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Speaker 1: Welcome to Stuff to Blow your Mind from how Stuff

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Speaker 1: Works dot com. Hey you welcome to Stuff to Blow

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Speaker 1: your Mind. My name is Robert Laham and I'm Joe

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Speaker 1: McCormick and Robert. I want to hit you with a quote.

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Speaker 1: I'm sure you've heard this one a million times before.

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Speaker 1: It's a quote from the American writer Upton Sinclair. Uh,

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Speaker 1: and the quote goes like this. He says, it is

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Speaker 1: difficult to get a man to understand something when his

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Speaker 1: salary depends upon his not understanding it. H Well, that's

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Speaker 1: pretty apt. I'm not sure I've actually heard that one before,

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Speaker 1: but but that certainly has a ring of truth to it. Really,

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Speaker 1: you never heard that? I think I've heard that one.

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Speaker 1: People roll that out all the time when they're talking

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Speaker 1: about you know, industry shills, paid spokespeople, pr types. Um. Yeah, yeah. So.

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Speaker 1: Upton Sinclair ran for governor of California in the nineteen thirties,

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Speaker 1: and he claimed in a campaign retrospective that he used

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Speaker 1: to tell his rally audience is this and it's a

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Speaker 1: great line. There's plenty of truth to it. Right. Yeah.

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Speaker 1: By the way, for anyone who's not familiar up to

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Speaker 1: in Sinclair lived through nine sixty eight, and he was

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Speaker 1: the author of The Jungle and perhaps more known to

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Speaker 1: some of our listeners for his story Oil, which was

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Speaker 1: loosely adapted into the two thousand seventh film There Will

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Speaker 1: Be Blood. Always going to be remembered for a movie first.

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Speaker 1: But did he also write Boogie Nights the original version?

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Speaker 1: Maybe so, John, maybe so? But but no, not just

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Speaker 1: an author but also a politician. Yeah, so he was

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Speaker 1: used to talking about issues of public policy. I mean,

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Speaker 1: he was a politically concerned writer. I think a lot

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Speaker 1: of times people put him in categories like like with

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Speaker 1: Charles Dickens. You know, somebody who's known for writing fiction

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Speaker 1: but also for exposing the plight of the politically disadvantaged.

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Speaker 1: And so, yeah, this quote comes up a lot, like

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Speaker 1: if you're talking about a lawyer representing big tobacco back

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Speaker 1: in the day, who would come on TV and say

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Speaker 1: the science isn't settled yet, there's no proof cigarettes cause cancer,

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Speaker 1: or maybe a col industry lobbyists, maybe literally the same

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Speaker 1: exact person comes on TV a few decades later and says,

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Speaker 1: don't listen to the climate alarmist, that they're scientists on

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Speaker 1: both sides. You know, climate change isn't settled yet. When

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Speaker 1: you're hearing from people like this who are like paid

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Speaker 1: to represent a particular point of view, you obviously don't

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Speaker 1: have to be a super skeptic to realize you shouldn't

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Speaker 1: just take their word for it. Um, But people who

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Speaker 1: get paid to tell you that the grass is pink

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Speaker 1: and the sky is green are going to keep saying that.

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Speaker 1: You know, you're not going to change their mind by

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Speaker 1: offering them evidence or making good points or something, because

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Speaker 1: they're not here to figure out what's true. They're here

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Speaker 1: to say their lines. Yeah, I'm I'm always reminded of

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Speaker 1: the The Doctor character who would inevitably show up in

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Speaker 1: the late night infomercials for various products. Um, you know,

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Speaker 1: clearly they didn't just do a cold call and get

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Speaker 1: get somebody in there to uh to to show for

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Speaker 1: this product. Only Marw Burrow stimulates your cue zone when

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Speaker 1: it comes to people like that. I guess this is

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Speaker 1: kind of a tangent. But when it when it comes

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Speaker 1: to like people who shill for a particular you know,

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Speaker 1: point of view, or or spokespeople for some kind of

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Speaker 1: line on TV, I always kind of wonder, like, do

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Speaker 1: they end up really truly believing the thing that they're

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Speaker 1: paid to say, or is there some kind of cognitive

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Speaker 1: dissonance in their brain. I don't know what it's like

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Speaker 1: to be in that mind. Yeah, that's a great question though,

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Speaker 1: because I mean it's one thing for just like an

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Speaker 1: individual to endorse a product, you know, yeah, like reading

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Speaker 1: an ad or or even saying, hey, I tried out

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Speaker 1: this product. It's really great. You guys should give it

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Speaker 1: a try as well, which obviously we do on the show.

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Speaker 1: But but but when you get to that level where

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Speaker 1: you have an expert, when you have say a medical

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Speaker 1: doctor um, appearing on an infomercial or appearing even um,

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Speaker 1: you know, in some sort of governmental body and saying, yes,

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Speaker 1: I state my reputation on this, I state my professional

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Speaker 1: um expertise, uh, put it on the line in support

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Speaker 1: of this product or this industry and directly contradicting what

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Speaker 1: appears to be the preponderance of the thence right, that

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Speaker 1: that's what these industry shills come out to do, right,

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Speaker 1: they come out to tell you that the scientists are wrong.

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Speaker 1: But anyway, given evidence that has emerged in recent years,

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Speaker 1: I think maybe later on in this episode we should

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Speaker 1: come back and try to do an updated version of

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Speaker 1: this Upton Sinclair quote, because I think that the scope

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Speaker 1: of this quote is actually too limited by just focusing

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Speaker 1: on the salary. So so we'll come back to this,

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Speaker 1: But today we're gonna be talking about a form of

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Speaker 1: motivated reasoning, a form of motivated reasoning called motivated numerous ee,

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Speaker 1: and specifically how that relates to the idea of identity

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Speaker 1: protective cognition. And this has come up on the show before.

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Speaker 1: We talked about it in an episode a while back

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Speaker 1: called Science Communication Breakdown. I think that was like a

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Speaker 1: year and a half ago, or so, I believe so.

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Speaker 1: But it was based on when you had gone to

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Speaker 1: the World Science Festival and seen a talk that included

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Speaker 1: the work of the Yale psychologist Dan Kahan, who is

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Speaker 1: he does a lot of really interesting research about biases

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Speaker 1: and motivated reasoning and the ways in which our brains

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Speaker 1: fail to be rational in one way, sometimes by being

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Speaker 1: uh sort of subversively rational in another way. Yeah. Isn't

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Speaker 1: it interesting how we sometimes uh as seem to outsmart

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Speaker 1: ourselves in these matters. Yeah. So I want to start

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Speaker 1: by thinking about two different kinds of disagreements that come

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Speaker 1: up when people talk about politics. There are obviously lots

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Speaker 1: of different ways people can disagree about politics. Here here

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Speaker 1: are two different kinds of currently politically relevant statements. One

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Speaker 1: is somebody who says the government shouldn't have a right

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Speaker 1: to tax my income. Right, you might talk to like

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Speaker 1: a libertarian who says that. And then here's a different

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Speaker 1: politically relevant statement, human activity is the primary driver of

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Speaker 1: global climate change. Now, people have political arguments over statements

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Speaker 1: like both of these two all the time, but these

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Speaker 1: are not at all the same kind of statement. One

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Speaker 1: big difference is that the first statement is a statement

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Speaker 1: about val I'll use like you can't do a bunch

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Speaker 1: of empirical experiments to determine if it's correct or not

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Speaker 1: that the government should be allowed to tax people. That's

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Speaker 1: just a question about what you believe should be the case.

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Speaker 1: What about values and priorities, and about the priorities of

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Speaker 1: the person making the statement, right, it's a it's a

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Speaker 1: it's a commentary on how you think, or how one

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Speaker 1: group thinks politics should work or how government should work. Rather, uh,

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Speaker 1: and we shouldn't be confused by the idea of political science.

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Speaker 1: Political science, though a serious field, is a different matter

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Speaker 1: compared to the natural sciences. Well, it's certainly true that

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Speaker 1: with questions about like whether or not you should tax income,

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Speaker 1: you can approach that question from the point of optimizing

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Speaker 1: for certain goals, like if you specify a goal and

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Speaker 1: you compare different methods of achieving that goal, then you

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Speaker 1: can do that. But like, absent all of that kind

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Speaker 1: of framework, that's just a statement about values. On the

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Speaker 1: other hand, you've got the human activity is the primary

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Speaker 1: driver of global climate change change. That statement is not

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Speaker 1: like that. There simply is a fact of the matter,

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Speaker 1: either human activity is the primary cause of global climate

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Speaker 1: change or it isn't. And you can do empirical experiments

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Speaker 1: to test this hypothesis, and of course the answer is that, yes,

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Speaker 1: we now know that it is the primary driver of

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Speaker 1: global climate change with like a you know, ninety something

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Speaker 1: percent certainty. It's we really really strongly know this. Now.

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Speaker 1: This is undoubtedly the scientific consensus. Even though this question

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Speaker 1: is politically controversial, it's not scientifically controversial. And if you

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Speaker 1: doubt this, you actually have the ability to go look

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Speaker 1: up the evidence yourself. Especially that's one thing that the

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Speaker 1: internet is great for. You can go read the most

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Speaker 1: recent I p. C. C report. You can read the

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Speaker 1: thousands of individual studies, you can look at the data

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Speaker 1: and read the climate scientist's own words about how their

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Speaker 1: conclusions are drawn from the data of their experiments. And

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Speaker 1: if you actually do that, I think any reasonable person

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Speaker 1: should be able to conclude, of course, human activities the

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Speaker 1: primary cause of climate change change. And yet that's not

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Speaker 1: what happens, is it? Questions like this remain politically controversial,

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Speaker 1: with people often judging the answer in a way that

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Speaker 1: aligns with their political identity. Now, speaking of politics, I

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Speaker 1: just want to throw in a quick fact Lloyd here

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Speaker 1: about this episode. We were recording this on election day.

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Speaker 1: It will be published after election day. So yeah, so

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Speaker 1: we don't know what the outcome is going to be. Yeah. So,

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Speaker 1: so none of this, none of this is a commentary

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Speaker 1: on things that have not yet occurred as of this recording. Yeah,

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Speaker 1: And it's not really a commentary on politics per se.

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Speaker 1: It's a commentary on psychology really that that is going

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Speaker 1: to be at play and people of all political persuasions exactly.

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Speaker 1: So I think we should turn to look at the

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Speaker 1: big paper that we're going to be focusing on in

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Speaker 1: this episode. The the lead author was was Dan Kahan,

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Speaker 1: but the other authors include Ellen Peters, Rika Cantrell Dawson,

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Speaker 1: and Paul Slovak. And it's called Motivated Numerousy and Enlightened

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Speaker 1: Self Government, published in Behavioral Public Policy, I think first

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Speaker 1: published in Revised in and they start off by observing

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Speaker 1: the same kind of thing we've just been talking about

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Speaker 1: that Obviously, there are questions where people can argue about

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Speaker 1: their political values, but the politics is also full of

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Speaker 1: these arguments about purely empirical questions, many of which are

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Speaker 1: no longer in fact empirically controversial, like is climate change

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Speaker 1: driven by greenhouse gas emissions? The answer is yes, but

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Speaker 1: this is still politically controversial. Other questions like this that

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Speaker 1: they give a big list of them. One would be

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Speaker 1: like could we improve public safety by storing nuclear waste

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Speaker 1: deep underground? And that one is a yes as well.

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Speaker 1: I believe that's one that was brought up in the

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Speaker 1: Penal World Science Festival that Kahan spoke on, and that

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Speaker 1: was one that actually I seem to be more divisive. Um,

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Speaker 1: they kind of pulled the audience there at the World

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Speaker 1: Science Festival, so you know, for the most part of

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Speaker 1: very informed and curious bunch, but even they were not

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Speaker 1: as well informed, uh on this issue as they were

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Speaker 1: on some of these other issues we're talking about here. Yeah, Now,

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Speaker 1: not all of these questions are going to be as

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Speaker 1: settled with as much confidence as other ones are. So like,

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Speaker 1: we have a very high confidence now that greenhouse gas

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Speaker 1: emissions are driving climate change, but there could be other

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Speaker 1: questions that are in theory empirical, even if we don't

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Speaker 1: have a scientific consensus yet. I honestly don't know where

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Speaker 1: this this next question falls in, whether it's more settled

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Speaker 1: or less settled. But other questions would include things like, uh,

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Speaker 1: do gun control measures reduce violent crime or increase it? Uh?

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Speaker 1: Does public spending in the aftermath of an economic recession

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Speaker 1: increase the length of the recession or shorten it? And

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Speaker 1: so with some of these questions, we don't always yet

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Speaker 1: know the correct answer, but they are at least empirical.

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Speaker 1: You can do tests, and you can gather data, and

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Speaker 1: you can find with some degree of confidence that there

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Speaker 1: is a correct answer. It's not just going to be

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Speaker 1: an endless contest of values. Yes, it's in the domain

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Speaker 1: of science, and science can have at it. One of

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Speaker 1: the interesting things about a lot of these questions is

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Speaker 1: that they, for some reason almost always seem to concern

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Speaker 1: questions or perceptions of risk. I guess maybe that's just

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Speaker 1: what politics is about. Yeah, I think there is a

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Speaker 1: lot of risk analysis in politics. I mean, obviously there's

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Speaker 1: there's there's always a certain amount of fear mongering as well,

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Speaker 1: Like how do you how do you capitalize on the

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Speaker 1: sort of risks that that voters are considering? How do

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Speaker 1: you potentially stir up the flames or or or or

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Speaker 1: tap them down a bit depending on what kind of

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Speaker 1: a reaction you're looking for. Well, I guess you could

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Speaker 1: look at many major policy decisions as um as conflicts

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Speaker 1: between perceptions of different kinds of risks, right, Like, so

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Speaker 1: somebody will say, well, there's a certain amount of risk

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Speaker 1: we're running by not doing anything about global climate change.

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Speaker 1: Here the things that could result, and somebody else's yes,

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Speaker 1: But if we do something about it, we risk I

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Speaker 1: don't know, we risk not making enough money or something

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Speaker 1: or or perrap perhap halps, it's yeah, we risk hurting

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Speaker 1: ourselves in the short term or a lot of a

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Speaker 1: lot of times, the short term risk versus long term risk,

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Speaker 1: immediate risk versus more you know, elusive risks, Yeah. Now, obviously,

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Speaker 1: when you look at these questions that have been pretty

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Speaker 1: convincingly answered with empirical evidence, and yet intense disagreement persists

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Speaker 1: in politics, this obviously isn't helpful. Like there's enough under

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Speaker 1: dispute over what values should drive public policy that it

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Speaker 1: really doesn't help to add to that that, like unnecessary

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Speaker 1: dead end disputes about underlying empirical facts when the science

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Speaker 1: or the facts are actually pretty clear. So the question

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Speaker 1: is why how come you can have a question where

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Speaker 1: the evidence is very clear, such as the cause of

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Speaker 1: climate change being related to the burning of fossil fuels,

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Speaker 1: but the public not being in general agreement about it.

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Speaker 1: And this this paper looks at two major competing hypotheses

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Speaker 1: to explain this, like why people don't accept the facts

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Speaker 1: when the facts are pretty clear. And the first one

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Speaker 1: is the hypothesis they call the science comprehension thesis or

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Speaker 1: the SCT, and basically it goes like this, the public

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Speaker 1: in general has a pretty weak understanding of science. We

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Speaker 1: are likely to misunderstand what scientists are telling us. If

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Speaker 1: you put a scientific paper in front of us, we're

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Speaker 1: probably not gonna understand it. Thus, we're likely to be

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Speaker 1: misled by people who are trying to deceive us to

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Speaker 1: their own advantage. And I think unfortunately, or well, I

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Speaker 1: don't want to pre empt what we get to in

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Speaker 1: a bit, but I guess we could say unfortunately. This

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Speaker 1: hypothesis is pretty common among skeptics and science enthusiasts and

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Speaker 1: even scientists themselves, and I feel myself very drawn to

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Speaker 1: it because if you accept that the problem is, um,

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Speaker 1: we're just not scientifically literate enough to understand what's being

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Speaker 1: talked about, in a way, this is actually kind of hopeful,

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Speaker 1: especially if you're an educator or a science communicator, because

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Speaker 1: the problem is simply a lack of knowledge. There's just

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Speaker 1: a deficit that can be made up. And so if

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Speaker 1: you just you know, community, you give people better scientific education,

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Speaker 1: better communication of the scientific reality. Under this hypothesis, if

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Speaker 1: you just teach people better scientific literacy skills, they will

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Speaker 1: finally see the light and come around and accept the

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Speaker 1: empirically verifiable facts. Yeah, there's hoping this because you can

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Speaker 1: you can teach people about science. You can you can

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Speaker 1: teach people more about logical thinking as well. Um And

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Speaker 1: though of course I think that's clearly part of scientific

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Speaker 1: literacy as well. But but I can't help but think

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Speaker 1: back to, for instance, Carl Sagan's discussion of on the

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Speaker 1: Bologna Detection Kit, like, the problem is people don't have

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Speaker 1: the kit online, right, or they don't have all the

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Speaker 1: tools and the kit for instance, just to just to

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Speaker 1: blow through these really quickly. He goes into far more

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Speaker 1: detail in the demon Haunted world. But the nine tools

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Speaker 1: are and again abbreviated Number one. Whenever possible, there must

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Speaker 1: be independent confirmation of the facts. Facts and quotations uh

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Speaker 1: Number two. Encourage a substantive debate on the evidence by

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Speaker 1: knowledgeable proponents of all points of view. Number Number three.

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Speaker 1: Arguments from authority carry little weight. Authorities have made mistakes

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Speaker 1: in the past, they will do so again in the future.

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Speaker 1: In science, there are no authorities. At most there are experts.

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Speaker 1: Number four. Spin more than one hypothesis. Number five. Try

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Speaker 1: not to get overly attached to a hypothesis just because

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Speaker 1: it's yours. Hard Number six quantify If whatever it is

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Speaker 1: you're explaining has some measure, some numerical quantity attached to it,

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Speaker 1: you'll be much better able to discriminate among competing hypotheses.

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Speaker 1: This is why numbers are often useful in science exactly.

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Speaker 1: Number seven. If there's a chain of argument, every link

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Speaker 1: in the chain must work, including the premise, not just

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Speaker 1: most of them. Number eight Acam's razer. This is basically,

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Speaker 1: when you have um two hypotheses that explain data equally well,

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Speaker 1: you choose the simpler of the two. Right, So like

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Speaker 1: a dream or a hallucination is probably a better explanation

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Speaker 1: for your alien abduction experience than aliens coming here exactly.

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Speaker 1: And then finally, the knife tool in a Bolognay detection

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Speaker 1: kit always ask whether the hypothesis can be at least

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Speaker 1: in principle falsified. Propositions that are untestable or unfalsifiable are

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Speaker 1: not worth much. That's a really good kit. And I

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Speaker 1: think Carl Sagan, I don't want to put words in

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Speaker 1: his mouth, but I do think he he seems to

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Speaker 1: operate from that kind of hopeful scientific comprehension thesis point

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Speaker 1: of view. At least as best I can tell, it

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Speaker 1: seems like he thinks, you know, the problem with the

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Speaker 1: lack of scientific skepticism among the people is just that

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Speaker 1: they need access to better tools like this, and if

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Speaker 1: we can communicate those tools to them, they can bring

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Speaker 1: them online. And then they'll be more protected against the

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Speaker 1: titular Bolognay, yeah, I think so. Now back to this paper,

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Speaker 1: the authors write that on this hypothesis, on the science

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Speaker 1: comprehension thesis, the lack of comprehension skill causes people to

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Speaker 1: over rely on what's calling what's known as system one

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Speaker 1: thinking when judging empirical scientific questions like perceptions of risk.

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Speaker 1: Now we should mention a little bit about the difference

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Speaker 1: between these concepts of system one thinking and system to thinking.

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Speaker 1: This is big in the works of people like Daniel

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Speaker 1: Kanaman who have written about behavioral economics and the psychology

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Speaker 1: of bias and stuff that's right. It was key to

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Speaker 1: his two thousand and eleven book Thinking Fast and Slow. Um,

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Speaker 1: And we've talked about system one thinking system to thinking

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Speaker 1: on the show before I Think, I think so. Yeah.

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Speaker 1: The basic explanation here, system one thinking is all about fast, automatic, frequent, emotional, stereotypic,

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Speaker 1: and unconscious thinking. This is the theory. This is ruled

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Speaker 1: by heuristics, you know, shortcut ways of thinking. When you

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Speaker 1: when you look at two piles of things and want

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Speaker 1: to know how many, you know which pile has more

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Speaker 1: things in it. If you just judge by I don't

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Speaker 1: know your eyeball. It that system one system to thinking

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Speaker 1: would be what maybe you count the things in the pile? Right?

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Speaker 1: It is slow, effortful, infrequent, logical calculating, and conscious. This

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Speaker 1: reminds me a lot of the two fear networks that

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Speaker 1: were recently discussed in the show Yeah and the Slayer

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Speaker 1: episode Yeah. System two is all about avoiding the tiger

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Speaker 1: haunted thickets. Well, if you rely on system one, then

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Speaker 1: you're more of a tiger racer, a tiger boxer, or

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Speaker 1: just I guess, just a straight up tiger denier. And

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Speaker 1: you know, both of those systems are necessary actually because

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Speaker 1: we don't always have time to do deliberate, slow logical

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Speaker 1: calculating conscious thought a lot. You know, if we did

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Speaker 1: that about every decision we made, we couldn't live. That

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Speaker 1: would be no way to survive. You have to be

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Speaker 1: fast and reactive and unconscious about all kinds of things.

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Speaker 1: And so the question is how do you choose which

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Speaker 1: types of decisions and scenarios to apply these two different

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Speaker 1: thinking schema to on the science comprehension thesis, I think

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Speaker 1: the idea is that people are relying on system one

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Speaker 1: thinking to answer empirical questions about science that are politically relevant,

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Speaker 1: whereas they should be using their system to thinking to

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Speaker 1: get through the get through the fast reactive, stereotypic kind

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Speaker 1: of thing ing and come to the correct answer. Fun fact,

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Speaker 1: we used to be owned by a company that called

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Speaker 1: itself System one UH, named after this this mode of thinking.

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Speaker 1: But that's not the only hypothesis on offer. That's the

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Speaker 1: science comprehension thesis. The other hypothesis, the rival hypothesis, is

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Speaker 1: what if the problem with controversies over empirical questions is

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Speaker 1: not that they're caused by a deficit of knowledge or

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Speaker 1: cognitive skill UH. And this other idea the authors called

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Speaker 1: the identity protective cognition thesis or the i C t

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Speaker 1: They write, quote, whereas s CT attributes conflicts over decision

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Speaker 1: relevant science two deficits in science comprehension, I SET sees

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Speaker 1: the public's otherwise intact capacity to comprehend decision relevant science

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Speaker 1: as disabled by cultural and political conflict. In other words,

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Speaker 1: it's not that people can't understand the science, it's that

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Speaker 1: they could understand the issue if they were not politically

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Speaker 1: charged urged. And it is specifically the political charging of

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Speaker 1: the issue that makes it impossible for them to understand

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Speaker 1: what they otherwise might be able to. All right, so

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Speaker 1: I have to try and put this into tiger terms. Okay,

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Speaker 1: So it's like having the capabilities to avoid tiger kill zones,

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Speaker 1: but refusing to do so for political reasons. Right, Yes,

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Speaker 1: all your friends around you maybe are saying like, oh no,

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Speaker 1: that the people who say that the tigers hang out

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Speaker 1: in the jungle are dumb. They are the bad people,

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Speaker 1: real people, really, the good people all know that there

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Speaker 1: are no tigers in the jungle, that the tigers are

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Speaker 1: somewhere else. I do admit I love it anytime we

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Speaker 1: can put things in terms of big cat attacks. That

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Speaker 1: always just seems to really help explain the topic. You

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Speaker 1: should know, I'm picturing not a real tiger, but Tony

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Speaker 1: the tiger. Yeah, Tony the tiger mauling and killing people

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Speaker 1: a right. Okay, So here's the question. If this hypothesis

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Speaker 1: is correct, why would it be the case that political

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Speaker 1: charging of issues would make us enable to use our

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Speaker 1: normal reasoning faculties. Well, first of all, I mean think

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Speaker 1: about the Upton's and Claire quote. It's difficult to make

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Speaker 1: a person understand something when their salary depends on it.

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Speaker 1: Here we're not talking about a salary, but about something

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Speaker 1: else of immense psychic and material value, and that is

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Speaker 1: your membership, status and standing within a social group that

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Speaker 1: is in part defined by its commitment to certain moral

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Speaker 1: and political values. Well, I think that's very much like salary.

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Speaker 1: I mean, salary is money, money is life, money is happiness.

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Speaker 1: I mean we say it's not, but it is. Uh,

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Speaker 1: and then uh and then but but it is the

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Speaker 1: thing that allows us to eat and live and be

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Speaker 1: in most circumstances, certainly in the world that we've we've

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Speaker 1: we've made and remade for ourselves and likewise, in a

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Speaker 1: more primal sense, belonging to a group, being part of

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Speaker 1: a group, that is, that is survival for for the

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Speaker 1: Homo sapiens. Yes, that is how we have historically and

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Speaker 1: prehistorically managed to live. It's psychically necessary to us. It's

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Speaker 1: necessary for us to have good mental and in fact,

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Speaker 1: I think in some ways good physical health, to be

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Speaker 1: a member in good standing of a social group and

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Speaker 1: a social network. But if you want to go into

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Speaker 1: our you know, our our evolutionary history, it is literally

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Speaker 1: materially necessary to be accepted as a member of the

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Speaker 1: end group. If you're driven out of your hunter gatherer

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Speaker 1: tribe that things are not looking good for you, you're

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Speaker 1: just waiting to fall into a tiger thicket at that point, right.

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Speaker 1: And so, if all your friends and allies believe one

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Speaker 1: way about any politically charged issue, climate change or gun

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Speaker 1: control or whatever, and you put yourself at huge personal

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Speaker 1: risk by advocating a position that that group disagrees with,

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Speaker 1: you could be alienated from your social group. You could

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Speaker 1: lose connections that you depend on for mental health and survival. Thus,

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Speaker 1: you could definitely see identity protective cognition as a kind

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Speaker 1: of mental immune system. It protects the brain from beliefs

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Speaker 1: that could potentially cause you immense harm if you were

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Speaker 1: to express them. The brain detects a belief or an

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Speaker 1: idea that is a threat to your social identity, and

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Speaker 1: it puts up a wall against that belief and doesn't

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Speaker 1: let it in because it could hurt you. You know,

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Speaker 1: And I think we can all relate to this on

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Speaker 1: one level or another. You know, how many times have

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Speaker 1: any of us said, well, I refuse to believe that,

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Speaker 1: or I find that hard to believe U. And of

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Speaker 1: course there are a lot of examples that come up

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Speaker 1: in which the issues relate more clearly to personal belief

430
00:23:25,960 --> 00:23:29,560
Speaker 1: and and or just pure opinion and artistic value. For instance,

431
00:23:29,600 --> 00:23:33,120
Speaker 1: of a movie reviewer television reviewer tells me that an

432
00:23:33,200 --> 00:23:37,040
Speaker 1: upcoming Cohen Brothers movie isn't worth seeing. I generally find

433
00:23:37,080 --> 00:23:40,200
Speaker 1: that hard to believe until I see it for myself,

434
00:23:40,240 --> 00:23:43,000
Speaker 1: and say, in the case of Inside Lewyn Davis, I

435
00:23:43,119 --> 00:23:46,359
Speaker 1: end up agreeing with what Inside Lewyn Davis. You know

436
00:23:46,640 --> 00:23:51,639
Speaker 1: it was wonderfully made. Prepare to be ostracized, but you

437
00:23:51,720 --> 00:23:53,479
Speaker 1: know it was wonderfully made. But it was just not

438
00:23:53,640 --> 00:23:55,960
Speaker 1: my cup of tea. Oh I loved it. I love

439
00:23:56,160 --> 00:23:58,680
Speaker 1: Oscar Isaac. It was Oh man, he's such a great

440
00:23:58,720 --> 00:24:02,000
Speaker 1: singer to the music was wonderful. The music was was great.

441
00:24:02,119 --> 00:24:05,359
Speaker 1: It just did not It did not make me happy

442
00:24:06,160 --> 00:24:08,840
Speaker 1: or make me sad in an interesting way. You know,

443
00:24:09,720 --> 00:24:12,280
Speaker 1: I will, I will do my best not to fully

444
00:24:12,359 --> 00:24:16,040
Speaker 1: alienate you and throw you out into the cold. So

445
00:24:16,359 --> 00:24:18,280
Speaker 1: but that's one thing, right, Ultimately coming down to art

446
00:24:18,280 --> 00:24:20,719
Speaker 1: in personal opinion. Uh, And and there are I think

447
00:24:20,760 --> 00:24:23,000
Speaker 1: there are going to be certain areas where you are

448
00:24:23,000 --> 00:24:26,000
Speaker 1: going to be so attached to certain artistic values that

449
00:24:26,080 --> 00:24:29,320
Speaker 1: you're going to feel reluctant to state it because of

450
00:24:29,359 --> 00:24:32,400
Speaker 1: how it might affect your standing in a group. Oh yeah,

451
00:24:32,440 --> 00:24:35,440
Speaker 1: so that's a different kind of variation. Like there are

452
00:24:35,560 --> 00:24:40,679
Speaker 1: some unpopular aesthetic opinions that you're not really scared to

453
00:24:40,840 --> 00:24:43,960
Speaker 1: voice because you could abandon them if you needed to. Maybe,

454
00:24:44,000 --> 00:24:47,159
Speaker 1: but I really deeply held aesthetic preference that would be

455
00:24:47,520 --> 00:24:50,840
Speaker 1: unpopular you maybe just don't even bring up. Yeah, Like

456
00:24:50,880 --> 00:24:54,000
Speaker 1: I imagine a band abandoning suddenly abandoning your favorite rock

457
00:24:54,040 --> 00:24:56,000
Speaker 1: band in high school. You know that sort of thing.

458
00:24:56,520 --> 00:24:58,960
Speaker 1: But but clearly, you know a lot of these other

459
00:24:59,000 --> 00:25:01,240
Speaker 1: issues are all so are going to be different matters, say,

460
00:25:01,240 --> 00:25:03,960
Speaker 1: matters of hearsay or something that's just not completely provable

461
00:25:03,960 --> 00:25:06,200
Speaker 1: one way or another, uh, say, some bit of dirt

462
00:25:06,240 --> 00:25:08,280
Speaker 1: on a political candidate that can need to be confirmed

463
00:25:08,359 --> 00:25:11,280
Speaker 1: or denied. But then we have to come back to

464
00:25:11,320 --> 00:25:14,800
Speaker 1: those empirical questions, the ones where science can and does

465
00:25:14,920 --> 00:25:17,680
Speaker 1: weigh in on the matter. Yes, and fortunately, as the

466
00:25:17,720 --> 00:25:21,560
Speaker 1: authors point out, not that many empirical questions are really

467
00:25:21,720 --> 00:25:26,280
Speaker 1: likely to trigger identity protective cognition. Only empirical questions that

468
00:25:26,320 --> 00:25:30,720
Speaker 1: are unfortunate enough to get tagged as politically significant along

469
00:25:30,800 --> 00:25:34,359
Speaker 1: partisan lines really acquired this taint. For example, you know,

470
00:25:34,440 --> 00:25:38,359
Speaker 1: there's been a partisan divide over the HPV vaccine, probably

471
00:25:38,400 --> 00:25:41,360
Speaker 1: because it has some kind of perceived relevance to sexual

472
00:25:41,400 --> 00:25:44,600
Speaker 1: morality and young people. But there's no partisan divide on

473
00:25:44,640 --> 00:25:48,560
Speaker 1: the use of antibiotics to treat bacterial infections, and most

474
00:25:48,680 --> 00:25:51,520
Speaker 1: questions are more like the antibiotics. There's just there's not

475
00:25:51,560 --> 00:25:55,280
Speaker 1: a partisan divide about it. What you know, temperature, water

476
00:25:55,400 --> 00:26:00,000
Speaker 1: boils or scientific questions. There's just not really a partisan divide.

477
00:26:00,080 --> 00:26:03,199
Speaker 1: Dawn though, to come back to antibiotics, I see, I

478
00:26:03,240 --> 00:26:05,439
Speaker 1: see a dark future. I see there could be a

479
00:26:05,480 --> 00:26:09,359
Speaker 1: time where if members of one major political party but

480
00:26:09,480 --> 00:26:13,199
Speaker 1: not the other, happen to start talking about antibiotics, I

481
00:26:13,240 --> 00:26:17,000
Speaker 1: think you could quite easily see partisan associations arise, and

482
00:26:17,080 --> 00:26:20,640
Speaker 1: antibiotics could go from an issue that's non politicized where

483
00:26:20,640 --> 00:26:23,480
Speaker 1: pretty much everybody agrees to an issue that suddenly is

484
00:26:23,560 --> 00:26:27,520
Speaker 1: divided along partisan lines. Now that that seems sadly like

485
00:26:27,560 --> 00:26:29,200
Speaker 1: the kind of thing we would do. But to come

486
00:26:29,200 --> 00:26:31,680
Speaker 1: back on the other side, Okay, wait a minute. Don't

487
00:26:31,720 --> 00:26:36,840
Speaker 1: people also have an incentive to have correct beliefs obviously, right,

488
00:26:36,880 --> 00:26:39,639
Speaker 1: I mean right, yeah, I mean we It definitely pays

489
00:26:39,680 --> 00:26:43,919
Speaker 1: off to have a working, realistic model of how the

490
00:26:44,000 --> 00:26:46,760
Speaker 1: world works that you live in. But it pays off

491
00:26:46,760 --> 00:26:50,680
Speaker 1: in some ways that are much more personally immediately relevant

492
00:26:50,760 --> 00:26:54,000
Speaker 1: than others. Uh, depending on the issue. Think about it.

493
00:26:54,320 --> 00:26:58,000
Speaker 1: In policy relevant empirical questions like the impact of carbon

494
00:26:58,040 --> 00:27:03,000
Speaker 1: emissions or the act of gun control policies, the consequence

495
00:27:03,000 --> 00:27:07,399
Speaker 1: of one individual person being wrong is vanishingly small. But

496
00:27:07,560 --> 00:27:10,880
Speaker 1: for that one person, the consequence of being alienated from

497
00:27:10,920 --> 00:27:15,120
Speaker 1: their identity group is potentially massive. So on one decision,

498
00:27:15,160 --> 00:27:18,280
Speaker 1: you potentially cast one vote out of millions for a

499
00:27:18,320 --> 00:27:21,960
Speaker 1: poorly reasoned public policy, and on the other decision, you

500
00:27:22,000 --> 00:27:26,240
Speaker 1: could alienate or weaken your most important friendships, your work relationships,

501
00:27:26,240 --> 00:27:29,040
Speaker 1: and even your sense of self um and so the

502
00:27:29,080 --> 00:27:33,040
Speaker 1: author's right quote persistent conflict over risks and other policy

503
00:27:33,119 --> 00:27:37,800
Speaker 1: relevant facts reflects a tragedy of the science communications commons,

504
00:27:38,600 --> 00:27:43,600
Speaker 1: a misalignment between the individual interests that culturally diverse citizens

505
00:27:43,600 --> 00:27:47,280
Speaker 1: have informing beliefs that connect them to others who share

506
00:27:47,320 --> 00:27:50,879
Speaker 1: their distinctive understanding of the best life, and the collective

507
00:27:50,880 --> 00:27:53,679
Speaker 1: interests that members of all such groups share in the

508
00:27:53,800 --> 00:27:57,280
Speaker 1: enactment of public policies that enable them to pursue their

509
00:27:57,480 --> 00:28:01,480
Speaker 1: ends free from threats to their health and prosperity. Okay,

510
00:28:01,520 --> 00:28:03,119
Speaker 1: maybe we should take a quick break and when we

511
00:28:03,160 --> 00:28:05,919
Speaker 1: come back we can take a look at how we

512
00:28:05,960 --> 00:28:12,560
Speaker 1: can compare these two hypotheses. Alright, we're back, So, yeah,

513
00:28:12,600 --> 00:28:15,679
Speaker 1: we're gonna look at ways to compare these two hypotheses. Now,

514
00:28:15,680 --> 00:28:18,320
Speaker 1: of course, in all of this, I can't help but think, well,

515
00:28:18,359 --> 00:28:21,560
Speaker 1: why can't it be both? Why can't we can't we

516
00:28:21,600 --> 00:28:24,800
Speaker 1: have like both of these uh, these uh, these reasons

517
00:28:24,840 --> 00:28:29,080
Speaker 1: in play? You mean that? So we've got the two hypotheses,

518
00:28:29,119 --> 00:28:31,960
Speaker 1: the science comprehension thesis, which says that people come to

519
00:28:32,119 --> 00:28:37,480
Speaker 1: incorrect beliefs about scientifically are politically relevant empirical questions because

520
00:28:37,560 --> 00:28:41,400
Speaker 1: they lack the scientific literacy skills to understand the issues.

521
00:28:41,480 --> 00:28:43,480
Speaker 1: And then the other one says it's not that they

522
00:28:43,560 --> 00:28:46,320
Speaker 1: lack the skills to understand the issues, it's that they

523
00:28:46,360 --> 00:28:51,680
Speaker 1: are being selectively blinded from proper reasoning by identity protective

524
00:28:51,680 --> 00:28:55,640
Speaker 1: cognition that is socially conditioned. Right, the idea coming back

525
00:28:55,680 --> 00:28:57,720
Speaker 1: to Segan's toolkit. It's like, do I not have the

526
00:28:57,760 --> 00:28:59,960
Speaker 1: tools or is there just this like this, there is

527
00:29:00,040 --> 00:29:03,840
Speaker 1: a social and psychological reason for not using the tools

528
00:29:03,840 --> 00:29:06,080
Speaker 1: that I have. Well, I think technically you could have

529
00:29:06,200 --> 00:29:08,280
Speaker 1: both in a way. So the question would be, um,

530
00:29:09,520 --> 00:29:12,480
Speaker 1: can you show that these are are mutually exclusive, and

531
00:29:12,520 --> 00:29:14,520
Speaker 1: that would come through in the evidence. But you certainly

532
00:29:14,600 --> 00:29:20,000
Speaker 1: could have a population that has fewer science comprehension skills

533
00:29:20,080 --> 00:29:22,840
Speaker 1: than it could and so you could educate people in

534
00:29:22,880 --> 00:29:26,560
Speaker 1: science better and we would have higher scientific comprehension skills.

535
00:29:26,760 --> 00:29:30,960
Speaker 1: But also within that population, identity protective cognition could be

536
00:29:31,040 --> 00:29:34,440
Speaker 1: highly salient. So that's a good question. But if you

537
00:29:34,440 --> 00:29:38,040
Speaker 1: want to pit these two hypotheses against each other, you

538
00:29:38,080 --> 00:29:41,400
Speaker 1: can create just create conditions where they're obviously going to

539
00:29:41,480 --> 00:29:45,280
Speaker 1: be antagonistic as far as the data is concerned. So

540
00:29:45,400 --> 00:29:49,800
Speaker 1: here's one idea. If the science comprehension thesis is correct, right,

541
00:29:49,880 --> 00:29:53,560
Speaker 1: the problem is a deficit and understanding science. People who

542
00:29:53,560 --> 00:29:57,920
Speaker 1: are better at drawing correct conclusions from scientific data will

543
00:29:57,960 --> 00:30:00,880
Speaker 1: be better at it, whether or not the data concerns

544
00:30:00,920 --> 00:30:04,800
Speaker 1: politically relevant issues. Right, So it should mean that if

545
00:30:04,840 --> 00:30:08,120
Speaker 1: the s CT is correct, the science comprehension thesis, it

546
00:30:08,160 --> 00:30:12,440
Speaker 1: should mean that if you have scientific understanding skills like

547
00:30:12,680 --> 00:30:15,800
Speaker 1: numerous e, which is skill at using numbers and drawing

548
00:30:15,840 --> 00:30:20,360
Speaker 1: conclusions from from quantitative data. If you have high NUMEROUSY

549
00:30:20,720 --> 00:30:24,280
Speaker 1: you should be better at drawing the correct conclusions from data,

550
00:30:24,600 --> 00:30:28,720
Speaker 1: whether or not that data flatters your political perceptions. UM.

551
00:30:29,000 --> 00:30:32,000
Speaker 1: On the other hand, if the identity protective cognition thesis

552
00:30:32,080 --> 00:30:35,400
Speaker 1: is correct, people who are better at drawing correct conclusions

553
00:30:35,400 --> 00:30:39,160
Speaker 1: from scientific data will see this skill significantly hampered by

554
00:30:39,200 --> 00:30:42,720
Speaker 1: the introduction of a political identity threat. All right, so

555
00:30:42,760 --> 00:30:44,200
Speaker 1: I have a feeling we're gonna we're gonna look at

556
00:30:44,240 --> 00:30:48,560
Speaker 1: some experiments. Yes, So the experiment is big sample of

557
00:30:48,680 --> 00:30:52,720
Speaker 1: one thousand, one hundred and eleven demographically diverse and ideologically

558
00:30:52,760 --> 00:30:55,840
Speaker 1: diverse US adults. Uh, and you sort them according to

559
00:30:55,920 --> 00:30:59,000
Speaker 1: a couple of major factors. One is political ideology, so

560
00:30:59,040 --> 00:31:01,760
Speaker 1: they're sort of on on a scale of how liberal

561
00:31:01,880 --> 00:31:04,840
Speaker 1: or conservative they rate themselves. And then the next is

562
00:31:04,880 --> 00:31:08,320
Speaker 1: their numeracy skills, determined by a numeracy test. The author's

563
00:31:08,400 --> 00:31:11,680
Speaker 1: right quote a well established and highly studied construct and

564
00:31:11,760 --> 00:31:15,959
Speaker 1: NUMEROUSY encompasses not just mathematical ability, but also a disposition

565
00:31:16,000 --> 00:31:20,040
Speaker 1: to engage quantitative information in a reflective and systematic way

566
00:31:20,440 --> 00:31:23,240
Speaker 1: and to use it to support valid inferences. So it's

567
00:31:23,240 --> 00:31:25,560
Speaker 1: not just being good at math, but it's being able

568
00:31:25,600 --> 00:31:28,520
Speaker 1: to say, look at data in a study and figure

569
00:31:28,560 --> 00:31:31,600
Speaker 1: out what that data should tell you. So the authors

570
00:31:31,640 --> 00:31:34,880
Speaker 1: came up with a couple of fictional experiments, and they

571
00:31:34,880 --> 00:31:37,840
Speaker 1: took the results of these fictional experiments and asked the

572
00:31:37,880 --> 00:31:42,200
Speaker 1: participants to draw conclusions based on the results they showed them. Now,

573
00:31:42,360 --> 00:31:45,120
Speaker 1: both the results of the fictional experiment and the topic

574
00:31:45,160 --> 00:31:48,800
Speaker 1: of the experiment were manipulated to create different test conditions,

575
00:31:48,840 --> 00:31:51,120
Speaker 1: so the same results were offered in the context of

576
00:31:51,160 --> 00:31:54,080
Speaker 1: either being about quote the effectiveness of a new skin

577
00:31:54,200 --> 00:31:57,440
Speaker 1: rash treatment or quote the effectiveness of a ban on

578
00:31:57,640 --> 00:32:00,600
Speaker 1: carrying concealed weapons in public. One of those is going

579
00:32:00,640 --> 00:32:03,480
Speaker 1: to be more controversial than the other. Right, So what

580
00:32:03,520 --> 00:32:06,120
Speaker 1: they're saying is they they expect that the skin rash

581
00:32:06,160 --> 00:32:09,520
Speaker 1: treatment is not going to have any partisan significance unless

582
00:32:09,640 --> 00:32:12,720
Speaker 1: I don't know, major Republicans or Democrats start talking about

583
00:32:12,760 --> 00:32:15,440
Speaker 1: skin rashes a lot, but at this point it was

584
00:32:15,480 --> 00:32:19,600
Speaker 1: not politically relevant. The other is, of course, being about guns,

585
00:32:19,600 --> 00:32:22,400
Speaker 1: which is one of the most highly charged, politically charged

586
00:32:22,440 --> 00:32:26,320
Speaker 1: topics where people break down along partisan lines. Okay, so

587
00:32:26,400 --> 00:32:28,360
Speaker 1: imagine you're one of the people who's a subject in

588
00:32:28,360 --> 00:32:32,480
Speaker 1: this experiment, they will give you a table of results

589
00:32:32,520 --> 00:32:35,200
Speaker 1: to look at, and it might say it's say it's

590
00:32:35,240 --> 00:32:37,920
Speaker 1: you're in the skin rash condition. It might You'll have

591
00:32:37,920 --> 00:32:41,160
Speaker 1: a table of four numbers, and the different numbers represent

592
00:32:41,320 --> 00:32:44,960
Speaker 1: patients who did use a new skin cream and patients

593
00:32:44,960 --> 00:32:47,440
Speaker 1: who did not use a new skin cream. And then

594
00:32:47,480 --> 00:32:50,200
Speaker 1: the other axes of the table will be patients whose

595
00:32:50,320 --> 00:32:53,880
Speaker 1: rash got worse and patients whose rash got better. And

596
00:32:53,920 --> 00:32:56,200
Speaker 1: then you need to determine, based on the numbers and

597
00:32:56,280 --> 00:32:59,680
Speaker 1: the table, whether the skin cream is more helpful or

598
00:32:59,720 --> 00:33:02,960
Speaker 1: more harmful, and then substitute in the exact same thing

599
00:33:03,080 --> 00:33:06,560
Speaker 1: for instead of using patients using a skin cream, cities

600
00:33:06,600 --> 00:33:10,720
Speaker 1: that did or did not ban carrying concealed handguns in public,

601
00:33:11,080 --> 00:33:13,280
Speaker 1: and instead of the rash getting worse or the rash

602
00:33:13,280 --> 00:33:16,200
Speaker 1: getting better, it's crime went down or crime went up.

603
00:33:16,800 --> 00:33:19,720
Speaker 1: So the authors had three hypotheses three that they would

604
00:33:19,720 --> 00:33:23,200
Speaker 1: test here. One is that they guessed subjects scoring high

605
00:33:23,200 --> 00:33:25,520
Speaker 1: in numeracy would be more likely to get the right

606
00:33:25,600 --> 00:33:30,440
Speaker 1: result in both skin treatment conditions. And this is pretty straightforward. Basically,

607
00:33:30,480 --> 00:33:33,520
Speaker 1: they're saying people who have higher numeracy skills are more

608
00:33:33,560 --> 00:33:36,840
Speaker 1: likely to use deliberate system to thinking to work out

609
00:33:36,880 --> 00:33:40,080
Speaker 1: the covariance between the results and draw the correct conclusions.

610
00:33:40,080 --> 00:33:42,680
Speaker 1: They're more likely to get the skin rash thing right.

611
00:33:43,400 --> 00:33:46,480
Speaker 1: Hypothesis too, is based on the science comprehension thesis, So

612
00:33:46,520 --> 00:33:49,600
Speaker 1: if the science comprehension thesis is correct, they predict that

613
00:33:49,720 --> 00:33:53,520
Speaker 1: subjects scoring higher in numeracy QUOTE would be more likely

614
00:33:53,560 --> 00:33:56,040
Speaker 1: to construe the data correctly, not only when it was

615
00:33:56,080 --> 00:33:59,960
Speaker 1: consistent with their ideological predispositions, but also when it was

616
00:34:00,240 --> 00:34:04,240
Speaker 1: inconsistent with them, and thus they were likely to display

617
00:34:04,360 --> 00:34:08,800
Speaker 1: less ideological polarization than subjects lower in numeracy. In other words,

618
00:34:08,800 --> 00:34:12,000
Speaker 1: on the science comprehension thesis, if you're better at understanding

619
00:34:12,080 --> 00:34:15,600
Speaker 1: quantitative science, your interpretation of the results of the gun

620
00:34:15,600 --> 00:34:20,080
Speaker 1: band thing should be less affected by political bias. And then, finally,

621
00:34:20,120 --> 00:34:23,000
Speaker 1: they have a third hypothesis based on the identity protective

622
00:34:23,040 --> 00:34:27,719
Speaker 1: cognition thesis QUOTE. Ideological polarization in the gun band conditions

623
00:34:27,760 --> 00:34:31,719
Speaker 1: should be most extreme among those highest in numerous E.

624
00:34:32,360 --> 00:34:36,480
Speaker 1: Under this hypothesis, people high in NUMEROUSY are not immune

625
00:34:36,560 --> 00:34:40,480
Speaker 1: from identity protective cognition and will, like everyone else, always

626
00:34:40,560 --> 00:34:45,040
Speaker 1: seek ways to affirm their existing political beliefs, but using

627
00:34:45,080 --> 00:34:48,880
Speaker 1: their NUMEROUSY skills, they can use system to thinking to

628
00:34:48,960 --> 00:34:53,600
Speaker 1: draw correct but counterintuitive inferences from the data when it

629
00:34:53,680 --> 00:34:57,399
Speaker 1: flatters their beliefs, but detect that they should skip this

630
00:34:57,560 --> 00:35:00,160
Speaker 1: and use quick heuristics to arrive at the wrong, wrong

631
00:35:00,239 --> 00:35:04,600
Speaker 1: answer when that flatters their beliefs. So quote, if high

632
00:35:04,680 --> 00:35:09,640
Speaker 1: numerous E subjects use their special cognitive advantage selectively only

633
00:35:09,719 --> 00:35:14,400
Speaker 1: when doing so generates an ideologically congenial answer, but not otherwise,

634
00:35:14,719 --> 00:35:17,600
Speaker 1: they will end up even more polarized than their low

635
00:35:17,680 --> 00:35:21,840
Speaker 1: numerous EY counterparts. And so here we get to the results.

636
00:35:21,880 --> 00:35:25,160
Speaker 1: So first thing worth noting is that detecting covariance is

637
00:35:25,200 --> 00:35:28,520
Speaker 1: difficult if you're not experienced in it. So across all

638
00:35:28,640 --> 00:35:32,720
Speaker 1: test conditions, most people got the answers wrong. All test

639
00:35:32,760 --> 00:35:38,200
Speaker 1: conditions combine, fifty nine percent of subjects supplied the incorrect answer. Uh.

640
00:35:38,239 --> 00:35:40,080
Speaker 1: And this is probably because if you just look at

641
00:35:40,080 --> 00:35:43,000
Speaker 1: the numbers and use a quick heuristic or system one thinking,

642
00:35:43,239 --> 00:35:46,040
Speaker 1: you're likely to draw the opposite of the correct conclusion.

643
00:35:46,080 --> 00:35:48,400
Speaker 1: You'd actually have to do the math and compare some

644
00:35:48,520 --> 00:35:51,640
Speaker 1: ratios to come up with the correct answer, but the

645
00:35:51,680 --> 00:35:54,759
Speaker 1: results found hypothesis one, which was that if you're high

646
00:35:54,800 --> 00:35:56,920
Speaker 1: and numerous E, you're you've got a better chance of

647
00:35:56,920 --> 00:36:00,400
Speaker 1: getting the skin rash results correct. That was ordered by

648
00:36:00,400 --> 00:36:02,840
Speaker 1: the data. The better yard at numerocy, the more likely

649
00:36:02,920 --> 00:36:06,640
Speaker 1: you are to draw correct inferences from politically neutral data,

650
00:36:06,920 --> 00:36:09,839
Speaker 1: though most people were not very good at this um

651
00:36:09,960 --> 00:36:14,520
Speaker 1: hypothesis to which would be consistent with the scientific comprehension

652
00:36:14,560 --> 00:36:18,280
Speaker 1: thesis that people high in numeracy will show less polarization

653
00:36:18,360 --> 00:36:21,560
Speaker 1: on the gun band condition, This was not supported by

654
00:36:21,560 --> 00:36:26,160
Speaker 1: the data. Conversely, hypothesis three was supported by the data,

655
00:36:26,239 --> 00:36:28,839
Speaker 1: and and that one was that people with high NUMEROUSY

656
00:36:28,920 --> 00:36:33,279
Speaker 1: skills will show even more ideologically polarized judgments about the

657
00:36:33,320 --> 00:36:36,040
Speaker 1: results in the gun band condition. And so what the

658
00:36:36,040 --> 00:36:39,520
Speaker 1: authors conclude is that high numerous E partisans use their

659
00:36:39,560 --> 00:36:44,560
Speaker 1: skills selectively. When a laborious system to calculation will yield

660
00:36:44,600 --> 00:36:47,479
Speaker 1: results that are flattering to your political point of view,

661
00:36:47,880 --> 00:36:50,919
Speaker 1: you'll do it. But when it threatens your point of view,

662
00:36:51,120 --> 00:36:54,239
Speaker 1: you'll skip it. You'll skip system to reasoning and just

663
00:36:54,360 --> 00:36:59,680
Speaker 1: draw incorrect heuristic conclusions. Uh. And so a few takeaways here.

664
00:36:59,680 --> 00:37:01,960
Speaker 1: I think we should think about while we're discussing this

665
00:37:02,040 --> 00:37:05,080
Speaker 1: one is that I should stress this study doesn't show

666
00:37:05,160 --> 00:37:08,960
Speaker 1: that science education and science communication efforts are pointless or

667
00:37:09,120 --> 00:37:13,480
Speaker 1: bad or anything like that. Science comprehension skills, including numerous

668
00:37:13,520 --> 00:37:16,799
Speaker 1: e are crucial for answering all kinds of questions accurately

669
00:37:17,080 --> 00:37:19,400
Speaker 1: when a system one heuristic model would cause you to

670
00:37:19,440 --> 00:37:22,640
Speaker 1: come to the wrong conclusion. So it's kind of the baseline, right,

671
00:37:22,680 --> 00:37:26,959
Speaker 1: you've got to have scientific comprehension skills. But if these

672
00:37:27,000 --> 00:37:29,440
Speaker 1: results are valid, what they do show is that science

673
00:37:29,480 --> 00:37:33,560
Speaker 1: comprehension skills are not necessarily a protection against getting politically

674
00:37:33,640 --> 00:37:37,360
Speaker 1: charged science questions wrong. Because the brain uses its science

675
00:37:37,360 --> 00:37:41,239
Speaker 1: comprehension skills selectively. It's more likely to bring out the

676
00:37:41,280 --> 00:37:44,040
Speaker 1: big guns if they will help it protect its identity,

677
00:37:44,360 --> 00:37:47,279
Speaker 1: and it's more likely to surrender to heuristic thinking if

678
00:37:47,360 --> 00:37:50,360
Speaker 1: that's what protects your identity. Another way of putting it,

679
00:37:50,520 --> 00:37:54,399
Speaker 1: political identity can make you selectively bad at math, even

680
00:37:54,440 --> 00:37:57,759
Speaker 1: if you're normally good at math. And so in this week,

681
00:37:57,800 --> 00:37:59,160
Speaker 1: this is where we get into some of these theories

682
00:37:59,160 --> 00:38:03,360
Speaker 1: where we see, say you know an individual um that

683
00:38:03,360 --> 00:38:06,880
Speaker 1: that has a scientific background or PhD or what have you,

684
00:38:07,200 --> 00:38:10,120
Speaker 1: uh that you see showing up on the side of say,

685
00:38:10,120 --> 00:38:15,279
Speaker 1: climate change deniers, or or even something more ridiculous like

686
00:38:15,320 --> 00:38:18,439
Speaker 1: a like a like a flat earth belief system. Yeah,

687
00:38:18,560 --> 00:38:20,840
Speaker 1: I almost never see it with flat earth beliefs, but

688
00:38:21,000 --> 00:38:23,680
Speaker 1: you do see it with climate change most definitely. What

689
00:38:23,760 --> 00:38:27,560
Speaker 1: you notice with climate changes that like, um, sometimes people

690
00:38:27,840 --> 00:38:30,600
Speaker 1: come up with lists of scientists who don't agree with

691
00:38:30,640 --> 00:38:33,600
Speaker 1: the consensus on climate change, and usually almost none of

692
00:38:33,640 --> 00:38:37,600
Speaker 1: them work in fields relevant to climate change. Uh. You know,

693
00:38:37,640 --> 00:38:40,440
Speaker 1: they're not like climate scientists. I'm not saying there are

694
00:38:40,520 --> 00:38:43,920
Speaker 1: no climate scientists that disagree, but they're almost none. They

695
00:38:43,960 --> 00:38:46,520
Speaker 1: tend to be somebody like one example that often comes

696
00:38:46,600 --> 00:38:49,400
Speaker 1: up and I honestly can't remember to what extent his

697
00:38:49,520 --> 00:38:52,719
Speaker 1: disagreement is with it, but say, Freeman Dyson is an

698
00:38:52,760 --> 00:38:56,520
Speaker 1: individual of note who has at least at times cast

699
00:38:56,600 --> 00:38:59,760
Speaker 1: some doubt in the area, but is brilliant. Is Freeman

700
00:38:59,800 --> 00:39:02,960
Speaker 1: Dice and Susan was He's not a climate scientist, right,

701
00:39:03,120 --> 00:39:05,960
Speaker 1: It's it tends to be people commenting outside their area

702
00:39:05,960 --> 00:39:08,640
Speaker 1: of expertise, and yet they still have the aura of

703
00:39:08,719 --> 00:39:12,880
Speaker 1: credibility because it's like, well, these are smart people, they're scientists, right. Uh,

704
00:39:13,000 --> 00:39:15,239
Speaker 1: So you know, you'll see a list of scientists who

705
00:39:15,239 --> 00:39:18,799
Speaker 1: don't accept the consensus on climate change, and they might

706
00:39:18,840 --> 00:39:21,640
Speaker 1: be like petroleum engineers and stuff like that. You know,

707
00:39:21,760 --> 00:39:25,120
Speaker 1: so it's like, not like petroleum engineers aren't smart. I mean,

708
00:39:25,719 --> 00:39:27,880
Speaker 1: I'm sure all all these people are very smart people.

709
00:39:27,920 --> 00:39:31,520
Speaker 1: But it's just that having scientific comprehension skills does not

710
00:39:31,760 --> 00:39:37,120
Speaker 1: protect you against arriving at malinformed, bad conclusions that support

711
00:39:37,160 --> 00:39:39,680
Speaker 1: your identity. Now, of course, one of the tools and

712
00:39:39,680 --> 00:39:43,560
Speaker 1: seconds toolkit I had to do with replication. Yes, uh

713
00:39:43,600 --> 00:39:45,960
Speaker 1: so that's always a big question. And in fact I

714
00:39:46,280 --> 00:39:48,840
Speaker 1: found one thing that I wanted to explore real quickly.

715
00:39:49,120 --> 00:39:52,120
Speaker 1: If you follow psychology research and you saw something about

716
00:39:52,160 --> 00:39:56,439
Speaker 1: motivated numerousy failing replication in a recent study, I think

717
00:39:56,480 --> 00:39:59,840
Speaker 1: that's probably a reference to a conference paper draft presented

718
00:39:59,880 --> 00:40:03,200
Speaker 1: an seventeen that claimed, as part of its findings to

719
00:40:03,360 --> 00:40:07,120
Speaker 1: fail to replicate the motivated numeracy effect. And then Dan

720
00:40:07,200 --> 00:40:09,840
Speaker 1: Kahan and Ellen Peters, two of the original authors of

721
00:40:09,840 --> 00:40:13,560
Speaker 1: the first paper we were talking about, in response, defended

722
00:40:13,600 --> 00:40:16,520
Speaker 1: their paper as best as I can tell, quite successfully

723
00:40:16,560 --> 00:40:19,440
Speaker 1: by pointing out that the study that failed to replicate

724
00:40:19,520 --> 00:40:23,160
Speaker 1: the motivated reasoning effect. Uh number one had a very

725
00:40:23,160 --> 00:40:27,280
Speaker 1: small sample size and fifty five and was ideologically homogeneous.

726
00:40:27,360 --> 00:40:31,800
Speaker 1: It was basically liberal, and in a paper called rumors

727
00:40:31,800 --> 00:40:35,040
Speaker 1: of the non replication of the motivated numeracy effect are

728
00:40:35,080 --> 00:40:39,719
Speaker 1: greatly exaggerated, uh Kahan and Peters. They so they they

729
00:40:39,840 --> 00:40:43,600
Speaker 1: argue against this supposed failed replication, and they also present

730
00:40:43,640 --> 00:40:47,080
Speaker 1: the results of their own replication attempt with a with

731
00:40:47,120 --> 00:40:51,319
Speaker 1: a sample size of fife, in which they did successfully

732
00:40:51,320 --> 00:40:54,879
Speaker 1: replicate the findings of the original very closely. And so

733
00:40:55,200 --> 00:40:58,200
Speaker 1: as far as I can tell, motivated numeracy through identity

734
00:40:58,239 --> 00:41:01,920
Speaker 1: through identity protective cognition and is still pretty solid. It

735
00:41:02,000 --> 00:41:04,320
Speaker 1: looks solid to me. And also, as far as I

736
00:41:04,320 --> 00:41:07,080
Speaker 1: can tell, that's not just me defending a cherished belief

737
00:41:07,160 --> 00:41:10,200
Speaker 1: that's important to my identity through motivated judgment, because in fact,

738
00:41:10,560 --> 00:41:15,040
Speaker 1: I find I strongly dislike the idea of identity protective cognition.

739
00:41:15,760 --> 00:41:18,080
Speaker 1: I think I would much rather live in the world

740
00:41:18,080 --> 00:41:21,160
Speaker 1: of so many of our anthropogenic climate change accepting peers,

741
00:41:21,600 --> 00:41:23,920
Speaker 1: and where you know, it's the world where if you

742
00:41:23,920 --> 00:41:27,960
Speaker 1: could just educate people enough with better science literacy skills,

743
00:41:28,000 --> 00:41:31,560
Speaker 1: these dead end public disputes over pretty solid empirical science

744
00:41:31,600 --> 00:41:34,920
Speaker 1: could be resolved. What mean you could essentially win an

745
00:41:35,000 --> 00:41:39,440
Speaker 1: argument over these issues by presenting facts, presenting data, And

746
00:41:39,480 --> 00:41:41,480
Speaker 1: that's how a lot of these you know, like science

747
00:41:41,640 --> 00:41:44,360
Speaker 1: people want it to be like that, right, sciencey people

748
00:41:44,440 --> 00:41:46,560
Speaker 1: want to say, well I can, I'll just bring more

749
00:41:46,600 --> 00:41:49,600
Speaker 1: evidence you. I'll show up with even more references next time,

750
00:41:49,680 --> 00:41:53,160
Speaker 1: and that'll get them. But I'm afraid the evidence seems

751
00:41:53,160 --> 00:41:56,120
Speaker 1: to be coming in that it doesn't necessarily work that way.

752
00:41:56,120 --> 00:41:58,640
Speaker 1: And maybe, and you know, we shouldn't be all or

753
00:41:58,680 --> 00:42:01,000
Speaker 1: nothing in the way we talked about things. Different different

754
00:42:01,040 --> 00:42:04,160
Speaker 1: types of appeals will work with different people, but on average,

755
00:42:04,440 --> 00:42:07,680
Speaker 1: that does not appear to be how people work. All right, Well,

756
00:42:07,719 --> 00:42:09,120
Speaker 1: on that note, we're going to take a break, and

757
00:42:09,160 --> 00:42:11,359
Speaker 1: when we come back, we're gonna expand on the the

758
00:42:11,560 --> 00:42:14,480
Speaker 1: concept a little bit and talk about what can possibly

759
00:42:14,520 --> 00:42:20,520
Speaker 1: be done and talk about Scott Steiner. Oh, yes, thank alright,

760
00:42:20,520 --> 00:42:23,000
Speaker 1: we're back. So, Joe, were you familiar with the Scott

761
00:42:23,040 --> 00:42:25,640
Speaker 1: Steiner before I mentioned him to you? I was not

762
00:42:25,719 --> 00:42:29,399
Speaker 1: tremendously familiar, But you sent me the best video I've

763
00:42:29,400 --> 00:42:32,680
Speaker 1: seen all week. Yes, so this, uh, this was a

764
00:42:32,800 --> 00:42:35,759
Speaker 1: video and this is readily available online because it it

765
00:42:36,400 --> 00:42:39,560
Speaker 1: kind of went viral and became its own meme. But yeah,

766
00:42:39,560 --> 00:42:43,440
Speaker 1: it's a video of professional wrestler Scott Steiner, a k a.

767
00:42:43,560 --> 00:42:46,840
Speaker 1: Big Papa pump Um. Okay, yeah, well I think I

768
00:42:46,920 --> 00:42:48,919
Speaker 1: knew him better by that name. Yeah that was Yeah,

769
00:42:48,920 --> 00:42:51,560
Speaker 1: that was a moniker he adopted at one point. Uh

770
00:42:51,560 --> 00:42:54,680
Speaker 1: and it's This is a clip from a wrestling promotion

771
00:42:54,719 --> 00:42:56,640
Speaker 1: that was known in TOO in two thousand eight is

772
00:42:56,680 --> 00:42:59,480
Speaker 1: t n A. The promotion is now called Impact, and

773
00:43:00,000 --> 00:43:03,120
Speaker 1: Miner launched into a backstage promo that, in typical pro

774
00:43:03,160 --> 00:43:07,120
Speaker 1: wrestling fashion, is all shouty and laced in macho pravada,

775
00:43:08,239 --> 00:43:12,759
Speaker 1: but in a twist, it's also full of math and statistics.

776
00:43:12,800 --> 00:43:16,040
Speaker 1: So he makes the rigorous yes yes, and in this

777
00:43:16,080 --> 00:43:18,520
Speaker 1: particular promo he makes the following claims, I'm just gonna

778
00:43:18,560 --> 00:43:22,040
Speaker 1: roll through these in a normal human voice. Okay. So

779
00:43:22,040 --> 00:43:24,440
Speaker 1: he points out that normally a wrestler has a fifty

780
00:43:24,520 --> 00:43:29,160
Speaker 1: chance of winning a match, all else being equal share Okay, yeah,

781
00:43:29,200 --> 00:43:33,800
Speaker 1: but given his uh Big Papa pump superior genetics um,

782
00:43:33,840 --> 00:43:37,520
Speaker 1: his opponent Samoa Joe only has a chance of winning.

783
00:43:38,600 --> 00:43:40,319
Speaker 1: But it's a three way match as well, and it

784
00:43:40,360 --> 00:43:44,120
Speaker 1: involves Kurt Angle so each participant here has a thirty

785
00:43:44,160 --> 00:43:47,160
Speaker 1: three and a third percent chance of winning, but he

786
00:43:47,320 --> 00:43:50,920
Speaker 1: but since Kurt Angle, according to to Steiner, knows that

787
00:43:51,000 --> 00:43:55,160
Speaker 1: he cannot win, he won't try. Uh So Steiner presses

788
00:43:55,160 --> 00:43:58,719
Speaker 1: the following point quote, So, Samoa Joe, you take your

789
00:43:58,760 --> 00:44:00,920
Speaker 1: thirty three and one third chance, it's minus my twenty

790
00:44:01,160 --> 00:44:04,120
Speaker 1: percent chance, and you have an eight and one third

791
00:44:04,200 --> 00:44:07,200
Speaker 1: chance of winning at Sacrifice, Sacrifice being the name of

792
00:44:07,239 --> 00:44:10,800
Speaker 1: the pro wrestling event. But when you take my seventy

793
00:44:10,800 --> 00:44:13,319
Speaker 1: five percent chance of winning, if we were to go

794
00:44:13,400 --> 00:44:16,319
Speaker 1: one on one and then add sixty six and two

795
00:44:16,400 --> 00:44:20,080
Speaker 1: thirds per cents, I got one and forty one and

796
00:44:20,120 --> 00:44:23,960
Speaker 1: two thirds chance of winning at Sacrifice. See Samoa Joe.

797
00:44:24,000 --> 00:44:26,799
Speaker 1: The numbers don't lie, and they spelled disaster for you

798
00:44:27,040 --> 00:44:31,160
Speaker 1: at Sacrifice. Did you watch Sacrifice? Were you there? I

799
00:44:31,200 --> 00:44:33,279
Speaker 1: did not. I was not there. I did to watch

800
00:44:33,320 --> 00:44:35,200
Speaker 1: some clips from it. Looks like it was, you know,

801
00:44:35,280 --> 00:44:40,720
Speaker 1: pretty hard hitting match. Interestingly enough, um Samoa Joe. One

802
00:44:41,040 --> 00:44:44,440
Speaker 1: oh Man. However, Kurt Angle was injured and had to

803
00:44:44,440 --> 00:44:46,560
Speaker 1: be replaced by another wrestler, so one assumes that that

804
00:44:46,560 --> 00:44:50,440
Speaker 1: would have changed the equation somewhat. Despite having a negative

805
00:44:50,480 --> 00:44:55,239
Speaker 1: forty one chance of winning one. So um, yeah, this,

806
00:44:55,480 --> 00:44:59,239
Speaker 1: but as Steiner says, the numbers don't lie or do that?

807
00:44:59,760 --> 00:45:03,320
Speaker 1: Is this admittedly ridiculous example. Is this is this Scott

808
00:45:03,360 --> 00:45:07,360
Speaker 1: Steiner falling prey to a lack of understanding regarding numeracy

809
00:45:07,480 --> 00:45:10,360
Speaker 1: or is it motivated numeracy? Is he just so highly

810
00:45:10,400 --> 00:45:13,680
Speaker 1: motivated by his dislike of Samoa Joe and his belief

811
00:45:13,719 --> 00:45:16,439
Speaker 1: in his own superior genetics that he just so uh

812
00:45:16,480 --> 00:45:19,520
Speaker 1: you know, readily mishandles them. Uh. That might be a

813
00:45:19,560 --> 00:45:23,680
Speaker 1: better example of a mathematical incarnation of the Dunning Krueger effect.

814
00:45:23,960 --> 00:45:26,480
Speaker 1: That's sure. But this is where you believe that you

815
00:45:26,520 --> 00:45:31,200
Speaker 1: have more fluency in a particular area than you actually do. Yes,

816
00:45:31,280 --> 00:45:33,840
Speaker 1: because the we we should, we should get into it

817
00:45:33,840 --> 00:45:36,080
Speaker 1: at one time, the Dunning Kruger effect, because there's a

818
00:45:36,320 --> 00:45:39,040
Speaker 1: I know, there is a more nuanced understanding of it

819
00:45:39,080 --> 00:45:41,920
Speaker 1: than you usually see when it's deployed in the media

820
00:45:41,960 --> 00:45:44,160
Speaker 1: and stuff. But the basic idea is that with the

821
00:45:44,239 --> 00:45:47,600
Speaker 1: Dunning Krueger effect, if you are not very good within

822
00:45:47,640 --> 00:45:50,760
Speaker 1: a skill set or within a knowledge domain, you also

823
00:45:50,880 --> 00:45:55,239
Speaker 1: lack the meta cognitive capacities to understand what would make

824
00:45:55,320 --> 00:45:58,719
Speaker 1: somebody good at it. Thus you fail to grasp your

825
00:45:58,760 --> 00:46:02,680
Speaker 1: own shortcomings. And thus people who are very low skilled

826
00:46:02,760 --> 00:46:05,680
Speaker 1: or very low knowledge in a certain domain tend to

827
00:46:05,960 --> 00:46:09,680
Speaker 1: vastly overestimate their skills or their knowledge because they can't

828
00:46:09,800 --> 00:46:12,480
Speaker 1: know they can't know what they don't know. All right, Well,

829
00:46:12,520 --> 00:46:15,920
Speaker 1: I realized that this example was was maybe more entertaining

830
00:46:15,960 --> 00:46:19,520
Speaker 1: than helpful. Still my only opportunity to really work Scott

831
00:46:19,560 --> 00:46:22,000
Speaker 1: Steiner into an episode. Come on, we've been plowing through

832
00:46:22,040 --> 00:46:24,400
Speaker 1: a psychology paper. We've gotta have a little wrestling to

833
00:46:24,560 --> 00:46:27,160
Speaker 1: lighten the load. Alright, Well, well, now that we've lightened

834
00:46:27,480 --> 00:46:29,600
Speaker 1: the load, let's let's come back to like the big

835
00:46:29,719 --> 00:46:34,319
Speaker 1: remaining question you have? You have motivated numeracy? Uh is

836
00:46:34,400 --> 00:46:37,800
Speaker 1: the key thing that's happening here? If this is the

837
00:46:37,800 --> 00:46:41,440
Speaker 1: the enemy, the threat, then how do we deal with it? Yeah? Like?

838
00:46:41,480 --> 00:46:43,719
Speaker 1: What what can be done? And so? One thing I

839
00:46:43,719 --> 00:46:46,400
Speaker 1: would take away from this research is that good science

840
00:46:46,520 --> 00:46:51,440
Speaker 1: education and science communication are necessary, but not sufficient. Necessary

841
00:46:51,480 --> 00:46:55,600
Speaker 1: but not sufficient to produce a correctly informed citizen. Read

842
00:46:55,640 --> 00:46:59,720
Speaker 1: You can't have people making good judgments without understanding the facts.

843
00:47:00,120 --> 00:47:02,560
Speaker 1: But the better they understand the facts, the more they'll

844
00:47:02,680 --> 00:47:06,600
Speaker 1: use their understanding to support their identity derived point of view.

845
00:47:07,120 --> 00:47:09,839
Speaker 1: So Kahan and others proposed that the way to beat

846
00:47:09,920 --> 00:47:14,239
Speaker 1: motivated reasoning is not necessarily to improve the reasoning, but

847
00:47:14,320 --> 00:47:19,239
Speaker 1: to remove the motivation. To remove the motivation, I like that.

848
00:47:19,239 --> 00:47:23,000
Speaker 1: That reminds me so much of Krishna's words to Arginna

849
00:47:23,080 --> 00:47:27,480
Speaker 1: in the Hindu epic the Baka bad Ghita. Uh yeah, yeah,

850
00:47:27,520 --> 00:47:29,719
Speaker 1: if if if I may, I'd like to read, you know,

851
00:47:29,760 --> 00:47:32,600
Speaker 1: because having come from the quoting Scott Steiner, I obviously

852
00:47:32,680 --> 00:47:35,440
Speaker 1: want to move on to the other high literature. Yes,

853
00:47:35,880 --> 00:47:38,520
Speaker 1: uh so this is these are the words of of Krishna,

854
00:47:39,560 --> 00:47:42,840
Speaker 1: that man alone is wise, who keeps the mastery of himself.

855
00:47:43,440 --> 00:47:48,160
Speaker 1: If one ponders on objects of the sense, there springs attraction.

856
00:47:48,440 --> 00:47:53,800
Speaker 1: From attraction grows desire, Desire flames to fierce passion, passion

857
00:47:53,880 --> 00:47:58,920
Speaker 1: breeds recklessness. Then the memory all betrayed. Lets noble purpose

858
00:47:59,040 --> 00:48:02,560
Speaker 1: go and say apps the mind. Until purpose, mind and

859
00:48:02,640 --> 00:48:06,760
Speaker 1: man are all undone. But if one deals with objects

860
00:48:06,800 --> 00:48:10,160
Speaker 1: of the sense, not loving and not hating, making them

861
00:48:10,200 --> 00:48:14,399
Speaker 1: serve his free soul, which rests serenely Lord Low, such

862
00:48:14,440 --> 00:48:18,480
Speaker 1: a man comes to tranquility, and out of that tranquility

863
00:48:18,560 --> 00:48:22,239
Speaker 1: shall rise the end and healing of his earthly pains.

864
00:48:22,280 --> 00:48:25,839
Speaker 1: Since the will governed sets the soul at peace. I'd

865
00:48:25,840 --> 00:48:28,160
Speaker 1: say the will governed as much asier said than done,

866
00:48:28,200 --> 00:48:30,480
Speaker 1: isn't it. Oh yeah, I mean that's why we've clearly

867
00:48:30,480 --> 00:48:32,279
Speaker 1: we're still struggling with it. And uh, you know, and

868
00:48:32,320 --> 00:48:33,960
Speaker 1: I don't want to you know, obviously this is a

869
00:48:34,160 --> 00:48:38,319
Speaker 1: this is the work of immense literary significance and in

870
00:48:38,360 --> 00:48:41,280
Speaker 1: deep philosophy. But but yeah, this idea of of acting

871
00:48:41,280 --> 00:48:45,480
Speaker 1: without passion seems to to line up reasonably well with

872
00:48:45,560 --> 00:48:50,680
Speaker 1: this idea of tackling various um uh you know, innumerable

873
00:48:50,760 --> 00:48:55,680
Speaker 1: um problems without bringing in this political motivation. Yeah. Though,

874
00:48:55,680 --> 00:48:58,600
Speaker 1: of course it seems very unfortunate that I think a

875
00:48:58,600 --> 00:49:02,000
Speaker 1: lot of this motivation comes in unconsciously, right, because I mean,

876
00:49:02,040 --> 00:49:04,160
Speaker 1: we we I guess we haven't really addressed this so far.

877
00:49:04,239 --> 00:49:07,360
Speaker 1: But you have to assume that people are not generally

878
00:49:07,400 --> 00:49:09,480
Speaker 1: and you probably know from your own experience at least

879
00:49:09,480 --> 00:49:12,600
Speaker 1: if it's like mine, they're not generally thinking like, Okay,

880
00:49:12,640 --> 00:49:15,520
Speaker 1: how should I trick myself right now to come to

881
00:49:15,560 --> 00:49:19,279
Speaker 1: the wrong conclusion because it would be socially acceptable. It

882
00:49:19,320 --> 00:49:22,239
Speaker 1: doesn't feel like that to think about political issues that

883
00:49:22,520 --> 00:49:26,160
Speaker 1: you know, are empirical issues that are politically relevant. Um,

884
00:49:26,200 --> 00:49:28,120
Speaker 1: it just feels like, well, I'm just trying to figure

885
00:49:28,120 --> 00:49:30,360
Speaker 1: out what's right, but obviously I must be doing this

886
00:49:30,440 --> 00:49:33,320
Speaker 1: at least sometimes. Yeah, we're just kind of we're often

887
00:49:33,480 --> 00:49:36,080
Speaker 1: just we're swimming through life. We're not necessarily thinking about

888
00:49:36,080 --> 00:49:38,920
Speaker 1: the individual strokes. You know, it all kind of comes

889
00:49:38,960 --> 00:49:41,600
Speaker 1: together and we end up making these mistakes and cognition

890
00:49:41,840 --> 00:49:44,320
Speaker 1: and to reemphasize what the authors of that original paper

891
00:49:44,360 --> 00:49:48,640
Speaker 1: we're talking about, I mean, in a way, this is rational.

892
00:49:48,719 --> 00:49:51,120
Speaker 1: It's rational in a perverse way. Not in a good

893
00:49:51,120 --> 00:49:53,560
Speaker 1: way that ultimately creates the most benefit, but in a

894
00:49:53,640 --> 00:49:56,640
Speaker 1: kind of short term perversity. It is rational. Like you

895
00:49:56,680 --> 00:50:00,759
Speaker 1: will sometimes hear people talking about or lament in politics,

896
00:50:00,760 --> 00:50:03,480
Speaker 1: how others just won't do what's rational. But given a

897
00:50:03,520 --> 00:50:08,239
Speaker 1: certain interpretation of rational self interest, this irrational relationship with

898
00:50:08,280 --> 00:50:12,600
Speaker 1: empirical questions makes perfect sense. The author's right quote what

899
00:50:12,719 --> 00:50:15,840
Speaker 1: any individual member of the public thinks about the reality

900
00:50:15,840 --> 00:50:19,360
Speaker 1: of climate change, the hazards of nuclear waste disposal, the

901
00:50:19,400 --> 00:50:23,560
Speaker 1: efficacy of gun control is too inconsequential to influence the

902
00:50:23,680 --> 00:50:26,520
Speaker 1: risk that that person, or anyone he or she cares

903
00:50:26,560 --> 00:50:30,960
Speaker 1: about faces. Nevertheless, given what positions on these issues signify

904
00:50:31,000 --> 00:50:34,800
Speaker 1: about a person's defining commitments, forming a belief at odds

905
00:50:34,840 --> 00:50:38,080
Speaker 1: with the one that predominates on it within important affinity

906
00:50:38,120 --> 00:50:40,839
Speaker 1: groups of which such a person as a member could

907
00:50:40,880 --> 00:50:45,879
Speaker 1: expose him or her to an array of highly unpleasant consequences. Thus, like,

908
00:50:46,560 --> 00:50:50,640
Speaker 1: we know that it's radically consequential, what in general public

909
00:50:50,680 --> 00:50:54,160
Speaker 1: policy is about climate change or gun policy or something.

910
00:50:54,200 --> 00:50:57,840
Speaker 1: You know, these are hugely important questions, but the impact

911
00:50:57,960 --> 00:51:02,439
Speaker 1: of one individual person his opinion feel small enough that

912
00:51:02,600 --> 00:51:07,160
Speaker 1: you basically the consequences of that are almost irrelevant. It's like,

913
00:51:07,239 --> 00:51:09,799
Speaker 1: what's really relevant is how is this affecting me in

914
00:51:09,800 --> 00:51:11,920
Speaker 1: my day to day? And now it's primarily affecting you

915
00:51:11,960 --> 00:51:14,520
Speaker 1: in your day to day? Is the social consequences of

916
00:51:14,560 --> 00:51:18,120
Speaker 1: the beliefs you express? But obviously that's not what we want, right, Like,

917
00:51:18,280 --> 00:51:22,400
Speaker 1: we want everybody making rational decisions, having correct empirical information

918
00:51:22,440 --> 00:51:25,000
Speaker 1: to reason from. Of course they're still gonna argue about

919
00:51:25,000 --> 00:51:28,319
Speaker 1: political values, but at least having everybody except the same

920
00:51:28,400 --> 00:51:32,640
Speaker 1: set of correct facts when correct facts are on the table, right,

921
00:51:32,719 --> 00:51:34,359
Speaker 1: I mean, a lot of it comes kind of comes

922
00:51:34,400 --> 00:51:36,840
Speaker 1: down to the fact that we are a short sighted

923
00:51:36,880 --> 00:51:41,360
Speaker 1: species that can, you know, barely see beyond our own horizon.

924
00:51:41,440 --> 00:51:44,120
Speaker 1: But but we are attempting to see beyond that arizon.

925
00:51:44,160 --> 00:51:48,160
Speaker 1: We are trying to to to maintain a world or

926
00:51:48,160 --> 00:51:51,120
Speaker 1: create a world that can be sustained in some fashion.

927
00:51:51,200 --> 00:51:53,439
Speaker 1: We you know that the the old addage, of course,

928
00:51:53,520 --> 00:51:58,040
Speaker 1: is making thinking about your children and your grandchildren when

929
00:51:58,120 --> 00:52:02,480
Speaker 1: when you're making decisions such as ease. But historically it's

930
00:52:02,520 --> 00:52:05,399
Speaker 1: not the sort of thing that we're great at as

931
00:52:05,440 --> 00:52:08,480
Speaker 1: a species. And yeah, and so it's clearly not enough

932
00:52:08,600 --> 00:52:11,160
Speaker 1: just to tell people like, well, here's a problem with

933
00:52:11,200 --> 00:52:14,840
Speaker 1: how you're probably thinking. You're probably doing identity protective cognition,

934
00:52:14,840 --> 00:52:17,120
Speaker 1: and you need to stop it. You know that that

935
00:52:17,280 --> 00:52:19,840
Speaker 1: that's just obviously not going to work as just asking

936
00:52:19,840 --> 00:52:22,520
Speaker 1: somebody to shut their mind their ears off, like like, oh, yeah,

937
00:52:22,520 --> 00:52:24,600
Speaker 1: they're really going to listen to you now, buddy. Yeah,

938
00:52:24,640 --> 00:52:26,600
Speaker 1: I mean, and they're they're probably not even doing it

939
00:52:26,640 --> 00:52:29,000
Speaker 1: on purpose, right, I mean, you and I are doing

940
00:52:29,000 --> 00:52:31,759
Speaker 1: it sometimes, we're not doing it on purpose. The people

941
00:52:31,800 --> 00:52:34,200
Speaker 1: who do this, they're not doing it out of a

942
00:52:34,600 --> 00:52:38,120
Speaker 1: will to deceive themselves. Is just happening as part of

943
00:52:38,160 --> 00:52:41,880
Speaker 1: what the brain does, even unconsciously. So the question is,

944
00:52:41,880 --> 00:52:45,600
Speaker 1: could you do something external? Could you create a state

945
00:52:45,640 --> 00:52:48,880
Speaker 1: of affairs that would change the incentive structure? Do what

946
00:52:48,960 --> 00:52:52,319
Speaker 1: the author said and somehow change the motivation. If you

947
00:52:52,360 --> 00:52:55,520
Speaker 1: can't change the reasoning and motivated reasoning, maybe you can

948
00:52:55,600 --> 00:52:59,120
Speaker 1: change the motivation and motivated reasoning. So here's one thing

949
00:52:59,160 --> 00:53:05,000
Speaker 1: I'm thinking about. Most politically relevant. Numeracy is basically recreational, right,

950
00:53:05,360 --> 00:53:07,560
Speaker 1: Like you need to get the numbers right when you're

951
00:53:07,600 --> 00:53:10,600
Speaker 1: calculating your bank balance. But if you get the numbers

952
00:53:10,600 --> 00:53:13,280
Speaker 1: wrong when you're talking about gun control or climate change,

953
00:53:13,560 --> 00:53:17,279
Speaker 1: there's no immediately detectable consequence to you, as long as

954
00:53:17,320 --> 00:53:19,360
Speaker 1: you get them wrong in the way that your social

955
00:53:19,400 --> 00:53:22,680
Speaker 1: group approves of. And this is not true of every

956
00:53:22,719 --> 00:53:26,600
Speaker 1: person in every context. For example, why does scientists working

957
00:53:26,680 --> 00:53:30,560
Speaker 1: within their own fields UH tend usually to get the

958
00:53:30,640 --> 00:53:34,040
Speaker 1: numbers right? Of course, not always, but usually, like, regardless

959
00:53:34,080 --> 00:53:37,360
Speaker 1: of whatever their political opinions are, if they're doing work

960
00:53:37,440 --> 00:53:40,120
Speaker 1: within their field, they tend to get it right most

961
00:53:40,160 --> 00:53:43,520
Speaker 1: of the time. Well, because they're gonna be other scientists

962
00:53:43,560 --> 00:53:46,480
Speaker 1: that are going to be attempting to UH to perform

963
00:53:46,480 --> 00:53:49,000
Speaker 1: the same experiment to see if they get the same results.

964
00:53:49,040 --> 00:53:51,040
Speaker 1: They're gonna be people reading it, and if they see

965
00:53:51,080 --> 00:53:53,239
Speaker 1: the error, they are going to they are going to

966
00:53:53,280 --> 00:53:56,200
Speaker 1: correct them on it. I mean, that's part of the process. Yeah,

967
00:53:56,239 --> 00:53:59,280
Speaker 1: there's a strong incentive to get the numbers right. Failed

968
00:53:59,360 --> 00:54:03,080
Speaker 1: numeracy in your own published research is potentially a major

969
00:54:03,120 --> 00:54:06,040
Speaker 1: blow to your credibility, to your career, to your standing

970
00:54:06,080 --> 00:54:09,000
Speaker 1: among your professional peers and stuff. So I wonder if

971
00:54:09,040 --> 00:54:13,480
Speaker 1: it's possible to change the incentive structure for non scientists

972
00:54:13,480 --> 00:54:16,160
Speaker 1: to somehow be more like that. This might be just

973
00:54:16,200 --> 00:54:19,680
Speaker 1: completely impossible fantasy, but is there a way you could

974
00:54:19,680 --> 00:54:22,960
Speaker 1: make it so that getting the factually correct answer is

975
00:54:23,040 --> 00:54:26,920
Speaker 1: incentivized in and in the social situations of lay people,

976
00:54:27,239 --> 00:54:30,480
Speaker 1: and arriving at conclusions in agreement with your social group

977
00:54:30,560 --> 00:54:34,000
Speaker 1: is not especially incentivized that maybe is that just a

978
00:54:34,040 --> 00:54:37,440
Speaker 1: totally unrealistic hope. Can human nature change that much? And

979
00:54:37,560 --> 00:54:39,839
Speaker 1: it does sound kind of daunting, like like what kind

980
00:54:39,840 --> 00:54:43,719
Speaker 1: of structure or system would enforce that? And then how

981
00:54:43,760 --> 00:54:45,759
Speaker 1: does it know? How do you roll it out successfully?

982
00:54:45,840 --> 00:54:49,040
Speaker 1: I'm some I'm sure some tech billionaire has some kind

983
00:54:49,080 --> 00:54:51,400
Speaker 1: of nightmaresh idea for an app that would do that,

984
00:54:51,440 --> 00:54:54,879
Speaker 1: but in fact we just destroy everything. They're all sorts

985
00:54:54,920 --> 00:54:58,359
Speaker 1: of sort of black mirror esque solutions that come to mind,

986
00:54:58,440 --> 00:55:00,600
Speaker 1: but they all have like a black mirror s twist

987
00:55:00,640 --> 00:55:02,680
Speaker 1: where you can see how it would screw things up,

988
00:55:02,800 --> 00:55:06,000
Speaker 1: or where people would essentially rebel against it and say, like,

989
00:55:06,040 --> 00:55:09,280
Speaker 1: you know what, I don't I don't really want Facebook

990
00:55:09,360 --> 00:55:12,520
Speaker 1: or Twitter or what have you coming along and calling

991
00:55:12,600 --> 00:55:15,360
Speaker 1: me on things that I've said that we're incorrect in

992
00:55:15,360 --> 00:55:18,200
Speaker 1: the past. Maybe about just why my account instead suffering

993
00:55:18,239 --> 00:55:22,799
Speaker 1: that embarrassment. Yeah, okay, here's another idea. Maybe some way

994
00:55:22,840 --> 00:55:27,160
Speaker 1: to fight the motivation. Perhaps this social support networks and

995
00:55:27,239 --> 00:55:31,440
Speaker 1: structures that are not dependent on ideological agreement. Like if

996
00:55:31,480 --> 00:55:35,279
Speaker 1: people really strongly felt confident that their friendships and their

997
00:55:35,320 --> 00:55:39,000
Speaker 1: work and family relationships were safe and would not suffer

998
00:55:39,040 --> 00:55:42,720
Speaker 1: at all no degree of alienation or weakening of relationships

999
00:55:42,760 --> 00:55:47,120
Speaker 1: from disagreement over political issues, maybe that would remove the incentive.

1000
00:55:47,360 --> 00:55:49,200
Speaker 1: Does that make sense? Like if people felt that they

1001
00:55:49,200 --> 00:55:52,240
Speaker 1: could disagree with their social group and not not risk

1002
00:55:52,280 --> 00:55:55,480
Speaker 1: anything by doing that, then there would so no longer

1003
00:55:55,600 --> 00:56:00,480
Speaker 1: be a protective motivation in what beliefs you whold so

1004
00:56:00,480 --> 00:56:03,880
Speaker 1: you're saying, basically, make our the social groups, making they're

1005
00:56:03,920 --> 00:56:07,920
Speaker 1: more making them more open to free discussion, more accepting

1006
00:56:07,960 --> 00:56:10,600
Speaker 1: of disagreement. I guess. So, I mean that at least

1007
00:56:10,600 --> 00:56:14,480
Speaker 1: seems like a possibility. Um. And maybe the way, maybe

1008
00:56:14,480 --> 00:56:16,279
Speaker 1: one way of addressing that is not that you can

1009
00:56:16,320 --> 00:56:20,680
Speaker 1: really change the nature of people's family and friendship relationships

1010
00:56:20,719 --> 00:56:22,880
Speaker 1: like that all that much, but if you could have

1011
00:56:23,040 --> 00:56:27,000
Speaker 1: I don't know, uh, supplemental social dynamics like this may

1012
00:56:27,000 --> 00:56:32,440
Speaker 1: be one thing that community style groups like church congregations

1013
00:56:32,520 --> 00:56:35,839
Speaker 1: and things like that are useful for, and that they

1014
00:56:35,880 --> 00:56:38,400
Speaker 1: provide sort of like outside of the family and the

1015
00:56:38,440 --> 00:56:41,960
Speaker 1: small friend group, they provide like a backup social situation

1016
00:56:42,480 --> 00:56:46,280
Speaker 1: where you you can retreat if you are feeling down

1017
00:56:46,320 --> 00:56:48,480
Speaker 1: in your other relationships. Though not to say that no

1018
00:56:48,680 --> 00:56:53,279
Speaker 1: certain church congregations have ever made people feel alienated for disagreeing.

1019
00:56:53,360 --> 00:56:55,880
Speaker 1: Oh yeah, I mean, I guess the thing. But you know,

1020
00:56:55,880 --> 00:56:59,920
Speaker 1: I'm just saying, like supplemental social safety nets, I guess right. Well,

1021
00:57:00,000 --> 00:57:02,520
Speaker 1: I could see where different groups, I mean, different social

1022
00:57:02,520 --> 00:57:05,680
Speaker 1: groups can serve as the backup depending on what's happening

1023
00:57:05,680 --> 00:57:07,520
Speaker 1: in your life. I mean, I can imagine a scenario

1024
00:57:07,560 --> 00:57:11,520
Speaker 1: in which certainly a church could be the the fallback,

1025
00:57:12,000 --> 00:57:14,759
Speaker 1: but also scenarios in which work social group could be

1026
00:57:14,760 --> 00:57:17,720
Speaker 1: the fallback or just uh, you know, your your your

1027
00:57:17,720 --> 00:57:20,800
Speaker 1: home life, so your home, social your family can't times

1028
00:57:20,800 --> 00:57:22,920
Speaker 1: do the fall it. You know, Well, my friends are

1029
00:57:22,960 --> 00:57:24,720
Speaker 1: mad at me because of what I said about but

1030
00:57:24,760 --> 00:57:28,480
Speaker 1: at least ways, at least I'm doing okay work. Uh.

1031
00:57:29,240 --> 00:57:31,360
Speaker 1: I don't know. It's like one of the ideas, it seems,

1032
00:57:31,440 --> 00:57:32,760
Speaker 1: one of the ideas that comes to mind here is

1033
00:57:32,760 --> 00:57:36,640
Speaker 1: like you'd almost want to have just social groups that

1034
00:57:36,720 --> 00:57:41,800
Speaker 1: are more adherent to scientific insensus. I hate to come

1035
00:57:41,840 --> 00:57:46,560
Speaker 1: back to to that, but because ultimately you have if

1036
00:57:46,600 --> 00:57:49,040
Speaker 1: if that is not present in uh, in one of

1037
00:57:49,080 --> 00:57:51,680
Speaker 1: these social structures, I mean, it's there's going to be

1038
00:57:51,720 --> 00:57:56,440
Speaker 1: a high possibility that some other factor is going to

1039
00:57:56,600 --> 00:57:59,800
Speaker 1: be more pressing in the worldview. And certainly one sees

1040
00:58:00,240 --> 00:58:02,640
Speaker 1: in religious groups, I mean not all religious groups, but

1041
00:58:02,840 --> 00:58:06,680
Speaker 1: there are certainly religious groups out there, uh that have

1042
00:58:06,680 --> 00:58:11,320
Speaker 1: have beliefs that run very counter to scientific consensus. Now

1043
00:58:11,360 --> 00:58:13,560
Speaker 1: do they do so in a detrimental fashion? I mean

1044
00:58:13,640 --> 00:58:16,280
Speaker 1: that's it's going to depend Yeah, again, I don't. I mean,

1045
00:58:16,360 --> 00:58:19,240
Speaker 1: as with all these questions like Is there any way

1046
00:58:19,280 --> 00:58:22,720
Speaker 1: to actually engineer that or is that just impossible? Well, no,

1047
00:58:22,800 --> 00:58:24,720
Speaker 1: I think we have We need to create a new religion.

1048
00:58:24,720 --> 00:58:27,720
Speaker 1: That's what we're coming down to, you know. Yeah, the

1049
00:58:28,200 --> 00:58:32,160
Speaker 1: an open discussion science first, religion. Uh, they can just

1050
00:58:32,400 --> 00:58:36,720
Speaker 1: sweep across the sweep across the land from shore to

1051
00:58:36,800 --> 00:58:39,600
Speaker 1: shore and uh and and make a better world for

1052
00:58:39,640 --> 00:58:41,840
Speaker 1: the future. Well, I'll let you carry the croak of

1053
00:58:41,880 --> 00:58:45,720
Speaker 1: priests and profit on that one. But okay, here's maybe

1054
00:58:45,720 --> 00:58:48,960
Speaker 1: one more way another. Basically, I'm just offering different ways

1055
00:58:49,000 --> 00:58:51,400
Speaker 1: you could approach the motivation problem. I don't know of

1056
00:58:51,480 --> 00:58:54,320
Speaker 1: any specifics that you could create, But here's another way

1057
00:58:54,360 --> 00:58:56,840
Speaker 1: of approaching it. What if there is a way to

1058
00:58:57,440 --> 00:59:01,480
Speaker 1: shield facts from acquiring in the first place what Kahan

1059
00:59:01,600 --> 00:59:06,880
Speaker 1: and co authors call quote antagonistic cultural meanings. In other words,

1060
00:59:06,960 --> 00:59:10,200
Speaker 1: if you can't fix public and understanding by making people

1061
00:59:10,240 --> 00:59:13,919
Speaker 1: better at science comprehension, and you can't program people not

1062
00:59:14,000 --> 00:59:16,640
Speaker 1: to be incentivized first and foremost by a sense of

1063
00:59:16,680 --> 00:59:20,440
Speaker 1: partisan social belonging, maybe the best way to protect facts

1064
00:59:20,840 --> 00:59:23,560
Speaker 1: is to find a way to never let them become

1065
00:59:23,560 --> 00:59:27,600
Speaker 1: politically charged in the first place. If there's a if

1066
00:59:27,640 --> 00:59:29,880
Speaker 1: somebody could figure out a way to do that or

1067
00:59:29,920 --> 00:59:33,160
Speaker 1: at least lessen the probability that would happen. That also

1068
00:59:33,240 --> 00:59:35,760
Speaker 1: seems like a very useful thing, a good way to

1069
00:59:35,800 --> 00:59:38,480
Speaker 1: fight this problem. But it may also be impossible because

1070
00:59:38,720 --> 00:59:43,400
Speaker 1: there's again political incentive for people to politicize certain issues. Yeah,

1071
00:59:43,400 --> 00:59:45,840
Speaker 1: I believe Ka Kahan has definitely talked about this before.

1072
00:59:46,200 --> 00:59:48,560
Speaker 1: I believe he touched on the idea of of not

1073
00:59:48,680 --> 00:59:53,120
Speaker 1: necessarily like outright preventing, but like identifying when it is

1074
00:59:53,200 --> 00:59:57,320
Speaker 1: beginning to take place, and in finding ways to intervene

1075
00:59:57,400 --> 01:00:00,680
Speaker 1: and keep it from being so highly politici because it's like,

1076
01:00:00,680 --> 01:00:03,600
Speaker 1: you know, barnacles building up on a ship or something, right, Yes,

1077
01:00:04,240 --> 01:00:06,840
Speaker 1: like when you detect and maybe you have a process

1078
01:00:06,920 --> 01:00:10,720
Speaker 1: for when you detect that a an empirical scientific question

1079
01:00:10,840 --> 01:00:14,960
Speaker 1: is starting to become an issue of political significance suddenly.

1080
01:00:15,000 --> 01:00:17,400
Speaker 1: What you want is to get all the politicians and

1081
01:00:17,480 --> 01:00:21,400
Speaker 1: political actors to stop talking about it immediately and instead

1082
01:00:21,480 --> 01:00:26,360
Speaker 1: get politically neutral celebrities and spokespeople and stuff to talk

1083
01:00:26,360 --> 01:00:29,760
Speaker 1: about it. Yeah. I feel like that's a pretty good idea.

1084
01:00:29,800 --> 01:00:32,080
Speaker 1: I think it probably has a thirty three and one

1085
01:00:32,120 --> 01:00:34,680
Speaker 1: third percent chance of success. But if you add that

1086
01:00:34,760 --> 01:00:37,640
Speaker 1: to the forty six and one half percent chance, then

1087
01:00:37,640 --> 01:00:39,960
Speaker 1: you're really getting steinorific. Yeah, you might get up to

1088
01:00:41,840 --> 01:00:46,280
Speaker 1: chance of winning. You know. One of the things that

1089
01:00:46,280 --> 01:00:48,840
Speaker 1: that can hunt it all right. In their paper that

1090
01:00:49,000 --> 01:00:52,720
Speaker 1: thought was really interesting is that they point out that people,

1091
01:00:52,840 --> 01:00:56,640
Speaker 1: even when experts in other fields are primarily as humans

1092
01:00:56,720 --> 01:01:01,320
Speaker 1: experts about quote, identifying who knows what about what? That

1093
01:01:01,440 --> 01:01:03,760
Speaker 1: sort of is the main way our brains work, right,

1094
01:01:03,840 --> 01:01:07,440
Speaker 1: That's like our primary capacity is figuring out who knows

1095
01:01:07,480 --> 01:01:10,480
Speaker 1: about what things? Right? Yeah, I mean to come back

1096
01:01:10,480 --> 01:01:12,760
Speaker 1: to Sagan's point of view, you know, it's it's it

1097
01:01:12,960 --> 01:01:15,400
Speaker 1: should be certainly less about trying to figure out who's

1098
01:01:15,400 --> 01:01:17,680
Speaker 1: the authority and just looking at who is the best

1099
01:01:17,680 --> 01:01:20,480
Speaker 1: and expert in a given field and being able to

1100
01:01:20,520 --> 01:01:23,400
Speaker 1: sort of weigh what they're saying and why they're saying it.

1101
01:01:23,560 --> 01:01:26,480
Speaker 1: But oftentimes we use this capacity of looking at who

1102
01:01:26,520 --> 01:01:29,000
Speaker 1: knows what about what not to figure out who has

1103
01:01:29,080 --> 01:01:32,320
Speaker 1: the real who's got the best expertise to offer? But

1104
01:01:32,920 --> 01:01:35,640
Speaker 1: with the best expertise is saying what I want to

1105
01:01:35,880 --> 01:01:38,880
Speaker 1: hear said exactly, Yes, who is saying what I want

1106
01:01:38,880 --> 01:01:41,680
Speaker 1: to hear said or what my social group believes in

1107
01:01:41,880 --> 01:01:44,320
Speaker 1: the best way, so I can say it the same

1108
01:01:44,360 --> 01:01:48,280
Speaker 1: way anyway, Eugenius is out there, who who can think

1109
01:01:48,360 --> 01:01:52,160
Speaker 1: of more specific and possibly effective ways to undercut the

1110
01:01:52,200 --> 01:01:57,960
Speaker 1: motivation part of motivated reasoning and uh, politically relevant empirical questions?

1111
01:01:58,400 --> 01:02:01,200
Speaker 1: Let us know what are those ideas you have? Indeed,

1112
01:02:01,200 --> 01:02:04,000
Speaker 1: this is one of those areas where this this hypothesis

1113
01:02:04,000 --> 01:02:06,440
Speaker 1: is so new I don't even think we probably have

1114
01:02:06,560 --> 01:02:09,640
Speaker 1: the science fiction to level at it. So you the listener,

1115
01:02:09,680 --> 01:02:13,240
Speaker 1: will be creating the science fiction uh that might in

1116
01:02:13,320 --> 01:02:16,520
Speaker 1: some way inform what we actually do about it. Yeah,

1117
01:02:16,520 --> 01:02:19,800
Speaker 1: and this whole field identity protective cognition in a way

1118
01:02:19,880 --> 01:02:23,080
Speaker 1: is still developing, so more research could change what seems

1119
01:02:23,120 --> 01:02:25,840
Speaker 1: to be true about it today. But I don't know.

1120
01:02:26,080 --> 01:02:28,000
Speaker 1: It's one of those where I feel like I'm very

1121
01:02:28,120 --> 01:02:32,000
Speaker 1: interested in this research, but it's not necessarily encouraging. I

1122
01:02:32,400 --> 01:02:35,120
Speaker 1: want to go back to the science comprehension thesis world.

1123
01:02:35,120 --> 01:02:37,520
Speaker 1: I want to live in the place where you can

1124
01:02:37,600 --> 01:02:40,040
Speaker 1: just where you can just tell people more, share more

1125
01:02:40,080 --> 01:02:44,040
Speaker 1: knowledge with more enthusiasm, model the correct kinds of critical

1126
01:02:44,080 --> 01:02:47,160
Speaker 1: thinking and all that and uh and bring people aboard.

1127
01:02:47,240 --> 01:02:49,440
Speaker 1: But it's just not that easy, is it, right? Or

1128
01:02:49,480 --> 01:02:52,280
Speaker 1: it's just not enough. I mean it kind of comes

1129
01:02:52,360 --> 01:02:55,320
Speaker 1: back though again to the GETA and and and other

1130
01:02:55,480 --> 01:02:59,120
Speaker 1: older works that taught about like self awareness, because that's

1131
01:02:59,200 --> 01:03:02,880
Speaker 1: ultimately what we're talking thing about is new ways to

1132
01:03:03,120 --> 01:03:06,520
Speaker 1: become aware of how our brains are working and how

1133
01:03:06,560 --> 01:03:09,360
Speaker 1: in some cases we our brains our minds are are

1134
01:03:09,440 --> 01:03:13,920
Speaker 1: tricking ourselves into um and clinging to beliefs that simply

1135
01:03:14,000 --> 01:03:16,640
Speaker 1: don't hold up. Yeah. Oh and one of the things,

1136
01:03:16,720 --> 01:03:18,720
Speaker 1: of course, we've always got to mention. We mentioned this

1137
01:03:18,840 --> 01:03:21,840
Speaker 1: and pretty much anytime we talk about bias or something,

1138
01:03:22,120 --> 01:03:24,520
Speaker 1: you're sitting out there thinking, right now, yeah, this is

1139
01:03:24,560 --> 01:03:29,000
Speaker 1: what other people do. Yeah, but it's we can all

1140
01:03:29,000 --> 01:03:32,240
Speaker 1: look to examples in our own lives. Big ones, small ones,

1141
01:03:32,520 --> 01:03:35,440
Speaker 1: Uh ones you you can't recognize and don't even know

1142
01:03:35,560 --> 01:03:40,360
Speaker 1: you do. Yeah, exactly, I got to remove that plank. Alright. Well,

1143
01:03:40,360 --> 01:03:42,040
Speaker 1: on that note, we're gonna go ahead and close out

1144
01:03:42,040 --> 01:03:45,320
Speaker 1: this episode. As always, head on over to stuff to

1145
01:03:45,320 --> 01:03:47,520
Speaker 1: Blow your Mind dot com because that is our mothership.

1146
01:03:47,560 --> 01:03:50,360
Speaker 1: That is where you'll find all the podcast episodes. You'll

1147
01:03:50,400 --> 01:03:53,400
Speaker 1: find links out there our various social media accounts. There's

1148
01:03:53,400 --> 01:03:55,320
Speaker 1: also a tab at the top of the page where

1149
01:03:55,320 --> 01:03:57,400
Speaker 1: you can go to our store and get all sorts

1150
01:03:57,440 --> 01:04:01,200
Speaker 1: of cool merchandise. Some of it shows specific like uh,

1151
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Speaker 1: the Great Basilisk or the Cambrian Life shirt. Other other

1152
01:04:05,200 --> 01:04:07,920
Speaker 1: stuff is just has to do with our logo. But

1153
01:04:07,960 --> 01:04:10,080
Speaker 1: it's a great way to support our show if you

1154
01:04:10,080 --> 01:04:11,800
Speaker 1: want to spend a few bucks and get something cool

1155
01:04:11,840 --> 01:04:14,520
Speaker 1: to stick on your laptop or your shirt. On the

1156
01:04:14,520 --> 01:04:16,000
Speaker 1: other hand, if you want to support the show without

1157
01:04:16,000 --> 01:04:19,480
Speaker 1: spending a dime, just rate and review the show wherever

1158
01:04:19,560 --> 01:04:21,800
Speaker 1: you have the power to do so. Thank you so

1159
01:04:21,880 --> 01:04:25,680
Speaker 1: much to our wonderful audio producers Alex Williams and Tarry Harrison.

1160
01:04:26,160 --> 01:04:27,960
Speaker 1: If you would like to get in touch with us

1161
01:04:28,000 --> 01:04:30,440
Speaker 1: with feedback on this episode or any other with your

1162
01:04:30,520 --> 01:04:33,960
Speaker 1: ideas of how to take the motivation out of motivated

1163
01:04:34,000 --> 01:04:37,600
Speaker 1: numeracy and motivated reasoning. If you want to let us

1164
01:04:37,600 --> 01:04:39,600
Speaker 1: know where you listen from, how you found out about

1165
01:04:39,600 --> 01:04:41,920
Speaker 1: the show, or suggest a topic for the future. If

1166
01:04:41,960 --> 01:04:44,560
Speaker 1: I didn't already say that, uh, either way, you can

1167
01:04:44,600 --> 01:04:47,840
Speaker 1: email us at blow the Mind at how stuff works

1168
01:04:48,120 --> 01:04:59,480
Speaker 1: dot com well more on this and thousands of other

1169
01:04:59,560 --> 01:05:12,520
Speaker 1: topics because it how stuff works dot Com. Then I

1170
01:05:12,600 --> 01:05:13,600
Speaker 1: think the biggest man