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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 Lamb and I'm Joe McCormick.

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Speaker 1: And today we're going to do an episode following up

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Speaker 1: on a panel Robert saw this year at the World

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Speaker 1: Science Festival in New York. So we're gonna talk about

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Speaker 1: topics having to do with bias, belief, public opinion, and

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Speaker 1: science communication. And I think we should start in some

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Speaker 1: territory that's sure to annoy at least a few listeners

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Speaker 1: right from the get go. So Robert, I got a

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Speaker 1: pop quiz for you. Don't look at the numbers. If

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Speaker 1: you had to guess how many Americans do you think

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Speaker 1: accept the scientific consensus on global warming? Oh, well, of

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Speaker 1: course that's a big one, because of course, when you

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Speaker 1: when you go to answer that question, you think about

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Speaker 1: your your immediate you know, sphere of influence and the

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Speaker 1: people you know, or perhaps you think of media representations

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Speaker 1: on the question. So it depends on on on on

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Speaker 1: the reporting, on the on the panels of experts you're

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Speaker 1: presented with. I would I would tend to just off

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Speaker 1: the top of my head, and I'd want to rate

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Speaker 1: it of people except the scientific consensus, you're not too

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Speaker 1: far off, but that's optimistic. Uh so. Gallup has been

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Speaker 1: tracking Americans beliefs about global warming for over a decade now,

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Speaker 1: and some of the questions they tend to ask people

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Speaker 1: are more subjective. It's things like do you worry a

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Speaker 1: great deal about global warming? And uh, intent of people

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Speaker 1: said yes. That's up from thirty seven percent in sixteen,

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Speaker 1: and up from thirty two percent in But technically, I mean,

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Speaker 1: it's worth pointing out that there's no objective fact about

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Speaker 1: whether you should be worried or not. Maybe you don't care.

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Speaker 1: Oh yeah, it comes down to worry and what what? What?

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Speaker 1: To what extent? Are you worrying a great deal about something? Right? Like,

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Speaker 1: you can you can realize something is a vital threat

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Speaker 1: to the human race. You can just not care anyway. Right,

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Speaker 1: you can say, well, if it happens, it happens, or

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Speaker 1: you know or what or well that's a problem for

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Speaker 1: the next generation to figure out. There are various ways

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Speaker 1: of calculating that question in your head. Right. It also

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Speaker 1: hinges on the word worry, like maybe you do care

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Speaker 1: about fighting climate change, but you wouldn't characterize your feeling

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Speaker 1: as worried. You're invigorated by the idea of trying to

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Speaker 1: do something about it. Right, Are you worried about it

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Speaker 1: versus do you think this is this is a problem

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Speaker 1: that government should work together too to address. Right. That

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Speaker 1: Some of the other questions have straightforwardly right or wrong answers.

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Speaker 1: For example, in seventy one percent of Americans said they

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Speaker 1: agree that most scientists believe global warming is occurring. Right,

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Speaker 1: So the question is, do you think most scientists believe

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Speaker 1: global warming is occurring? Se said yes. That's up from

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Speaker 1: sixty in sixteen and sixty, so there's there's a climb

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Speaker 1: in that number. More people are saying yes, I think

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Speaker 1: most scientists believe that the Earth is warming. There is

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Speaker 1: just an objective fact to the matter about whether most

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Speaker 1: scientists or most climate scientists believe the planet is warming.

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Speaker 1: They do. There's no debate about that. Now, what has

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Speaker 1: been reasonably debated is the exact figure of the agreement,

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Speaker 1: because it's not necessarily easy to calculate exactly what numbers

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Speaker 1: of scientists agree with a certain proposition. Right, Yeah, I mean,

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Speaker 1: if you if you just give yourself this assignment and

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Speaker 1: start hitting Wikipedia. Yeah, you're gonna find lists of scientists

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Speaker 1: who or either opponents or proponents. But then when you

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Speaker 1: start trying to peal back and figure out who these

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Speaker 1: people are and what their field of expertise are, it

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Speaker 1: just gets increasingly complicated. Right, But there are studies that

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Speaker 1: look into this. They try to impose a methodology and say, Okay,

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Speaker 1: what do scientists think or what has the published literature said. Now,

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Speaker 1: one study like this was published in two thousand nine

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Speaker 1: in EOS Transactions of the American Geophysical Union, Uh good

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Speaker 1: good professional publication title there, and it's called Examining the

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Speaker 1: Scientific Consensus on Climate Change. And so what they did

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Speaker 1: is they sent invitations out to more than ten thousand

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Speaker 1: Earth scientists, basically all of the geoscientists they could find

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Speaker 1: at universities and public research institutions, with two survey questions.

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Speaker 1: And these were the two questions. First question, when compared

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Speaker 1: with pre eighteen hundreds levels, do you think that mean

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Speaker 1: global temperatures have generally risen, fallen, or remained relatively constant?

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Speaker 1: And then the second question, do you think human activity

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Speaker 1: is a significant contributing factor in changing mean global temperatures?

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Speaker 1: So of the people they pinned with this survey, three thousand,

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Speaker 1: one forty six geoscientists responded. They said, that's about a

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Speaker 1: standard survey response rate over all of the earth sciences.

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Speaker 1: Nine of participants answered risen to the first question. Now,

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Speaker 1: that's geoscientists. That's people who study the Earth in anyway,

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Speaker 1: so geologist, ocean hydrologists, meteorologists, economic geologists. Um, what about

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Speaker 1: people who study the climate specifically? Well, Of the subset

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Speaker 1: of respondents who were experts in climate science and had

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Speaker 1: published more than half of their recent papers weighing in

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Speaker 1: on the subject of climate change, ninety six point two

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Speaker 1: percent or seventy six of seventy nine answered risen to

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Speaker 1: the first question. That's a high that's a high percentage

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Speaker 1: right there, right yeah. And there have been multiple other

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Speaker 1: studies that use different methodologies to ask slightly different questions,

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Speaker 1: but all of them have found overwhelming agreement among scientists

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Speaker 1: in general, and especially among climate scientists in particular, that

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Speaker 1: the planet is rapidly warming. So those seventy one percent

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Speaker 1: of Americans who say that most scientists believe global warming

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Speaker 1: is happening, they are factually correct. Those who disagreed are incorrect.

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Speaker 1: Though it is worth saying that a large share of

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Speaker 1: the people who didn't agree with that said they were unsure.

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Speaker 1: So if you're unsure, you're unsure. But if you if

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Speaker 1: you didn't agree with that, you are incorrect. But then,

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Speaker 1: of course, there you get into questions in the global

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Speaker 1: warming debate that are not quite as cut and dry

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Speaker 1: as whether the majority of scientists agree that the Earth

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Speaker 1: is warming. For example, what's causing the warming? Is global

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Speaker 1: warming caused by human activity primarily greenhouse gas emissions, or

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Speaker 1: by natural causes? Well, it shouldn't be surprising that there

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Speaker 1: have been plenty of attempts to study the opinion of

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Speaker 1: scientists on this question as well. So, for example, in

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Speaker 1: that same survey from two thousand nine we just mentioned

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Speaker 1: a d two percent of all Earth scientists said yes

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Speaker 1: that humans are a major contributing factor, and nine seven

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Speaker 1: point four percent of active climate researchers said yes. Again,

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Speaker 1: these are high percentages. If these were the experts telling

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Speaker 1: me that I should cut something out of my diet

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Speaker 1: or maybe, you know, make other some sort of major

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Speaker 1: change in my life, I would be seriously inclined to

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Speaker 1: listen to them. Okay, sure, but maybe maybe you say, well,

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Speaker 1: that's just one study, but has anybody else studied this?

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Speaker 1: Actually yes. So a commonly cited figure is from a

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Speaker 1: study in the journal Environmental Research Letters by John Cook

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Speaker 1: at All that what they did is looked at abstracts

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Speaker 1: of published papers on the subject. They looked at about

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Speaker 1: twelve thousand research papers published over the previous two decades,

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Speaker 1: and uh they found quote, sixty six point four percent

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Speaker 1: of abstracts expressed no position on anthropogenic global warming, meaning

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Speaker 1: human cause global warming, thirty two point six percent endorsed it,

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Speaker 1: and zero point seven percent rejected it, and zero point

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Speaker 1: three percent were uncertain about the cause of global warming.

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Speaker 1: So this means that among papers that expressed a view

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Speaker 1: on the cause of global warming, ninety seven point one

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Speaker 1: percent endorsed the consensus. Now, I want to just cut

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Speaker 1: in are real quick and say if it sounds like

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Speaker 1: we're just hitting you over the head with a bunch

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Speaker 1: of figures and numbers to drive home the fact that

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Speaker 1: climate change is caused by hum an activity, the vast

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Speaker 1: majority of this episode is dealing not with those facts,

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Speaker 1: but how we process those facts. Right, We just want

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Speaker 1: to establish clearly what the scientific consensus is beyond any

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Speaker 1: reasonable doubts. We will get to science, communication and public

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Speaker 1: consumption of the information in a bit right. Uh. So

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Speaker 1: you've got this Cook paper, and obviously there's a lot

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Speaker 1: of people in the general public don't agree with climate change.

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Speaker 1: So there's been plenty of criticisms of the cook papers methodology.

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Speaker 1: For example, in there was a Dutch born economist named

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Speaker 1: Richard Toll who criticized the Cook study and tried to

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Speaker 1: revise the estimates down. Uh. Toll is often cited as

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Speaker 1: a critic of the consensus on global warming. But even

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Speaker 1: when he revised the numbers down, he recrunched them and said, no,

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Speaker 1: actually it's not as high as they said. He found

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Speaker 1: nine one percent agreement instead of agreement. And I should

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Speaker 1: add that Cook also defended their original figure on in

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Speaker 1: a response article that attributed tolls lower figure of to

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Speaker 1: a math era one last study before we move on, Uh,

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Speaker 1: andreg at all expert credibility and Climate Change Proceedings of

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Speaker 1: the National Academy of Sciences two thousand ten. They used

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Speaker 1: a data set of one thousand, three seventy two climate

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Speaker 1: researchers to determine that of climate scientists in general were

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Speaker 1: convinced that human activity was the main cause of global

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Speaker 1: warming and of climate scientists who were actively publishing in

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Speaker 1: the field, between nine and percent of them agreed with

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Speaker 1: the findings of the Intergovernmental Panel on Climate Change, the

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Speaker 1: i p c C, which concluded that the main reason

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Speaker 1: the climate is changing is human activity, primarily greenhouse gas emissions.

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Speaker 1: So this is pretty unambiguous anyway you cut it. The

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Speaker 1: large majority of scientists, especially those that study the climate

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Speaker 1: directly and published in the field, agree that the Earth

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Speaker 1: is warming and that human activity is the main cause.

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Speaker 1: But let's go back to those gallop results. Only of

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Speaker 1: Americans agree with the objective fact that the majority of

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Speaker 1: scientists believe the Earth is warming, and only sixty eight

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Speaker 1: percent of Americans agree with the clear consensus that human

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Speaker 1: activity is a significant cause of this warming. And this

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Speaker 1: gap between the expert consensus in public opinion is sometimes

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Speaker 1: maddening to science and science communicators, Like if you are

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Speaker 1: a dissenting climate researcher and you've got some reason of

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Speaker 1: your own to disagree, sure, but why would non experts

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Speaker 1: like you and me disagree with the overwhelming majority of

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Speaker 1: people who know what they're talking about. And the ungenerous

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Speaker 1: question that comes out of frustration with this situation is

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Speaker 1: why don't they believe in science? And is that really

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Speaker 1: what's going on? So that's a question we wanted to

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Speaker 1: ask today is the city. Is the situation really that

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Speaker 1: they don't believe in science or is that there's something

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Speaker 1: particular to this issue where their ability to judge science

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Speaker 1: has been corrupted. Now, obviously you're listening to a science podcast,

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Speaker 1: so I'm we're not gonna try and belabor the point

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Speaker 1: here too much. But of course science, science is the

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Speaker 1: true path as a systematic exploration of the universe and

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Speaker 1: the properties that government science. I mean, this has allowed

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Speaker 1: humanity a path out of darkness, ignorance, and disease. It

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Speaker 1: underlies all the marvels of modern technology and provides us

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Speaker 1: with a chance for humanities a long term survival on

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Speaker 1: and potentially beyond Earth. I mean, if you listen to

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Speaker 1: the podcast, you know I'm not saying that that science

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Speaker 1: will answer all of life's questions, especially in teleological questions

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Speaker 1: related to him like why do I exist? What's my

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Speaker 1: purpose in life? Right? But if you're trying to answer

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Speaker 1: facts about the material universe with reliable accuracy, uh, I

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Speaker 1: mean we're science advocates around here, right, you have the

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Speaker 1: science you have the scientists, and here's the thing. For

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Speaker 1: the most part, we trust it, or at the very

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Speaker 1: least we claim to trust it. That's right. Is pointed

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Speaker 1: out in an often cited uh Pew research study from

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Speaker 1: two thousands sixteen. Americans in particular trust only the military

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Speaker 1: over scientists. So there's a whole ranking. Yeah. So they

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Speaker 1: repeatedly do this study where they'll put up public institutions

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Speaker 1: and they'll ask you how much trust you have in them,

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Speaker 1: like great deal of trust, a significant amount of trust,

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Speaker 1: not too much, exactly like that, And people will rank

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Speaker 1: these different institutions, including things like the media. You can

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Speaker 1: guess where that goes. But how does the ranking work out? Well,

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Speaker 1: based on this two thousand and sixteen ranking, it goes

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Speaker 1: as follows military at the top, then medical scientists, scientists

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Speaker 1: k through twelve principles, religious leaders, news, media, business, and

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Speaker 1: finally elected officials. Yeah. Now, one of the main people

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Speaker 1: we're gonna be talking about from this seventeen World Science

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Speaker 1: Festival panel, Dan Kahan, is what he made this point

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Speaker 1: percentages for trusting these groups a great deal, because, like

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Speaker 1: Uh So, the percentages for trusting these groups a great

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Speaker 1: deal range from thirty three for the military and twenty

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Speaker 1: four and twenty one for medical scientists and scientists, respectively,

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Speaker 1: down to a mere three percent for elected officials. Just

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Speaker 1: to let you know what politicians, Yeah, I don't know

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Speaker 1: why we don't trust them more. But but particularly noteworthy

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Speaker 1: and scientists. So, uh, that alone, I think it is

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Speaker 1: is telling about our overall cultural trust in scientific expertise.

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Speaker 1: And of course that's not been getting into the fact

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Speaker 1: also talking about military scientists. But that's that's kind of

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Speaker 1: a separate question. Yeah, and so that's interesting. Generally, people

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Speaker 1: say they put at least a fair amount of trust

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Speaker 1: and scientists. A lot of people put a great deal

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Speaker 1: of trust in scientists. People claim to believe in science,

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Speaker 1: people claim to say that scientists are working with the

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Speaker 1: public's best interests at heart. But there are particular issues

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Speaker 1: where for some reason, the public's understanding of the scientific

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Speaker 1: consensus gets very out of whack. Yeah. Yeah, we have

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Speaker 1: this polarizing effect, or seemingly polarized based on what we

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Speaker 1: see and what we hear in the media. That how

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Speaker 1: can we hold such high opinions of scientists and science

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Speaker 1: in general and disagree on the clear scientific consensus regarding,

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Speaker 1: for instance, human driven climate change? Why are so many

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Speaker 1: of us scared away by the prospect of Frankenstein food? Um?

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Speaker 1: And then other other topics, of course are vaccine safety,

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Speaker 1: uh for um, evolution and whether your kids should watch

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Speaker 1: Dinosaur Train? What does Dinosaur train? Dinosaur Trains a lovely

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Speaker 1: show by the Jim Hinson Company where dinosaurs travel in

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Speaker 1: time on a train through a wormhole and your child

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Speaker 1: memorizes all of these complex dinosaur names. That's wonderful. Yeah.

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Speaker 1: And then of course another big one, This is what

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Speaker 1: I hadn't really thought about much, but I understand you've

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Speaker 1: you've covered in the past for Harvard thinking. Yeah, is

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Speaker 1: the deep geological isolation of nuclear waste. Yeah, so, high

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Speaker 1: level radioactive waste. I mean, there's pretty clear scientific consensus

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Speaker 1: that the best thing we need to do with it

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Speaker 1: general public, even the educated public, people who say they're

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Speaker 1: into science, you you do not get the high levels

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Speaker 1: of agreement with the scientific consensus on this. And what

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Speaker 1: some of these UH things should indicate is, I mean,

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Speaker 1: it's no secret that the consensus on climate change has

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Speaker 1: been particularly identified with one half of the political spectrum,

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Speaker 1: at least in the United States. But this is not

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Speaker 1: to criticize conservatives, because there are plenty of these issues

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Speaker 1: where where. Apparently if you pull people, liberals are more

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Speaker 1: out of tune with the scientific consensus than conservatives are.

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Speaker 1: Like last time I saw more liberals were out of

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Speaker 1: tune with the scientific consensus on vaccine safety, namely that

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Speaker 1: they are safe uh than conservatives. Now we've already mentioned

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Speaker 1: that the the World Science Festival panel that I attended

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Speaker 1: UH and then you watched online, and then you too, listener,

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Speaker 1: can watch online. I'll include either a link or embedded

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Speaker 1: video of it on the landing page for this episode

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Speaker 1: Stuff to Abow your Mind dot com. But the title

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Speaker 1: the discussion was Science in a Polarized World. It was

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Speaker 1: moderated by author and journalist John don Van UH and

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Speaker 1: the panelist included astrophysicist France Cordova, physicist and World Science

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Speaker 1: Festival co founder Brian Greene, geneticist Sir Paul Nurse, and

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Speaker 1: most notably that one of the individuals we are probably

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Speaker 1: gonna spend the most time with here today, Ale law

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Speaker 1: professor and science communication expert Dan Kahan. He's the Elizabeth K.

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Speaker 1: Dollard Professor of Law and Professor of Psychology at Yale

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Speaker 1: Law School, and his primary research interests are risk perception,

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Speaker 1: science communication, and the application of decision science to law

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Speaker 1: and policy making. Yeah, so I was looking at some

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Speaker 1: of his research in preparing for this episode, and it's

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Speaker 1: an interesting thing he's doing. Obviously he's not the only

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Speaker 1: person doing it, but trying to apply, for example, psychology

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Speaker 1: Harvard Law Review that are saying, hey, you know, judges

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Speaker 1: should be aware that human brains tend to work like X, Y,

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Speaker 1: and Z like. For example, one thing that judges might

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Speaker 1: really benefit from being aware of is that research shows

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Speaker 1: that when you tell people to be rational and objective,

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Speaker 1: they do not become more rational and objective to get

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Speaker 1: more entrenched. Al Right. So in this discussion panel everybody.

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Speaker 1: He had some great commentary on on polarization regarding a

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Speaker 1: scientific consensus. Um Sir Paul Nurse was wonderful. Uh, Brian Green,

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Speaker 1: the physicist, it just was really fired up for this

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Speaker 1: one and it was just you know, enjoy to watch

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Speaker 1: and listen to. But one of the things that Brian

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Speaker 1: Green was saying in the panel was, you know, so

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Speaker 1: we've got this problem of polarization of public opinion on

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Speaker 1: scientific issues. There were some issues where the public, as

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Speaker 1: we talked about at the beginning of the episode, just

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Speaker 1: doesn't line up with what scientists are saying. Why is that?

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Speaker 1: And I think Brian Green was coming at it from

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Speaker 1: the point of view like, if we could just make

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Speaker 1: them understand science better, if we could just teach, we

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Speaker 1: could educate people better in the scientific process and what

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Speaker 1: science is about, then they would agree with the scientific consensus.

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Speaker 1: And Kahan had a really interesting response to this. I

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Speaker 1: think his answer was was, don't get ahead of yourself.

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Speaker 1: That's not necessarily the case, right, because I mean, because

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Speaker 1: Brian Green's question makes sense, right because you think, well,

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Speaker 1: you want to say, what do you realize that that

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Speaker 1: science is to quote Sir Paul Nurse, tentative knowledge that

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Speaker 1: we're it's it's not a complete, prepackaged understanding the universe.

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Speaker 1: It's a continued exploration. You want to say, don't you

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Speaker 1: understand the mistakes are part of it. This was definitely

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Speaker 1: Brian Green's argument that if you want to talk about

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Speaker 1: people who are skeptical of climate change, talk to the

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Speaker 1: climate change scientists who have studied it, because science is

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Speaker 1: about you. If you you have a hypothesis and you're

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Speaker 1: studying it, you were skeptical about it every step of

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Speaker 1: the way. Yeah, if you're not being skeptical about the

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Speaker 1: theory you advocate, then you're not doing science right, right,

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Speaker 1: You're not being very good at your job. So all

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Speaker 1: of the that, that entire argument makes sense, and it

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Speaker 1: does lead one to think, all right, it's just it's

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Speaker 1: scientific literacy or lack of reasoning that's being employed here.

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Speaker 1: But yeah, Kahan is saying that you'll find plenty of

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Speaker 1: people resisting sientific consensus who are highly literate in science

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Speaker 1: and highly logical, and they just wind up applying their

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Speaker 1: cognitive resources to fit their beliefs in worldview. In fact,

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Speaker 1: it gets crazier than this, because it's not just that

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Speaker 1: people who are highly educated in science, who apparently, if

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Speaker 1: you test them, they understand how science works. It's not

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Speaker 1: just that they can disagree with the scientific consensus, but

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Speaker 1: that people who have more rational capacity and who have

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Speaker 1: greater cognitive resources in fact, tend to apply those more

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Speaker 1: strongly on entrenching themselves against the scientific consensus when they

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Speaker 1: disagree with it. It's like people who are better educated

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Speaker 1: in science are better at coming up with reasons to

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Speaker 1: explain why they don't agree with scientists or why they

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Speaker 1: don't agree. Not just don't agree with scientists, but don't

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Speaker 1: agree about even objective facts like the fact that the

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Speaker 1: majority of scientists to endorse the fact that the Earth

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Speaker 1: is warming. Yeah, and it is worth noting here that

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Speaker 1: when we're talking about these these dissenting individuals, like it's

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Speaker 1: generally it's not across the board. They're not they're not

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Speaker 1: dissenting on all scientific consensus. It's about a particular topic,

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Speaker 1: be that topic climate change, or be that topic, um,

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Speaker 1: you know, vaccines or genetically modified organisms. Yeah, and it's

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Speaker 1: it's also important Kahan points out that you also see

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Speaker 1: this this sort of thing outside of science. You see

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Speaker 1: you see the same process involved with say, abortion or

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Speaker 1: military recruitment, any issue in which protests becomes a badge

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Speaker 1: of identity. And this place specifically into one paper that

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Speaker 1: Khan refers to, which is this paper they saw a protest. Uh.

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Speaker 1: So this comes up in the conversation where you you

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Speaker 1: can show people video of a protest taking place, right,

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Speaker 1: and you ask them just objective facts about what they

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Speaker 1: see at the video. Did you see the protesters, uh,

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Speaker 1: screaming in someone's face, did you see the protesters bocking

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Speaker 1: someone's path? Did you see the protesters doing this and that?

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Speaker 1: All these sort of negative behaviors that would render the

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Speaker 1: protest in a bad light. And it turns out people

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Speaker 1: claim to see different things in the video depending on

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Speaker 1: whether they think the politics of the protesters line up

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Speaker 1: with their own. So if you show a person with

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Speaker 1: certain politics a video and you say that it's people

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Speaker 1: protesting outside an abortion clinic, they'll have a very different

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Speaker 1: report of what they see in the video than if

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Speaker 1: you tell them that it's protesters outside a military recruiting

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Speaker 1: center protesting. Don't ask, don't tell. And this is a

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Speaker 1: specific example of motivated reasoning. Maybe we can talk about

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Speaker 1: motivated reasoning more later on, but it's the fact that

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Speaker 1: we we just do not process the facts of reality

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Speaker 1: and the evidence of our senses with perfect objectivity. We

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Speaker 1: in fact process them in a highly goal oriented way,

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Speaker 1: and a lot of times that goal is I don't

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Speaker 1: want people like me or the people in my group

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Speaker 1: to look bad. All Right, we're gonna take a quick break,

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Speaker 1: and when we come back, we're gonna jump jump right

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Speaker 1: back into Kahan's research. Than all right, we're back. So

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Speaker 1: one of the things that people obviously do when they

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Speaker 1: are motivated to arrive at a certain conclusion is that

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Speaker 1: they cherry pick facts. Right that you can. You can

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Speaker 1: always find stuff that makes your worldview look better or

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Speaker 1: stuff that makes the other person's worldview look better. And

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Speaker 1: it is in fact, incredibly easy and causes almost no

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Speaker 1: cognitive dissonance whatsoever for people to just say, Okay, this

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Speaker 1: fact that supports what I already believe. That's a good

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Speaker 1: fact that's legit and real and should be included. And

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Speaker 1: a fact I encounter that doesn't support my point of view, well,

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Speaker 1: that's that's a bunch of bunk. You know, why would

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Speaker 1: anybody believe that? And it extends to experts as well,

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Speaker 1: so that the same thing. It's like, here's this, uh,

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Speaker 1: this this expert, and I'm using expert in quotation marks

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Speaker 1: because to what degree they're an expert also depends on

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Speaker 1: your cherry picking this individual. Let's say this individual with

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Speaker 1: some sort of scientific background. Uh, they're making a statement.

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Speaker 1: I will consider them more of an expert based on

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Speaker 1: how their opinion matches up with my preconceived beliefs and worldview. Yeah,

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Speaker 1: and this is another point Kahan makes it it comes

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Speaker 1: straight out of that. So he says, it's not that

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Speaker 1: people who don't, for example, except the consensus on climate

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Speaker 1: change or on vaccine safety, don't believe in scientific expertise.

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Speaker 1: They do. They do believe in scientific expertise generally statistically

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Speaker 1: they do, but they don't think that people who disagree

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Speaker 1: with them are legitimate experts. Now, Kahan wrote about this

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Speaker 1: in a very recent paper like this month. Um, misconceptions, misinformation,

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Speaker 1: and logic of identity, protective cognition. When we're talking about

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Speaker 1: your views as as a badge of identity, that's what

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Speaker 1: we're getting to here. Um. This came out June for

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Speaker 1: the Cultural Cognition Project. So in this paper, Kahn tackles

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Speaker 1: what he refers to as the public irrationality thesis or pit.

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Speaker 1: So this is something he's setting up to be an

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Speaker 1: opposition to. Right. This is the idea that we can

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Speaker 1: touched on earlier, the idea that the general public largely

430
00:25:25,600 --> 00:25:29,840
Speaker 1: quote display only modest familiarity with fundamental scientific findings and

431
00:25:29,920 --> 00:25:33,800
Speaker 1: lack proficiency in the forms of critical reasoning essential to

432
00:25:33,880 --> 00:25:38,440
Speaker 1: science comprehension unquote and uh, and they're therefore easily swayed

433
00:25:38,480 --> 00:25:41,840
Speaker 1: by special interest groups who muddy the waters with non

434
00:25:41,920 --> 00:25:44,639
Speaker 1: scientific information. Yeah, I think this is a is a

435
00:25:44,680 --> 00:25:49,320
Speaker 1: common thesis people on both sides of a contentious issue

436
00:25:49,359 --> 00:25:52,560
Speaker 1: of fact in the public debate sphere. They just tend

437
00:25:52,600 --> 00:25:54,920
Speaker 1: to think that, well, people on the other side are

438
00:25:55,000 --> 00:25:59,240
Speaker 1: just ignorant, and they just they just don't understand and

439
00:25:59,280 --> 00:26:01,960
Speaker 1: they're just being swayed by propaganda. Yeah, you listen to

440
00:26:02,000 --> 00:26:05,120
Speaker 1: the wrong news channel, you listen to the wrong radical

441
00:26:05,480 --> 00:26:08,600
Speaker 1: and now you have you have a faulty understanding of

442
00:26:08,600 --> 00:26:13,640
Speaker 1: the facts. So Gahan argues that Pitt reflects a misconception

443
00:26:13,640 --> 00:26:18,040
Speaker 1: of science communication, like a basic misconception. Controversy over so

444
00:26:18,080 --> 00:26:23,560
Speaker 1: called decision relevant science is increasingly tied to identity protective cognition.

445
00:26:23,640 --> 00:26:27,480
Speaker 1: This is the quote tendency to selectively credit and discredit

446
00:26:27,760 --> 00:26:32,640
Speaker 1: evidence in patterns that reflect people's commitments to competing cultural groups.

447
00:26:33,000 --> 00:26:35,400
Speaker 1: And that's a concept, he says, it's rooted in the

448
00:26:35,440 --> 00:26:37,719
Speaker 1: two thousand to two thousand sixteen work of D. K.

449
00:26:37,920 --> 00:26:40,960
Speaker 1: Sherman and G. L. Cohen. Right, so maybe we should

450
00:26:40,960 --> 00:26:43,520
Speaker 1: try to go a little bit deeper into where this

451
00:26:43,640 --> 00:26:49,000
Speaker 1: idea of of identity protective cognition comes from. So obviously

452
00:26:49,160 --> 00:26:51,560
Speaker 1: there are a lot of ways to be wrong. Right.

453
00:26:51,920 --> 00:26:55,199
Speaker 1: You can be mistaken due to pure error, right, But

454
00:26:55,600 --> 00:26:58,600
Speaker 1: as we've already shown, you can also be mistaken for

455
00:26:58,640 --> 00:27:01,880
Speaker 1: a reason. Our our brains are not so made as

456
00:27:01,920 --> 00:27:05,480
Speaker 1: to perceive and judge the world objectively, like when you're reasoning,

457
00:27:05,640 --> 00:27:10,200
Speaker 1: and perceptions are skewed by a desire, conscious or unconscious,

458
00:27:10,240 --> 00:27:14,320
Speaker 1: to reach particular conclusions. This is what we call motivated reasoning.

459
00:27:14,800 --> 00:27:17,760
Speaker 1: And Kahan in an article he did for the Harvard

460
00:27:17,800 --> 00:27:20,919
Speaker 1: Law Review in that he reproduced an exerpt from on

461
00:27:20,960 --> 00:27:25,280
Speaker 1: his blog, he he said, quote motivated reasoning refers to

462
00:27:25,359 --> 00:27:29,760
Speaker 1: the unconscious tendency of individuals to process information in a

463
00:27:29,840 --> 00:27:34,119
Speaker 1: manner that suits some end or goal extrinsic to the

464
00:27:34,160 --> 00:27:38,880
Speaker 1: formation of accurate beliefs. That unconscious part. It is very

465
00:27:38,880 --> 00:27:41,840
Speaker 1: critical because because no, but we're not arguing that argument.

466
00:27:41,880 --> 00:27:43,920
Speaker 1: Here is not that someone is saying, well, I don't

467
00:27:44,040 --> 00:27:46,480
Speaker 1: I don't like this climate change. This is the expert

468
00:27:46,520 --> 00:27:48,760
Speaker 1: for me. This is it, or or vice versa, theyd

469
00:27:48,840 --> 00:27:51,679
Speaker 1: someone saying, Oh, I'm I don't really like the idea

470
00:27:51,680 --> 00:27:55,399
Speaker 1: of these GMO foods. I'm gonna listen to this expert

471
00:27:55,480 --> 00:27:59,200
Speaker 1: right here. This is taking place, uh in the unconscious. Yeah.

472
00:27:59,240 --> 00:28:01,640
Speaker 1: You you don't even and realize when it's going on.

473
00:28:02,280 --> 00:28:05,320
Speaker 1: And so there there is a classic, highly cited paper

474
00:28:05,320 --> 00:28:07,480
Speaker 1: in the history of psychology that he goes back to

475
00:28:07,480 --> 00:28:10,800
Speaker 1: to talk about early examples of motivated reasoning, and this

476
00:28:10,880 --> 00:28:12,760
Speaker 1: is a precedent, I guess for his They Saw a

477
00:28:12,760 --> 00:28:15,760
Speaker 1: protest paper. The original one was this paper called They

478
00:28:15,800 --> 00:28:19,199
Speaker 1: Saw a Game a case study, and it goes to

479
00:28:19,240 --> 00:28:22,359
Speaker 1: stuff that has nothing to do with politics, absolutely nothing.

480
00:28:22,600 --> 00:28:25,040
Speaker 1: You can take the politics and you can take the

481
00:28:25,080 --> 00:28:28,640
Speaker 1: science completely out and you still get the exact same effects.

482
00:28:28,640 --> 00:28:31,400
Speaker 1: And what this is is there was a football game

483
00:28:31,760 --> 00:28:35,879
Speaker 1: between Dartmouth and Princeton in ninette that had some highly

484
00:28:35,920 --> 00:28:40,000
Speaker 1: controversial behavior, leading injuries for a few players. Players were

485
00:28:40,160 --> 00:28:44,920
Speaker 1: hurting each other in the Researchers in this study recruited

486
00:28:45,160 --> 00:28:49,080
Speaker 1: Dartmouth and Princeton students to review footage of what happened

487
00:28:49,400 --> 00:28:52,640
Speaker 1: and answer questions about what they saw, and it turns

488
00:28:52,680 --> 00:28:57,520
Speaker 1: out what they saw depended on their school allegiance. Dartmouth

489
00:28:57,560 --> 00:29:02,080
Speaker 1: students claimed to see things favorable to Dartmouth's reputation. Princeton

490
00:29:02,160 --> 00:29:06,000
Speaker 1: students claim to see things favorable to Princeton's reputation. They

491
00:29:06,000 --> 00:29:09,520
Speaker 1: didn't just have different opinions about the game, they apparently

492
00:29:09,600 --> 00:29:13,840
Speaker 1: perceived a different reality based on institutional allegiance. They were

493
00:29:13,840 --> 00:29:18,280
Speaker 1: not reasoning impartially but in a motivated way, and lots

494
00:29:18,280 --> 00:29:20,840
Speaker 1: of studies over the years have reflected other versions of

495
00:29:20,880 --> 00:29:24,160
Speaker 1: these findings. It's totally clear when people have a goal,

496
00:29:24,560 --> 00:29:27,840
Speaker 1: when they consciously or unconsciously want things to be a

497
00:29:27,840 --> 00:29:32,080
Speaker 1: certain way, they're usually not capable of reasoning and perceiving

498
00:29:32,160 --> 00:29:36,440
Speaker 1: reality impartially. And to get back to the main example

499
00:29:36,480 --> 00:29:38,880
Speaker 1: of this that we we came in with is the

500
00:29:38,920 --> 00:29:43,760
Speaker 1: idea of identity protective cognition. We want to affirm our

501
00:29:43,800 --> 00:29:47,520
Speaker 1: membership in reference groups because we're social creatures, right, I mean,

502
00:29:48,560 --> 00:29:51,080
Speaker 1: one of one of the main things that's been hypothesized

503
00:29:51,120 --> 00:29:54,960
Speaker 1: that our brains evolved to do is to manage social relationships.

504
00:29:54,960 --> 00:29:58,360
Speaker 1: We were just talking about the social brain hypothesis another episode.

505
00:29:59,040 --> 00:30:02,000
Speaker 1: Uh yeah on one of the main things we appear

506
00:30:02,080 --> 00:30:05,720
Speaker 1: to be optimized for is for group membership and group

507
00:30:05,800 --> 00:30:10,040
Speaker 1: solidarity and understanding group dynamics. Yeah, I mean, survival has

508
00:30:10,080 --> 00:30:13,080
Speaker 1: a almost has a different definition when you're talking about

509
00:30:13,080 --> 00:30:17,680
Speaker 1: an individual versus a a larger especially a global culture.

510
00:30:18,040 --> 00:30:21,600
Speaker 1: We didn't evolve to save the planet from the human

511
00:30:21,640 --> 00:30:25,680
Speaker 1: caused climate change or or meteorites. Uh. We evolve to

512
00:30:25,760 --> 00:30:31,280
Speaker 1: survive um social dynamics, to to adapt our thinking to

513
00:30:31,320 --> 00:30:33,920
Speaker 1: fit in with the group that has access to the fire,

514
00:30:33,960 --> 00:30:37,600
Speaker 1: that has access to the uh to to the food

515
00:30:37,960 --> 00:30:40,840
Speaker 1: and the shelter that is necessary for survival. Yeah. And

516
00:30:40,920 --> 00:30:44,960
Speaker 1: so we deeply, deeply want we're highly motivated to affirm

517
00:30:45,000 --> 00:30:48,960
Speaker 1: our membership in reference groups and the character and the

518
00:30:49,040 --> 00:30:52,560
Speaker 1: reputation of those groups. When those things are at stake,

519
00:30:52,680 --> 00:30:56,400
Speaker 1: we are highly motivated to defend them. So the idea

520
00:30:56,440 --> 00:31:00,320
Speaker 1: here's the culture comes before fact. Perception of what acts

521
00:31:00,400 --> 00:31:03,760
Speaker 1: even are are shaped by values. So many of these

522
00:31:03,920 --> 00:31:08,080
Speaker 1: individual members of the public simply have a quote bigger

523
00:31:08,080 --> 00:31:11,880
Speaker 1: personal stake in fitting in with important affinity groups than

524
00:31:11,920 --> 00:31:15,280
Speaker 1: informing correct perceptions of scientific evidence. Yet again, this is

525
00:31:15,280 --> 00:31:19,000
Speaker 1: not necessarily done consciously. In fact, it's almost never done consciously.

526
00:31:19,080 --> 00:31:23,160
Speaker 1: You don't think I'm sacrificing knowing the truth for fitting

527
00:31:23,200 --> 00:31:25,680
Speaker 1: in with my group. That's just what your brain does

528
00:31:25,880 --> 00:31:28,200
Speaker 1: and doesn't really let you in on the fact that

529
00:31:28,200 --> 00:31:31,120
Speaker 1: that's what it's doing. Yeah, and the members of the

530
00:31:31,120 --> 00:31:33,600
Speaker 1: public that are most polarized over a topic are the

531
00:31:33,600 --> 00:31:36,680
Speaker 1: ones that have the highest degree of scientific comprehension. Where

532
00:31:36,680 --> 00:31:39,320
Speaker 1: I discussed that this is the nature of the dissenting expert.

533
00:31:39,800 --> 00:31:43,600
Speaker 1: The problem, then, Kahan points out, is not a gullible public,

534
00:31:43,640 --> 00:31:49,719
Speaker 1: not this pit scenario, but quote, a polluted science communication environment. Now,

535
00:31:50,200 --> 00:31:52,800
Speaker 1: he referred to a two thousand eleven study that that

536
00:31:52,880 --> 00:31:56,400
Speaker 1: he himself worked on with Jenkins, Smith and Brahmin, in

537
00:31:56,440 --> 00:32:00,480
Speaker 1: which a scientist headshot and credentials were presented along with

538
00:32:00,560 --> 00:32:04,760
Speaker 1: attributed quotes about climate change, and whether he this individual

539
00:32:04,880 --> 00:32:07,200
Speaker 1: was a true expert in the eyes of the subject

540
00:32:07,200 --> 00:32:11,400
Speaker 1: depended entirely on their particulars and their views. So people

541
00:32:11,480 --> 00:32:15,080
Speaker 1: are simply quote, using the consistency of new evidence with

542
00:32:15,200 --> 00:32:18,280
Speaker 1: their group's positions to determine whether the evidence should be

543
00:32:18,320 --> 00:32:21,760
Speaker 1: given any weight at all. And this is how deniers

544
00:32:22,280 --> 00:32:25,560
Speaker 1: of scientific consensus become stuck in their opinions. Right, So,

545
00:32:25,640 --> 00:32:29,560
Speaker 1: if somebody presents you an alternative opinion, the scientist comes

546
00:32:29,600 --> 00:32:32,560
Speaker 1: on TV or writes a book or something like that

547
00:32:33,080 --> 00:32:37,520
Speaker 1: and says, look, here's what the science says. It's pretty clear.

548
00:32:37,680 --> 00:32:40,520
Speaker 1: This is why scientists agree. This is where the consensus

549
00:32:40,520 --> 00:32:42,840
Speaker 1: comes from, and here's why the public should agree with

550
00:32:42,880 --> 00:32:45,560
Speaker 1: it too. If you are part of a group that

551
00:32:45,680 --> 00:32:50,480
Speaker 1: is culturally polarized against that scientific position, you don't think

552
00:32:50,560 --> 00:32:54,120
Speaker 1: I'm being anti science. You just think this person isn't

553
00:32:54,120 --> 00:32:57,160
Speaker 1: a real expert. Why should I trust what they say? Indeed,

554
00:32:57,200 --> 00:32:59,240
Speaker 1: and I also want to point out that the con

555
00:32:59,320 --> 00:33:03,440
Speaker 1: touches on on disinformation. He says that disinformation doesn't seem

556
00:33:03,440 --> 00:33:05,880
Speaker 1: to have as much impact as you might think. And

557
00:33:05,920 --> 00:33:08,640
Speaker 1: the embar in mind that there are several flavors of misinformation.

558
00:33:08,640 --> 00:33:13,320
Speaker 1: There's self misinformation, there's motivated consumption of misinformation. They're straight

559
00:33:13,400 --> 00:33:17,280
Speaker 1: up fake news. Uh. Kahan states that while such misinformation

560
00:33:17,560 --> 00:33:20,760
Speaker 1: certainly does have an impact on the world, UH, the

561
00:33:20,840 --> 00:33:23,040
Speaker 1: reality is is a little bit different. He says what

562
00:33:23,080 --> 00:33:27,240
Speaker 1: these individuals do with misinformation in most circumstances will not

563
00:33:27,440 --> 00:33:30,719
Speaker 1: differ from what they would have done without it. So

564
00:33:30,840 --> 00:33:34,400
Speaker 1: I find this whole scenario very, very illuminating. Uh, you know,

565
00:33:34,440 --> 00:33:36,760
Speaker 1: it's it's help It's a helpful model not only in

566
00:33:36,920 --> 00:33:40,960
Speaker 1: understanding or trying to understand individuals who have a differing

567
00:33:41,000 --> 00:33:43,840
Speaker 1: opinion in your own on scientific consensus, but also to

568
00:33:43,840 --> 00:33:46,160
Speaker 1: to self reflect and and try and think, well, how

569
00:33:46,160 --> 00:33:48,640
Speaker 1: do I think about scientific consensus? Yeah. Well, one of

570
00:33:48,680 --> 00:33:50,480
Speaker 1: the things that you should really take away from this,

571
00:33:50,600 --> 00:33:53,960
Speaker 1: and we should emphasize this very strongly, is that this

572
00:33:54,000 --> 00:33:58,840
Speaker 1: applies to you too. It applies to me and to you. Um,

573
00:33:58,880 --> 00:34:02,920
Speaker 1: it's not so much surprising that motivated reasoning happens, or

574
00:34:02,960 --> 00:34:07,280
Speaker 1: that identity protective cognition happens, but it's surprising that it

575
00:34:07,320 --> 00:34:10,799
Speaker 1: applies to you because it doesn't feel like it does. Yeah. Yeah,

576
00:34:10,800 --> 00:34:13,200
Speaker 1: when it just feels like I'm being objective, I'm trying

577
00:34:13,239 --> 00:34:15,640
Speaker 1: to figure out what's true, it's those other people who

578
00:34:15,719 --> 00:34:19,279
Speaker 1: are reasoning from their cultural point of view, right, Yeah,

579
00:34:19,320 --> 00:34:21,360
Speaker 1: I mean that that is how how it how it feels.

580
00:34:21,360 --> 00:34:23,479
Speaker 1: I mean, that's the that's one of the tricky parts

581
00:34:23,520 --> 00:34:25,759
Speaker 1: about this is you. You can't simply hold up the

582
00:34:25,800 --> 00:34:28,759
Speaker 1: mirror uh so much and say look, look, look how

583
00:34:28,800 --> 00:34:31,080
Speaker 1: you're thinking. Look at look at the way you're processing

584
00:34:31,080 --> 00:34:34,680
Speaker 1: your information. Yeah. And so this actually leads to problems,

585
00:34:34,680 --> 00:34:37,880
Speaker 1: and Cohn writes about this in uh. In that piece

586
00:34:37,880 --> 00:34:40,480
Speaker 1: in the Harvard Law Review, he points out how this

587
00:34:40,560 --> 00:34:44,879
Speaker 1: leads to really bad cultural situations, where so you've got

588
00:34:44,920 --> 00:34:48,160
Speaker 1: your group and another group who are both motivated to

589
00:34:48,200 --> 00:34:51,400
Speaker 1: perceive facts differently for reasons having nothing to do with

590
00:34:51,440 --> 00:34:55,240
Speaker 1: forming accurate beliefs. You know, you're both using motivated reasoning.

591
00:34:55,800 --> 00:35:00,000
Speaker 1: Each group correctly perceives that the other group is used

592
00:35:00,160 --> 00:35:05,120
Speaker 1: motivated reasoning, but each group incorrectly believes that it is

593
00:35:05,200 --> 00:35:09,000
Speaker 1: just looking at the plane obvious objective truth. And of course,

594
00:35:09,000 --> 00:35:10,920
Speaker 1: when you feel like, well, I'm just looking at the

595
00:35:10,960 --> 00:35:15,480
Speaker 1: plane obvious objective truth and this other group is deluding themselves,

596
00:35:15,640 --> 00:35:19,560
Speaker 1: that can lead to feelings of disgust and polarization. You're like,

597
00:35:19,640 --> 00:35:24,319
Speaker 1: why won't they accept reality? Why are they being so dishonest? Yeah,

598
00:35:24,320 --> 00:35:28,000
Speaker 1: And in this the divide deepens even more, right, Yeah,

599
00:35:28,120 --> 00:35:31,520
Speaker 1: And of course leads to these uh, these partisanship situations.

600
00:35:31,560 --> 00:35:34,280
Speaker 1: And of course this makes the problem even worse because

601
00:35:34,320 --> 00:35:37,680
Speaker 1: once you get entrenched partisanship on an issue in the

602
00:35:37,680 --> 00:35:41,840
Speaker 1: public conversation, this provides even more incentive to group a

603
00:35:41,960 --> 00:35:46,520
Speaker 1: line right, and so it re reinforces the motivated reasoning

604
00:35:46,560 --> 00:35:49,800
Speaker 1: that caused you to divide in the first place. Now,

605
00:35:49,920 --> 00:35:52,520
Speaker 1: there are some things that you might think you could

606
00:35:52,520 --> 00:35:55,279
Speaker 1: do to solve the problem. For one thing, you could say, hey,

607
00:35:55,840 --> 00:35:59,239
Speaker 1: what if we just tell people, no, don't don't think

608
00:35:59,239 --> 00:36:01,840
Speaker 1: with your culture, I don't think with your identity, be

609
00:36:02,080 --> 00:36:05,839
Speaker 1: rational and be objective. Does that solve the problem? Well,

610
00:36:05,920 --> 00:36:10,560
Speaker 1: Khan says, research says no. When people use motivated reasoning,

611
00:36:10,800 --> 00:36:14,399
Speaker 1: they tend to believe they're already being objective. They think, yes,

612
00:36:14,440 --> 00:36:16,759
Speaker 1: I am being objective. And this is due to what

613
00:36:16,800 --> 00:36:20,560
Speaker 1: he calls naive realism. This is just the belief that, well,

614
00:36:20,600 --> 00:36:22,960
Speaker 1: what I'm looking at is is a clear and and

615
00:36:23,040 --> 00:36:27,600
Speaker 1: accurate perception of reality. So we're we're all correctly perceiving

616
00:36:27,600 --> 00:36:31,200
Speaker 1: that other people are reasoning with motivation. We're buying into

617
00:36:31,320 --> 00:36:34,160
Speaker 1: naive realism about our own points of view, saying well,

618
00:36:34,200 --> 00:36:36,680
Speaker 1: I'm just looking at the facts. And this leads to

619
00:36:36,760 --> 00:36:40,600
Speaker 1: that horrible state of affairs of of cultural cognition, where

620
00:36:40,680 --> 00:36:45,440
Speaker 1: where partisanship rules. Uh, these certain issues that have been

621
00:36:45,480 --> 00:36:50,760
Speaker 1: infected with the toxic sludge of culture bleeding into questions

622
00:36:50,760 --> 00:36:53,040
Speaker 1: of fact. I always end up coming back to Dr

623
00:36:53,080 --> 00:36:55,719
Speaker 1: Seuss when thinking about these these issues, and not only

624
00:36:55,760 --> 00:36:59,600
Speaker 1: the sneeches, the starbellied sneeches, who who are so caught

625
00:36:59,680 --> 00:37:02,279
Speaker 1: up in in the the identity of their groups that

626
00:37:02,320 --> 00:37:05,560
Speaker 1: they're only cured of it due to just catastrophe. And

627
00:37:05,600 --> 00:37:07,879
Speaker 1: then there's a shorter story in that same book where

628
00:37:07,880 --> 00:37:10,720
Speaker 1: we have the North going Zacks in the South going Zacks.

629
00:37:10,719 --> 00:37:13,319
Speaker 1: These two individuals that need in the desert going in

630
00:37:13,360 --> 00:37:16,600
Speaker 1: a straight line, and they neither one budgets. They can't

631
00:37:16,600 --> 00:37:18,360
Speaker 1: move through each other, but neither one is going to

632
00:37:18,440 --> 00:37:21,600
Speaker 1: go around. Uh. And it just over time like a

633
00:37:21,640 --> 00:37:25,200
Speaker 1: city is built around them while they're just frozen in

634
00:37:25,239 --> 00:37:30,319
Speaker 1: their their their their their unshakable ability to either compromise

635
00:37:30,440 --> 00:37:33,480
Speaker 1: or to understand each other. Yeah. Well, so this situation

636
00:37:33,560 --> 00:37:37,360
Speaker 1: can really induce feelings of despair. I mean, there are

637
00:37:37,440 --> 00:37:40,680
Speaker 1: multiple problems here, one of which is that some issues

638
00:37:40,719 --> 00:37:45,000
Speaker 1: are becoming infected with this with this motivated reasoning, this

639
00:37:45,120 --> 00:37:49,239
Speaker 1: cultural cognition. A toxin is how Kahan referred to it

640
00:37:49,280 --> 00:37:52,800
Speaker 1: as a pollutant. Yeah, it's a pollutant that just infects

641
00:37:52,800 --> 00:37:55,960
Speaker 1: certain issues and then makes it impossible to have a

642
00:37:56,000 --> 00:37:59,000
Speaker 1: clear discussion on them because you get people retrenched in

643
00:37:59,040 --> 00:38:02,840
Speaker 1: their positions and don't budge. But then the retrenchment leads

644
00:38:02,880 --> 00:38:06,200
Speaker 1: to the general worsening of the situation. It's a it's

645
00:38:06,239 --> 00:38:09,280
Speaker 1: a self reinforcing cycle that just gets worse and worse.

646
00:38:09,560 --> 00:38:13,040
Speaker 1: It's like everybody's identity and their politics has all just

647
00:38:13,200 --> 00:38:17,120
Speaker 1: drained out into this, into this body of water. How

648
00:38:17,120 --> 00:38:19,279
Speaker 1: do you unpollute that enough that you can have the

649
00:38:19,400 --> 00:38:23,359
Speaker 1: unpolluted discussion again? Now, maybe that's what we should turn

650
00:38:23,400 --> 00:38:26,160
Speaker 1: to next. If you're hearing this and you're you're following

651
00:38:26,200 --> 00:38:28,560
Speaker 1: along with us, like, if you agree that these are

652
00:38:28,640 --> 00:38:31,200
Speaker 1: valid ways of examining what's going on in in these

653
00:38:31,200 --> 00:38:34,879
Speaker 1: public conversations, uh, you might be feeling to spare right,

654
00:38:35,000 --> 00:38:36,319
Speaker 1: How do we ever get out of this? If we

655
00:38:36,400 --> 00:38:40,520
Speaker 1: all use motivated reasoning and there are these horrible situations

656
00:38:40,600 --> 00:38:44,560
Speaker 1: where issues of fact and scientific questions are just polluted

657
00:38:44,600 --> 00:38:47,879
Speaker 1: by cultural partisanship, how do we get out of it?

658
00:38:48,480 --> 00:38:50,359
Speaker 1: We'll take a quick break and when we come back,

659
00:38:50,640 --> 00:39:01,040
Speaker 1: we can discuss why it's not necessarily always time to despair. Alright,

660
00:39:01,040 --> 00:39:03,800
Speaker 1: we're back. Okay, So we were saying, it can feel

661
00:39:03,840 --> 00:39:06,000
Speaker 1: like it's time to despair once you look at the

662
00:39:06,040 --> 00:39:12,080
Speaker 1: situation of partisanship, partisan reasoning, cultural cognition. But it's not

663
00:39:12,160 --> 00:39:14,920
Speaker 1: necessarily time to despair. First of all, if you're just

664
00:39:14,960 --> 00:39:17,719
Speaker 1: thinking about motivated reasoning and you accept the fact that

665
00:39:17,800 --> 00:39:20,240
Speaker 1: you use it too, it's not just those other people,

666
00:39:20,680 --> 00:39:23,520
Speaker 1: it's me, it's you. We all use it. How can

667
00:39:23,600 --> 00:39:26,799
Speaker 1: we ever know anything is true? Well, i'd say two

668
00:39:26,840 --> 00:39:29,560
Speaker 1: things to that. First of all, not every question is

669
00:39:29,600 --> 00:39:33,239
Speaker 1: settled through motivated reasoning, right, There are plenty of questions

670
00:39:33,280 --> 00:39:36,920
Speaker 1: where we actually do have the primary motivation of just

671
00:39:36,960 --> 00:39:41,440
Speaker 1: getting an accurate answer people. People do show identity splitting

672
00:39:41,440 --> 00:39:44,680
Speaker 1: on whether climate change is dangerous, but they don't show

673
00:39:44,719 --> 00:39:47,759
Speaker 1: identity based splitting on issues like whether X rays are

674
00:39:47,800 --> 00:39:50,760
Speaker 1: harmful to the human body. If you pull people based

675
00:39:50,800 --> 00:39:54,360
Speaker 1: on their ideology and political affiliation, all the other stuff

676
00:39:54,360 --> 00:39:57,640
Speaker 1: you'd be looking for their you know, liberals and conservatives,

677
00:39:58,280 --> 00:40:00,440
Speaker 1: or these other groups that are oft inside, like the

678
00:40:00,560 --> 00:40:06,239
Speaker 1: hierarchical individualist versus the egalitarian communitarian. These groups are in

679
00:40:06,280 --> 00:40:09,080
Speaker 1: agreement X rays are equally harmful to the human bodies

680
00:40:09,160 --> 00:40:13,000
Speaker 1: say they yeah, because, as Khan points out again, these

681
00:40:13,080 --> 00:40:17,920
Speaker 1: these instances of polarization over scientific consensus, these are are

682
00:40:17,960 --> 00:40:21,200
Speaker 1: pathological in the sense that they're harmful, but they're also rare. Yeah.

683
00:40:21,239 --> 00:40:24,200
Speaker 1: So there, it's just these certain issues that we're reasoning

684
00:40:24,200 --> 00:40:27,239
Speaker 1: this way about. Not everything suffers from this problem. Many

685
00:40:27,280 --> 00:40:30,919
Speaker 1: issues are uncontroversial. We generally approached them with no real

686
00:40:31,000 --> 00:40:35,600
Speaker 1: motivation other than just knowing what's true. The problem is

687
00:40:35,719 --> 00:40:39,279
Speaker 1: that even though you're not always using motivated reasoning, you're

688
00:40:39,280 --> 00:40:42,839
Speaker 1: probably not going to know it when you are, uh,

689
00:40:43,120 --> 00:40:48,680
Speaker 1: using motivated reasoning apparently feels similar to using actual objective reasoning.

690
00:40:49,440 --> 00:40:51,919
Speaker 1: You can you can know this firsthand by the fact

691
00:40:51,960 --> 00:40:54,880
Speaker 1: that you don't ever think you're using motivated reasoning. You

692
00:40:54,960 --> 00:40:57,680
Speaker 1: think you're just honestly judging things. But you also know

693
00:40:57,800 --> 00:41:01,279
Speaker 1: you're not right about everything. You're some of those things

694
00:41:01,280 --> 00:41:04,720
Speaker 1: you believe you're definitely wrong about, even though it feels

695
00:41:04,760 --> 00:41:08,600
Speaker 1: like you're just clearly judging what's true. So is there

696
00:41:08,640 --> 00:41:11,520
Speaker 1: any way to know what's true when issues are controversial

697
00:41:11,560 --> 00:41:14,839
Speaker 1: and when we're motivated to reason one way or another. Well,

698
00:41:14,880 --> 00:41:17,200
Speaker 1: I'd say this is when we come back to our

699
00:41:17,239 --> 00:41:22,120
Speaker 1: starting principle going with science, right, Science is exactly a

700
00:41:22,160 --> 00:41:25,839
Speaker 1: way of getting around motivated reasoning and bias if you're

701
00:41:25,880 --> 00:41:28,040
Speaker 1: doing it right. I mean, of course it's possible to

702
00:41:28,120 --> 00:41:31,040
Speaker 1: be really bad at science, but if you're following the

703
00:41:31,040 --> 00:41:34,120
Speaker 1: norms of science, what it is supposed to do is

704
00:41:34,160 --> 00:41:37,280
Speaker 1: make it really hard to get away with motivated reasoning

705
00:41:37,320 --> 00:41:40,480
Speaker 1: for an extended period of time. You've got obstacles built

706
00:41:40,560 --> 00:41:45,160
Speaker 1: into science that are specifically designed to kill motivated reasoning.

707
00:41:45,680 --> 00:41:49,840
Speaker 1: So you've got rigorous empirical method using objective measurement criteria,

708
00:41:49,920 --> 00:41:52,760
Speaker 1: trying to take your own subjective judgments out of things.

709
00:41:53,440 --> 00:41:57,120
Speaker 1: You've got blinding and double blinding of experiments where you know,

710
00:41:57,160 --> 00:41:59,040
Speaker 1: you get people who don't even know what's going on

711
00:41:59,160 --> 00:42:02,560
Speaker 1: to perform the experiment. People in the experiment don't necessarily

712
00:42:02,640 --> 00:42:06,200
Speaker 1: know what's going on. You've got peer review by critical experts,

713
00:42:06,200 --> 00:42:09,960
Speaker 1: you've got replication attempts, you've got professional competition. This is

714
00:42:10,000 --> 00:42:12,480
Speaker 1: the thing that often doesn't get emphasized enough, is that

715
00:42:12,800 --> 00:42:17,280
Speaker 1: there's professional and career based incentive in science to disprove

716
00:42:17,360 --> 00:42:20,719
Speaker 1: the consensus. Right. Yeah, and and again, like like we said,

717
00:42:20,719 --> 00:42:25,040
Speaker 1: skepticism is built into the recipe, right, So if you're

718
00:42:25,040 --> 00:42:28,120
Speaker 1: doing science in a motivated way, your science number one

719
00:42:28,280 --> 00:42:30,839
Speaker 1: is not going to look very strong to begin with,

720
00:42:31,120 --> 00:42:33,279
Speaker 1: and number two, you're not going to get away with

721
00:42:33,320 --> 00:42:35,560
Speaker 1: it for very long. People are going to figure out

722
00:42:35,560 --> 00:42:37,560
Speaker 1: what you're up to. And we've seen examples of this

723
00:42:37,719 --> 00:42:40,960
Speaker 1: when people get caught doing scientific fraud. It seems to

724
00:42:41,040 --> 00:42:44,359
Speaker 1: be fairly rare, but they get caught. Maybe people can't

725
00:42:44,360 --> 00:42:48,480
Speaker 1: replicate your results, people start noticing your regularities in your data.

726
00:42:48,520 --> 00:42:50,839
Speaker 1: I mean, it's a system that is just not very

727
00:42:50,920 --> 00:42:53,640
Speaker 1: forgiving to this kind of nonsense. Yeah, I mean, I don't.

728
00:42:53,719 --> 00:42:56,000
Speaker 1: It depends in each case, like I guess whether it

729
00:42:56,040 --> 00:42:59,800
Speaker 1: falls with fraud or bad science. But with artificial g

730
00:43:00,040 --> 00:43:02,960
Speaker 1: vity or gravity repelling technology is one example where you

731
00:43:03,000 --> 00:43:05,160
Speaker 1: do see studies that have come out where someone claims

732
00:43:05,200 --> 00:43:08,279
Speaker 1: to have developed a means of achieving this. Yeah, but

733
00:43:08,440 --> 00:43:11,040
Speaker 1: they can't be replicated. It doesn't, it doesn't work. It

734
00:43:11,080 --> 00:43:13,759
Speaker 1: doesn't it doesn't pass the tests that are built into

735
00:43:13,800 --> 00:43:16,880
Speaker 1: the scientific process, right, And so this is why science

736
00:43:16,920 --> 00:43:20,200
Speaker 1: is a good way of arriving at correct conclusions about

737
00:43:20,200 --> 00:43:21,880
Speaker 1: the world. I mean, you're not if you go with

738
00:43:21,920 --> 00:43:25,400
Speaker 1: the scientific consensus, you might not be right. Every time,

739
00:43:25,400 --> 00:43:28,040
Speaker 1: But it's your best bet for being right the most

740
00:43:28,239 --> 00:43:32,040
Speaker 1: times of any other thing, you'd go with um. So

741
00:43:32,680 --> 00:43:35,160
Speaker 1: the problem is, of course we can't all be scientists,

742
00:43:35,800 --> 00:43:39,319
Speaker 1: and even scientists themselves can't use all the tools of

743
00:43:39,360 --> 00:43:43,520
Speaker 1: science to solve every controversial question they encounter. Right, So,

744
00:43:43,719 --> 00:43:45,840
Speaker 1: even if you're a scientist, there's tons of stuff in

745
00:43:45,880 --> 00:43:48,719
Speaker 1: your life where you can't bring to bear all of

746
00:43:48,760 --> 00:43:53,960
Speaker 1: that machinery of skepticism and empiricism and impartiality, where you've

747
00:43:54,000 --> 00:43:56,359
Speaker 1: just got to work like everybody else. You've got to

748
00:43:56,400 --> 00:43:59,879
Speaker 1: decide on some issue of public substance what you think

749
00:44:00,000 --> 00:44:03,920
Speaker 1: about it without having the most impartial method possible. So

750
00:44:03,960 --> 00:44:06,280
Speaker 1: the question there, I guess is how can we avoid

751
00:44:06,400 --> 00:44:11,000
Speaker 1: deluding ourselves on issues where identity protective cognition come into play,

752
00:44:11,040 --> 00:44:14,759
Speaker 1: where we can't use the scientific method. Yeah, Like, one

753
00:44:14,760 --> 00:44:17,440
Speaker 1: of the points Gone brings up is like, how do

754
00:44:17,480 --> 00:44:20,520
Speaker 1: you how do you avoid these scenarios in the future,

755
00:44:20,520 --> 00:44:22,000
Speaker 1: Because there's one thing to figure out, how do we

756
00:44:22,200 --> 00:44:25,719
Speaker 1: unpollute this pool of scientific communication? But then how do

757
00:44:25,800 --> 00:44:27,800
Speaker 1: we how do we avoid polluting this one? How do

758
00:44:27,880 --> 00:44:30,880
Speaker 1: we avoid polluting pools that don't really exist yet? As

759
00:44:30,880 --> 00:44:33,640
Speaker 1: a matter of like, public consideration. Yeah. In fact that

760
00:44:33,680 --> 00:44:36,120
Speaker 1: he mentions in the panel that we should have quote

761
00:44:36,120 --> 00:44:40,480
Speaker 1: a science of science communication um, meaning that science communicators

762
00:44:40,520 --> 00:44:43,319
Speaker 1: should have some experiments they can draw on that show

763
00:44:43,440 --> 00:44:46,840
Speaker 1: them how to predict when an issue it's some just

764
00:44:47,000 --> 00:44:52,000
Speaker 1: innocuous question of fact, will become politicized where people suddenly

765
00:44:52,080 --> 00:44:54,480
Speaker 1: take cultural positions on it. I mean, there are a

766
00:44:54,520 --> 00:44:57,680
Speaker 1: lot of variables involved here. It depends on you know,

767
00:44:57,760 --> 00:45:02,480
Speaker 1: who's who's relaying the information there, Uh, that their identity is,

768
00:45:02,520 --> 00:45:06,080
Speaker 1: what what ideals they're pushing on everybody, and how that

769
00:45:06,160 --> 00:45:08,920
Speaker 1: ends up polluting the message. It also depends on a

770
00:45:08,960 --> 00:45:12,120
Speaker 1: number of cultural problems. I mean there are certain polarizing

771
00:45:12,160 --> 00:45:14,880
Speaker 1: issues that are issues here in the United States that

772
00:45:14,920 --> 00:45:18,080
Speaker 1: are not so in Europe, such as such as climate change,

773
00:45:18,520 --> 00:45:21,759
Speaker 1: and then the reverses. You see stuff like genetically modified

774
00:45:22,160 --> 00:45:25,880
Speaker 1: organisms being more of a of a hot topic in

775
00:45:26,320 --> 00:45:29,160
Speaker 1: say England than it is in the States. Totally. Yeah,

776
00:45:29,160 --> 00:45:31,919
Speaker 1: in the in the UK, there's way more controversy over

777
00:45:32,040 --> 00:45:34,200
Speaker 1: GM crops than there is in the United States. Not

778
00:45:34,239 --> 00:45:37,040
Speaker 1: to say there's not some controversy here, and so yeah,

779
00:45:37,080 --> 00:45:39,400
Speaker 1: how do you predict it? I mean, con mentions the

780
00:45:39,400 --> 00:45:42,560
Speaker 1: possibility of well, maybe you can run simulations, if there's

781
00:45:42,600 --> 00:45:45,520
Speaker 1: some sort of simulation system you could employ, which I

782
00:45:45,600 --> 00:45:47,800
Speaker 1: love because they instantly get this sort of star trek

783
00:45:48,280 --> 00:45:52,319
Speaker 1: Um hollow deck scenario where we're running simulations and and

784
00:45:52,440 --> 00:45:56,759
Speaker 1: trying to catch these these polarization points, these confusions, these

785
00:45:56,760 --> 00:45:59,880
Speaker 1: pollution points before they occur, and forget how do you

786
00:46:00,040 --> 00:46:02,640
Speaker 1: davocating how to communicate ahead of them? Yeah, and some

787
00:46:02,719 --> 00:46:05,520
Speaker 1: things are going to be more predictable than others, Like

788
00:46:05,640 --> 00:46:10,360
Speaker 1: there are some facts of science that, if true, tend

789
00:46:10,400 --> 00:46:13,839
Speaker 1: to be unfriendly to the world view that certain people hold,

790
00:46:13,840 --> 00:46:17,239
Speaker 1: tend to be unfriendly to their values. A couple of

791
00:46:17,239 --> 00:46:20,520
Speaker 1: examples Kahn gives is that if you're generally more of

792
00:46:20,560 --> 00:46:25,319
Speaker 1: an individualist and an anti communitary and action person, this

793
00:46:25,360 --> 00:46:28,760
Speaker 1: may make you inherently opposed to the idea of climate change,

794
00:46:28,800 --> 00:46:31,120
Speaker 1: because really the only way that you can do anything

795
00:46:31,120 --> 00:46:35,640
Speaker 1: about climate change is with organized communitary and action. Likewise,

796
00:46:35,719 --> 00:46:38,160
Speaker 1: if you are a person whose values are sort of

797
00:46:38,239 --> 00:46:42,480
Speaker 1: anti big business, that might predispose you to be against

798
00:46:42,480 --> 00:46:45,600
Speaker 1: GMOs because you see them as like a tool that's

799
00:46:45,600 --> 00:46:48,680
Speaker 1: being used by large agrib business to to get their

800
00:46:48,719 --> 00:46:51,880
Speaker 1: profits and to drive other people out of business. And

801
00:46:51,880 --> 00:46:54,560
Speaker 1: and you know, and whip the environment into the shape

802
00:46:54,600 --> 00:46:57,640
Speaker 1: they want it. And I mean, it'd be worth pointing

803
00:46:57,640 --> 00:47:01,200
Speaker 1: out that like you could, for example, use GMOs if

804
00:47:01,200 --> 00:47:03,000
Speaker 1: you're a big business in a way that would be

805
00:47:03,520 --> 00:47:06,960
Speaker 1: very unethical, very damaging to the environment. I mean, the

806
00:47:07,200 --> 00:47:10,480
Speaker 1: whole thing about the scientific consensus on GMOs is that

807
00:47:10,520 --> 00:47:15,279
Speaker 1: there's nothing inherently dangerous about GMOs as a rule, But

808
00:47:15,520 --> 00:47:19,680
Speaker 1: any individual genetically modified organism could be dangerous, just as

809
00:47:19,719 --> 00:47:22,520
Speaker 1: any other organism could. Yeah, that the process is not

810
00:47:22,640 --> 00:47:26,480
Speaker 1: the problem. The potential problem is in the product that's

811
00:47:26,520 --> 00:47:29,359
Speaker 1: created with it, which can be said of most processes. Yeah,

812
00:47:29,360 --> 00:47:32,080
Speaker 1: it can be said just as equally of products that

813
00:47:32,080 --> 00:47:35,680
Speaker 1: are created through traditional agriculture. It's not it's not the

814
00:47:35,760 --> 00:47:39,480
Speaker 1: gene and the lab that makes the problem. But on

815
00:47:39,520 --> 00:47:42,359
Speaker 1: the other hand, uh Kahn points out, you know, there

816
00:47:42,360 --> 00:47:44,759
Speaker 1: there can be other things that are not nearly as

817
00:47:44,840 --> 00:47:49,200
Speaker 1: determined by our core values. It's not necessarily the conservative

818
00:47:49,280 --> 00:47:52,920
Speaker 1: values or liberal values or whatever other kind of dichotomy

819
00:47:52,960 --> 00:47:56,680
Speaker 1: you want to establish in the culture determine how how

820
00:47:56,719 --> 00:47:58,520
Speaker 1: your opinion comes out on them. Some things are much

821
00:47:58,560 --> 00:48:02,320
Speaker 1: more accidental. It can just be that some prominent figure

822
00:48:02,320 --> 00:48:04,600
Speaker 1: on one side of the political spectrum just sort of

823
00:48:04,640 --> 00:48:09,080
Speaker 1: declares for one side of a factual disagreement, and because

824
00:48:09,120 --> 00:48:12,520
Speaker 1: of group affiliation and identity, the groups just start lining

825
00:48:12,600 --> 00:48:16,240
Speaker 1: up accordingly, even though it's not determined by anything inherent

826
00:48:16,280 --> 00:48:20,000
Speaker 1: to their values. Yeah, you know what I mean? Yeah, yeah, Yeah,

827
00:48:20,000 --> 00:48:21,520
Speaker 1: that that's a that's a good point because it's it's

828
00:48:21,560 --> 00:48:25,040
Speaker 1: easier to see it coming together a polarization effect occurring

829
00:48:25,239 --> 00:48:28,759
Speaker 1: when either the problem or the solution either disagree or

830
00:48:28,840 --> 00:48:31,680
Speaker 1: line up with your worldview, such as well, the solution

831
00:48:31,760 --> 00:48:33,600
Speaker 1: is for us all to come together as a as

832
00:48:33,640 --> 00:48:35,759
Speaker 1: a nation that have some sort of top down governmental

833
00:48:35,960 --> 00:48:38,239
Speaker 1: fix that's going to disagree with some people's world views.

834
00:48:38,280 --> 00:48:40,600
Speaker 1: If the if the solution is we're all going to

835
00:48:40,680 --> 00:48:43,880
Speaker 1: eat you know, plants that grow naturally in harmony with

836
00:48:44,239 --> 00:48:47,440
Speaker 1: mother Earth, that's going to fit another worldview more than another.

837
00:48:48,400 --> 00:48:51,520
Speaker 1: But but when it occurs outside of those parameters, yeah,

838
00:48:51,520 --> 00:48:54,640
Speaker 1: it becomes increasingly different. It's like, what is it a

839
00:48:54,640 --> 00:48:57,880
Speaker 1: slow newsweek? Is it just is this just a topic

840
00:48:57,960 --> 00:49:01,880
Speaker 1: that happened to be out there during particular politicians campaign

841
00:49:01,960 --> 00:49:03,360
Speaker 1: and they just took it up and ran with it,

842
00:49:03,360 --> 00:49:05,560
Speaker 1: pressed to distract from something else. I mean, I think

843
00:49:05,600 --> 00:49:08,600
Speaker 1: it looks very very possible that there can be issues

844
00:49:08,680 --> 00:49:12,799
Speaker 1: where there is cultural cognition going on, where society divides

845
00:49:12,920 --> 00:49:16,360
Speaker 1: on a question of scientific fact along cultural lines in

846
00:49:16,360 --> 00:49:18,640
Speaker 1: a way that really just doesn't have very much to

847
00:49:18,680 --> 00:49:22,160
Speaker 1: do with ideology or values at all. It's just one

848
00:49:22,239 --> 00:49:25,400
Speaker 1: side picked one side arbitrarily, and then the other side,

849
00:49:25,719 --> 00:49:29,400
Speaker 1: because they know they always disagree, picks the other side.

850
00:49:29,800 --> 00:49:33,400
Speaker 1: All right. So in terms of solutions here or possible solutions,

851
00:49:33,440 --> 00:49:35,200
Speaker 1: I mean, on one hand, there I feel like there

852
00:49:35,320 --> 00:49:37,600
Speaker 1: is validity in the idea that yes, we have to

853
00:49:37,640 --> 00:49:41,960
Speaker 1: continue to trust science as a process and trust scientists

854
00:49:41,960 --> 00:49:45,040
Speaker 1: that are speaking on behalf of it. Yeah. I mean,

855
00:49:45,120 --> 00:49:47,760
Speaker 1: if you're a person who if you're not yourself a scientist,

856
00:49:47,840 --> 00:49:50,040
Speaker 1: if you're not yourself an expert, and you don't have

857
00:49:50,120 --> 00:49:53,320
Speaker 1: a good reason based in expertise in the subject matter

858
00:49:53,360 --> 00:49:56,000
Speaker 1: for disagreeing with the consensus, I'd say it's usually your

859
00:49:56,040 --> 00:49:58,120
Speaker 1: best bet to go with what most of the people

860
00:49:58,120 --> 00:50:00,640
Speaker 1: who know what they're talking about are saying. Yeah. And

861
00:50:00,640 --> 00:50:03,840
Speaker 1: and then beyond that, I agree with without Khan is

862
00:50:03,840 --> 00:50:06,880
Speaker 1: saying that we do need a science of science communication,

863
00:50:07,120 --> 00:50:11,239
Speaker 1: We need ability to to predict and maneuver around potential

864
00:50:11,640 --> 00:50:15,400
Speaker 1: pollution points in our communication of science. Yeah, how can you?

865
00:50:15,440 --> 00:50:21,279
Speaker 1: How can you preemptively defend contentious facts about reality from

866
00:50:21,320 --> 00:50:25,839
Speaker 1: becoming politicized or becoming subject to cultural cognition? That would

867
00:50:25,840 --> 00:50:29,000
Speaker 1: be a really good thing. One thing that Kahan offers

868
00:50:29,040 --> 00:50:31,879
Speaker 1: that I think is very interesting is that I get

869
00:50:31,880 --> 00:50:34,920
Speaker 1: the impression that he subscribes to what he calls them

870
00:50:35,239 --> 00:50:38,960
Speaker 1: the law of social proof, meaning that if you want

871
00:50:39,000 --> 00:50:41,880
Speaker 1: to convince somebody to to agree on a point of

872
00:50:41,920 --> 00:50:46,400
Speaker 1: fact that they are resistant to for cultural reasons, don't

873
00:50:46,440 --> 00:50:49,319
Speaker 1: try to keep giving them more evidence that you're right

874
00:50:49,360 --> 00:50:52,160
Speaker 1: and they're wrong, because that's just not what works on us.

875
00:50:52,200 --> 00:50:55,000
Speaker 1: I mean, that's what should work on On one hand,

876
00:50:55,280 --> 00:50:57,239
Speaker 1: we feel like we should do that because that would

877
00:50:57,239 --> 00:51:00,560
Speaker 1: be the logical thing to do, but psychologically that is

878
00:51:00,560 --> 00:51:03,480
Speaker 1: not what changes people's positions. What would probably be more

879
00:51:03,480 --> 00:51:07,879
Speaker 1: effective is if they simply see people who are culturally

880
00:51:08,040 --> 00:51:11,200
Speaker 1: like them and part of their in group agreeing with

881
00:51:11,239 --> 00:51:14,640
Speaker 1: this fact. But then that can also come back into

882
00:51:14,680 --> 00:51:17,839
Speaker 1: science communication, like who who are the science communicators? Then

883
00:51:18,160 --> 00:51:20,759
Speaker 1: that are that are reaching out to these groups that

884
00:51:20,920 --> 00:51:24,120
Speaker 1: have a certain amount of polarization present within them. Well,

885
00:51:24,120 --> 00:51:27,319
Speaker 1: it makes me think that if if, what science communicators

886
00:51:27,360 --> 00:51:29,680
Speaker 1: want to do is try to get everybody across different

887
00:51:29,680 --> 00:51:32,160
Speaker 1: cultural groups on the same page. One thing that should

888
00:51:32,200 --> 00:51:36,759
Speaker 1: really be encouraged is cultural diversity of science communication, is

889
00:51:36,840 --> 00:51:40,760
Speaker 1: that there should be people from all different cultural groups

890
00:51:40,840 --> 00:51:45,400
Speaker 1: within a society, all communicating like, hey, here's what the

891
00:51:45,440 --> 00:51:48,440
Speaker 1: science says. So at least when it's a question of science,

892
00:51:48,760 --> 00:51:51,400
Speaker 1: you can be on the same page and not bring

893
00:51:51,400 --> 00:51:53,920
Speaker 1: in these cultural issues because it's not just people from

894
00:51:53,960 --> 00:51:56,560
Speaker 1: that other cultural group telling you what the science says.

895
00:51:56,760 --> 00:51:59,680
Speaker 1: You're hearing about it from people like you, so they

896
00:51:59,800 --> 00:52:02,360
Speaker 1: have it. Hopefully we gave you some some new tools

897
00:52:02,440 --> 00:52:06,799
Speaker 1: to illuminate not only the understanding of others, but growing understandings,

898
00:52:06,840 --> 00:52:09,920
Speaker 1: and also to better understand how science communication is working

899
00:52:09,920 --> 00:52:14,400
Speaker 1: and how these these blockages, the scientific communication breakdowns are occurring,

900
00:52:14,400 --> 00:52:16,720
Speaker 1: and how we might even treat them. Yeah, I hope,

901
00:52:16,880 --> 00:52:19,520
Speaker 1: I hope today maybe we said something of substance that

902
00:52:19,560 --> 00:52:22,320
Speaker 1: will help people. Uh, I don't know, bridge the partisan

903
00:52:22,440 --> 00:52:24,520
Speaker 1: divide and come to some agreement about the things we

904
00:52:24,560 --> 00:52:28,759
Speaker 1: should be able to agree on. Um. But yeah, it's tough, man,

905
00:52:29,680 --> 00:52:32,480
Speaker 1: the partisan divide. It's the thing I often think about

906
00:52:32,560 --> 00:52:35,800
Speaker 1: in in how we communicate stuff like this and it

907
00:52:35,920 --> 00:52:38,040
Speaker 1: can get you down at times, but we shouldn't despair.

908
00:52:38,120 --> 00:52:40,000
Speaker 1: We should try to find ways to get around it.

909
00:52:40,640 --> 00:52:43,440
Speaker 1: Come together, have have one of those big happy Uh

910
00:52:43,680 --> 00:52:47,160
Speaker 1: what were you talking about? Grow grow food together? Oh yes, yes,

911
00:52:47,360 --> 00:52:52,359
Speaker 1: uh in a nice communal Kumbaya moment. Yeah, you might

912
00:52:52,400 --> 00:52:55,319
Speaker 1: have just alienated something. Oh yeah, probably did so. Hey

913
00:52:55,400 --> 00:52:58,160
Speaker 1: did we alienate you? Did we did? We did we

914
00:52:58,239 --> 00:53:00,839
Speaker 1: illuminate anything for you? We of course love to hear

915
00:53:00,880 --> 00:53:03,000
Speaker 1: from you. Check out the podcast at stuff to bow

916
00:53:03,000 --> 00:53:05,160
Speaker 1: your Mind dot com, where you'll find all the episodes, videos,

917
00:53:05,719 --> 00:53:08,200
Speaker 1: blog post and links out to our various social media

918
00:53:08,239 --> 00:53:11,160
Speaker 1: accounts and uh and certainly you can always contact us

919
00:53:11,160 --> 00:53:14,160
Speaker 1: their Facebook, Twitter, Tumbler, Instagram, you name it. And if

920
00:53:14,160 --> 00:53:15,840
Speaker 1: you want to get in touch with us directly to

921
00:53:15,960 --> 00:53:18,040
Speaker 1: let us know feedback on this episode or any other,

922
00:53:18,160 --> 00:53:21,000
Speaker 1: or to suggest a future episode topic, you can email

923
00:53:21,080 --> 00:53:23,600
Speaker 1: us at blow the Mind at how stuff works dot

924
00:53:23,640 --> 00:53:36,440
Speaker 1: com for more on this and thousands of other topics.

925
00:53:36,680 --> 00:54:00,000
Speaker 1: Is it how stuff works dot com