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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 Charles w Chuck Bryant, the best all around Boy,

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Speaker 2: and there's Jerry Rowland. And this is stuff you should

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Speaker 2: know about.

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Speaker 1: Really, wish I hadn't have told you that. I think

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Speaker 1: I've told you that before, though, so you forgot the

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Speaker 1: first time, so maybe you'll forget again.

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Speaker 2: I can't you know how people keep lists of like

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Speaker 2: episodes we say we should do, or movies we've mentioned,

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Speaker 2: or something like that. They should keep a list of

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Speaker 2: the different things that we've talked about and then completely

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Speaker 2: forgot we talked about, because I'm sure it would be extensive.

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Speaker 1: Yeah, big list.

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Speaker 2: So speaking of big list, Chuck, we're talking today about priming,

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Speaker 2: which is the present tense of prime us, and what

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Speaker 2: it refers to is a a psychological term where you

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Speaker 2: are prompted to respond in a certain way, behave in

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Speaker 2: a certain way, choose a certain selection based on some

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Speaker 2: prompt that was given to you without your knowledge. It

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Speaker 2: could either be so flashed so fast or something on

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Speaker 2: a computer screen that your conscious awareness didn't pick up

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Speaker 2: on it, or it could just be presented to you

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Speaker 2: in a way that you're not you're not aware that

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Speaker 2: it's actually related to the thing that you're being say,

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Speaker 2: tested on.

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Speaker 1: Yeah, exactly. And this is a big, big thing in

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Speaker 1: cognitive psychology, and you know, we're going to kind of

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Speaker 1: get into the ins and outs of it. Some of it,

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Speaker 1: there's there's a lot of for some of it, there's

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Speaker 1: a lot of studies that sort of indicate that there's

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Speaker 1: a lot to it. There's another part of this that

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Speaker 1: we'll get to where there's a lot of studies that

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Speaker 1: aren't so great or that have been fudged or data's

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Speaker 1: been massaged or completely fabricated, where it seems like sometimes

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Speaker 1: they've been found out as far as researchers go. But

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Speaker 1: it all comes down to memory. And if you're if

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Speaker 1: you're talking to a cognitive psychologist, they'll say that you

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Speaker 1: have a couple of ways that you remember things. One

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Speaker 1: is your explicit memory or your active or conscious memory.

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Speaker 1: Like if you're trying to think of something and you

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Speaker 1: actively engage your memory to think of a thing, that's

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Speaker 1: what that is.

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Speaker 2: If somebody asked, like what was Chuck's senior superlative in

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Speaker 2: high school? I would think back to a fact that

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Speaker 2: I learned, and I would say best all round boy, is.

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Speaker 1: It going to be one of those? And then implicit

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Speaker 1: memory is the other one. And that's just what you

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Speaker 1: remember on an unconscious level. So if someone asked me

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Speaker 1: what was your senior superlo, wouldn't even have to think

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Speaker 1: about it, best all around boy, boom, it just pops

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Speaker 1: right out as if it were my name.

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Speaker 2: Right, So those are and this is really kind of

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Speaker 2: like breaking it down to a really rough basic level.

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Speaker 2: It's much more complex than that. Well, we bring that

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Speaker 2: up because yours. It's tapping into the different ways that

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Speaker 2: you access memories that priming is based on. Yeah, and

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Speaker 2: you said it falls under the rubric of cognitive psychology,

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Speaker 2: and that is true. Cognitive psychology has shown very clearly

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Speaker 2: that this actually works. That if you give somebody, say

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Speaker 2: a word, and you show them a list of associated words,

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Speaker 2: it's going to just happen that they they're able to

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Speaker 2: pick out the associated word faster than other words. There, Well,

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Speaker 2: we'll give you some examples. Rather than just me mushing

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Speaker 2: it all together. Let's let's tease it out a little

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Speaker 2: bit like a eighties per.

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Speaker 1: Yeah, oh boy, Yeah, Let let's do that, because I

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Speaker 1: think if we give some of these examples it'll make

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Speaker 1: a little more sense. For instance, this first exercise that

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Speaker 1: Dave helped us out with this they found this one.

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Speaker 1: It's called the lexical decision, and that is the idea

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Speaker 1: that if you put a word up on a screen

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Speaker 1: for a couple of seconds as a priming word, then

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Speaker 1: as the subject, will indicate the next word on the

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Speaker 1: screen if it's real word or if it's a nonsense word,

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Speaker 1: but that will be influenced that's your lexical decision you're making,

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Speaker 1: but that will be influenced by that priming word that

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Speaker 1: you saw. So in this case, Dave, do you use

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Speaker 1: the example Doctor You're sitting there in the lab. Bill

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Speaker 1: Murray's on the other side, got the shaker already. Doctor

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Speaker 1: pops up on the screen for a couple of seconds.

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Speaker 1: That's your priming word, and then all of a sudden

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Speaker 1: other words pop up after that, and our job as

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Speaker 1: a subject is to say whether that's a real word

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Speaker 1: or a nonsense word. And obviously you would think if

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Speaker 1: the word nurse pops up, you're going to be recognizing

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Speaker 1: that a lot faster. So it's all about sort of

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Speaker 1: the speed at which you recognize this as a real

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Speaker 1: or nonsense word. Nurse would be a much faster decision

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Speaker 1: than if it was basketball, right.

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Speaker 2: And it's basically words that share a similar category are

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Speaker 2: more easily accessed once you've been primed, almost like the

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Speaker 2: way that we sort things is by putting them into

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Speaker 2: large categories like doctor, nurse, hospital, stethoscope, right, and then

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Speaker 2: once you open that category by thinking of doctor, you're

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Speaker 2: going to be able to access the other stuff in

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Speaker 2: that category much more easily than say something in a

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Speaker 2: totally different category like cheese in the same category as delicious,

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Speaker 2: that kind of stuff. Right, That seems for sure, that

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Speaker 2: seems like what it's tapping into, and it actually seems

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Speaker 2: to reveal that that's kind of how we store memories.

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Speaker 2: There's another another demonstration that was pretty famous that shows

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Speaker 2: for sure. Again, I just want to get this across.

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Speaker 2: Cognitive priming really works. If you give people a list

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Speaker 2: of words and one of those words has a letter missing,

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Speaker 2: say a vowel, that could make multiple different words they're

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Speaker 2: going to choose or they're going to fill it in

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Speaker 2: differently based on the other words in the list. So,

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Speaker 2: for example, if you have a list of words bread, milk, hot,

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Speaker 2: and then the last word is so o, blank p,

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Speaker 2: poop sure or soup Oh, I think would be a

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Speaker 2: good one, right, yeah, And depending on whether you know

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Speaker 2: how to spell, you might spell it with an oh

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Speaker 2: or you doesn't matter, You're still getting the point across, right.

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Speaker 1: Or in another example of the same, you know, missing letter,

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Speaker 1: if it was shower, water, wash, so blank p, you

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Speaker 1: might say poop there too. Actually, soap would probably be

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Speaker 1: the word. So you know, it's just a very intuitive

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Speaker 1: kind of thing happening because your brain is working in

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Speaker 1: a very unconscious way because or I guess non conscious,

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Speaker 1: because it is primed by the words that precede the

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Speaker 1: one with a missing letter.

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Speaker 2: Right. And then there's also another another There's a number

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Speaker 2: of different ways of priming people cognitively, but one that

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Speaker 2: just makes total sense, and I think we've all run

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Speaker 2: into is repetition priming, to where if you are shown,

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Speaker 2: like say, a pair of words, maybe sometimes one of

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Speaker 2: which is a nonsense word, and you have to pick

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Speaker 2: out the nonsense words. If you see, like in a

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Speaker 2: list over and over again, doctor and nurse, doctor and nurse,

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Speaker 2: it comes up three times out of a seven word

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Speaker 2: pair list. You're going to move through those much more

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Speaker 2: quickly because it's right there in the forefront of your mind,

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Speaker 2: so it hasn't faded yet, so you're just going to

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Speaker 2: pick it out faster and faster the more it's repeated.

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Speaker 2: That's a form of priming too.

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Speaker 1: Yeah, and these are all really super basic, you know,

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Speaker 1: examples of cognitive priming, But it does show a lot

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Speaker 1: about how our memory works. That were not just computers

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Speaker 1: where we can just access a file by clicking on

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Speaker 1: it very easily. We develop shortcuts in our brain. We

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Speaker 1: take shortcuts when we see words relating them to other words,

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Speaker 1: and so it just makes sense, you know, you could

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Speaker 1: access something much much faster because you have been primed.

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Speaker 1: In other words, our implicit memory can be tapped into

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Speaker 1: a lot quicker than our explicit memory.

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Speaker 2: Right. And at the end of each of these experiments,

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Speaker 2: it kind of quickly became tradition where the researcher would

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Speaker 2: stand up and point at this and be like, you've

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Speaker 2: been primed, maybe adding a boo yah once in a while.

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Speaker 1: Yeah, and Deon Sanders just moon walks through the background.

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Speaker 2: Really, so we know that this is true also, not

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Speaker 2: just because study after study has shown that this is

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Speaker 2: actually correct. But when you put somebody in the Wonder Machine,

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Speaker 2: the fMRI gotta do it. Yeah, they found that, like

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Speaker 2: if you ask somebody the name of let's say, Anya

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Speaker 2: Taylor Joy, say who was the star of the Witch,

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Speaker 2: but also the Gorge, which isn't that good?

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Speaker 1: I thought that was fairly entertaining for a not great movie.

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Speaker 1: By the way, The Gorge, Yeah, it was highly watchable

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Speaker 1: on like a rainy, not feeling so great day kind

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Speaker 1: of movie.

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Speaker 2: Way, agreed, Yeah, agreed. It just wow, it takes a

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Speaker 2: sudden turn. It's just really surprising. But yes, I agree.

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Speaker 2: I think that's a good way to put it. You

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Speaker 2: would stop and think like, oh, Anya Taylor Joy. And

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Speaker 2: by the way, the Queen's Gambit is one of the

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Speaker 2: best things I've ever seen in my entire life.

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Speaker 1: Oh yeah, yeah, the chess one. Yeah, that was great.

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Speaker 2: Okay, but if you also said, you know, name a

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Speaker 2: animal that you think of when we'd say the word dog.

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Speaker 2: What they found in the fMRI is that different parts

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Speaker 2: of your brain light up. So we do know that

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Speaker 2: priming does have a certain effect and it is different

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Speaker 2: than our normal kind of conscious recall, I.

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Speaker 1: Wonder if when they go into the fMRI Wonder Machine room.

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Speaker 1: Now the person running it just goes I'm telling you

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Speaker 1: what's gonna happen. Some part of light up and then

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Speaker 1: another part of light up, and you'll all be super happy.

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Speaker 2: Right, you've been primed.

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Speaker 1: You've been primed in Deander's very slowly moonwalks.

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Speaker 2: So, okay, that's cognitive priming. Now, imagine if you were

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Speaker 2: a psychologist and you said cognitive priming is kind of boring.

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Speaker 2: What if we could use that same stuff to get

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Speaker 2: people to eat more cheeseburgers, Yeah, or to vote for

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Speaker 2: a particular candidate in a political election, Like, what if

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Speaker 2: priming works for that?

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Speaker 1: Yeah? And that's you know, it's all fun in games

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Speaker 1: to just sort of look at these little experiments on

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Speaker 1: a campus somewhere. But if it has a real world use,

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Speaker 1: especially when it comes to marketing and advertising and stuff

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Speaker 1: like that, you can bet corporations are going to sink

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Speaker 1: some money into doing that.

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Speaker 2: Yes, So this is the handoff right here between cognitive

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Speaker 2: psychology to social psychology. And social psychology has studied priming

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Speaker 2: in great detail. It was a huge hit. You'll remember

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Speaker 2: back to nudge Economics, we talked about it in our

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Speaker 2: PR Live episode, like it was a big deal in

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Speaker 2: like the Late ought to about the twenty twelve. I

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Speaker 2: think something like those is just a big deal, and

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Speaker 2: it makes a lot of sense, Like it's the basis

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Speaker 2: of things like ideas like McDonald's uses red and yellow

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Speaker 2: in its logo because those colors are associated with excitement

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Speaker 2: or energy, your happiness, or they call it a happy

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Speaker 2: meal because over time your kid will associate McDonald's with

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Speaker 2: being happy or I'm loving it. It's just a jingle

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Speaker 2: or whatever, and it makes sense. It's very catchy, but

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Speaker 2: there's some part of your mind that it has been

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Speaker 2: primed to later on associate McDonald's with love, a positive feeling.

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Speaker 2: All of this is these examples of social psychology research

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Speaker 2: supporting this idea that you can prime human beings to

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Speaker 2: behave in a certain way just by using these same

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Speaker 2: techniques that cognitive psychologists prove work. Dog cat soup, soap,

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Speaker 2: that kind of stuff.

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Speaker 1: Yeah, as long as you beat them over the head

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Speaker 1: with it over and over and over again, because repetition

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Speaker 1: is one of the real keys here. Your brain just

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Speaker 1: being repeatedly exposed to that stimuli is going to you know,

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Speaker 1: strengthen that association over time, and brands know that, and

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Speaker 1: that's why it drives you crazy during you know, especially

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Speaker 1: like sports playoffs or something, when you see those same

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Speaker 1: ads over and over and over again. Yeah, it's a

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Speaker 1: it's big business. They're putting a lot of money into

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Speaker 1: into you know, sort of manipulating your brain essentially.

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Speaker 2: Yeah, I've said it before and I'll say it again.

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Speaker 2: For some reason, some ad exec chose morning reruns of Murder.

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Speaker 2: She wrote, all right, start TV over the air channel

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Speaker 2: network for the Burger King terrible singing.

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Speaker 1: There had to be some research into their viewership or something, right, it.

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Speaker 2: Had to be. But the thing is, every single other

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Speaker 2: ad is for like human life insurance or health insurance

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Speaker 2: or dental insurance because you're you're just retired and now

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Speaker 2: you have Medicaid. Like every other ad that's almost stood out,

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Speaker 2: you know what I mean.

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Speaker 1: Well, you know, our senior friends in the world are

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Speaker 1: very concerned about their health and their healthcare, and they

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Speaker 1: still love those cheeseburgers. That's two things we know.

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Speaker 2: I guess that's it. But it really grated on my

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Speaker 2: nerves because I watched that almost every day.

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Speaker 1: All right, why don't we take a break here. That's

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Speaker 1: a good table setting, I think, and we'll get back

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Speaker 1: to how this is used in media and politics. Should

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Speaker 1: be no surprise right after this. All right, so we're back,

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Speaker 1: as promised. We're going to talk a little bit about

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Speaker 1: the media and politics, hopefully in a way that's just

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Speaker 1: sort of quick and easy and doesn't ruffle too many feathers,

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Speaker 1: because what we've seen is media of all stripes do this,

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Speaker 1: and politicians of all stripes do this. So it's not

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Speaker 1: singling anybody out. But when the media gets involved in

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Speaker 1: this kind of thing to shape public opinion, they do

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Speaker 1: so sort of a three pronged fork, which is agenda setting, framing,

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Speaker 1: and priming. Agenda setting being like, hey, what's what are

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Speaker 1: we going to focus on editorially maybe in today's paper

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Speaker 1: or every day until the election? How are you going

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Speaker 1: to frame this thing to suit what we feel is

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Speaker 1: probably our slant? And then priming it's basically, you know,

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Speaker 1: similar to the first two, but this is the subconscious

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Speaker 1: things where we're going to repeat things. We're going to

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Speaker 1: use certain words, certain images as to just sort of

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Speaker 1: reinforce how we framed it.

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Speaker 2: Yeah, so just for a quick example, let's say the

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Speaker 2: news decides to focus on crime, and that the framing

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Speaker 2: they uses that crime is on the rise, and you

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Speaker 2: hear about this over and over again, night after night

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Speaker 2: on the news, or you read about it over and

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Speaker 2: over again on your favorite news site. Eventually you're going

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Speaker 2: to be primed to think that crime is on the rise,

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Speaker 2: and you might even be a little scared of it.

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Speaker 2: Or relating to politics, a politician might latch onto that

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Speaker 2: and be like, crime's really hot right now. We're going

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Speaker 2: to make crime like the main point. We're going to

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Speaker 2: use it to set the agenda of our campaign, and

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Speaker 2: we're going to tap into that fear that this person,

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Speaker 2: this lack of safety that the people out there watching

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Speaker 2: the news feel because they're being told over and over

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Speaker 2: again crime is on the rise. Whether it is or not,

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Speaker 2: as irrelevant.

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Speaker 1: Yeah, there's no data, feel that it is exactly.

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Speaker 2: They feel that crime is on the rise. So we

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Speaker 2: a politician, are going to tap into that and use

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Speaker 2: it to helpfully get an election, because now we can

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Speaker 2: prime them to get that emotional response out of them

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Speaker 2: over and over again until we finally move them to

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Speaker 2: the voting stations to vote for me the politician.

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Speaker 1: That's right, I'd vote for you.

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Speaker 2: By the way, I would never ever run for public life.

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Speaker 1: Oh of course you wouldn't, but I would never. I

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Speaker 1: would vote for you for like, you know, Neighborhood Watch

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Speaker 1: or something.

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Speaker 2: Would you vote for me for best all Round? Boy?

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Speaker 1: Oh man, I mean you got that one in the bag, buddy.

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Speaker 1: You don't need my.

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Speaker 2: Vote, thank you.

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Speaker 1: Dog Whistling is something that you know, you've heard more

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Speaker 1: and more as a sort of a term used and

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Speaker 1: you know, social media politics, on the news, stuff like that,

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Speaker 1: And that is when you use a word or phrase

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Speaker 1: that you sort of are in not sort of, you

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Speaker 1: are very much intentionally associating with a negative racial stereotype.

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Speaker 1: But it's not explicit, so it operates on the unconscious level.

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Speaker 1: And this is where they tap into implicit bias, something

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Speaker 1: that everybody has, and you know, using these coded words.

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Speaker 1: Basically there are things that I will never take one

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Speaker 1: of these. It scares me to death. So I would

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Speaker 1: never sit down for an implicit bias test because I

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Speaker 1: don't want to know what my implicit biases are. It's

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Speaker 1: absolutely terrifying because we all have them. I guess that's

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Speaker 1: probably cowardly and I should know what my implicit biases

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Speaker 1: are so I can try and correct those. But it's

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Speaker 1: basically when they sit you down and then you say,

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Speaker 1: all right, respond very quickly as quick as you can

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Speaker 1: to a series of images or words, and anything within

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Speaker 1: two hundred and six hundred milliseconds is your implicit memory.

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Speaker 1: Anything after that you're thinking about, and they that's explicit memory.

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Speaker 2: Right. Two things about that. One If every if implicit

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Speaker 2: bias is a universal thing and everybody has it, you're free, Okay,

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Speaker 2: I have to feel as bad about it. I mean,

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Speaker 2: it's definitely something you should work to correct, if you say,

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Speaker 2: but you don't have to feel like it's just you.

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Speaker 1: Right.

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Speaker 2: That's one thing. And then the second thing, too, Chuck,

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Speaker 2: is if it is true that we all are implicitly biased,

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Speaker 2: say like along racial lines. I feel like you could

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Speaker 2: explain it by saying, by it's an example of our

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Speaker 2: evolutionary history not being caught up yet to our current

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Speaker 2: social history. Like when we foreign modern societies, we jumped

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Speaker 2: light years ahead as far as evolution goes, and the

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Speaker 2: way that we think and see the world just completely

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Speaker 2: just hit warp speed. But evolutionarily there's still a big

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Speaker 2: lag catching up to it. So we're fearful of people

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Speaker 2: who don't look like us, or have a slightly different culture,

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Speaker 2: live in a different nation, just evolutionarily speaking, even though

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Speaker 2: we know we shouldn't feel that way, that that's not

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Speaker 2: actually how things are. That conflict between those two things

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Speaker 2: is what the real issue is.

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Speaker 1: Yeah, oh boy, that really lets us all off the hook.

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Speaker 2: I guess, right, So we don't have to do anything

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Speaker 2: about it. It'll work itself out in ten thousand years.

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Speaker 1: Well, here's the thing, though, is when people sit down

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Speaker 1: for these tests, they found that even people who explicitly

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Speaker 1: disagree with any kind of racial stereotype and like, I

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Speaker 1: would never do that, I don't have those at all.

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Speaker 1: They even while I was about to say, fail those tests,

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Speaker 1: they even chart on those tests as showing implicit bias.

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Speaker 2: Right, so we've made it pretty squarely into social psychology

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Speaker 2: territory I now. And there were some there were some

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Speaker 2: early experiments trying to figure out how you can persuade

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Speaker 2: people using priming.

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Speaker 1: Right, he's like the seventies, right, yeah.

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Speaker 2: Late seventies, early eighties, and so one of the first

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Speaker 2: ones came out of nineteen seventy nine, and it was

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Speaker 2: a social psychology experiment where you gave people scrambled, like

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Speaker 2: a scrambled word list and said make a sentence out

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Speaker 2: of this, right, And so you would get something neutral

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Speaker 2: like her found new eye and I knew her is

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Speaker 2: a sentence you could make out of that, or you

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Speaker 2: would give something like leg break arm his You could say,

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Speaker 2: break his arm. And so one group was given more

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Speaker 2: hostile word scrambles to make sentences out of the other

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Speaker 2: was more neutral, And then that was part one. After

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Speaker 2: that they were asked to consider a hypothetical scenario in

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Speaker 2: which somebody's kind of ambiguously responding or interacting with somebody.

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Speaker 2: I think I saw that Donald is refusing to pay

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Speaker 2: rent until his landlord paints his apartment, and the people

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Speaker 2: who were given the hostile word scrambles rated Donald is

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Speaker 2: much more hostile than the people who are given the

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Speaker 2: neutral word scrambles. So what they're showing is that you

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Speaker 2: can nudge people toward forming an impression about someone they

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Speaker 2: know basically nothing about, based on priming them, yeah, to

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Speaker 2: feel one way or another about them. And then that

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Speaker 2: was like, if we can do that, man, what else

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Speaker 2: can we do? That really opened the floodgates.

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Speaker 1: Yeah, and you know we're about to tick through a

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Speaker 1: series of little things like that that might be mind

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Speaker 1: blowing for you where you're like, oh my god, I

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Speaker 1: can't believe that works, but just you know, sort of

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Speaker 1: put that in your pocket for now. There was one

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Speaker 1: study where it was again it was unscrambling sentences. Half

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Speaker 1: the people were given sentences that had words that were

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Speaker 1: you know, like healthier, healthy, and active lifestyle words. The

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Speaker 1: other half got neutral words, and afterward they said, all right,

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Speaker 1: you guys are great. You can go ahead and leave there.

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Speaker 1: You can you know, go up the stairs there and

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Speaker 1: get out of here to the parking deck up there,

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Speaker 1: or you can take the elevator. And people who unscrambled

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Speaker 1: the healthy words were more likely to take the stairs afterward,

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Speaker 1: so you know, they're like, hey, that's pretty much proof

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Speaker 1: right there. They had been primed with these healthy words

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Speaker 1: to make a healthier decision afterward.

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Speaker 2: Right And well, like you said, we'll talk about some

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Speaker 2: of the more shocking or surprising studies, but before that,

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Speaker 2: we need to mention a guy named John Barg who

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Speaker 2: became basically the rock star of this field starting in

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Speaker 2: the nineties. He essentially wrote a paper that said, like

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Speaker 2: all of this is possible, Like, here's how you do that.

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Speaker 2: Here's how you take the findings of cognitive priming and

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Speaker 2: turn it into social or behavioral priming. And he was

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Speaker 2: very famous for a couple of studies, many studies, but

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Speaker 2: there's two that really stuck out to me that Let

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Speaker 2: me give you an example. One is that he tested

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Speaker 2: whether something is as random as temperature could affect your

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Speaker 2: impression of another person.

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Speaker 1: Yeah, I thought that was interesting because it was like

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Speaker 1: they'd use words and finally like, hey, I wonder if

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Speaker 1: words worked, if images only alone could work? And then

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Speaker 1: he was like, hold my beer, right, what about just

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Speaker 1: temperature alone? So in this one, he asked participants to

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Speaker 1: hold a something warm, like a cup of coffee or

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Speaker 1: warm cup of soup or something, or a frosty cold beverage,

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Speaker 1: and then they bring them in a room where they

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Speaker 1: have a conversation with a stranger and they found that

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Speaker 1: or he found he claimed to find that people who

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Speaker 1: were with that warm bread beverage had a warmer impression

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Speaker 1: of that stranger afterward, and people who had that cold

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Speaker 1: beverage frostier had eight you know, they felt frostier toward

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Speaker 1: that person.

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Speaker 2: Right, So that means that you can nudge people to

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Speaker 2: feel a certain way based on metaphor. Priming using metaphor

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Speaker 2: and not even words or images, but temperature.

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Speaker 1: Yeah.

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Speaker 2: Another one that he figured out was and this is

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Speaker 2: the one he's really famous for. I think this is

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Speaker 2: the one that came from his nineteen ninety six paper.

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Speaker 2: But he tested to see how certain kinds of words

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Speaker 2: affect certain kinds of behavior. And he took some nineteen

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Speaker 2: twenty twenty one year old students and had them do

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Speaker 2: that famous word scramble. Priming researchers love word scrambles. Yeah,

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Speaker 2: and some had just a normal neutral set of words

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Speaker 2: to pick out from. Another had sets of words that

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Speaker 2: were associated with being old, so straight ahead that you'd

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Speaker 2: be like, these are all old people words, but they

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Speaker 2: were like bingo or florida or wrinkles, that kind of stuff. Right.

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Speaker 2: That was part one, And then the students who were

434
00:25:13,000 --> 00:25:16,080
Speaker 2: participating thought that they were done at that point, but

435
00:25:16,240 --> 00:25:18,320
Speaker 2: he said, Okay, now we've got to do part two.

436
00:25:18,400 --> 00:25:20,159
Speaker 2: But we're gonna have to go to the end of

437
00:25:20,200 --> 00:25:22,440
Speaker 2: the hall and turn right and where there's another lab

438
00:25:22,480 --> 00:25:24,119
Speaker 2: we need to go to for the second part of

439
00:25:24,160 --> 00:25:27,000
Speaker 2: this test. But the real thing he was doing was

440
00:25:27,080 --> 00:25:29,639
Speaker 2: clocking how long it took the students to make it

441
00:25:29,680 --> 00:25:32,119
Speaker 2: from one end of the hall to the other. And

442
00:25:32,160 --> 00:25:35,439
Speaker 2: he found the people who had who had worked with

443
00:25:35,520 --> 00:25:40,040
Speaker 2: word scrambles that had age or old elder related words

444
00:25:40,920 --> 00:25:44,760
Speaker 2: walked slower than the people who had the neutral words.

445
00:25:45,280 --> 00:25:48,560
Speaker 2: And this was the one this experiment, Chuck, is what

446
00:25:49,119 --> 00:25:52,879
Speaker 2: broke open. This is what led to nudge economics. This

447
00:25:52,920 --> 00:25:56,640
Speaker 2: is what led to governments saying like, man, we could

448
00:25:56,720 --> 00:25:59,359
Speaker 2: use this to like move people in a way that

449
00:25:59,440 --> 00:26:02,600
Speaker 2: we want them. Two that's healthier and happier. This is

450
00:26:02,640 --> 00:26:03,679
Speaker 2: the study that did that.

451
00:26:04,520 --> 00:26:06,720
Speaker 1: Yeah, and here's my I mean, I hope I'm not

452
00:26:06,760 --> 00:26:09,679
Speaker 1: giving anything away for act three when we sort of

453
00:26:10,240 --> 00:26:13,880
Speaker 1: rained down our judgment upon this stuff. But like even

454
00:26:13,880 --> 00:26:15,760
Speaker 1: when I was first going through this, I was like,

455
00:26:16,560 --> 00:26:19,000
Speaker 1: I even know, there are so many variables to account

456
00:26:19,040 --> 00:26:21,400
Speaker 1: for in these experiments that there's no way they're accounting

457
00:26:21,440 --> 00:26:24,720
Speaker 1: for him like the taking the stairs or taking the elevator,

458
00:26:25,240 --> 00:26:27,639
Speaker 1: like who was tired that day, who had a bum

459
00:26:27,680 --> 00:26:31,280
Speaker 1: ankle or who you know, There's just so many different

460
00:26:31,359 --> 00:26:33,639
Speaker 1: variables to account for on why someone would take the

461
00:26:33,640 --> 00:26:37,520
Speaker 1: stairs or an elevator, or why somebody would walk slower

462
00:26:37,520 --> 00:26:39,600
Speaker 1: down the hall. Maybe they were super bored by this

463
00:26:39,720 --> 00:26:42,480
Speaker 1: experiment and so they walked a little more sluggish or

464
00:26:42,480 --> 00:26:45,719
Speaker 1: something like that, and those types of variables there's they

465
00:26:45,720 --> 00:26:47,840
Speaker 1: weren't accounting for. Ever, it seems like.

466
00:26:48,480 --> 00:26:51,800
Speaker 2: I think we can just go ahead and reveal. Now

467
00:26:52,119 --> 00:26:52,600
Speaker 2: what do you think?

468
00:26:53,080 --> 00:26:54,119
Speaker 1: Yeah? Sure, so.

469
00:26:54,400 --> 00:26:57,199
Speaker 2: I wish you had been illuminary in the field of

470
00:26:57,200 --> 00:27:00,520
Speaker 2: social psychology and priming research back in the late nineties

471
00:27:00,560 --> 00:27:02,480
Speaker 2: or eight two thousands, because you could have derailed this

472
00:27:02,560 --> 00:27:05,800
Speaker 2: whole thing before it ever got started. Because if that

473
00:27:05,840 --> 00:27:09,200
Speaker 2: seemed ridiculous to you, that idea that you could suggest

474
00:27:10,040 --> 00:27:13,520
Speaker 2: old related or age related words to twenty year olds

475
00:27:13,600 --> 00:27:15,840
Speaker 2: and they're going to walk slower because they were just

476
00:27:15,880 --> 00:27:19,840
Speaker 2: thinking about being elderly. Yeah, if that seems ridiculous to you,

477
00:27:19,840 --> 00:27:23,520
Speaker 2: you are one hundred percent right. Okay, it's a ridiculous study,

478
00:27:24,000 --> 00:27:28,320
Speaker 2: and it's ridiculous that the entire field of social psychology, economics,

479
00:27:28,640 --> 00:27:32,960
Speaker 2: the politics paid attention to this and went all in

480
00:27:33,080 --> 00:27:35,800
Speaker 2: on it. But what we have, what we're actually talking

481
00:27:35,800 --> 00:27:39,600
Speaker 2: about today, is one of the biggest black eyes in

482
00:27:39,640 --> 00:27:44,400
Speaker 2: the history of psychology that didn't involve torturing human beings.

483
00:27:44,640 --> 00:27:47,399
Speaker 1: Yeah, but you know, let's tick through a few of

484
00:27:47,440 --> 00:27:50,200
Speaker 1: these that you found kind of quickly, because I think

485
00:27:50,240 --> 00:27:54,000
Speaker 1: it just illustrates, though, how a company or or a

486
00:27:54,040 --> 00:27:56,800
Speaker 1: political party would really latch onto this when they see

487
00:27:56,800 --> 00:27:59,520
Speaker 1: stuff like this right without kind of like critically thinking

488
00:27:59,520 --> 00:28:03,200
Speaker 1: on how they got there. One study exposure to fishy

489
00:28:03,240 --> 00:28:07,919
Speaker 1: smells would induce suspicion on trust based economic exchanges in

490
00:28:07,920 --> 00:28:11,040
Speaker 1: a trust game. In other words, like they smelled something fishy,

491
00:28:11,160 --> 00:28:13,480
Speaker 1: so they're going to carry that over as in something

492
00:28:13,480 --> 00:28:14,199
Speaker 1: smells fishy.

493
00:28:15,320 --> 00:28:18,439
Speaker 2: Can you imagine reading that in a scholarly, scholarly journal

494
00:28:18,440 --> 00:28:20,520
Speaker 2: and being like, man, that's that's really crazy.

495
00:28:20,600 --> 00:28:22,639
Speaker 1: I would say this paper smells like tuna.

496
00:28:25,040 --> 00:28:28,400
Speaker 2: There's another one. Remember power poses. I specifically remember John

497
00:28:28,480 --> 00:28:32,120
Speaker 2: Hodgman realizing that I was nervous backstage at the Bellhouse

498
00:28:32,119 --> 00:28:35,000
Speaker 2: once and telling me to do a power pose. Oh really,

499
00:28:35,040 --> 00:28:36,800
Speaker 2: to get over my stage fright. And I was like,

500
00:28:36,840 --> 00:28:39,400
Speaker 2: this isn't working. The reason it doesn't work is because

501
00:28:39,440 --> 00:28:41,920
Speaker 2: that was a finding from priming.

502
00:28:43,040 --> 00:28:46,760
Speaker 1: Oh man, did you did you text him and say

503
00:28:47,040 --> 00:28:48,040
Speaker 1: you're full of crap?

504
00:28:48,640 --> 00:28:52,960
Speaker 2: No, I'm gonna let him hear this episode. Okay, there's

505
00:28:53,000 --> 00:28:55,760
Speaker 2: another one. If you make a frownie face and you're

506
00:28:55,760 --> 00:28:58,800
Speaker 2: shown upsetting pictures, you will self report that should be

507
00:28:58,840 --> 00:29:02,400
Speaker 2: a red flag in and of us that you were

508
00:29:02,560 --> 00:29:07,880
Speaker 2: upset by pictures of starving children, people arguing accident victims

509
00:29:07,880 --> 00:29:11,720
Speaker 2: that had been maimed more than people who weren't making

510
00:29:11,720 --> 00:29:14,200
Speaker 2: a frownie face at the time they saw the pictures.

511
00:29:14,520 --> 00:29:19,320
Speaker 1: Yeah, here's one that's interesting to me. Money primed people

512
00:29:19,360 --> 00:29:22,320
Speaker 1: are more selfish. So if you're I guess they did

513
00:29:22,360 --> 00:29:25,120
Speaker 1: this experiment with people who made a lot of money

514
00:29:25,120 --> 00:29:26,280
Speaker 1: and people who didn't.

515
00:29:27,200 --> 00:29:30,760
Speaker 2: I think it was they were winning money in like games.

516
00:29:30,960 --> 00:29:33,520
Speaker 1: Oh okay, okay, But the long and short of it is,

517
00:29:34,040 --> 00:29:36,200
Speaker 1: if you were one of the money people, quote unquote,

518
00:29:36,760 --> 00:29:39,680
Speaker 1: then you would not if someone spilled their pencils like

519
00:29:39,680 --> 00:29:41,640
Speaker 1: one of the researchers, like, oh look at me, I

520
00:29:41,640 --> 00:29:44,160
Speaker 1: spilled all these pencils, they wouldn't pick up as many

521
00:29:44,200 --> 00:29:46,720
Speaker 1: pencils as someone who didn't have the money. So people

522
00:29:46,720 --> 00:29:51,280
Speaker 1: without money are kinder. I can see where that's going

523
00:29:51,320 --> 00:29:53,480
Speaker 1: in a way, But there are also just so many

524
00:29:53,560 --> 00:29:55,200
Speaker 1: variables in that, you.

525
00:29:55,160 --> 00:29:59,480
Speaker 2: Know, right, And also if your study is supporting just

526
00:29:59,520 --> 00:30:02,000
Speaker 2: a general moral judgment against.

527
00:30:01,600 --> 00:30:04,120
Speaker 1: A certain group, yeah.

528
00:30:03,440 --> 00:30:06,160
Speaker 2: It may have been biased in and of itself. Right,

529
00:30:06,200 --> 00:30:08,880
Speaker 2: it's a good point. There's another one. I love this.

530
00:30:08,960 --> 00:30:10,160
Speaker 1: At least last two are great.

531
00:30:10,480 --> 00:30:14,600
Speaker 2: If you think about stabbing a coworker in the back metaphorically,

532
00:30:15,360 --> 00:30:19,080
Speaker 2: you are more inclined when given a choice to buy soap, detergent,

533
00:30:19,200 --> 00:30:22,240
Speaker 2: or disinfectant than you are to buy batteries, juice, or

534
00:30:22,280 --> 00:30:23,440
Speaker 2: candy bars.

535
00:30:25,160 --> 00:30:26,560
Speaker 1: Out damned spot.

536
00:30:26,680 --> 00:30:29,200
Speaker 2: Yes exactly. Why don't you give him the last one? Chuck?

537
00:30:30,440 --> 00:30:33,920
Speaker 1: Okay, if you were in this experiment, you were induced

538
00:30:33,920 --> 00:30:37,480
Speaker 1: to lie to an imaginary person, either in an email

539
00:30:37,520 --> 00:30:39,560
Speaker 1: or a phone call. And then and a test that

540
00:30:39,600 --> 00:30:44,280
Speaker 1: followed that of the desirability of different products. People who

541
00:30:44,320 --> 00:30:47,280
Speaker 1: lied on the people who lied on the telephone they

542
00:30:47,280 --> 00:30:52,160
Speaker 1: actually said to lie out loud, preferred mouthwash over soap,

543
00:30:52,240 --> 00:30:54,120
Speaker 1: and people who typed it out and light in the

544
00:30:54,120 --> 00:30:56,640
Speaker 1: email preferred soap to mouthwash.

545
00:30:56,840 --> 00:31:00,280
Speaker 2: Yeah, the first group preferred mouthwash to soap and less

546
00:31:00,280 --> 00:31:05,960
Speaker 2: the soap was lifeboy. Right, So okay, yes, we should

547
00:31:05,960 --> 00:31:08,880
Speaker 2: probably rein it back a little bit. Yeah, because our

548
00:31:09,000 --> 00:31:11,640
Speaker 2: bias is showing. But for good reason. I mean, we

549
00:31:11,680 --> 00:31:14,840
Speaker 2: should say priming is not just this point of ridicule. This,

550
00:31:15,680 --> 00:31:19,960
Speaker 2: it just comple the bottom fell out of this really hot,

551
00:31:20,120 --> 00:31:24,120
Speaker 2: super sexy field of research that everyone had bought into,

552
00:31:24,880 --> 00:31:28,440
Speaker 2: like a mud slide going down a mountain, Like it

553
00:31:28,720 --> 00:31:32,920
Speaker 2: just erupted. It was. It went so south so fast

554
00:31:33,280 --> 00:31:37,760
Speaker 2: that today, about about almost fifteen years on, since the

555
00:31:37,920 --> 00:31:42,400
Speaker 2: everybody was like, this is all made up. It's essentially

556
00:31:42,480 --> 00:31:45,160
Speaker 2: a discredited field. Like there's almost no one working in

557
00:31:45,200 --> 00:31:47,920
Speaker 2: this anymore, because most people are like, this isn't true.

558
00:31:50,040 --> 00:31:52,160
Speaker 1: Yeah, but that's not to say that they're like people

559
00:31:52,160 --> 00:31:55,680
Speaker 1: didn't get a lot of like legitimate recognition for this stuff.

560
00:31:55,720 --> 00:31:59,040
Speaker 1: Like there were people won Nobel prizes who worked on

561
00:31:59,080 --> 00:32:02,000
Speaker 1: this stuff and wrote selling books, who worked on this

562
00:32:02,040 --> 00:32:05,240
Speaker 1: stuff and made millions of dollars, like speaking to corporations

563
00:32:05,920 --> 00:32:08,520
Speaker 1: about how they can better, you know, take advantage of

564
00:32:08,520 --> 00:32:12,360
Speaker 1: their consumers. So it was it was swallowed, hook line

565
00:32:12,400 --> 00:32:12,920
Speaker 1: and sinker.

566
00:32:13,400 --> 00:32:16,520
Speaker 2: So let's give you an example. Daniel Kahneman. He was

567
00:32:16,560 --> 00:32:22,120
Speaker 2: a Nobel already a Nobel Prize winning economist. He wrote

568
00:32:22,160 --> 00:32:25,640
Speaker 2: Thinking Fast and Slow, and it was essentially an introduction

569
00:32:26,200 --> 00:32:32,760
Speaker 2: to nudge economics and priming for the average person. And

570
00:32:32,800 --> 00:32:35,680
Speaker 2: it was a huge bestseller. I mean, everybody was reading

571
00:32:35,760 --> 00:32:37,280
Speaker 2: that book back in the day. I think it was

572
00:32:37,360 --> 00:32:39,280
Speaker 2: two thousand and two.

573
00:32:40,000 --> 00:32:41,080
Speaker 1: Okay, yeah.

574
00:32:41,880 --> 00:32:47,760
Speaker 2: In this book he says, this is a quote. Disbelief

575
00:32:47,880 --> 00:32:50,720
Speaker 2: is not an option. The results are not made up,

576
00:32:51,000 --> 00:32:54,120
Speaker 2: nor are they statistical flukes. You have no choice but

577
00:32:54,200 --> 00:32:57,400
Speaker 2: to accept that the major conclusions of these studies are true.

578
00:32:58,120 --> 00:33:00,680
Speaker 2: You have no choice but to say that this is true.

579
00:33:00,720 --> 00:33:03,600
Speaker 2: So just get on board. This was like a Nobel

580
00:33:03,640 --> 00:33:07,400
Speaker 2: prize wing economist who was a psychologist himself, who wrote

581
00:33:07,440 --> 00:33:09,600
Speaker 2: that to the rest of the world, saying like, don't

582
00:33:09,600 --> 00:33:12,600
Speaker 2: even question priming. This is true. Let's figure out how

583
00:33:12,640 --> 00:33:14,960
Speaker 2: to use it to persuade people to do what we want.

584
00:33:15,400 --> 00:33:18,480
Speaker 1: Yeah, there was another guy. We talked about the nudge earlier.

585
00:33:19,080 --> 00:33:21,680
Speaker 1: In two thousand and eight, another Nobel winning economist named

586
00:33:21,720 --> 00:33:26,360
Speaker 1: Richard thaller Co wrote a bestseller called Nudge Colon Improving

587
00:33:26,400 --> 00:33:29,360
Speaker 1: Decisions about health, wealth, and Happiness. And in that book

588
00:33:29,400 --> 00:33:34,400
Speaker 1: he talked about social priming experiences, benign paternalism, or that

589
00:33:34,520 --> 00:33:36,640
Speaker 1: nudge that you know you kind of mentioned earlier, towards

590
00:33:36,760 --> 00:33:40,200
Speaker 1: making a better decision. And then there's this other economists

591
00:33:40,240 --> 00:33:43,800
Speaker 1: name they're all economists, Daniel what's his name, Eirily?

592
00:33:44,320 --> 00:33:46,480
Speaker 2: I think so, yeah, And he.

593
00:33:46,360 --> 00:33:49,920
Speaker 1: Tested a lot of these supposed nudges. In one experiment,

594
00:33:50,000 --> 00:33:53,800
Speaker 1: he had kids students grade their own tests, but before

595
00:33:53,960 --> 00:33:57,600
Speaker 1: had half of them write out as many of the

596
00:33:57,600 --> 00:34:00,440
Speaker 1: Ten Commandments as they could remember. And what he found

597
00:34:00,560 --> 00:34:03,680
Speaker 1: was that those students were less likely to cheat on

598
00:34:03,760 --> 00:34:07,200
Speaker 1: their self graded testing because they had just been primed

599
00:34:07,600 --> 00:34:09,319
Speaker 1: to sort of think of like, hey, what's the right

600
00:34:09,360 --> 00:34:09,799
Speaker 1: thing to do?

601
00:34:10,520 --> 00:34:14,160
Speaker 2: Totally makes sense? Why even question it. He worked with

602
00:34:14,400 --> 00:34:18,840
Speaker 2: a car insurance company for another study. Car insurance companies

603
00:34:18,880 --> 00:34:21,680
Speaker 2: will sometimes have you say how many miles you've driven

604
00:34:21,800 --> 00:34:26,200
Speaker 2: in a year, and you can just lie and your

605
00:34:26,239 --> 00:34:28,560
Speaker 2: insurance rates will be affected on whether you lie or not.

606
00:34:28,840 --> 00:34:30,520
Speaker 2: So to find out if you could make people be

607
00:34:30,640 --> 00:34:35,560
Speaker 2: more honest about that, Erarily introduced an honesty pledge that

608
00:34:35,600 --> 00:34:38,520
Speaker 2: the person would sign at the top of their insurance

609
00:34:38,920 --> 00:34:42,839
Speaker 2: agreement or contract saying I won't fudge these numbers. And

610
00:34:42,960 --> 00:34:46,920
Speaker 2: those people fudge their numbers less than people who didn't

611
00:34:46,960 --> 00:34:50,640
Speaker 2: have that pledge to sign. So to prompt people to

612
00:34:50,719 --> 00:34:54,360
Speaker 2: be honest, whether it's the Ten Commandments or pledging honesty

613
00:34:54,480 --> 00:34:57,399
Speaker 2: or whatever, they're going to be more honest. And that's

614
00:34:57,400 --> 00:35:01,080
Speaker 2: a perfect example of the nudge economic that just swept

615
00:35:01,120 --> 00:35:04,200
Speaker 2: the world at the turn of the two thousands to

616
00:35:04,280 --> 00:35:05,360
Speaker 2: the twenty tens.

617
00:35:05,640 --> 00:35:08,120
Speaker 1: Yeah, I wonder if they asked any of the participants

618
00:35:08,120 --> 00:35:09,960
Speaker 1: coming in, are you.

619
00:35:09,960 --> 00:35:13,279
Speaker 2: Honest generally or not self report?

620
00:35:15,840 --> 00:35:17,920
Speaker 1: Maybe we should take a break here. Yeah, it's a

621
00:35:17,920 --> 00:35:19,640
Speaker 1: good time for a break, and we'll come back and

622
00:35:19,680 --> 00:35:22,239
Speaker 1: sort of talk about the big problem with all this,

623
00:35:22,440 --> 00:35:25,920
Speaker 1: which is, you know, scientifically speaking, the replication of these

624
00:35:25,920 --> 00:35:26,920
Speaker 1: studies right after this.

625
00:36:16,640 --> 00:36:19,680
Speaker 2: Okay, Chuck, So we've talked about this before. The replication

626
00:36:19,800 --> 00:36:24,880
Speaker 2: crisis in science. I think it's in the physical sciences.

627
00:36:24,920 --> 00:36:29,040
Speaker 2: It's all over where people have had these landmark papers,

628
00:36:29,080 --> 00:36:32,439
Speaker 2: groundbreaking papers that have changed the world, changed how people act,

629
00:36:32,840 --> 00:36:37,200
Speaker 2: changed where research dollars go. When somebody finally gets around

630
00:36:37,520 --> 00:36:40,520
Speaker 2: to trying to replicate that exact same experiment, they come

631
00:36:40,600 --> 00:36:44,080
Speaker 2: up with different results, often negative results. They can't replicate

632
00:36:44,120 --> 00:36:47,120
Speaker 2: the results that that study found. And like I said,

633
00:36:47,200 --> 00:36:50,600
Speaker 2: that's throughout science this is a big problem. But in

634
00:36:50,640 --> 00:36:56,440
Speaker 2: psychology and more specifically social psychology, the replication crisis is

635
00:36:56,680 --> 00:37:02,000
Speaker 2: shaking the foundations of the thee and a reason why.

636
00:37:02,200 --> 00:37:05,319
Speaker 2: Let's do a little thought experiment here. Imagine that you're

637
00:37:05,320 --> 00:37:09,920
Speaker 2: a social psychologist, researcher, Okay, you're at the university and

638
00:37:09,960 --> 00:37:12,960
Speaker 2: you've come up with an experiment that you believe the

639
00:37:13,080 --> 00:37:17,920
Speaker 2: results of which will be used a corporations, political campaigns

640
00:37:18,360 --> 00:37:22,200
Speaker 2: all over the place to persuade everyday people to change

641
00:37:22,239 --> 00:37:26,400
Speaker 2: their behavior which will shape the world for years to come.

642
00:37:27,239 --> 00:37:30,359
Speaker 2: But you don't really feel like leaving your office that day,

643
00:37:30,640 --> 00:37:32,920
Speaker 2: so you just bring in a bunch of students, maybe

644
00:37:32,960 --> 00:37:36,359
Speaker 2: fifteen of them, experiment on them, and then extrapolate those

645
00:37:36,400 --> 00:37:41,000
Speaker 2: findings to a human universality, and then it gets packaged

646
00:37:41,200 --> 00:37:45,280
Speaker 2: and exported to corporations and political campaigns. That, in a nutshell,

647
00:37:45,320 --> 00:37:46,440
Speaker 2: is social psychology.

648
00:37:46,760 --> 00:37:49,759
Speaker 1: Yeah, I mean, that's what happened. We have some specific

649
00:37:49,800 --> 00:37:53,960
Speaker 1: examples of like real mouthfeasance that happened in these studies.

650
00:37:54,600 --> 00:37:57,719
Speaker 1: In some cases even not just like oh, you know,

651
00:37:58,280 --> 00:38:00,879
Speaker 1: they didn't account for all the very like in some

652
00:38:00,920 --> 00:38:04,680
Speaker 1: cases they massage data. That Daniel Areley guy that we

653
00:38:04,680 --> 00:38:09,400
Speaker 1: were talking about, yeah, and his colleague Francesca Gino, they

654
00:38:09,400 --> 00:38:12,480
Speaker 1: were accused of massaging the data just to fit their hypothesis.

655
00:38:13,040 --> 00:38:16,320
Speaker 1: Turns out that car insurance company came back and said

656
00:38:16,880 --> 00:38:20,000
Speaker 1: that data doesn't even match what we sent you and

657
00:38:20,040 --> 00:38:24,000
Speaker 1: what you published in that honesty pledge situation. Yeah, there

658
00:38:24,040 --> 00:38:29,360
Speaker 1: was another guy, a Dutch guy named Diedrich Stoppel. He

659
00:38:29,520 --> 00:38:32,400
Speaker 1: was a big, sort of big name in this field.

660
00:38:33,120 --> 00:38:35,240
Speaker 1: He fabricated studies out of whole cloth.

661
00:38:36,120 --> 00:38:39,160
Speaker 2: Yeah, for sure. So it's bad for psychology, bad for

662
00:38:39,320 --> 00:38:43,400
Speaker 2: social psychology. But priming research, it was just this is

663
00:38:43,760 --> 00:38:46,279
Speaker 2: like it must have just felt like a bloodbath day

664
00:38:46,320 --> 00:38:49,160
Speaker 2: after day if you were in the field, just bad

665
00:38:49,239 --> 00:38:52,960
Speaker 2: news after bad news. It got so bad that ten

666
00:38:53,040 --> 00:38:56,160
Speaker 2: years after he wrote Thinking Fast and Slow, Daniel Kahneman

667
00:38:56,520 --> 00:38:59,160
Speaker 2: wrote an open letter but it was actually directed to

668
00:38:59,480 --> 00:39:03,719
Speaker 2: John Barr saying like something, this is really bad, that

669
00:39:05,320 --> 00:39:08,160
Speaker 2: priming research is the poster child for doubts about the

670
00:39:08,160 --> 00:39:11,719
Speaker 2: integrity of psychological research. And he warned of becoming train

671
00:39:11,800 --> 00:39:15,280
Speaker 2: wreck and was basically saying like, hey man, you guys,

672
00:39:15,320 --> 00:39:17,520
Speaker 2: I'm just a believer. I'm just a fan. You guys

673
00:39:17,560 --> 00:39:21,120
Speaker 2: screwed this up. You better fix it. And there's actually

674
00:39:21,160 --> 00:39:24,840
Speaker 2: I found a really great website called Replicable Replicability Index.

675
00:39:24,880 --> 00:39:29,560
Speaker 2: It's a blog by Ulric Shimack at the University of Toronto,

676
00:39:30,200 --> 00:39:33,960
Speaker 2: and they have a something called the Reconstruction of a

677
00:39:34,120 --> 00:39:37,040
Speaker 2: train Wreck where they go through and pick out like

678
00:39:37,280 --> 00:39:41,200
Speaker 2: how Kahman sold this to the public and misrepresented it

679
00:39:41,280 --> 00:39:44,959
Speaker 2: all sorts of different ways himself. So he's passed on RIP.

680
00:39:45,200 --> 00:39:47,319
Speaker 2: I don't like to speak ill of the dead. And

681
00:39:47,360 --> 00:39:50,520
Speaker 2: everything leading up to that, from what I understand, was

682
00:39:50,600 --> 00:39:54,000
Speaker 2: a great career, but he just hitched his wagonto the

683
00:39:54,040 --> 00:39:57,240
Speaker 2: wrong thing and then tried to distance himself from as

684
00:39:57,480 --> 00:39:58,520
Speaker 2: much as he could.

685
00:39:58,840 --> 00:40:01,560
Speaker 1: Yeah, and you know, the community that worked in that

686
00:40:01,600 --> 00:40:04,120
Speaker 1: field certainly woke up, and they weren't just like this

687
00:40:04,160 --> 00:40:05,560
Speaker 1: is no big deal. They were like, all right, this

688
00:40:05,600 --> 00:40:08,919
Speaker 1: is a real problem. So they tried have tried since

689
00:40:08,960 --> 00:40:10,440
Speaker 1: then to try and clean things up a bit and

690
00:40:10,480 --> 00:40:15,640
Speaker 1: address what they call qrps, or questionable research practices, of

691
00:40:15,680 --> 00:40:17,799
Speaker 1: which there are many. We're going to talk about some

692
00:40:17,840 --> 00:40:21,520
Speaker 1: of them right now, but one obviously you sort of

693
00:40:21,560 --> 00:40:24,160
Speaker 1: mentioned this was they're just really small studies. You can't

694
00:40:24,160 --> 00:40:27,480
Speaker 1: make these big, huge conclusions about human behavior when you

695
00:40:27,520 --> 00:40:31,040
Speaker 1: studied twelve, you know, college freshmen, you know, on a

696
00:40:31,080 --> 00:40:36,160
Speaker 1: Saturday morning. As part of the open science movement, and

697
00:40:36,200 --> 00:40:38,200
Speaker 1: this is pretty cool. I didn't know about this, but

698
00:40:38,400 --> 00:40:42,720
Speaker 1: researchers are now encouraged to register their studies ahead of time,

699
00:40:42,920 --> 00:40:46,600
Speaker 1: including what their hypothesis is before they collect the data,

700
00:40:47,440 --> 00:40:51,000
Speaker 1: and that'll keep them from what's called HARKing hypothesizing after

701
00:40:51,200 --> 00:40:54,000
Speaker 1: results are known. So basically like, hey, here's what I

702
00:40:54,040 --> 00:40:55,920
Speaker 1: think is going to happen. Now, let's do the study.

703
00:40:56,000 --> 00:40:59,600
Speaker 1: But it's officially registered, so I can't sort of pick

704
00:40:59,640 --> 00:41:01,520
Speaker 1: and choose what I what I look at.

705
00:41:02,080 --> 00:41:05,640
Speaker 2: Right, there's another big problem called pea hacking, which is

706
00:41:06,440 --> 00:41:10,319
Speaker 2: taking data and then making it work statistically so that

707
00:41:10,440 --> 00:41:15,440
Speaker 2: these random flukes suddenly became statistically significant. Yeah, that's a

708
00:41:15,440 --> 00:41:20,080
Speaker 2: big problem. And that I read that it's not so

709
00:41:20,239 --> 00:41:24,440
Speaker 2: much that researchers were sitting there purposely massaging their data

710
00:41:24,480 --> 00:41:27,080
Speaker 2: over and over and over again to tease out some

711
00:41:27,120 --> 00:41:30,319
Speaker 2: results that they could publish, but it was more like

712
00:41:30,320 --> 00:41:35,880
Speaker 2: they were just falling force flukes being more significant than

713
00:41:35,960 --> 00:41:38,520
Speaker 2: they were. That's what I read that that was really

714
00:41:38,520 --> 00:41:40,760
Speaker 2: the big problem. That it wasn't like an entire field

715
00:41:40,760 --> 00:41:41,600
Speaker 2: of bad actors.

716
00:41:41,680 --> 00:41:44,719
Speaker 1: Yeah, I mean I sort of get it in a way.

717
00:41:44,719 --> 00:41:48,040
Speaker 1: And we've talked about this in our Scientific Method show

718
00:41:48,080 --> 00:41:50,719
Speaker 1: and other episodes too, where you know, you put all

719
00:41:50,719 --> 00:41:52,480
Speaker 1: this time and you want to you want to you

720
00:41:52,520 --> 00:41:55,040
Speaker 1: want your thing to pan out that you think is true.

721
00:41:55,120 --> 00:41:58,520
Speaker 1: So I get the inclination, but you can't you can't

722
00:41:58,520 --> 00:42:00,640
Speaker 1: budge numbers, you can't look at stuff that just backs

723
00:42:00,719 --> 00:42:02,959
Speaker 1: up your conclusion. You can't throw stuff in the file

724
00:42:03,040 --> 00:42:05,600
Speaker 1: drawer that doesn't you got and this is one of

725
00:42:05,640 --> 00:42:07,320
Speaker 1: the things they talked about, the file drawer, like you

726
00:42:07,400 --> 00:42:11,319
Speaker 1: got to make all this stuff public. And part of

727
00:42:11,360 --> 00:42:13,200
Speaker 1: the problem is the media, because they want to write

728
00:42:13,200 --> 00:42:17,000
Speaker 1: about something splashy and super interesting, right, and so like

729
00:42:17,040 --> 00:42:18,799
Speaker 1: there's a lot of things that play here that go

730
00:42:18,840 --> 00:42:20,880
Speaker 1: into why somebody would do this beyond just being like,

731
00:42:20,960 --> 00:42:22,120
Speaker 1: you know, you're a bad person.

732
00:42:22,560 --> 00:42:25,560
Speaker 2: Yeah, and so there's something called publisher perish, like you

733
00:42:25,680 --> 00:42:28,560
Speaker 2: basically are advancing your careers if you get published in

734
00:42:28,600 --> 00:42:32,200
Speaker 2: an academic journal. The problem is academic journals they're like

735
00:42:32,239 --> 00:42:34,480
Speaker 2: the media. They wanted it to be splashing and sexy,

736
00:42:34,640 --> 00:42:37,759
Speaker 2: so they don't really publish negative results anyway. So even

737
00:42:37,800 --> 00:42:40,280
Speaker 2: if you wanted to, you'd have a hard time getting

738
00:42:40,280 --> 00:42:43,560
Speaker 2: it published in a legitimate journal these days. So that's

739
00:42:43,560 --> 00:42:47,000
Speaker 2: a big problem. Ultimately, Chuck the biggest problem, and this

740
00:42:47,080 --> 00:42:50,920
Speaker 2: is what really tripped up social psychology. It's the drum

741
00:42:50,960 --> 00:42:53,920
Speaker 2: that you've been beating this whole time. Humans are not

742
00:42:54,880 --> 00:42:58,759
Speaker 2: predictable computers who will respond in a predictable way if

743
00:42:58,760 --> 00:43:01,960
Speaker 2: you give them a specific stimulus. That's just not how

744
00:43:02,040 --> 00:43:02,879
Speaker 2: humans work now.

745
00:43:02,920 --> 00:43:06,160
Speaker 1: And even the same person will react on a different

746
00:43:06,200 --> 00:43:09,799
Speaker 1: day to the same exact experiment depending on the host

747
00:43:09,880 --> 00:43:10,480
Speaker 1: of factors.

748
00:43:11,040 --> 00:43:15,040
Speaker 2: Sure, so the same person will respond differently, you better

749
00:43:15,040 --> 00:43:18,480
Speaker 2: believe different people will respond differently to the same stimulus.

750
00:43:18,840 --> 00:43:22,040
Speaker 2: And then it also depends on who's presenting the stimulus.

751
00:43:22,760 --> 00:43:26,640
Speaker 2: Is it being presented by a grad student who's posing

752
00:43:26,680 --> 00:43:29,000
Speaker 2: as one of the participants, or is it like a

753
00:43:29,040 --> 00:43:33,360
Speaker 2: professor wearing a white lab coat for some reason. That's

754
00:43:33,480 --> 00:43:36,680
Speaker 2: definitely going to shape the information or the stimulus that's

755
00:43:36,719 --> 00:43:41,160
Speaker 2: being received. And when you put all this together, it's

756
00:43:41,520 --> 00:43:48,160
Speaker 2: essentially impossible to replicate a priming study, and if you

757
00:43:48,239 --> 00:43:51,640
Speaker 2: get the same results, that's essentially a statistical fluke from

758
00:43:51,640 --> 00:43:52,440
Speaker 2: what I understand.

759
00:43:53,040 --> 00:43:56,000
Speaker 1: Yeah, I mean, we've talked about the Stanford prison experiment

760
00:43:56,040 --> 00:43:59,200
Speaker 1: a few times, and looking back, like especially when you

761
00:43:59,239 --> 00:44:02,520
Speaker 1: see the movie I talk about who's presenting the experiment,

762
00:44:02,560 --> 00:44:05,839
Speaker 1: it's like those guards showed up. It's like, you, guys,

763
00:44:05,920 --> 00:44:08,720
Speaker 1: you look ridiculous. You know, you look like an eighteen

764
00:44:08,800 --> 00:44:11,560
Speaker 1: year old who painted on a mustache and put on

765
00:44:11,600 --> 00:44:14,239
Speaker 1: some you know, mirror aviator sunglasses trying to be a

766
00:44:14,239 --> 00:44:14,759
Speaker 1: prison guard.

767
00:44:15,000 --> 00:44:17,600
Speaker 2: Yeah, and developed a southern accent. That one always stuck

768
00:44:17,640 --> 00:44:18,040
Speaker 2: out to me.

769
00:44:18,239 --> 00:44:18,880
Speaker 1: Yeah, exactly.

770
00:44:19,480 --> 00:44:22,040
Speaker 2: So one reason why, I mean, when you're looking back,

771
00:44:22,280 --> 00:44:24,719
Speaker 2: so we should say that there are still people I

772
00:44:24,719 --> 00:44:27,680
Speaker 2: think I did say working in this field earnestly, but

773
00:44:27,719 --> 00:44:30,080
Speaker 2: they're essentially going through and picking out what could be

774
00:44:30,800 --> 00:44:31,799
Speaker 2: salvaged from it.

775
00:44:31,840 --> 00:44:32,040
Speaker 1: Yeah.

776
00:44:32,080 --> 00:44:35,640
Speaker 2: What they're finding is that priming does work, but it,

777
00:44:35,840 --> 00:44:38,240
Speaker 2: like we said, it's going to work differently for different people.

778
00:44:38,280 --> 00:44:42,239
Speaker 2: There's very few universalities, if any. But one thing that

779
00:44:42,280 --> 00:44:44,759
Speaker 2: they have figured out that is legit with social or

780
00:44:44,800 --> 00:44:48,440
Speaker 2: behavioral priming is that you can be primed most easily

781
00:44:48,480 --> 00:44:51,520
Speaker 2: and most reliably if it's pointing you in a direction

782
00:44:51,640 --> 00:44:54,279
Speaker 2: you already want to go. So let's say you want

783
00:44:54,280 --> 00:44:55,200
Speaker 2: to lose weight.

784
00:44:55,239 --> 00:44:55,880
Speaker 1: Uh huh.

785
00:44:55,920 --> 00:44:58,719
Speaker 2: If you're given a menu that has words like light

786
00:44:59,320 --> 00:45:02,000
Speaker 2: or diet on it or something like that, you're more

787
00:45:02,160 --> 00:45:05,920
Speaker 2: likely to choose those items than somebody who isn't interested

788
00:45:05,960 --> 00:45:10,520
Speaker 2: in dieting. Yeah, that's essentially what it got reduced back to,

789
00:45:10,640 --> 00:45:15,320
Speaker 2: which is just barely beyond cognitive priming. But it's legitimate,

790
00:45:15,320 --> 00:45:17,920
Speaker 2: and that's where they're starting out from. Again, that's the

791
00:45:17,960 --> 00:45:20,600
Speaker 2: current state of social priming. But just one more thing

792
00:45:20,640 --> 00:45:24,160
Speaker 2: I wanted to talk about, Chuck, is why everyone bought

793
00:45:24,200 --> 00:45:24,719
Speaker 2: into this?

794
00:45:26,280 --> 00:45:28,680
Speaker 1: Well, why do you think everyone bought into it?

795
00:45:28,880 --> 00:45:32,880
Speaker 2: I'll tell you why. Thank you for asking. Because it

796
00:45:32,920 --> 00:45:36,719
Speaker 2: reduces humans to an understandable, predictable state.

797
00:45:37,120 --> 00:45:39,640
Speaker 1: Yeah, which is very easy to market and sell to.

798
00:45:40,120 --> 00:45:43,719
Speaker 2: Yeah, and understand, not feel threatened by, but also to

799
00:45:43,800 --> 00:45:47,640
Speaker 2: feel superior to. I ran across one explanation called MPC

800
00:45:47,840 --> 00:45:51,800
Speaker 2: theory non player character theory, like referencing background characters and

801
00:45:51,920 --> 00:45:54,680
Speaker 2: video games. We don't think for themselves. They're just kind

802
00:45:54,680 --> 00:46:00,560
Speaker 2: of automated, and something like social priming unders scores that

803
00:46:00,640 --> 00:46:03,480
Speaker 2: idea that other people are like that. I'm not like that.

804
00:46:03,760 --> 00:46:06,479
Speaker 2: I can think for myself. Nobody's gonna doupe me into

805
00:46:06,480 --> 00:46:09,920
Speaker 2: eating a cheeseburger voting for them. Yeah, But other people

806
00:46:10,000 --> 00:46:14,000
Speaker 2: that happens too, And that's what priming research supported. That

807
00:46:14,200 --> 00:46:17,359
Speaker 2: other people are non player characters, which elevates your sense

808
00:46:17,400 --> 00:46:22,200
Speaker 2: of superiority and your sense of intelligence while also deflating

809
00:46:22,560 --> 00:46:24,719
Speaker 2: that of other people in your mind.

810
00:46:25,120 --> 00:46:27,480
Speaker 1: I have to say, though, we said the word cheeseburger

811
00:46:27,520 --> 00:46:29,719
Speaker 1: so many times I can't remember the last cheeseburger I had.

812
00:46:29,760 --> 00:46:30,400
Speaker 1: All I want right now?

813
00:46:30,440 --> 00:46:31,759
Speaker 2: It's a cheeseburger, is that right?

814
00:46:32,239 --> 00:46:33,200
Speaker 1: Yeah?

815
00:46:33,280 --> 00:46:35,239
Speaker 2: Okay, well we'll get you one. What kind do you want?

816
00:46:36,520 --> 00:46:40,200
Speaker 1: Well, you know, Dave said something about nasty McDonald's cheezburgers

817
00:46:40,200 --> 00:46:42,600
Speaker 1: and that I was like, what are you talking about?

818
00:46:42,680 --> 00:46:45,880
Speaker 2: Mc donalds. It's a classic, it's the best of the best.

819
00:46:46,360 --> 00:46:48,839
Speaker 1: Yeah. Well, you know, they may not like it. Maybe

820
00:46:48,840 --> 00:46:49,919
Speaker 1: he's a hearty's guy.

821
00:46:50,280 --> 00:46:52,560
Speaker 2: Maybe he's one of those guys who eats his cheeseburgers

822
00:46:52,560 --> 00:46:53,520
Speaker 2: on a briosh moon.

823
00:46:54,120 --> 00:46:57,800
Speaker 1: Oh an egg roll? Uh?

824
00:46:57,920 --> 00:46:58,680
Speaker 2: You got anything else?

825
00:46:58,960 --> 00:47:01,279
Speaker 1: I got nothing else. These are always fun. I like

826
00:47:01,320 --> 00:47:02,439
Speaker 1: these they are.

827
00:47:02,440 --> 00:47:05,319
Speaker 2: We don't very frequently tee off on something, but it

828
00:47:05,360 --> 00:47:07,400
Speaker 2: does feel good when we do once in a while.

829
00:47:09,239 --> 00:47:12,319
Speaker 2: Chuck just said yeah, and as everybody who's ever listened

830
00:47:12,360 --> 00:47:15,279
Speaker 2: to the podcast knows, he just unlocked listener mail.

831
00:47:18,600 --> 00:47:20,879
Speaker 1: Hey guys, to listen to your wonderful podcast about three

832
00:47:20,880 --> 00:47:22,359
Speaker 1: hours a day of my way to and from work.

833
00:47:22,360 --> 00:47:24,840
Speaker 1: I want to say thank you for all of that

834
00:47:24,880 --> 00:47:27,400
Speaker 1: over the years. I had a very funny incident the

835
00:47:27,480 --> 00:47:29,759
Speaker 1: other day listening to the Sea Monkeys episode. I was

836
00:47:29,800 --> 00:47:32,200
Speaker 1: coming home after a long day and that episode came on.

837
00:47:32,280 --> 00:47:35,520
Speaker 1: I was really tired, and when I originally fell asleep

838
00:47:35,920 --> 00:47:38,520
Speaker 1: against the window, I take it the person isn't driving.

839
00:47:40,480 --> 00:47:42,319
Speaker 1: When I originally fell asleep, I was listening to the

840
00:47:42,360 --> 00:47:44,439
Speaker 1: history of sea monkeys. To my surprise, though, I woke

841
00:47:44,520 --> 00:47:49,080
Speaker 1: up and heard White Supremacists and the KKK. I panic

842
00:47:49,160 --> 00:47:50,719
Speaker 1: and jumped out of my seat, thinking I had missed

843
00:47:50,719 --> 00:47:53,040
Speaker 1: my stop by a long shot and I had finished

844
00:47:53,040 --> 00:47:55,920
Speaker 1: an episode and started a completely different one. I was

845
00:47:55,960 --> 00:47:58,359
Speaker 1: not expecting the sea monkey episode to take that turn,

846
00:47:58,400 --> 00:48:00,520
Speaker 1: you guys. I was relieved to find out that not

847
00:48:00,600 --> 00:48:02,520
Speaker 1: much time had passed and I didn't miss my stop,

848
00:48:02,800 --> 00:48:04,680
Speaker 1: but also a little distressed to find out the dark

849
00:48:04,760 --> 00:48:08,120
Speaker 1: history of such an innocent children's produt. Yeah, I live

850
00:48:08,120 --> 00:48:10,600
Speaker 1: in Istanbul as a foreigner. In your podcast gives me

851
00:48:10,600 --> 00:48:12,879
Speaker 1: a little taste of home. It's comforting on those days

852
00:48:12,880 --> 00:48:15,200
Speaker 1: when I want to tune into two smart guys talking

853
00:48:15,239 --> 00:48:18,520
Speaker 1: about something interesting. Really appreciate you for all the work

854
00:48:18,560 --> 00:48:20,040
Speaker 1: you do and all the fun moments you've given me

855
00:48:20,040 --> 00:48:24,400
Speaker 1: on the bus. And that is from Katie Sesenler.

856
00:48:25,160 --> 00:48:28,480
Speaker 2: Katie knows how to speak our language, doesn't she Katie

857
00:48:28,520 --> 00:48:31,760
Speaker 2: really knows, she knows how to flatter. Thanks a lot, Katie,

858
00:48:31,880 --> 00:48:34,560
Speaker 2: have fun in Istanbul. That's very exciting and thrilling, and

859
00:48:34,600 --> 00:48:37,080
Speaker 2: I'm glad you did not miss your stop. If you

860
00:48:37,120 --> 00:48:39,040
Speaker 2: want to be like Katie and let us know where

861
00:48:39,080 --> 00:48:41,920
Speaker 2: you live and some funny story about stuff you Should Know,

862
00:48:42,200 --> 00:48:44,960
Speaker 2: we love that kind of thing. You can wrap it up,

863
00:48:45,160 --> 00:48:47,040
Speaker 2: spank it on the bottom, and send it off to

864
00:48:47,280 --> 00:48:53,160
Speaker 2: Stuff podcast at iHeartRadio dot com.

865
00:48:53,280 --> 00:48:56,160
Speaker 1: Stuff you Should Know is a production of iHeartRadio. For

866
00:48:56,239 --> 00:48:59,680
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867
00:48:59,719 --> 00:49:02,360
Speaker 1: pod Tests, or wherever you listen to your favorite shows.