1 00:00:01,760 --> 00:00:04,440 Speaker 1: You're listening to Math and Magic, a production of I 2 00:00:04,640 --> 00:00:10,240 Speaker 1: Heart Radio. Your brain's job is to put together a 3 00:00:10,320 --> 00:00:14,200 Speaker 1: model of reality, but all we ever get is our 4 00:00:14,240 --> 00:00:19,119 Speaker 1: own version of reality. I am Bob Pittman, and welcome 5 00:00:19,160 --> 00:00:21,720 Speaker 1: to this episode of Math and Magic, Stories from the 6 00:00:21,760 --> 00:00:25,320 Speaker 1: Frontiers and Marketing. Today, we're really going to the frontier. 7 00:00:25,600 --> 00:00:28,040 Speaker 1: Our guest is going to take us to the area 8 00:00:28,200 --> 00:00:31,600 Speaker 1: most of us would not really think about as marketing, 9 00:00:32,159 --> 00:00:36,320 Speaker 1: yet it underpins everything we as marketers do. It's the 10 00:00:36,360 --> 00:00:40,440 Speaker 1: brain who we fundamentally are, and the how behind all 11 00:00:40,479 --> 00:00:43,200 Speaker 1: those decisions that are key to marketing and oh yeah, 12 00:00:43,400 --> 00:00:46,800 Speaker 1: life itself. Today, we're fortunate to have Dr David Eagleman. 13 00:00:47,240 --> 00:00:49,360 Speaker 1: It's hard to put a label on him, but let's 14 00:00:49,360 --> 00:00:53,320 Speaker 1: call him a neuroscientist who happens to teach brain plasticity 15 00:00:53,440 --> 00:00:57,240 Speaker 1: at Stanford and is a best selling author, TV host 16 00:00:57,840 --> 00:01:01,360 Speaker 1: and very serious entrepreneur. A Fortunately for us, he is 17 00:01:01,400 --> 00:01:04,400 Speaker 1: also a failed stand up comedian, and what a ways 18 00:01:04,680 --> 00:01:07,160 Speaker 1: that would have been had he succeeded at that. David 19 00:01:07,160 --> 00:01:09,880 Speaker 1: wrote a book Incognito, The Secret Lives of the Brain, 20 00:01:10,240 --> 00:01:12,800 Speaker 1: which I've given to other marketers over the years as 21 00:01:12,840 --> 00:01:16,080 Speaker 1: what I considered to be the most important marketing book 22 00:01:16,240 --> 00:01:19,680 Speaker 1: ever written, but that barely scratches the surface of who 23 00:01:19,720 --> 00:01:23,120 Speaker 1: he is and his contributions. A native of New Mexico, 24 00:01:23,640 --> 00:01:26,680 Speaker 1: he was always the smart kid and wildly curious, and 25 00:01:26,720 --> 00:01:29,880 Speaker 1: it took him on an electric path. He considered being 26 00:01:29,880 --> 00:01:33,800 Speaker 1: a writer, electrical engineer, screenwriter, and was even a soldier 27 00:01:34,160 --> 00:01:38,120 Speaker 1: in the Israeli Army. He has important works and thoughts 28 00:01:38,480 --> 00:01:42,360 Speaker 1: on who we really are, empathy, perceptions of time, consciousness, 29 00:01:42,680 --> 00:01:45,440 Speaker 1: and even the possibilities for the afterlives. We got a 30 00:01:45,480 --> 00:01:48,800 Speaker 1: lot to cover today, David, welcome, Bob's great to be here. 31 00:01:49,080 --> 00:01:51,440 Speaker 1: Before we get started, I want to do a little 32 00:01:51,480 --> 00:01:55,320 Speaker 1: warm up. It's called you in sixty seconds. Ready to go? 33 00:01:55,560 --> 00:01:59,480 Speaker 1: Do you prefer Instagram or Twitter? Twitter, West World or 34 00:01:59,480 --> 00:02:04,280 Speaker 1: Game of Thrown? I can't arbitrate between those two. That's 35 00:02:04,280 --> 00:02:06,720 Speaker 1: a good answer. Early riser or night owl? Night O 36 00:02:07,200 --> 00:02:11,440 Speaker 1: Houston or San Francisco, San Francisco, call or text call 37 00:02:11,840 --> 00:02:15,079 Speaker 1: Left brain or right brain, no difference between them. Salty 38 00:02:15,160 --> 00:02:20,040 Speaker 1: or sweet, salty, coffee or tea coffee. Grants or venture capital, 39 00:02:20,400 --> 00:02:25,880 Speaker 1: vesture capital, cats or dogs, dogs, introvert or extrovert extrovert. 40 00:02:26,280 --> 00:02:31,880 Speaker 1: Airplanes or helicopters, airplanes, WEB two or WEB three WEB 41 00:02:31,880 --> 00:02:35,639 Speaker 1: two Slow and steady or pedal to the metal, pedal 42 00:02:35,680 --> 00:02:40,160 Speaker 1: to the metal, crypto or dollars dollars Conscious or subconscious? 43 00:02:42,800 --> 00:02:45,760 Speaker 1: Trust your subconscious? Come on, my subconscious is telling me 44 00:02:45,800 --> 00:02:48,280 Speaker 1: to say the conscious. I like that. It's about to 45 00:02:48,280 --> 00:02:52,040 Speaker 1: get harder. Smartest person you know, my father, favorite writer, 46 00:02:52,840 --> 00:02:59,840 Speaker 1: Gabriel Garcia Marquez, childhood hero, Carl Sagan, first job, bus Boy, 47 00:03:00,200 --> 00:03:03,920 Speaker 1: last book you read, Entangled Life. It's something you can't 48 00:03:03,960 --> 00:03:12,079 Speaker 1: live without. Family, favorite city, Its Stanable, Guilty Pleasure, Sylvester 49 00:03:12,160 --> 00:03:17,119 Speaker 1: Stallone Movies. If you could have one superpower, what would 50 00:03:17,160 --> 00:03:20,320 Speaker 1: it be? The ability to see more of the electro 51 00:03:20,400 --> 00:03:24,240 Speaker 1: magnetic spectrum? Oh? I like that. Okay, let's get going 52 00:03:24,440 --> 00:03:28,200 Speaker 1: and incognito. You talk about this idea that our conscious 53 00:03:28,240 --> 00:03:30,440 Speaker 1: self is just a small fraction of who we are. 54 00:03:30,800 --> 00:03:34,600 Speaker 1: Can you explain this concept? Yeah, It turns out most 55 00:03:34,639 --> 00:03:36,840 Speaker 1: of what your brain is doing you don't have any 56 00:03:36,880 --> 00:03:41,000 Speaker 1: conscious access to. So what you believe, the actions you take, 57 00:03:41,960 --> 00:03:45,800 Speaker 1: all these things are generated by these parts of your 58 00:03:45,840 --> 00:03:48,600 Speaker 1: brain that you have no acquaintance with. So that's in 59 00:03:48,640 --> 00:03:52,680 Speaker 1: neuroscience what we call the unconscious, or sometimes the subconscious 60 00:03:52,720 --> 00:03:55,600 Speaker 1: brain and the conscious mind, which is the part of 61 00:03:55,600 --> 00:03:58,080 Speaker 1: you that wakes up in the morning. That is like 62 00:03:58,120 --> 00:04:02,040 Speaker 1: a broom closet in the mansion of the brain. You know, 63 00:04:02,080 --> 00:04:04,240 Speaker 1: think about when you have an idea and you say, oh, 64 00:04:04,280 --> 00:04:06,840 Speaker 1: I'm a genius. It wasn't exactly you that thought of it. 65 00:04:06,880 --> 00:04:09,040 Speaker 1: Your brain has been working on that for days or weeks, 66 00:04:09,560 --> 00:04:13,400 Speaker 1: evaluating hypotheses behind the scenes, and at some point it 67 00:04:13,440 --> 00:04:15,120 Speaker 1: cooks something up and it serves it to you and 68 00:04:15,120 --> 00:04:17,800 Speaker 1: you say, oh, I'm so smart. You know. For those 69 00:04:17,800 --> 00:04:20,240 Speaker 1: of us in the creative business, we often talk about 70 00:04:20,240 --> 00:04:22,320 Speaker 1: how do you come up with these sort of ideas? 71 00:04:22,720 --> 00:04:25,200 Speaker 1: And I think most of as share this view that 72 00:04:25,640 --> 00:04:28,040 Speaker 1: you know, you you load up your brain, you think 73 00:04:28,080 --> 00:04:30,440 Speaker 1: about the issue, get all the data, and then you 74 00:04:30,480 --> 00:04:33,280 Speaker 1: forget about it, and at some moment, which you're sort 75 00:04:33,320 --> 00:04:36,240 Speaker 1: of the most in the zone mind is a fifty 76 00:04:36,279 --> 00:04:39,520 Speaker 1: minute hot shower in the morning, suddenly ideas pop into 77 00:04:39,520 --> 00:04:42,200 Speaker 1: my head. So you're saying, my subconscious have been doing 78 00:04:42,360 --> 00:04:44,800 Speaker 1: lots of work and when it's ready, it just throws 79 00:04:44,800 --> 00:04:46,400 Speaker 1: it up to me at the easiest point it can 80 00:04:46,440 --> 00:04:50,680 Speaker 1: access me. Yeah, that's exactly right. We uh, it's a 81 00:04:50,680 --> 00:04:54,159 Speaker 1: funny existence that we have because we don't know what's 82 00:04:54,200 --> 00:04:57,640 Speaker 1: happening under the hood. But then things just sort of 83 00:04:57,680 --> 00:05:01,719 Speaker 1: appear like mind pops. We say, oh, yeah, hey, that works. 84 00:05:01,839 --> 00:05:03,520 Speaker 1: Oh I just thought of that person's name, or oh 85 00:05:03,560 --> 00:05:06,320 Speaker 1: I just thought of a way to view this problem 86 00:05:06,360 --> 00:05:08,680 Speaker 1: differently and get past it. I just wrote my speech 87 00:05:08,680 --> 00:05:10,200 Speaker 1: in my head. Let me rush out of the shower 88 00:05:10,200 --> 00:05:12,000 Speaker 1: and write it down real quickly while it's still in 89 00:05:12,040 --> 00:05:15,719 Speaker 1: my head. Exactly. You use the example of someone hearing 90 00:05:15,720 --> 00:05:18,160 Speaker 1: their name in a restaurant just sort of Wow, they 91 00:05:18,200 --> 00:05:21,440 Speaker 1: just said my name. Your point was that your subconscious 92 00:05:21,520 --> 00:05:24,320 Speaker 1: been listening to every conversation in the room. Is that right, Well, yeah, 93 00:05:24,440 --> 00:05:27,279 Speaker 1: all the conversations in your room are hitting your ear drum. 94 00:05:27,400 --> 00:05:30,599 Speaker 1: And even though you think I'm ignoring everything except for 95 00:05:30,600 --> 00:05:33,000 Speaker 1: the conversation right in front of me, your brain can't help. 96 00:05:33,520 --> 00:05:36,320 Speaker 1: But here those other conversations, and then it has to 97 00:05:36,560 --> 00:05:39,600 Speaker 1: try to cut out the conversation that's happening in front 98 00:05:39,680 --> 00:05:42,159 Speaker 1: of you. It's called the cocktail party effect. When somebody 99 00:05:42,200 --> 00:05:45,800 Speaker 1: says your name in a different conversation, you're aware of it, 100 00:05:45,800 --> 00:05:49,440 Speaker 1: which means you have been listening to everything. So let's 101 00:05:49,480 --> 00:05:54,279 Speaker 1: go through for a marketing standpoint, when you're unconscious, subconscious, 102 00:05:54,680 --> 00:05:58,279 Speaker 1: here's a message over and over. Does it examine the 103 00:05:58,360 --> 00:06:01,200 Speaker 1: claim or does it up that based on how many 104 00:06:01,240 --> 00:06:03,840 Speaker 1: times it's heard it? How does that work? It's a 105 00:06:03,880 --> 00:06:05,960 Speaker 1: little bit of both. I mean, when you hear something, 106 00:06:06,000 --> 00:06:08,920 Speaker 1: you can be skeptical about it and you know, chew 107 00:06:08,960 --> 00:06:10,680 Speaker 1: on it and so on. The thing is that there's 108 00:06:10,680 --> 00:06:13,359 Speaker 1: something called the illusion of truth, which has been studied 109 00:06:13,360 --> 00:06:16,040 Speaker 1: for a while, which is, if you have heard something 110 00:06:16,440 --> 00:06:19,680 Speaker 1: several times, you tend to rate it as being more 111 00:06:19,720 --> 00:06:23,000 Speaker 1: truthful than the first time you hear it. So politicians, 112 00:06:23,000 --> 00:06:25,800 Speaker 1: of course take advantage of this all the time. Everyone 113 00:06:25,880 --> 00:06:28,000 Speaker 1: does this in various ways in their life. But yes, 114 00:06:28,040 --> 00:06:30,680 Speaker 1: if something is said many times, you think that it 115 00:06:30,760 --> 00:06:33,680 Speaker 1: somehow must be true. So we're going through it, of 116 00:06:33,720 --> 00:06:36,080 Speaker 1: course in this country right now, with trying to figure 117 00:06:36,120 --> 00:06:39,520 Speaker 1: out what's real news fake news? How we wind up 118 00:06:39,520 --> 00:06:43,520 Speaker 1: with this polarization. How is this piece of our brain 119 00:06:43,640 --> 00:06:46,560 Speaker 1: and ourself playing into that. This has been an area 120 00:06:46,600 --> 00:06:48,920 Speaker 1: of great interest for me lately, so my whole life. 121 00:06:48,960 --> 00:06:52,880 Speaker 1: I've been studying this issue about our internal model, which 122 00:06:52,920 --> 00:06:55,560 Speaker 1: is to say, you know, your brain is locked in 123 00:06:55,720 --> 00:06:58,719 Speaker 1: silence and darkness inside your skull. It doesn't have access 124 00:06:58,760 --> 00:07:01,400 Speaker 1: to the outside world all ever has or the little 125 00:07:01,680 --> 00:07:05,000 Speaker 1: dribbles of signals that gets through these spheres in the 126 00:07:05,040 --> 00:07:07,520 Speaker 1: front of your head, your eyes, or through air compression 127 00:07:07,520 --> 00:07:10,560 Speaker 1: waves and your ears or things that touches and so on, 128 00:07:11,120 --> 00:07:13,840 Speaker 1: and so your brain's job is to put together a 129 00:07:13,920 --> 00:07:17,600 Speaker 1: model of reality. But all we ever get is our 130 00:07:17,640 --> 00:07:20,440 Speaker 1: own version of reality. And the part that's been fascinating 131 00:07:20,480 --> 00:07:23,440 Speaker 1: to me lately watching the polarization is the way that 132 00:07:23,480 --> 00:07:27,600 Speaker 1: we all believe our own reality so firmly, so every 133 00:07:27,600 --> 00:07:32,440 Speaker 1: single person thinks, Okay, look, I understand the truth, and 134 00:07:32,760 --> 00:07:34,760 Speaker 1: I have no idea what those other people are doing, 135 00:07:34,760 --> 00:07:38,600 Speaker 1: their trolls or their misinformed They're just ignorant. And if 136 00:07:38,640 --> 00:07:40,200 Speaker 1: I could just sit down and talk to them, or 137 00:07:40,200 --> 00:07:42,800 Speaker 1: if I could just shout loudly enough in all caps 138 00:07:42,840 --> 00:07:46,760 Speaker 1: on Twitter, they would have to understand the genius of 139 00:07:46,800 --> 00:07:50,000 Speaker 1: what I'm saying and would come to repair their ways. 140 00:07:50,560 --> 00:07:52,840 Speaker 1: And the part that's been so fascinating to me is 141 00:07:52,880 --> 00:07:57,760 Speaker 1: that we all believe this all the way down, that 142 00:07:57,760 --> 00:08:00,360 Speaker 1: that we have the truth and that other people do not. 143 00:08:00,880 --> 00:08:03,920 Speaker 1: So let's talk a bit about news organizations the old 144 00:08:04,000 --> 00:08:08,480 Speaker 1: days pre social that a news organization would get information, 145 00:08:08,520 --> 00:08:11,600 Speaker 1: they would gather it, they would usually examine it, they 146 00:08:11,600 --> 00:08:16,360 Speaker 1: would process it. Some people do argue, curate it and 147 00:08:16,400 --> 00:08:18,960 Speaker 1: put it out. Do you think that it's very hard 148 00:08:19,040 --> 00:08:23,120 Speaker 1: for a society to function without that going on? That 149 00:08:23,200 --> 00:08:25,840 Speaker 1: if we left it to everybody who all always thinks 150 00:08:25,840 --> 00:08:28,760 Speaker 1: the right and gives everybody the microphone, which of course 151 00:08:29,040 --> 00:08:32,920 Speaker 1: social does, that we have a problem. You know, it's funny. 152 00:08:33,120 --> 00:08:38,600 Speaker 1: I think that it's easy to engage in retrospective romanticization, 153 00:08:38,640 --> 00:08:41,640 Speaker 1: where we think that it used to be sort of 154 00:08:41,679 --> 00:08:44,319 Speaker 1: better and normal, But if you look at the twentieth century, 155 00:08:44,840 --> 00:08:48,040 Speaker 1: it's far more bloody than anything we've had in the 156 00:08:48,040 --> 00:08:50,600 Speaker 1: twenty one century. I mean, take not only the World Wars, 157 00:08:50,640 --> 00:08:53,640 Speaker 1: but you know, the the Communist revolution in China, or 158 00:08:53,640 --> 00:08:56,959 Speaker 1: what happened with Nazi Germany, or the Hutu massacre the 159 00:08:57,040 --> 00:09:00,319 Speaker 1: Tutsi and Rwanda, or so on. It was an awful 160 00:09:00,440 --> 00:09:03,960 Speaker 1: time pre social media. What that demonstrates is you don't 161 00:09:03,960 --> 00:09:06,920 Speaker 1: need social media for humans to get very caught up 162 00:09:07,280 --> 00:09:09,760 Speaker 1: in their particular view and think the other side is 163 00:09:09,800 --> 00:09:12,480 Speaker 1: wrong to the point of picking up arms and and 164 00:09:12,600 --> 00:09:16,920 Speaker 1: murdering other people, and in fact, news in previous centuries 165 00:09:17,280 --> 00:09:20,959 Speaker 1: has always been just as biased in completely nutty ways. 166 00:09:21,000 --> 00:09:24,400 Speaker 1: For example, people used to do pamphlets. If you look 167 00:09:24,440 --> 00:09:26,720 Speaker 1: at let's say the American Nazi Party, you know they 168 00:09:26,800 --> 00:09:29,719 Speaker 1: print pamphlets with all kinds of the craziest stuff and 169 00:09:29,760 --> 00:09:31,640 Speaker 1: they mail it to people's houses. Is how you know, 170 00:09:31,679 --> 00:09:34,280 Speaker 1: went last century, and you've got tens of thousands of 171 00:09:34,280 --> 00:09:36,440 Speaker 1: people on the mailing list and so on. It's like 172 00:09:36,559 --> 00:09:38,959 Speaker 1: social media in that way. It's not different. And I 173 00:09:39,000 --> 00:09:42,120 Speaker 1: think it's probably a dangerous illusion if we think that 174 00:09:42,320 --> 00:09:44,719 Speaker 1: somehow people used to tell the truth and things used 175 00:09:44,720 --> 00:09:47,520 Speaker 1: to be peaceful, because it's quite the opposite of that. 176 00:09:48,000 --> 00:09:50,559 Speaker 1: So the question I think is, you know, does social 177 00:09:50,600 --> 00:09:53,000 Speaker 1: media have anything to do with what's going on now? 178 00:09:53,360 --> 00:09:56,000 Speaker 1: I think maybe a tiny bit, but that's not the 179 00:09:56,080 --> 00:09:59,720 Speaker 1: driver of it. What the driver is is the human 180 00:10:00,040 --> 00:10:03,600 Speaker 1: capacity to make in groups and now groups, and this 181 00:10:03,679 --> 00:10:07,040 Speaker 1: is something that you know, evolutionary theorists are. This goes 182 00:10:07,080 --> 00:10:10,000 Speaker 1: back to the days when we've functioned in small tribes. 183 00:10:10,120 --> 00:10:12,640 Speaker 1: Let's say a hundred and fifty people, and you know, 184 00:10:12,800 --> 00:10:14,760 Speaker 1: you knew the people in your tribe, so you would 185 00:10:14,800 --> 00:10:16,760 Speaker 1: trust them, and the tribe on the other side of 186 00:10:16,800 --> 00:10:18,960 Speaker 1: the hill that was to them, and you would, you know, 187 00:10:19,000 --> 00:10:22,600 Speaker 1: be suspicious of them. It's just extraordinarily easy for us 188 00:10:22,640 --> 00:10:25,160 Speaker 1: to assign labels and it doesn't take much to say, oh, 189 00:10:25,240 --> 00:10:27,199 Speaker 1: you're on the other side, you're them. You did an 190 00:10:27,200 --> 00:10:30,720 Speaker 1: experiment I think about empathy and was it a knife 191 00:10:30,760 --> 00:10:33,400 Speaker 1: going into a hand? So we put people in the 192 00:10:33,440 --> 00:10:36,800 Speaker 1: brain scanner it's called a functional magnetic residence imaging scanner, 193 00:10:36,920 --> 00:10:40,840 Speaker 1: and we show them six hands on the screen, and 194 00:10:41,080 --> 00:10:43,280 Speaker 1: the computer goes around, do do do and picks one 195 00:10:43,320 --> 00:10:45,280 Speaker 1: of the hands, and then you see it gets stabbed 196 00:10:45,280 --> 00:10:50,320 Speaker 1: with the syringe needle, and that evokes essentially an empathic 197 00:10:50,400 --> 00:10:54,000 Speaker 1: response from you. Your brain lights up what we summarize 198 00:10:54,040 --> 00:10:56,800 Speaker 1: as the pain matrix, which is to say, even though 199 00:10:56,840 --> 00:10:59,960 Speaker 1: it's not your hand getting stabbed, you feel the pain 200 00:11:00,160 --> 00:11:01,920 Speaker 1: of someone else's hand getting staff and this is the 201 00:11:02,200 --> 00:11:05,800 Speaker 1: neural basis of empathy. Okay, But now what we do 202 00:11:06,600 --> 00:11:09,120 Speaker 1: is we then put a one word label on each 203 00:11:09,160 --> 00:11:13,080 Speaker 1: hand Christian, Jewish, Muslim, Scientologists, Hindu, atheist, and the computer 204 00:11:13,160 --> 00:11:15,160 Speaker 1: goes around, picks a hand. You see the hand gets stabbed, 205 00:11:15,160 --> 00:11:17,480 Speaker 1: and the question is does your brain care as much 206 00:11:18,120 --> 00:11:21,760 Speaker 1: when it's a member of your outgroup getting stabbed. Now, 207 00:11:21,960 --> 00:11:24,079 Speaker 1: And it turns out that's the answer, is that you 208 00:11:24,240 --> 00:11:27,240 Speaker 1: don't care as much when it's not your label getting stabbed. 209 00:11:27,280 --> 00:11:30,439 Speaker 1: Of course, we measured all people from all groups, and 210 00:11:30,559 --> 00:11:32,880 Speaker 1: you care more when you see your label, and you 211 00:11:32,960 --> 00:11:35,439 Speaker 1: care less when you see other labels. And this is 212 00:11:35,520 --> 00:11:38,920 Speaker 1: very depressing. How low level and instant this is where 213 00:11:38,960 --> 00:11:42,800 Speaker 1: your brain has its in groups and has its outgroups. Now, interestingly, 214 00:11:43,120 --> 00:11:46,360 Speaker 1: it's a little bit flexible. So if we then put 215 00:11:46,360 --> 00:11:50,120 Speaker 1: a one word sentence on that says the year is five, 216 00:11:50,280 --> 00:11:53,480 Speaker 1: and these three groups have teamed up against these three groups, 217 00:11:53,679 --> 00:11:57,640 Speaker 1: the computer randomly picks the division. Now you care more 218 00:11:57,800 --> 00:12:01,200 Speaker 1: about your allies. So whatever groups have just been randomly 219 00:12:01,200 --> 00:12:03,800 Speaker 1: assigned to your now you care when you see them 220 00:12:03,840 --> 00:12:06,640 Speaker 1: get stabbed more than you cared a second ago about it. 221 00:12:07,040 --> 00:12:09,280 Speaker 1: So anything we can do to organize people in bigger 222 00:12:09,280 --> 00:12:12,160 Speaker 1: groups is probably helpful. Huh, that's exactly right. And this 223 00:12:12,240 --> 00:12:14,720 Speaker 1: is what I've been working on, is how do we 224 00:12:14,920 --> 00:12:19,120 Speaker 1: complexify our group memberships. I think this is really going 225 00:12:19,160 --> 00:12:22,880 Speaker 1: to be the key to solving the polarization issue that 226 00:12:22,920 --> 00:12:25,320 Speaker 1: we're facing, which is to say, how do we figure 227 00:12:25,320 --> 00:12:29,240 Speaker 1: out what are the threads that link us across the 228 00:12:29,400 --> 00:12:32,360 Speaker 1: easy group divisions. So I'll just give an example the 229 00:12:32,600 --> 00:12:36,200 Speaker 1: Iroquois Native Americans. For for hundreds of years, there were 230 00:12:36,200 --> 00:12:39,320 Speaker 1: the six tribes that fought in these bloody wars. And 231 00:12:39,360 --> 00:12:42,400 Speaker 1: eventually a new leader came along, came to be known 232 00:12:42,400 --> 00:12:45,320 Speaker 1: as the Great Peacemaker, and and his idea was he said, look, 233 00:12:45,920 --> 00:12:48,840 Speaker 1: let's take these six tribes and for each member of 234 00:12:48,880 --> 00:12:52,120 Speaker 1: the tribe, assign them to a clan. So you're a 235 00:12:52,160 --> 00:12:54,520 Speaker 1: member of the Beaver clan or the Eagle clan, or 236 00:12:54,520 --> 00:12:57,440 Speaker 1: the Elk clan or so on. And now it's harder 237 00:12:57,880 --> 00:13:00,280 Speaker 1: for one tribe to attack another tribe because these two 238 00:13:00,280 --> 00:13:02,400 Speaker 1: guys here both members of the Beaver clan, and these 239 00:13:02,400 --> 00:13:04,480 Speaker 1: two guys remembers the Eagle clan. So they've got these 240 00:13:04,480 --> 00:13:08,760 Speaker 1: other relationships that cross cut what seems to be the 241 00:13:08,800 --> 00:13:13,960 Speaker 1: original group, and so it makes it more complexified, and 242 00:13:13,960 --> 00:13:16,640 Speaker 1: and that actually brought peace to these to these tribes 243 00:13:16,679 --> 00:13:19,480 Speaker 1: that were attacking each other. So one of my interests 244 00:13:19,520 --> 00:13:22,640 Speaker 1: right now is how to make what I'm calling algorithms 245 00:13:22,679 --> 00:13:26,200 Speaker 1: for unity, which is, let's say it's on social media. 246 00:13:26,280 --> 00:13:28,760 Speaker 1: Let's imagine that you and I had totally different views 247 00:13:28,840 --> 00:13:32,640 Speaker 1: on some whatever on our political parties. How do we 248 00:13:32,679 --> 00:13:34,559 Speaker 1: make it so that you post something and it happens 249 00:13:34,559 --> 00:13:36,719 Speaker 1: to be something I like to that, something about your 250 00:13:36,800 --> 00:13:40,120 Speaker 1: dog or something about surfing or whatever, and so I 251 00:13:40,200 --> 00:13:44,000 Speaker 1: like that post. And specifically, what the algorithm looks for 252 00:13:44,480 --> 00:13:47,880 Speaker 1: is posts that are cross cutting like that, that reach 253 00:13:48,000 --> 00:13:51,120 Speaker 1: across party lines, and those are the ones that can 254 00:13:51,160 --> 00:13:55,640 Speaker 1: amplified instead of the ones that are merely within party lines. 255 00:13:55,960 --> 00:13:58,640 Speaker 1: Let's go back in time to put you in context. 256 00:13:58,960 --> 00:14:01,120 Speaker 1: You were born in New Mexican go the dawn of 257 00:14:01,160 --> 00:14:04,400 Speaker 1: the nineteen seventies, youngest son of a psychiatrist and a 258 00:14:04,400 --> 00:14:08,240 Speaker 1: biology teacher. Can you describe that that time and place 259 00:14:08,320 --> 00:14:10,959 Speaker 1: that you grew up in. Yeah, I was outside of 260 00:14:10,960 --> 00:14:14,800 Speaker 1: the Albuquerque city limits, up in the mountains. So in retrospect, 261 00:14:14,840 --> 00:14:17,800 Speaker 1: it was a very isolated sort of childhood. And as 262 00:14:17,840 --> 00:14:20,120 Speaker 1: I said, I'm a real extrovert. So as soon as 263 00:14:20,120 --> 00:14:23,280 Speaker 1: I was able to, I got myself into big cities. 264 00:14:23,320 --> 00:14:27,200 Speaker 1: And I've always loved being right in the thick of things. 265 00:14:27,400 --> 00:14:29,280 Speaker 1: But you know, it was it was a nice childhood. 266 00:14:29,400 --> 00:14:32,920 Speaker 1: My father had built an extensive library, you know, he 267 00:14:32,920 --> 00:14:36,840 Speaker 1: actually built the shelves like library stacks that you could 268 00:14:36,840 --> 00:14:40,480 Speaker 1: walk around and pick books. He was a terrific reader, 269 00:14:40,640 --> 00:14:43,560 Speaker 1: was fluent in eight languages, and so we had books 270 00:14:43,560 --> 00:14:46,560 Speaker 1: of all languages and all types. I guess in retrospect, 271 00:14:46,600 --> 00:14:50,360 Speaker 1: it was really wonderful to grow up with not much 272 00:14:50,400 --> 00:14:54,080 Speaker 1: else to do. Besides that, you're obviously very curious guy, 273 00:14:54,120 --> 00:14:56,520 Speaker 1: and this sort of has driven you in your whole life. 274 00:14:57,080 --> 00:15:01,800 Speaker 1: Wasn't that curiosity influenced by all this? Or was it biology? 275 00:15:02,040 --> 00:15:05,480 Speaker 1: Was it just genetics? Impossible to know? In fact, the 276 00:15:05,560 --> 00:15:07,960 Speaker 1: nature versus nurture question, of course, is a dead question 277 00:15:07,960 --> 00:15:12,840 Speaker 1: of biology, because it's always both. But happily my parents 278 00:15:13,080 --> 00:15:17,000 Speaker 1: both were real feeders of that curiosity. And every night 279 00:15:17,000 --> 00:15:19,360 Speaker 1: at the dinner table, we would talk about things, and 280 00:15:19,640 --> 00:15:22,320 Speaker 1: they would ask me questions, and you know, we'd pull 281 00:15:22,360 --> 00:15:25,600 Speaker 1: out a map of the world and talk about different countries, 282 00:15:25,640 --> 00:15:28,000 Speaker 1: and you know, my father would pose questions to us, 283 00:15:28,040 --> 00:15:29,640 Speaker 1: most of which we didn't know the answer to. And 284 00:15:29,880 --> 00:15:31,560 Speaker 1: you know, the time we learned all this stuff, it 285 00:15:31,600 --> 00:15:33,880 Speaker 1: was really lovely to have that time. And I feel 286 00:15:33,920 --> 00:15:37,200 Speaker 1: like as the world has sped up, yeah, mostly because 287 00:15:37,240 --> 00:15:39,480 Speaker 1: of the Internet and social media and video games and 288 00:15:39,480 --> 00:15:42,680 Speaker 1: so on, it's harder to rope our kids into just 289 00:15:42,760 --> 00:15:45,760 Speaker 1: spending time with us doing that. Now, I will say, 290 00:15:45,760 --> 00:15:49,160 Speaker 1: on the other hand, I am very cyber optimistic about 291 00:15:49,200 --> 00:15:51,680 Speaker 1: what's going on. I think that there is a good 292 00:15:51,760 --> 00:15:54,400 Speaker 1: chance that our children will grow up to be smarter 293 00:15:55,040 --> 00:16:00,080 Speaker 1: than we are because they have so much opportunity to 294 00:16:00,160 --> 00:16:03,920 Speaker 1: tap into the entirety of human kinds knowledge. With any 295 00:16:04,000 --> 00:16:06,920 Speaker 1: question that they want to ask Alexa, Google Home, they're 296 00:16:07,000 --> 00:16:09,800 Speaker 1: getting the information that they need. And so you know, 297 00:16:09,880 --> 00:16:13,560 Speaker 1: I happily had my very smart parents to ask questions too. 298 00:16:14,080 --> 00:16:16,840 Speaker 1: But these kids can ask a question and watch a 299 00:16:16,920 --> 00:16:19,720 Speaker 1: Ted talk and in fifteen minutes get the world's expert 300 00:16:19,760 --> 00:16:21,640 Speaker 1: on some topic giving the best talk of their life 301 00:16:21,680 --> 00:16:23,720 Speaker 1: where they can see a little video on something. And 302 00:16:23,760 --> 00:16:26,840 Speaker 1: the reason this matters is because they're getting the answers 303 00:16:27,320 --> 00:16:30,240 Speaker 1: right when they're curious about the question. And that makes 304 00:16:30,240 --> 00:16:32,640 Speaker 1: a big difference from the point of view of brain plasticity, 305 00:16:32,640 --> 00:16:35,800 Speaker 1: which is to say, things stick if you care about 306 00:16:35,800 --> 00:16:38,320 Speaker 1: the answer, and it's harder to make things stick if 307 00:16:38,360 --> 00:16:42,320 Speaker 1: you are just being told in a classroom that you 308 00:16:42,400 --> 00:16:45,800 Speaker 1: need to know this particular fact. So supposedly you wrote 309 00:16:45,800 --> 00:16:48,160 Speaker 1: your first words at age two as a kid, you 310 00:16:48,200 --> 00:16:50,960 Speaker 1: could repeat back to some on a list of random objects, 311 00:16:51,200 --> 00:16:53,960 Speaker 1: and even in reverse order, if they ask is that 312 00:16:54,400 --> 00:16:59,640 Speaker 1: brain power or is that something that's learned? And how 313 00:16:59,640 --> 00:17:01,800 Speaker 1: did that define you? I mean, you must have been 314 00:17:01,800 --> 00:17:05,360 Speaker 1: the smart kid. Was that your image as a kid 315 00:17:05,359 --> 00:17:09,959 Speaker 1: of yourself self image? That's interesting? I guess it was. 316 00:17:10,520 --> 00:17:12,440 Speaker 1: But you know, some of these things are just party tricks. 317 00:17:12,440 --> 00:17:16,840 Speaker 1: They are just intellectual exercises. That's no different than learning 318 00:17:16,840 --> 00:17:19,040 Speaker 1: how to do a layup or something. So you know, 319 00:17:19,080 --> 00:17:21,919 Speaker 1: the thing about memorizing a list of items, There are 320 00:17:21,920 --> 00:17:24,159 Speaker 1: all these memory tricks that you can use for that. 321 00:17:24,240 --> 00:17:26,159 Speaker 1: And I did that as a kid, and you know, 322 00:17:26,280 --> 00:17:29,880 Speaker 1: really really enjoyed doing that sort of thing. But yeah, 323 00:17:29,920 --> 00:17:34,440 Speaker 1: I think it's so helpful to to get the proper 324 00:17:34,600 --> 00:17:39,639 Speaker 1: feedback about, hey, you've done something that's smart and and 325 00:17:39,760 --> 00:17:42,040 Speaker 1: isn't that great? And you know, my parents always gave 326 00:17:42,119 --> 00:17:44,520 Speaker 1: such wonderful feedback about that sort of thing, and when 327 00:17:44,520 --> 00:17:46,679 Speaker 1: I came home with good grades and so on, And 328 00:17:46,720 --> 00:17:50,040 Speaker 1: that matters because when you're making choices later in life 329 00:17:50,359 --> 00:17:53,280 Speaker 1: about whether to stay up all night really dig into 330 00:17:53,280 --> 00:17:56,080 Speaker 1: the book and really understand something all the way down 331 00:17:56,080 --> 00:17:59,000 Speaker 1: to the bottom, or to take the lazy way out 332 00:17:59,440 --> 00:18:02,760 Speaker 1: it's used. Well, if you are used to making sure 333 00:18:02,760 --> 00:18:05,359 Speaker 1: that you understand something all the way down, what lesson 334 00:18:05,720 --> 00:18:10,280 Speaker 1: do you use today that you know came from your childhood? Ah? 335 00:18:10,760 --> 00:18:14,160 Speaker 1: I think it's never giving up. One example, I happen 336 00:18:14,160 --> 00:18:16,000 Speaker 1: to be sitting in front of my bookshelf right now, 337 00:18:16,040 --> 00:18:19,280 Speaker 1: and there's some copies in my book Some which I'm 338 00:18:19,280 --> 00:18:22,800 Speaker 1: really happy to say became an international bestseller and has 339 00:18:22,800 --> 00:18:24,600 Speaker 1: had a real wonderful life. It got turned into two 340 00:18:24,680 --> 00:18:27,399 Speaker 1: operas and stuff like that. But the point is I 341 00:18:27,480 --> 00:18:30,280 Speaker 1: wrote this book Some it's a book of fiction, and 342 00:18:30,840 --> 00:18:35,080 Speaker 1: I submitted it to so many agents and publishers and 343 00:18:35,160 --> 00:18:37,960 Speaker 1: got a stack of rejection letters as high as the book. 344 00:18:38,760 --> 00:18:42,480 Speaker 1: And people some people said nice things like I really 345 00:18:42,520 --> 00:18:44,240 Speaker 1: love this book, but I have no idea how to 346 00:18:44,240 --> 00:18:45,760 Speaker 1: market a book like this. I wouldn't even know where 347 00:18:45,760 --> 00:18:47,960 Speaker 1: in the book store to put a book like this. 348 00:18:48,080 --> 00:18:49,800 Speaker 1: Tell us about the book for the people who don't 349 00:18:49,800 --> 00:18:52,600 Speaker 1: know what some as. It's a book of forty short stories, 350 00:18:52,760 --> 00:18:56,440 Speaker 1: it's literary fiction, and all forty stories are mutually exclusive, 351 00:18:56,480 --> 00:19:00,560 Speaker 1: so they all tell incompatible version of what's going on 352 00:19:00,640 --> 00:19:03,119 Speaker 1: with our lives here. It was just so hard to 353 00:19:03,119 --> 00:19:06,560 Speaker 1: get published but the point is probably well well passed 354 00:19:06,560 --> 00:19:09,360 Speaker 1: the point where everyone thought I was nuts. I just 355 00:19:09,480 --> 00:19:12,560 Speaker 1: kept sending out letters. I just kept making phone calls 356 00:19:12,600 --> 00:19:16,119 Speaker 1: and asking people if they had a connection and working 357 00:19:16,119 --> 00:19:18,800 Speaker 1: on it until until finally, through a string of connections, 358 00:19:18,800 --> 00:19:21,480 Speaker 1: I got introduced to an agent and she said, Okay, 359 00:19:21,520 --> 00:19:23,640 Speaker 1: I'll take you on as a hip pocket client, which 360 00:19:23,640 --> 00:19:25,560 Speaker 1: means you're not really a client, but I'll just keeping 361 00:19:25,560 --> 00:19:28,080 Speaker 1: in my pocket in case I see an opportunity. And 362 00:19:28,119 --> 00:19:30,359 Speaker 1: then she called me two days later and she was 363 00:19:30,400 --> 00:19:34,640 Speaker 1: as surprised I was, said, Wow, random house just bought this. Anyway, 364 00:19:34,760 --> 00:19:37,720 Speaker 1: the key is I've gone about everything in my life 365 00:19:37,760 --> 00:19:41,520 Speaker 1: that way, and like everybody, I've had so many rejections. 366 00:19:41,560 --> 00:19:44,520 Speaker 1: I I imagine that I may have more rejections than 367 00:19:44,560 --> 00:19:47,960 Speaker 1: the average person, just because I just keep getting rejected 368 00:19:48,040 --> 00:19:51,359 Speaker 1: until at some point I know moral math and magic. 369 00:19:51,520 --> 00:19:58,080 Speaker 1: Right after this quick break, welcome back to math and magic. 370 00:19:58,240 --> 00:20:01,040 Speaker 1: Let's hear more from my conversation with d David Eagleman. 371 00:20:02,680 --> 00:20:05,760 Speaker 1: You did graduate from college in a degree in British 372 00:20:05,800 --> 00:20:08,000 Speaker 1: and American literature, and you went on to get your 373 00:20:08,040 --> 00:20:11,800 Speaker 1: pH d in neuroscience, but along the way you joined 374 00:20:11,840 --> 00:20:14,919 Speaker 1: the Israeli Army, You spent a semester at Oxford, you 375 00:20:15,040 --> 00:20:18,399 Speaker 1: had a try as a screenwriter and even a stand 376 00:20:18,440 --> 00:20:22,320 Speaker 1: up comic. So what was driving this and how did 377 00:20:22,359 --> 00:20:25,520 Speaker 1: that send you to where you are today? Yeah, I 378 00:20:25,600 --> 00:20:29,560 Speaker 1: was really trying to find what was fitting because I 379 00:20:29,600 --> 00:20:32,480 Speaker 1: had been good in science all growing up, and so 380 00:20:32,880 --> 00:20:35,240 Speaker 1: everyone told me you should be a scientist, which which 381 00:20:35,320 --> 00:20:38,320 Speaker 1: was good advice, except that no one really knew the 382 00:20:38,400 --> 00:20:40,800 Speaker 1: details of how to steer me. So, you know, one 383 00:20:40,840 --> 00:20:43,520 Speaker 1: person said, hey, you should go into waste management. There's 384 00:20:43,520 --> 00:20:45,520 Speaker 1: a lot of money in that. And someone said, oh, 385 00:20:45,560 --> 00:20:48,400 Speaker 1: you should be an actuary. I looked into that stuff 386 00:20:48,440 --> 00:20:51,480 Speaker 1: about five seconds and it's like chewing on autumn leaves. 387 00:20:51,520 --> 00:20:54,400 Speaker 1: So everyone, I feel like, was giving me a well 388 00:20:54,480 --> 00:20:56,960 Speaker 1: meaning advice that didn't fit me at all. And so 389 00:20:57,160 --> 00:21:00,280 Speaker 1: when I went to college, I was studying all kinds science. 390 00:21:00,320 --> 00:21:02,040 Speaker 1: I was doing electrical engineering for a while, I was 391 00:21:02,080 --> 00:21:03,880 Speaker 1: doing space physics for a while. I was always doing 392 00:21:03,960 --> 00:21:06,760 Speaker 1: British American literature in the background, because I that was 393 00:21:06,880 --> 00:21:10,000 Speaker 1: my first love. But I couldn't find what I was 394 00:21:10,040 --> 00:21:13,560 Speaker 1: going to do as far as career wise goes, and 395 00:21:13,600 --> 00:21:15,560 Speaker 1: so that's why I dropped out of college. I wasn't 396 00:21:15,560 --> 00:21:17,520 Speaker 1: really enjoying college that much, and I just felt there's 397 00:21:17,680 --> 00:21:20,760 Speaker 1: so much of the world to do and explore, and 398 00:21:20,840 --> 00:21:22,680 Speaker 1: so I went off and did that for a while. 399 00:21:22,880 --> 00:21:25,560 Speaker 1: You know, all of that added so much depth and 400 00:21:25,640 --> 00:21:28,360 Speaker 1: color to my experience that I think I probably wouldn't 401 00:21:28,359 --> 00:21:31,880 Speaker 1: have had otherwise. And what happened is the last semester 402 00:21:31,920 --> 00:21:34,679 Speaker 1: of my senior year, that's when I took a neuroscience class, 403 00:21:35,040 --> 00:21:38,200 Speaker 1: and a gentleman teaching the class was probably eighty five 404 00:21:38,280 --> 00:21:42,000 Speaker 1: years old, and he was using literature from the nineties sixties. 405 00:21:42,400 --> 00:21:45,359 Speaker 1: But it didn't matter. I just I was immediately hooked 406 00:21:45,359 --> 00:21:48,280 Speaker 1: on it and read every book at the library on neuroscience. 407 00:21:48,600 --> 00:21:52,320 Speaker 1: And so when I went to apply for neuroscience graduate school, 408 00:21:52,840 --> 00:21:54,639 Speaker 1: I said to them in the interview, look, I know 409 00:21:54,720 --> 00:21:58,240 Speaker 1: I don't have any biology on my transcript, but asked 410 00:21:58,240 --> 00:21:59,520 Speaker 1: me what I know, and I'll tell you because I've 411 00:21:59,520 --> 00:22:01,560 Speaker 1: read every look in the library on this, and so 412 00:22:01,800 --> 00:22:03,680 Speaker 1: you know, they interviewed me and asked me, and I 413 00:22:04,160 --> 00:22:06,919 Speaker 1: guess I just squeaked in there. Let's jump to the 414 00:22:06,920 --> 00:22:09,919 Speaker 1: perception of time. At age six, I was put on 415 00:22:09,920 --> 00:22:11,800 Speaker 1: a horse by an uncle and all of a sudden, 416 00:22:11,800 --> 00:22:14,480 Speaker 1: the horse reared up, and I still remember, I mean 417 00:22:14,520 --> 00:22:17,679 Speaker 1: to this day, sweating thinking about it, every detail of 418 00:22:17,720 --> 00:22:21,200 Speaker 1: trying to hold onto that saddle horn and then beginning 419 00:22:21,200 --> 00:22:23,760 Speaker 1: to slide off the back of the horse, and my 420 00:22:23,920 --> 00:22:27,919 Speaker 1: life went into completely slow motion. I see a story 421 00:22:28,000 --> 00:22:31,880 Speaker 1: from you that was was your childhood story about falling 422 00:22:31,920 --> 00:22:34,359 Speaker 1: off the roof of a house and time moving to 423 00:22:34,480 --> 00:22:37,560 Speaker 1: slow motion. And I know even at that moment as 424 00:22:37,560 --> 00:22:41,919 Speaker 1: a kid, that aroused your curiosity. So later as a scientist, 425 00:22:42,000 --> 00:22:44,479 Speaker 1: what did you learn about that? So the main thing 426 00:22:44,520 --> 00:22:47,000 Speaker 1: I learned is, you know, when I had fallen off 427 00:22:47,040 --> 00:22:49,320 Speaker 1: through if it seemed like things were moving in slow motion. 428 00:22:49,320 --> 00:22:51,000 Speaker 1: But when I learned as a scientist that no one 429 00:22:51,040 --> 00:22:54,560 Speaker 1: had ever actually run this study of does time going 430 00:22:54,600 --> 00:22:57,000 Speaker 1: slow motion when you're in fear for your life? So 431 00:22:57,359 --> 00:22:59,760 Speaker 1: I tried to figure out how would I study this 432 00:23:00,040 --> 00:23:02,240 Speaker 1: because it's actually quite important. And also, you know, you 433 00:23:02,280 --> 00:23:05,480 Speaker 1: could build completely different kinds of dashboards for cars or 434 00:23:05,480 --> 00:23:08,560 Speaker 1: for fighter jets or whatever if you could feed information 435 00:23:08,600 --> 00:23:11,520 Speaker 1: to the pilot faster when things were really hitting the fan. 436 00:23:11,720 --> 00:23:14,120 Speaker 1: But it turns out it had never been studied, of course, 437 00:23:14,160 --> 00:23:17,639 Speaker 1: because you can't put people in life threatening situations. So 438 00:23:17,880 --> 00:23:20,119 Speaker 1: I figured out how to do that. I you know, 439 00:23:20,119 --> 00:23:22,439 Speaker 1: I took my whole team to the amusement park. We 440 00:23:22,440 --> 00:23:24,480 Speaker 1: went on the scariest rides. It turns out none of 441 00:23:24,520 --> 00:23:27,560 Speaker 1: that was scary enough to induce the effect. But I 442 00:23:27,640 --> 00:23:30,879 Speaker 1: finally found something called scad diving, which is where you 443 00:23:30,960 --> 00:23:34,960 Speaker 1: dropped from a hundred and fifty foot tall tower backwards 444 00:23:35,000 --> 00:23:37,240 Speaker 1: in free fall and you're caught in a net below 445 00:23:37,320 --> 00:23:40,240 Speaker 1: going seventy miles and that, it turns out, is scary 446 00:23:40,320 --> 00:23:42,480 Speaker 1: enough that you really feel like the whole event took 447 00:23:42,560 --> 00:23:45,520 Speaker 1: much longer than it did. So with my students, I 448 00:23:45,640 --> 00:23:48,119 Speaker 1: built a device that sits on the wrist and it 449 00:23:48,240 --> 00:23:51,000 Speaker 1: flashes information at you in such a way that we 450 00:23:51,040 --> 00:23:55,120 Speaker 1: could measure whether people were actually seeing in slow motion 451 00:23:55,880 --> 00:23:59,600 Speaker 1: during the event or not. And uh, skip to the punchline. 452 00:23:59,600 --> 00:24:03,480 Speaker 1: It turns out you cannot actually see in slow motion 453 00:24:03,560 --> 00:24:07,400 Speaker 1: during the event. Nonetheless, you believe that the event took 454 00:24:07,520 --> 00:24:10,320 Speaker 1: much longer. So it turns out the whole thing is 455 00:24:10,320 --> 00:24:14,160 Speaker 1: a trick of memory. When something very scary is happening, 456 00:24:14,200 --> 00:24:17,760 Speaker 1: you're laying down more memory, because that's what memory is for. 457 00:24:17,960 --> 00:24:20,159 Speaker 1: When things are hitting the fan, that's when you write 458 00:24:20,160 --> 00:24:24,200 Speaker 1: down everything going on, and when you read that back out, 459 00:24:24,720 --> 00:24:26,800 Speaker 1: it seems you've taken longer. You have much more memory 460 00:24:26,840 --> 00:24:29,040 Speaker 1: than normal. You know, when you're in a car accident 461 00:24:29,160 --> 00:24:31,320 Speaker 1: and you're watching the hood crumple and the rear view 462 00:24:31,320 --> 00:24:34,040 Speaker 1: mirror fall off, and the other driver's facial expression and 463 00:24:34,040 --> 00:24:37,320 Speaker 1: so on, you're writing down every detail of that. So 464 00:24:37,400 --> 00:24:38,879 Speaker 1: when you say what does happen, what does happen? What 465 00:24:38,880 --> 00:24:41,080 Speaker 1: does happened? You think, oh, well, there's that, there's that, 466 00:24:41,160 --> 00:24:42,919 Speaker 1: so your brain estimate, so that must have taken a 467 00:24:42,920 --> 00:24:46,040 Speaker 1: long time, but in fact it's not moving in slow motion. 468 00:24:46,200 --> 00:24:48,120 Speaker 1: And of course part of the reason you can evidence 469 00:24:48,200 --> 00:24:49,960 Speaker 1: is for yourself is you know, the person on this 470 00:24:50,200 --> 00:24:53,400 Speaker 1: seat next to you who's screaming no, it doesn't sound 471 00:24:53,400 --> 00:24:57,080 Speaker 1: like they're saying no like that, And that's because you 472 00:24:57,119 --> 00:24:59,040 Speaker 1: know it would have to sound like that if time 473 00:24:59,080 --> 00:25:01,480 Speaker 1: we're actually running in emotion. But instead it's just a 474 00:25:01,520 --> 00:25:04,800 Speaker 1: density of memory issue. So when we look back on 475 00:25:04,840 --> 00:25:07,160 Speaker 1: our life, and you know, I hear people talk about 476 00:25:07,160 --> 00:25:09,919 Speaker 1: this that we really tell our story of our life 477 00:25:09,960 --> 00:25:12,600 Speaker 1: through all the things that were bad that happened to us, 478 00:25:12,720 --> 00:25:16,040 Speaker 1: scary stuff. So you're really saying that that's true. The 479 00:25:16,160 --> 00:25:19,359 Speaker 1: memory is much more filled with that than it is 480 00:25:19,400 --> 00:25:21,119 Speaker 1: oh wow, it's a beautiful day to day and I 481 00:25:21,160 --> 00:25:23,280 Speaker 1: walked to work and the sun was beaming on my head. 482 00:25:23,520 --> 00:25:26,120 Speaker 1: It's anything that's emotionally salient, so that can be good 483 00:25:26,200 --> 00:25:27,840 Speaker 1: or bad. So it can be your wedding day and 484 00:25:27,880 --> 00:25:29,520 Speaker 1: the birth of your children, and so it can be 485 00:25:29,560 --> 00:25:32,280 Speaker 1: good memories too. It's just anything that is routine. The 486 00:25:32,320 --> 00:25:34,800 Speaker 1: brain doesn't bother writing down. So for example, the first 487 00:25:34,800 --> 00:25:37,560 Speaker 1: time you ever drove to your work, it maybe seems 488 00:25:37,560 --> 00:25:39,040 Speaker 1: to take a bit of a long time, but after 489 00:25:39,080 --> 00:25:42,280 Speaker 1: a while, it essentially seems like zero time because you're 490 00:25:42,320 --> 00:25:46,359 Speaker 1: just running as an unconscious automaton and you're not writing 491 00:25:46,359 --> 00:25:48,840 Speaker 1: down any new memories. So when you arrive at work 492 00:25:48,880 --> 00:25:52,240 Speaker 1: and say, okay, what just happened, you can't really remember anything. 493 00:25:52,359 --> 00:25:55,720 Speaker 1: So the whole key to making it seem as though 494 00:25:55,760 --> 00:26:00,440 Speaker 1: you've lived longer is to always seek new cha lenges 495 00:26:00,480 --> 00:26:03,600 Speaker 1: so that you're writing down new memories. Question for those 496 00:26:03,640 --> 00:26:05,959 Speaker 1: of us on the back end of life, why has 497 00:26:06,000 --> 00:26:09,280 Speaker 1: my memory gone to hell? Um? A big part of that, 498 00:26:09,320 --> 00:26:11,919 Speaker 1: again has to do with this issue that memory is 499 00:26:11,960 --> 00:26:15,439 Speaker 1: for things that are novel and as you grow older, 500 00:26:15,480 --> 00:26:18,480 Speaker 1: you've sort of figured things out. You you know how 501 00:26:18,480 --> 00:26:20,760 Speaker 1: to run a company, Bob, and you know how people 502 00:26:20,840 --> 00:26:23,840 Speaker 1: act generally, and you know the spectrum of personality types, 503 00:26:23,880 --> 00:26:25,600 Speaker 1: and you know, even when you travel to a new city, 504 00:26:25,600 --> 00:26:28,200 Speaker 1: you've kind of seen it all before. You know what, Oh, 505 00:26:28,280 --> 00:26:31,000 Speaker 1: here's a cool tourist spot, here's the Starbucks, here's the hotel. 506 00:26:31,240 --> 00:26:33,200 Speaker 1: You sort of know how things going. So your brain 507 00:26:33,320 --> 00:26:35,840 Speaker 1: just doesn't write things down, and the way it used 508 00:26:35,880 --> 00:26:38,520 Speaker 1: to in memory is simply not as important. But again, 509 00:26:38,640 --> 00:26:43,119 Speaker 1: this is the key to seeking novelty, to putting yourself 510 00:26:43,160 --> 00:26:46,399 Speaker 1: in situations where you're always in between the levels of 511 00:26:46,400 --> 00:26:50,040 Speaker 1: frustrating but achievable, because then your brain starts writing things down. 512 00:26:50,080 --> 00:26:53,080 Speaker 1: And of course you know this. If you spend two 513 00:26:53,119 --> 00:26:55,560 Speaker 1: weeks at home doing the same old thing, when you 514 00:26:55,600 --> 00:26:57,679 Speaker 1: look back, that you just have no memory of it. 515 00:26:57,720 --> 00:27:00,840 Speaker 1: But if you go on a vacation for a weekend 516 00:27:00,960 --> 00:27:04,880 Speaker 1: to burning Man, to Burning Man, exactly you come back 517 00:27:04,920 --> 00:27:07,879 Speaker 1: and you have to remember every day exactly right. Is 518 00:27:07,880 --> 00:27:10,480 Speaker 1: that why my pattern recognitions is so good because I've 519 00:27:10,600 --> 00:27:13,240 Speaker 1: now seen it all that's exactly right. So so there's 520 00:27:13,280 --> 00:27:15,840 Speaker 1: always this trade off that happens, which is you become 521 00:27:16,080 --> 00:27:18,440 Speaker 1: expert at the things that you're doing. That's why you're 522 00:27:18,480 --> 00:27:21,720 Speaker 1: able to run companies, because you are trained like a 523 00:27:21,760 --> 00:27:24,159 Speaker 1: machine to do that kind of work. And that's the 524 00:27:24,160 --> 00:27:26,320 Speaker 1: good news. The bad news is you have less novelty. 525 00:27:26,800 --> 00:27:29,320 Speaker 1: Let's talk about your TV work. You have been an 526 00:27:29,320 --> 00:27:32,080 Speaker 1: advisor for all sorts of folks. You've had your own 527 00:27:32,359 --> 00:27:35,400 Speaker 1: TV show, You've helped other people with theirs, you were 528 00:27:35,440 --> 00:27:39,000 Speaker 1: even a scientific advisor for Westworld. Where did this itch 529 00:27:39,080 --> 00:27:42,440 Speaker 1: come from? What motivates this for a scientist like you? 530 00:27:42,440 --> 00:27:43,920 Speaker 1: You know I mentioned at the beginning when you asked 531 00:27:43,920 --> 00:27:47,240 Speaker 1: you what childhood hero was, it was Carl Sagan, And 532 00:27:47,480 --> 00:27:50,360 Speaker 1: it's because he was a great scientist. He was such 533 00:27:50,400 --> 00:27:56,520 Speaker 1: an insightful thinker, and also he was able to phrase 534 00:27:56,600 --> 00:27:59,760 Speaker 1: things so that we could all tap directly into the 535 00:27:59,800 --> 00:28:02,199 Speaker 1: beauty of of what was going on around us in 536 00:28:02,240 --> 00:28:05,120 Speaker 1: the world. And so I watched him at nine years 537 00:28:05,160 --> 00:28:08,520 Speaker 1: old and just immediately thought that's what I wanted to do. 538 00:28:08,680 --> 00:28:10,199 Speaker 1: And in fact, my parents when I was growing up, 539 00:28:10,200 --> 00:28:12,520 Speaker 1: they would they wouldn't say, oh, David's gonna be the 540 00:28:12,520 --> 00:28:14,720 Speaker 1: next president United States, he say, they said, David's gonna 541 00:28:14,720 --> 00:28:16,200 Speaker 1: be the next Carl Sagan, Like from the time I 542 00:28:16,240 --> 00:28:18,359 Speaker 1: was a little kid, they were saying that stuff. So 543 00:28:18,600 --> 00:28:21,240 Speaker 1: when I reached a point where I could, you know, 544 00:28:21,280 --> 00:28:24,040 Speaker 1: I've been running my neuroscience career for a long time, 545 00:28:24,440 --> 00:28:28,200 Speaker 1: started proposing to various production companies this idea of doing 546 00:28:28,240 --> 00:28:31,400 Speaker 1: a series called the Brain, which is equivalent to Cosmos, 547 00:28:31,480 --> 00:28:35,280 Speaker 1: but but the inner cosmos. And uh, it's interesting because 548 00:28:35,280 --> 00:28:38,400 Speaker 1: that's another example of getting rejected so many times, and 549 00:28:38,480 --> 00:28:42,360 Speaker 1: people essentially uniformly told me, look, you can't do a 550 00:28:42,400 --> 00:28:45,160 Speaker 1: show like Carl Sagan anymore because audiences don't have that 551 00:28:45,160 --> 00:28:48,120 Speaker 1: same kind of audience loyalty and attention span. They said, 552 00:28:48,120 --> 00:28:50,120 Speaker 1: you have to do something like, you know, stand in 553 00:28:50,160 --> 00:28:52,040 Speaker 1: an airplane and take your shirt off and jump out 554 00:28:52,080 --> 00:28:53,640 Speaker 1: of the plane and then talk about stress on the 555 00:28:53,680 --> 00:28:56,040 Speaker 1: way down and so. And I said, you know, I 556 00:28:56,080 --> 00:28:58,880 Speaker 1: want to do something with his seriousness of purpose. I 557 00:28:58,960 --> 00:29:01,160 Speaker 1: don't want to do something us to get eyeballs and 558 00:29:01,200 --> 00:29:03,480 Speaker 1: things like that. So anyway, I kept going and kept 559 00:29:03,520 --> 00:29:06,400 Speaker 1: talking to production companies. I finally found one that got it, 560 00:29:06,480 --> 00:29:08,600 Speaker 1: that got exactly what I wanted to do, and we 561 00:29:08,680 --> 00:29:11,520 Speaker 1: pitched it to PBS together and uh, and then I 562 00:29:11,560 --> 00:29:13,920 Speaker 1: was able to make the Brain exactly the way that 563 00:29:13,960 --> 00:29:16,560 Speaker 1: I wanted to. So very happy that I was able 564 00:29:16,640 --> 00:29:19,480 Speaker 1: to show some people wrong. It was nominated for an Emmy, 565 00:29:19,560 --> 00:29:22,280 Speaker 1: and it, you know, has maintained a real place in 566 00:29:22,680 --> 00:29:25,720 Speaker 1: a very small segment of the public. But anyone who 567 00:29:25,760 --> 00:29:28,280 Speaker 1: cares about stuff like that, who cares about the brain 568 00:29:28,400 --> 00:29:31,080 Speaker 1: and learning and being exposed to the beauty of something. 569 00:29:31,320 --> 00:29:35,120 Speaker 1: And so increasingly when I teach my classes at Stanford, 570 00:29:35,480 --> 00:29:37,760 Speaker 1: I run into students who say, Hey, when I was 571 00:29:37,800 --> 00:29:40,840 Speaker 1: thirteen years old, I watched your series The Brain, and 572 00:29:40,920 --> 00:29:44,000 Speaker 1: that's why I'm here, That's why I'm now at Stanford 573 00:29:44,080 --> 00:29:47,520 Speaker 1: studying this. So it's really it's very rewarding. Do you 574 00:29:47,560 --> 00:29:49,680 Speaker 1: think your TV work is just an outgrowth of being 575 00:29:49,720 --> 00:29:52,960 Speaker 1: a professor? I feel like it's different in the sense 576 00:29:53,000 --> 00:29:55,720 Speaker 1: that the TV work in a sense, it's putting on 577 00:29:55,800 --> 00:30:00,560 Speaker 1: a completely different hat and it's trying to figure out, Okay, 578 00:30:00,600 --> 00:30:03,040 Speaker 1: I'm not having to make sure that you know this 579 00:30:03,120 --> 00:30:04,840 Speaker 1: and this and this and this, but instead, how do 580 00:30:04,920 --> 00:30:07,640 Speaker 1: I tell stories? How do I lead the audience through 581 00:30:07,680 --> 00:30:09,920 Speaker 1: a journey where by the end of it they've learned 582 00:30:09,960 --> 00:30:13,200 Speaker 1: and they've been turned on something that's that's changed their 583 00:30:13,240 --> 00:30:16,560 Speaker 1: internal model and you know, hopefully blown their mind, because 584 00:30:17,160 --> 00:30:19,880 Speaker 1: everything that I get to do every day is mind 585 00:30:19,880 --> 00:30:22,080 Speaker 1: blowing stuff. But you have to take the time to 586 00:30:22,200 --> 00:30:24,680 Speaker 1: go on the journey to get there, to see who 587 00:30:24,720 --> 00:30:28,840 Speaker 1: we are, what your existence is. The brain is essentially 588 00:30:29,080 --> 00:30:32,000 Speaker 1: the densest representation of who you are, and as I mentioned, 589 00:30:32,000 --> 00:30:34,360 Speaker 1: you know, even the tiniest bit of damage to your 590 00:30:34,360 --> 00:30:36,480 Speaker 1: brain a tumor, stroke, traumatic brain injury and anything like 591 00:30:36,560 --> 00:30:39,920 Speaker 1: that will change who you are and how you decide 592 00:30:40,000 --> 00:30:43,520 Speaker 1: and what your consciousness is. It is an opportunity to 593 00:30:43,600 --> 00:30:47,080 Speaker 1: look under the hood there and I love when people 594 00:30:47,160 --> 00:30:49,880 Speaker 1: come with me to do that examination. When we did 595 00:30:49,920 --> 00:30:52,240 Speaker 1: you in sixty seconds at the top of the episode, 596 00:30:52,760 --> 00:30:57,040 Speaker 1: you said, venture, not grants. Why be in business? You're 597 00:30:57,040 --> 00:30:59,800 Speaker 1: in academia. Where did this come from? Okay, so I've 598 00:30:59,800 --> 00:31:03,560 Speaker 1: been academia my whole life, and I got very used 599 00:31:03,600 --> 00:31:05,160 Speaker 1: to it. You know, the way you write a grant 600 00:31:05,200 --> 00:31:07,680 Speaker 1: and nowadays grants to the National Institutes of Health in 601 00:31:07,680 --> 00:31:10,240 Speaker 1: the National Science Foundation, even for the best people, there's 602 00:31:10,280 --> 00:31:12,920 Speaker 1: a ten percent chance of of hitting on a grant. 603 00:31:13,120 --> 00:31:15,080 Speaker 1: So I've been doing that for a long time. But 604 00:31:15,160 --> 00:31:17,640 Speaker 1: what happened is in I gave a talk at Ted 605 00:31:18,040 --> 00:31:20,080 Speaker 1: on this research that we've been doing in my lab 606 00:31:20,880 --> 00:31:24,360 Speaker 1: about sensory substitution, which is passing information into the brain 607 00:31:24,840 --> 00:31:28,120 Speaker 1: via unusual channels, for example through patterns vibration on the skin. 608 00:31:28,400 --> 00:31:30,640 Speaker 1: And so I gave this talk at TED and immediately 609 00:31:30,680 --> 00:31:32,800 Speaker 1: a number of venture capitalists came up and said, Hey, 610 00:31:32,840 --> 00:31:34,880 Speaker 1: we want to fund this as a company. So I 611 00:31:34,960 --> 00:31:38,240 Speaker 1: made this very sharp turn in my life and started 612 00:31:38,280 --> 00:31:41,680 Speaker 1: a company called Neo Sensory. And for the last seven 613 00:31:41,760 --> 00:31:43,960 Speaker 1: or eight years now I've been running that company and 614 00:31:44,120 --> 00:31:47,960 Speaker 1: it has been amazing. So I'm still I'm teaching at Stanford, 615 00:31:48,000 --> 00:31:50,680 Speaker 1: but I'm no longer running a lab anymore, and instead 616 00:31:50,680 --> 00:31:52,640 Speaker 1: I spend ninety of my time running, you know, a 617 00:31:52,720 --> 00:31:56,120 Speaker 1: CEO of this company. What it has taught me is 618 00:31:56,200 --> 00:31:58,760 Speaker 1: to be able to see the walls of the fish 619 00:31:58,800 --> 00:32:02,920 Speaker 1: bowl that is academia. And I also, by the way, 620 00:32:02,960 --> 00:32:04,680 Speaker 1: because I have a foot in both worlds, now, I 621 00:32:04,800 --> 00:32:07,080 Speaker 1: also see the walls of the fish bowl of the 622 00:32:07,160 --> 00:32:10,360 Speaker 1: entrepreneurial world. But by having the opportunity to be in 623 00:32:10,360 --> 00:32:12,720 Speaker 1: two different worlds and switch back and forth, I can 624 00:32:12,800 --> 00:32:17,400 Speaker 1: finally see what the opportunities and the limitations are in 625 00:32:17,760 --> 00:32:20,400 Speaker 1: both of these But anyway, I think venture capital can 626 00:32:20,440 --> 00:32:24,200 Speaker 1: just move things along much faster. I actually the work 627 00:32:24,240 --> 00:32:26,800 Speaker 1: that I've been doing at Neo Century, I actually had 628 00:32:26,840 --> 00:32:29,719 Speaker 1: applied for a grant to the n i H and 629 00:32:29,760 --> 00:32:33,680 Speaker 1: the NSF, and both of those got rejected with the statement, 630 00:32:34,120 --> 00:32:38,080 Speaker 1: and I'm quoting, they said, uh, this is not incremental enough. 631 00:32:38,880 --> 00:32:41,320 Speaker 1: Because I always thought incremental was a bad word, but 632 00:32:41,320 --> 00:32:43,480 Speaker 1: they actually meant they wanted to be more incremental, and 633 00:32:43,520 --> 00:32:45,200 Speaker 1: I was trying to take too big a leap. And 634 00:32:45,240 --> 00:32:49,080 Speaker 1: so for that, I find venture capital so exciting that 635 00:32:49,120 --> 00:32:52,000 Speaker 1: people say, you know what, You've convinced us that the 636 00:32:52,080 --> 00:32:54,880 Speaker 1: data is there, Let's let's go for it. So in 637 00:32:54,960 --> 00:32:57,680 Speaker 1: two thousand thirteen, you wrote in New York Times op 638 00:32:57,840 --> 00:33:01,360 Speaker 1: ed about what our brains can teach is basically asking 639 00:33:01,560 --> 00:33:05,520 Speaker 1: you know, why fund brain science? A decade later, what 640 00:33:05,560 --> 00:33:10,000 Speaker 1: do you think? Wow, that's an interesting question. I still 641 00:33:10,320 --> 00:33:12,840 Speaker 1: am such a believer in funding brain science. But back 642 00:33:12,840 --> 00:33:15,680 Speaker 1: when I wrote that, I was probably really thinking about 643 00:33:16,240 --> 00:33:19,360 Speaker 1: the n I H in the NSF. And now I'm 644 00:33:19,400 --> 00:33:22,800 Speaker 1: so pleased to see all the other opportunities going on. 645 00:33:22,880 --> 00:33:26,160 Speaker 1: So not only is their venture capital when appropriate, but 646 00:33:26,200 --> 00:33:28,720 Speaker 1: they're also private philanthropists. But there are all these new 647 00:33:28,760 --> 00:33:32,000 Speaker 1: things that are coming along with Web three, these neuroscience dows, 648 00:33:32,600 --> 00:33:36,400 Speaker 1: these decentralized autonomous organizations where people are collecting up money 649 00:33:36,760 --> 00:33:40,520 Speaker 1: and figuring out, hey, how can we help fund good 650 00:33:40,560 --> 00:33:44,840 Speaker 1: neuroscience research in this new Web three kind of way. Anyway, 651 00:33:44,840 --> 00:33:48,360 Speaker 1: it's just it's wonderful to me to see that neuroscience 652 00:33:48,400 --> 00:33:51,680 Speaker 1: has has gotten more attention and that people care about 653 00:33:51,720 --> 00:33:53,960 Speaker 1: funding it. So let me hit a couple of quick 654 00:33:54,000 --> 00:33:57,280 Speaker 1: topics here before we come to an end. Knowing what 655 00:33:57,400 --> 00:34:01,800 Speaker 1: you know and the conversation we've had a day, our 656 00:34:01,840 --> 00:34:04,479 Speaker 1: schools teaching the right way? If not, how should they 657 00:34:04,520 --> 00:34:07,400 Speaker 1: be teaching? How should we redo education? You know what? 658 00:34:07,640 --> 00:34:10,200 Speaker 1: I think there is a way to redo education, but 659 00:34:10,239 --> 00:34:13,359 Speaker 1: it's very simple. Redo is too strong a verb for it. 660 00:34:13,840 --> 00:34:18,480 Speaker 1: The whole key is to teach our children the skills 661 00:34:18,760 --> 00:34:21,600 Speaker 1: of creativity, which is about taking all the stuff that's 662 00:34:21,640 --> 00:34:24,800 Speaker 1: just gone in to your head and bending and breaking 663 00:34:24,800 --> 00:34:28,120 Speaker 1: and blending it to come up with new versions of things. 664 00:34:28,280 --> 00:34:31,279 Speaker 1: That's really the heart of both art and science, and 665 00:34:31,320 --> 00:34:34,120 Speaker 1: it's what we summarize as creativity. It's just this constant 666 00:34:34,200 --> 00:34:37,360 Speaker 1: remixing of what's under the hood there Now, what happens 667 00:34:37,360 --> 00:34:40,400 Speaker 1: in our schools currently is they teach all semester and 668 00:34:40,440 --> 00:34:42,160 Speaker 1: you take a test at the end, and if you 669 00:34:42,719 --> 00:34:45,200 Speaker 1: regurgitate properly, then you get a good grade. And that's it. 670 00:34:45,800 --> 00:34:48,200 Speaker 1: All we need to do is add an extra week 671 00:34:48,239 --> 00:34:50,480 Speaker 1: in there in the end where we say, great, we've 672 00:34:50,520 --> 00:34:53,040 Speaker 1: taught you all the foundational stuff you need. Now for 673 00:34:53,080 --> 00:34:55,360 Speaker 1: your final project. I want you to take all of 674 00:34:55,400 --> 00:34:58,320 Speaker 1: that stuff and remix it. I've touched you how to 675 00:34:58,320 --> 00:35:01,160 Speaker 1: do electrical engineering with this stuff. Now invent a new thing. Okay, 676 00:35:01,160 --> 00:35:03,320 Speaker 1: I've taught to you all these you know, great artists. 677 00:35:03,480 --> 00:35:05,880 Speaker 1: I want you to mix up their art styles and 678 00:35:05,920 --> 00:35:07,839 Speaker 1: make some new thing. I've taught you all these great 679 00:35:08,000 --> 00:35:10,359 Speaker 1: whatever writers. I want you to come up with your 680 00:35:10,400 --> 00:35:13,200 Speaker 1: own version that's a mixture of blending and breaking and 681 00:35:13,239 --> 00:35:15,719 Speaker 1: bending of what you have learned so far. So it 682 00:35:15,760 --> 00:35:18,279 Speaker 1: would actually be very easy. There's just no emphasis on 683 00:35:18,320 --> 00:35:20,560 Speaker 1: this in the in the school systems currently. But this 684 00:35:20,640 --> 00:35:23,759 Speaker 1: is something I've been trying to spread my previous book before, 685 00:35:23,840 --> 00:35:25,920 Speaker 1: that's what was called The Runaway Species that I co 686 00:35:25,960 --> 00:35:28,920 Speaker 1: authored with my friend Tony Brandt because a musician, and 687 00:35:29,239 --> 00:35:31,279 Speaker 1: we have been trying to spread the word on this 688 00:35:31,400 --> 00:35:34,319 Speaker 1: too to schools because it costs almost nothing and uh 689 00:35:34,640 --> 00:35:37,319 Speaker 1: just requires a tiny compression of what is already there. 690 00:35:37,480 --> 00:35:39,360 Speaker 1: So here we are on a podcast. I would be 691 00:35:39,400 --> 00:35:42,240 Speaker 1: remiss if I didn't ask this question. Audio is hot 692 00:35:42,280 --> 00:35:46,280 Speaker 1: at I heart were totally in the audio radio, podcast, etcetera. 693 00:35:46,600 --> 00:35:51,680 Speaker 1: Explain my business to me. How does hearing something differ 694 00:35:51,760 --> 00:35:55,279 Speaker 1: from seeing something? How does conversation differ from reading? Any 695 00:35:55,320 --> 00:35:59,080 Speaker 1: difference in memory of audio versus visual? You know, it 696 00:35:59,120 --> 00:36:01,040 Speaker 1: depends a bit on the person. So we've heard of 697 00:36:01,120 --> 00:36:03,600 Speaker 1: visual learners and auditory learners, and that's true. You know, 698 00:36:03,640 --> 00:36:06,439 Speaker 1: people are different places on the spectrum. So some people 699 00:36:06,440 --> 00:36:09,160 Speaker 1: are gonna love podcasts and other people won't. What has 700 00:36:09,200 --> 00:36:12,280 Speaker 1: been fascinating to me, I mean, you're in this world. 701 00:36:12,280 --> 00:36:14,880 Speaker 1: I'm just watching from a distance. But fascinating to me 702 00:36:15,040 --> 00:36:18,920 Speaker 1: was the rise of popularity podcasts during the pandemic because 703 00:36:19,200 --> 00:36:21,319 Speaker 1: people realize, you know, I want to walk, I want 704 00:36:21,320 --> 00:36:23,640 Speaker 1: to garden, I want to wash the dishes, whatever, and 705 00:36:23,680 --> 00:36:27,440 Speaker 1: so they found it so convenient to be able to 706 00:36:27,480 --> 00:36:30,440 Speaker 1: strap in some EarPods and go and and be a 707 00:36:30,480 --> 00:36:32,799 Speaker 1: part of what's happening and learn new things. You know, 708 00:36:32,800 --> 00:36:37,000 Speaker 1: there's something so wonderful about eavesdropping on the conversation of 709 00:36:37,000 --> 00:36:39,560 Speaker 1: of other people who are talking about interesting things, hopefully, 710 00:36:39,840 --> 00:36:43,080 Speaker 1: And that's why podcasts are really here to stay, because 711 00:36:43,360 --> 00:36:45,440 Speaker 1: other activities are here to stay, as in you know, 712 00:36:45,480 --> 00:36:48,439 Speaker 1: gardening and washing dishes and walking and a million other things. 713 00:36:48,680 --> 00:36:51,719 Speaker 1: You could go back in time, what advice would you 714 00:36:51,760 --> 00:36:54,600 Speaker 1: give your twenty year old self. Just keep going, Just 715 00:36:54,680 --> 00:36:58,120 Speaker 1: keep banging on all these doors, and eventually the doors 716 00:36:58,120 --> 00:37:02,479 Speaker 1: will open. So you had this remarkable life, You've done 717 00:37:02,520 --> 00:37:05,840 Speaker 1: so much, yet you're lucky, you're still young. What do 718 00:37:05,880 --> 00:37:09,160 Speaker 1: you plan to do with the next chapter? What's the 719 00:37:09,200 --> 00:37:13,960 Speaker 1: big unanswered question that fascinates you now? Mm hmm, I 720 00:37:14,000 --> 00:37:18,160 Speaker 1: guess I'd say there's three things there. One is scientifically, 721 00:37:19,000 --> 00:37:21,720 Speaker 1: I still have a hundred questions on the plate about 722 00:37:21,800 --> 00:37:24,560 Speaker 1: the brain. I'll presumably be studying this till the till 723 00:37:24,560 --> 00:37:26,479 Speaker 1: the day that I die. In fact, back in two 724 00:37:26,719 --> 00:37:29,960 Speaker 1: four I wrote an article and Discover magazine the cover 725 00:37:30,120 --> 00:37:34,319 Speaker 1: article which was called ten Unsolved Questions of Neuroscience. And 726 00:37:34,440 --> 00:37:37,760 Speaker 1: they're still unsolved all these years later, And so there's 727 00:37:37,800 --> 00:37:40,440 Speaker 1: plenty plenty of stuff to sink my teeth into. In 728 00:37:40,560 --> 00:37:42,759 Speaker 1: terms of great unsolved questions. One of the things I've 729 00:37:42,800 --> 00:37:46,600 Speaker 1: been working on lately is the question of why we dream. 730 00:37:46,640 --> 00:37:49,239 Speaker 1: I mean, it's bizarre that we spend every night of 731 00:37:49,239 --> 00:37:53,920 Speaker 1: our lives having these completely weird movies that were the 732 00:37:53,960 --> 00:37:56,759 Speaker 1: star of and it's never been explained why we dream. 733 00:37:56,800 --> 00:37:59,040 Speaker 1: So I have a new hypothsitive that that I've been 734 00:37:59,040 --> 00:38:01,200 Speaker 1: publishing about last few years. Tell us that one real 735 00:38:01,280 --> 00:38:04,560 Speaker 1: quickly because it's really interesting. Yeah. It's simply that when 736 00:38:04,600 --> 00:38:08,080 Speaker 1: the planet rotates into darkness, the visual system is at 737 00:38:08,120 --> 00:38:11,040 Speaker 1: a disadvantage because you can still hear and smell and 738 00:38:11,080 --> 00:38:13,640 Speaker 1: touch and taste in the dark, but you can't see 739 00:38:13,680 --> 00:38:16,960 Speaker 1: obviously in evolutionary times before electricity, is what I'm talking about. 740 00:38:17,320 --> 00:38:21,000 Speaker 1: So what we know is that when data coming into 741 00:38:21,000 --> 00:38:24,640 Speaker 1: a particular sense stops, the other senses try to take 742 00:38:24,680 --> 00:38:26,560 Speaker 1: it over. And what we've learned in neuroscience in last 743 00:38:26,600 --> 00:38:30,080 Speaker 1: fifteen years is how rapidly these takeovers can occur. And 744 00:38:30,120 --> 00:38:32,839 Speaker 1: so what I realized is the brain needs some way 745 00:38:32,840 --> 00:38:36,600 Speaker 1: of defending the visual territory, and so the way it 746 00:38:36,640 --> 00:38:39,360 Speaker 1: does this is every ninety minutes, it just blasts random 747 00:38:39,400 --> 00:38:43,520 Speaker 1: activity into the visual system to keep it defended against 748 00:38:43,680 --> 00:38:46,040 Speaker 1: takeover from hearing and touch and so on. So I 749 00:38:46,080 --> 00:38:50,040 Speaker 1: call this the defensive activation theory. It's the only hypothesis 750 00:38:50,080 --> 00:38:54,800 Speaker 1: on dreaming that provides quantitative predictions about how much different 751 00:38:54,800 --> 00:38:58,000 Speaker 1: animal species will sleep depending on how flexible their brains are, 752 00:38:58,360 --> 00:39:01,200 Speaker 1: and the predictions come out spaw on and so I'm 753 00:39:01,239 --> 00:39:04,359 Speaker 1: super excited about that. So scientifically, I'm gonna keep doing that. 754 00:39:04,719 --> 00:39:08,160 Speaker 1: In terms of my writing, I still have at least 755 00:39:08,160 --> 00:39:11,120 Speaker 1: six more books that are outlined and all in various 756 00:39:11,200 --> 00:39:14,080 Speaker 1: stages of pregnancy, and so those will all happen. But 757 00:39:14,160 --> 00:39:18,640 Speaker 1: as I said, I'm always interested in new methods, uh 758 00:39:19,000 --> 00:39:23,399 Speaker 1: pedagogically speaking, for for transmitting knowledge, and who the heck 759 00:39:23,480 --> 00:39:25,880 Speaker 1: knows what's gonna exist in twenty years from now. We 760 00:39:25,960 --> 00:39:28,719 Speaker 1: might have really really new methods for that, and so 761 00:39:28,760 --> 00:39:31,560 Speaker 1: I'm really excited to jump on that. And then finally, 762 00:39:31,760 --> 00:39:35,960 Speaker 1: entrepreneurial wise, I'm looking way ahead, you know, once I'm 763 00:39:36,000 --> 00:39:38,840 Speaker 1: done with my current company, Neo Sensory, I'm looking ahead 764 00:39:38,880 --> 00:39:42,879 Speaker 1: to my next company, which will be about brain plasticity, 765 00:39:42,880 --> 00:39:47,080 Speaker 1: but specifically, how do we build machines that take advantage 766 00:39:47,640 --> 00:39:50,320 Speaker 1: of what we've learned about brain plasticity. In other words, 767 00:39:50,800 --> 00:39:53,799 Speaker 1: the way we build everything now is based on these 768 00:39:53,840 --> 00:39:57,120 Speaker 1: principles of hardware and then software that runs on top 769 00:39:57,120 --> 00:39:58,720 Speaker 1: of that, and you make this very clean and efficient 770 00:39:58,719 --> 00:40:01,040 Speaker 1: hardware and clean efficient soft where And that's a cool 771 00:40:01,040 --> 00:40:02,920 Speaker 1: way of building things, but it's not at all the 772 00:40:02,920 --> 00:40:05,960 Speaker 1: way that mother Nature does things. And so the situation 773 00:40:06,000 --> 00:40:08,080 Speaker 1: we run into is, you know, when the Mars rover 774 00:40:08,520 --> 00:40:11,720 Speaker 1: gets its wheel stuck, it dies, and that's what happened 775 00:40:11,719 --> 00:40:14,319 Speaker 1: to Curiosity. But you know when a wolf gets its 776 00:40:14,400 --> 00:40:16,440 Speaker 1: leg caught in a trap, it choose its leg off 777 00:40:16,440 --> 00:40:18,319 Speaker 1: and figures out how to walk on three legs. Because 778 00:40:18,600 --> 00:40:22,520 Speaker 1: real animals don't give up. They figure out how to 779 00:40:23,160 --> 00:40:26,279 Speaker 1: change things around and and be creative and change their 780 00:40:26,320 --> 00:40:29,560 Speaker 1: body plan and make things happen. And so I want 781 00:40:29,600 --> 00:40:32,600 Speaker 1: to figure out how we can build machines that take 782 00:40:32,640 --> 00:40:37,520 Speaker 1: advantage of what we're learning in neuroscience. Pretty cool, and TV, 783 00:40:37,640 --> 00:40:39,400 Speaker 1: what are you gonna do in TV? I'm working on 784 00:40:39,440 --> 00:40:43,440 Speaker 1: several things right now. One is a fiction series and 785 00:40:43,440 --> 00:40:46,000 Speaker 1: then I have several documentaries. Let's end the way we 786 00:40:46,040 --> 00:40:48,960 Speaker 1: always end on math and magic. We always ask our 787 00:40:49,040 --> 00:40:52,120 Speaker 1: guests to tell us the greatest math mind they know, 788 00:40:52,320 --> 00:40:55,319 Speaker 1: and usually within the context of marketing, that's some big 789 00:40:55,360 --> 00:40:58,759 Speaker 1: analytical person and the greatest magician. And again in the 790 00:40:58,800 --> 00:41:02,879 Speaker 1: business world, that's usually some intuitive, creative ideas. Person. Now 791 00:41:02,920 --> 00:41:05,120 Speaker 1: in your case, you're probably gonna have a little different 792 00:41:05,200 --> 00:41:07,359 Speaker 1: view of that. But in your case, who would you 793 00:41:07,360 --> 00:41:10,480 Speaker 1: give us, Let's put him on the pedestal greatest math person, 794 00:41:11,040 --> 00:41:16,759 Speaker 1: greatest magic, magician, mathematician. I would say Pythagoras, because you know, 795 00:41:16,840 --> 00:41:20,240 Speaker 1: he obviously was mathematical genius and had his whole school 796 00:41:20,360 --> 00:41:22,600 Speaker 1: and you know, came up with such new ideas. But 797 00:41:22,600 --> 00:41:24,799 Speaker 1: but what I find particularly interesting about him is he 798 00:41:24,880 --> 00:41:28,799 Speaker 1: had synesthesia, which means a blending of the senses. So 799 00:41:28,800 --> 00:41:31,319 Speaker 1: when he would think of numbers, that would trigger a 800 00:41:31,400 --> 00:41:34,480 Speaker 1: color experience for him, each number had a color, a size, 801 00:41:34,480 --> 00:41:37,319 Speaker 1: of shape, things like that. And synesthesia is one of 802 00:41:37,360 --> 00:41:39,560 Speaker 1: the things that I've been studying in my lab for 803 00:41:39,600 --> 00:41:43,319 Speaker 1: many years as an alternative form of consciousness, and so 804 00:41:43,400 --> 00:41:47,239 Speaker 1: it's very cool to see a mathematician who leveraged his 805 00:41:47,320 --> 00:41:50,840 Speaker 1: synesthesia for magician. So I've been teaching a class at 806 00:41:50,840 --> 00:41:54,480 Speaker 1: Stanford called the Brain and Literature, and what I realized 807 00:41:54,560 --> 00:41:59,000 Speaker 1: is that mystery novelists are doing precisely what magicians do, 808 00:41:59,120 --> 00:42:02,880 Speaker 1: which is they lead you down a particular garden path, 809 00:42:03,040 --> 00:42:05,400 Speaker 1: so that everyone in the audience or the reader in 810 00:42:05,400 --> 00:42:08,040 Speaker 1: this case thinks, Okay, I got it, I got the 811 00:42:08,080 --> 00:42:10,560 Speaker 1: next thing, and so on, and the writer or the 812 00:42:10,600 --> 00:42:16,959 Speaker 1: magician is simply taking advantage of of your assumptions about things. 813 00:42:16,960 --> 00:42:20,279 Speaker 1: So I would list Arthur Conan Doyle, the writer of 814 00:42:20,320 --> 00:42:25,120 Speaker 1: the Sherlock Holmes Mysteries, as my favorite magician because he 815 00:42:25,320 --> 00:42:28,480 Speaker 1: does exactly the same sort of tricks, such that everything 816 00:42:28,560 --> 00:42:30,239 Speaker 1: is right there in front of you, but you don't 817 00:42:30,239 --> 00:42:32,719 Speaker 1: see it until he wants you to. I was gonna 818 00:42:32,719 --> 00:42:34,520 Speaker 1: ask you if we're living in a simulation, but I 819 00:42:34,520 --> 00:42:37,719 Speaker 1: thought maybe that's a step too far. Well, you know 820 00:42:38,280 --> 00:42:43,280 Speaker 1: the answer to that is, um, we certainly maybe um. 821 00:42:43,480 --> 00:42:45,760 Speaker 1: And there are many philosophers who take that very seriously 822 00:42:45,800 --> 00:42:47,880 Speaker 1: now as as a real possibility, and some make the 823 00:42:47,960 --> 00:42:52,680 Speaker 1: argument that it's almost certainly the probability. David, thanks for 824 00:42:52,719 --> 00:42:55,520 Speaker 1: giving us so much to think about, by the way, 825 00:42:55,520 --> 00:42:57,719 Speaker 1: not only on this episode, but on everything you do, 826 00:42:58,080 --> 00:43:00,440 Speaker 1: and for giving us some science that under ends our 827 00:43:00,480 --> 00:43:03,480 Speaker 1: world of marketing. Thank you so much, Bob for having me. 828 00:43:05,480 --> 00:43:06,960 Speaker 1: Here are a few things I've picked up in my 829 00:43:07,000 --> 00:43:11,440 Speaker 1: conversation with David one when executing on something new, embraced 830 00:43:11,520 --> 00:43:14,640 Speaker 1: the familiar. David shares that our brains are wired to 831 00:43:14,640 --> 00:43:17,920 Speaker 1: have stronger convictions in the messages we hear most often. 832 00:43:18,360 --> 00:43:21,600 Speaker 1: As marketers, we should remember that when we're tempted to 833 00:43:21,640 --> 00:43:24,760 Speaker 1: freshen up a campaign, we should keep elements of classic 834 00:43:24,840 --> 00:43:29,680 Speaker 1: messaging in order to forge stronger relationships with consumers. Two 835 00:43:30,239 --> 00:43:34,120 Speaker 1: Always seek new challenges, As David says, memories preserve for 836 00:43:34,200 --> 00:43:37,720 Speaker 1: things that are new and novel. David cites scientific research 837 00:43:37,840 --> 00:43:40,280 Speaker 1: that shows the more familiar we become with the task, 838 00:43:40,680 --> 00:43:44,680 Speaker 1: the less energy our brains assigned to it. Implement slight 839 00:43:44,800 --> 00:43:48,680 Speaker 1: changes to your routine so you can remain sharp and attentive. 840 00:43:49,400 --> 00:43:52,799 Speaker 1: Three Find what fits. David talks about how he had 841 00:43:52,800 --> 00:43:55,880 Speaker 1: a myriad of interest growing up, like literature, physics, and 842 00:43:55,960 --> 00:43:59,720 Speaker 1: stand up comedy. Despite getting pushed toward different career paths 843 00:43:59,760 --> 00:44:03,560 Speaker 1: by family and advisors, David kept pursuing his passions until 844 00:44:03,719 --> 00:44:10,120 Speaker 1: something neuroscience fit. Four. Push through rejection. So often we 845 00:44:10,160 --> 00:44:13,239 Speaker 1: think that ambition ends with either success or failure, but 846 00:44:13,320 --> 00:44:16,240 Speaker 1: David reminds us that we not our wins or losses, 847 00:44:16,560 --> 00:44:19,800 Speaker 1: decide where our ambition takes us. I love how, David 848 00:44:19,840 --> 00:44:23,480 Speaker 1: says his book Some was and I quote rejected until 849 00:44:23,520 --> 00:44:26,160 Speaker 1: it wasn't. So if you're hitting a roadblock while launching 850 00:44:26,160 --> 00:44:29,600 Speaker 1: a new idea, try pushing through the roadblock may only 851 00:44:29,640 --> 00:44:34,880 Speaker 1: stop you until it doesn't. I'm Bob Pittman. Thanks for listening. 852 00:44:36,000 --> 00:44:38,400 Speaker 1: That's it for today's episode. Thanks so much for listening 853 00:44:38,400 --> 00:44:40,800 Speaker 1: to Math and Magic, a production of I Heart Radio. 854 00:44:40,960 --> 00:44:43,360 Speaker 1: The show is hosted by Bob Pittman. Special thanks to 855 00:44:43,480 --> 00:44:46,120 Speaker 1: Susan Ward for booking and wrangling our wonderful talent, which 856 00:44:46,160 --> 00:44:48,880 Speaker 1: is no small feat Marissa Brown for pulling research, our 857 00:44:49,000 --> 00:44:52,440 Speaker 1: editors Derek Clements, Mary Dow and Ryan Murdoch, our producer 858 00:44:52,520 --> 00:44:55,760 Speaker 1: Morgan Levoy, our executive producer Nikki Etor, and of course 859 00:44:55,920 --> 00:44:59,399 Speaker 1: Gayle Raoel, Eric Angel Noel, and everyone who helped bring 860 00:44:59,480 --> 00:45:01,600 Speaker 1: this show to your ears. Until next time,