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Speaker 1: a restaurant and they might have one super expensive bottle

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Speaker 1: of wine on the menu, and maybe it's even placed

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Speaker 1: at the top so you see it first, and then

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Speaker 1: the other bottles of wine might seem like a decent

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Speaker 1: deal after that, even if they're also overpriced.

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Speaker 2: Yeah, exploitation, but.

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Speaker 1: All wine and restaurants is overpriced. I hope everyone realizes that.

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Speaker 2: I mean buy a lot, right, Yeah, I think that

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Speaker 2: most of their margin on that.

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Speaker 1: Oh, totally. It's it's frustrating, but it's that's that's the business.

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Speaker 2: Well, that's also I think, chuck. This anchoring bias is

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Speaker 2: why they say you should never lead in a negotiation

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Speaker 2: with your actual price that you want to go high

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Speaker 2: or lower depending on your position.

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Speaker 1: Oh like if they ask what you want to get paid?

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Speaker 2: Yeah, or like you're trying question or something like that.

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Speaker 1: You know, yeah, I hate all that stuff.

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Speaker 2: Yeah. Yeah. If somebody it's like, well, what do you

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Speaker 2: think I should get paid?

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Speaker 1: You know, what are you looking to make at this job?

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Speaker 1: But that's what I'm saying, what you make is should

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Speaker 1: always be the answer.

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Speaker 2: Exactly. You just want to add I don't know, fifty

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Speaker 2: percent to what you actually want, and then you're away

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Speaker 2: for negotiation. There's the one other thing that's related to

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Speaker 2: this framing bias, and that's basically the same thing, but

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Speaker 2: rather than the first piece of information guiding you, this

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Speaker 2: is more directly guiding you. So for example, some drug

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Speaker 2: maker says ten percent of patients die, You're like, oh god,

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Speaker 2: that's a lot, right. You could say at the opposite weight,

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Speaker 2: ninety percent of patients live and you're like, oh that's great,

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Speaker 2: the same amount of people dying. It's just framed differently

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Speaker 2: to exploit your your response.

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Speaker 1: To exploit your aversion to dying.

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Speaker 2: And that's a human thing, isn't it?

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Speaker 1: For sure? Shall we take a break? Yeah, all right,

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Speaker 1: Josh said human things. So it's time for a break

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Speaker 1: and we're going to come back with more biases right

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Speaker 1: after this.

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Speaker 2: All right, chuck up to bat is number twenty three

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Speaker 2: availability heuristic?

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Speaker 1: Can we put like a stadium echo effect on that?

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Speaker 2: Next to bat? Many motos?

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Speaker 1: Very nice?

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Speaker 2: So what is that availability heuristic? And I'm sure this

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Speaker 2: has happened to you before.

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Speaker 1: Right, Yeah, none of these have ever happened to you,

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Speaker 1: which is a funny thing about us doing this episode.

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Speaker 1: But the availability heuristic is what you have available to

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Speaker 1: call up in your brain at any given moment, So

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Speaker 1: you're you're going to rely more on what you can

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Speaker 1: immediately think of in the moment, and chances are what

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Speaker 1: you're immediately able to think of in the moment as

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Speaker 1: something that probably aligns with your worldview or something like that,

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Speaker 1: which is a sort of a well, we won't talk

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Speaker 1: about the sea bias because that's coming up.

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Speaker 2: Well, yeah, or something that like really kind of goosed

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Speaker 2: you emotionally, like that's that's very available because it's wow,

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Speaker 2: you know, loud and scary in your mind kind of

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Speaker 2: you know, like if you saw something about a plane

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Speaker 2: crash in the last like day or so, right when

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Speaker 2: somebody asks you how frequent plane crashes are, you're probably

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Speaker 2: going to give a much higher estimate than you would

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Speaker 2: have before that, you know, maybe based on the number

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Speaker 2: of times you've flown and nothing bad happened.

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Speaker 1: Yeah, that's a good one.

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Speaker 2: There's also an attentional blindness. And before anybody, before we

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Speaker 2: talk about this, because we're gonna spoil it. Yeah, yeah,

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Speaker 2: I want to send everybody, if you have the means

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Speaker 2: to do this, go onto YouTube and search for selective

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Speaker 2: attention test. And this is Daniel Simon's YouTube channel, and

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Speaker 2: then watch the test where the people are passing the

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Speaker 2: basketball back and forth. We'll wait a second.

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Speaker 1: Dude, Okay, that's enough.

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Speaker 2: All right, great, so hopefully you press pause and you

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Speaker 2: didn't just try to watch it while we were while

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Speaker 2: Chuck was doing the Jeopardy theme.

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Speaker 1: It is short, but it's not that short.

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Speaker 2: It's like a minute and a half or something, right, Yeah,

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Speaker 2: So tell them about this video, Chuck, because it's pretty great.

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Speaker 1: That's right. In the video, they have a group of

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Speaker 1: what was it like six people, probably six on the nose,

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Speaker 1: six college students I guess, Three are wearing white shirts,

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Speaker 1: three are not wearing white shirts, and they're in a

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Speaker 1: very tight, small circle. It looks very awkward. They have

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Speaker 1: a I think was it two basketballs. There's two groups, yeah,

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Speaker 1: two groups, two basketballs, and you're what you're told is

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Speaker 1: the tasket hand is to count the number of times

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Speaker 1: that people in white the white team are passing the basketball.

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Speaker 1: So you're counting, right, one, two, three, four, four, five, six, seven,

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Speaker 1: and that's all you're supposed to do. And at the

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Speaker 1: end you're supposed to say, you know how many times

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Speaker 1: they pass a basketball?

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Speaker 2: Right?

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Speaker 1: And now, now, hit them with the good stuff.

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Speaker 2: So apparently half of the people who do this, which

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Speaker 2: is astounding to me, half of the people who watch

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Speaker 2: this video and take this test don't notice that in

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Speaker 2: the middle of it, a person in a gorilla suit

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Speaker 2: walks into frame and turns to the camera and I

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Speaker 2: think beats on their chest and then walks out of frame.

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Speaker 2: Like in the middle of these people throwing these basketballs

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Speaker 2: around the gorilla, half of the people are paying such

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Speaker 2: close attention to counting how many times the people wearing

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Speaker 2: white T shirts are passing the basket well that they

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Speaker 2: do not notice the gorilla until the end of the

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Speaker 2: video when it's pointed out.

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00:23:05,880 --> 00:23:08,920
Speaker 1: Yeah, and we're assuming it's a person in a gorilla costume.

435
00:23:09,040 --> 00:23:10,520
Speaker 2: I'm hoping, first of all.

436
00:23:10,400 --> 00:23:11,919
Speaker 1: That might be a bias at play, that it's not

437
00:23:11,960 --> 00:23:12,879
Speaker 1: a real gorilla.

438
00:23:13,040 --> 00:23:15,200
Speaker 2: Well, I guess it depends on the amount of funding

439
00:23:15,240 --> 00:23:15,560
Speaker 2: they had.

440
00:23:15,800 --> 00:23:20,439
Speaker 1: Yeah, it looks actually like the gorilla from trading places.

441
00:23:19,320 --> 00:23:23,920
Speaker 2: Totally, which is like, were they even trying? No, Okay, kid,

442
00:23:24,119 --> 00:23:25,360
Speaker 2: I just wanted to make sure.

443
00:23:25,520 --> 00:23:28,159
Speaker 1: Uh, did you watch this video Before you knew about this.

444
00:23:28,520 --> 00:23:32,120
Speaker 2: I had heard it from some friends who were who

445
00:23:32,160 --> 00:23:34,720
Speaker 2: do magic, and they were basically talking about this on

446
00:23:34,800 --> 00:23:36,440
Speaker 2: a little podcast that they made.

447
00:23:36,880 --> 00:23:39,480
Speaker 1: Who Bo Bobo, You are friends who do magic?

448
00:23:41,119 --> 00:23:43,960
Speaker 2: So you know our friend Toby. Oh, yeah, he has

449
00:23:44,119 --> 00:23:45,760
Speaker 2: very good friends that do magic.

450
00:23:46,200 --> 00:23:46,440
Speaker 1: Wow.

451
00:23:46,680 --> 00:23:50,560
Speaker 2: I became Yes, I became kind of friends with him.

452
00:23:50,560 --> 00:23:52,399
Speaker 2: So yeah, I guess I do. I have friends that

453
00:23:52,440 --> 00:23:53,000
Speaker 2: do magic.

454
00:23:53,480 --> 00:23:55,879
Speaker 1: Well, buddy, next time we are in Los Angeles at

455
00:23:55,920 --> 00:23:58,360
Speaker 1: the same time, our good friend and friend of the show,

456
00:23:58,400 --> 00:24:02,120
Speaker 1: Adam Pranica, Yeah, is a member of the Magic Castle

457
00:24:03,000 --> 00:24:05,600
Speaker 1: and it's one of his and his wife Elaine's favorite

458
00:24:05,600 --> 00:24:08,119
Speaker 1: thing to do is to take friends to the Magic Castle.

459
00:24:08,200 --> 00:24:09,440
Speaker 1: So have you ever been?

460
00:24:09,800 --> 00:24:10,000
Speaker 2: No?

461
00:24:10,280 --> 00:24:11,480
Speaker 1: And it is great fun.

462
00:24:11,800 --> 00:24:14,960
Speaker 2: Adam Pranika just keeps getting better and better, doesn't he.

463
00:24:15,440 --> 00:24:17,080
Speaker 1: Yeah. I haven't been with him, but I've been a

464
00:24:17,119 --> 00:24:19,639
Speaker 1: couple of times, once many many years ago, then another

465
00:24:19,720 --> 00:24:21,560
Speaker 1: probably ten years ago. But it's a lot of fun.

466
00:24:21,880 --> 00:24:25,280
Speaker 1: I'm a big fan of magic. Yeah, And it's pretty magical.

467
00:24:25,280 --> 00:24:27,760
Speaker 1: When people don't see that gorilla in a very tight frame,

468
00:24:28,359 --> 00:24:31,240
Speaker 1: it's not like it's on a big basketball court and

469
00:24:31,280 --> 00:24:33,439
Speaker 1: the gorilla sneaks in there like there's six people and

470
00:24:33,480 --> 00:24:35,919
Speaker 1: then there's a seventh. Very clearly it is.

471
00:24:36,080 --> 00:24:38,000
Speaker 2: It is very obvious so I mean, what this is

472
00:24:38,040 --> 00:24:41,720
Speaker 2: showing is that our attention is limited, right, and we're

473
00:24:41,760 --> 00:24:45,560
Speaker 2: really focused on a task. You you saw that gorilla.

474
00:24:46,080 --> 00:24:48,560
Speaker 2: Those half of the people who didn't notice the gorilla,

475
00:24:48,600 --> 00:24:50,960
Speaker 2: you still saw it, but you're still focused on the task.

476
00:24:51,000 --> 00:24:53,199
Speaker 2: That your brain was just getting rid of information that

477
00:24:53,280 --> 00:24:57,840
Speaker 2: was unrelated to the task because it's not pertinent. It

478
00:24:57,840 --> 00:25:00,880
Speaker 2: can become pertinent though, when that gorilla decide to attack you.

479
00:25:01,680 --> 00:25:04,800
Speaker 2: And so this is a cognitive bias we have where

480
00:25:04,800 --> 00:25:10,159
Speaker 2: we're ignoring potentially unimportant information to take in the stuff

481
00:25:10,200 --> 00:25:12,480
Speaker 2: that's related to the task at hand. Yeah.

482
00:25:12,520 --> 00:25:14,199
Speaker 1: Well, you know where they could really get away with

483
00:25:14,280 --> 00:25:17,080
Speaker 1: this because where you have great concentration. Is it a

484
00:25:17,119 --> 00:25:20,240
Speaker 1: professional sports game on the jumbo tron when they have

485
00:25:20,359 --> 00:25:23,480
Speaker 1: the baseball under the helmet or whatever, uh huh, and

486
00:25:23,520 --> 00:25:25,440
Speaker 1: then they're moving them around and you got to find

487
00:25:25,520 --> 00:25:27,840
Speaker 1: you know, it's like three card money, yep, Because you're

488
00:25:27,840 --> 00:25:30,040
Speaker 1: concentrating so hard on that, they could they could put

489
00:25:30,080 --> 00:25:32,320
Speaker 1: whatever they wanted on that screen while that's going on,

490
00:25:32,560 --> 00:25:35,320
Speaker 1: And I bet you most people would not or maybe

491
00:25:35,359 --> 00:25:37,159
Speaker 1: I guess it's half if that's what they found.

492
00:25:37,640 --> 00:25:40,000
Speaker 2: Yeah, I'll bet you're right. I'll bet you're right, man.

493
00:25:40,200 --> 00:25:41,520
Speaker 1: I was just trying to think of something where you're

494
00:25:41,520 --> 00:25:44,560
Speaker 1: super trying to follow because I was happy I came

495
00:25:44,640 --> 00:25:46,520
Speaker 1: up with the correct amount of passes at the end.

496
00:25:46,440 --> 00:25:48,919
Speaker 2: You did. You said, what does it mean if I

497
00:25:49,000 --> 00:25:52,000
Speaker 2: noticed the gorilla and got the correct number of passes?

498
00:25:52,040 --> 00:25:54,119
Speaker 2: And I said, it means you're a perfect human.

499
00:25:53,960 --> 00:25:55,639
Speaker 1: That's right, which we all know is not true.

500
00:25:57,240 --> 00:25:58,880
Speaker 2: None of us are, Chuck, None of us are.

501
00:25:59,119 --> 00:25:59,520
Speaker 1: Yeah, I know.

502
00:26:00,080 --> 00:26:02,359
Speaker 2: So there's another one that you may have heard of before,

503
00:26:02,400 --> 00:26:04,200
Speaker 2: even if you've not heard of any of these other ones,

504
00:26:04,200 --> 00:26:07,000
Speaker 2: called the Dunning Kruger effect. It became kind of viral

505
00:26:07,119 --> 00:26:11,320
Speaker 2: because if you take it through the pop culture meat grinder,

506
00:26:11,800 --> 00:26:14,159
Speaker 2: it becomes much more simplified and kind of loses some

507
00:26:14,240 --> 00:26:18,199
Speaker 2: of its actuality. But yeah, people still like it because

508
00:26:18,520 --> 00:26:20,240
Speaker 2: it's a good way to put other people down.

509
00:26:20,720 --> 00:26:23,320
Speaker 1: Yeah, it is. This is the idea that the correct

510
00:26:23,520 --> 00:26:27,000
Speaker 1: idea is that people with a little understanding in an

511
00:26:27,040 --> 00:26:31,119
Speaker 1: area tend to overestimate their ability and their knowledge about something,

512
00:26:31,359 --> 00:26:34,400
Speaker 1: right because they don't They know so little they don't

513
00:26:34,400 --> 00:26:37,080
Speaker 1: even know what they don't know. Kind of right exactly,

514
00:26:37,560 --> 00:26:39,760
Speaker 1: But what you were talking about, it's kind of been

515
00:26:39,800 --> 00:26:44,679
Speaker 1: transformed into like morons have them are the most like braggadocious,

516
00:26:44,800 --> 00:26:46,320
Speaker 1: which can be true.

517
00:26:47,000 --> 00:26:48,960
Speaker 2: It can be you know. I think that's one of

518
00:26:49,000 --> 00:26:51,159
Speaker 2: the things. Like you said, you can be right with

519
00:26:51,240 --> 00:26:53,680
Speaker 2: cognitive biases, you're not wrong with them all the time.

520
00:26:53,720 --> 00:26:56,119
Speaker 2: So yeah, that kind of supports that, But that's not

521
00:26:56,160 --> 00:26:58,919
Speaker 2: what the Dunning Kruger effect actually says. You said it.

522
00:26:59,240 --> 00:27:02,639
Speaker 2: And then there's the opposite way too, where the more

523
00:27:02,720 --> 00:27:06,040
Speaker 2: experience you have, the more expert you are in a field,

524
00:27:06,640 --> 00:27:10,320
Speaker 2: the more you assume that it should be easier for

525
00:27:10,400 --> 00:27:11,199
Speaker 2: you than it is.

526
00:27:12,240 --> 00:27:15,600
Speaker 1: Yeah. That's a very valuable thing to understand, I think,

527
00:27:15,800 --> 00:27:19,360
Speaker 1: and you get much further in life if people are like, well,

528
00:27:19,400 --> 00:27:22,719
Speaker 1: you're the expert, and the experts usually the one going yeah,

529
00:27:22,760 --> 00:27:25,360
Speaker 1: but I don't know, maybe we should hold off because

530
00:27:25,440 --> 00:27:26,560
Speaker 1: you know X, Y and Z.

531
00:27:27,040 --> 00:27:30,440
Speaker 2: Right. Yeah, So that's the actual Dunning Kruger effect, and

532
00:27:30,520 --> 00:27:33,520
Speaker 2: I saw that it's it's being assailed right now. People

533
00:27:33,560 --> 00:27:36,480
Speaker 2: are showing a question even the basic version of it,

534
00:27:36,560 --> 00:27:39,120
Speaker 2: like the actually academic version of it. Yeah, so we'll

535
00:27:39,119 --> 00:27:40,080
Speaker 2: see what happens with that.

536
00:27:40,400 --> 00:27:44,199
Speaker 1: Oh interesting. We've got the gambler's fallacy next, and that

537
00:27:44,359 --> 00:27:49,760
Speaker 1: is oh boy, if you have ever gambled anywhere. But

538
00:27:49,800 --> 00:27:51,560
Speaker 1: if you like go to casinos and stuff like that,

539
00:27:52,200 --> 00:27:54,360
Speaker 1: you're going to see this all over the place. You're

540
00:27:54,359 --> 00:27:56,600
Speaker 1: going to hear it spoken out loud. And this is

541
00:27:56,640 --> 00:28:00,960
Speaker 1: the idea that you find patterns where there are no patterns.

542
00:28:01,119 --> 00:28:01,639
Speaker 2: Yeah.

543
00:28:01,720 --> 00:28:04,120
Speaker 1: So if you're at the blackjack table and you hear

544
00:28:04,200 --> 00:28:08,040
Speaker 1: the person next to you like, well, oh, man, see,

545
00:28:08,040 --> 00:28:10,280
Speaker 1: I've lost four in a row, so I'm gonna bet, like,

546
00:28:10,840 --> 00:28:13,160
Speaker 1: I'm gonna go all in on this because I'm bound

547
00:28:13,200 --> 00:28:14,760
Speaker 1: to win because I've lost four in a row. There's

548
00:28:14,760 --> 00:28:16,280
Speaker 1: no way I'm gonna lose five in a row.

549
00:28:16,400 --> 00:28:19,440
Speaker 2: Right. The problem is is those each of those hands

550
00:28:19,440 --> 00:28:22,480
Speaker 2: of blackjack are unrelated to one another. They don't form

551
00:28:22,520 --> 00:28:26,080
Speaker 2: a pattern. But you are predicting a pattern that just

552
00:28:26,119 --> 00:28:29,560
Speaker 2: doesn't exist. Yeah, that means you're a fallacious gambler.

553
00:28:29,960 --> 00:28:33,040
Speaker 1: I can get you in real trouble. I mean it's

554
00:28:33,200 --> 00:28:35,040
Speaker 1: you can do the same thing on the playground with

555
00:28:35,119 --> 00:28:37,600
Speaker 1: coin tosses. In fact, coin tosses, I think is a

556
00:28:37,600 --> 00:28:39,920
Speaker 1: lot of a lot of times the way they sort

557
00:28:39,920 --> 00:28:41,600
Speaker 1: of try and prove.

558
00:28:41,440 --> 00:28:45,440
Speaker 2: This, Yeah, because each coin toss, considering like you're playing

559
00:28:45,480 --> 00:28:48,480
Speaker 2: with a perfect unflogged coin that has no bias whatsoever.

560
00:28:49,120 --> 00:28:51,840
Speaker 2: Each coin toss is totally unrelated to the last. So

561
00:28:52,440 --> 00:28:54,560
Speaker 2: you could get one hundred heads in a row and

562
00:28:54,560 --> 00:28:56,880
Speaker 2: that doesn't mean anything. It doesn't mean a tail is coming,

563
00:28:57,320 --> 00:29:00,720
Speaker 2: because that each of those hundred heads in each of

564
00:29:00,760 --> 00:29:03,440
Speaker 2: those coin tosses had nothing to do with the last

565
00:29:03,440 --> 00:29:04,760
Speaker 2: one or the next one.

566
00:29:05,120 --> 00:29:07,240
Speaker 1: I know. That's hard to break out of, though, because

567
00:29:07,240 --> 00:29:10,000
Speaker 1: it seems very human to think like they've flipped foreheads

568
00:29:10,000 --> 00:29:11,360
Speaker 1: in a row. There's no way there's going to be

569
00:29:11,360 --> 00:29:11,640
Speaker 1: a fit.

570
00:29:12,080 --> 00:29:14,320
Speaker 2: Well, that's another reason why this is so hard. We're

571
00:29:14,400 --> 00:29:17,560
Speaker 2: hardwired to find patterns and stuff. It's a way to

572
00:29:17,680 --> 00:29:20,240
Speaker 2: navigate the world. The way we navigate the world is

573
00:29:20,280 --> 00:29:22,920
Speaker 2: by finding patterns so that we can recognize things in

574
00:29:22,920 --> 00:29:26,440
Speaker 2: the future and thus spend less energy getting back to homeostasis.

575
00:29:26,800 --> 00:29:28,920
Speaker 1: That's right, This is all so interesting to me.

576
00:29:29,160 --> 00:29:30,120
Speaker 2: I love this stuff.

577
00:29:30,880 --> 00:29:33,240
Speaker 1: I knew that you loved it. This is Josh Clark Central.

578
00:29:33,680 --> 00:29:36,840
Speaker 2: I love observing it because I can't grasp what it

579
00:29:36,880 --> 00:29:39,000
Speaker 2: feels like to suffer any of these, So just to

580
00:29:39,080 --> 00:29:42,480
Speaker 2: discuss it and in this way is really fascinating to me.

581
00:29:42,960 --> 00:29:45,880
Speaker 1: All right, let's talk about the base rate fallacy That

582
00:29:45,960 --> 00:29:49,480
Speaker 1: means you put more weight on just like one very

583
00:29:49,480 --> 00:29:52,600
Speaker 1: specific piece of information instead of looking at all the

584
00:29:52,640 --> 00:29:54,640
Speaker 1: pieces of information that have come your way.

585
00:29:54,840 --> 00:29:58,920
Speaker 2: Yeah, and usually it's individuated information, meaning like say some

586
00:29:59,120 --> 00:30:02,920
Speaker 2: quality or careacteristic of one person, and then you're ignoring

587
00:30:03,000 --> 00:30:06,440
Speaker 2: the base rate, which is like pure statistical information about

588
00:30:06,520 --> 00:30:09,440
Speaker 2: what you're trying to figure out. And a really good

589
00:30:09,480 --> 00:30:11,880
Speaker 2: example of this is like, let's say that you are

590
00:30:12,000 --> 00:30:14,600
Speaker 2: looking at somebody who is super fit, a woman who's

591
00:30:14,680 --> 00:30:18,000
Speaker 2: very fit and athletic, and you're asked, do you think

592
00:30:18,000 --> 00:30:21,640
Speaker 2: that woman is a personal trainer or a teacher, Because

593
00:30:21,800 --> 00:30:24,280
Speaker 2: the basically the only evidence you have there is that

594
00:30:24,320 --> 00:30:28,280
Speaker 2: this woman is very athletic and fit. You might say

595
00:30:28,360 --> 00:30:31,480
Speaker 2: personal trainer. But if you took all the base rate

596
00:30:31,560 --> 00:30:35,880
Speaker 2: information into account, you would know that even the very say,

597
00:30:36,040 --> 00:30:40,280
Speaker 2: very small portion of teachers who are very fit and

598
00:30:40,320 --> 00:30:44,080
Speaker 2: athletic may be small compared to the total number of teachers,

599
00:30:44,400 --> 00:30:48,040
Speaker 2: it's still much larger than the total number of personal

600
00:30:48,080 --> 00:30:51,960
Speaker 2: trainers in the world. So statistically speaking, it's much likelier

601
00:30:52,280 --> 00:30:55,479
Speaker 2: that that very fit athletic woman is a teacher and

602
00:30:55,520 --> 00:30:57,960
Speaker 2: not a personal trainer. You don't do that because you

603
00:30:58,000 --> 00:31:01,840
Speaker 2: think personal trainer athletic fit must be a personal trainer.

604
00:31:02,080 --> 00:31:05,280
Speaker 2: You've just fallen prey to the base rate fallacy.

605
00:31:05,360 --> 00:31:08,520
Speaker 1: My friend, Yeah, but she has on yoga pants and

606
00:31:08,560 --> 00:31:12,880
Speaker 1: hokuhs exactly. That doesn't narrow down anything these days.

607
00:31:13,560 --> 00:31:18,080
Speaker 2: You know, how about the mere exposure effect, Chuck, and like,

608
00:31:18,240 --> 00:31:20,320
Speaker 2: mirror is part of it. I'm not making a judgment

609
00:31:20,360 --> 00:31:20,960
Speaker 2: about it.

610
00:31:21,120 --> 00:31:25,760
Speaker 1: That's right. That means just merely being exposed to something

611
00:31:26,640 --> 00:31:30,840
Speaker 1: has a vast impact. So the more we experience something,

612
00:31:31,240 --> 00:31:33,200
Speaker 1: the more you like it, which is why you see

613
00:31:33,200 --> 00:31:36,400
Speaker 1: that commercial for the thing over and over and over,

614
00:31:36,520 --> 00:31:40,160
Speaker 1: that burger king ad over and over and over. Although

615
00:31:40,200 --> 00:31:42,920
Speaker 1: I wouldn't say that you might like that one the

616
00:31:42,920 --> 00:31:44,200
Speaker 1: more you heard it.

617
00:31:44,320 --> 00:31:45,800
Speaker 2: That's the outlier for me too.

618
00:31:46,600 --> 00:31:50,840
Speaker 1: But that's the idea. Though there's just just mere exposure,

619
00:31:51,080 --> 00:31:51,800
Speaker 1: we'll get you there.

620
00:31:52,000 --> 00:31:54,880
Speaker 2: And then there's a related thing called the illusory truth effect,

621
00:31:54,880 --> 00:32:00,600
Speaker 2: which is basically that repeated exposure to a lie causes

622
00:32:00,640 --> 00:32:02,560
Speaker 2: you to eventually believe in it if you hear it

623
00:32:02,680 --> 00:32:06,920
Speaker 2: enough times, even if you initially knew that it wasn't true.

624
00:32:07,160 --> 00:32:10,000
Speaker 2: So that makes me wonder if like it just wears

625
00:32:10,000 --> 00:32:12,040
Speaker 2: you down over time, like your brain is tired of

626
00:32:12,080 --> 00:32:15,560
Speaker 2: defending itself against being assailed with a lie. And it's

627
00:32:15,600 --> 00:32:17,520
Speaker 2: just like, fine, that's true. I don't I don't care.

628
00:32:18,440 --> 00:32:22,200
Speaker 1: Yeah, I mean sure, politics certainly comes to mind. Repeat

629
00:32:22,240 --> 00:32:23,840
Speaker 1: the lie, repeat the lie, repeat the lie.

630
00:32:23,920 --> 00:32:26,280
Speaker 2: Yeah, And I mean like it's a viable way to

631
00:32:26,360 --> 00:32:30,000
Speaker 2: exploit people's cognitive biases in that In that respect.

632
00:32:30,680 --> 00:32:33,240
Speaker 1: Should we end up should we close out with the

633
00:32:33,240 --> 00:32:35,200
Speaker 1: big daddy of them all, the big c the big

634
00:32:35,200 --> 00:32:36,200
Speaker 1: confirmation bias?

635
00:32:36,320 --> 00:32:38,000
Speaker 2: Yeah, let's do it, baby.

636
00:32:38,080 --> 00:32:40,640
Speaker 1: All right, why don't you start this one?

637
00:32:41,320 --> 00:32:43,880
Speaker 2: Okay, So there's a guy named Peter Wasason back in

638
00:32:43,920 --> 00:32:48,800
Speaker 2: the sixties. He coined the term confirmation bias, and he

639
00:32:48,880 --> 00:32:51,680
Speaker 2: basically had an experiment that's really clever. It's hard to

640
00:32:51,760 --> 00:32:55,440
Speaker 2: understand at first, but it's very clever. He basically said, hey,

641
00:32:55,600 --> 00:32:58,360
Speaker 2: here is a sequence of numbers two, four, and six.

642
00:32:59,480 --> 00:33:02,680
Speaker 2: Figure out what the pattern is. Just to be clear,

643
00:33:02,760 --> 00:33:06,960
Speaker 2: this is really hard to explain. If if you find

644
00:33:07,000 --> 00:33:10,600
Speaker 2: somebody who can explain this, well you'll get it. But

645
00:33:10,840 --> 00:33:13,360
Speaker 2: I don't think I'm a candidate for that. I think

646
00:33:13,400 --> 00:33:15,400
Speaker 2: we all know that I'm not going to explain this

647
00:33:15,560 --> 00:33:16,000
Speaker 2: very well.

648
00:33:16,360 --> 00:33:17,680
Speaker 1: Oh I don't think that's true.

649
00:33:17,720 --> 00:33:18,720
Speaker 2: Do you want to take a crack?

650
00:33:19,240 --> 00:33:23,120
Speaker 1: Okay, The original numbers were two four six, and people

651
00:33:23,240 --> 00:33:26,240
Speaker 1: might tend to go with, like, all right, eight ten twelve,

652
00:33:26,960 --> 00:33:28,920
Speaker 1: and they thinking it might be all right, it's even

653
00:33:29,000 --> 00:33:32,960
Speaker 1: number sequence ascending even number, right, and they would say, no,

654
00:33:33,080 --> 00:33:35,560
Speaker 1: that's not correct. And you said, well, maybe it's four

655
00:33:35,640 --> 00:33:38,360
Speaker 1: eight twelve and it's like doubled or something, and they

656
00:33:38,400 --> 00:33:43,120
Speaker 1: would say, well, that's also incorrect. And then you're at

657
00:33:43,120 --> 00:33:46,200
Speaker 1: WIT's end because what you haven't done is just done

658
00:33:46,240 --> 00:33:50,320
Speaker 1: any ascending order. You didn't go one seventy nine, three hundred.

659
00:33:50,760 --> 00:33:53,920
Speaker 2: All right, let me take a crack at it. You ready?

660
00:33:54,280 --> 00:33:54,640
Speaker 1: Sure?

661
00:33:55,120 --> 00:33:58,320
Speaker 2: So the original numbers two four six, and the participants

662
00:33:58,360 --> 00:34:00,760
Speaker 2: would try to come up with the explanation of why,

663
00:34:01,080 --> 00:34:04,880
Speaker 2: like what are the what pattern are those numbers following?

664
00:34:05,040 --> 00:34:05,200
Speaker 1: Right?

665
00:34:05,880 --> 00:34:11,200
Speaker 2: So you might say the like does eight ten twelve

666
00:34:11,280 --> 00:34:14,560
Speaker 2: work and they would say yes, and you'd say, okay,

667
00:34:14,600 --> 00:34:16,680
Speaker 2: well then you're just looking at even numbers and they

668
00:34:16,680 --> 00:34:20,040
Speaker 2: would say, no, you still got this right, This still

669
00:34:20,080 --> 00:34:23,280
Speaker 2: fits the pattern, but your your hypothesis for it is wrong.

670
00:34:23,800 --> 00:34:25,200
Speaker 1: Right, that's yeah, that's the key.

671
00:34:25,360 --> 00:34:28,439
Speaker 2: Right. Here's where the confirmation bias came in. People would

672
00:34:28,520 --> 00:34:32,799
Speaker 2: then go back and continue trying to find versions that

673
00:34:32,960 --> 00:34:36,600
Speaker 2: fit their hypothesis to explain this even though it was wrong. Yeah,

674
00:34:36,719 --> 00:34:39,920
Speaker 2: rather than take their hypothesis and say, Okay, this is right,

675
00:34:40,160 --> 00:34:42,880
Speaker 2: this fits the pattern, but it's still not correct and

676
00:34:42,920 --> 00:34:47,120
Speaker 2: start trying to break their original hypothesis by coming up

677
00:34:47,120 --> 00:34:50,520
Speaker 2: with like just completely random stuff that doesn't fit their

678
00:34:50,800 --> 00:34:54,240
Speaker 2: original hypothesis, in which case they might have said something

679
00:34:54,320 --> 00:34:57,160
Speaker 2: like does one, six or twenty seven work and they

680
00:34:57,200 --> 00:35:00,680
Speaker 2: would say no, that's that doesn't fit, and then that

681
00:35:00,760 --> 00:35:03,480
Speaker 2: might lead the person to see that actually, the only

682
00:35:03,640 --> 00:35:06,279
Speaker 2: the only thing that has to be correct to be

683
00:35:06,360 --> 00:35:08,160
Speaker 2: part of the model is that the numbers have to

684
00:35:08,239 --> 00:35:13,479
Speaker 2: ascend in order. That was it. But people, people, man,

685
00:35:14,200 --> 00:35:14,440
Speaker 2: just you.

686
00:35:14,480 --> 00:35:16,799
Speaker 1: Might even try, you might even try and break it

687
00:35:16,840 --> 00:35:20,400
Speaker 1: by saying three, five, seven, but you're still using that

688
00:35:20,480 --> 00:35:25,440
Speaker 1: original a version of that original hypothesis.

689
00:35:25,000 --> 00:35:27,839
Speaker 2: Exactly that that there's yes, say like that you think

690
00:35:27,840 --> 00:35:29,760
Speaker 2: it goes up by two or something like that. Yes,

691
00:35:30,239 --> 00:35:33,719
Speaker 2: You're like very few people go back and try to

692
00:35:33,760 --> 00:35:37,479
Speaker 2: break their own hypothesis, And that's the point of confirmation bias.

693
00:35:37,560 --> 00:35:40,360
Speaker 2: Let's move on from that experiment. The point of confirmation

694
00:35:40,480 --> 00:35:43,120
Speaker 2: bias that this shows if you actually can understand it

695
00:35:43,160 --> 00:35:47,279
Speaker 2: from other people than us, is that we tend to

696
00:35:47,320 --> 00:35:51,200
Speaker 2: take our initial ideas, our beliefs in a lot of cases,

697
00:35:51,600 --> 00:35:54,960
Speaker 2: and look for information that supports those and discard information

698
00:35:55,080 --> 00:35:56,200
Speaker 2: that doesn't support it.

699
00:35:57,000 --> 00:35:59,400
Speaker 1: Right, And that's of course you know mentioned politics a

700
00:35:59,440 --> 00:36:01,680
Speaker 1: minute ago. That where you most firmly see that these

701
00:36:01,760 --> 00:36:06,880
Speaker 1: days is you are in a media bubble. Probably, I

702
00:36:06,880 --> 00:36:09,399
Speaker 1: don't know a ton of people that get their news

703
00:36:09,480 --> 00:36:14,160
Speaker 1: sources from completely disparate points of view, and news these

704
00:36:14,280 --> 00:36:17,680
Speaker 1: days that you're getting is so a lot of it

705
00:36:17,719 --> 00:36:19,839
Speaker 1: is so slanted to begin with. It's probably not even

706
00:36:19,880 --> 00:36:23,840
Speaker 1: the best example anymore to use, but you're probably a

707
00:36:23,840 --> 00:36:26,160
Speaker 1: long way of saying, you're probably going to be seeking

708
00:36:26,200 --> 00:36:28,520
Speaker 1: out news that confirms your beliefs because you don't want

709
00:36:28,560 --> 00:36:29,360
Speaker 1: your beliefs challenged.

710
00:36:29,560 --> 00:36:32,440
Speaker 2: Yes, I mean, I, like everybody else, I have trouble

711
00:36:32,480 --> 00:36:34,440
Speaker 2: with that as well. But I have to boast you

712
00:36:34,520 --> 00:36:38,000
Speaker 2: me is actually really good about getting news from different sources.

713
00:36:38,719 --> 00:36:39,160
Speaker 1: Yeah.

714
00:36:39,200 --> 00:36:41,480
Speaker 2: And one reason that I find it difficult to do

715
00:36:41,560 --> 00:36:45,640
Speaker 2: is because I have like physical reactions sometimes. Yeah, And

716
00:36:45,800 --> 00:36:47,799
Speaker 2: that is a thing that's one of the reasons why

717
00:36:47,800 --> 00:36:50,200
Speaker 2: they think we have we use confirmation biases because it

718
00:36:50,280 --> 00:36:53,319
Speaker 2: sucks to be to have your beliefs challenged, right, It's

719
00:36:53,360 --> 00:36:56,319
Speaker 2: really difficult to overcome that. And there's this thing called

720
00:36:56,360 --> 00:37:01,160
Speaker 2: belief perseverance, which is, even when you're beliefs are challenged with,

721
00:37:01,280 --> 00:37:06,799
Speaker 2: say like an indisputable fact, you can still use confirmation

722
00:37:06,880 --> 00:37:12,440
Speaker 2: bias to preserve that belief because we usually attach our identity,

723
00:37:12,520 --> 00:37:15,040
Speaker 2: or build our identity around our beliefs. That's who we are.

724
00:37:15,200 --> 00:37:18,200
Speaker 2: So it's like we're being personally attacked. And then even

725
00:37:18,239 --> 00:37:20,560
Speaker 2: more than that, there's the backfire effect. Right did you

726
00:37:20,560 --> 00:37:21,000
Speaker 2: see that?

727
00:37:21,760 --> 00:37:22,160
Speaker 1: I did not.

728
00:37:22,520 --> 00:37:26,000
Speaker 2: So the backfire effect says that in the in the

729
00:37:26,040 --> 00:37:30,880
Speaker 2: face of being presented with information that is that basically

730
00:37:30,880 --> 00:37:34,200
Speaker 2: counters your own beliefs, it can make you actually solidify

731
00:37:34,800 --> 00:37:39,000
Speaker 2: your original incorrect belief in the first place. Right, You'll

732
00:37:39,040 --> 00:37:42,680
Speaker 2: you'll you'll believe it even more strongly, even though you've

733
00:37:42,719 --> 00:37:46,560
Speaker 2: just been given facts that contradict it. So we really

734
00:37:46,920 --> 00:37:51,320
Speaker 2: really hang on to our beliefs as much as possible.

735
00:37:51,320 --> 00:37:54,680
Speaker 2: And that is a huge, huge thing that humans trip over.

736
00:37:54,840 --> 00:37:58,759
Speaker 2: That confirmation bias is probably the grandaddy of all biases.

737
00:37:58,840 --> 00:38:03,120
Speaker 1: I think, Yeah, that's why I saved it for last Yeah,

738
00:38:03,160 --> 00:38:04,680
Speaker 1: and you know, a lot of reasons people do this.

739
00:38:04,840 --> 00:38:08,520
Speaker 1: You might be protecting your yourself, like your self esteem,

740
00:38:08,600 --> 00:38:11,880
Speaker 1: because otherwise you can you're admitting that you're may have

741
00:38:11,920 --> 00:38:14,759
Speaker 1: been wrong about something, and it, you know, takes a

742
00:38:14,760 --> 00:38:17,839
Speaker 1: big person to do that. You want to believe that

743
00:38:17,960 --> 00:38:21,440
Speaker 1: you're right about stuff. And it also might just be

744
00:38:21,480 --> 00:38:25,239
Speaker 1: difficult to process more than one hypothesis at once. It

745
00:38:25,520 --> 00:38:27,120
Speaker 1: might just be a little too brain breaking.

746
00:38:27,440 --> 00:38:30,440
Speaker 2: Yeah, because once you lock into an explanation, your brain

747
00:38:30,600 --> 00:38:32,879
Speaker 2: just it's like, I know, we've got it. We don't

748
00:38:32,880 --> 00:38:35,960
Speaker 2: have to figure this other thing out. Homeostasis, homeostasis. You know.

749
00:38:36,520 --> 00:38:39,200
Speaker 2: It's it is very hard to entertain something that is

750
00:38:40,200 --> 00:38:42,160
Speaker 2: counter to what we already think is true.

751
00:38:42,680 --> 00:38:45,319
Speaker 1: That's right, all right everyone. As you can tell by

752
00:38:45,360 --> 00:38:48,600
Speaker 1: the clock, we are taking our second break, this is

753
00:38:48,640 --> 00:38:49,880
Speaker 1: a long one, and we're going to come back and

754
00:38:49,920 --> 00:38:51,880
Speaker 1: talk about behavioral economics right after that.

755
00:38:52,080 --> 00:38:54,600
Speaker 2: Well, wait, before we do that, let's try to explain

756
00:38:54,640 --> 00:38:56,200
Speaker 2: this confirmation by a study again.

757
00:38:56,600 --> 00:39:29,600
Speaker 1: Yeah, we should all right, we'll be right back, Okay,

758
00:39:29,840 --> 00:39:33,000
Speaker 1: I promised talk about behavioral economics. A lot of the

759
00:39:33,000 --> 00:39:38,319
Speaker 1: work that Spirsky and Konoman did was super applicable and

760
00:39:38,520 --> 00:39:40,239
Speaker 1: kind of revolutionary in a lot of ways for the

761
00:39:40,239 --> 00:39:44,480
Speaker 1: world of economics and how people buying behavior is affected.

762
00:39:45,200 --> 00:39:47,520
Speaker 1: They didn't invent it, like Adam Smith wrote about stuff

763
00:39:47,560 --> 00:39:50,919
Speaker 1: like this, and starting about World War two is when

764
00:39:51,440 --> 00:39:54,239
Speaker 1: they started really kind of homing in on stuff like this,

765
00:39:54,760 --> 00:39:58,640
Speaker 1: like using mathematical models, and it all kind of started

766
00:39:58,640 --> 00:40:03,359
Speaker 1: with the assumption that people and companies and organizations are

767
00:40:03,440 --> 00:40:06,839
Speaker 1: really just trying to pursue their self interest at the end.

768
00:40:06,719 --> 00:40:08,160
Speaker 2: Of the day. Yeah, and they're going to make the

769
00:40:08,160 --> 00:40:12,040
Speaker 2: most rational decision. Yeah, and that's just they And they

770
00:40:12,040 --> 00:40:15,080
Speaker 2: were like, yes, we know people make irrational decisions, but

771
00:40:15,320 --> 00:40:17,480
Speaker 2: these are outliers. Like if you take all of the

772
00:40:17,520 --> 00:40:20,960
Speaker 2: information and their data and aggregate, you will see that

773
00:40:21,080 --> 00:40:24,279
Speaker 2: humans generally try to make the most rational decision. That's

774
00:40:24,440 --> 00:40:28,120
Speaker 2: just not true. People don't do that. We make all

775
00:40:28,160 --> 00:40:32,840
Speaker 2: sorts of irrational decisions that very frequently run counter to

776
00:40:32,920 --> 00:40:37,160
Speaker 2: our own best interests. And again we'll even reject stuff

777
00:40:37,280 --> 00:40:40,480
Speaker 2: like information that would help us make decisions to our

778
00:40:40,480 --> 00:40:44,120
Speaker 2: own best interests if they counter our beliefs. So there's

779
00:40:44,120 --> 00:40:47,120
Speaker 2: a guy named Richard Thaler who ended up becoming a

780
00:40:47,160 --> 00:40:50,600
Speaker 2: colleague of Diversky and Konnoment, and he took some of

781
00:40:50,640 --> 00:40:56,080
Speaker 2: their papers, and he realized that these mistakes, these cognitive biases,

782
00:40:56,480 --> 00:41:00,960
Speaker 2: they can be predictable. Right, you can actually map how

783
00:41:01,040 --> 00:41:04,600
Speaker 2: somebody's going to make a bad decision. And this became

784
00:41:04,640 --> 00:41:06,960
Speaker 2: the basis of behavioral economics.

785
00:41:07,200 --> 00:41:12,880
Speaker 1: Yeah, he well, let's talk about this this prospect theory,

786
00:41:12,880 --> 00:41:16,640
Speaker 1: because this was from Tversky and Konomon. It was an

787
00:41:16,680 --> 00:41:21,080
Speaker 1: article from nineteen seventy nine from this idea of prospect

788
00:41:21,160 --> 00:41:25,160
Speaker 1: theory colon an Analysis of Decision under Risk, and Livia

789
00:41:25,160 --> 00:41:27,919
Speaker 1: says it's probably the most cited economics paper of all time.

790
00:41:28,040 --> 00:41:31,600
Speaker 1: Like this was a revolutionary, landmark paper and they didn't

791
00:41:31,600 --> 00:41:33,799
Speaker 1: write a ton of papers for researchers. They did like

792
00:41:33,840 --> 00:41:37,319
Speaker 1: eight totalah, which just shows what an outsize impact they had.

793
00:41:38,440 --> 00:41:41,600
Speaker 1: But they talk about in this paper a lot of

794
00:41:41,880 --> 00:41:46,640
Speaker 1: attitudes about risk. One is loss a version, which is

795
00:41:46,680 --> 00:41:49,760
Speaker 1: the idea that you're going to experience more emotional suffering

796
00:41:49,800 --> 00:41:53,600
Speaker 1: when you lose money, then you will gain happiness if

797
00:41:53,600 --> 00:41:58,120
Speaker 1: you gain something, So you may pass up an offer

798
00:41:58,520 --> 00:42:00,759
Speaker 1: that gives you equal odds of winning twenty five or

799
00:42:00,800 --> 00:42:04,080
Speaker 1: losing twenty. There was another example I think kind of

800
00:42:04,080 --> 00:42:06,360
Speaker 1: gets it across more is there was an experiment in

801
00:42:06,400 --> 00:42:09,560
Speaker 1: nineteen ninety six where they gave participants a lottery ticket

802
00:42:10,160 --> 00:42:12,680
Speaker 1: and before you scratched it off or whatever, let's say

803
00:42:12,719 --> 00:42:14,120
Speaker 1: it was a scratch off. They said, all right, well,

804
00:42:14,160 --> 00:42:17,680
Speaker 1: hold on, before you do that, I'll give you another

805
00:42:17,760 --> 00:42:21,840
Speaker 1: lottery ticket plus ten dollars in cash. And for no

806
00:42:22,400 --> 00:42:26,359
Speaker 1: logical reason at all, people tended to think that that

807
00:42:26,400 --> 00:42:29,279
Speaker 1: first ticket was the one, even though there was no

808
00:42:29,400 --> 00:42:31,399
Speaker 1: it was a lottery ticket, there was no difference at all.

809
00:42:31,719 --> 00:42:33,560
Speaker 1: They would turn down that extra ten bucks. I think

810
00:42:33,640 --> 00:42:35,480
Speaker 1: less than fifty percent of them took that deal.

811
00:42:35,760 --> 00:42:38,760
Speaker 2: Yeah, because in giving away or trading that first ticket,

812
00:42:38,840 --> 00:42:42,680
Speaker 2: they risked a loss even though the gain was right there.

813
00:42:42,880 --> 00:42:46,480
Speaker 2: Just trading the ticket, you got an extra ten bucks, right, Yeah,

814
00:42:45,960 --> 00:42:51,120
Speaker 2: that's fairly irrational. We also have a lot of trouble

815
00:42:51,760 --> 00:42:56,160
Speaker 2: with rare events. Yeah, we tend to overestimate them. It

816
00:42:56,200 --> 00:42:57,680
Speaker 2: can be a positive event and it can be a

817
00:42:57,680 --> 00:43:02,480
Speaker 2: negative event. But we're really bad at probabilities and statistics.

818
00:43:02,960 --> 00:43:09,960
Speaker 2: And this is essentially it's like you won't let your

819
00:43:10,040 --> 00:43:12,960
Speaker 2: kid walk to school because you're afraid of your kid

820
00:43:12,960 --> 00:43:15,560
Speaker 2: being kidnapped, even though the chance of your kid being

821
00:43:15,640 --> 00:43:21,200
Speaker 2: kidnapped is just ridiculously low. It's technically irrational, even though

822
00:43:21,360 --> 00:43:24,440
Speaker 2: very few people would fault you for that, but it's

823
00:43:24,480 --> 00:43:26,040
Speaker 2: still an irrational decision.

824
00:43:26,640 --> 00:43:29,560
Speaker 1: Yeah, for sure. We talked about that in the.

825
00:43:31,200 --> 00:43:33,280
Speaker 2: Well was it free range kids?

826
00:43:33,960 --> 00:43:36,560
Speaker 1: Maybe I can't remember, tied in with a satanic panic

827
00:43:36,600 --> 00:43:38,920
Speaker 1: and stuff like that I think definitely did back in

828
00:43:38,960 --> 00:43:42,800
Speaker 1: the day. Relative rather than absolute terms. That is a theory,

829
00:43:43,960 --> 00:43:48,319
Speaker 1: monetary theory where and there's a great example. You might

830
00:43:48,400 --> 00:43:50,399
Speaker 1: drive an extra ten minutes in a car to buy

831
00:43:50,400 --> 00:43:52,920
Speaker 1: a shirt that you know is selling the shirt for

832
00:43:52,960 --> 00:43:55,120
Speaker 1: twenty bucks rather than the one closer to you that

833
00:43:55,160 --> 00:43:57,520
Speaker 1: sells it for thirty. But you're not going to do

834
00:43:57,560 --> 00:44:00,040
Speaker 1: that because that saves you ten bucks. You won't s

835
00:44:00,200 --> 00:44:04,279
Speaker 1: twenty dollars on a car even though you may even

836
00:44:04,400 --> 00:44:06,960
Speaker 1: have to drive five minutes down the road, because you're like, oh,

837
00:44:07,040 --> 00:44:10,120
Speaker 1: it's twenty dollars, the car is twenty thousand. But it's

838
00:44:10,200 --> 00:44:13,440
Speaker 1: really a relative, you know, absolute thing, as you're saving

839
00:44:13,800 --> 00:44:15,360
Speaker 1: twice as much money as you did on that T

840
00:44:15,480 --> 00:44:16,080
Speaker 1: shirt purchase.

841
00:44:16,320 --> 00:44:19,840
Speaker 2: Right, But it's like you said, it's all relative again,

842
00:44:20,160 --> 00:44:25,040
Speaker 2: totally irrational. But all this stuff relates to economics, and

843
00:44:25,480 --> 00:44:27,520
Speaker 2: like you said, this stuff can be replicated. There's a

844
00:44:27,560 --> 00:44:32,560
Speaker 2: twenty twenty study that looked at the prospect theory in particular,

845
00:44:33,120 --> 00:44:37,279
Speaker 2: and this major study was conducted in nineteen countries and

846
00:44:37,360 --> 00:44:40,960
Speaker 2: thirteen different languages and held up not bad. No, that's

847
00:44:41,040 --> 00:44:44,440
Speaker 2: not bad at all. And so it's not just economics.

848
00:44:44,480 --> 00:44:48,160
Speaker 2: It's not just being exploited by the wine list or

849
00:44:49,400 --> 00:44:53,480
Speaker 2: you know, Kentucky Fried Chicken or something like that to

850
00:44:53,600 --> 00:44:57,960
Speaker 2: make you buy their stuff. This actually this can have

851
00:44:58,040 --> 00:45:01,360
Speaker 2: like life and death consequences too, although I guess so

852
00:45:01,480 --> 00:45:04,279
Speaker 2: can wine. In Kentucky Fried chicken.

853
00:45:04,719 --> 00:45:06,960
Speaker 1: You know what you're gonna get a Kentucky fried chicken?

854
00:45:07,000 --> 00:45:08,080
Speaker 1: What PEPSI?

855
00:45:08,760 --> 00:45:11,359
Speaker 2: That's right, you will get some pepsi. You know how

856
00:45:11,400 --> 00:45:13,880
Speaker 2: I know that cause you just had Kentucky fried Chicken.

857
00:45:14,480 --> 00:45:16,720
Speaker 1: I did. After our tour. I was a little tired

858
00:45:16,760 --> 00:45:19,240
Speaker 1: and needed just some fried chicken. So I got fried chicken.

859
00:45:19,280 --> 00:45:21,360
Speaker 2: What do you get just the original or extra crispy?

860
00:45:21,360 --> 00:45:23,280
Speaker 2: Because you're crazy if you don't get extra crispy.

861
00:45:23,760 --> 00:45:28,400
Speaker 1: I get extra crispy. But they were out they could satisfy.

862
00:45:28,480 --> 00:45:30,400
Speaker 1: I got the three piece. They had two more pieces

863
00:45:30,400 --> 00:45:32,160
Speaker 1: of extra crispy, and they asked if one piece of

864
00:45:33,239 --> 00:45:36,319
Speaker 1: O R was available, and I was like, yeah, sure,

865
00:45:36,320 --> 00:45:37,920
Speaker 1: I'm not gonna not eat a piece of chicken.

866
00:45:40,400 --> 00:45:43,200
Speaker 2: It is good. They do chicken, right, Yeah they do.

867
00:45:43,920 --> 00:45:45,520
Speaker 2: Did you get the mashed potatoes and gravy?

868
00:45:45,960 --> 00:45:48,680
Speaker 1: You know it? Buddy times too, and extra biscuit. I

869
00:45:48,680 --> 00:45:52,760
Speaker 1: went all in. It was a rare treat eating frenzy.

870
00:45:52,880 --> 00:45:53,920
Speaker 2: Did you drink a PEPSI?

871
00:45:54,360 --> 00:45:54,719
Speaker 1: I did?

872
00:45:54,840 --> 00:45:58,160
Speaker 2: Awesome. Well that all fits somehow. I don't know how,

873
00:45:58,239 --> 00:46:02,960
Speaker 2: but it somehow fits this this episode so where I

874
00:46:03,040 --> 00:46:05,120
Speaker 2: was saying that this can be life and death, as

875
00:46:05,160 --> 00:46:08,840
Speaker 2: with medicine, because although doctors have God complexes and like

876
00:46:08,880 --> 00:46:11,759
Speaker 2: to present themselves as infallible, they are quite fallible. They're

877
00:46:11,840 --> 00:46:15,359
Speaker 2: humans and they can suffer the same cognitive biases as us.

878
00:46:15,800 --> 00:46:19,240
Speaker 2: But they have your life in their hands. We rarely

879
00:46:19,280 --> 00:46:21,200
Speaker 2: have others' lives in our hands.

880
00:46:21,880 --> 00:46:23,439
Speaker 1: Yeah. Do you watch the show the Pit?

881
00:46:24,000 --> 00:46:26,759
Speaker 2: I tried and I just did not grab me. I

882
00:46:26,800 --> 00:46:28,960
Speaker 2: gave it like ten minutes, but I hear nothing but

883
00:46:29,040 --> 00:46:29,640
Speaker 2: good things.

884
00:46:30,160 --> 00:46:31,799
Speaker 1: Yeah, I mean, I really like it. I've never been

885
00:46:31,840 --> 00:46:33,680
Speaker 1: a hospital show guy, so this is kind of one

886
00:46:33,719 --> 00:46:36,000
Speaker 1: of my first forays into it, but I like it

887
00:46:36,000 --> 00:46:38,320
Speaker 1: a lot. I haven't started season two, but I noticed

888
00:46:38,320 --> 00:46:42,440
Speaker 1: when reading through these bias like medical biases that they do,

889
00:46:43,320 --> 00:46:45,520
Speaker 1: or at least Noah Wiley does a really good job

890
00:46:45,560 --> 00:46:49,759
Speaker 1: on the show with these younger residents trying to bust

891
00:46:49,840 --> 00:46:51,840
Speaker 1: through and a lot of this stuff comes up. He

892
00:46:51,880 --> 00:46:57,040
Speaker 1: doesn't say, hey, that's affect heuristic. He just will talk

893
00:46:57,080 --> 00:46:59,440
Speaker 1: about what that is. And now that I know the definitions,

894
00:46:59,440 --> 00:47:01,640
Speaker 1: I'm like, oh, he's talking about as an outcome bias

895
00:47:02,239 --> 00:47:05,200
Speaker 1: or an anchoring bias. It's fairly interesting.

896
00:47:05,320 --> 00:47:08,160
Speaker 2: Yeah. Rather than say, like being presented with a really

897
00:47:08,239 --> 00:47:10,680
Speaker 2: high price for a bottle of wine to make the

898
00:47:10,719 --> 00:47:14,080
Speaker 2: other overpriced wine seem like a bargain, this can be

899
00:47:14,200 --> 00:47:17,760
Speaker 2: like your first lab work comes back and that forms

900
00:47:17,800 --> 00:47:23,480
Speaker 2: the anchoring biased impression of your condition. And even as

901
00:47:23,640 --> 00:47:27,080
Speaker 2: new lab work comes back, that doctor may fail to

902
00:47:27,520 --> 00:47:31,440
Speaker 2: adjust their view of your condition because they're not taking

903
00:47:31,480 --> 00:47:34,040
Speaker 2: into account this new stuff. They're giving more weight to

904
00:47:34,080 --> 00:47:37,560
Speaker 2: that original, that original number. So yeah, and that's just

905
00:47:37,600 --> 00:47:39,959
Speaker 2: the anchoring bias. The way that it can affect there's,

906
00:47:40,040 --> 00:47:41,759
Speaker 2: like you said, there's all sorts of other ways for

907
00:47:41,800 --> 00:47:44,279
Speaker 2: it to happen, and all of it can result in

908
00:47:44,719 --> 00:47:48,800
Speaker 2: poorer outcomes for patients just because their doctors are humans,

909
00:47:49,239 --> 00:47:52,919
Speaker 2: and we don't really approach cognitive biases in a really

910
00:47:53,960 --> 00:47:55,560
Speaker 2: methodical or deliberate way.

911
00:47:56,640 --> 00:47:58,640
Speaker 1: Yeah. They In fact, now that I'm thinking about it,

912
00:47:58,640 --> 00:48:00,359
Speaker 1: they do this so much on the show. The show

913
00:48:00,400 --> 00:48:05,600
Speaker 1: could be called medical confirmation bias the show because you

914
00:48:05,640 --> 00:48:08,000
Speaker 1: see it all the time. Outcome bias is when a

915
00:48:08,080 --> 00:48:10,960
Speaker 1: shift in the patient's health you're convinced is the result

916
00:48:10,960 --> 00:48:13,600
Speaker 1: of a treatment, like it's because of that thing I did,

917
00:48:13,880 --> 00:48:17,759
Speaker 1: or affect heuristic that I mentioned, an emotional reaction to

918
00:48:17,800 --> 00:48:21,799
Speaker 1: a patient, you know, kind of overrunning you know, deliberating

919
00:48:21,800 --> 00:48:24,000
Speaker 1: on this thing in a logical way. This happens all

920
00:48:24,080 --> 00:48:25,000
Speaker 1: the time on the show.

921
00:48:25,480 --> 00:48:28,600
Speaker 2: Yeah. Well, another field that it happens with this forensic science,

922
00:48:28,640 --> 00:48:31,000
Speaker 2: which we've gone to great links to kind of point out,

923
00:48:31,120 --> 00:48:35,120
Speaker 2: is junks in the most in most cases, and a

924
00:48:35,160 --> 00:48:37,759
Speaker 2: lot of that junk is just based on cognitive biases.

925
00:48:38,719 --> 00:48:41,080
Speaker 1: Yeah, for sure. I mean certainly the way they do

926
00:48:41,120 --> 00:48:45,120
Speaker 1: lineups is flawed. I mean the way they I feel like,

927
00:48:45,200 --> 00:48:46,759
Speaker 1: you're right, we've done this a lot on the on

928
00:48:46,800 --> 00:48:49,239
Speaker 1: the show. The way they have done a lot of

929
00:48:49,280 --> 00:48:53,359
Speaker 1: this is super flawed. And I think maybe they're looking

930
00:48:53,360 --> 00:48:54,960
Speaker 1: at it some but not a lot.

931
00:48:55,239 --> 00:49:00,759
Speaker 2: No, So if you want to fight cognitive bias in

932
00:49:00,800 --> 00:49:03,920
Speaker 2: your own mind, Chuck, what do you do? What do

933
00:49:04,000 --> 00:49:04,279
Speaker 2: you do?

934
00:49:05,800 --> 00:49:07,560
Speaker 1: Well, there's a list of good tips here, and I

935
00:49:07,560 --> 00:49:10,640
Speaker 1: think these are pretty good tips. The first one is

936
00:49:10,719 --> 00:49:12,640
Speaker 1: just being aware that you have these, which is something

937
00:49:12,680 --> 00:49:14,400
Speaker 1: that we've already kind of kind of worked through on

938
00:49:14,440 --> 00:49:16,200
Speaker 1: this episode, except for you, of course, because you don't

939
00:49:16,200 --> 00:49:21,680
Speaker 1: have these. Sure, but studies show that like just being aware,

940
00:49:22,360 --> 00:49:24,359
Speaker 1: it's not one of those things where like, well, being

941
00:49:24,360 --> 00:49:26,440
Speaker 1: aware is have the problem. It's like being aware seems

942
00:49:26,480 --> 00:49:27,600
Speaker 1: like two percent of the problem.

943
00:49:27,760 --> 00:49:32,480
Speaker 2: Yeah, it's like you're aware that you have an unconscious bias.

944
00:49:32,520 --> 00:49:35,560
Speaker 2: It doesn't make you understand the bias. You just know

945
00:49:35,719 --> 00:49:39,200
Speaker 2: that they're there, Right, that's the problem with is unconscious.

946
00:49:40,000 --> 00:49:40,359
Speaker 1: What else?

947
00:49:40,520 --> 00:49:43,400
Speaker 2: There are some like actual things you can do like

948
00:49:43,560 --> 00:49:48,040
Speaker 2: delay decision making. Yeah, don't don't come to snap judgments.

949
00:49:48,480 --> 00:49:52,279
Speaker 2: Go get more information, Go get information from a contradictory

950
00:49:52,320 --> 00:49:55,800
Speaker 2: source or different source or something like that. And then

951
00:49:55,920 --> 00:49:57,839
Speaker 2: it like kind of tied into that. You can have

952
00:49:57,960 --> 00:50:02,080
Speaker 2: like personal like rule like if there's a big decision,

953
00:50:02,320 --> 00:50:06,520
Speaker 2: you will not make that decision until you've slept on it. Yeah,

954
00:50:06,560 --> 00:50:10,799
Speaker 2: for example, don't buy a TV unless your friend says, yeah,

955
00:50:11,040 --> 00:50:12,040
Speaker 2: good idea.

956
00:50:12,960 --> 00:50:17,200
Speaker 1: Try and consider your past experience for sure, because optimism

957
00:50:17,280 --> 00:50:21,600
Speaker 1: biased could come into play, like hey, worked out last time, yeah,

958
00:50:21,640 --> 00:50:23,880
Speaker 1: Like why would I why would I take more time

959
00:50:24,080 --> 00:50:24,600
Speaker 1: this time?

960
00:50:24,920 --> 00:50:27,160
Speaker 2: Yeah? And that's another way that you can kind of

961
00:50:27,200 --> 00:50:29,680
Speaker 2: do that. An exercise you can do is write down

962
00:50:29,880 --> 00:50:33,000
Speaker 2: your expectations for an outcome and then go back and

963
00:50:33,040 --> 00:50:34,960
Speaker 2: look at it afterward and see if you were right

964
00:50:35,040 --> 00:50:38,000
Speaker 2: or not. Can kind of help you realize like, uh,

965
00:50:38,080 --> 00:50:40,759
Speaker 2: I do kind of tend toward the optimism bias.

966
00:50:41,000 --> 00:50:43,280
Speaker 1: Yeah, because I believe that was one of the other biases.

967
00:50:43,480 --> 00:50:46,440
Speaker 1: Is even like it is hard to recognize because you're

968
00:50:46,440 --> 00:50:49,520
Speaker 1: biased and that you misremember what you thought going into it.

969
00:50:49,520 --> 00:50:51,280
Speaker 1: So writing it down is a good that's a good.

970
00:50:51,120 --> 00:50:54,360
Speaker 2: One, right, But if you're super super unconsciously biased, you

971
00:50:54,440 --> 00:50:57,319
Speaker 2: might be like someone else wrote this in my handwriting, Right,

972
00:50:58,040 --> 00:50:59,120
Speaker 2: I've never been this wrong.

973
00:51:00,920 --> 00:51:03,320
Speaker 1: What about Thomas Bayes and Baysian reasoning.

974
00:51:03,840 --> 00:51:06,480
Speaker 2: So he was a minister from the eighteenth century, and

975
00:51:06,560 --> 00:51:10,640
Speaker 2: he basically came up with a standardized formula for taking

976
00:51:10,680 --> 00:51:14,840
Speaker 2: into account the probability of an outcome, right that things

977
00:51:14,960 --> 00:51:18,120
Speaker 2: aren't essentially so I saw this on less wrong dot

978
00:51:18,200 --> 00:51:21,080
Speaker 2: org founded by one of the guys who wrote, if

979
00:51:21,120 --> 00:51:24,480
Speaker 2: anyone builds it, everyone dies about Ai Eliotz or Yukowski.

980
00:51:25,080 --> 00:51:27,120
Speaker 2: The whole point of less wrong dot org is to

981
00:51:27,360 --> 00:51:31,439
Speaker 2: overcome your biases in a methodical way. And they love

982
00:51:31,520 --> 00:51:34,600
Speaker 2: Baysian reasoning, and it basically says, there's no such thing

983
00:51:34,640 --> 00:51:39,440
Speaker 2: as something is just true. Everything is just a probability,

984
00:51:39,480 --> 00:51:42,560
Speaker 2: and you can kind of try to determine how probable

985
00:51:42,640 --> 00:51:46,600
Speaker 2: something is based on whatever evidence you can gather about it.

986
00:51:46,680 --> 00:51:48,440
Speaker 2: Just basically going through life like that.

987
00:51:49,080 --> 00:51:53,600
Speaker 1: You know, who hates that website? Who l E s

988
00:51:53,800 --> 00:51:57,120
Speaker 1: R O NNG That dude who started his own personal

989
00:51:57,280 --> 00:52:01,000
Speaker 1: comedy website right, less wrong dot God. That's right, he's

990
00:52:01,040 --> 00:52:01,880
Speaker 1: just getting smashed.

991
00:52:03,560 --> 00:52:04,279
Speaker 2: What else, Chuck?

992
00:52:06,800 --> 00:52:08,840
Speaker 1: What else is I cultivate a growth mindset?

993
00:52:09,080 --> 00:52:09,920
Speaker 2: That's a big one.

994
00:52:10,239 --> 00:52:13,480
Speaker 1: Hey, I make mistakes. I screw things up, and like

995
00:52:13,600 --> 00:52:16,120
Speaker 1: I need to recognize that and try and grow from

996
00:52:16,120 --> 00:52:21,080
Speaker 1: that rather than you know, just being confirmed with my

997
00:52:21,120 --> 00:52:22,280
Speaker 1: own biases constantly.

998
00:52:22,640 --> 00:52:24,960
Speaker 2: Yeah, maybe like looking around at some of the ways

999
00:52:25,000 --> 00:52:29,319
Speaker 2: that you're commonly exploited, say like by advertisers, Like scarcity

1000
00:52:29,400 --> 00:52:31,880
Speaker 2: is one when somebody says act now supplies are limited.

1001
00:52:31,920 --> 00:52:37,719
Speaker 2: They're creating a scarcity mindset in you social proof basically

1002
00:52:37,800 --> 00:52:40,200
Speaker 2: like these people like this, so you probably should too,

1003
00:52:40,200 --> 00:52:41,719
Speaker 2: and you're like, oh, I should like that too.

1004
00:52:42,320 --> 00:52:42,760
Speaker 1: Yeah.

1005
00:52:42,800 --> 00:52:45,680
Speaker 2: And then two other things I saw. There's something called

1006
00:52:46,360 --> 00:52:54,280
Speaker 2: cognitive bias mod modification I think is what it is. Okay,

1007
00:52:54,400 --> 00:52:57,400
Speaker 2: you can use this for like treating anxiety, right, Like

1008
00:52:57,480 --> 00:53:02,879
Speaker 2: people like tend to seek out negative facial expressions. Oh yeah,

1009
00:53:02,920 --> 00:53:07,040
Speaker 2: and this treatment is like like here's a thousand frownie faces.

1010
00:53:07,520 --> 00:53:10,840
Speaker 2: Find the smiley face in there, and just screen after screen,

1011
00:53:10,880 --> 00:53:13,359
Speaker 2: you're looking for the smiley face, and you're training your

1012
00:53:13,360 --> 00:53:18,640
Speaker 2: brain to stop putting as much weight on negative facial expressions,

1013
00:53:19,160 --> 00:53:23,839
Speaker 2: just using like basically exploiting your cognitive bias to get

1014
00:53:23,880 --> 00:53:25,440
Speaker 2: over your cognitive bias.

1015
00:53:25,840 --> 00:53:26,480
Speaker 1: Oh wow.

1016
00:53:26,719 --> 00:53:30,839
Speaker 2: And then the last thing, Chuck is apparently AI are

1017
00:53:30,880 --> 00:53:35,560
Speaker 2: starting to show signs of emergent cognitive biases because they

1018
00:53:35,640 --> 00:53:40,279
Speaker 2: use heuristics too, so they're starting to make cognitive they're

1019
00:53:40,280 --> 00:53:44,000
Speaker 2: starting to make errors in judgment in predictable ways, which

1020
00:53:44,000 --> 00:53:46,600
Speaker 2: are cognitive biases, just like humans.

1021
00:53:47,280 --> 00:53:48,879
Speaker 1: Rob Zombie more human than human.

1022
00:53:49,080 --> 00:53:51,479
Speaker 2: That's right, you got anything else?

1023
00:53:51,920 --> 00:53:52,759
Speaker 1: I got nothing else.

1024
00:53:52,840 --> 00:53:56,520
Speaker 2: This is a good one. This is fun. Chuck, I'm going, well,

1025
00:53:56,560 --> 00:53:59,160
Speaker 2: since Chuck and I both liked this episode, that means

1026
00:53:59,160 --> 00:54:02,440
Speaker 2: we have no choice but for listener mail to be triggered.

1027
00:54:02,719 --> 00:54:06,799
Speaker 1: Right now, I'm going to call this follow up on

1028
00:54:07,080 --> 00:54:11,920
Speaker 1: a Sebastopol because I wondered what the connection there was, Hey, guys,

1029
00:54:12,200 --> 00:54:15,960
Speaker 1: because if you didn't listen Sebastopol, California, and we were

1030
00:54:15,960 --> 00:54:20,160
Speaker 1: talking about the Sebastopold in the Crimean War, and I

1031
00:54:20,200 --> 00:54:22,879
Speaker 1: was like, there's no way that's a coincidence. And it's not. Hey, guys,

1032
00:54:22,920 --> 00:54:26,200
Speaker 1: listening to the podcast on the Light Brigade from Sonoma County.

1033
00:54:26,320 --> 00:54:31,400
Speaker 1: Our Sebastopol was named after Sebastopod. And here's a little information.

1034
00:54:31,640 --> 00:54:35,440
Speaker 1: The settlement was apparently originally named pine Grove, and the

1035
00:54:35,520 --> 00:54:40,040
Speaker 1: name changed to Sebastopol was attributed to a bar fight

1036
00:54:40,120 --> 00:54:44,879
Speaker 1: in the eighteen fifties which allegedly compared by a bystander

1037
00:54:45,440 --> 00:54:49,799
Speaker 1: to the long siege of the seaport of Sebastopol during

1038
00:54:49,840 --> 00:54:53,440
Speaker 1: the Crimean War. Wow, so the original name survives in

1039
00:54:53,480 --> 00:54:56,440
Speaker 1: the name of the Pine Grove General Store downtown only,

1040
00:54:57,280 --> 00:55:00,920
Speaker 1: and that is it. There's also the rush there Russian

1041
00:55:00,960 --> 00:55:04,000
Speaker 1: River Valley, so apparently there are some Russian influence in

1042
00:55:04,040 --> 00:55:05,480
Speaker 1: that area which I didn't know about. And that is

1043
00:55:05,480 --> 00:55:06,279
Speaker 1: from Marsha Ford.

1044
00:55:06,920 --> 00:55:09,879
Speaker 2: Yeah. Also, we want to apologize to all of our

1045
00:55:10,600 --> 00:55:13,759
Speaker 2: Iron Maiden fans who wrote in to be like, yeah,

1046
00:55:14,120 --> 00:55:17,000
Speaker 2: that song of the Trooper is about that whole battle.

1047
00:55:18,000 --> 00:55:21,120
Speaker 1: Yeah, I didn't know. I am not. I like Iron Maiden,

1048
00:55:21,640 --> 00:55:23,600
Speaker 1: but I didn't have as much shame upon my head

1049
00:55:23,640 --> 00:55:26,760
Speaker 1: as you. But you didn't reading the lyrics, it doesn't

1050
00:55:26,800 --> 00:55:31,640
Speaker 1: say you know crimean war in charge of the light Brigade,

1051
00:55:31,640 --> 00:55:31,920
Speaker 1: does it?

1052
00:55:32,400 --> 00:55:34,880
Speaker 2: I don't know. I haven't heard it in a while.

1053
00:55:35,200 --> 00:55:37,239
Speaker 2: I'm a big fan of the poster. I love the

1054
00:55:37,280 --> 00:55:38,120
Speaker 2: poster a lot.

1055
00:55:38,440 --> 00:55:39,040
Speaker 1: Yeah, me too.

1056
00:55:39,920 --> 00:55:42,399
Speaker 2: Well, sorry all of you Iron Maiden fans out there.

1057
00:55:42,440 --> 00:55:43,879
Speaker 2: We'll try to do better next time.

1058
00:55:44,040 --> 00:55:45,160
Speaker 1: Yeah, missed opportunity.

1059
00:55:45,200 --> 00:55:47,480
Speaker 2: Who is that that wrote in about Sebastopol?

1060
00:55:48,000 --> 00:55:49,040
Speaker 1: That was Marcia, I believe.

1061
00:55:49,080 --> 00:55:53,120
Speaker 2: Thanks Marcia, Marcia, Marsha Marsha. We really appreciate you, and

1062
00:55:53,160 --> 00:55:55,600
Speaker 2: if you want to be like Marcia, you can email

1063
00:55:55,680 --> 00:55:58,520
Speaker 2: us as well. Send it off to Stuff podcast at

1064
00:55:58,520 --> 00:56:03,520
Speaker 2: iHeartRadio dot com.

1065
00:56:03,680 --> 00:56:06,560
Speaker 1: Stuff you Should Know is a production of iHeartRadio. For

1066
00:56:06,640 --> 00:56:10,839
Speaker 1: more podcasts Myheart Radio, visit the iHeartRadio, app, Apple podcasts,

1067
00:56:10,920 --> 00:56:12,760
Speaker 1: or wherever you listen to your favorite shows.