1 00:00:00,440 --> 00:00:04,440 Speaker 1: Look ahead, imagine more, gain insight for your industry with 2 00:00:04,480 --> 00:00:08,480 Speaker 1: forward thinking advice from the professionals at Cone Resnick. Is 3 00:00:08,480 --> 00:00:11,559 Speaker 1: your business ready to break through? Find out more at 4 00:00:11,640 --> 00:00:19,279 Speaker 1: Cone resnick dot com slash Breakthrough. This is Masters in 5 00:00:19,360 --> 00:00:23,680 Speaker 1: Business with Barry Ridholts on Bloomberg Radio. This week. On 6 00:00:23,720 --> 00:00:29,200 Speaker 1: the podcast, I had an absolutely fascinating and fantastic conversation 7 00:00:29,920 --> 00:00:36,160 Speaker 1: with a rock star professor of neural linguistics and cognitive 8 00:00:36,200 --> 00:00:41,920 Speaker 1: psychology and all sorts of other interesting uh fields of 9 00:00:41,960 --> 00:00:46,879 Speaker 1: study within the world of cognition and psychology, Professor Stephen 10 00:00:46,920 --> 00:00:51,640 Speaker 1: Pinker at Harvard University. This guy is a rock star. 11 00:00:51,880 --> 00:00:55,400 Speaker 1: This was really one of those conversations that was just 12 00:00:56,160 --> 00:01:00,760 Speaker 1: so fascinating and went in so many different directions. Uh. 13 00:01:00,840 --> 00:01:04,640 Speaker 1: He's a psychologist, but he also has a really fascinating 14 00:01:05,360 --> 00:01:08,880 Speaker 1: uh quantitative background, and so he's a guy that's actually 15 00:01:09,720 --> 00:01:13,720 Speaker 1: especially driven by data. When you think of things like 16 00:01:14,040 --> 00:01:18,160 Speaker 1: neural linguistics or or visual cognition, you don't think in 17 00:01:18,280 --> 00:01:22,119 Speaker 1: terms of of how's the math behind this? But he 18 00:01:22,280 --> 00:01:26,520 Speaker 1: has a mind that looks at the world very much 19 00:01:26,520 --> 00:01:30,960 Speaker 1: through a quantitative filter. Uh. He he wrote a number, 20 00:01:31,280 --> 00:01:35,400 Speaker 1: by the way, uh one a ton of awards at 21 00:01:35,480 --> 00:01:40,280 Speaker 1: at Harvard and throughout the sciences, highly highly regarded. His 22 00:01:40,400 --> 00:01:45,720 Speaker 1: books have are also really really well reviewed, very notable. 23 00:01:46,440 --> 00:01:50,640 Speaker 1: Uh the book Better Better Nature of Our Angels, why 24 00:01:50,800 --> 00:01:54,800 Speaker 1: violence has decreased around the world and indeed when you 25 00:01:54,800 --> 00:01:58,280 Speaker 1: look at it quantitatively, violence and war and crime or 26 00:01:58,320 --> 00:02:01,200 Speaker 1: at record low levels. I know it doesn't look that 27 00:02:01,240 --> 00:02:04,680 Speaker 1: way when you when you see the news, but that's 28 00:02:04,720 --> 00:02:09,600 Speaker 1: a fascinating conversation. It's I love that sort of counterintuitive. 29 00:02:09,600 --> 00:02:12,960 Speaker 1: Here's what everybody believes, it's all wrong, here's the data 30 00:02:12,960 --> 00:02:16,919 Speaker 1: and proving it. Uh. And and he brings that approach 31 00:02:17,000 --> 00:02:21,840 Speaker 1: to everything he touches. His His work on how the 32 00:02:22,280 --> 00:02:28,519 Speaker 1: mind works is really fascinating. How children acquire language skills 33 00:02:29,000 --> 00:02:33,240 Speaker 1: and why that is a significant evolutionary development amongst humans 34 00:02:33,800 --> 00:02:39,679 Speaker 1: is really, you know, groundbreaking, fascinating stuff. Um. You may 35 00:02:39,720 --> 00:02:42,799 Speaker 1: not think that there's an immediate application to the world 36 00:02:42,919 --> 00:02:47,040 Speaker 1: of investing, but he's just one of these people who 37 00:02:47,120 --> 00:02:51,880 Speaker 1: are so interesting and so knowledgeable and has such an 38 00:02:51,919 --> 00:02:57,320 Speaker 1: interesting model in his mind for how to approach thinking 39 00:02:57,360 --> 00:03:00,880 Speaker 1: about the world that I can't help but think that 40 00:03:00,919 --> 00:03:04,600 Speaker 1: there are lessons for investors in this. So a little 41 00:03:04,639 --> 00:03:09,000 Speaker 1: off the beaten path, but absolutely fascinating. Here is my 42 00:03:09,160 --> 00:03:16,880 Speaker 1: conversation with Professor Steven Pinker. This is Masters in Business 43 00:03:16,960 --> 00:03:21,280 Speaker 1: with Barry Ridholts on Bloomberg Radio. My special guest today 44 00:03:21,320 --> 00:03:25,240 Speaker 1: is Professor Steven Pinker. He is a rock star professor 45 00:03:25,320 --> 00:03:29,280 Speaker 1: of cognitive science and psychology at Harvard, where he holds 46 00:03:29,280 --> 00:03:33,760 Speaker 1: the title of Johnstone Family Professor in the psych Department. 47 00:03:34,120 --> 00:03:39,640 Speaker 1: He is a psychologist, linguist, and popular science author, specializing 48 00:03:39,680 --> 00:03:43,320 Speaker 1: in visual cognition and psycho linguistics. I think you can 49 00:03:43,400 --> 00:03:47,160 Speaker 1: find a lot of what we talked about today absolutely fascinating. 50 00:03:47,400 --> 00:03:50,920 Speaker 1: He's won numerous awards from the National Economy of Sciences, 51 00:03:51,200 --> 00:03:55,240 Speaker 1: the Royal Institute, the Cognitive Neuroscience Society. He is the 52 00:03:55,320 --> 00:04:00,160 Speaker 1: author of The Language Instinct, How the Mind Works, the 53 00:04:00,200 --> 00:04:03,320 Speaker 1: Better Angels of Our Nature, The Stuff of Thought, and 54 00:04:03,400 --> 00:04:07,560 Speaker 1: most recently The Sense of Style. He is also on 55 00:04:07,640 --> 00:04:12,920 Speaker 1: the usage panel of the American Heritage dis Dictionary. Professor Pinker, 56 00:04:13,000 --> 00:04:15,920 Speaker 1: welcome to Bloomberg. Thank you. First question, I have to 57 00:04:15,960 --> 00:04:21,120 Speaker 1: ask you what is visual cognition and psycholinguistics? Those are 58 00:04:21,160 --> 00:04:24,600 Speaker 1: two sub topics in the field of cognitive science, which 59 00:04:24,680 --> 00:04:27,000 Speaker 1: is how do we think? What? What is the nature 60 00:04:27,000 --> 00:04:31,320 Speaker 1: of intelligence. Visual cognition is how we um interpret what 61 00:04:31,360 --> 00:04:33,080 Speaker 1: we see, or how we think about what we see. 62 00:04:33,160 --> 00:04:35,200 Speaker 1: How do you recognize the face of a friend? How 63 00:04:35,200 --> 00:04:37,760 Speaker 1: do you find an object when you're rummaging through a drawer? 64 00:04:38,160 --> 00:04:41,880 Speaker 1: How do you imagine things that that are hypothetical, like 65 00:04:42,080 --> 00:04:44,560 Speaker 1: what would my living room look like if the couch 66 00:04:44,680 --> 00:04:46,560 Speaker 1: was on the other side, or what would the smalllecule 67 00:04:46,600 --> 00:04:50,320 Speaker 1: look like if I rotated in three dimensions? How do 68 00:04:50,440 --> 00:04:55,160 Speaker 1: we allocate attention across the visual field? How how does 69 00:04:55,200 --> 00:04:59,000 Speaker 1: your airport screener look for the hidden weapon in those 70 00:04:59,680 --> 00:05:02,720 Speaker 1: false colored X ray images and so on, or not 71 00:05:02,880 --> 00:05:07,120 Speaker 1: find them? So we have learned indeed, so that is 72 00:05:07,160 --> 00:05:09,839 Speaker 1: a problem in visual cognition. It's not vision in the 73 00:05:09,880 --> 00:05:13,400 Speaker 1: sense of seeing color and motion and uh and sharp detail, 74 00:05:13,560 --> 00:05:15,560 Speaker 1: but it's the next step up in the brain, namely, 75 00:05:15,600 --> 00:05:20,640 Speaker 1: how do you how does the visual world um interact 76 00:05:20,720 --> 00:05:23,039 Speaker 1: with what you know, what you see, what you think about. 77 00:05:23,400 --> 00:05:26,640 Speaker 1: So that raises an interesting question. How much of what 78 00:05:26,880 --> 00:05:30,719 Speaker 1: the average person perceives as a three hundred and sixty 79 00:05:30,760 --> 00:05:34,200 Speaker 1: d Greek construct of the universe around them, how much 80 00:05:34,200 --> 00:05:36,279 Speaker 1: of that is accurate and how much of that is 81 00:05:36,360 --> 00:05:40,680 Speaker 1: the brain filling in projecting. I don't want to say fabricating, 82 00:05:40,760 --> 00:05:44,560 Speaker 1: but filling in the blind spots and blank spaces is 83 00:05:44,600 --> 00:05:48,960 Speaker 1: what we see actually there or maybe not so much. Well, 84 00:05:49,000 --> 00:05:51,799 Speaker 1: when when we're not hallucinating and we're looking at something, 85 00:05:51,839 --> 00:05:54,080 Speaker 1: then we can we can see things vertically, and we 86 00:05:54,120 --> 00:05:57,839 Speaker 1: do it much better than any robot or artificial intelligence system. 87 00:05:57,960 --> 00:06:00,520 Speaker 1: That's why it's taken Google so long to develop a 88 00:06:00,520 --> 00:06:03,279 Speaker 1: self driving car. They're trying to bring it to the 89 00:06:03,320 --> 00:06:06,440 Speaker 1: and exceed the level of a human visual system. On 90 00:06:06,440 --> 00:06:09,280 Speaker 1: the other hand, there is an illusion that we have 91 00:06:09,480 --> 00:06:13,719 Speaker 1: a wall to wall tableau of visual detail. Uh. And 92 00:06:13,839 --> 00:06:16,719 Speaker 1: that is constructed by the brain because even if you 93 00:06:17,160 --> 00:06:21,600 Speaker 1: if you hold your hand out maybe eight inches from 94 00:06:21,800 --> 00:06:24,920 Speaker 1: the where you're looking, you can't even count the fingers 95 00:06:24,960 --> 00:06:28,600 Speaker 1: your vision. The acuity of your vision falls off really dramatically, 96 00:06:28,920 --> 00:06:32,360 Speaker 1: but your eyes are constantly flitting around UH. And so 97 00:06:32,480 --> 00:06:39,039 Speaker 1: your brain constructs an illusion of a continuously detailed visual world. 98 00:06:39,040 --> 00:06:42,520 Speaker 1: But outside the phobia, the spot that you're actually looking at, 99 00:06:42,920 --> 00:06:47,599 Speaker 1: vision is surprisingly course, and we rely on expectations and memories. 100 00:06:48,200 --> 00:06:51,080 Speaker 1: I could picture hundreds of listeners holding their arms out 101 00:06:51,120 --> 00:06:53,400 Speaker 1: and saying, you know, I can't count how many fingers 102 00:06:53,440 --> 00:06:55,560 Speaker 1: I have, And it doesn't have to be in the 103 00:06:55,600 --> 00:06:57,279 Speaker 1: in your peripheral vision. It just has to be a 104 00:06:57,320 --> 00:06:59,760 Speaker 1: few inches away from the direction of your gaze. So, 105 00:07:00,000 --> 00:07:03,360 Speaker 1: and people talk about tunnel vision or hyper focus. Really 106 00:07:03,560 --> 00:07:05,680 Speaker 1: that's the normal state of there's a sense in which 107 00:07:05,680 --> 00:07:07,520 Speaker 1: we all have tunnel vision. We don't realize it because 108 00:07:07,520 --> 00:07:10,720 Speaker 1: our eyeballs move around so quickly. And then psycho linguistics, 109 00:07:10,760 --> 00:07:12,920 Speaker 1: that was the second half of your question. That's another 110 00:07:12,960 --> 00:07:16,520 Speaker 1: topic in cognitive science, and that is the psychology of language. 111 00:07:16,600 --> 00:07:19,600 Speaker 1: How do we understand speech, how do we produce speech, 112 00:07:19,720 --> 00:07:23,680 Speaker 1: how do children learn their mother tongue? Where does language 113 00:07:24,000 --> 00:07:26,520 Speaker 1: come from, who decides what the rules are, how does 114 00:07:26,560 --> 00:07:30,000 Speaker 1: it change over time? How do we read? All of 115 00:07:30,040 --> 00:07:34,000 Speaker 1: those are topics in psycho linguistics, the psychology of language. So, 116 00:07:34,040 --> 00:07:35,880 Speaker 1: I don't remember which book it was. It might have 117 00:07:36,000 --> 00:07:38,360 Speaker 1: been the Stuff of Thought, or it might have been 118 00:07:39,200 --> 00:07:43,640 Speaker 1: um How the mind works. You talked about how words 119 00:07:43,680 --> 00:07:46,400 Speaker 1: sometimes are aunt amount of poetic, and it just made 120 00:07:46,440 --> 00:07:50,080 Speaker 1: me think of the mel Brooks call Rehner routine the 121 00:07:50,120 --> 00:07:53,160 Speaker 1: two thousand year Old Man, where they discuss how egg 122 00:07:53,440 --> 00:07:56,760 Speaker 1: and shower are automoto poetic and I won't spoil it. 123 00:07:56,800 --> 00:07:59,320 Speaker 1: People should go find it on YouTube. It's hilarious. But 124 00:07:59,440 --> 00:08:04,120 Speaker 1: how much of actual language is because things sound like 125 00:08:04,720 --> 00:08:09,000 Speaker 1: the way they are a bit uh, and so it's 126 00:08:09,040 --> 00:08:14,200 Speaker 1: not a complete coincidence that, say, the word malefluous solus 127 00:08:14,280 --> 00:08:17,680 Speaker 1: and the word cantankerous reminds you of a person with 128 00:08:17,720 --> 00:08:20,480 Speaker 1: a lot of sharp edges. On the other hand, that 129 00:08:20,600 --> 00:08:23,120 Speaker 1: only goes so far, because if you could really predict 130 00:08:23,160 --> 00:08:25,280 Speaker 1: what a word meant by what it sounded like, then 131 00:08:25,400 --> 00:08:27,640 Speaker 1: you wouldn't have to go through the laborious process of 132 00:08:27,720 --> 00:08:30,760 Speaker 1: learning another second language. All the words would would be 133 00:08:31,000 --> 00:08:33,560 Speaker 1: the meetings would be obvious. So most of the language 134 00:08:33,600 --> 00:08:36,480 Speaker 1: is arbitrary, but there is a little bit of correlation 135 00:08:36,520 --> 00:08:39,280 Speaker 1: between sound and meaning. So I just got back from Europe, 136 00:08:39,280 --> 00:08:41,920 Speaker 1: and I know most of what you've worked on has 137 00:08:42,040 --> 00:08:45,520 Speaker 1: come out of the English language. But why is it 138 00:08:45,640 --> 00:08:49,360 Speaker 1: that when a non speaker of let's say, French or 139 00:08:49,400 --> 00:08:53,559 Speaker 1: Italian listens, it sounds so melodic, or if you listen 140 00:08:53,640 --> 00:08:56,920 Speaker 1: to German it sounds so harsh and guttural. What is 141 00:08:56,920 --> 00:09:02,719 Speaker 1: it about those languages that give those things that that sensation. Well, 142 00:09:02,760 --> 00:09:06,599 Speaker 1: it's a component of language called phonology, the the the 143 00:09:07,240 --> 00:09:11,480 Speaker 1: sound pattern of the language, and that includes the set 144 00:09:11,480 --> 00:09:13,959 Speaker 1: of vowels and consonants that you're allowed to use. We 145 00:09:14,000 --> 00:09:16,559 Speaker 1: don't have in English, but you have it in Hebrew 146 00:09:16,640 --> 00:09:21,240 Speaker 1: and in uh German. Uh. It also includes the melody 147 00:09:21,280 --> 00:09:25,120 Speaker 1: and rhythm of speech, what's called prosody. Prosody, yes, so 148 00:09:25,160 --> 00:09:27,480 Speaker 1: that that's kind of the aspect of language that you 149 00:09:27,480 --> 00:09:31,320 Speaker 1: can hear behind a closed door, and you can often 150 00:09:31,360 --> 00:09:35,320 Speaker 1: recognize a language just from its prosody. I'm Barry RIDHLTZ. 151 00:09:35,320 --> 00:09:38,480 Speaker 1: You're listening to Masters in Business on Bloomberg Radio. My 152 00:09:38,559 --> 00:09:42,480 Speaker 1: special guest today is Professor Stephen pinker Uh. He teaches 153 00:09:42,520 --> 00:09:48,280 Speaker 1: at Harvard in the Psychology Department, studying cognition and psycholinguistics. 154 00:09:48,720 --> 00:09:50,839 Speaker 1: One of your more recent books was called The Bitter 155 00:09:50,920 --> 00:09:54,800 Speaker 1: Angels of Our Nature, How violence has declines, And when 156 00:09:54,840 --> 00:09:59,600 Speaker 1: you see the data on this, it's pretty it's pretty astonishing. 157 00:09:59,600 --> 00:10:02,600 Speaker 1: In the pres reference to Better Angels, you say the 158 00:10:02,679 --> 00:10:05,880 Speaker 1: present day we are blessed by an unprecedented level of 159 00:10:05,960 --> 00:10:10,520 Speaker 1: peaceful coexistence, but that seems to be contradicted by the 160 00:10:10,559 --> 00:10:13,800 Speaker 1: news headlines we see every day. How do you reconcile 161 00:10:13,920 --> 00:10:17,160 Speaker 1: the two because you can't get an accurate picture of 162 00:10:17,200 --> 00:10:20,320 Speaker 1: the world by looking at the headlines. The headlines are 163 00:10:20,320 --> 00:10:22,720 Speaker 1: about things that happen, they're not about things that don't happen. 164 00:10:22,880 --> 00:10:24,800 Speaker 1: And as long as the rate of violence hasn't fallen 165 00:10:24,840 --> 00:10:27,920 Speaker 1: to zero, they're always going to be enough violent incidents 166 00:10:27,960 --> 00:10:30,920 Speaker 1: to fill the news. And we can lose sight of 167 00:10:31,000 --> 00:10:35,679 Speaker 1: the vast amounts of the world that that are at peace. Currently. 168 00:10:35,760 --> 00:10:37,920 Speaker 1: The there's a there is a zone of war that 169 00:10:38,160 --> 00:10:43,280 Speaker 1: stretches pretty much from Nigeria through um Sub Saharan Africa, 170 00:10:43,360 --> 00:10:46,440 Speaker 1: into the Middle East and then down into Pakistan. But 171 00:10:46,600 --> 00:10:49,679 Speaker 1: five six of the world is at peace. And areas 172 00:10:49,679 --> 00:10:53,000 Speaker 1: of the world that had were torn by war for 173 00:10:53,280 --> 00:10:57,440 Speaker 1: centuries haven't had a war in in decades. Western Europe 174 00:10:57,480 --> 00:11:00,600 Speaker 1: one of the most historically violent part so the world, 175 00:11:00,880 --> 00:11:04,600 Speaker 1: hasn't had a war in in seventy years. Southeast Asia, 176 00:11:04,920 --> 00:11:07,680 Speaker 1: those of us who grew up remembering the war in Vietnam, Yes, 177 00:11:07,800 --> 00:11:09,720 Speaker 1: and and there has not been a war in Southeast 178 00:11:09,720 --> 00:11:13,200 Speaker 1: Asia and since a small skirmish between China and Vietnam 179 00:11:13,200 --> 00:11:16,800 Speaker 1: in the late eighties. Uh then, and it isn't just war, 180 00:11:16,960 --> 00:11:20,079 Speaker 1: it's also one on one crime, which actually kills more 181 00:11:20,120 --> 00:11:23,040 Speaker 1: people than wars in most years, other than in world wars. 182 00:11:23,920 --> 00:11:26,920 Speaker 1: But the rate of crime has gone down. Everyone knows 183 00:11:26,960 --> 00:11:28,560 Speaker 1: that it's gone down in the United States, but it 184 00:11:28,559 --> 00:11:31,080 Speaker 1: seems to have gone down globally as well, especially if 185 00:11:31,120 --> 00:11:33,000 Speaker 1: you look at even earlier periods in the history the 186 00:11:33,000 --> 00:11:36,960 Speaker 1: Middle Ages, the rate of homicide was about thirty five 187 00:11:37,040 --> 00:11:40,360 Speaker 1: times what it is today. So there was that wasn't 188 00:11:40,360 --> 00:11:42,720 Speaker 1: even more dramatic decline, and it's homicides that killed the 189 00:11:42,760 --> 00:11:46,440 Speaker 1: majority of people. So you mentioned the Middle Ages in 190 00:11:46,520 --> 00:11:50,480 Speaker 1: the book you described the six major historical clients of violence. 191 00:11:51,200 --> 00:11:53,760 Speaker 1: Let's skip ahead too. I think it was the third 192 00:11:53,840 --> 00:11:57,040 Speaker 1: or fourth decline, which was the the invention of the 193 00:11:57,040 --> 00:12:00,880 Speaker 1: printing press and the spread of literal Why is that 194 00:12:01,000 --> 00:12:05,160 Speaker 1: someonepactful on reducing violence. Well, it's a conjecture. The the 195 00:12:05,160 --> 00:12:08,600 Speaker 1: phenomenon we're trying to explain is why there was a 196 00:12:08,640 --> 00:12:12,480 Speaker 1: cascade of humanitarian reforms around the time of the European 197 00:12:12,559 --> 00:12:15,600 Speaker 1: Enlightenment the second half of the eighteenth century. Also, of 198 00:12:15,600 --> 00:12:19,720 Speaker 1: course the time of the American Declaration of Independence and 199 00:12:19,760 --> 00:12:22,080 Speaker 1: Bill of Rights, which was a kind of product of 200 00:12:22,120 --> 00:12:24,599 Speaker 1: the Enlightenment. Why why did people wake up in the 201 00:12:24,640 --> 00:12:27,520 Speaker 1: eighteenth century and say, well, maybe we should stop burning heretics, 202 00:12:28,120 --> 00:12:32,200 Speaker 1: or maybe we should stop executing people for stealing a cabbage. Uh, 203 00:12:32,360 --> 00:12:35,400 Speaker 1: maybe slavery isn't such a great idea when you come 204 00:12:35,400 --> 00:12:38,120 Speaker 1: down and try to justify it. Maybe we should stop 205 00:12:38,120 --> 00:12:41,240 Speaker 1: watching animals tear each other apart for entertainment. Maybe we 206 00:12:41,240 --> 00:12:44,880 Speaker 1: should stop throwing debtors in in prison. So all of 207 00:12:44,880 --> 00:12:48,280 Speaker 1: these reforms concentrated in a in a few decades, and 208 00:12:48,360 --> 00:12:51,200 Speaker 1: we have to ask why, then, why did it take 209 00:12:51,240 --> 00:12:53,840 Speaker 1: people millennia to figure out that might be something a 210 00:12:53,880 --> 00:12:58,120 Speaker 1: wee bit wrong with slavery, and so one the first 211 00:12:58,160 --> 00:13:02,000 Speaker 1: hypothesis is, well, maybe people got richer, and uh, if 212 00:13:02,040 --> 00:13:07,600 Speaker 1: your life is more pleasant, then you value life more generally, 213 00:13:07,640 --> 00:13:09,600 Speaker 1: and so you value the lives of others. But the 214 00:13:09,640 --> 00:13:12,520 Speaker 1: timing doesn't work because people only started to get rich 215 00:13:12,600 --> 00:13:15,880 Speaker 1: other than the kings and aristocrats in the nineteenth century, 216 00:13:16,320 --> 00:13:19,800 Speaker 1: and these reforms were really products of the eighteenth century. 217 00:13:19,840 --> 00:13:23,319 Speaker 1: So I suggested that maybe it's the rise of literacy 218 00:13:23,520 --> 00:13:26,560 Speaker 1: and printing in the exchange of ideas, and that was 219 00:13:26,600 --> 00:13:30,720 Speaker 1: the only technology that showed an increase in productivity prior 220 00:13:30,760 --> 00:13:33,880 Speaker 1: to the Industrial Revolution. The cost of printing a book 221 00:13:34,280 --> 00:13:37,760 Speaker 1: plunged in the seventeenth and eighteen centuries. There was kind 222 00:13:37,800 --> 00:13:39,760 Speaker 1: of an early version of Moore's law if you look 223 00:13:39,800 --> 00:13:42,720 Speaker 1: at the cost of producing a book. More and more 224 00:13:42,720 --> 00:13:46,000 Speaker 1: people were reading. They were best sellers. There were novels. 225 00:13:46,120 --> 00:13:49,240 Speaker 1: They're also pamphlets in the first newspapers. So the world 226 00:13:49,280 --> 00:13:53,200 Speaker 1: got more connected. People could exchange ideas, bad ideas could 227 00:13:53,200 --> 00:13:57,400 Speaker 1: be criticized. People would get together in also in cities, 228 00:13:57,559 --> 00:14:03,520 Speaker 1: in uh coffee houses and pubs and saloons to exchange ideas, 229 00:14:04,080 --> 00:14:06,960 Speaker 1: and it's possible that first of all, that could increase 230 00:14:07,040 --> 00:14:10,560 Speaker 1: your your circle of empathy. It's harder to dehumanize people 231 00:14:10,600 --> 00:14:14,400 Speaker 1: when you read their words, when you see what life 232 00:14:14,480 --> 00:14:17,400 Speaker 1: was like from their point of view. And also when 233 00:14:17,400 --> 00:14:20,880 Speaker 1: you have ideas being brought together and people debating them 234 00:14:20,880 --> 00:14:24,400 Speaker 1: and arguing them over them, then bad ideas tend to 235 00:14:24,400 --> 00:14:26,800 Speaker 1: be filtered out. So the idea that the reason that 236 00:14:26,840 --> 00:14:30,560 Speaker 1: there was a crop failure is because of witchcraft, for example, 237 00:14:30,600 --> 00:14:33,160 Speaker 1: the reason that there was an epidemic is because the 238 00:14:33,240 --> 00:14:36,440 Speaker 1: Jews poisoned the wells. Slavery is a good thing because 239 00:14:36,480 --> 00:14:39,200 Speaker 1: Africans can't do anything but be slaves. All of these 240 00:14:39,280 --> 00:14:42,720 Speaker 1: toxic ideas could start to seem ridiculous when you know 241 00:14:42,880 --> 00:14:47,120 Speaker 1: more about the world, and that was conceivably accelerated by 242 00:14:47,200 --> 00:14:52,040 Speaker 1: the exchange of printed matter. So education helps reduce violence 243 00:14:52,120 --> 00:14:56,800 Speaker 1: by making people less likely to believe silliness and nonsense 244 00:14:56,840 --> 00:15:00,560 Speaker 1: and more likely to understand basic science and the logic 245 00:15:00,680 --> 00:15:06,160 Speaker 1: of This causes that not witchcraft on average over the 246 00:15:06,200 --> 00:15:08,640 Speaker 1: long run, not in every case, because there have been 247 00:15:08,640 --> 00:15:14,480 Speaker 1: toxic ideas that have been advanced by intellectuals. Uh Communism, 248 00:15:14,520 --> 00:15:18,200 Speaker 1: for example, responsible for massive numbers of deaths, was an 249 00:15:18,200 --> 00:15:21,960 Speaker 1: intellectual movement, and Nazism there were plenty of Nazi professors. 250 00:15:22,400 --> 00:15:24,680 Speaker 1: But I think that when you have freedom of speech, 251 00:15:24,720 --> 00:15:27,240 Speaker 1: freedom of expression, so you don't get thrown in jail 252 00:15:27,400 --> 00:15:30,920 Speaker 1: by criticizing a bad idea, then it's more likely that 253 00:15:31,000 --> 00:15:33,160 Speaker 1: the bad ideas will be exposed. And it's not a 254 00:15:33,160 --> 00:15:38,400 Speaker 1: coincidence that repressive regimes are also repressive in um clapping 255 00:15:38,400 --> 00:15:42,320 Speaker 1: down on free speech. So we've seen a huge decrease 256 00:15:42,320 --> 00:15:44,640 Speaker 1: in crime and violence in the United States over the 257 00:15:44,640 --> 00:15:48,960 Speaker 1: past thirty years. Some people have attributed it to things 258 00:15:49,000 --> 00:15:52,880 Speaker 1: like the ending of lead paper and apartments. Other people 259 00:15:52,960 --> 00:15:57,440 Speaker 1: have looked at the removal of various outlives to gasoline 260 00:15:57,440 --> 00:16:01,320 Speaker 1: taking the lead out of gasoline. UH. The guys from 261 00:16:01,320 --> 00:16:04,120 Speaker 1: Freakonomics even have gone so far as to suggest Roe v. 262 00:16:04,280 --> 00:16:09,000 Speaker 1: Wade is a factor. Why the huge fall off just 263 00:16:09,080 --> 00:16:14,200 Speaker 1: in the most recent few decades. Yes, so, starting there 264 00:16:14,360 --> 00:16:17,520 Speaker 1: was an eight year decline of violent crime in the 265 00:16:17,560 --> 00:16:20,360 Speaker 1: United States, which brought it down to almost half of 266 00:16:20,400 --> 00:16:24,560 Speaker 1: its peak UH. And then surprisingly around O seven oh eight, 267 00:16:24,560 --> 00:16:27,480 Speaker 1: there was a second decline which no one predicted. Everyone said, well, 268 00:16:27,480 --> 00:16:30,040 Speaker 1: we have great recession, unemployment, crime is going to go up, 269 00:16:30,080 --> 00:16:32,880 Speaker 1: in equality UH and and crime went down instead of 270 00:16:32,920 --> 00:16:35,680 Speaker 1: going up. H. The honest answer is, no one really 271 00:16:35,680 --> 00:16:39,360 Speaker 1: knows what all the causes were. Probably the cute theories 272 00:16:39,640 --> 00:16:44,000 Speaker 1: like lead in the gasoline abortion are are wrong um 273 00:16:44,200 --> 00:16:46,960 Speaker 1: the AUH. And it may be that a number of 274 00:16:47,000 --> 00:16:50,480 Speaker 1: things went right around the same time. Among them were 275 00:16:51,240 --> 00:16:53,440 Speaker 1: an increase in the number of police in a change 276 00:16:53,440 --> 00:16:58,120 Speaker 1: in police tactics of policing got smarter nationwide, The homicide 277 00:16:58,200 --> 00:17:00,880 Speaker 1: rate absolutely plunged. In rates of other types of crime 278 00:17:00,920 --> 00:17:03,920 Speaker 1: like rape and assault and UH and robbery also went down. 279 00:17:04,359 --> 00:17:06,359 Speaker 1: And then there are also changes that no one really 280 00:17:06,440 --> 00:17:09,880 Speaker 1: can completely explain in the culture because there you also 281 00:17:09,920 --> 00:17:14,200 Speaker 1: had a decline in teenage pregnancy, decline in insurance fraud, 282 00:17:14,240 --> 00:17:18,040 Speaker 1: a decline in drug use. So somehow people got a 283 00:17:18,080 --> 00:17:20,640 Speaker 1: little bit more civilized starting in the nineties on top 284 00:17:20,680 --> 00:17:25,360 Speaker 1: of these other changes. So last question. You've noted that 285 00:17:25,440 --> 00:17:31,399 Speaker 1: the economic benefits of affluence, really a post nineteenth century phenomena, 286 00:17:31,640 --> 00:17:35,520 Speaker 1: did not have a big impact on on violent crime. Well, 287 00:17:35,560 --> 00:17:37,680 Speaker 1: what why do you imagine that is? Well, it didn't 288 00:17:37,680 --> 00:17:43,840 Speaker 1: have an impact on institutionalized violence, on slavery, on grizzly 289 00:17:43,920 --> 00:17:47,480 Speaker 1: torture as a form of criminal punishment like breaking on 290 00:17:47,520 --> 00:17:51,320 Speaker 1: the wheel and burning on this, on the stake um, 291 00:17:51,760 --> 00:17:55,320 Speaker 1: on hundreds of capital crimes on the lawbooks, so it's 292 00:17:55,359 --> 00:17:58,199 Speaker 1: really meaning crimes that there was a death penalty for 293 00:17:58,880 --> 00:18:02,600 Speaker 1: but did not involve homicide exactly. That's right, criticizing the 294 00:18:02,680 --> 00:18:08,119 Speaker 1: Royal garden being in the company. Listen, I understand some 295 00:18:08,200 --> 00:18:09,880 Speaker 1: of these other things, but if you're going to criticize 296 00:18:09,880 --> 00:18:16,919 Speaker 1: the royal that's it. That's amazing that a capital It 297 00:18:17,000 --> 00:18:20,439 Speaker 1: is amazing. England had four capital crimes on the books 298 00:18:20,440 --> 00:18:25,040 Speaker 1: and the prior to the nineteenth century, So the affluence 299 00:18:25,359 --> 00:18:29,680 Speaker 1: didn't didn't institution I think affluence does affect certain kinds 300 00:18:29,680 --> 00:18:33,080 Speaker 1: of violence. So, for example, countries that are at the 301 00:18:33,200 --> 00:18:35,840 Speaker 1: rock bottom end of the poverty scale and make less 302 00:18:35,840 --> 00:18:40,240 Speaker 1: than less than fift dollars GDP per capita are much 303 00:18:40,280 --> 00:18:44,280 Speaker 1: more likely to have civil wars, although once you climb 304 00:18:44,359 --> 00:18:47,320 Speaker 1: above that level then there isn't a clear relationship between 305 00:18:47,359 --> 00:18:50,639 Speaker 1: civil war and affluence. But certainly rock bottom poverty is 306 00:18:50,640 --> 00:18:55,159 Speaker 1: a contributor to civil war um in general, but not 307 00:18:55,160 --> 00:18:58,720 Speaker 1: not always. It's the poorer sectors of psycho of society 308 00:18:58,800 --> 00:19:01,040 Speaker 1: that are more crime prone. So there is there is 309 00:19:01,040 --> 00:19:03,879 Speaker 1: some relationship, but it's not a not a perfect relationship. 310 00:19:03,960 --> 00:19:06,919 Speaker 1: I'm Barry rit Halt. You're listening to Masters in Business 311 00:19:07,000 --> 00:19:11,400 Speaker 1: on Bloomberg Radio. My special guest today is Professor Stephen Pinker. 312 00:19:11,760 --> 00:19:15,640 Speaker 1: He is professor of cognitive science and psychology at Harvard, 313 00:19:16,320 --> 00:19:20,480 Speaker 1: author of numerous books, winner of numerous awards. His most 314 00:19:20,560 --> 00:19:24,480 Speaker 1: recent book is Sense of Style. And let's talk a 315 00:19:24,560 --> 00:19:28,359 Speaker 1: little bit about the way people communicate today with the 316 00:19:28,400 --> 00:19:33,560 Speaker 1: written word. What's the impact of the digital realm on writing? 317 00:19:34,800 --> 00:19:36,880 Speaker 1: I don't think there's a simple answer to that, because 318 00:19:36,880 --> 00:19:40,000 Speaker 1: there isn't one thing called writing. And when people it's 319 00:19:40,000 --> 00:19:42,080 Speaker 1: a question, I get a lot. Well, now that people 320 00:19:42,080 --> 00:19:44,960 Speaker 1: are writing a hundred forty characters for Twitter and instant 321 00:19:44,960 --> 00:19:49,240 Speaker 1: messages and emails, isn't the language going to deteriorate? And 322 00:19:49,359 --> 00:19:52,679 Speaker 1: the answer is no, because we don't just write in 323 00:19:53,160 --> 00:19:57,840 Speaker 1: tweets or in instant messages. We all command of variety 324 00:19:58,119 --> 00:20:01,600 Speaker 1: of styles for different formats. We don't speak the same 325 00:20:01,600 --> 00:20:05,959 Speaker 1: way when giving a lecture as we do um speaking 326 00:20:05,960 --> 00:20:09,320 Speaker 1: to our family across the dinner table. Uh. Text message 327 00:20:09,359 --> 00:20:11,800 Speaker 1: is going to be different from a funeral ooration. And 328 00:20:11,840 --> 00:20:14,720 Speaker 1: so just looking at one kind of writing and saying well, 329 00:20:14,800 --> 00:20:16,840 Speaker 1: that's what's going to happen to the language in general 330 00:20:17,000 --> 00:20:21,280 Speaker 1: just isn't valid because we tailor our our language to 331 00:20:21,320 --> 00:20:25,040 Speaker 1: the medium. What about technologies like power point and the 332 00:20:25,160 --> 00:20:30,679 Speaker 1: tendency for people to try and communicate by numbered bullet points. 333 00:20:31,600 --> 00:20:35,560 Speaker 1: There there's good power point, there's bad power point. Uh. 334 00:20:35,680 --> 00:20:40,840 Speaker 1: In science of scientific presentations are done in power point. Um. 335 00:20:40,920 --> 00:20:44,880 Speaker 1: Science has not shut down or or even slow down. It's, 336 00:20:45,119 --> 00:20:48,840 Speaker 1: if anything, accelerating, and power Point by mixing text and 337 00:20:49,040 --> 00:20:53,480 Speaker 1: images can be and video and audio can be remarkably powerful. 338 00:20:54,119 --> 00:20:58,280 Speaker 1: We've all sat through horrific power point presentations where just 339 00:20:58,359 --> 00:21:01,919 Speaker 1: binalities are are broken up into bullets. Uh so, but 340 00:21:01,960 --> 00:21:03,840 Speaker 1: it's like it's like writing and saying, like, what does 341 00:21:04,000 --> 00:21:06,560 Speaker 1: what does print do to the language? Well, there's there's 342 00:21:06,520 --> 00:21:08,400 Speaker 1: a lot of drivell that people write down, and there's 343 00:21:08,400 --> 00:21:10,720 Speaker 1: a lot of brilliance that people write down, but they 344 00:21:10,840 --> 00:21:15,400 Speaker 1: met PowerPoint medium opens up huge possibilities. Sturgeon's law applies 345 00:21:15,440 --> 00:21:19,600 Speaker 1: to everything. In other words, exactly. Um So, so let's 346 00:21:19,640 --> 00:21:22,760 Speaker 1: talk a little bit about how vocabulary and grammar of 347 00:21:22,840 --> 00:21:27,240 Speaker 1: English have changed, what drives these changes, and how much 348 00:21:27,280 --> 00:21:32,040 Speaker 1: has the English language changed just over the past century. Uh, 349 00:21:32,160 --> 00:21:36,480 Speaker 1: it hasn't changed so much that you can't understand uh 350 00:21:36,840 --> 00:21:39,399 Speaker 1: writing that was set down a hundred years ago. You know, 351 00:21:39,440 --> 00:21:41,359 Speaker 1: if you have a look at a copy of the 352 00:21:41,359 --> 00:21:44,639 Speaker 1: New York Times from and we can understand pretty much 353 00:21:44,680 --> 00:21:47,560 Speaker 1: all of it. But it feels different. The style was 354 00:21:47,600 --> 00:21:51,880 Speaker 1: more formal. There's been an informalization of style that might 355 00:21:51,960 --> 00:21:55,600 Speaker 1: parallel the informalization of everything else. The fact that gentlemen 356 00:21:55,640 --> 00:22:00,560 Speaker 1: don't wear ties everywhere, and that women don't wear white gloves, us, 357 00:22:00,600 --> 00:22:03,359 Speaker 1: the fact that we refer to each other on a 358 00:22:03,400 --> 00:22:06,359 Speaker 1: first name basis instead of Mr. And Mrs So and 359 00:22:06,400 --> 00:22:08,880 Speaker 1: so all the time, and writing has gotten more casual 360 00:22:08,920 --> 00:22:12,960 Speaker 1: as society has gotten more democratic, or at least the 361 00:22:13,280 --> 00:22:18,120 Speaker 1: look and feel is more democratized. Uh, And vocabulary definitely 362 00:22:18,119 --> 00:22:22,600 Speaker 1: turns over. If you look at an episode of contemporary 363 00:22:23,040 --> 00:22:25,800 Speaker 1: show that was set in the past, like Downton Abbey, 364 00:22:26,160 --> 00:22:28,800 Speaker 1: linguists of often had a field day at flagging all 365 00:22:28,840 --> 00:22:31,120 Speaker 1: of the idioms and figures of speech that just didn't 366 00:22:31,160 --> 00:22:34,880 Speaker 1: exist in the nineteen teens that the writers, UH kind 367 00:22:34,880 --> 00:22:37,760 Speaker 1: of anachronistically put in. There's all there's a lot of turnover. 368 00:22:37,800 --> 00:22:40,439 Speaker 1: A lot of it is. It is kind of random. 369 00:22:40,600 --> 00:22:44,040 Speaker 1: There's drift in and out. People an old saying will 370 00:22:44,080 --> 00:22:47,760 Speaker 1: will just sound fusty and old fashioned, and younger people 371 00:22:48,280 --> 00:22:50,959 Speaker 1: stop using it, and they'll introduce new figures of speech. 372 00:22:51,320 --> 00:22:53,719 Speaker 1: And so there's a constant turnover, which is why if 373 00:22:53,720 --> 00:22:55,720 Speaker 1: you go back more than a hundred years, so you 374 00:22:55,760 --> 00:22:59,000 Speaker 1: go back to Shakespeare, it's not often not that easy 375 00:22:59,080 --> 00:23:03,879 Speaker 1: to understand what the references were because the vocabulary is obsolete. 376 00:23:05,040 --> 00:23:07,280 Speaker 1: One of the things you you wrote in the book 377 00:23:07,280 --> 00:23:10,960 Speaker 1: that I thought was quite interesting, many of the alleged 378 00:23:11,040 --> 00:23:15,359 Speaker 1: rules of writing are actually superstitions. Explain what that means. 379 00:23:15,720 --> 00:23:18,840 Speaker 1: So many of us have been under the impression that 380 00:23:18,880 --> 00:23:21,600 Speaker 1: you wouldn't ought not to split an infinitive. So instead 381 00:23:21,640 --> 00:23:24,639 Speaker 1: of saying um, as Captain Cricket did, too boldly go 382 00:23:24,760 --> 00:23:26,879 Speaker 1: where no man has gone before, you should say to 383 00:23:27,200 --> 00:23:30,480 Speaker 1: go boldly where no man has gone before. That's a 384 00:23:30,520 --> 00:23:35,160 Speaker 1: perfect example of a superstition. There's absolutely no reason to 385 00:23:35,240 --> 00:23:39,200 Speaker 1: avoid a split infinitive. The whole rule came from a 386 00:23:39,280 --> 00:23:42,200 Speaker 1: kind of thick witted analogy to Latin, where you can't 387 00:23:42,240 --> 00:23:45,399 Speaker 1: split an infinitive. But there's abolutely no reason to avoid 388 00:23:45,400 --> 00:23:50,800 Speaker 1: spending with the proposition. Exactly so Shakespeare wrote, we are 389 00:23:51,160 --> 00:23:53,240 Speaker 1: such stuff as dreams are made on. You're gonna go 390 00:23:53,280 --> 00:23:57,080 Speaker 1: tell Shakespeare that he made a grammatical error. Absolutely not. 391 00:23:57,800 --> 00:24:00,439 Speaker 1: I'm Barry rid Helts. You're listening to Man Sessters in 392 00:24:00,480 --> 00:24:04,159 Speaker 1: Business on Bloomberg Radio. My guest today is Professor Stephen 393 00:24:04,200 --> 00:24:08,719 Speaker 1: pinker Uh, professor of psychology and linguistics at Harvard University. 394 00:24:09,400 --> 00:24:12,880 Speaker 1: Let's jump into some of the really fascinating things that 395 00:24:12,960 --> 00:24:17,760 Speaker 1: you have written about. There's there's one that really struck me. 396 00:24:18,240 --> 00:24:22,520 Speaker 1: Let's go start right off on the wonky linguistical things. 397 00:24:22,560 --> 00:24:27,720 Speaker 1: What's the difference between common knowledge and shared knowledge? Because 398 00:24:27,720 --> 00:24:31,199 Speaker 1: they seem so similar. Yeah. Shared knowledge is when you 399 00:24:31,240 --> 00:24:34,800 Speaker 1: know something and I know something. Common knowledge is a 400 00:24:34,960 --> 00:24:39,359 Speaker 1: term from game theory and logic is when I know something, 401 00:24:39,440 --> 00:24:41,359 Speaker 1: you know something. I know that you know it, you 402 00:24:41,359 --> 00:24:42,800 Speaker 1: know that I know it, I know that you know 403 00:24:42,920 --> 00:24:44,480 Speaker 1: that I know that you know that I know it, 404 00:24:44,640 --> 00:24:47,919 Speaker 1: at infinitum, and that makes a difference. It makes a 405 00:24:47,920 --> 00:24:52,160 Speaker 1: difference in um logically, there's certain things that you can 406 00:24:52,960 --> 00:24:56,320 Speaker 1: deduce if something is commonly known that as you know 407 00:24:56,400 --> 00:24:58,040 Speaker 1: that I know that you know it, and it makes 408 00:24:58,080 --> 00:24:59,840 Speaker 1: a difference. I think in our everyday lives when we 409 00:24:59,840 --> 00:25:03,040 Speaker 1: have an expression like the emperor's new clothes, what are 410 00:25:03,040 --> 00:25:05,000 Speaker 1: we referring to? And the little boy said, the emperor 411 00:25:05,040 --> 00:25:07,680 Speaker 1: is naked. He wasn't telling anyone anything that they didn't 412 00:25:07,680 --> 00:25:09,800 Speaker 1: already know. They could see the emperor was naked. So 413 00:25:09,840 --> 00:25:12,280 Speaker 1: why did it? Why was it such a big deal? Well, 414 00:25:12,480 --> 00:25:15,280 Speaker 1: at that moment, everyone knew that everyone else knew that 415 00:25:15,320 --> 00:25:17,800 Speaker 1: everyone else knew that everyone else knew that the emperor 416 00:25:17,840 --> 00:25:20,560 Speaker 1: was naked, and that allowed them to challenge the emperor's 417 00:25:20,600 --> 00:25:23,640 Speaker 1: authority by breaking out into laughter. But before the little 418 00:25:23,640 --> 00:25:26,000 Speaker 1: boy said that, people didn't realize that they had a 419 00:25:26,040 --> 00:25:28,760 Speaker 1: shared knowledge of his there was could there could be 420 00:25:28,840 --> 00:25:30,800 Speaker 1: a you know, a little scintilla of doubt. You know, 421 00:25:30,880 --> 00:25:32,919 Speaker 1: I can see it, but how do I know that 422 00:25:32,960 --> 00:25:34,520 Speaker 1: everyone else can see it? And how do I know 423 00:25:34,600 --> 00:25:36,920 Speaker 1: that they know that that I know? And that makes 424 00:25:36,920 --> 00:25:39,720 Speaker 1: it by it makes a difference in technology, especially for 425 00:25:40,000 --> 00:25:43,399 Speaker 1: uh what they call network externalities, That is when the 426 00:25:43,440 --> 00:25:46,600 Speaker 1: advantage of a technology depends on everyone else using it, 427 00:25:47,080 --> 00:25:51,280 Speaker 1: and so to generate a next network externality, you need 428 00:25:51,320 --> 00:25:53,960 Speaker 1: to generate common knowledge. And the best example is from 429 00:25:54,000 --> 00:25:59,439 Speaker 1: Michael Choa is when Apple introduced the Macintosh back I 430 00:25:59,440 --> 00:26:01,800 Speaker 1: think it was the most expensive commercial ever made. They 431 00:26:01,840 --> 00:26:05,239 Speaker 1: introduced it and the Super Bowl played once directed by 432 00:26:05,320 --> 00:26:10,359 Speaker 1: Ridley Scott, exactly the famous commercial. Now, the thing is, 433 00:26:10,440 --> 00:26:13,040 Speaker 1: no matter how good a computer Macintosh is, no one 434 00:26:13,119 --> 00:26:15,160 Speaker 1: is going to buy it if they think that they're 435 00:26:15,200 --> 00:26:17,440 Speaker 1: the only one buying it, because there won't be enough software, 436 00:26:17,480 --> 00:26:20,200 Speaker 1: there won't be enough peripherals. You have to know two things. 437 00:26:20,280 --> 00:26:22,440 Speaker 1: You have to know. Number one, it's a good computer. 438 00:26:22,880 --> 00:26:26,080 Speaker 1: Number two, everyone knows that. Everyone knows that. Everyone knows 439 00:26:26,119 --> 00:26:28,720 Speaker 1: that it's a good computer. And that's why you had 440 00:26:28,760 --> 00:26:32,800 Speaker 1: to introduce it with a um with something that made 441 00:26:32,840 --> 00:26:34,840 Speaker 1: a splash that you knew when you were watching the 442 00:26:34,840 --> 00:26:37,280 Speaker 1: super Bowl that the whole country is watching the super Bowl, 443 00:26:37,560 --> 00:26:39,439 Speaker 1: and so you knew that this product was going to 444 00:26:39,480 --> 00:26:42,159 Speaker 1: be its advantages were going to be common knowledge as 445 00:26:42,160 --> 00:26:44,719 Speaker 1: opposed to share knowledge. So so it's more than just 446 00:26:44,800 --> 00:26:47,760 Speaker 1: the network effects like a fax machine or what have you. 447 00:26:48,240 --> 00:26:53,280 Speaker 1: It's the network effect plus everybody recognizing that this is now. Well, 448 00:26:53,320 --> 00:26:55,320 Speaker 1: that's how you create the network effects. You have to 449 00:26:55,359 --> 00:26:58,480 Speaker 1: generate common knowledge. And the easiest way to generate common 450 00:26:58,520 --> 00:27:01,680 Speaker 1: knowledge is if there's an event that everyone can witness 451 00:27:01,840 --> 00:27:06,000 Speaker 1: while knowing that everyone else's witnessing the super bowls. That's 452 00:27:06,000 --> 00:27:07,800 Speaker 1: why the ads in the super Bowl get almost as 453 00:27:07,840 --> 00:27:10,040 Speaker 1: much coverage of the super Bowl itself, because that's where 454 00:27:10,480 --> 00:27:13,439 Speaker 1: companies that introduce a product that depends on a network 455 00:27:13,480 --> 00:27:17,080 Speaker 1: effect will introduce the company. Monster dot com is another example. 456 00:27:17,240 --> 00:27:19,280 Speaker 1: Might be a great employment site, but who's going to 457 00:27:19,359 --> 00:27:23,119 Speaker 1: go there unless they think that employers are advertising jobs 458 00:27:23,320 --> 00:27:25,600 Speaker 1: and vice versa. Who's going to advertise there unless you 459 00:27:25,600 --> 00:27:27,240 Speaker 1: know that people are going to be looking for jobs, 460 00:27:27,520 --> 00:27:29,200 Speaker 1: And so you make a big splash on a super 461 00:27:29,200 --> 00:27:34,840 Speaker 1: Bowl at Let let me continue along u surprisingly interesting things. 462 00:27:35,520 --> 00:27:38,520 Speaker 1: Why do we have facial expressions and what functions do 463 00:27:38,560 --> 00:27:42,240 Speaker 1: they serve. Yeah, it's not just too you might say 464 00:27:42,240 --> 00:27:44,120 Speaker 1: it wouldn't it be best to keep a poker face 465 00:27:44,160 --> 00:27:47,320 Speaker 1: and not to know, not to show your cards. Uh. 466 00:27:47,800 --> 00:27:54,000 Speaker 1: Facial expressions can um can signal the credibility of a 467 00:27:54,119 --> 00:27:57,000 Speaker 1: threat or a promise, and in a study that I 468 00:27:57,000 --> 00:27:59,480 Speaker 1: did with Ian Reid and Peter de Sholey, we found 469 00:27:59,520 --> 00:28:03,840 Speaker 1: that threats are more credible when they're delivered with an 470 00:28:03,880 --> 00:28:09,120 Speaker 1: angry expression and tone of voice, because they are involuntary 471 00:28:09,200 --> 00:28:11,679 Speaker 1: unless you're a really really good actor, unless they're perceived 472 00:28:11,720 --> 00:28:14,919 Speaker 1: as real as sincere, because they are sincere in most cases. 473 00:28:15,080 --> 00:28:17,640 Speaker 1: So people aren't that good at controlling their facial expressions. 474 00:28:17,640 --> 00:28:20,560 Speaker 1: So we've seen I've read about other studies where they 475 00:28:20,600 --> 00:28:24,560 Speaker 1: look at people smiling and apparently an actual smile involves 476 00:28:24,600 --> 00:28:27,520 Speaker 1: the eyes, and a fake smile just involves the mouth, 477 00:28:27,920 --> 00:28:31,720 Speaker 1: and on a subconscious level, people can see and and 478 00:28:31,720 --> 00:28:35,159 Speaker 1: tell the difference. Is am I telling that right? Or 479 00:28:35,320 --> 00:28:37,520 Speaker 1: absolutely sure? When the you know, when the flight attendant 480 00:28:37,560 --> 00:28:40,320 Speaker 1: says bye bye, bye bye with the grin pasted on 481 00:28:40,320 --> 00:28:43,360 Speaker 1: her face, you know that she's not actually experiencing joy. 482 00:28:43,760 --> 00:28:46,240 Speaker 1: And it's usually because the sincere smile as along with 483 00:28:46,360 --> 00:28:49,280 Speaker 1: a crinkling of the eyes, not just the mouth in 484 00:28:49,320 --> 00:28:53,160 Speaker 1: a U shape. So this combines both the visual cognition 485 00:28:53,440 --> 00:28:56,800 Speaker 1: and the uh the language aspect of this as well, 486 00:28:56,840 --> 00:28:59,960 Speaker 1: doesn't it. Indeed, And talking to speaking of common knowledge, 487 00:29:00,000 --> 00:29:02,479 Speaker 1: which we discussed earlier in the program, why do people blush? 488 00:29:02,640 --> 00:29:06,400 Speaker 1: That's a puzzle that I've thought about, and I think 489 00:29:06,400 --> 00:29:09,760 Speaker 1: that which is unlike other facial expressions, which are conveyed 490 00:29:09,760 --> 00:29:13,120 Speaker 1: by contracting muscles, with blushing, You've got this rush of 491 00:29:13,160 --> 00:29:16,000 Speaker 1: blood to the face. And I think it's because it's um. 492 00:29:16,280 --> 00:29:19,120 Speaker 1: It generates common knowledge. That is when the thing about 493 00:29:19,120 --> 00:29:22,880 Speaker 1: blushing is you uh. When you blush, you feel it 494 00:29:22,920 --> 00:29:25,200 Speaker 1: from the inside and you display it from the on 495 00:29:25,240 --> 00:29:28,360 Speaker 1: the outside, and you know that everyone knows that you're blushing. 496 00:29:28,760 --> 00:29:30,960 Speaker 1: And in fact, when someone says you're a blushing that 497 00:29:31,040 --> 00:29:33,240 Speaker 1: makes it all the worst. You blush all the more 498 00:29:33,320 --> 00:29:36,080 Speaker 1: beat red. So with the genuine and sincere and both 499 00:29:36,080 --> 00:29:39,840 Speaker 1: parties understand what it means exactly, it's I I messed up, 500 00:29:40,000 --> 00:29:41,680 Speaker 1: and I know that I messed up. And I'm not 501 00:29:41,680 --> 00:29:44,160 Speaker 1: trying to pull anything over on you. I'm not not 502 00:29:44,200 --> 00:29:47,280 Speaker 1: a psychopath. I'm not a cheater. I have the same 503 00:29:47,360 --> 00:29:49,840 Speaker 1: standards that you did, and I know that I messed up, 504 00:29:50,040 --> 00:29:52,360 Speaker 1: which is a way of knowing that the person is 505 00:29:52,400 --> 00:29:54,239 Speaker 1: less likely to mess up in the future if at 506 00:29:54,280 --> 00:29:58,920 Speaker 1: least he recognizes that he messed up. So blushing embarrassment 507 00:29:59,120 --> 00:30:04,239 Speaker 1: isn't acknowledgement of common ethical standards? Is that? Is that 508 00:30:04,280 --> 00:30:08,720 Speaker 1: how common morality? Am I mistating that? That's right, common norms, 509 00:30:08,760 --> 00:30:11,200 Speaker 1: that's right, But in particular the common knowledge, in that 510 00:30:11,320 --> 00:30:15,320 Speaker 1: technical sense of if I'm blushing, then not, I know 511 00:30:15,520 --> 00:30:17,640 Speaker 1: that you know that I know that I've messed up, 512 00:30:17,880 --> 00:30:21,040 Speaker 1: and that means that I, by blushing, I'm acknowledging that 513 00:30:21,080 --> 00:30:24,440 Speaker 1: I'm playing by the same rules. So I mentioned morality 514 00:30:24,480 --> 00:30:27,640 Speaker 1: and ethics. One of the columns you had written, I 515 00:30:27,680 --> 00:30:30,760 Speaker 1: think it was for The Times talked about three people, 516 00:30:31,440 --> 00:30:36,480 Speaker 1: Mother Teresa, Bill Gates, and Norman Borlog And if you 517 00:30:36,520 --> 00:30:40,200 Speaker 1: were to ask various people, who's the most admirable of 518 00:30:40,280 --> 00:30:45,960 Speaker 1: these three people, who is the most um has had 519 00:30:46,000 --> 00:30:49,920 Speaker 1: the greatest impact on on humans? Most most people get 520 00:30:49,920 --> 00:30:52,200 Speaker 1: the answers to that wrong. Yes, I mean the common 521 00:30:52,240 --> 00:30:54,640 Speaker 1: answer as well. You know Mother Teresa, she said, you 522 00:30:54,640 --> 00:30:56,520 Speaker 1: know a saint. She is the most. In fact, if 523 00:30:56,520 --> 00:30:59,239 Speaker 1: you ask someone who is the most moral person of 524 00:30:59,280 --> 00:31:02,520 Speaker 1: the entry, they would say, oh, I guess Mother Teresa. 525 00:31:02,800 --> 00:31:05,040 Speaker 1: And I think, what exactly did she do? I mean, 526 00:31:05,520 --> 00:31:07,240 Speaker 1: you know, she washed the feet of some you know, 527 00:31:07,320 --> 00:31:12,040 Speaker 1: lepers and brought them well help them. I mean, how 528 00:31:12,080 --> 00:31:17,200 Speaker 1: did she actually make them less poor? Temporarily for a 529 00:31:17,240 --> 00:31:19,320 Speaker 1: meal or two? But for a meal or two. But 530 00:31:19,360 --> 00:31:23,040 Speaker 1: then you look at um Bill Gates, and I used 531 00:31:23,080 --> 00:31:25,520 Speaker 1: the example at a time before he became famous as 532 00:31:25,560 --> 00:31:28,160 Speaker 1: a philanthropist and was just starting the Bill and Melinda 533 00:31:28,200 --> 00:31:32,600 Speaker 1: Gates Foundation. Um, he's trying to conquer infectious disease in 534 00:31:32,640 --> 00:31:38,360 Speaker 1: the developing world with a the chance of improving the 535 00:31:38,440 --> 00:31:41,760 Speaker 1: lives of tens of millions of people, of saving tens 536 00:31:41,760 --> 00:31:44,720 Speaker 1: of millions of lives. Then Norman borlog I threw in, 537 00:31:44,760 --> 00:31:48,520 Speaker 1: because that's a fascinating so I never heard of him. 538 00:31:48,080 --> 00:31:52,720 Speaker 1: He won the Nobel Peace Price for uh inventing the 539 00:31:52,760 --> 00:31:57,320 Speaker 1: Green Revolution in the nineteen sixties, developing crops and methods 540 00:31:57,320 --> 00:32:01,200 Speaker 1: of farming that multiplied the amount of food that an 541 00:32:01,240 --> 00:32:04,320 Speaker 1: acre of land would deliver. He's credited with saving a 542 00:32:04,400 --> 00:32:07,000 Speaker 1: billion lives, more than anyone in history, and no one's 543 00:32:07,000 --> 00:32:10,200 Speaker 1: heard of him he wins, and yet is totally unknown. 544 00:32:10,800 --> 00:32:13,920 Speaker 1: So what it shows is that our sense that our 545 00:32:14,000 --> 00:32:18,280 Speaker 1: own ascription of morality, who we revere, who we don't 546 00:32:18,280 --> 00:32:21,320 Speaker 1: care about, who we even maybe even revile. By the way, 547 00:32:21,360 --> 00:32:23,400 Speaker 1: the other reason I chose Bill Gates was at the 548 00:32:23,400 --> 00:32:27,160 Speaker 1: time that he was associated with ms DOS and windows 549 00:32:28,400 --> 00:32:30,520 Speaker 1: everyone hated and so everyone hated him. He got up 550 00:32:30,680 --> 00:32:33,120 Speaker 1: someone through a pianist face. There were I Hate Gates 551 00:32:33,200 --> 00:32:37,920 Speaker 1: websites at the time. Uh. It so our our description 552 00:32:37,960 --> 00:32:41,600 Speaker 1: of morality, who we give brownie points to is very 553 00:32:41,680 --> 00:32:44,000 Speaker 1: loosely related to how much good they do in the world. 554 00:32:44,360 --> 00:32:47,440 Speaker 1: And uh, it's actually a quirk of our own nature 555 00:32:47,520 --> 00:32:49,880 Speaker 1: of who we admire. I think it relates to who 556 00:32:49,920 --> 00:32:52,160 Speaker 1: we would like to have on our side and our 557 00:32:52,200 --> 00:32:55,320 Speaker 1: foxhole part of our community, and it isn't really closely 558 00:32:55,360 --> 00:32:58,240 Speaker 1: related to how much good they do in the world. So, 559 00:32:59,080 --> 00:33:01,360 Speaker 1: if people want to find line more of your writings 560 00:33:01,440 --> 00:33:04,400 Speaker 1: online in addition to all your various books, where's the 561 00:33:04,440 --> 00:33:07,480 Speaker 1: best place to send them? Stephen Pinker dot com is 562 00:33:07,520 --> 00:33:11,240 Speaker 1: my website that has links to all of my articles, 563 00:33:11,520 --> 00:33:13,800 Speaker 1: as well as two pages for each one of my books. 564 00:33:14,560 --> 00:33:17,719 Speaker 1: If you enjoy this conversation. Be sure and stick around 565 00:33:17,840 --> 00:33:20,360 Speaker 1: and listen to our podcast extras, where we keep the 566 00:33:20,400 --> 00:33:24,520 Speaker 1: tape rolling and continue to chat about all things cognitive 567 00:33:24,600 --> 00:33:27,920 Speaker 1: and linguistic. Be sure and check out my daily column 568 00:33:27,920 --> 00:33:31,400 Speaker 1: on Bloomberg View dot com or follow me on Twitter 569 00:33:31,720 --> 00:33:35,320 Speaker 1: at rit Halts. I'm Barry rit Halts. You're listening to 570 00:33:35,440 --> 00:33:39,520 Speaker 1: Masters in Business on Bloomberg Radio. Are you looking to 571 00:33:39,560 --> 00:33:42,440 Speaker 1: take your business to the next level? The accounting, tax 572 00:33:42,520 --> 00:33:46,240 Speaker 1: and advisory professionals from cone Resnick can guide you. Cone 573 00:33:46,280 --> 00:33:50,920 Speaker 1: Resnick delivers industry expertise and forward thinking perspective that can 574 00:33:50,920 --> 00:33:56,880 Speaker 1: help turn business possibilities into business opportunities. Look ahead, gain insight, 575 00:33:57,200 --> 00:34:00,960 Speaker 1: imagine more. Is your business ready to break through? Learn 576 00:34:01,040 --> 00:34:05,240 Speaker 1: more at cone Resnick dot com slash Breakthrough, cone Reisneck 577 00:34:05,480 --> 00:34:10,719 Speaker 1: Accounting Text Advisory. Welcome to the podcast, Steve, I don't 578 00:34:10,760 --> 00:34:14,080 Speaker 1: want to call you Professor Panker. Steve, what Steve? Thank 579 00:34:14,120 --> 00:34:16,520 Speaker 1: you so much for doing this. This is really I 580 00:34:16,560 --> 00:34:20,200 Speaker 1: love this stuff. I find it endlessly fascinating and anytime 581 00:34:20,360 --> 00:34:25,560 Speaker 1: I can weave cognition into what investors should be looking at, 582 00:34:25,600 --> 00:34:29,960 Speaker 1: thinking about, and just stimulating their thought process to develop 583 00:34:30,320 --> 00:34:35,400 Speaker 1: better mental models and better processes to approach this stuff. 584 00:34:35,520 --> 00:34:39,239 Speaker 1: I think it's just fascinating, and it's your work is 585 00:34:39,280 --> 00:34:43,200 Speaker 1: so diverse that you're obviously fascinated by so much of this. 586 00:34:43,360 --> 00:34:47,080 Speaker 1: It's apparent and everything I read of yours absolutely and 587 00:34:47,120 --> 00:34:49,560 Speaker 1: thank you for having me on. Well, it's been it's 588 00:34:49,600 --> 00:34:52,719 Speaker 1: been my pleasure. There are some questions we didn't get 589 00:34:52,760 --> 00:34:56,640 Speaker 1: to before I do my standard questions, but some of 590 00:34:56,680 --> 00:34:59,440 Speaker 1: these I just have to come back to. So in 591 00:34:59,520 --> 00:35:03,239 Speaker 1: the ninet fifties, comic books were going to turn juveniles 592 00:35:03,320 --> 00:35:07,880 Speaker 1: into delinquents. And what happened subsequent to that, Yeah, the 593 00:35:08,040 --> 00:35:11,920 Speaker 1: nineties were was a decade of very low crime, and 594 00:35:11,920 --> 00:35:15,000 Speaker 1: then the nines same thing. Video games We're going to 595 00:35:15,080 --> 00:35:18,800 Speaker 1: cause people to become ultra violent, especially the first person 596 00:35:18,840 --> 00:35:21,840 Speaker 1: shooter games. It's easy to imagine how that might happen, 597 00:35:21,920 --> 00:35:23,879 Speaker 1: but it didn't happen. That was the era in which 598 00:35:23,920 --> 00:35:29,520 Speaker 1: crime plummeted, and then in general, television, transistor radio's rock 599 00:35:29,560 --> 00:35:33,520 Speaker 1: and roll music videos were decades where people were supposed 600 00:35:33,560 --> 00:35:36,680 Speaker 1: to get stupid. That's right, and actually i Q scores 601 00:35:36,719 --> 00:35:40,600 Speaker 1: have been increasing for decades, the so called Flynn effect. Well, 602 00:35:40,719 --> 00:35:42,719 Speaker 1: crime did go up in the nineteen sixties and it 603 00:35:42,840 --> 00:35:47,319 Speaker 1: stayed high from the sixties through the early nineties, so 604 00:35:47,480 --> 00:35:49,759 Speaker 1: not all of so. So the people who said that 605 00:35:51,200 --> 00:35:53,359 Speaker 1: when rock and roll was coming in that they would 606 00:35:53,400 --> 00:35:57,719 Speaker 1: lead to a breakdown of order and safety weren't completely wrong, 607 00:35:57,800 --> 00:36:01,120 Speaker 1: and that there was a crime shot up by a 608 00:36:01,200 --> 00:36:03,960 Speaker 1: factor of more than two. So it was rock and roll. 609 00:36:04,000 --> 00:36:07,759 Speaker 1: It wasn't an ill considered war in Southeast Asia. It 610 00:36:07,840 --> 00:36:11,160 Speaker 1: was music. Well it wasn't rock and roll. But but 611 00:36:11,360 --> 00:36:14,960 Speaker 1: although it wasn't, the war in Asia probably didn't lead 612 00:36:15,000 --> 00:36:17,759 Speaker 1: to a rise in street crime in uh in the 613 00:36:17,840 --> 00:36:22,360 Speaker 1: United States either, but it did lead to So what 614 00:36:22,360 --> 00:36:24,960 Speaker 1: what I remember New York City in the seventies was 615 00:36:25,040 --> 00:36:30,120 Speaker 1: a disaster. What led to a breakdown in those basic 616 00:36:30,239 --> 00:36:36,000 Speaker 1: societal norms of not robbing and killing and raping? How 617 00:36:36,040 --> 00:36:38,840 Speaker 1: does that go off the rails? Again, the the honest 618 00:36:38,880 --> 00:36:41,000 Speaker 1: answers that we don't know for sure, but a number 619 00:36:41,000 --> 00:36:45,040 Speaker 1: of things happened. The baby boomer generation reached its crime 620 00:36:45,080 --> 00:36:48,759 Speaker 1: prone years in the sixties. That wasn't enough to explain it, 621 00:36:48,800 --> 00:36:52,680 Speaker 1: that would explain why violent crime would increase by violent 622 00:36:52,719 --> 00:36:56,600 Speaker 1: crime in fact increase, But it may be that having 623 00:36:56,600 --> 00:37:00,240 Speaker 1: a whole generation coming of age at the same time 624 00:37:00,640 --> 00:37:05,200 Speaker 1: overwhelmed societies defenses, and it was a time in which 625 00:37:05,239 --> 00:37:09,680 Speaker 1: there was a civil unrest, change in rights. There was 626 00:37:09,719 --> 00:37:11,400 Speaker 1: a whole bunch of a whole bunch of things, and 627 00:37:11,480 --> 00:37:15,600 Speaker 1: a general you know, we we grew up uh in it. 628 00:37:15,640 --> 00:37:19,680 Speaker 1: There was a decline respect for authority. The police backed off. 629 00:37:21,440 --> 00:37:25,239 Speaker 1: There is some truth to the the explanation that the 630 00:37:25,280 --> 00:37:28,400 Speaker 1: criminal justice system was less likely to put people behind bars? 631 00:37:28,840 --> 00:37:32,719 Speaker 1: What about the broken window thesis? To any of that, 632 00:37:33,120 --> 00:37:35,720 Speaker 1: there there is evidence that that that the broken windows 633 00:37:35,719 --> 00:37:38,680 Speaker 1: effect is real. That is, if a neighborhood shows signs 634 00:37:38,719 --> 00:37:43,279 Speaker 1: of disrepair, the famous broken windows, graffiti, turnstile jumping, and 635 00:37:43,280 --> 00:37:47,560 Speaker 1: so on, then that conveys the message that that this 636 00:37:47,680 --> 00:37:49,520 Speaker 1: is a place where the rules are not enforced. And 637 00:37:49,520 --> 00:37:53,440 Speaker 1: there's some experiments that show that increasing the general appearance 638 00:37:53,480 --> 00:37:56,319 Speaker 1: of order leads to a decrease in in the rate 639 00:37:56,360 --> 00:37:59,040 Speaker 1: of crime. It doesn't deserve all the credit, but it 640 00:37:59,080 --> 00:38:02,360 Speaker 1: may be deserved part of it. I find that I 641 00:38:02,400 --> 00:38:06,239 Speaker 1: find that fascinating. Let me see what else I skipped through. Oh, 642 00:38:06,320 --> 00:38:08,600 Speaker 1: let's talk about the long piece. When we were talking 643 00:38:08,600 --> 00:38:13,719 Speaker 1: about from Better Angels, the various six phases of um 644 00:38:13,840 --> 00:38:18,480 Speaker 1: decrease in violence. What what exactly is the long piece 645 00:38:18,480 --> 00:38:21,240 Speaker 1: and what was the cause of it? Long piece refers 646 00:38:21,280 --> 00:38:24,799 Speaker 1: to the fact that war between great powers that the 647 00:38:24,800 --> 00:38:29,040 Speaker 1: eight pound guerrillas of the world um has kind of stopped. 648 00:38:29,080 --> 00:38:31,120 Speaker 1: The last one that we had was in nineteen fifty 649 00:38:31,200 --> 00:38:32,960 Speaker 1: three with the end of the Korean War, with the 650 00:38:33,040 --> 00:38:35,279 Speaker 1: United States on one side in China on the other. 651 00:38:36,040 --> 00:38:38,600 Speaker 1: Was that really a US versus China though, I mean, 652 00:38:38,640 --> 00:38:40,399 Speaker 1: I know there were a lot of proxies, but it 653 00:38:40,480 --> 00:38:44,400 Speaker 1: wasn't like World War Two where Germany and Russia and 654 00:38:44,600 --> 00:38:48,879 Speaker 1: US and Japan were literally doing battles with each other. Well, 655 00:38:48,920 --> 00:38:53,160 Speaker 1: there was a coalition that the United Nations authorized, with 656 00:38:53,239 --> 00:38:57,200 Speaker 1: of course the United States contributing the most troops and weaponry, 657 00:38:57,440 --> 00:39:00,400 Speaker 1: and of course the North Korea had its own army, 658 00:39:00,640 --> 00:39:04,680 Speaker 1: but supported by overtly by China and with the USS 659 00:39:04,719 --> 00:39:10,279 Speaker 1: are definitely helping, although not sending troops. But through most 660 00:39:10,280 --> 00:39:13,960 Speaker 1: of history the great powers were always at each other's throats, 661 00:39:14,120 --> 00:39:17,319 Speaker 1: and then after World War Two that that kind of 662 00:39:17,760 --> 00:39:22,080 Speaker 1: um went out of style, as did wars between developed states, 663 00:39:22,200 --> 00:39:25,239 Speaker 1: that is, rich countries. We we think of war is 664 00:39:25,280 --> 00:39:27,759 Speaker 1: something that takes place in the poor, backward parts of 665 00:39:27,800 --> 00:39:29,600 Speaker 1: the world. But it used to be that it was 666 00:39:29,600 --> 00:39:32,160 Speaker 1: the rich countries that were constantly at war, and that 667 00:39:32,239 --> 00:39:36,520 Speaker 1: has gone out of style, and wars between countries in general. 668 00:39:36,600 --> 00:39:41,239 Speaker 1: Most wars now are civil wars, wars between interstate war 669 00:39:41,280 --> 00:39:45,520 Speaker 1: as opposed to interstate war, and UM. A number of things. 670 00:39:45,640 --> 00:39:48,759 Speaker 1: One is that the world has become more globalized. When 671 00:39:48,800 --> 00:39:52,400 Speaker 1: you when there's more trade, there is less of an 672 00:39:52,440 --> 00:39:54,600 Speaker 1: incentive to go to war. You don't. You don't kill 673 00:39:54,640 --> 00:39:58,960 Speaker 1: your customers because your supply chain or your supply chain exactly, 674 00:39:59,000 --> 00:40:01,560 Speaker 1: you don't. It's cheaper to buy things than to steal 675 00:40:01,600 --> 00:40:04,360 Speaker 1: them than you don't. Uh, You're you're less likely to 676 00:40:04,640 --> 00:40:08,560 Speaker 1: plunder and invade. There's been with the United Nations, there 677 00:40:08,560 --> 00:40:12,680 Speaker 1: has been a norm that borders are pretty much grandfathered in, 678 00:40:12,920 --> 00:40:15,680 Speaker 1: So you don't push borders around by force. You don't 679 00:40:15,920 --> 00:40:20,320 Speaker 1: swallow states that states are are now considered to be immortal. 680 00:40:20,480 --> 00:40:22,960 Speaker 1: They can break apart, but they can't be swallowed by 681 00:40:23,000 --> 00:40:27,840 Speaker 1: their neighbors. Uh. There is more democracy, and on average, 682 00:40:27,880 --> 00:40:30,279 Speaker 1: democracies are a little bit less likely to wage war, 683 00:40:30,320 --> 00:40:33,160 Speaker 1: at least on each other. And I think there's more 684 00:40:33,320 --> 00:40:35,840 Speaker 1: of a respect for human life. The idea that you 685 00:40:35,880 --> 00:40:38,640 Speaker 1: should die for your country, that it's glorious and sweet 686 00:40:38,680 --> 00:40:41,839 Speaker 1: and the best thing that you could do. And conversely, 687 00:40:41,880 --> 00:40:46,319 Speaker 1: that leaders can sacrifice millions of their own young men 688 00:40:46,600 --> 00:40:50,319 Speaker 1: for the glory of the empire is uh less an 689 00:40:50,320 --> 00:40:52,880 Speaker 1: operation now than it used to be. So before I 690 00:40:52,920 --> 00:40:55,799 Speaker 1: get to my favorite questions, I would be remiss if 691 00:40:55,800 --> 00:41:00,719 Speaker 1: I didn't ask about the debate over gene editing. If 692 00:41:00,960 --> 00:41:04,440 Speaker 1: people not familiar with Crisper. There's been a huge number 693 00:41:04,440 --> 00:41:08,640 Speaker 1: of articles, most recently in Wired magazine, describing how this 694 00:41:08,719 --> 00:41:13,359 Speaker 1: has made what was once time consuming, complex and expensive, 695 00:41:14,280 --> 00:41:19,160 Speaker 1: very inexpensive and relatively easy to do. What is the 696 00:41:19,239 --> 00:41:24,600 Speaker 1: advances in biotech say about current morality and why are 697 00:41:24,640 --> 00:41:28,120 Speaker 1: some people on one side or the other of that debate? 698 00:41:28,600 --> 00:41:32,040 Speaker 1: There's there's a widespread fear that first popped up when 699 00:41:32,120 --> 00:41:35,480 Speaker 1: Dolly the Sheep was cloned in and it's been revived 700 00:41:35,520 --> 00:41:38,839 Speaker 1: now with the development of Crisper Cassinine making it easy 701 00:41:38,880 --> 00:41:41,680 Speaker 1: to edit genes that will be designing our own children 702 00:41:41,840 --> 00:41:44,680 Speaker 1: very soon. We'll we'll put in the gene for musical ability, 703 00:41:44,880 --> 00:41:48,480 Speaker 1: or athletic ability or high i Q. I think that's 704 00:41:48,600 --> 00:41:52,080 Speaker 1: very likely, unlikely. I think it is possible. That you 705 00:41:52,120 --> 00:41:57,120 Speaker 1: could edit out disease genes. But having looked at the 706 00:41:57,160 --> 00:42:00,480 Speaker 1: genetic basis of personality and intelligence, I can tell you 707 00:42:00,560 --> 00:42:03,359 Speaker 1: that there ain't no i Q gene. There are. There 708 00:42:03,360 --> 00:42:05,880 Speaker 1: may be a thousand genes, each one of which raises 709 00:42:06,160 --> 00:42:08,640 Speaker 1: or lowers your i Q by a tenth of a point, 710 00:42:09,280 --> 00:42:11,840 Speaker 1: but the single gene that's going to give you musical 711 00:42:11,880 --> 00:42:14,960 Speaker 1: talent or athletic ability just doesn't exist. That's just not 712 00:42:15,000 --> 00:42:17,400 Speaker 1: the way genes work. There is a genetic basis to 713 00:42:17,520 --> 00:42:22,240 Speaker 1: talent and personality, but seems to be distributed across hundreds 714 00:42:22,320 --> 00:42:24,960 Speaker 1: or thousands of genes, each with a tiny effect, many 715 00:42:25,000 --> 00:42:28,279 Speaker 1: of which may have side effects. That is, there may 716 00:42:28,320 --> 00:42:32,440 Speaker 1: be a gene that increases your um likelihood of being smart, 717 00:42:32,520 --> 00:42:35,640 Speaker 1: but also slightly increases your rate of having bipolar disorder 718 00:42:36,719 --> 00:42:41,000 Speaker 1: or of some kind of degenerative disease. So I don't 719 00:42:41,040 --> 00:42:44,959 Speaker 1: see parents taking the chance with their children at any 720 00:42:45,000 --> 00:42:48,560 Speaker 1: time soon of mucking around with the with the embryo 721 00:42:48,840 --> 00:42:51,439 Speaker 1: by putting in a few genes, each of which might 722 00:42:51,560 --> 00:42:55,000 Speaker 1: increase the i Q by a tiny fraction of a point, 723 00:42:55,080 --> 00:42:59,000 Speaker 1: but might also introduce some some risks. So I'm glad 724 00:42:59,040 --> 00:43:02,080 Speaker 1: I brought that up because us. I've read some of 725 00:43:02,120 --> 00:43:04,760 Speaker 1: the pieces you've written on that and thought it was interesting. 726 00:43:04,880 --> 00:43:07,160 Speaker 1: Let me get to my I know I only have 727 00:43:07,200 --> 00:43:09,080 Speaker 1: you for another ten minutes or so, so let me 728 00:43:09,120 --> 00:43:12,759 Speaker 1: get to some of my favorite questions I asked all 729 00:43:12,840 --> 00:43:17,239 Speaker 1: my guests. So your background is really kind of interesting. 730 00:43:17,360 --> 00:43:20,480 Speaker 1: Did you know you always wanted to be a professor? 731 00:43:20,520 --> 00:43:23,360 Speaker 1: Did you always want to go into teaching? I certainly 732 00:43:23,440 --> 00:43:26,600 Speaker 1: enjoyed teaching from the time I was in uh in college. 733 00:43:26,680 --> 00:43:29,600 Speaker 1: I put myself through college by tutoring high school students 734 00:43:29,600 --> 00:43:32,920 Speaker 1: in math and science. Uh For a while, I thought, you, 735 00:43:33,000 --> 00:43:34,960 Speaker 1: maybe I'd would be fun to be a high school 736 00:43:34,960 --> 00:43:41,800 Speaker 1: math teacher, but I UH my mother, among others, convinced 737 00:43:41,800 --> 00:43:45,120 Speaker 1: me that university was really a place for me. That 738 00:43:46,000 --> 00:43:50,200 Speaker 1: adding to knowledge as well as transmitting knowledge was seemed 739 00:43:50,200 --> 00:43:54,240 Speaker 1: to be what I enjoyed doing. Nicely done, rose Um. 740 00:43:54,480 --> 00:43:57,680 Speaker 1: Next question, early mentors. Who were some of the people 741 00:43:57,760 --> 00:44:03,200 Speaker 1: who uh um entered you and gave you intellectual direction 742 00:44:04,440 --> 00:44:08,280 Speaker 1: As an undergraduate, I was a student at McGill University 743 00:44:08,360 --> 00:44:10,719 Speaker 1: and was in the psychology department there, and I worked 744 00:44:10,719 --> 00:44:13,760 Speaker 1: in a lab of a cognitive psychologist named Albert Bregman. 745 00:44:13,800 --> 00:44:17,520 Speaker 1: Who studied auditory pattern perception, how we the brain makes 746 00:44:17,560 --> 00:44:21,560 Speaker 1: sense of the world of sound. In university, one of 747 00:44:21,600 --> 00:44:25,239 Speaker 1: my advisors was Roger Brown, the great social psychologist and 748 00:44:25,400 --> 00:44:28,360 Speaker 1: founder of the study of child language acquisition in children 749 00:44:28,520 --> 00:44:31,320 Speaker 1: and a gifted writer, and I think I took lessons 750 00:44:31,360 --> 00:44:34,520 Speaker 1: from him on writing how to try to write stylishly. 751 00:44:35,239 --> 00:44:39,440 Speaker 1: Stephen Kostlin was my mentor in visual cognition. He's now 752 00:44:39,520 --> 00:44:44,640 Speaker 1: the academic dean of Minerva University, a startup university based 753 00:44:44,640 --> 00:44:50,719 Speaker 1: in San Francisco. Interesting is interesting group, and it seems, um, 754 00:44:50,800 --> 00:44:54,759 Speaker 1: you took something from each of those folks and took 755 00:44:55,040 --> 00:44:57,760 Speaker 1: one more is a Joan Bresnan, who is a linguist 756 00:44:57,880 --> 00:44:59,560 Speaker 1: at m I T. At the time she was a 757 00:44:59,600 --> 00:45:03,399 Speaker 1: student of Noam Chomskys. She was my post doctoral advisor. Ah, 758 00:45:03,440 --> 00:45:06,520 Speaker 1: there you go. That that's quite a quite a list. 759 00:45:06,760 --> 00:45:13,280 Speaker 1: You referenced Chomsky quite frequently in many of the books, obviously, Um, 760 00:45:13,360 --> 00:45:18,000 Speaker 1: he is a leader in this field. Uh. My next 761 00:45:18,080 --> 00:45:21,560 Speaker 1: question is what are some of your favorite books, whether 762 00:45:21,600 --> 00:45:25,560 Speaker 1: it relates to UH language and and linguistics or or 763 00:45:25,600 --> 00:45:28,799 Speaker 1: anything else. Noam Chomsky was my colleague a M I 764 00:45:28,800 --> 00:45:30,799 Speaker 1: T for twenty one years. And he was in a 765 00:45:30,840 --> 00:45:33,360 Speaker 1: different department, but he was certainly an influence from the 766 00:45:33,360 --> 00:45:36,160 Speaker 1: time that I was an undergraduate, particularly his books in 767 00:45:36,160 --> 00:45:40,040 Speaker 1: in linguistics. Language in Mind was a book that I 768 00:45:40,080 --> 00:45:43,080 Speaker 1: read as an undergraduate. I don't certainly don't share his politics, 769 00:45:43,280 --> 00:45:46,520 Speaker 1: but uh and I nor do I subscribe to his 770 00:45:46,600 --> 00:45:50,399 Speaker 1: particular theory of how language works. But he broke open 771 00:45:50,480 --> 00:45:54,160 Speaker 1: the field of language and really deserves credit for the 772 00:45:54,200 --> 00:45:57,759 Speaker 1: modern understanding of language. Any other books stand out as 773 00:45:57,960 --> 00:46:03,839 Speaker 1: whether it's fiction non fixed in related Uh, Well, I'm 774 00:46:03,880 --> 00:46:06,759 Speaker 1: married to a novelist, Rebecca Goldstein, and her book The 775 00:46:06,760 --> 00:46:09,640 Speaker 1: Mind Body Problem I read many years before I met her. 776 00:46:09,800 --> 00:46:13,520 Speaker 1: They really remember that old ad? The guy Victor Kayam 777 00:46:13,600 --> 00:46:15,759 Speaker 1: and got a Shavery said, I liked it, so what 778 00:46:15,840 --> 00:46:18,520 Speaker 1: the company bought the company? Well, I liked the novelists 779 00:46:18,560 --> 00:46:22,600 Speaker 1: so much that I married her. That that's very very funny. Um, So, 780 00:46:22,880 --> 00:46:27,920 Speaker 1: since you really started in psycholinguistics and and visual cognition, 781 00:46:28,440 --> 00:46:30,560 Speaker 1: what are some of the major changes that have taken 782 00:46:30,600 --> 00:46:33,960 Speaker 1: place in that industry or that field of study? I 783 00:46:34,000 --> 00:46:37,760 Speaker 1: should really call it, certainly the rise of neuroimaging. Functional 784 00:46:37,840 --> 00:46:42,960 Speaker 1: magnetic resonance imaging was revolutionized the field, being able to 785 00:46:43,000 --> 00:46:45,719 Speaker 1: see what part of the brain lights up in response 786 00:46:45,760 --> 00:46:52,359 Speaker 1: to different stimulus, different processes. Is that is that specifically 787 00:46:53,080 --> 00:46:55,839 Speaker 1: what you're referring to exactly, yeah, and that that has 788 00:46:55,880 --> 00:46:59,720 Speaker 1: been the single biggest change, And how does that manifest 789 00:46:59,800 --> 00:47:02,920 Speaker 1: this often in the study of language, well, you can 790 00:47:02,960 --> 00:47:07,040 Speaker 1: see how words are processed in the brain. UM. You 791 00:47:07,080 --> 00:47:12,000 Speaker 1: can see how um grammatical processing is implemented. That is, 792 00:47:12,840 --> 00:47:15,280 Speaker 1: by and again by grammatical processing, I don't mean rules 793 00:47:15,280 --> 00:47:18,799 Speaker 1: like avoiding dangling participles. I mean just ordering words in 794 00:47:18,840 --> 00:47:20,960 Speaker 1: a way that makes sense what we do every time 795 00:47:20,960 --> 00:47:23,920 Speaker 1: we open our mouths and produce a sentence. And you 796 00:47:23,960 --> 00:47:28,160 Speaker 1: can see also the pattern of information flow from one 797 00:47:28,160 --> 00:47:30,200 Speaker 1: part of the brain to another, because it's not as 798 00:47:30,200 --> 00:47:33,680 Speaker 1: if one blob of the brain is responsible for all 799 00:47:33,719 --> 00:47:38,560 Speaker 1: the language there is. You have to coordinate your understanding 800 00:47:38,560 --> 00:47:41,640 Speaker 1: of what words mean, your knowledge of in English syntax, 801 00:47:41,880 --> 00:47:45,440 Speaker 1: the control of the muscles of your tongue. UH. In conversation, 802 00:47:45,520 --> 00:47:47,719 Speaker 1: you go back and forth between speaking and listening, So 803 00:47:47,760 --> 00:47:51,759 Speaker 1: it also involves hooking up uh speech information coming in 804 00:47:51,800 --> 00:47:54,719 Speaker 1: from the ear. UH. You have to hold things in 805 00:47:54,800 --> 00:47:57,000 Speaker 1: memory as you start a sentence. You have to know 806 00:47:57,080 --> 00:48:00,200 Speaker 1: where you're going. So, a lot of different part of 807 00:48:00,239 --> 00:48:03,320 Speaker 1: the brain are involved in language, forming a kind of network, 808 00:48:03,640 --> 00:48:06,200 Speaker 1: and narrow imaging helps you see the different parts of the 809 00:48:06,160 --> 00:48:09,160 Speaker 1: the network and how they interact. I recall reading about 810 00:48:09,200 --> 00:48:13,759 Speaker 1: aphasiacs and other damages to physical damages to the brain. 811 00:48:14,239 --> 00:48:19,600 Speaker 1: How important is looking at damage, physical trauma and disease 812 00:48:19,840 --> 00:48:24,279 Speaker 1: too learning about language. Is that something that we did 813 00:48:24,440 --> 00:48:28,720 Speaker 1: decades ago and figured out, oh, this injury causes this result. 814 00:48:29,080 --> 00:48:31,000 Speaker 1: Have we moved beyond that or is that still a 815 00:48:31,080 --> 00:48:37,160 Speaker 1: key part of recognizing how these brain components developed. It 816 00:48:37,280 --> 00:48:39,040 Speaker 1: still is a key part. It used to be the 817 00:48:39,120 --> 00:48:41,279 Speaker 1: only way that you could understand language in the brain. 818 00:48:41,719 --> 00:48:44,560 Speaker 1: Um Now it's still important even in the era of 819 00:48:44,640 --> 00:48:47,480 Speaker 1: neuro imaging, because your imaging tells you what is active 820 00:48:47,880 --> 00:48:50,759 Speaker 1: when you are engaged in a task, but it doesn't 821 00:48:50,760 --> 00:48:53,680 Speaker 1: tell you what's necessary for all you know, it could 822 00:48:53,680 --> 00:48:55,960 Speaker 1: be like the lights that flash on a on a computer, 823 00:48:56,200 --> 00:48:58,560 Speaker 1: that you turn off the lights, the computer still does 824 00:48:58,600 --> 00:49:00,720 Speaker 1: its thing. It's kind of a spell low for effect, 825 00:49:00,920 --> 00:49:03,360 Speaker 1: and you never know just from the fact that blood 826 00:49:03,440 --> 00:49:05,680 Speaker 1: is going to a particular area of the brain whether 827 00:49:06,160 --> 00:49:09,360 Speaker 1: actually at part of the brain is necessary for the 828 00:49:09,440 --> 00:49:12,040 Speaker 1: person to do what they're doing. With brain damage, you're 829 00:49:12,080 --> 00:49:15,560 Speaker 1: removing a component and or nature is removing a component, 830 00:49:15,719 --> 00:49:17,520 Speaker 1: and you're seeing what they can no longer do. So 831 00:49:17,560 --> 00:49:20,960 Speaker 1: it's still a supplementary form of information and an important one. 832 00:49:21,280 --> 00:49:24,280 Speaker 1: So you work with a lot of college kids, people 833 00:49:24,280 --> 00:49:27,360 Speaker 1: who are just starting out their career. When a millennial 834 00:49:27,480 --> 00:49:31,320 Speaker 1: or recent graduate comes to you and says, I'm thinking 835 00:49:31,400 --> 00:49:35,520 Speaker 1: about a career in linguistics and visual cognition and in 836 00:49:35,640 --> 00:49:39,279 Speaker 1: any of the subsectors of psychology that you focus on, 837 00:49:39,840 --> 00:49:42,799 Speaker 1: what sort of advice do you give them? If you're 838 00:49:42,920 --> 00:49:46,000 Speaker 1: if you're passionate about something, and if you're if you 839 00:49:46,040 --> 00:49:49,640 Speaker 1: can see yourself throwing yourself into it, doing a lot 840 00:49:49,640 --> 00:49:52,560 Speaker 1: of work, then you should pursue it as a career, 841 00:49:53,040 --> 00:49:57,080 Speaker 1: even if the academic jug market is discouraging, which of 842 00:49:57,080 --> 00:50:00,760 Speaker 1: course it is um it has been at various times, 843 00:50:00,760 --> 00:50:02,439 Speaker 1: such as when I was a student, and I remember, 844 00:50:02,480 --> 00:50:05,040 Speaker 1: I remember the advice that I got from Ronald Melzack, 845 00:50:05,160 --> 00:50:08,360 Speaker 1: a professor of psychology at McGill, pioneer in the study 846 00:50:08,400 --> 00:50:12,600 Speaker 1: of pain. He said, look at the bright side. People die, people, 847 00:50:13,200 --> 00:50:16,480 Speaker 1: people retire, people get higher paying jobs. In industry. There's 848 00:50:16,480 --> 00:50:20,160 Speaker 1: always turnover, even if the market is contracting, if you 849 00:50:20,360 --> 00:50:22,520 Speaker 1: think you're good at it, if you're willing to to 850 00:50:23,280 --> 00:50:25,640 Speaker 1: dedicate yourself to it, if it excites you enough that 851 00:50:25,719 --> 00:50:28,239 Speaker 1: it doesn't feel like work but it feels like play, 852 00:50:28,320 --> 00:50:30,520 Speaker 1: there will always be openings. And so I tell students, 853 00:50:30,520 --> 00:50:33,960 Speaker 1: if they're really passionate about some intellectual topic, not to 854 00:50:34,160 --> 00:50:38,040 Speaker 1: just automatically go into law or finance or consulting because 855 00:50:38,080 --> 00:50:40,399 Speaker 1: that's the easy path, but that it really is still 856 00:50:40,400 --> 00:50:43,000 Speaker 1: possible to make a career in what you what you love. 857 00:50:44,200 --> 00:50:47,120 Speaker 1: And our our last question is what is it that 858 00:50:47,160 --> 00:50:51,760 Speaker 1: you know about cognition and linguistics today that you wish 859 00:50:51,760 --> 00:50:56,760 Speaker 1: you knew when you started thirty years ago? Oh? Well 860 00:50:56,760 --> 00:51:02,520 Speaker 1: that UM, I think that any cognitive or psychological trait 861 00:51:02,640 --> 00:51:06,720 Speaker 1: both has a heritable basis but is distributed over hundreds 862 00:51:06,760 --> 00:51:08,560 Speaker 1: or thousands of genes. That there is not going to 863 00:51:08,600 --> 00:51:13,239 Speaker 1: be a gene for x UM, That that there's a 864 00:51:13,280 --> 00:51:16,040 Speaker 1: lot of information that can come out of looking at 865 00:51:16,560 --> 00:51:20,960 Speaker 1: large data sets. That you're understanding of a subject is 866 00:51:21,000 --> 00:51:24,520 Speaker 1: only as good as the data that you can examine, 867 00:51:24,600 --> 00:51:28,640 Speaker 1: and that to understand something you've got to um look 868 00:51:28,960 --> 00:51:30,799 Speaker 1: at as large a set of data as you can 869 00:51:30,840 --> 00:51:34,919 Speaker 1: find Professor Panker, thank you so much for being so 870 00:51:35,040 --> 00:51:38,799 Speaker 1: generous with your time that this has been just absolutely fascinating. 871 00:51:39,480 --> 00:51:43,000 Speaker 1: If you enjoy this conversation UH, and others like this, 872 00:51:43,440 --> 00:51:45,400 Speaker 1: be sure and look up an inch or down an 873 00:51:45,440 --> 00:51:48,160 Speaker 1: inch at any of the other ninety two or so 874 00:51:48,239 --> 00:51:52,200 Speaker 1: such conversations we've had over the past two years. Be 875 00:51:52,360 --> 00:51:55,320 Speaker 1: sure to check out my daily column. It used to 876 00:51:55,360 --> 00:51:58,360 Speaker 1: be Bloomberg View dot com, but I am now seeing 877 00:51:58,400 --> 00:52:01,640 Speaker 1: that it is Bloomberg dot com. You can follow me 878 00:52:01,680 --> 00:52:05,600 Speaker 1: on Twitter at rid Halts Um. I would be remiss 879 00:52:05,640 --> 00:52:09,959 Speaker 1: if I did not think Uh, Taylor Riggs for being 880 00:52:09,960 --> 00:52:13,839 Speaker 1: our booker, Charlie Volmer for being our producer engineer, and 881 00:52:13,920 --> 00:52:18,120 Speaker 1: Michael Batnick, head of research. I'm Barry Ri Halts. You're 882 00:52:18,200 --> 00:52:21,440 Speaker 1: listening or you've been listening to Masters in Business on 883 00:52:21,520 --> 00:52:26,319 Speaker 1: Bloomberg Radio. Look Ahead, Imagine more, gain insight for your 884 00:52:26,360 --> 00:52:30,440 Speaker 1: industry with forward thinking advice from the professionals at Cone Resnick. 885 00:52:31,000 --> 00:52:33,960 Speaker 1: Is your business ready to break through? Find out more 886 00:52:34,040 --> 00:52:36,799 Speaker 1: at Cone Resnick dot com slash Breakthrough