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Speaker 1: M This is Mesters in Business with very Results on

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Speaker 1: Bluebird Radio. Here this week on the podcast, boy Do

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Speaker 1: I have a fascinating guest, Professor Richard Nisbet of the

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Speaker 1: University of Michigan. This could be the most influential academic

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Speaker 1: that the average person has never heard of. His work

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Speaker 1: has touched on everything UM from psychology to intelligence, to

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Speaker 1: childhood and child rearing, UH to pharmaceutical side effects. It's

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Speaker 1: it's just endlessly astonishing who he is, the research he's done,

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Speaker 1: understanding the impact of culture and society on UM, just

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Speaker 1: how we think and how different let's just use East

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Speaker 1: versus West as examples as different parts of the world

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Speaker 1: approach problem solving and societal issues and economic sen just

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Speaker 1: endlessly fascinating. He's written a dozen books, the most recent

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Speaker 1: of which UH is Thinking, a memoir, which was quite fascinating.

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Speaker 1: He's one of these people. I don't want to say

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Speaker 1: he name dropped because he worked with all these people,

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Speaker 1: but he just so casually works in UM various characters

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Speaker 1: from you know, the canon of twentieth century psychology and

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Speaker 1: economics and and UH academia because he was really there

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Speaker 1: as all these things were being developed. UM. I mentioned

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Speaker 1: During the interview, Professor David Dunning of Dunning Krueger is

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Speaker 1: the one who said, Hey, I work with Richard Nisbet.

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Speaker 1: You should really talk to him, and and really, what

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Speaker 1: more do you need than that as an introduction. I

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Speaker 1: wish we had another two hours. The conversation was absolutely fascinating.

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Speaker 1: It's a deep dive into intelligence and thinking and how

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Speaker 1: we get smarter both as individuals and society. Absolutely fascinating.

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Speaker 1: I'm going to stop here and say, with no further

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Speaker 1: ado my conversation with Richard Nisbet. This is Mesters in

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Speaker 1: Business with very Renaults on Bloomberg Radio. My extra special

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Speaker 1: guest this week is Professor Richard Nesbitt. He is the

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Speaker 1: co director of the Culture and Cognition Program at the

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Speaker 1: University of Michigan, focusing on culture and reasoning and basic

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Speaker 1: cognitive processes. No less than Malcolm Gladwell called him the

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Speaker 1: most influential thinker in my life. And when Professor David

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Speaker 1: Dunning yes that Dunning offered to make an introduction, I

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Speaker 1: jumped at the chance. Professor Richard Nisbit, welcome to Bloomberg.

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Speaker 1: Thank you. It's my pleasure to have you. Before we

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Speaker 1: dive in, I just want to give you a little

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Speaker 1: bit about my background, because I have no real psychology background.

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Speaker 1: My biases the world's of behavioral finance and cognitive eras

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Speaker 1: really in the context of investing decisions, especially bed investing decisions.

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Speaker 1: So pardons some of the naivete that I may exhibit

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Speaker 1: in some of my questions. There's like the slightest bit

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Speaker 1: of overlap between what I've looked at and and your

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Speaker 1: whole career, and that's why I found it so interesting.

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Speaker 1: And and let's start with your career. There's nothing really

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Speaker 1: in your background growing up in El Paso and in

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Speaker 1: California that suggests an academic career in psychology. What led

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Speaker 1: you to the study of human reasoning and decision making? Oh,

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Speaker 1: I think it was just luck and key, and that's

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Speaker 1: what I was meant to do. But I learned it

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Speaker 1: very early. Fortunately, I read Calvin Hall's Primer of Freudian

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Speaker 1: Psychology and it was just za, that's it, That's what

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Speaker 1: I'm gonna do, right right. Interesting that I really like

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Speaker 1: the idea that has been talked about, and you you

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Speaker 1: reference it um in your book Thinking, a Memoir, which

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Speaker 1: we'll talk about in in a few moments, that the

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Speaker 1: human propensity for floored reasoning was advantageous on the Savannah,

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Speaker 1: but it really doesn't service well in a modern society.

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Speaker 1: Tell us a little bit about that, right, Um, well,

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Speaker 1: there's a whole enterprise I'm sure you're aware of and

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Speaker 1: psychology and economics showing Uh, people's reasoning is flawed in

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Speaker 1: many respects, and most of them was Many people said, well,

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Speaker 1: that can't be. You know, I managed to get through

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Speaker 1: my day pretty well. Uh. That's sometimes um self delusional,

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Speaker 1: but but by and large it's correct to say we

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Speaker 1: were not terrible at eavening across the boards. It's just

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Speaker 1: that the Industrial Revolution and then in spades, the information revolution,

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Speaker 1: just changed the nature of what we need to do. Uh,

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Speaker 1: and in our reasoning in everyday life. Uh. It gave

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Speaker 1: us data, It gave us uh numbers, Uh, it gave

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Speaker 1: us graphs, It gave us reports from people that we

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Speaker 1: never heard of. We encounter people that we don't know

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Speaker 1: at all. All of this is just completely unanticipatable from

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Speaker 1: the life of a hunter gatherer. So the problem is

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Speaker 1: the rug has been pulled out from under us, so

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Speaker 1: we make errors all the time. Quite quite interesting, some

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Speaker 1: of the research that you've done on cognition is really

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Speaker 1: quite fascinating because it's so challenging to figure out what's

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Speaker 1: actually going on in people's minds. I was kind of

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Speaker 1: intrigued by some of the research that was done on

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Speaker 1: birthwater and how that impacts people's career choices and aversion

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Speaker 1: to risk taking or not. Tell us a little bit,

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Speaker 1: or even how they embrace risky your sports. Tell us

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Speaker 1: a little bit about birthwater and and how did you

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Speaker 1: figure out that was significant? Well? Um, I was my

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Speaker 1: advisor UH and graduate school at Columbia, Stanley sh Actor

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Speaker 1: UH studied birth order and one thing he discovered is

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Speaker 1: that firstborn females are more frightened of the prospect of

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Speaker 1: electric shock than later born females. And I thought that's

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Speaker 1: interesting because I've always been kind of afraid of getting hurt,

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Speaker 1: and my younger brother was getting hurt all the time

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Speaker 1: when he was a kid. UM, So I sort of

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Speaker 1: filed that away. And then I heard of one of

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Speaker 1: these people who looks at primates UH, and she studied monkeys, UH,

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Speaker 1: and she just made this offhand observation that when a

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Speaker 1: monkey mother has her first baby, she's all arms and

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Speaker 1: legs and tails. Keep it in the tree to keep

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Speaker 1: it from falling thirty down thirty feet down from the

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Speaker 1: forest canopy. Uh. By the time her fourth or fifth

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Speaker 1: kid has come along, the kid falls out. Damn, I'm

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Speaker 1: gonna have to pick the thing up. Um. She just

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Speaker 1: so first borns are protected, uh in a way that

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Speaker 1: later borns aren't. So I said, I said, how can

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Speaker 1: I test this? And I started looking at uh sports

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Speaker 1: and uh the birth order of people who played dangerous

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Speaker 1: sports versus non dangerous sports. And it turns out there

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Speaker 1: later born is about more likely to play a dangerous

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Speaker 1: sport than a firstborn. So. Um, and that's I just

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Speaker 1: found out recently. There have been twenty studies since, all

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Speaker 1: supporting that general statistic and is a giant number. We're

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Speaker 1: usually looking for a couple of percent here or there

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Speaker 1: to identify some difference of of note, this is clearly

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Speaker 1: not only replicable, but very significant. Yeah, it's it's surprisingly strong.

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Speaker 1: If if I had been predicting what I'd find, I say, oh,

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Speaker 1: maybe a ten or fifteen percent edge. But no, it's huge.

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Speaker 1: So another one of your books, Geography of Thought, you

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Speaker 1: put forth the theory Asians and Westerners have maintained very

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Speaker 1: different systems of thought for thousands of years and these

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Speaker 1: differences are scientifically measurable now now that's when it was

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Speaker 1: first introduced. Was a pretty radical premise, but you had

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Speaker 1: a data to back it up. Tell us a bit

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Speaker 1: about that. Well, it is a radical premise. And Um,

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Speaker 1: I just happened to have a student a number of

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Speaker 1: years ago from China, a really brilliant guy who's actually

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Speaker 1: now dean of social sciences at Kingwa University, which is

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Speaker 1: the top university in China. Uh, and actually had been

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Speaker 1: working together for a while. He said, you know, Dick,

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Speaker 1: you and I think completely differently about a lot of

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Speaker 1: different kinds of things. And I said, well, tell me more,

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Speaker 1: and he gave me a version of the following. He said,

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Speaker 1: you think very analytically, very linearly. When you look at

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Speaker 1: some object or person, you are you thinking what are

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Speaker 1: the attributes of that object or person? Uh? And you

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Speaker 1: come up with rules? Are you you consult your rules

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Speaker 1: about that kind of object or person to figure how

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Speaker 1: it's going to react, how it's going to behave um?

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Speaker 1: He said, I, as Chinese, UH think much more Um. Holistically,

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Speaker 1: I pay much more attention to the context of everything,

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Speaker 1: objects and people. I pay attention to relationships, I pay

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Speaker 1: attention to similarities. Uh, and this bruces all kinds of

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Speaker 1: different conclusions between you and me looking at the same situation.

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Speaker 1: Now that's actually he didn't quite say all of that,

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Speaker 1: but he said stuff that was similar to that. In

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Speaker 1: what I just said is a summary of our results. UM.

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Speaker 1: And speaking of big effects, not everything I do get

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Speaker 1: big effects, I can assure you. But Uh, I didn't

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Speaker 1: actually believe him exactly. Although I had read a book

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Speaker 1: by Nakamura called Ways of Thinking of Eastern People, so

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Speaker 1: I was prepared for some differences. Certainly, not the different

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Speaker 1: is as large as we found. Uh. My favorite examples

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Speaker 1: of these cognitive differences really have to do with perception

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Speaker 1: more than reasoning, although there are plenty of big differences

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Speaker 1: for reasoning. UM. Were one of our studies, and this

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Speaker 1: was done with Takahiko Masuda. Uh. We showed underwater scenes

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Speaker 1: two Americans in Japanese for twenty seconds, UH, and then

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Speaker 1: we asked them, what tell me what you saw. The

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Speaker 1: Americans will say something like, well, I saw three big

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Speaker 1: fish swimming off to the left. Um. They had one

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Speaker 1: fin on top, they had pink stipples on their bellies. UM.

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Speaker 1: There were rocks and shells on the bottom. So on

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Speaker 1: the Japanese nearly always start with the context. I said,

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Speaker 1: I saw what looked like a stream. The water was green. Uh,

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Speaker 1: there were rocks and plants on the bottom. Uh, there

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Speaker 1: were three big fish swimming off to the left. Now,

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Speaker 1: in total, we got more reports about context from the Japanese,

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Speaker 1: from the Americans, then from the Americans, and more observations

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Speaker 1: about relationships like the frog was on the lily pad

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Speaker 1: um and uh so Uh it turns out and and

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Speaker 1: the difference there between number of observations about the context,

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Speaker 1: it's increase over what Americans do. Uh. And that's done

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Speaker 1: with no loss of information about the central objects. Uh.

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Speaker 1: So there and so we wanted to see, well, what

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Speaker 1: are they doing? Are they looking at this this thing differently?

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Speaker 1: And the answer is yes they are. Um. We put

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Speaker 1: a GisMo on their heads, um with a camera back

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Speaker 1: at their eyes, so we know where they're looking at

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Speaker 1: every moment. This is done with still photos uh and uh.

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Speaker 1: We show people the photos for ten or twenty seconds,

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Speaker 1: um and um. What we find is that the Americans

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Speaker 1: spend almost all their time looking at the object, the

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Speaker 1: front of it, at the back of it, at the

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Speaker 1: top of it, at the bottom of etcetera. The Japanese

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Speaker 1: uh spend much much more time on the context, and

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Speaker 1: they're actually constantly looking back and forth between the context

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Speaker 1: and the object. Um. So uh. They they're not just

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Speaker 1: seeing the object and the context, they're seeing relationships between

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Speaker 1: the object uh and the context. So uh. This is

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Speaker 1: the The effects are huge for perception. Uh. There are

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Speaker 1: some very large effects for cognition as well, which I

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Speaker 1: can tell you about if you're interested. Uh. So um.

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Speaker 1: One thing that we do, we do very simple studies

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Speaker 1: to make our points. Uh. We show people a picture

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Speaker 1: of a monkey. Uh. Ah. Let's let's get me take

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Speaker 1: a better example. We show people a picture of a

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Speaker 1: cow uh grass uh and um and a monkey uh.

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Speaker 1: And we say which two of these pictures go together? Uh.

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Speaker 1: The Americans said, well, the monkey and the cow go

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Speaker 1: to better go together because they're both animals. The Asians

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Speaker 1: are much more likely to say, well, the cow and

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Speaker 1: the grass go together because the cow eats the grass.

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Speaker 1: So they're seeing relationships automatically that are not so salient

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Speaker 1: to us. Uh and uh. They're paying much more attention

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Speaker 1: to the object. Uh. Some of the more consequential differences

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Speaker 1: have to do with understanding human behavior. UM. There is

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Speaker 1: UH an error UH in reasoning that people make. UM.

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Speaker 1: I wonder how many of your guests have heard of.

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Speaker 1: This error is called the fundamental attribution errors. So we

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Speaker 1: tend to attribute the causes of behavior to a person's

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Speaker 1: attributes UM. That is, UM, his personality, his abilities, his attitudes. UH,

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Speaker 1: and we tend to ignore the con text. UH. East

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Speaker 1: Asians are much more likely to pick up on the

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Speaker 1: context and to attribute behavior to the context than we are.

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Speaker 1: UM and UH. An example of this UH is UH

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Speaker 1: we do a study where we say, we've asked that

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Speaker 1: this person you're about to hear from UH, We've asked

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Speaker 1: him to please state the case uh as if you

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Speaker 1: were in a debate for why marijuana should be legalized

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Speaker 1: or some other topic and so UM and we say,

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Speaker 1: either this person gave this talk in response to a

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Speaker 1: psychology professor's request in response to a political science assignment,

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Speaker 1: or UH in respond to a debate coach who said,

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Speaker 1: I want you to give me the pro arguments. UH

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Speaker 1: as if you were in a debate and people then

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Speaker 1: hear this UH speech and now you asked them, UH,

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Speaker 1: what are you suppose this guy actually thinks uh and um.

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Speaker 1: People are hugely influenced by what he's said, even though

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Speaker 1: they know he was chosen randomly to give this particular talk.

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Speaker 1: Now Asians East Asians. When I say Asians, I nearly

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Speaker 1: always meet East Asians, Japanese, Chinese uh and um and Koreans,

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Speaker 1: but others as well. Uh we um tell people, Uh, well,

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Speaker 1: they've been told that this person was required to give

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Speaker 1: this each what do they think? They tell us? Americans teals, well,

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Speaker 1: he thinks pretty much what he said. Asians don't do that.

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Speaker 1: I don't really nobody thinks because he was assigned to

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Speaker 1: do this by somebody else. And that effect is really huge.

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Speaker 1: I mean, we make the error in that situation of

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Speaker 1: very big airror that they are much less likely to make.

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Speaker 1: They do make the fundamental attribution error also, but it's

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Speaker 1: just not nearly as frequently. I'm not nearly as strong

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Speaker 1: an effect. We're going to talk more about the fundamental

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Speaker 1: attribution err in a little bit, but I have to

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Speaker 1: ask what is the societal or cultural basis for this

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Speaker 1: difference between looking at things either with or without context

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Speaker 1: and relationships, and how does it manifest itself in in

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Speaker 1: each society. Clearly there's a different structure in both, and

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Speaker 1: the behaviors and economies and everything else are so different.

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Speaker 1: Tell us a little bit about what leads to this

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Speaker 1: and then where it goes from there. Uh. Well, first

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Speaker 1: of all, point out you, as you did earlier, that

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Speaker 1: the differences go back thousands of years UH and UM.

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Speaker 1: For example, for example, if you look at UM physics

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Speaker 1: beliefs in the West and in the East, UM, the

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Speaker 1: it turns out Westerners have always tended to have physical

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Speaker 1: beliefs which were of a variety of the fundamental attribution

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Speaker 1: Here they try to explain the behavior of objects purely

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Speaker 1: in terms of properties of a object. So you know,

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Speaker 1: the object felled because it's heavy. The Easterns have only

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Speaker 1: objects fell because the material supporting it wasn't sufficient to

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Speaker 1: bear the way. So they understand the relation between the

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Speaker 1: context and UH and the object and UH. Years ago

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Speaker 1: already Chinese were well aware of the concept of action

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Speaker 1: at a distance UM. So they understood the reason for

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Speaker 1: the tides, for example, which was not understood even by Galileo. Uh,

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Speaker 1: he didn't get it right. And they also had a

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Speaker 1: good concept of magnetism UH, and of acoustics. So they

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Speaker 1: they had very accurate lay intuitive physics as compared to Westerners. Now,

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Speaker 1: so that and the and the category and the logic

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Speaker 1: by the way, uh apropool of analytic reasoning. UH logic

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Speaker 1: was the story goes invented by Aristotle because he got

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Speaker 1: sick of hearing allows the arguments in the marketplace and

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Speaker 1: the political assembly. Um So he said, okay, can we

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Speaker 1: agree that if your argument has this structure, it's allows

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Speaker 1: the argument. Uh So. UH logic was formalized very early

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Speaker 1: in the West. It was never very much of an

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Speaker 1: interest in the East. Uh. And it was never formalized.

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Speaker 1: And there was only a very brief period in the

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Speaker 1: third century BC when there was any concern with logic

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Speaker 1: at all, and the it basically dropped out of of

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Speaker 1: the intellectual armaments of the East. So why do we

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Speaker 1: get these perceptual differences and these cognitive differences and also

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Speaker 1: as a consequence, the social differences? Uh? I believed early

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Speaker 1: on without much of it's that it was because of

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Speaker 1: the type of of economy uh that East Asians had

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Speaker 1: of versus Europeans. Uh. East Asians had a great especially

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Speaker 1: in China. I mean terrific circumstances for mass agriculture. Mass Agriculture,

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Speaker 1: especially rice culture, demands lots of cooperation from people. So

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Speaker 1: effective action depends on my looking at what you're doing

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Speaker 1: and understanding the relationship between your action and my action

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Speaker 1: and so on. So I'm constantly looking out there, and

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Speaker 1: I'm seeing the reasons for behavior, I'm seeing the context

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Speaker 1: that creates them, and just and incidentally, if I'm paying

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Speaker 1: attention out there, I have better understanding of physics. Um so.

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Speaker 1: Um And I had a very sharp Turkish student a

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Speaker 1: while back, who's oh, and I should say, what what

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Speaker 1: was the situation in Greece. Well, Greece was mountains descending

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Speaker 1: to the sea. Mass Agriculture is just not in the

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Speaker 1: cards in that situation. People to make a living by

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Speaker 1: trading and fishing and kitchen gardening and so on. Um So,

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Speaker 1: it's they don't have to pay attention to and then

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Speaker 1: herding sheepherding, especially goat. Uh So, they don't have to

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Speaker 1: pay attention to other people in leading an effective life.

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Speaker 1: Um So, this Turkish student says, why I know an

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Speaker 1: interesting town in Turkey where there are three major occupations farmers, fishers,

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Speaker 1: and herders. Now, if you guys are right about why

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Speaker 1: uh people think holistically in the East and analytically in

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Speaker 1: the west. Uh. Then we ought to find that the

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Speaker 1: farmers and the fishers think more holistically than the herders.

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Speaker 1: And that turns out to be true. So the herder

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Speaker 1: sees the monkey and the cow as being related, and

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Speaker 1: the and the farmers and the fishers see the cow

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Speaker 1: and the grass uh to be um related. And uh.

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Speaker 1: So then the really beautiful study on this was a

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Speaker 1: former student of mine looked at UH reasoning in China saying, well,

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Speaker 1: people ought to be more holistic and South China than

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Speaker 1: in North China because, Uh, you make your living historically

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Speaker 1: in the south by rice farming, which is extremely social dependent.

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Speaker 1: I mean, you can't do anything without lots of people cooperating,

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Speaker 1: and it's wheat farming in the north of China, which

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Speaker 1: is much less dependent on other people. Sure enough, people

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Speaker 1: reason more holistically in the South of China than in

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Speaker 1: the North of China. Quite a very long answer to

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Speaker 1: your question, No, but it's quite fascinating. Some of those

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Speaker 1: things I found in the book were really fascinating and curious.

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Speaker 1: And there's a thread that runs through all of this

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Speaker 1: UH that that is just intriguing. I want to start

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Speaker 1: with something that I thought was an urban legend, but

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Speaker 1: you presented very differently. One of the few factors that

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Speaker 1: predict success as a scientist is the amount of time

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Speaker 1: and childhood that person is sick. So explain that. Well,

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Speaker 1: my theory about that is who knows, it's a Factum,

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Speaker 1: we don't really know why. But my theory is that

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Speaker 1: if you're sick, you spend a lot of time in

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Speaker 1: bed by yourself, and there's not much to do. This

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Speaker 1: is pre TV. There's not much to do but read

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Speaker 1: and think. Uh. And um, this is gonna stand you

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Speaker 1: in good stead for certain kinds of occupations. Um, actually,

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Speaker 1: I think most occupations. Um. So then the reason that's

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Speaker 1: in my book, Uh, there's in the fact that it's

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Speaker 1: sort of an interesting tidbit, Uh, is that I spent

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Speaker 1: a huge amount of time alone wandering the the area

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Speaker 1: of El Paso, Texas, which is right across the border

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Speaker 1: from Warez. And Uh it was it was a wonderful

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Speaker 1: place to wander around. I would go down to the

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Speaker 1: Rio grand I would go to the irrigation canals and

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Speaker 1: look at the cat catfish and the crayfish. Uh. There

348
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Speaker 1: was a UM kind of motel for Mexican farm workers.

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Speaker 1: UM that I would uh go down and visit those folks,

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Speaker 1: usually taking a bunch of Walt Disney comics which I

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Speaker 1: had paid ten cents for and I charged them five

352
00:27:13,160 --> 00:27:16,840
Speaker 1: cents uh or and I much later I realized this

353
00:27:16,920 --> 00:27:22,440
Speaker 1: is a parado improving exchange. I got some money which

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Speaker 1: for the my comics, and they got their comics cheaper

355
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Speaker 1: than they would have had for them in the store. UM.

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Speaker 1: So it was it. I wandered around a lot, so

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Speaker 1: I was thinking a lot, and I was something of

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Speaker 1: an icelet that were not other kids in my neighborhood.

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Speaker 1: So I did a lot of reading these days that

360
00:27:43,080 --> 00:27:49,360
Speaker 1: wouldn't happen they be doing um looking at uh Instagram

361
00:27:49,520 --> 00:27:54,439
Speaker 1: or um playing video games. Right, there's no downtime in

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00:27:54,480 --> 00:27:59,760
Speaker 1: those potential negative ramifications for that. So let's talk about

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Speaker 1: some of your other research that that is somewhat intriguing.

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Speaker 1: Telling people about drug side effects has no impact on them,

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Speaker 1: and this eventually leads to regulation about all the drug

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Speaker 1: disclosures we see on pharmaceuticals and TV ads for different drugs.

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Speaker 1: You're credited or blamed or at least your research is

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Speaker 1: credited or blamed for those disclosures. Tell us how you

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00:28:26,119 --> 00:28:31,919
Speaker 1: came to that conclusion. Well, um, very early on, I

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Speaker 1: did a study where I asked people to take a

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Speaker 1: series of electric shocks of increasing intensity, and I asked

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Speaker 1: him to when can you first feel it? When does

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Speaker 1: it first become painful? And when is it too painful

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Speaker 1: to bear? By the way. A lot of people say, well,

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Speaker 1: I would never. I would walk out of the room. No,

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Speaker 1: you wouldn't. Everybody trying to be cooperative, and I presented

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Speaker 1: is a reasonable thing for them to be doing for science.

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Speaker 1: People will do it, um. But for some of these people,

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Speaker 1: I say, I want you to take this pills called

380
00:29:07,240 --> 00:29:14,080
Speaker 1: sproxin uh and um. It will cause your heart to

381
00:29:14,480 --> 00:29:16,320
Speaker 1: race a bit. It will come back and make your

382
00:29:16,400 --> 00:29:20,600
Speaker 1: breathing heavier and more irregular. If you may find that

383
00:29:20,760 --> 00:29:24,480
Speaker 1: your palms are a little bit sweaty, you may get

384
00:29:24,520 --> 00:29:29,000
Speaker 1: a tightness feeling in your stomach. These are the symptoms

385
00:29:29,040 --> 00:29:33,120
Speaker 1: people experience when you're giving them electric shock. They're the

386
00:29:33,160 --> 00:29:37,000
Speaker 1: arousal symptoms that just come with the territory. Other people

387
00:29:37,000 --> 00:29:39,920
Speaker 1: will say it'll just give them a few junk symptoms.

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00:29:39,920 --> 00:29:44,920
Speaker 1: It will cause headache and some matching. Uh and sure enough,

389
00:29:45,000 --> 00:29:47,280
Speaker 1: and this is for some reason you're asking them about

390
00:29:47,320 --> 00:29:50,800
Speaker 1: things all all the big effects studies of ever an

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Speaker 1: enormous effect. The people who have been told that the

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Speaker 1: arousal will accompany the drug H take four times the

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00:30:00,040 --> 00:30:04,720
Speaker 1: amperation of other people. Uh. And, by the way, and

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Speaker 1: this gets opens up a line of work which we

395
00:30:07,120 --> 00:30:09,680
Speaker 1: may want to talk about later. The reason he is

396
00:30:09,720 --> 00:30:13,040
Speaker 1: completely unconscious. You you ask people, you know, tell me

397
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Speaker 1: what did you think about the drug while you were

398
00:30:15,880 --> 00:30:18,720
Speaker 1: taking the shock? And I wasn't thinking about the drug

399
00:30:18,760 --> 00:30:22,360
Speaker 1: at all. Yes, they were, and what they were thinking

400
00:30:22,600 --> 00:30:27,840
Speaker 1: was they're getting more and more aroused as this thing

401
00:30:27,960 --> 00:30:30,200
Speaker 1: that I've been sure enough, their heart rate, they're breathing

402
00:30:30,880 --> 00:30:33,480
Speaker 1: patterns and so on or changing, just like we said

403
00:30:33,520 --> 00:30:38,160
Speaker 1: they would. They attribute the arousal, which otherwise would be

404
00:30:38,240 --> 00:30:42,840
Speaker 1: multiplying the pain, the pain experience. Uh. They're treating as

405
00:30:43,000 --> 00:30:46,120
Speaker 1: an external, external thing. It has nothing to do with

406
00:30:46,600 --> 00:30:53,640
Speaker 1: you know, my thoughts or emotions or uh anything else.

407
00:30:53,720 --> 00:30:58,080
Speaker 1: It's just it's just that drug that caused those symptoms.

408
00:30:58,760 --> 00:31:05,120
Speaker 1: Um and Uh. I did one other study which showed

409
00:31:05,120 --> 00:31:09,720
Speaker 1: a therapeutic out come for this kind of thing I

410
00:31:09,800 --> 00:31:15,480
Speaker 1: advertised for insomniacs. This was done at Yale UH with

411
00:31:15,560 --> 00:31:22,080
Speaker 1: Yale students UH for a study on on dreams. Allegedly. UM,

412
00:31:22,120 --> 00:31:24,320
Speaker 1: so I found out how long it took them to

413
00:31:24,360 --> 00:31:27,600
Speaker 1: get to sleep the previous two nights before they come

414
00:31:27,640 --> 00:31:33,560
Speaker 1: into the lab UH, and I tell them for the

415
00:31:33,600 --> 00:31:37,800
Speaker 1: next two nights, I want you to take this pill

416
00:31:37,920 --> 00:31:42,520
Speaker 1: before you go to sleep. And it's an arousal pill. UH.

417
00:31:42,920 --> 00:31:45,840
Speaker 1: It's going to cause you know, this meal may become

418
00:31:46,600 --> 00:31:50,880
Speaker 1: your breathing may may become irregular, your heart rate may

419
00:31:51,160 --> 00:31:55,959
Speaker 1: become faster, et cetera, all the arousal symptoms. Other people

420
00:31:56,520 --> 00:31:59,800
Speaker 1: we say nothing to about the effects of this pill,

421
00:32:00,520 --> 00:32:04,600
Speaker 1: or we actually say it will reduce their arousal symptoms.

422
00:32:05,680 --> 00:32:09,680
Speaker 1: And our anticipation was, I mean, here, this is based

423
00:32:09,680 --> 00:32:13,240
Speaker 1: on my myself as an insomniac. I used to you know,

424
00:32:13,360 --> 00:32:17,800
Speaker 1: lie in bed and you know, thinking about the day

425
00:32:17,960 --> 00:32:20,960
Speaker 1: and the future, and there were things I was concerned about,

426
00:32:21,040 --> 00:32:24,040
Speaker 1: and I start thinking about them and I get kind

427
00:32:24,040 --> 00:32:26,360
Speaker 1: of worked up, and so I'm saying, you know, I'm

428
00:32:26,400 --> 00:32:29,120
Speaker 1: getting aroused, and that done. That's a very bad idea.

429
00:32:30,280 --> 00:32:34,680
Speaker 1: So we anticipated and found that the people that we

430
00:32:34,920 --> 00:32:40,760
Speaker 1: had given the arousal instructions to got to sleep more quickly.

431
00:32:41,360 --> 00:32:47,600
Speaker 1: The nights that they took the bill, the people we said, uh,

432
00:32:47,720 --> 00:32:53,600
Speaker 1: the pill will will reduce your arousal, took longer to

433
00:32:53,640 --> 00:32:57,280
Speaker 1: get to sleep because we're thinking, you know, the the

434
00:32:57,280 --> 00:33:01,960
Speaker 1: the arousal that they experience because of they're worried about

435
00:33:02,080 --> 00:33:09,000
Speaker 1: the situation with their girlfriend or our exam tomorrow whatever. Um. Though,

436
00:33:09,160 --> 00:33:14,480
Speaker 1: the arousal that occurs as a result of these these scary,

437
00:33:14,600 --> 00:33:21,480
Speaker 1: unpleasant thoughts. Um They they'll they'll say, well, it must

438
00:33:21,480 --> 00:33:25,080
Speaker 1: be particularly bad tonight because I've got this pill that's

439
00:33:25,080 --> 00:33:27,520
Speaker 1: supposed to call me down. And by the way, that

440
00:33:27,600 --> 00:33:30,960
Speaker 1: was based on a personal experience as well. I after

441
00:33:31,000 --> 00:33:34,560
Speaker 1: I had insomnia for quite a while as undergraduate, I said, okay,

442
00:33:34,560 --> 00:33:37,560
Speaker 1: I'm gonna give up. I'm gonna take a sleeping drug.

443
00:33:37,600 --> 00:33:41,760
Speaker 1: And I took a sumin X and lay in bed

444
00:33:41,880 --> 00:33:45,120
Speaker 1: waiting for it to take effects, which it never did,

445
00:33:45,160 --> 00:33:47,920
Speaker 1: And it took me longer to get to sleep because

446
00:33:48,560 --> 00:33:51,320
Speaker 1: I said, well, this this ought to get rid of

447
00:33:51,320 --> 00:33:55,360
Speaker 1: those irreleval symptoms that I have, and it didn't. So

448
00:33:56,280 --> 00:33:59,320
Speaker 1: years later somebody said, well, samin X is practically useless.

449
00:33:59,480 --> 00:34:02,960
Speaker 1: So uh, it was never going to have an effect.

450
00:34:03,200 --> 00:34:07,960
Speaker 1: So so what's the relationships Taken the pill at bedtime

451
00:34:08,040 --> 00:34:10,960
Speaker 1: and think it will reduce arousal, it takes them longer

452
00:34:11,000 --> 00:34:15,719
Speaker 1: to get to sleep because they think, look, I've got

453
00:34:15,320 --> 00:34:19,800
Speaker 1: it at least as much arousal as usual and even

454
00:34:19,800 --> 00:34:24,000
Speaker 1: though I've got a pill in me. So Uh, the

455
00:34:24,040 --> 00:34:28,800
Speaker 1: general point made by those two studies is that uh,

456
00:34:29,080 --> 00:34:34,320
Speaker 1: you can have uh the effects of a pill. Uh

457
00:34:34,600 --> 00:34:38,719
Speaker 1: that uh that that that aren't really there there there

458
00:34:38,920 --> 00:34:44,240
Speaker 1: arousal is not being produced by that pill or reduced

459
00:34:44,280 --> 00:34:51,319
Speaker 1: by that pill, but by other experiences. Um. So this

460
00:34:51,480 --> 00:34:58,640
Speaker 1: work got picked up by the f D a M

461
00:34:58,680 --> 00:35:03,280
Speaker 1: because drug companies did not want to tell people about

462
00:35:04,000 --> 00:35:07,040
Speaker 1: the side effects that they might have from the drug,

463
00:35:07,880 --> 00:35:11,239
Speaker 1: because they said, well, the power of suggestion. We're psychologists, Sara,

464
00:35:11,239 --> 00:35:14,239
Speaker 1: and we know that the power of suggestion is such

465
00:35:14,320 --> 00:35:17,240
Speaker 1: that people are going to be experiencing these things that

466
00:35:18,040 --> 00:35:22,200
Speaker 1: uh they're not really uh have anything to do with

467
00:35:22,400 --> 00:35:25,960
Speaker 1: the pill, but they're gonna attribute they're gonna uh they'll

468
00:35:26,000 --> 00:35:29,960
Speaker 1: they'll they'll invent these effects. They'll start having these effects

469
00:35:30,040 --> 00:35:33,160
Speaker 1: if you tell them about it, because via the power

470
00:35:33,160 --> 00:35:37,520
Speaker 1: of suggestion. So let me jump in here and ask

471
00:35:37,560 --> 00:35:43,680
Speaker 1: a quick question. What's the relationship between the expected physiological

472
00:35:43,920 --> 00:35:47,960
Speaker 1: arousal in this external source and what I think lay

473
00:35:48,000 --> 00:35:54,839
Speaker 1: people think of as the placebo effect. Well, the placebo

474
00:35:54,960 --> 00:36:04,480
Speaker 1: effect is, in fact, you get certain symptoms, uh, certain experiences. Uh.

475
00:36:04,520 --> 00:36:08,879
Speaker 1: And if you've been given uh what is actually a

476
00:36:08,920 --> 00:36:12,719
Speaker 1: placebo uh and told that it will improve your condition

477
00:36:12,800 --> 00:36:18,480
Speaker 1: the pain or uh, your flu symptoms or whatever, people

478
00:36:18,520 --> 00:36:22,239
Speaker 1: will in fact often experience those things or think that

479
00:36:22,280 --> 00:36:26,520
Speaker 1: they're experiencing those things. So there is definitely a phenomenon

480
00:36:26,680 --> 00:36:29,319
Speaker 1: of the placebo effect. But in a way, this is

481
00:36:29,360 --> 00:36:35,680
Speaker 1: the opposite. In fact, I mean, this pill is not

482
00:36:35,800 --> 00:36:41,080
Speaker 1: causing any any symptoms of any kind, but they they

483
00:36:41,400 --> 00:36:47,120
Speaker 1: they think that the pill is causing certain symptoms. So,

484
00:36:48,200 --> 00:36:50,879
Speaker 1: but it very much depends on the symptoms. If it's

485
00:36:50,880 --> 00:36:56,680
Speaker 1: arousal symptoms, uh, they will attribute any naturally occurring arousal

486
00:36:57,120 --> 00:37:02,319
Speaker 1: to the pill. Uh and uh if it's uh decreasing

487
00:37:02,320 --> 00:37:08,240
Speaker 1: arousal symptoms, they'll attribute any arousal to their own state,

488
00:37:08,560 --> 00:37:13,800
Speaker 1: to their actual state, which they will assume is worse

489
00:37:14,520 --> 00:37:16,920
Speaker 1: than they might have thought. So you can read into

490
00:37:16,960 --> 00:37:22,080
Speaker 1: people's experiences arousal. Let's due to some external source or

491
00:37:22,160 --> 00:37:27,640
Speaker 1: you can read it out. So so the FDA requires

492
00:37:27,640 --> 00:37:33,240
Speaker 1: that all possible side effects be reported. Um. You mentioned

493
00:37:33,320 --> 00:37:39,440
Speaker 1: David Dunning earlier on that on your program. Uh, and

494
00:37:39,480 --> 00:37:45,800
Speaker 1: he's done a fascinating study showing that um, people who

495
00:37:45,920 --> 00:37:50,160
Speaker 1: have been uh well just a second, let me let

496
00:37:50,160 --> 00:37:53,760
Speaker 1: me make sure I get the the story right about

497
00:37:53,800 --> 00:37:59,920
Speaker 1: what he's finding. Yeah. Uh, Dunning has found that, uh,

498
00:38:00,320 --> 00:38:05,920
Speaker 1: people are less likely to pay attention to side effects

499
00:38:06,480 --> 00:38:09,279
Speaker 1: if you list twenty seven side effects, you know, like

500
00:38:09,360 --> 00:38:12,080
Speaker 1: the TV ads where there's may call blah blah blah

501
00:38:12,080 --> 00:38:17,040
Speaker 1: blah blah blah. The more symptoms you give them, the

502
00:38:17,160 --> 00:38:20,319
Speaker 1: less attention they paid to any of them, and the

503
00:38:20,400 --> 00:38:23,400
Speaker 1: less likely they are to remember it later. If you

504
00:38:23,520 --> 00:38:26,879
Speaker 1: just tell them a few of the very most important ones,

505
00:38:27,960 --> 00:38:33,160
Speaker 1: they're likely to retain that information. Um. So you know,

506
00:38:33,960 --> 00:38:38,720
Speaker 1: psychology has a lot to do with what drug effects are,

507
00:38:38,719 --> 00:38:43,400
Speaker 1: and what drugs side effects are, and what people attribute

508
00:38:43,800 --> 00:38:48,960
Speaker 1: the side effects too. So so let's jump to attribution theory.

509
00:38:49,719 --> 00:38:55,879
Speaker 1: What is the fundamental attribution error in acting versus observing.

510
00:38:55,880 --> 00:39:00,640
Speaker 1: I'm kind of intrigued in my field that when someone

511
00:39:00,719 --> 00:39:05,719
Speaker 1: sees a successful investor, a trader, um they tend to say, well,

512
00:39:05,760 --> 00:39:08,640
Speaker 1: that guy got lucky, But when they themselves are doing

513
00:39:09,400 --> 00:39:13,319
Speaker 1: pretty good, instead of crediting luck, it's obviously due to

514
00:39:13,360 --> 00:39:17,399
Speaker 1: their own skill set. How is that related to the

515
00:39:17,600 --> 00:39:21,800
Speaker 1: attribution theory, Well, in a way, that's almost the opposite

516
00:39:21,800 --> 00:39:32,520
Speaker 1: of the attributionary and the fundamental attributionary attribute behavior to

517
00:39:33,160 --> 00:39:35,640
Speaker 1: an attribute of the person, a personality, trade, or an

518
00:39:35,680 --> 00:39:41,239
Speaker 1: ability that really should be understood in terms of context

519
00:39:41,920 --> 00:39:48,839
Speaker 1: or situation. Um so um. But in showing that this

520
00:39:49,160 --> 00:39:52,799
Speaker 1: is a there's a difference in our likelihood that we

521
00:39:52,880 --> 00:39:59,480
Speaker 1: make this error for ourselves versus others. Um. If you,

522
00:40:00,680 --> 00:40:07,960
Speaker 1: um uh, show people a video of someone behaving in

523
00:40:08,000 --> 00:40:10,799
Speaker 1: a particular way and ask them why they did it,

524
00:40:10,880 --> 00:40:14,600
Speaker 1: they'll they'll go for the for the person's attributes, for

525
00:40:14,640 --> 00:40:17,920
Speaker 1: their traits instead of paying attention to the context. But

526
00:40:18,000 --> 00:40:21,839
Speaker 1: if it's they themselves who are doing this, they're they're

527
00:40:21,840 --> 00:40:26,000
Speaker 1: they're they're they're responding to the significant extent to the context.

528
00:40:26,040 --> 00:40:30,000
Speaker 1: So they say they attribute the behavior their own behavior,

529
00:40:30,120 --> 00:40:32,920
Speaker 1: they're much more likely to attribute it to the context

530
00:40:32,960 --> 00:40:37,160
Speaker 1: of the situation than they would the same behavior by

531
00:40:37,160 --> 00:40:43,400
Speaker 1: someone else. That's quite interesting. You mentioned an endless array

532
00:40:43,480 --> 00:40:48,359
Speaker 1: of different. Um, very famous psychologists and sociologists that you've

533
00:40:48,360 --> 00:40:52,400
Speaker 1: worked with over the years. Two of the people you

534
00:40:52,520 --> 00:40:57,320
Speaker 1: discuss really stand out and and I have to reference

535
00:40:57,360 --> 00:41:00,520
Speaker 1: both of them. Um, there's an old joke about Amos

536
00:41:00,560 --> 00:41:03,759
Speaker 1: Diversky that says, you know, the amis Diversky i Q

537
00:41:03,960 --> 00:41:07,799
Speaker 1: test is the smarter a person is the sooner they

538
00:41:07,840 --> 00:41:12,520
Speaker 1: figure out that Diversky is smarter than them. Um, is

539
00:41:12,520 --> 00:41:15,640
Speaker 1: that your line in the book. It seems to suggest

540
00:41:15,760 --> 00:41:18,720
Speaker 1: you're the person who coined that. Yes, I'm the person

541
00:41:18,719 --> 00:41:22,239
Speaker 1: who claims that it's become I mean every psychologist knows

542
00:41:22,320 --> 00:41:25,520
Speaker 1: that line for sure. I mean it's interesting that you know,

543
00:41:25,640 --> 00:41:28,080
Speaker 1: the smarter you are quickly to realize you're not as

544
00:41:28,120 --> 00:41:30,719
Speaker 1: smart as someone else. Actually, this is related to a

545
00:41:30,800 --> 00:41:35,520
Speaker 1: dunning finding, right, I mean, Um, the more knowledgeable people are,

546
00:41:35,560 --> 00:41:42,520
Speaker 1: the more the better calibrated they are uh for uh

547
00:41:42,880 --> 00:41:48,680
Speaker 1: for understanding uh the world they understand. They know very

548
00:41:48,719 --> 00:41:50,880
Speaker 1: little about science. So I'll tell you I don't know.

549
00:41:51,400 --> 00:41:53,080
Speaker 1: You asked me a question I can't answer. I don't

550
00:41:53,080 --> 00:41:56,080
Speaker 1: I don't know enough. More ignorant people will give you

551
00:41:56,120 --> 00:41:59,719
Speaker 1: an answer because they don't realize they don't have the

552
00:42:00,120 --> 00:42:05,680
Speaker 1: withal meta cognition as a distinct skill set from actual

553
00:42:06,080 --> 00:42:11,000
Speaker 1: knowledge of the space exactly. So the smarter somebody is,

554
00:42:11,080 --> 00:42:15,120
Speaker 1: the more likely they are to stand aside and not

555
00:42:15,360 --> 00:42:18,239
Speaker 1: and not give you an opinion about something if they

556
00:42:18,280 --> 00:42:23,640
Speaker 1: don't have feel like they have sufficient sufficient knowledge. And

557
00:42:23,800 --> 00:42:26,360
Speaker 1: we're going to spend a little more time talking about

558
00:42:26,800 --> 00:42:31,200
Speaker 1: intelligence and i Q and how malleable and modifiable that

559
00:42:31,400 --> 00:42:35,640
Speaker 1: is in a moment. But there's a quote you paraphrase

560
00:42:35,760 --> 00:42:38,719
Speaker 1: of of Hans. I'm not sure I'm pronouncing his name right,

561
00:42:38,760 --> 00:42:43,560
Speaker 1: Hans cell you Hans Um. Success in a science is

562
00:42:43,640 --> 00:42:50,520
Speaker 1: a multiplicative function. It's intelligence times education times, ambition times,

563
00:42:50,560 --> 00:42:55,000
Speaker 1: curiosity times, hard work times, the ability to get along

564
00:42:55,040 --> 00:42:57,920
Speaker 1: with people and not just super high i Q s.

565
00:42:58,440 --> 00:43:03,600
Speaker 1: Explain that it's quite a fast an eating little formula. Right. Well,

566
00:43:03,800 --> 00:43:05,600
Speaker 1: you have to have all those things to be a

567
00:43:05,640 --> 00:43:08,560
Speaker 1: successful scientist, and my my guess is that you have

568
00:43:08,680 --> 00:43:10,600
Speaker 1: to have pretty much all those things to be a

569
00:43:10,640 --> 00:43:17,239
Speaker 1: successful to be successful in business. Um and um, I

570
00:43:17,280 --> 00:43:21,640
Speaker 1: mean I have I know someone who had a tested

571
00:43:21,680 --> 00:43:25,000
Speaker 1: i Q of a hundred and eighty four and he

572
00:43:25,120 --> 00:43:27,520
Speaker 1: was incredible. I mean you didn't have to be with

573
00:43:27,640 --> 00:43:30,000
Speaker 1: him for very long to realize, oh my god, this

574
00:43:30,000 --> 00:43:35,839
Speaker 1: guy is really super super smart. Um. And but he

575
00:43:35,960 --> 00:43:41,160
Speaker 1: was slightly autistic. I mean, he was slightly off. He

576
00:43:41,760 --> 00:43:46,320
Speaker 1: would make comments that were inappropriate. Uh, he would laugh

577
00:43:46,400 --> 00:43:49,919
Speaker 1: when that wasn't the right reaction and so on. Uh.

578
00:43:50,000 --> 00:43:54,560
Speaker 1: And he had a PhD from a major university. Uh,

579
00:43:54,600 --> 00:44:01,120
Speaker 1: and he only managed to get very middling of a jobs.

580
00:44:01,560 --> 00:44:04,040
Speaker 1: Academic jobs in his career should have been, you know,

581
00:44:04,120 --> 00:44:08,120
Speaker 1: at a major university, and he was on very minor places.

582
00:44:08,160 --> 00:44:10,640
Speaker 1: So that's a case of you know that that the

583
00:44:10,719 --> 00:44:16,160
Speaker 1: particular thing that he lacked was ability to get along

584
00:44:16,200 --> 00:44:19,959
Speaker 1: with people. But ambition is another thing that you often

585
00:44:20,000 --> 00:44:22,600
Speaker 1: see if somebody he's got the whole package, but it

586
00:44:22,719 --> 00:44:27,520
Speaker 1: just doesn't care enough to to to put it all

587
00:44:27,560 --> 00:44:31,920
Speaker 1: into into play. There's a quote that makes this point,

588
00:44:31,960 --> 00:44:34,840
Speaker 1: by the way, which I really love from Warren Buffett,

589
00:44:35,640 --> 00:44:38,720
Speaker 1: who says, you know, investing is not a game where

590
00:44:39,719 --> 00:44:43,120
Speaker 1: the smartest guy wins. If you've got an i Q

591
00:44:43,280 --> 00:44:46,040
Speaker 1: of a hundred and sixty, you may as well sell

592
00:44:46,080 --> 00:44:49,759
Speaker 1: thirty of those points because you're not going to need him.

593
00:44:49,800 --> 00:44:52,120
Speaker 1: And there's there's a lot to that. You know, it's

594
00:44:52,200 --> 00:44:58,640
Speaker 1: not just raw processing power, it's judgment and decision making

595
00:44:58,960 --> 00:45:02,400
Speaker 1: and the ability to seek context, and a lot of

596
00:45:02,440 --> 00:45:07,320
Speaker 1: those things aren't necessarily i Q there, they're a different

597
00:45:07,320 --> 00:45:11,279
Speaker 1: form of intelligence, which kind of goes to and I

598
00:45:11,280 --> 00:45:13,879
Speaker 1: don't wanna I don't wanna give the game away, because

599
00:45:13,880 --> 00:45:16,440
Speaker 1: we'll talk about this in a moment, but it kind

600
00:45:16,480 --> 00:45:19,960
Speaker 1: of goes to the concept that there are a lot

601
00:45:20,000 --> 00:45:27,040
Speaker 1: of different ways to improve intelligence and improve, um, how

602
00:45:27,080 --> 00:45:30,160
Speaker 1: successful a person can be. And you point out in

603
00:45:30,200 --> 00:45:35,600
Speaker 1: the book that the average IQ score in developed Western

604
00:45:35,600 --> 00:45:39,520
Speaker 1: economies has risen over the past century. Tell us a

605
00:45:39,520 --> 00:45:43,240
Speaker 1: little bit about that, right, well, that it act actually

606
00:45:43,239 --> 00:45:48,000
Speaker 1: probably started with the industrial or revolution. Um. Suddenly you

607
00:45:48,200 --> 00:45:51,239
Speaker 1: have a workforce that needs to be able to read

608
00:45:51,280 --> 00:45:56,920
Speaker 1: and write and do arithmetic. So you start increasing the

609
00:45:56,960 --> 00:46:01,080
Speaker 1: amount of schooling, introducing it are if it's already they're

610
00:46:01,320 --> 00:46:06,640
Speaker 1: sort of an embryo. You enhance the education. Uh. And

611
00:46:07,360 --> 00:46:12,520
Speaker 1: for the last two years, the amount of time that

612
00:46:12,760 --> 00:46:16,680
Speaker 1: people spend in school is just kept on increasing. Ah.

613
00:46:17,480 --> 00:46:20,680
Speaker 1: And this is hard to believe it. Right after World

614
00:46:20,719 --> 00:46:22,880
Speaker 1: War Two only about six or eight percent of the

615
00:46:22,920 --> 00:46:27,080
Speaker 1: population had a college degree. Uh. And college does make

616
00:46:27,160 --> 00:46:29,560
Speaker 1: you smarter. And by the way, I had a data

617
00:46:29,560 --> 00:46:31,640
Speaker 1: about that if you want to hear about that. To

618
00:46:31,760 --> 00:46:35,160
Speaker 1: my surprise, I really didn't. I just thought, you know,

619
00:46:35,280 --> 00:46:38,120
Speaker 1: college teaches you stuff. I didn't realize it actually makes

620
00:46:38,120 --> 00:46:40,800
Speaker 1: you smarter and lots of very important ways. Well, it

621
00:46:41,600 --> 00:46:44,520
Speaker 1: does both, right. It teaches you things, but it also

622
00:46:44,880 --> 00:46:48,960
Speaker 1: gives you a framework to think about things. It's more

623
00:46:49,000 --> 00:46:52,200
Speaker 1: than just here's a list of data points, memorize them.

624
00:46:52,239 --> 00:46:56,240
Speaker 1: It's here's how to Here's a set of cognitive rules

625
00:46:56,719 --> 00:47:01,680
Speaker 1: and mental models that you can use to solve problems exactly.

626
00:47:02,360 --> 00:47:08,279
Speaker 1: UM so UM that An example UM that I like

627
00:47:08,520 --> 00:47:12,960
Speaker 1: is UM I have lots of lots of my studies

628
00:47:13,000 --> 00:47:17,480
Speaker 1: and lots of Firsky economist studies. By the way, UM

629
00:47:17,560 --> 00:47:20,040
Speaker 1: I had to do with showing the errors and reasoning

630
00:47:20,080 --> 00:47:24,480
Speaker 1: that people make because they don't think statistically or probabilistically

631
00:47:25,239 --> 00:47:28,960
Speaker 1: when they should. I mean, it's a these habits of

632
00:47:29,000 --> 00:47:33,399
Speaker 1: thought are not they're not common. We don't we don't

633
00:47:33,440 --> 00:47:37,480
Speaker 1: we're not typically taught them in such a way that

634
00:47:37,600 --> 00:47:43,239
Speaker 1: they can make contact with everyday life events. UM. For example,

635
00:47:43,400 --> 00:47:47,719
Speaker 1: they're one of their most famous studies. Uh. Is you

636
00:47:47,800 --> 00:47:52,000
Speaker 1: say there's a town where there are two hospitals, one

637
00:47:52,040 --> 00:47:55,520
Speaker 1: has about fifteen births per day, and the other has

638
00:47:55,560 --> 00:47:59,680
Speaker 1: about sixty births per day. At which of these hospitals

639
00:47:59,719 --> 00:48:02,000
Speaker 1: do you think there would be more days in the

640
00:48:02,120 --> 00:48:09,120
Speaker 1: year when or more of the births were boys. Most

641
00:48:09,160 --> 00:48:13,120
Speaker 1: people say that it shouldn't make any difference. Uh, And

642
00:48:13,239 --> 00:48:16,280
Speaker 1: about as many say it would be the larger hospital

643
00:48:16,520 --> 00:48:21,880
Speaker 1: as would be the smaller hospital. In fact, it's astronomically

644
00:48:22,000 --> 00:48:26,560
Speaker 1: more likely to happen at the smaller, smaller one. Right,

645
00:48:26,600 --> 00:48:28,600
Speaker 1: You're more likely to get a random outcome with a

646
00:48:28,719 --> 00:48:31,600
Speaker 1: smaller data set than you are with the larger data

647
00:48:31,640 --> 00:48:34,640
Speaker 1: is four times larger data set, Right, I mean you're

648
00:48:34,680 --> 00:48:39,680
Speaker 1: gonna go far away from if there's three births uh

649
00:48:39,800 --> 00:48:45,680
Speaker 1: per day, because uh, it's gonna be either two thirds

650
00:48:45,800 --> 00:48:53,120
Speaker 1: zero three thirds boys. That's um so, uh so that's

651
00:48:53,120 --> 00:48:56,160
Speaker 1: the case. We understand the law of large numbers. By

652
00:48:56,160 --> 00:49:00,480
Speaker 1: the way, even a hunter gatherers understood the law of

653
00:49:00,560 --> 00:49:04,000
Speaker 1: large numbers and a lot of important contexts. But we

654
00:49:04,160 --> 00:49:07,480
Speaker 1: don't have it stored away as an abstract rule to

655
00:49:08,160 --> 00:49:11,480
Speaker 1: any time there's a little light that goes on in

656
00:49:11,480 --> 00:49:15,480
Speaker 1: our heads. Data. Oh yeah, what's the end? What's the

657
00:49:15,560 --> 00:49:20,879
Speaker 1: number of cases? My favorite example of somebody not using

658
00:49:20,920 --> 00:49:25,120
Speaker 1: the law of large numbers and not understanding that the

659
00:49:25,160 --> 00:49:31,240
Speaker 1: more variable the dimension you're looking at, the more data

660
00:49:31,400 --> 00:49:34,080
Speaker 1: it's important to have to make a judgment. And this

661
00:49:34,200 --> 00:49:36,800
Speaker 1: comes from when I went for the first time to Europe.

662
00:49:37,280 --> 00:49:41,440
Speaker 1: I spent about a week in London week or ten days,

663
00:49:41,600 --> 00:49:44,800
Speaker 1: and it was absolutely gorgeous weather. I'm in blue sky,

664
00:49:45,120 --> 00:49:48,600
Speaker 1: seventy degrees every single day, and I came away start

665
00:49:48,719 --> 00:49:51,200
Speaker 1: with a lingering feeling that, you know, the British or

666
00:49:51,239 --> 00:49:54,440
Speaker 1: such complainers, they actually have wonderful weather, but they're always

667
00:49:54,480 --> 00:49:59,440
Speaker 1: complaining about the rain. Well, I I got h what

668
00:49:59,480 --> 00:50:01,680
Speaker 1: I deserve. The next time I went to London. It

669
00:50:01,840 --> 00:50:08,000
Speaker 1: never stopped dreaming the entire time. So now I wasn't

670
00:50:08,040 --> 00:50:11,400
Speaker 1: so foolish just to think that weather isn't as variable

671
00:50:12,200 --> 00:50:14,680
Speaker 1: in England as it is anywhere else. But I just

672
00:50:14,760 --> 00:50:17,239
Speaker 1: didn't take the trouble to say, wait a minute, how

673
00:50:17,320 --> 00:50:20,920
Speaker 1: much evidence do I really have about what the weather is?

674
00:50:21,880 --> 00:50:26,960
Speaker 1: Um so any rate. I studied people's failure to apply

675
00:50:27,080 --> 00:50:31,160
Speaker 1: the law of large numbers when they should. They're attributing

676
00:50:32,080 --> 00:50:36,480
Speaker 1: causality when the data they have is purely correlational. I

677
00:50:36,520 --> 00:50:44,280
Speaker 1: studied their inability to apply the regression, statistical regression concept, uh,

678
00:50:44,320 --> 00:50:48,880
Speaker 1: two problems and so on and UH. When I was

679
00:50:49,280 --> 00:50:51,080
Speaker 1: doing that work, I used to say, you know, not

680
00:50:51,120 --> 00:50:53,360
Speaker 1: only are we stupid, but you can't make a smarter

681
00:50:53,560 --> 00:50:56,160
Speaker 1: with these things. So when I said, well, you know,

682
00:50:57,320 --> 00:50:59,720
Speaker 1: I'm going to prove that, I mean, I don't actually

683
00:50:59,719 --> 00:51:02,319
Speaker 1: know for a fact that you know, you can't teach

684
00:51:02,360 --> 00:51:06,920
Speaker 1: people these things. To my astonishment, in fifteen or twenty minutes,

685
00:51:07,080 --> 00:51:12,360
Speaker 1: you can teach the law of large numbers, the regression principle, UH,

686
00:51:12,440 --> 00:51:17,560
Speaker 1: the cost cost benefit principles, including a crucially important one

687
00:51:18,320 --> 00:51:31,320
Speaker 1: of uh of of the cost principle. UH that UM,

688
00:51:31,360 --> 00:51:36,280
Speaker 1: I can't retrieve money I've already spent by consuming something.

689
00:51:37,040 --> 00:51:39,680
Speaker 1: So you know, I got go to a movie and

690
00:51:39,760 --> 00:51:42,560
Speaker 1: it's a lousy movie. You know, I may say, well,

691
00:51:42,920 --> 00:51:45,239
Speaker 1: I don't want this thing, is hardly worth it. I

692
00:51:45,320 --> 00:51:48,000
Speaker 1: just assumed be sitting at home. But you know, I

693
00:51:48,040 --> 00:51:50,080
Speaker 1: don't want to waste the money I paid. You know

694
00:51:50,440 --> 00:51:55,520
Speaker 1: which an economist says, honk wrong. You can't waste that money,

695
00:51:55,840 --> 00:51:59,480
Speaker 1: you already wasted it. Uh. All you can do now

696
00:51:59,640 --> 00:52:04,319
Speaker 1: is has send good money after bad. Uh. You talk

697
00:52:04,400 --> 00:52:08,799
Speaker 1: about this with books, And I had this experience more

698
00:52:08,920 --> 00:52:13,680
Speaker 1: or less around the same time you did the realization that, hey,

699
00:52:13,719 --> 00:52:17,440
Speaker 1: I'm not liking this book. I'm under no obligation to

700
00:52:17,520 --> 00:52:20,680
Speaker 1: finish it. It's not homework. I didn't sign a contract

701
00:52:20,880 --> 00:52:23,799
Speaker 1: if I'm not enjoying it. Put it aside. I know

702
00:52:23,920 --> 00:52:27,640
Speaker 1: people who have a real emotional difficulty in saying no, no,

703
00:52:27,719 --> 00:52:30,000
Speaker 1: I started it, I have to finish it. Well, I

704
00:52:30,480 --> 00:52:34,080
Speaker 1: was absolutely you know, pathological. I mean, if I started,

705
00:52:34,239 --> 00:52:36,960
Speaker 1: by God, I was going to finish it. Uh. And

706
00:52:37,160 --> 00:52:38,960
Speaker 1: and I don't know who told me that that's a

707
00:52:39,000 --> 00:52:42,560
Speaker 1: good rule. I guess I just invented it myself. But

708
00:52:42,560 --> 00:52:46,439
Speaker 1: of course it's a perfectly terrible rule. Uh. Whatever point

709
00:52:46,520 --> 00:52:50,800
Speaker 1: you're not enjoying or you're not learning, then you should stop. Um.

710
00:52:51,040 --> 00:52:56,840
Speaker 1: People don't have that principle, Uh, at such a level

711
00:52:56,920 --> 00:53:00,520
Speaker 1: of abstraction that it will make contact with all the

712
00:53:00,719 --> 00:53:04,840
Speaker 1: problems in life that it should so. Um so, But

713
00:53:05,120 --> 00:53:08,520
Speaker 1: you teach them in the laboratory just a very few

714
00:53:08,680 --> 00:53:12,640
Speaker 1: problems using one of these principles, or actually just giving

715
00:53:12,680 --> 00:53:18,080
Speaker 1: them the abstract principles, and either either one will help people.

716
00:53:18,160 --> 00:53:21,360
Speaker 1: And if you give them both abstract and concrete cases,

717
00:53:21,400 --> 00:53:25,240
Speaker 1: of course, um, they do even better. So I decided

718
00:53:25,239 --> 00:53:27,560
Speaker 1: to say, well, what what does college do for you

719
00:53:28,080 --> 00:53:31,400
Speaker 1: on these things? And I gave them a package of

720
00:53:31,480 --> 00:53:36,840
Speaker 1: problems like the the the that are solvable by using

721
00:53:36,840 --> 00:53:40,200
Speaker 1: the some cost rule, or the law of large numbers,

722
00:53:40,400 --> 00:53:45,080
Speaker 1: or the concept of statistical regression, which is that, uh,

723
00:53:45,320 --> 00:53:51,719
Speaker 1: extreme events are not likely to be so extreme the

724
00:53:51,800 --> 00:53:55,680
Speaker 1: next time they're encountered. We don't have that wired into us.

725
00:53:55,760 --> 00:54:00,080
Speaker 1: I mean, it's a tremendously important principle that we you

726
00:54:00,120 --> 00:54:04,279
Speaker 1: know it, just it doesn't come with the hardware or

727
00:54:04,320 --> 00:54:08,640
Speaker 1: the software for that matter, or the software. It doesn't

728
00:54:08,640 --> 00:54:13,279
Speaker 1: come with this when in in a few million years

729
00:54:13,320 --> 00:54:18,240
Speaker 1: of evolution would we have ever experienced things like exponential

730
00:54:18,320 --> 00:54:23,640
Speaker 1: growth or compounding. If it never comes up, it's just

731
00:54:23,719 --> 00:54:31,080
Speaker 1: a total blind spot to us until we're walked through. Right. Well,

732
00:54:31,200 --> 00:54:35,400
Speaker 1: Although that said, I've watched people get the money whole

733
00:54:35,520 --> 00:54:38,839
Speaker 1: problem explained to them. The you know do do you

734
00:54:39,239 --> 00:54:42,600
Speaker 1: do you stay with your original choice of the three doors,

735
00:54:43,120 --> 00:54:45,840
Speaker 1: or once a door is revealed, do you switch? Even

736
00:54:46,000 --> 00:54:49,560
Speaker 1: after you lay out the statistics to people, a lot

737
00:54:49,600 --> 00:54:52,640
Speaker 1: of people still refuse to accept that the odds have

738
00:54:52,760 --> 00:54:57,719
Speaker 1: changed and you should switch. Right, So we looked at

739
00:54:57,760 --> 00:55:00,400
Speaker 1: all of these kinds of things, and I'm and they

740
00:55:00,440 --> 00:55:03,320
Speaker 1: were just simple problems like I've just been talking about.

741
00:55:03,440 --> 00:55:07,120
Speaker 1: Or you tell people, uh, you know, at the end

742
00:55:07,160 --> 00:55:11,440
Speaker 1: of uh, the first couple of weeks the baseball season,

743
00:55:11,480 --> 00:55:15,080
Speaker 1: there are usually several batters with averages of four fifty

744
00:55:15,200 --> 00:55:18,040
Speaker 1: or higher, but no one ever finishes the season with

745
00:55:18,200 --> 00:55:22,239
Speaker 1: that an average. Why do you suppose that is? Now,

746
00:55:22,239 --> 00:55:24,239
Speaker 1: if you ask this question of the U of M

747
00:55:24,320 --> 00:55:28,279
Speaker 1: freshman before he set foot in the classroom, he will say,

748
00:55:28,760 --> 00:55:31,880
Speaker 1: and I'm making it he because he's more likely to

749
00:55:31,920 --> 00:55:35,359
Speaker 1: know about baseball. Uh. He will say, well, you know

750
00:55:35,480 --> 00:55:39,640
Speaker 1: the pictures make the necessary adjustments. Or you know, they

751
00:55:40,160 --> 00:55:42,439
Speaker 1: they they're doing so well, they kind of goof off

752
00:55:42,480 --> 00:55:49,239
Speaker 1: and stop trying as hard. After uh education lasting four

753
00:55:49,320 --> 00:55:51,799
Speaker 1: years at the University of Michigan, they're like they said, well,

754
00:55:51,800 --> 00:55:54,840
Speaker 1: wait a minute, early on in the season, there haven't

755
00:55:54,840 --> 00:55:59,320
Speaker 1: been that many at bats. If you think about it, uh,

756
00:56:00,000 --> 00:56:03,640
Speaker 1: after your first at back, your your average is either

757
00:56:03,760 --> 00:56:07,480
Speaker 1: zero or one. Uh So when you get more and more,

758
00:56:07,760 --> 00:56:10,520
Speaker 1: but nobody really has a four fifty level of ability

759
00:56:10,560 --> 00:56:14,040
Speaker 1: and that's going to show over the long haul. So

760
00:56:14,320 --> 00:56:19,799
Speaker 1: I mean it goes from like giving you that kind

761
00:56:19,840 --> 00:56:24,680
Speaker 1: of answer to sixty or see that kind of answer.

762
00:56:24,719 --> 00:56:29,680
Speaker 1: I mean, college is hugely important for making you smarter.

763
00:56:30,480 --> 00:56:36,000
Speaker 1: Peter teal to the contrary. Notwithstanding uh and uh so

764
00:56:36,120 --> 00:56:38,520
Speaker 1: this was I was bowled over, But this I couldn't again.

765
00:56:39,480 --> 00:56:42,880
Speaker 1: It's a tremendously strong effect. I mean, it's people go

766
00:56:43,280 --> 00:56:49,759
Speaker 1: on average from answering of our problems correctly to our

767
00:56:49,760 --> 00:56:53,520
Speaker 1: problems correctly. None of these things that you know, the

768
00:56:53,760 --> 00:56:59,279
Speaker 1: statistical rules, probabilistic rules, economic rules, psychological rules, none of

769
00:56:59,280 --> 00:57:05,600
Speaker 1: these things, uh are taught explicitly nearly as much as

770
00:57:05,600 --> 00:57:09,279
Speaker 1: they should be. I went and got after I did

771
00:57:09,320 --> 00:57:11,759
Speaker 1: the study, I went and got an economic economic couple

772
00:57:11,800 --> 00:57:16,760
Speaker 1: of economic economics texts, uh to see what they did

773
00:57:16,760 --> 00:57:20,000
Speaker 1: with the sunk cost, And both of them they spoke

774
00:57:20,040 --> 00:57:24,360
Speaker 1: of it only in business situations. Uh. They didn't say, oh,

775
00:57:24,400 --> 00:57:28,800
Speaker 1: by the way, this principle is important to you every day,

776
00:57:29,400 --> 00:57:33,400
Speaker 1: um in many many ways, or the opportunity cost principle.

777
00:57:33,400 --> 00:57:37,160
Speaker 1: They're they're not, they're not. They make no effort, And

778
00:57:37,200 --> 00:57:42,320
Speaker 1: statistics courses make no effort to say, look, statistics is relevant,

779
00:57:42,520 --> 00:57:46,720
Speaker 1: you know all the time, and probability and so on.

780
00:57:47,760 --> 00:57:53,880
Speaker 1: So there are opportunities to do uh greatly improve people's education.

781
00:57:54,000 --> 00:57:55,960
Speaker 1: Won't affect their i Q, but as sure as that

782
00:57:56,520 --> 00:58:01,440
Speaker 1: makes them smarter. Uh. And it's tragedy that these things

783
00:58:01,600 --> 00:58:04,880
Speaker 1: are rarely taught, certainly not in the way of UH

784
00:58:05,400 --> 00:58:08,040
Speaker 1: that will make contact with every other live events in

785
00:58:08,160 --> 00:58:12,520
Speaker 1: high school. Instead, we make high school students take algebra,

786
00:58:12,720 --> 00:58:15,160
Speaker 1: which they're not gonna use. I mean a tiny fraction

787
00:58:15,200 --> 00:58:19,200
Speaker 1: are ever going to use in algebra. Uh. And tragically

788
00:58:19,480 --> 00:58:22,919
Speaker 1: a lot of people drop out of high school because

789
00:58:22,920 --> 00:58:27,440
Speaker 1: they can't hack algebra well, algebra to go out of

790
00:58:27,440 --> 00:58:30,640
Speaker 1: the curriculum, as far as I'm concerned. And um, and

791
00:58:30,720 --> 00:58:36,840
Speaker 1: put probability, economics and statistics in. Yeah, I think that's

792
00:58:37,200 --> 00:58:42,480
Speaker 1: a fabulous suggestion. Last question in this segments on thinking.

793
00:58:43,000 --> 00:58:46,920
Speaker 1: You've now been at the University of Michigan for quite

794
00:58:46,920 --> 00:58:50,880
Speaker 1: a long while. Uh, tell us about how you came

795
00:58:51,000 --> 00:58:56,280
Speaker 1: to leave Yale and end up at Michigan, right, Uh, well,

796
00:58:56,360 --> 00:59:01,160
Speaker 1: my story there appall's most people who hear it. That's

797
00:59:01,160 --> 00:59:05,840
Speaker 1: why I asked, UM, so I was at Yale, personally

798
00:59:05,880 --> 00:59:09,720
Speaker 1: got the university, and I went to Michigan. Despite the

799
00:59:09,760 --> 00:59:14,520
Speaker 1: fact that the day of my interview it was in February.

800
00:59:15,280 --> 00:59:17,960
Speaker 1: An arbor is not a lovely place in February. It

801
00:59:18,040 --> 00:59:21,400
Speaker 1: may surprise you, don't know. And this was a particularly

802
00:59:21,520 --> 00:59:25,480
Speaker 1: unlovely day because there was dirty, patchy snow lying all

803
00:59:25,480 --> 00:59:30,480
Speaker 1: around the very cold um And I had was interviewed

804
00:59:30,520 --> 00:59:34,000
Speaker 1: by the executive Committee of the department, and uh, they

805
00:59:34,280 --> 00:59:36,760
Speaker 1: most of the time, and the discussion was spent with

806
00:59:36,880 --> 00:59:40,880
Speaker 1: him discussing baseball, which I know nothing about and isn't

807
00:59:41,240 --> 00:59:46,240
Speaker 1: really relevant to my career. Uh. And I spent the

808
00:59:46,280 --> 00:59:49,400
Speaker 1: bulk of my time, well not the bulk, but the

809
00:59:49,520 --> 00:59:52,760
Speaker 1: single the single person. I spent most time with a

810
00:59:52,880 --> 00:59:57,440
Speaker 1: colossal bore. But I went to the University of Michigan anyway.

811
00:59:57,600 --> 00:59:59,840
Speaker 1: In fact, I made up my mind I would go

812
01:00:00,040 --> 01:00:04,600
Speaker 1: the University of Michigan absolutely regardless of what happened on

813
01:00:05,240 --> 01:00:09,560
Speaker 1: when I was there, because I had heard only good

814
01:00:09,600 --> 01:00:15,320
Speaker 1: things about the university, about the psychology department, about the town, etcetera.

815
01:00:16,680 --> 01:00:20,400
Speaker 1: I'm going to improve my life and all of these ways,

816
01:00:20,480 --> 01:00:22,480
Speaker 1: and I'm not going to pay any attention to the

817
01:00:22,600 --> 01:00:26,000
Speaker 1: concrete data which I get. And that's a huge bias

818
01:00:26,080 --> 01:00:29,040
Speaker 1: that we have. You know, if if if I saw it,

819
01:00:29,040 --> 01:00:33,520
Speaker 1: it must be real. Well, no, it might not be. Uh,

820
01:00:33,560 --> 01:00:36,320
Speaker 1: you have better ways of finding out what an object

821
01:00:36,400 --> 01:00:40,520
Speaker 1: is like than examining it yourself. That's what we have

822
01:00:41,480 --> 01:00:44,840
Speaker 1: speech for and the ability to read, and we can

823
01:00:45,560 --> 01:00:50,000
Speaker 1: get information rather than relying on our own senses. And

824
01:00:50,040 --> 01:00:53,120
Speaker 1: it was, in fact the absolute right decision for me,

825
01:00:54,320 --> 01:00:58,800
Speaker 1: so I got what I deserved. Quite quite interesting. Let's

826
01:00:58,880 --> 01:01:03,080
Speaker 1: talk a little bit about some of the impacts of

827
01:01:03,120 --> 01:01:08,120
Speaker 1: your research on geography of thought. I'm kind of intrigued

828
01:01:08,640 --> 01:01:12,560
Speaker 1: by the conclusion that we focus way too much on

829
01:01:12,600 --> 01:01:15,760
Speaker 1: genetics and we really should be paying a whole lot

830
01:01:15,880 --> 01:01:20,320
Speaker 1: more attention to the environment, the culture, and the society.

831
01:01:20,400 --> 01:01:25,080
Speaker 1: Tell us a little bit about that, right, Well, Um,

832
01:01:24,600 --> 01:01:29,520
Speaker 1: I had studied reasoning for many, many years, and if

833
01:01:29,560 --> 01:01:33,080
Speaker 1: you're a psychologist, to sort of keep up on the

834
01:01:33,120 --> 01:01:36,120
Speaker 1: literature on many fields and one of them and most

835
01:01:36,120 --> 01:01:40,280
Speaker 1: psychologists know something about his intelligence. But for some reason

836
01:01:41,520 --> 01:01:45,800
Speaker 1: I got to thinking seriously about the intelligence literature. And

837
01:01:45,840 --> 01:01:48,240
Speaker 1: the more I thought and the more I read, the

838
01:01:48,280 --> 01:01:53,120
Speaker 1: more I realized that psychologists had gotten things desperately wrong

839
01:01:54,080 --> 01:01:58,120
Speaker 1: with intelligence. And there's a book that has all these

840
01:01:58,200 --> 01:02:01,680
Speaker 1: desperately wrong things, and it's called the Bell Curve. And

841
01:02:01,800 --> 01:02:05,800
Speaker 1: most of your listeners believe what the Bell curve tells them.

842
01:02:05,920 --> 01:02:13,960
Speaker 1: They believe that genetics is accounts for um of the

843
01:02:14,080 --> 01:02:18,400
Speaker 1: variation that you find an intelligence. They think that early

844
01:02:18,920 --> 01:02:22,400
Speaker 1: childhood environment is not all that important unless it's their

845
01:02:22,440 --> 01:02:24,320
Speaker 1: own kid and they've got to get there desperate to

846
01:02:24,360 --> 01:02:27,880
Speaker 1: get him in the very best uh daycare situation, which

847
01:02:27,920 --> 01:02:39,840
Speaker 1: is a mistake. Um. They um. They underestimate the lifetime

848
01:02:40,520 --> 01:02:45,160
Speaker 1: learning opportunities that make us uh much smarter or not,

849
01:02:45,320 --> 01:02:50,280
Speaker 1: as the case may bee. UH. They think that blacks

850
01:02:50,280 --> 01:02:54,200
Speaker 1: and whites have are separated in their i Q scores

851
01:02:54,240 --> 01:02:58,280
Speaker 1: on the average by fifteen points. They think, well, probably

852
01:02:58,480 --> 01:03:03,360
Speaker 1: maybe some of that genetic um. They think Asians have

853
01:03:03,600 --> 01:03:07,760
Speaker 1: higher i Q s than than your people of European

854
01:03:07,800 --> 01:03:11,360
Speaker 1: descent um and so on and all of that is

855
01:03:11,400 --> 01:03:15,840
Speaker 1: wrong um and uh. And I wrote a book called

856
01:03:16,040 --> 01:03:20,520
Speaker 1: Intelligence and How to Get It uh, which shows what's

857
01:03:20,520 --> 01:03:24,360
Speaker 1: wrong with all of that. Uh. The arguments are pretty

858
01:03:24,360 --> 01:03:28,600
Speaker 1: complicated in the case of, for example, how much of

859
01:03:29,040 --> 01:03:34,919
Speaker 1: i Q is due to your genes? UM. The most

860
01:03:35,000 --> 01:03:37,920
Speaker 1: interesting thing I can say about it, first of all,

861
01:03:37,920 --> 01:03:40,960
Speaker 1: it's it's I don't know what the contribution of genes is.

862
01:03:41,040 --> 01:03:45,440
Speaker 1: I know it's less than sixty, way less than of

863
01:03:46,240 --> 01:03:51,120
Speaker 1: the variation. Uh. And the most interesting and important thing

864
01:03:51,160 --> 01:03:56,320
Speaker 1: I can say about genes and intelligence is that the

865
01:03:56,320 --> 01:04:01,360
Speaker 1: the contribution to the i Q of a population of

866
01:04:01,520 --> 01:04:07,000
Speaker 1: upper middle class people only is about eight. It is

867
01:04:07,120 --> 01:04:12,000
Speaker 1: huge there. They're The variation that you see between, you know,

868
01:04:12,480 --> 01:04:16,080
Speaker 1: between people who were raised in upper middle class environments

869
01:04:16,240 --> 01:04:22,720
Speaker 1: is largely variation that's produced by their genes. In this country,

870
01:04:23,440 --> 01:04:27,440
Speaker 1: the contribution to i Q of genes of the lower

871
01:04:27,520 --> 01:04:33,840
Speaker 1: class is practically zero. How could that possibly be well,

872
01:04:34,440 --> 01:04:38,520
Speaker 1: because upper middle class families are all alike. Uh, they're

873
01:04:38,520 --> 01:04:43,200
Speaker 1: like happy families. The happy families are all alike. Upper

874
01:04:43,200 --> 01:04:46,280
Speaker 1: middle class families are all alike with respect to cotny

875
01:04:46,400 --> 01:04:49,320
Speaker 1: skill that they're they're very good, and there's not that

876
01:04:49,400 --> 01:04:55,680
Speaker 1: much variation. I mean, lawyer Smith and businesswoman Jones, their

877
01:04:55,800 --> 01:05:00,000
Speaker 1: kids are all getting essentially the same environment we respect

878
01:05:00,080 --> 01:05:03,760
Speaker 1: to cognitive skill training. So wait, let me interrupt you here.

879
01:05:03,800 --> 01:05:08,040
Speaker 1: So when you say the same environment there they read.

880
01:05:08,080 --> 01:05:11,760
Speaker 1: Their parents pay attention to them, They go to even

881
01:05:11,800 --> 01:05:16,240
Speaker 1: a decent school. And so everybody who has that similar

882
01:05:17,120 --> 01:05:22,320
Speaker 1: upper middle class or even middle class background is gonna

883
01:05:22,520 --> 01:05:25,680
Speaker 1: take the most advantage of their own genetic background, but

884
01:05:25,880 --> 01:05:31,560
Speaker 1: working class students you're suggesting get almost none of that, Yeah,

885
01:05:31,560 --> 01:05:35,360
Speaker 1: because of the chaos that you find UH in in

886
01:05:35,440 --> 01:05:38,760
Speaker 1: many of those families. And this has nothing to do

887
01:05:38,840 --> 01:05:41,439
Speaker 1: with race, This has nothing to do with religion. This

888
01:05:41,520 --> 01:05:45,880
Speaker 1: is really everybody just scrapping really hard to make ends meet,

889
01:05:46,280 --> 01:05:52,120
Speaker 1: and it it's not the ideal circumstance for raising a child, right.

890
01:05:52,320 --> 01:05:55,760
Speaker 1: And you know, some environments obviously lower class environments are

891
01:05:55,800 --> 01:05:57,600
Speaker 1: as good as you would ever find in an upper

892
01:05:57,600 --> 01:06:02,560
Speaker 1: middle class. But some of the environments are chaos chaotic

893
01:06:02,640 --> 01:06:06,880
Speaker 1: in the extreme, uh, and not much goes on that's

894
01:06:06,920 --> 01:06:15,560
Speaker 1: going to facilitate uh somebody rising to their level of ability. Um.

895
01:06:15,600 --> 01:06:21,200
Speaker 1: And when the environment is massively different across UH individuals,

896
01:06:21,480 --> 01:06:25,680
Speaker 1: as it is in the lower classes, then uh, it's

897
01:06:25,680 --> 01:06:28,920
Speaker 1: the environment that's going to drive the bus. And genes

898
01:06:28,960 --> 01:06:33,080
Speaker 1: are not really much relevant. A little side points that

899
01:06:33,200 --> 01:06:38,480
Speaker 1: all make here, and this is that this is true

900
01:06:40,240 --> 01:06:46,560
Speaker 1: only in the US among rich countries. In Scandinavia especially,

901
01:06:48,040 --> 01:06:51,280
Speaker 1: the contribution of genes to i Q is just as

902
01:06:51,320 --> 01:06:55,040
Speaker 1: great for the lower classes people with the least money

903
01:06:55,080 --> 01:06:59,280
Speaker 1: as it is the most money, because they're they're making

904
01:06:59,360 --> 01:07:02,720
Speaker 1: sure that the poorest have good things going on in

905
01:07:02,760 --> 01:07:07,360
Speaker 1: their environment, schooling and so on. So that um so

906
01:07:07,520 --> 01:07:13,800
Speaker 1: the genes can't express themselves. Uh in the lower classes

907
01:07:13,800 --> 01:07:22,600
Speaker 1: in Europe, especially Scandinavia. Um so. Um So that's that's

908
01:07:22,640 --> 01:07:26,160
Speaker 1: the most interesting thing I can say about genes and

909
01:07:26,240 --> 01:07:29,760
Speaker 1: the environment. Well, let's stay with this is that, because

910
01:07:29,800 --> 01:07:35,760
Speaker 1: you really raise too fascinating questions, and I'm going to

911
01:07:35,840 --> 01:07:38,680
Speaker 1: assume you answered it in intelligence and how to get it?

912
01:07:39,040 --> 01:07:43,920
Speaker 1: The first is what should parents of any economic background

913
01:07:44,480 --> 01:07:49,120
Speaker 1: be doing to help their children become as intelligent and

914
01:07:49,200 --> 01:07:52,400
Speaker 1: successful as possible? And then the bigger question is what

915
01:07:52,480 --> 01:07:57,960
Speaker 1: should the US as a society be doing? Yeah, all right,

916
01:07:58,600 --> 01:08:06,480
Speaker 1: of great Christians. Uh there it is wonderful anthropological work

917
01:08:06,720 --> 01:08:10,360
Speaker 1: that's gone on. Looking at where you just live with

918
01:08:10,440 --> 01:08:14,560
Speaker 1: a family for a few weeks or months and see

919
01:08:14,600 --> 01:08:18,360
Speaker 1: what goes on. Uh in the upper middle class family,

920
01:08:19,080 --> 01:08:23,479
Speaker 1: Uh that they typically eat dinner together, and the dinner

921
01:08:23,479 --> 01:08:26,519
Speaker 1: table conversation is kind of like a tennis game. I mean,

922
01:08:27,560 --> 01:08:30,639
Speaker 1: dad says something, and mom says something, and the kid

923
01:08:30,720 --> 01:08:34,000
Speaker 1: says something, and the dad responds to what the kids said,

924
01:08:34,040 --> 01:08:37,920
Speaker 1: and so on. They learned how to think to a

925
01:08:37,960 --> 01:08:42,840
Speaker 1: significant extent in family gatherings. These kids get taken to

926
01:08:42,920 --> 01:08:49,120
Speaker 1: the museum, they get read to, uh, often by quite

927
01:08:49,200 --> 01:08:54,600
Speaker 1: good books. Uh, in the lower class family. And and

928
01:08:54,640 --> 01:08:57,960
Speaker 1: this is intact. I mean we're not talking about pathological

929
01:08:58,040 --> 01:09:01,400
Speaker 1: I mean it's you know, debts. Dad's a lunch pale dad,

930
01:09:01,479 --> 01:09:09,479
Speaker 1: and and and mom's uh homemaker. Um, intact family. Uh.

931
01:09:10,200 --> 01:09:13,760
Speaker 1: There isn't much in the way of conversation with the kid.

932
01:09:13,800 --> 01:09:16,200
Speaker 1: You tell the kid what to do, and the kid

933
01:09:16,240 --> 01:09:18,960
Speaker 1: asks you for something, and that it's just not at

934
01:09:18,960 --> 01:09:23,400
Speaker 1: all the same thing. Uh. There often is some reading

935
01:09:23,720 --> 01:09:25,800
Speaker 1: uh in the lower class on it. You know, there

936
01:09:25,880 --> 01:09:28,360
Speaker 1: may be some little golden books. I don't know if

937
01:09:28,360 --> 01:09:31,320
Speaker 1: they still have those, But when the most of this

938
01:09:31,479 --> 01:09:36,280
Speaker 1: research was initially done, uh, those were common. Um. And

939
01:09:36,680 --> 01:09:43,519
Speaker 1: kids may get read to some Uh. And they don't

940
01:09:43,520 --> 01:09:49,440
Speaker 1: tend to go to museums. Uh. They don't have their information,

941
01:09:49,920 --> 01:09:56,160
Speaker 1: their their fiction of the top level, most interesting, most

942
01:09:56,320 --> 01:10:02,479
Speaker 1: likely to be food for thought. So Uh, there is

943
01:10:02,560 --> 01:10:07,280
Speaker 1: a huge difference between upper and lower class in cognity skills.

944
01:10:07,320 --> 01:10:10,479
Speaker 1: Some of that is undoubtedly genetic. I have no idea

945
01:10:10,840 --> 01:10:15,280
Speaker 1: what fraction, but we know most of it is environmental.

946
01:10:15,960 --> 01:10:21,599
Speaker 1: And this comes from studies of adoption. Uh, what happens

947
01:10:21,640 --> 01:10:24,800
Speaker 1: to a kid from a lower class who gets adopted

948
01:10:24,920 --> 01:10:29,479
Speaker 1: into a middle class family as compared to the kid

949
01:10:29,520 --> 01:10:33,360
Speaker 1: who stays in the family of origin. And the answer

950
01:10:33,439 --> 01:10:36,400
Speaker 1: is that's worth twelve to eighteen points in i Q.

951
01:10:36,960 --> 01:10:40,760
Speaker 1: I mean, that's enormous. That's the difference between somebody who

952
01:10:40,840 --> 01:10:44,040
Speaker 1: might or might not graduate from high school versus someone

953
01:10:44,120 --> 01:10:46,720
Speaker 1: that you would expect with complete college and might well

954
01:10:46,800 --> 01:10:52,280
Speaker 1: go on to post graduate work. Um. So uh. And

955
01:10:52,720 --> 01:10:56,680
Speaker 1: that's that shows how important the environment is. And that's

956
01:10:56,680 --> 01:11:01,479
Speaker 1: that's the end of the question. What should a country

957
01:11:01,520 --> 01:11:04,479
Speaker 1: like the United States be doing? If we have the

958
01:11:04,520 --> 01:11:07,439
Speaker 1: goal of hey, we want to see more of our

959
01:11:07,520 --> 01:11:10,559
Speaker 1: kids succeed. We want to level the playing field and

960
01:11:10,600 --> 01:11:15,360
Speaker 1: make sure everybody has the opportunity to do their best.

961
01:11:15,479 --> 01:11:19,800
Speaker 1: What is Scandinaview doing that we're not? Well? First of all,

962
01:11:19,840 --> 01:11:22,840
Speaker 1: they have they have daycare from I don't actually the

963
01:11:22,840 --> 01:11:31,840
Speaker 1: word go um and uh daycare is it's important. I

964
01:11:31,840 --> 01:11:35,720
Speaker 1: mean a good daycare program is important for intelligence for sure.

965
01:11:35,760 --> 01:11:39,640
Speaker 1: If we with the respect to specifically I quh, the

966
01:11:39,760 --> 01:11:46,080
Speaker 1: very best daycare programs that we have might may have

967
01:11:46,160 --> 01:11:49,640
Speaker 1: an early effect that tends to largely fade on i

968
01:11:49,840 --> 01:11:56,479
Speaker 1: q UM. But the Nobel Prize winning economist Heckman at

969
01:11:56,680 --> 01:12:02,439
Speaker 1: University of Chicago is summarize the evidence of what happens

970
01:12:02,479 --> 01:12:07,839
Speaker 1: to these kids thirty years after they either have daycare

971
01:12:07,920 --> 01:12:11,679
Speaker 1: or don't, and the differences I seem to be only

972
01:12:11,720 --> 01:12:17,400
Speaker 1: telling you about really big effects today. It's like increase

973
01:12:17,439 --> 01:12:20,200
Speaker 1: in the likelihood that the kid will graduate from high school,

974
01:12:20,560 --> 01:12:24,280
Speaker 1: a like increasing the likelier that to go to college,

975
01:12:25,000 --> 01:12:30,000
Speaker 1: UH having cutting in half of a likelihood that the

976
01:12:30,120 --> 01:12:32,000
Speaker 1: kid is going to go to jail or be a

977
01:12:32,040 --> 01:12:35,880
Speaker 1: public charge. I mean, stuff is being learned in there

978
01:12:35,920 --> 01:12:42,680
Speaker 1: about dealing with other people and self control, cooperation and

979
01:12:42,720 --> 01:12:47,120
Speaker 1: so on that are tremendously not not i q but

980
01:12:47,280 --> 01:12:51,639
Speaker 1: they're cognitive skills and social skills with a huge payoffs.

981
01:12:51,680 --> 01:12:55,679
Speaker 1: And by the way, the economists have shown that good

982
01:12:55,760 --> 01:13:01,639
Speaker 1: daycare UH pays back UH in a ratio of nine

983
01:13:01,760 --> 01:13:05,599
Speaker 1: to one, that is, nine dollars gained for every one

984
01:13:05,640 --> 01:13:10,040
Speaker 1: dollar spent on daycare. The gain to the treasury is

985
01:13:10,439 --> 01:13:15,080
Speaker 1: significant over the lifetime of a kid. So, I mean

986
01:13:15,080 --> 01:13:19,599
Speaker 1: it's just so important that early childhood education and there

987
01:13:19,640 --> 01:13:25,120
Speaker 1: and to the policy thing. Of course, in the the democrats,

988
01:13:25,520 --> 01:13:31,639
Speaker 1: UM three point five trillion. Hope. Uh, there is early

989
01:13:32,120 --> 01:13:37,479
Speaker 1: childhood care for everybody. That that's a huge investment. I mean,

990
01:13:37,520 --> 01:13:41,519
Speaker 1: it's a hugely valuable investment, not to mention the human

991
01:13:41,600 --> 01:13:47,559
Speaker 1: suffering and and h and human well being that are involved.

992
01:13:48,600 --> 01:13:52,160
Speaker 1: Quite quite intriguing. I want to talk about some of

993
01:13:52,200 --> 01:13:58,040
Speaker 1: the things you've discussed about the state of of psychological

994
01:13:58,120 --> 01:14:02,960
Speaker 1: research and academic research in general. Uh, most importantly with

995
01:14:03,080 --> 01:14:08,960
Speaker 1: the reproduction problem. Tell us what's happening, um in the

996
01:14:09,040 --> 01:14:15,760
Speaker 1: ability to reproduce academic research? Right. Well, this is a

997
01:14:15,760 --> 01:14:18,840
Speaker 1: matter of some passion for me, as you might imagine.

998
01:14:19,840 --> 01:14:25,439
Speaker 1: Several years ago, UH, psychologist named Brian Noseck and a

999
01:14:25,479 --> 01:14:31,320
Speaker 1: lot of other psychologists published an article in Science claiming

1000
01:14:31,400 --> 01:14:37,439
Speaker 1: that only of the psychology experiments that they attempted to

1001
01:14:37,479 --> 01:14:42,280
Speaker 1: replicate actually did replicate. UM. By the way, even if

1002
01:14:42,280 --> 01:14:46,559
Speaker 1: that were true, which it isn't, it would be better

1003
01:14:46,600 --> 01:14:51,559
Speaker 1: than what the drug companies do. Uh. Their replication rate

1004
01:14:51,640 --> 01:15:00,559
Speaker 1: is significantly lower than But the research was deep flawed

1005
01:15:00,640 --> 01:15:05,400
Speaker 1: in many ways. For one thing, UH, a lot of

1006
01:15:05,400 --> 01:15:09,120
Speaker 1: the studies were chosen because someone found the results to

1007
01:15:09,160 --> 01:15:14,479
Speaker 1: be implausible. So only fifty of the on their face

1008
01:15:14,520 --> 01:15:18,840
Speaker 1: of it, implausible research uh got reproduced would be the

1009
01:15:18,840 --> 01:15:24,639
Speaker 1: way to describe what they did. But they they did

1010
01:15:25,000 --> 01:15:29,760
Speaker 1: a lot of things that were astonishingly far from what

1011
01:15:29,880 --> 01:15:35,120
Speaker 1: the original investigators did. Uh. Italians were asked of opinions

1012
01:15:35,160 --> 01:15:38,680
Speaker 1: about African Americans. Well, they don't have the same understanding

1013
01:15:38,720 --> 01:15:43,040
Speaker 1: of African Americans in Italy that we do. Uh. People

1014
01:15:43,120 --> 01:15:48,720
Speaker 1: are asked, um how they would feel about, you know,

1015
01:15:48,840 --> 01:15:53,120
Speaker 1: flubbing a question in college class, but people who uh

1016
01:15:53,360 --> 01:15:57,800
Speaker 1: and replicating this by somebody who makes an error uh

1017
01:15:57,920 --> 01:16:03,080
Speaker 1: in business quite a different kind of thing. Uh. And

1018
01:16:03,880 --> 01:16:08,599
Speaker 1: if you, if you improve on this situation, you'll get

1019
01:16:08,600 --> 01:16:18,840
Speaker 1: about of people of studies replicating. Just simply asking the

1020
01:16:18,920 --> 01:16:22,120
Speaker 1: investigator of the studies said, look, this is our understanding

1021
01:16:22,160 --> 01:16:26,080
Speaker 1: of what you did. Uh? Is that right? And they say, yeah,

1022
01:16:26,160 --> 01:16:30,519
Speaker 1: go ahead, or know you're missing something there? Uh. Just

1023
01:16:30,760 --> 01:16:38,639
Speaker 1: that gets it up. But in fact there was. It's

1024
01:16:38,720 --> 01:16:45,040
Speaker 1: extremely misleading to talk about the replicability of randomly selected studies,

1025
01:16:45,120 --> 01:16:51,400
Speaker 1: let alone the replicability of implausible seeming studies. Because I've

1026
01:16:51,479 --> 01:16:55,360
Speaker 1: asked I started asking people after this came out, Uh,

1027
01:16:55,479 --> 01:17:00,799
Speaker 1: can you think of any finding in psychology which seemed

1028
01:17:01,240 --> 01:17:05,720
Speaker 1: interesting and important? When it came out. Uh, that then

1029
01:17:05,840 --> 01:17:11,680
Speaker 1: turns out not to be the case and doesn't replicate. Um,

1030
01:17:12,080 --> 01:17:15,360
Speaker 1: and uh people have to think for a while. I've

1031
01:17:15,360 --> 01:17:17,760
Speaker 1: only got a list of about a half dozen studies

1032
01:17:17,800 --> 01:17:21,559
Speaker 1: like that at this point. So that's enough to tell

1033
01:17:21,600 --> 01:17:25,000
Speaker 1: you we don't have a replicability problem. It's a certain class,

1034
01:17:25,040 --> 01:17:28,240
Speaker 1: you know, run of them l studies are Uh. But

1035
01:17:28,360 --> 01:17:33,760
Speaker 1: if studies you know, have been vetted appropriately. Uh, there's

1036
01:17:33,960 --> 01:17:39,120
Speaker 1: there's a very low rate of misinformation getting into the system.

1037
01:17:39,240 --> 01:17:43,240
Speaker 1: And you're talking about actual lab studies, not things like

1038
01:17:44,200 --> 01:17:49,479
Speaker 1: Freud's abstract theory of the ID subsequently being replaced by

1039
01:17:49,520 --> 01:17:56,320
Speaker 1: a different thesis of psychology. You're talking about actual experimental psychology. Right.

1040
01:17:56,479 --> 01:18:00,200
Speaker 1: Let me give you an example. Uh. It's a one

1041
01:18:00,200 --> 01:18:04,320
Speaker 1: thing that we haven't gotten onto is how incredibly much

1042
01:18:04,360 --> 01:18:11,879
Speaker 1: we are affected by small, situational, contextual things in our lives. Uh,

1043
01:18:12,280 --> 01:18:16,640
Speaker 1: they were affected unconsciously. One of clever study you have

1044
01:18:16,840 --> 01:18:22,680
Speaker 1: people hold a pencil in their mouth with the end

1045
01:18:22,680 --> 01:18:25,200
Speaker 1: of the pencil pointing in, which gives you kind of

1046
01:18:25,240 --> 01:18:29,639
Speaker 1: a sour puss and feel when you do that. Are

1047
01:18:29,800 --> 01:18:34,000
Speaker 1: you have them hold it between their teeth, Uh, horizontal

1048
01:18:34,040 --> 01:18:37,200
Speaker 1: to the floor, which makes it makes it you feel

1049
01:18:37,240 --> 01:18:40,400
Speaker 1: like you're smiling. And you have people look at cartoons

1050
01:18:41,160 --> 01:18:44,519
Speaker 1: under one of these conditions or the other, and sure enough,

1051
01:18:44,560 --> 01:18:48,160
Speaker 1: people who have been forced to smile I think the

1052
01:18:48,200 --> 01:18:52,920
Speaker 1: cartoons are funnier. So then there's like ten or twenty

1053
01:18:53,040 --> 01:18:57,880
Speaker 1: studies attempting to replicate that effect and they don't find it.

1054
01:18:57,920 --> 01:19:00,840
Speaker 1: So that study just drops up. People's stop referring to this.

1055
01:19:00,920 --> 01:19:03,920
Speaker 1: It doesn't work. It turns out that all of the

1056
01:19:04,000 --> 01:19:07,559
Speaker 1: studies that attempted to replicate this have a video camera

1057
01:19:07,640 --> 01:19:11,559
Speaker 1: on the person. Very the person knows that he's being watched.

1058
01:19:11,560 --> 01:19:16,040
Speaker 1: The whole point about this thing is that you're unaware

1059
01:19:16,280 --> 01:19:19,439
Speaker 1: in a conscious way of the fact that your facial

1060
01:19:19,479 --> 01:19:23,240
Speaker 1: expressions has been manipulated, and that's what allows the effect

1061
01:19:23,400 --> 01:19:26,400
Speaker 1: to occur. So sure enough, when you do it with

1062
01:19:26,760 --> 01:19:29,240
Speaker 1: the video camera, you don't get the effect. When you

1063
01:19:29,280 --> 01:19:32,160
Speaker 1: do it without the video camera, you do get the effect.

1064
01:19:32,920 --> 01:19:38,639
Speaker 1: Um so, uh so the the work is is really

1065
01:19:38,760 --> 01:19:44,560
Speaker 1: very bad. On the other hand, I will say that, uh,

1066
01:19:44,880 --> 01:19:49,880
Speaker 1: that's article started a movement to look at the practices

1067
01:19:49,960 --> 01:19:53,840
Speaker 1: of psychologists research practices, and there have been a number

1068
01:19:53,840 --> 01:19:58,640
Speaker 1: of improvements. Uh uh, you're more likely. Going back to

1069
01:19:58,680 --> 01:20:00,519
Speaker 1: the law of large numbers, you're more like it to

1070
01:20:00,560 --> 01:20:03,360
Speaker 1: get a spurious result if you use a small number

1071
01:20:03,400 --> 01:20:09,720
Speaker 1: of subjects, because you you may uh you know, get

1072
01:20:09,760 --> 01:20:12,599
Speaker 1: something happening that isn't very likely to happen. You just

1073
01:20:13,040 --> 01:20:17,000
Speaker 1: use the small enough sample that you get it. I

1074
01:20:17,120 --> 01:20:19,920
Speaker 1: went back and looked at some of the studies early

1075
01:20:20,000 --> 01:20:23,920
Speaker 1: in my career, and I was appalled at how few

1076
01:20:23,960 --> 01:20:29,080
Speaker 1: subjects I used, uh, including the studies that I mentioned

1077
01:20:29,120 --> 01:20:37,080
Speaker 1: about externalizing arousal effects ends of twelve per sell and

1078
01:20:37,120 --> 01:20:41,439
Speaker 1: it's it's just it's inappropriate. Uh. And over time, like

1079
01:20:41,560 --> 01:20:48,240
Speaker 1: everybody else, drifted to much larger numbers of cases than that. UM.

1080
01:20:48,280 --> 01:20:51,800
Speaker 1: But now no one would ever use in as low

1081
01:20:51,920 --> 01:20:54,760
Speaker 1: as most of us used at least some of the

1082
01:20:54,840 --> 01:20:59,920
Speaker 1: time decades ago. UM. And there there are other pract

1083
01:21:00,080 --> 01:21:02,880
Speaker 1: just this to some good, some not so good. But

1084
01:21:03,439 --> 01:21:06,280
Speaker 1: the ones that are good, they're they're valuable that they

1085
01:21:06,320 --> 01:21:11,440
Speaker 1: have changed people's behavior. So so, speaking of doing experiments,

1086
01:21:11,560 --> 01:21:16,719
Speaker 1: I'm kind of fascinated by things like introspective reports, where

1087
01:21:16,760 --> 01:21:22,000
Speaker 1: people are verbally describing what they're thinking about how they're thinking,

1088
01:21:22,080 --> 01:21:25,840
Speaker 1: and you kind of reach the conclusion that these sorts

1089
01:21:25,840 --> 01:21:30,240
Speaker 1: of things. At best, people can explain what they think

1090
01:21:30,280 --> 01:21:34,639
Speaker 1: they're thinking about, but not really how they actually think.

1091
01:21:35,000 --> 01:21:39,000
Speaker 1: Tell us a bit about that, right, well. Um, some

1092
01:21:39,080 --> 01:21:43,559
Speaker 1: of my favorite studies here about showing that we don't

1093
01:21:43,560 --> 01:21:46,760
Speaker 1: know what's going on in our heads. One beautiful one

1094
01:21:46,880 --> 01:21:52,000
Speaker 1: shows people of matrix of four cells up, down, right, left,

1095
01:21:53,120 --> 01:21:55,280
Speaker 1: and it tells us such a I'm gonna put an

1096
01:21:55,479 --> 01:21:59,040
Speaker 1: X on the computer screen and one of those uh

1097
01:21:59,520 --> 01:22:03,760
Speaker 1: quadr and Uh, I want you to predict where it's

1098
01:22:03,760 --> 01:22:09,120
Speaker 1: going to appear. So early on the subject does terribly,

1099
01:22:09,920 --> 01:22:14,479
Speaker 1: and he gets better and better as time goes on.

1100
01:22:15,360 --> 01:22:19,200
Speaker 1: That's because there are rules that are determining where that

1101
01:22:19,479 --> 01:22:24,360
Speaker 1: X appears. It never appears in the same quadrant twice. Uh.

1102
01:22:24,439 --> 01:22:30,120
Speaker 1: It has to appear in quadrants to before it can

1103
01:22:30,320 --> 01:22:37,559
Speaker 1: appear in quadrant four, ETCETERA complicated rule system. Uh. And

1104
01:22:37,840 --> 01:22:40,120
Speaker 1: we know that people have learned the rules because when

1105
01:22:40,160 --> 01:22:43,320
Speaker 1: you change the rules, they fall back down to chance level.

1106
01:22:44,200 --> 01:22:46,600
Speaker 1: Now you ask this, if you say, tell me you

1107
01:22:46,760 --> 01:22:48,800
Speaker 1: you do it. You were doing so great there and

1108
01:22:48,800 --> 01:22:52,360
Speaker 1: then kind of you know, honest to say, you kind

1109
01:22:52,360 --> 01:22:55,320
Speaker 1: of fell apart. What happened? And I said, well, I

1110
01:22:55,800 --> 01:23:00,320
Speaker 1: just I just lost the rhythm. Um. We had psychology. Uh.

1111
01:23:00,400 --> 01:23:04,439
Speaker 1: Some professors us some of the subjects, and one of

1112
01:23:04,479 --> 01:23:10,960
Speaker 1: them said, well, I think you were showing me distracting symbols. Uh, subliminally.

1113
01:23:12,520 --> 01:23:18,799
Speaker 1: So here are people learning rules uh that are quite complicated. Uh,

1114
01:23:18,800 --> 01:23:21,639
Speaker 1: and no awareness that they've learned those rules at all.

1115
01:23:24,680 --> 01:23:30,519
Speaker 1: What's that? That's quite interesting? Yeah. So Uh. The Nobel

1116
01:23:30,680 --> 01:23:39,040
Speaker 1: Prize winner economist, organizational psychologist, um cognity psychologist Herbert Simon

1117
01:23:39,520 --> 01:23:43,559
Speaker 1: uh won the Nobel Prize. And you know, for while

1118
01:23:43,560 --> 01:23:46,840
Speaker 1: I was going around saying I do think a loud protocols.

1119
01:23:46,880 --> 01:23:50,400
Speaker 1: I have people solve problems and they think aloud, and

1120
01:23:50,640 --> 01:23:53,519
Speaker 1: that tells me the process that they're using to solve

1121
01:23:53,600 --> 01:23:58,000
Speaker 1: the problem. And my bryans action, Well, no, it it

1122
01:23:58,160 --> 01:24:02,760
Speaker 1: just says that they tell you how their theory of

1123
01:24:02,840 --> 01:24:05,280
Speaker 1: how they would have solved the problem, which happens to

1124
01:24:05,439 --> 01:24:10,040
Speaker 1: correspond to your theory. But that not isn't necessarily what's

1125
01:24:10,080 --> 01:24:16,080
Speaker 1: going on. Uh, And uh these studies established that. And

1126
01:24:16,080 --> 01:24:18,920
Speaker 1: and there was a controversy that went back and forth

1127
01:24:18,960 --> 01:24:24,360
Speaker 1: between uh Simon and me. Uh and uh he actually

1128
01:24:24,439 --> 01:24:28,439
Speaker 1: gave me a beautiful example of this. He says, you know,

1129
01:24:28,520 --> 01:24:34,040
Speaker 1: when when people play their first chess game. Um, it's

1130
01:24:34,240 --> 01:24:40,400
Speaker 1: uh uh. They they play, they make several moves, and

1131
01:24:40,400 --> 01:24:43,240
Speaker 1: then you asked them why you did that, and they

1132
01:24:43,240 --> 01:24:44,680
Speaker 1: can't tell you that I don't I don't know. I

1133
01:24:44,760 --> 01:24:47,599
Speaker 1: just was moving things randomly. I don't know why I

1134
01:24:47,640 --> 01:24:51,080
Speaker 1: was doing that. And Simon says, no, Actually, they were

1135
01:24:51,120 --> 01:24:56,320
Speaker 1: following rules. It's called Duffer's rules. Everybody plays the same

1136
01:24:56,360 --> 01:24:59,840
Speaker 1: way before they really have gotten any expertise at all.

1137
01:25:00,840 --> 01:25:02,840
Speaker 1: It says, then if they continue to play, and they

1138
01:25:02,880 --> 01:25:06,040
Speaker 1: read books and they talk to other chess players, they

1139
01:25:06,080 --> 01:25:10,519
Speaker 1: are now beautifully cognizant of what's going on, and there

1140
01:25:10,680 --> 01:25:13,479
Speaker 1: they can tell exactly why they moved the rook to

1141
01:25:14,600 --> 01:25:17,080
Speaker 1: this position instead of the bishop and so on, and

1142
01:25:17,160 --> 01:25:20,879
Speaker 1: they're quite accurate. Then if you look at chess masters,

1143
01:25:20,920 --> 01:25:25,439
Speaker 1: I mean, world class players, they're no longer accurate about

1144
01:25:25,479 --> 01:25:29,080
Speaker 1: what they're doing because they've forgotten some of the stuff

1145
01:25:29,120 --> 01:25:32,800
Speaker 1: they learned in books in a in a conscious way.

1146
01:25:33,439 --> 01:25:37,360
Speaker 1: And it's precisely because their masters is that they've invented,

1147
01:25:37,439 --> 01:25:41,599
Speaker 1: but they've induced certain rules that they're following, but they're

1148
01:25:41,680 --> 01:25:46,360
Speaker 1: they're hopeless. An example, an even simpler example is uh, language,

1149
01:25:46,720 --> 01:25:50,040
Speaker 1: we don't know what grammar is. If you try to

1150
01:25:50,240 --> 01:25:57,519
Speaker 1: have people explain English grammar, they're hopeless. They can't say

1151
01:25:57,520 --> 01:26:03,360
Speaker 1: a single correct thing. Uh. Nevertheless, we rarely violate those rules.

1152
01:26:03,400 --> 01:26:06,439
Speaker 1: We just don't have a conscious grasp on what they are.

1153
01:26:07,200 --> 01:26:12,960
Speaker 1: So we're constantly learning things, inducing things uh that uh

1154
01:26:13,200 --> 01:26:17,240
Speaker 1: we're not aware of in a conscious way, solving problems

1155
01:26:17,320 --> 01:26:21,880
Speaker 1: in an unconscious way. Um and uh. One of my

1156
01:26:23,040 --> 01:26:29,599
Speaker 1: very favorite studies, you have people in a lab. There's

1157
01:26:29,720 --> 01:26:33,120
Speaker 1: two ropes hanging down from the ceiling. There's a bunch

1158
01:26:33,160 --> 01:26:37,000
Speaker 1: of objects lying around on the tables in there, and

1159
01:26:37,040 --> 01:26:40,200
Speaker 1: the experimenter says, I want you to tie the two

1160
01:26:40,439 --> 01:26:44,160
Speaker 1: ropes together. But the problem is they're too short to

1161
01:26:44,160 --> 01:26:47,000
Speaker 1: do that. So you have to find a way. And

1162
01:26:47,080 --> 01:26:51,320
Speaker 1: one way is very obvious. You tie an extinction cord

1163
01:26:52,040 --> 01:26:54,439
Speaker 1: to one and you go over and grab the other,

1164
01:26:54,560 --> 01:26:57,200
Speaker 1: and you can now tie them together. And there are

1165
01:26:57,240 --> 01:27:00,400
Speaker 1: a lot of things like that. And after the subject

1166
01:27:00,400 --> 01:27:07,000
Speaker 1: has been stumped for a while, Uh, the experimenter says, uh, okay,

1167
01:27:07,439 --> 01:27:14,840
Speaker 1: so um uh, they do do it another way. Bill

1168
01:27:14,880 --> 01:27:17,439
Speaker 1: can do nothing, So then he goes he's been wandering

1169
01:27:17,479 --> 01:27:20,920
Speaker 1: around the lab while this is going on, handling around.

1170
01:27:21,560 --> 01:27:26,479
Speaker 1: He casually puts one of the ropes into motion. Then,

1171
01:27:26,840 --> 01:27:31,240
Speaker 1: typically within forty five seconds, the subject grabs a rope,

1172
01:27:31,520 --> 01:27:34,439
Speaker 1: ties a heavy object like a pair of pliers, to

1173
01:27:34,600 --> 01:27:37,800
Speaker 1: a second moving like a pendulum, goes to the other rope,

1174
01:27:38,120 --> 01:27:42,760
Speaker 1: grabs the pendulum rope and ties them together, and the

1175
01:27:42,840 --> 01:27:45,559
Speaker 1: investigator says that that's very that's very clever. How did

1176
01:27:45,560 --> 01:27:48,080
Speaker 1: you happen to think of that? And again, this is

1177
01:27:48,360 --> 01:27:52,160
Speaker 1: another one of the studies to use psychology professors as subjects.

1178
01:27:52,160 --> 01:27:57,800
Speaker 1: And one says, well, um, I thought of uh, monkeys, uh,

1179
01:27:58,360 --> 01:28:05,760
Speaker 1: swinging through trees across the river. The imagery occurred simultaneously

1180
01:28:06,160 --> 01:28:08,920
Speaker 1: with the solution, and he said, do you think it

1181
01:28:08,960 --> 01:28:12,000
Speaker 1: could have influenced you the fact that I put the

1182
01:28:12,120 --> 01:28:14,519
Speaker 1: rope in emotion? No, no, no, no, no. It's all

1183
01:28:14,560 --> 01:28:19,200
Speaker 1: about monkeys and rivers and swinging. And people have no

1184
01:28:19,360 --> 01:28:23,320
Speaker 1: idea why they were able to solve that problem. Now,

1185
01:28:23,600 --> 01:28:27,600
Speaker 1: this is not just a curio. It turns out that

1186
01:28:27,720 --> 01:28:32,120
Speaker 1: the unconscious mind is going on solving problems all the

1187
01:28:32,200 --> 01:28:36,559
Speaker 1: time for free. We're not aware of what's going on,

1188
01:28:36,640 --> 01:28:40,920
Speaker 1: but it's happening, and that's that's great. I mean, uh,

1189
01:28:41,120 --> 01:28:44,920
Speaker 1: it's uh. Most of the stuff that that goes on

1190
01:28:46,880 --> 01:28:50,240
Speaker 1: is accurate. So that's reminds me a little bit of

1191
01:28:50,280 --> 01:28:55,200
Speaker 1: some of the split brain experiments where people who have

1192
01:28:55,320 --> 01:28:58,840
Speaker 1: had their I guess it's the corpus colossum severed. So

1193
01:28:59,000 --> 01:29:01,280
Speaker 1: what they've seen, their eye goes to the left side

1194
01:29:01,320 --> 01:29:03,080
Speaker 1: of the brain, the left eye goes to the right side.

1195
01:29:03,400 --> 01:29:06,240
Speaker 1: But they're not communicating. And if they're showing shown two

1196
01:29:06,240 --> 01:29:10,360
Speaker 1: different things, two different images in each eye, they're unaware

1197
01:29:10,360 --> 01:29:14,200
Speaker 1: of why they're actually engaging in the sort of behavior

1198
01:29:14,240 --> 01:29:17,519
Speaker 1: they do based on whether something was shown to one

1199
01:29:17,560 --> 01:29:21,120
Speaker 1: side of their brain or the other. Is that related

1200
01:29:21,120 --> 01:29:25,519
Speaker 1: to this? It's a perfect example to bring to this.

1201
01:29:26,320 --> 01:29:31,720
Speaker 1: Those people are confabulating that they don't that the investigator

1202
01:29:31,760 --> 01:29:35,479
Speaker 1: has shown them something to the left eye. Uh, and

1203
01:29:36,000 --> 01:29:39,519
Speaker 1: that gets incorporated in the person's thinking about what's going on.

1204
01:29:39,760 --> 01:29:43,960
Speaker 1: But but it's not conscious, it's not been presented to

1205
01:29:44,000 --> 01:29:50,120
Speaker 1: the verbal hemisphere. So yeah, it's exactly that. It's a contabulation.

1206
01:29:50,560 --> 01:29:55,559
Speaker 1: We're going we're confabulating all the time. So when somebody

1207
01:29:55,560 --> 01:29:59,400
Speaker 1: asked me why I did something, Uh, if it's a psychologist,

1208
01:29:59,439 --> 01:30:02,320
Speaker 1: I say, well, you know, bear in mind who I am.

1209
01:30:03,880 --> 01:30:09,200
Speaker 1: I wrote you know all those articles about unconscious reasoning.

1210
01:30:09,280 --> 01:30:11,720
Speaker 1: So I don't really know why I did what it is,

1211
01:30:11,760 --> 01:30:15,760
Speaker 1: but I'll give you a story which sounds acceptable to

1212
01:30:15,840 --> 01:30:18,679
Speaker 1: me and will do you? Now, you you can only

1213
01:30:18,720 --> 01:30:22,519
Speaker 1: do this with psychologists. You can't. You can't just do

1214
01:30:22,560 --> 01:30:25,519
Speaker 1: this with aunt maa do well, thank you finally lost it?

1215
01:30:26,320 --> 01:30:32,320
Speaker 1: Um so? Uh, but we can put the unconscious to

1216
01:30:32,920 --> 01:30:36,559
Speaker 1: doing more work than it than it does and uh

1217
01:30:37,640 --> 01:30:40,919
Speaker 1: if you, but you have to give it some material

1218
01:30:41,120 --> 01:30:45,519
Speaker 1: to work over. So I use my teaching technique, my

1219
01:30:45,600 --> 01:30:49,920
Speaker 1: seminar teaching techniques. I hand out thought questions one week

1220
01:30:49,960 --> 01:30:52,599
Speaker 1: for and that will be the what we'll focus on

1221
01:30:52,960 --> 01:30:57,519
Speaker 1: in the seminar next week. And my joke about that is, uh,

1222
01:30:57,760 --> 01:31:00,720
Speaker 1: this is simple somewhat like the thought questions you may

1223
01:31:00,720 --> 01:31:03,400
Speaker 1: have seen in other classes, with the difference being that

1224
01:31:03,479 --> 01:31:08,000
Speaker 1: I expect you to have thought about these questions. Uh. Now,

1225
01:31:08,640 --> 01:31:11,120
Speaker 1: if I sit down just before class and write the

1226
01:31:11,160 --> 01:31:13,400
Speaker 1: thoughts questions out there, not going to be very good

1227
01:31:13,439 --> 01:31:15,920
Speaker 1: and the class is not going to be great. But

1228
01:31:16,000 --> 01:31:18,519
Speaker 1: if I just sit down for ten minutes and say, well,

1229
01:31:18,560 --> 01:31:21,200
Speaker 1: what are the main points? And I want this material

1230
01:31:21,280 --> 01:31:25,799
Speaker 1: to make and what would I do if somebody raises

1231
01:31:25,840 --> 01:31:30,639
Speaker 1: a you know particular point, how how would I deflect

1232
01:31:30,680 --> 01:31:34,439
Speaker 1: the discussion in that direction? And so on. I don't

1233
01:31:34,439 --> 01:31:37,200
Speaker 1: have to think for long. But then, you know, several

1234
01:31:37,280 --> 01:31:40,479
Speaker 1: days later, without having consciously thought about the thought questions

1235
01:31:40,560 --> 01:31:43,840
Speaker 1: at all, I sit down and start taking the thoughts

1236
01:31:44,040 --> 01:31:48,639
Speaker 1: questions by dictation. Just they start flowing out of my bid.

1237
01:31:49,120 --> 01:31:54,280
Speaker 1: And it turns out that if you ask highly creative people,

1238
01:31:54,360 --> 01:31:59,360
Speaker 1: I mean the great people uh point Array, the mathematician,

1239
01:32:00,479 --> 01:32:05,880
Speaker 1: Amy Lowell, the great uh poet Uh, if you ask them,

1240
01:32:05,920 --> 01:32:08,880
Speaker 1: you know, how did you come up with that? There?

1241
01:32:08,920 --> 01:32:11,400
Speaker 1: They say saying, well the point to Ray says, well,

1242
01:32:11,439 --> 01:32:15,080
Speaker 1: I put my foot on the I booth on vacation.

1243
01:32:15,160 --> 01:32:17,320
Speaker 1: I wasn't thinking about work at all, and at the

1244
01:32:17,360 --> 01:32:20,080
Speaker 1: moment I put my foot on the step of the bus,

1245
01:32:20,600 --> 01:32:23,640
Speaker 1: it occurred to me that the functions I needed to

1246
01:32:23,680 --> 01:32:27,600
Speaker 1: solve this problem where the very Fuxian functions that I

1247
01:32:27,640 --> 01:32:32,519
Speaker 1: had used to solve another problem years ago. Amy Lowell says,

1248
01:32:32,600 --> 01:32:35,680
Speaker 1: you know, I was in a art museum and I

1249
01:32:35,720 --> 01:32:39,840
Speaker 1: saw sculpture of bronze horses, and I thought, you know,

1250
01:32:39,920 --> 01:32:44,400
Speaker 1: it might be an interesting topic, uh for a um

1251
01:32:44,600 --> 01:32:48,360
Speaker 1: for a poem. Someday months later, she sits down, and

1252
01:32:48,439 --> 01:32:53,080
Speaker 1: as she says, the poem was there without having thought

1253
01:32:53,120 --> 01:32:55,800
Speaker 1: about it consciously in that period of time. The point

1254
01:32:55,840 --> 01:33:01,160
Speaker 1: being if you prime the unconscious with something, it'll still

1255
01:33:01,240 --> 01:33:03,840
Speaker 1: it'll golf doing work, including at night. I have a

1256
01:33:03,920 --> 01:33:08,440
Speaker 1: friend who said, you know, um, I'm working on Calculus

1257
01:33:08,520 --> 01:33:11,000
Speaker 1: homework problems, you know, and get the problems three and

1258
01:33:11,080 --> 01:33:14,439
Speaker 1: it's not. I can't figure it out and work for

1259
01:33:14,479 --> 01:33:17,400
Speaker 1: thirty minutes, nothing's happening. So it just goes to sleep,

1260
01:33:17,479 --> 01:33:20,479
Speaker 1: and often it's not. It wakes up. It's, oh, that's

1261
01:33:20,479 --> 01:33:26,000
Speaker 1: a beast up problem. That's all. That is amazing. A

1262
01:33:26,040 --> 01:33:30,040
Speaker 1: lot of the unconscious work, conveniently enough, gets done while

1263
01:33:30,080 --> 01:33:34,000
Speaker 1: we're asleep. Huh. That's absolutely fascinating. We've been speaking with

1264
01:33:34,040 --> 01:33:38,280
Speaker 1: Professor Richard Nisbet of the University of Michigan. If you

1265
01:33:38,439 --> 01:33:41,320
Speaker 1: enjoy this conversation, we'll be sure to check out any

1266
01:33:41,320 --> 01:33:45,439
Speaker 1: of the previous four hundred UH interviews we've conducted. You

1267
01:33:45,479 --> 01:33:48,840
Speaker 1: can find those at iTunes or Spotify or or your

1268
01:33:48,880 --> 01:33:52,920
Speaker 1: favorite podcast UH source. We love your comments, feedback and

1269
01:33:53,040 --> 01:33:56,879
Speaker 1: suggestions right to us at m IB podcast at Bloomberg

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Speaker 1: dot net. Sign up for my daily reads at helps

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Speaker 1: dot com. Check out my regular column at Bloomberg dot

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Speaker 1: com slash Opinion. Follow me on Twitter at Ridholts. I

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Speaker 1: would be remiss if I did not thank the crack

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Speaker 1: staff that helps me do these each week. Mohammed is

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Speaker 1: my audio engineer, Paris Wald is my producer. A tick

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Speaker 1: of Valbron is our project manager. Michael Batnick is our

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Speaker 1: head of research. I'm Barry Hults. You've been listening to

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Speaker 1: Master's in Business on Bloomberg Radio.