1 00:00:05,040 --> 00:00:08,479 Speaker 1: How can we understand what music is about from the 2 00:00:08,520 --> 00:00:13,200 Speaker 1: point of view of neuroscience. Can music be leveraged to 3 00:00:13,280 --> 00:00:18,120 Speaker 1: help with anxiety disorders, or with dementia or with Parkinson's disease? 4 00:00:18,840 --> 00:00:22,080 Speaker 1: Is music universal or does it have to do with 5 00:00:22,120 --> 00:00:25,840 Speaker 1: what you have absorbed in your lifetime? How is music 6 00:00:26,360 --> 00:00:30,400 Speaker 1: like a language but one with very particular structure and 7 00:00:30,440 --> 00:00:33,319 Speaker 1: therefore high predictability. And what does this have to do 8 00:00:33,680 --> 00:00:36,919 Speaker 1: with Stevie Wonder on the High Hat or the relationship 9 00:00:37,040 --> 00:00:42,920 Speaker 1: between music and color. Welcome to Inner Cosmos with me 10 00:00:43,080 --> 00:00:46,680 Speaker 1: David Eagleman. I'm a neuroscientist and an author at Stanford 11 00:00:47,000 --> 00:00:50,520 Speaker 1: and in these episodes we sail deeply into our three 12 00:00:50,600 --> 00:00:54,920 Speaker 1: pound universe to understand why and how our lives look 13 00:00:54,960 --> 00:01:08,200 Speaker 1: the way they do. Today's episode is about music and 14 00:01:08,240 --> 00:01:10,440 Speaker 1: the brain, and this is a topic that has been 15 00:01:10,480 --> 00:01:16,320 Speaker 1: requested by several different listeners, so I prioritized making this episode. Music. 16 00:01:16,520 --> 00:01:20,039 Speaker 1: I suspect is a popular topic because music can be 17 00:01:20,160 --> 00:01:25,400 Speaker 1: so emotive for us and so catchy and so meaningful. 18 00:01:26,319 --> 00:01:30,000 Speaker 1: My father, for example, who was the quintessential tough guy 19 00:01:30,480 --> 00:01:34,399 Speaker 1: when he would listen to Mozart or Brahms or Beethoven, 20 00:01:34,880 --> 00:01:38,720 Speaker 1: he would have tears streaming down his cheeks. And as 21 00:01:38,760 --> 00:01:41,479 Speaker 1: a child, I didn't have much understanding of classical music, 22 00:01:41,560 --> 00:01:44,880 Speaker 1: but that really caused me to wonder, what is going 23 00:01:44,920 --> 00:01:48,280 Speaker 1: on here? Why does this music wafting out of the 24 00:01:48,400 --> 00:01:54,760 Speaker 1: radio evoke such strong emotions and perhaps such deep memories 25 00:01:54,880 --> 00:01:57,160 Speaker 1: in my father? And as I got older and became 26 00:01:57,160 --> 00:02:00,440 Speaker 1: a neuroscientist, I wondered, is there something you unique in 27 00:02:00,440 --> 00:02:03,600 Speaker 1: the structure of the human brain that ties music so 28 00:02:03,880 --> 00:02:09,280 Speaker 1: closely with our emotional experiences? So I decided to do 29 00:02:09,360 --> 00:02:11,800 Speaker 1: an episode on this today, and I realized there was 30 00:02:11,840 --> 00:02:14,799 Speaker 1: no one better to ring up than my friend and colleague, 31 00:02:14,960 --> 00:02:19,239 Speaker 1: Daniel Leviton. He's the founding Dean of Arts and Humanities 32 00:02:19,280 --> 00:02:23,600 Speaker 1: at Minerva University in San Francisco and a professor emeritus 33 00:02:23,639 --> 00:02:28,360 Speaker 1: of psychology and neuroscience at McGill University in Montreal. You 34 00:02:28,480 --> 00:02:31,160 Speaker 1: may know Dan because he wrote a book called This 35 00:02:31,200 --> 00:02:35,280 Speaker 1: Is Your Brain on music, the Science of a Human Obsession, 36 00:02:35,320 --> 00:02:38,280 Speaker 1: which became a big New York Times bestseller, and he's 37 00:02:38,320 --> 00:02:41,440 Speaker 1: also written four other best selling books, including his latest, 38 00:02:41,520 --> 00:02:45,040 Speaker 1: which is called I Heard There Was a Secret Chord. 39 00:02:45,600 --> 00:02:49,480 Speaker 1: Music as Medicine. Now, as you may suspect. Dan is 40 00:02:49,480 --> 00:02:53,080 Speaker 1: also a very talented musician. He composes music and he's 41 00:02:53,160 --> 00:02:56,920 Speaker 1: worked on albums by Blue Oyster Cult and Chris Isaac 42 00:02:56,960 --> 00:03:00,799 Speaker 1: and Joe Satriani, among many others. He does this as 43 00:03:00,840 --> 00:03:04,960 Speaker 1: an advisory producer a recording engineer. So given his expertise, 44 00:03:05,040 --> 00:03:07,239 Speaker 1: I wanted to sit down with Dan to get his 45 00:03:07,320 --> 00:03:15,960 Speaker 1: take on music and the brain. Okay, Dan, what goes 46 00:03:16,040 --> 00:03:18,920 Speaker 1: on in the brain when you listen to music? 47 00:03:19,520 --> 00:03:23,079 Speaker 2: Yeah, well, it really is quite fascinating. So it begins 48 00:03:23,120 --> 00:03:27,240 Speaker 2: with the sound waves impinging on your ear drums. They 49 00:03:27,320 --> 00:03:30,520 Speaker 2: wiggle in and out, and all the information you have 50 00:03:30,800 --> 00:03:35,680 Speaker 2: about the auditory world comes from molecules vibrating in some medium, 51 00:03:35,760 --> 00:03:39,040 Speaker 2: in our case air, it could be underwater, and then 52 00:03:39,920 --> 00:03:41,840 Speaker 2: your ear drums just wiggle in and out, and then 53 00:03:41,880 --> 00:03:44,360 Speaker 2: your brain has to take that wiggling in and out 54 00:03:44,800 --> 00:03:48,960 Speaker 2: and extract from it all the different sounds a bird chirping, 55 00:03:49,040 --> 00:03:52,240 Speaker 2: a leaf blower going, the oboe in the symphony as 56 00:03:52,280 --> 00:03:56,360 Speaker 2: opposed to the French horns in a crowded room, the 57 00:03:56,440 --> 00:03:59,280 Speaker 2: conversation you're trying to listen to in front of you, 58 00:03:59,360 --> 00:04:01,560 Speaker 2: as well as that when you're eavesdropping in. It has 59 00:04:01,600 --> 00:04:04,000 Speaker 2: to separate all that out. And the way it does 60 00:04:04,040 --> 00:04:09,560 Speaker 2: that is that your brain has special processing circuits. I 61 00:04:09,600 --> 00:04:11,120 Speaker 2: was going to use the word designed, but of course 62 00:04:11,120 --> 00:04:16,440 Speaker 2: they weren't designed, but evolved to do different distinct functions. 63 00:04:16,520 --> 00:04:21,960 Speaker 2: One circuit processes the loudness is anything louder, is it 64 00:04:21,960 --> 00:04:25,280 Speaker 2: getting softer? And it follows that loudness trajectory, which can 65 00:04:25,800 --> 00:04:28,200 Speaker 2: be an important cue as. 66 00:04:28,000 --> 00:04:31,719 Speaker 3: To what's going on. Pitch duration. 67 00:04:32,760 --> 00:04:36,200 Speaker 2: The pitches get in a separate circuit, bound into a 68 00:04:36,279 --> 00:04:41,440 Speaker 2: representation of melody and harmony, the durations into a representation 69 00:04:41,520 --> 00:04:46,560 Speaker 2: of rhythm and meter, the loudness into accent structure, timbre, 70 00:04:47,160 --> 00:04:51,080 Speaker 2: which is the quality that distinguishes your voice from my 71 00:04:51,200 --> 00:04:53,920 Speaker 2: voice when we're saying the same thing, or a trumpet 72 00:04:53,920 --> 00:04:56,680 Speaker 2: from a piano when they're playing the same note. That's 73 00:04:56,680 --> 00:05:01,200 Speaker 2: a combination of spectral temporal information, in other words, pitch 74 00:05:01,600 --> 00:05:06,520 Speaker 2: and time and loudness. It all comes together later in 75 00:05:06,560 --> 00:05:10,040 Speaker 2: the brain and you just hear that song where later 76 00:05:10,120 --> 00:05:14,479 Speaker 2: in the brain is maybe forty milliseconds. It happens so 77 00:05:14,640 --> 00:05:17,920 Speaker 2: seamlessly that it just sounds like we're hearing the song, 78 00:05:18,000 --> 00:05:21,000 Speaker 2: but we're not. Our brain is hearing the pitch, the rhythm, 79 00:05:21,080 --> 00:05:25,479 Speaker 2: the loudness, the timber, and our evidence, David, that this 80 00:05:25,640 --> 00:05:29,560 Speaker 2: happens is not just from neuroimaging, but from patients. We 81 00:05:29,680 --> 00:05:33,720 Speaker 2: see patients with focal brain damage who suddenly lose their 82 00:05:33,800 --> 00:05:37,440 Speaker 2: perception of pitch, but they retain rhythm or vice versa. 83 00:05:38,000 --> 00:05:43,159 Speaker 1: Okay, so it's this terrifically complicated computational process. So why 84 00:05:43,200 --> 00:05:45,520 Speaker 1: does it end up feeling so emotional for us? 85 00:05:46,360 --> 00:05:46,680 Speaker 3: Well? 86 00:05:46,800 --> 00:05:52,200 Speaker 2: So I think there are both neurobiological reasons and evolutionary 87 00:05:52,240 --> 00:05:57,039 Speaker 2: reasons that are connected. Of course, the simple answer is 88 00:05:57,360 --> 00:06:01,880 Speaker 2: it's emotional for us because emotion circuits in the brain 89 00:06:01,880 --> 00:06:05,400 Speaker 2: are involved in music processing. And by that I mean, 90 00:06:05,600 --> 00:06:09,640 Speaker 2: among others, the well known reward center that you and 91 00:06:09,640 --> 00:06:12,279 Speaker 2: I have talked about in our own classes over the 92 00:06:12,320 --> 00:06:16,120 Speaker 2: years and with one another, the limbic system, the structures 93 00:06:16,160 --> 00:06:19,800 Speaker 2: like the nucleus of Cumban's, the amygdala, the ventral tegmental area. 94 00:06:20,279 --> 00:06:23,479 Speaker 2: This is the emotional core part of the reptilian brain 95 00:06:23,600 --> 00:06:26,440 Speaker 2: that you know motivates us to move out of the 96 00:06:26,440 --> 00:06:30,680 Speaker 2: way of some approaching danger or to signal pleasure when 97 00:06:30,680 --> 00:06:34,160 Speaker 2: we're hungry and we finally get a taste of something sweet. 98 00:06:34,839 --> 00:06:37,120 Speaker 2: Music activates that same center, and in fact, it was 99 00:06:37,200 --> 00:06:40,240 Speaker 2: my lab that was the first to show that our 100 00:06:40,240 --> 00:06:44,320 Speaker 2: brain produces dopamine in response to music listening, and later 101 00:06:44,360 --> 00:06:49,000 Speaker 2: we showed that our brain produces its own endogenous opioids 102 00:06:49,040 --> 00:06:51,479 Speaker 2: in response to music listening, all part of that well 103 00:06:51,520 --> 00:06:55,560 Speaker 2: known pleasure network. Now, of course, that raises the question 104 00:06:56,279 --> 00:07:01,920 Speaker 2: why why is you know, over ever solutionary timescals did 105 00:07:02,040 --> 00:07:05,920 Speaker 2: music hit that emotional center and hear The answer is 106 00:07:05,960 --> 00:07:11,200 Speaker 2: by fer Kate. One is that the well known startle response. 107 00:07:11,800 --> 00:07:16,560 Speaker 2: You hear a sudden, loud noise and you jump. That's 108 00:07:16,600 --> 00:07:23,080 Speaker 2: evolutionarily adaptive because you know, you know, even lizards, snakes, 109 00:07:23,080 --> 00:07:26,040 Speaker 2: reptiles have to move out of the way of something 110 00:07:26,080 --> 00:07:30,360 Speaker 2: that might step on them or smash them. And that 111 00:07:30,440 --> 00:07:34,840 Speaker 2: startle response in humans goes directly from the inner ear 112 00:07:35,440 --> 00:07:39,200 Speaker 2: to the cerebellum and the brain stem. Before we even 113 00:07:39,200 --> 00:07:42,520 Speaker 2: figure out what the sound is, we startle and that's 114 00:07:42,520 --> 00:07:46,000 Speaker 2: connected to emotion centers. And so music, because it's an 115 00:07:46,040 --> 00:07:50,560 Speaker 2: auditory stimulus, is hardwired to movement and to emotion. 116 00:07:51,080 --> 00:07:54,160 Speaker 1: So what happens in the brain when you play an instrument. 117 00:07:54,960 --> 00:07:58,360 Speaker 2: Playing instrument is one of the most neuroprotective things we 118 00:07:58,440 --> 00:08:02,560 Speaker 2: can do. Glistening activates every area of the brain that 119 00:08:02,600 --> 00:08:06,200 Speaker 2: we so far mapped, as does playing an instrument. But 120 00:08:06,320 --> 00:08:08,960 Speaker 2: the added advantage of playing an instrument is that it's 121 00:08:09,040 --> 00:08:14,680 Speaker 2: active rather than passive, and it involves prediction centers in 122 00:08:14,720 --> 00:08:19,240 Speaker 2: the prefrontal cortex, particularly broad An area forty seven, which 123 00:08:19,280 --> 00:08:21,920 Speaker 2: is a pattern detector. You know, Venode, Menna and I 124 00:08:21,960 --> 00:08:24,240 Speaker 2: at Stanford for thirty years have been looking at this 125 00:08:24,280 --> 00:08:27,200 Speaker 2: little sliver of tissue on either side of your between 126 00:08:27,200 --> 00:08:29,760 Speaker 2: your top of your ears and your eyeballs broad and 127 00:08:29,840 --> 00:08:33,120 Speaker 2: forty seven, and Michael Patritus and I at mcgil have 128 00:08:33,160 --> 00:08:37,000 Speaker 2: also looked at it as that part of the human 129 00:08:37,080 --> 00:08:45,400 Speaker 2: brain that primates lack that allows us to process temporal patterns, 130 00:08:45,440 --> 00:08:49,920 Speaker 2: either in vision, touch or sound. And so in playing 131 00:08:49,920 --> 00:08:53,000 Speaker 2: an instrument, you've got to plan what you want it 132 00:08:53,080 --> 00:08:57,000 Speaker 2: to sound like. Hopefully you've got some idea of what 133 00:08:57,040 --> 00:08:59,839 Speaker 2: you want to come out, and then you've got to 134 00:09:00,080 --> 00:09:03,120 Speaker 2: use a feedback loop to listen to what came out 135 00:09:03,920 --> 00:09:06,439 Speaker 2: and see how well it matches with what you intended 136 00:09:06,559 --> 00:09:10,480 Speaker 2: and that adjust or not. And Broaden forty seven is 137 00:09:10,520 --> 00:09:12,920 Speaker 2: a part of that, as well as other prefrontal areas 138 00:09:13,040 --> 00:09:14,479 Speaker 2: and temporal areas. 139 00:09:15,880 --> 00:09:17,760 Speaker 1: Before we move on some other questions, I want to 140 00:09:17,760 --> 00:09:22,120 Speaker 1: ask what got you into this intersection between music and 141 00:09:22,200 --> 00:09:22,680 Speaker 1: the brain. 142 00:09:23,800 --> 00:09:25,080 Speaker 3: It wasn't intentional. 143 00:09:25,920 --> 00:09:28,960 Speaker 2: I had dropped out of college after my sophomore year 144 00:09:29,080 --> 00:09:32,640 Speaker 2: to play in a series of bands, and that led 145 00:09:32,720 --> 00:09:36,080 Speaker 2: to me becoming a staff producer at Columbia Records in 146 00:09:36,120 --> 00:09:40,760 Speaker 2: the eighties and in the nineties, when the music business 147 00:09:41,040 --> 00:09:44,160 Speaker 2: seemed to be imploding, a bunch of us who had 148 00:09:44,240 --> 00:09:46,800 Speaker 2: entered the business around the same time, figured we needed 149 00:09:46,800 --> 00:09:49,840 Speaker 2: a plan B, that this may not be a sustainable career, 150 00:09:50,800 --> 00:09:53,480 Speaker 2: and so I went back and finished my bachelor's degree 151 00:09:53,640 --> 00:09:56,440 Speaker 2: at Stanford. I just worked in every lab that would 152 00:09:56,440 --> 00:10:01,079 Speaker 2: have me. Then I went to graduate school and Oregon 153 00:10:01,280 --> 00:10:05,040 Speaker 2: in order to work with Doug Hintsman and Helen Neville 154 00:10:05,200 --> 00:10:08,720 Speaker 2: a language specialist, and of course Mike Posner on neuroimaging. 155 00:10:09,320 --> 00:10:12,080 Speaker 2: And I was just doing all these things in parallel 156 00:10:12,360 --> 00:10:16,240 Speaker 2: and loving it. I mentioned all these names because they 157 00:10:16,240 --> 00:10:20,319 Speaker 2: were very important mentors to me, each of them. And 158 00:10:21,200 --> 00:10:24,440 Speaker 2: in the third year of my graduate program, Posner, who 159 00:10:24,480 --> 00:10:28,199 Speaker 2: was my principal advisor, said, you know you're gonna have 160 00:10:28,240 --> 00:10:32,240 Speaker 2: to specialize. What do you want to do when you 161 00:10:32,280 --> 00:10:35,120 Speaker 2: grow up. I said, well, I really love all of this. 162 00:10:35,280 --> 00:10:39,600 Speaker 2: I love psycholinguistics, I love memory, I love decision making, 163 00:10:40,200 --> 00:10:44,320 Speaker 2: and I had worked in all those areas. And he said, well, 164 00:10:44,440 --> 00:10:48,040 Speaker 2: you know, you have this background as a musician, and 165 00:10:48,080 --> 00:10:51,160 Speaker 2: there's a lot of competition for jobs in those other fields. 166 00:10:51,960 --> 00:10:54,840 Speaker 2: There's this emerging field of psychology of music with a 167 00:10:54,880 --> 00:10:57,080 Speaker 2: handful of people in it. Maybe if you go into 168 00:10:57,120 --> 00:11:00,360 Speaker 2: that field, there'll be a lot of low hanging fruit, 169 00:11:00,400 --> 00:11:03,719 Speaker 2: as it were, a lot of studies that have obviously 170 00:11:03,800 --> 00:11:06,120 Speaker 2: need to be done that haven't been done yet. And 171 00:11:06,200 --> 00:11:10,840 Speaker 2: in addition, although nobody's going to advertise for a music 172 00:11:10,880 --> 00:11:15,439 Speaker 2: psychology faculty member, all those other things apply to music psychology, 173 00:11:15,559 --> 00:11:17,880 Speaker 2: decision making, how do we decide what we want to 174 00:11:17,920 --> 00:11:22,160 Speaker 2: listen to? Cyclel linguistics, psychology of lyrics and music, individual 175 00:11:22,160 --> 00:11:25,960 Speaker 2: differences in musical taste, memory for music. So it was 176 00:11:26,040 --> 00:11:30,719 Speaker 2: Mike who said preciently that I should brand myself as 177 00:11:30,720 --> 00:11:34,360 Speaker 2: a music psychologist. Of course, back in those days, there 178 00:11:34,360 --> 00:11:37,800 Speaker 2: were no neuroscience departments, there were no neuroscience programs. You 179 00:11:37,800 --> 00:11:41,360 Speaker 2: could not study neuroscience as you and I know it. 180 00:11:41,840 --> 00:11:44,000 Speaker 2: And I'll make a distinction for our listeners between like 181 00:11:44,120 --> 00:11:48,600 Speaker 2: molecular neuroscience, which was done in biology departments where you 182 00:11:48,679 --> 00:11:50,800 Speaker 2: only look at a single neuron and you've never even 183 00:11:50,880 --> 00:11:54,120 Speaker 2: considered what a thought might be and what we call you. 184 00:11:54,160 --> 00:11:56,880 Speaker 2: And I work in the area systems neuroscience, where we're 185 00:11:56,880 --> 00:12:01,720 Speaker 2: looking at the big ideas andions of neurons communicate with 186 00:12:01,760 --> 00:12:02,920 Speaker 2: other millions in neurons. 187 00:12:03,559 --> 00:12:08,360 Speaker 1: I know that you feel like that the music psychology 188 00:12:08,440 --> 00:12:12,040 Speaker 1: world was not particularly good let's say twenty years ago, 189 00:12:12,120 --> 00:12:14,640 Speaker 1: so give us a sense of what it was like 190 00:12:14,720 --> 00:12:16,640 Speaker 1: at that point when people thought about I'm going to 191 00:12:16,640 --> 00:12:19,040 Speaker 1: study the psychology music and what it's like now and 192 00:12:19,080 --> 00:12:19,760 Speaker 1: what's changed. 193 00:12:19,920 --> 00:12:22,319 Speaker 2: And there were a bunch of other people who hadn't 194 00:12:22,640 --> 00:12:27,679 Speaker 2: studied cognitive psychology but fancy themselves music psychologists, and they 195 00:12:27,679 --> 00:12:30,840 Speaker 2: did a bunch of bad studies, the poster child for 196 00:12:30,880 --> 00:12:36,520 Speaker 2: that being the Mozart effect study, which purported to show 197 00:12:36,559 --> 00:12:39,560 Speaker 2: that listening to Mozart for twenty minutes would make you smarter. 198 00:12:40,120 --> 00:12:43,640 Speaker 2: And there have now been literally one hundred studies that 199 00:12:43,760 --> 00:12:46,440 Speaker 2: shows that that was just bullshit. It was a poorly 200 00:12:46,480 --> 00:12:48,960 Speaker 2: controlled study, It was done by people who had no 201 00:12:49,080 --> 00:12:52,840 Speaker 2: experience in human experimental design. They stepped outside their lane, 202 00:12:53,160 --> 00:12:55,760 Speaker 2: and so there was a lot of garbage work being done. 203 00:12:56,200 --> 00:13:02,640 Speaker 2: What's different now is that we've had twenty years of 204 00:13:02,800 --> 00:13:06,440 Speaker 2: people applying to graduate school who knew they wanted to 205 00:13:06,480 --> 00:13:10,840 Speaker 2: study music psychology. Some of them went into music psychologists' 206 00:13:10,920 --> 00:13:14,559 Speaker 2: labs like mine. Others did what I did. They went 207 00:13:14,559 --> 00:13:17,679 Speaker 2: into the lab of a memory person or an attention 208 00:13:18,320 --> 00:13:23,640 Speaker 2: or brain imaging person and just used that interest in 209 00:13:23,720 --> 00:13:25,040 Speaker 2: music to design studies. 210 00:13:25,679 --> 00:13:27,760 Speaker 1: And so what sort of things have come out in 211 00:13:27,800 --> 00:13:29,800 Speaker 1: the last twenty years. Part of this has to do 212 00:13:29,840 --> 00:13:32,840 Speaker 1: with the advent of neuroimaging, right. 213 00:13:33,240 --> 00:13:36,000 Speaker 2: Well, that really, that really is what kicked it off, because, 214 00:13:36,080 --> 00:13:41,319 Speaker 2: as you know, David, the study of emotion was rather unseemly. 215 00:13:41,920 --> 00:13:45,240 Speaker 2: I think it began with the foundation of the first 216 00:13:45,280 --> 00:13:49,360 Speaker 2: psychology labs in the world by Vuntenfeckner in Europe. Of course, 217 00:13:49,400 --> 00:13:52,840 Speaker 2: William James was always more interested in the esthetics and 218 00:13:52,960 --> 00:13:56,400 Speaker 2: artistics side, but it was the behaviorist movement of the 219 00:13:56,440 --> 00:14:00,640 Speaker 2: fifties led by BF Skinner, that if something wasn't deservable 220 00:14:00,679 --> 00:14:04,960 Speaker 2: and replicable, it wasn't worth study. And so emotions just 221 00:14:05,040 --> 00:14:07,920 Speaker 2: seemed too squishy, and music as the language of emotion 222 00:14:08,440 --> 00:14:11,600 Speaker 2: seemed like the squishiest of all. What happened with the 223 00:14:11,600 --> 00:14:16,439 Speaker 2: first studies of neuroimaging, which Mike Posner was part of 224 00:14:16,800 --> 00:14:20,040 Speaker 2: nineteen ninety eight ninety nine. We were able to actually 225 00:14:20,160 --> 00:14:25,360 Speaker 2: see pictures of the brain caught in the act of 226 00:14:25,440 --> 00:14:29,760 Speaker 2: thinking and remembering and imagining, and that gave it a 227 00:14:29,800 --> 00:14:36,680 Speaker 2: biological basis reality. You could replicate brain imaging experiments, and 228 00:14:36,760 --> 00:14:41,160 Speaker 2: so first the study of emotion, and then shortly followed 229 00:14:41,160 --> 00:14:43,480 Speaker 2: by the study of music with the first neu imaging 230 00:14:44,160 --> 00:14:48,080 Speaker 2: studies of music in the early two thousands, that put 231 00:14:48,080 --> 00:14:51,200 Speaker 2: it back on the table as something that was worthy 232 00:14:51,240 --> 00:14:54,000 Speaker 2: of study and could be done in a rigorous fashion. 233 00:14:54,720 --> 00:14:57,600 Speaker 1: So your latest book, I heard, There was a Secret Chord, 234 00:14:58,360 --> 00:15:02,040 Speaker 1: looks at this issue of muse musick as medicine. So 235 00:15:02,040 --> 00:15:03,760 Speaker 1: tell us about that. 236 00:15:03,760 --> 00:15:09,400 Speaker 2: That's something that I think most of us have experienced intuitively, certainly, 237 00:15:09,400 --> 00:15:12,400 Speaker 2: it goes back tens of thousands of years using music 238 00:15:12,480 --> 00:15:16,640 Speaker 2: to treat injury and disease to shamans and faith healers 239 00:15:16,720 --> 00:15:21,680 Speaker 2: and indigenous tribes. And it was really in the last 240 00:15:21,840 --> 00:15:26,720 Speaker 2: ten years. I would say that the idea that music 241 00:15:27,720 --> 00:15:33,440 Speaker 2: had an evidence base for treating injury disease promoting wellness, 242 00:15:34,120 --> 00:15:38,320 Speaker 2: helping with mental disorders like depression, post traumatic stress disorder, anxiety. 243 00:15:38,880 --> 00:15:42,440 Speaker 2: There's been eight thousand papers in the last two years 244 00:15:42,440 --> 00:15:45,800 Speaker 2: alone in peer review journals on medical applications of music, 245 00:15:46,320 --> 00:15:51,640 Speaker 2: and so about five years ago I became involved with 246 00:15:51,720 --> 00:15:54,800 Speaker 2: the National Institutes of Health and the White House Science Office, 247 00:15:55,320 --> 00:16:01,280 Speaker 2: leading various expert panels to figure out what do we 248 00:16:01,320 --> 00:16:02,960 Speaker 2: really know and what do we don't know and what 249 00:16:03,040 --> 00:16:06,080 Speaker 2: remains to be done. That led to a call for proposals. 250 00:16:06,120 --> 00:16:09,600 Speaker 2: The NIH put forty million into music and medicine research 251 00:16:10,320 --> 00:16:12,440 Speaker 2: a few years back. So I looked for a book 252 00:16:12,520 --> 00:16:14,560 Speaker 2: on music and medicine because I wanted to know what 253 00:16:14,640 --> 00:16:17,880 Speaker 2: the state of the art was, and I couldn't find one. 254 00:16:18,320 --> 00:16:20,560 Speaker 2: There were a lot of papers, but no books, and 255 00:16:20,640 --> 00:16:22,680 Speaker 2: so I ended up writing the book I wanted. 256 00:16:22,440 --> 00:16:25,040 Speaker 1: To read and so give us a sense when it 257 00:16:25,080 --> 00:16:29,640 Speaker 1: comes to something like, let's start with dementia, how would 258 00:16:29,720 --> 00:16:33,600 Speaker 1: music be useful in the case of something like Alzheimer's. 259 00:16:34,200 --> 00:16:37,480 Speaker 2: Not all dementia is Alzheimer's, of course, and not all 260 00:16:37,520 --> 00:16:42,360 Speaker 2: memory loss comes from Alzheimer's. But the most straightforward case 261 00:16:42,400 --> 00:16:46,240 Speaker 2: of somebody with profound memory loss, perhaps due to Alzheimer's, 262 00:16:46,280 --> 00:16:51,320 Speaker 2: Corsokov's stroke, whatever. They may not recognize where they are, 263 00:16:52,800 --> 00:16:55,680 Speaker 2: They may not recognize loved ones. They may not even 264 00:16:55,720 --> 00:17:01,400 Speaker 2: recognize themselves in the mirror. This is profoundly We've seen 265 00:17:01,480 --> 00:17:04,400 Speaker 2: patients who will walk by a mirror and think they're 266 00:17:04,480 --> 00:17:07,359 Speaker 2: talking to someone else, and then they get angry because 267 00:17:07,359 --> 00:17:09,360 Speaker 2: the person in the mirror appears to be mocking them 268 00:17:09,840 --> 00:17:14,200 Speaker 2: by gesturing the same way they are, and it causes 269 00:17:14,680 --> 00:17:19,760 Speaker 2: one of two reactions. Individuals either turn in on themselves, 270 00:17:19,800 --> 00:17:22,679 Speaker 2: fold in on unsells because the external world makes no 271 00:17:22,840 --> 00:17:29,080 Speaker 2: sense and they become somewhat catatonic, or they become angry, agitated, 272 00:17:29,119 --> 00:17:32,720 Speaker 2: and violent and in that case have to be medicated. 273 00:17:32,920 --> 00:17:36,600 Speaker 2: They might start beating up on their spouses, not recognizing them. 274 00:17:37,119 --> 00:17:43,200 Speaker 2: Music follows a kind of principle that computer science talks about, 275 00:17:43,240 --> 00:17:45,360 Speaker 2: which is first in, last out. 276 00:17:45,600 --> 00:17:48,200 Speaker 3: This is a holder for the old models of computer memory. 277 00:17:48,600 --> 00:17:50,600 Speaker 2: The first thing that goes into the memory is the 278 00:17:50,680 --> 00:17:54,000 Speaker 2: last thing to come out. And because we've been listening 279 00:17:54,040 --> 00:17:57,200 Speaker 2: to music in the womb most of us and through 280 00:17:57,240 --> 00:18:01,639 Speaker 2: our childhoods, those memories the most deeply embedded in the 281 00:18:01,680 --> 00:18:06,720 Speaker 2: brain and the most resilient and resistant to decay or damage. 282 00:18:06,760 --> 00:18:10,080 Speaker 2: And so if we play music from the youth of 283 00:18:10,119 --> 00:18:14,520 Speaker 2: an Alzheimer's patient, somebody with profound memory loss, play the 284 00:18:14,600 --> 00:18:17,040 Speaker 2: music from say the ages of twelve to fourteen or 285 00:18:17,080 --> 00:18:22,520 Speaker 2: sixteen that's preserved in most cases, and it allows them 286 00:18:22,920 --> 00:18:29,040 Speaker 2: to profoundly reconnect with a part of themselves they had lost. 287 00:18:29,720 --> 00:18:35,040 Speaker 2: It eases them, it comforts them, It triggers memories that 288 00:18:35,240 --> 00:18:39,560 Speaker 2: had been buried, and that kind of therapy or intervention 289 00:18:40,359 --> 00:18:43,600 Speaker 2: can pull them out of the state they're in and 290 00:18:43,720 --> 00:18:47,320 Speaker 2: actually have consequences for days or weeks where they come 291 00:18:47,359 --> 00:18:48,000 Speaker 2: alive again. 292 00:18:49,920 --> 00:18:55,399 Speaker 1: It doesn't cure or help the dementia the cognitive loss exactly, 293 00:18:55,600 --> 00:19:00,440 Speaker 1: but it triggers memories and pulls them back to state 294 00:19:01,359 --> 00:19:06,159 Speaker 1: where they've been. And it can also revivify skills that 295 00:19:06,200 --> 00:19:11,400 Speaker 1: someone has. For example, musicians with profound dementia who get 296 00:19:11,440 --> 00:19:14,119 Speaker 1: an instrument put in their hand and they go and 297 00:19:14,880 --> 00:19:16,880 Speaker 1: play again as though they're young. 298 00:19:17,640 --> 00:19:20,480 Speaker 2: It's really extraordinary. And we saw this play out in 299 00:19:20,520 --> 00:19:24,640 Speaker 2: recent years with Glenn Campbell first and then with Tony Bennett, 300 00:19:25,359 --> 00:19:28,359 Speaker 2: both of whom had profound memory loss and did not 301 00:19:28,520 --> 00:19:31,399 Speaker 2: know where they were. When Glenn did his final tour 302 00:19:32,640 --> 00:19:37,360 Speaker 2: with dementia and in the throes of Alzheimer's. He would 303 00:19:37,400 --> 00:19:39,520 Speaker 2: sometimes play a song two or three times in a 304 00:19:39,600 --> 00:19:41,560 Speaker 2: row because they didn't realize he had just played it, 305 00:19:42,160 --> 00:19:44,520 Speaker 2: or he'd forget what song he was supposed to play. 306 00:19:44,560 --> 00:19:47,199 Speaker 2: But once the notes, the first few notes happened, he 307 00:19:47,240 --> 00:19:50,440 Speaker 2: knew where he was. Say, with Tony Bennett, he could 308 00:19:50,480 --> 00:19:53,480 Speaker 2: sing for an hour and a half without stopping once 309 00:19:53,520 --> 00:19:57,400 Speaker 2: the music took over. These are what we cognitive scientists 310 00:19:58,160 --> 00:20:02,919 Speaker 2: call overlearned. They're not just in memory, but they're in 311 00:20:03,000 --> 00:20:06,400 Speaker 2: memory with thousands and thousands of traces overlaid on top 312 00:20:06,440 --> 00:20:10,119 Speaker 2: of one another and their procedural memory. Once your vocal 313 00:20:10,160 --> 00:20:13,560 Speaker 2: cords and your fingers get going, they kind of take over. Now, 314 00:20:13,600 --> 00:20:16,280 Speaker 2: to be clear, the memory is not in your fingers, 315 00:20:16,359 --> 00:20:18,679 Speaker 2: although it feels that way. If I were to scoop 316 00:20:18,720 --> 00:20:21,080 Speaker 2: your brain outside your head, your fingers would not keep 317 00:20:21,080 --> 00:20:23,639 Speaker 2: playing like a chicken with its head cut off. But 318 00:20:24,160 --> 00:20:29,560 Speaker 2: those pathways are so profoundly deeply embedded that yeah, you 319 00:20:29,600 --> 00:20:32,520 Speaker 2: can keep going even with Alzheimer's, and it gives the 320 00:20:32,600 --> 00:20:38,240 Speaker 2: patient a rare act of competence in a world in 321 00:20:38,280 --> 00:20:43,119 Speaker 2: which they're otherwise incompetent. It gives them agency in the world. 322 00:20:43,840 --> 00:20:47,919 Speaker 2: This can really affect their mood and their quality of 323 00:20:47,920 --> 00:20:49,399 Speaker 2: life and way of being in the world. 324 00:21:04,920 --> 00:21:06,359 Speaker 1: Now, one of the things I've talked about on in 325 00:21:06,440 --> 00:21:09,919 Speaker 1: previous episode is right Bo's law, which is where older 326 00:21:09,960 --> 00:21:13,600 Speaker 1: memories are more secure, they're burned down more deeply than 327 00:21:13,840 --> 00:21:16,119 Speaker 1: newer memories. And of course we see this with people 328 00:21:16,160 --> 00:21:16,919 Speaker 1: with cogno. 329 00:21:17,040 --> 00:21:18,800 Speaker 2: I thought you were going with Bow's law, like the 330 00:21:18,840 --> 00:21:23,879 Speaker 2: bow on a violin, the earliest violin pieces are the 331 00:21:23,880 --> 00:21:25,639 Speaker 2: ones that are the most embedded. 332 00:21:25,960 --> 00:21:30,800 Speaker 1: Yeah, nope, Riebo Ribot, Yeah, which is you know, he 333 00:21:30,920 --> 00:21:32,760 Speaker 1: was the first I think this is actually the first 334 00:21:32,840 --> 00:21:35,160 Speaker 1: rule in neurology, as in the oldest rule. 335 00:21:35,480 --> 00:21:35,720 Speaker 2: Yeah. 336 00:21:35,760 --> 00:21:40,520 Speaker 1: But anyway, he saw that, you know, things from childhood 337 00:21:40,680 --> 00:21:45,160 Speaker 1: were remembered by people with let's say dementia, and things 338 00:21:45,160 --> 00:21:47,560 Speaker 1: they did last week or last month were not remembered, 339 00:21:47,800 --> 00:21:49,760 Speaker 1: so older members more stable. Now, the reason this is 340 00:21:49,800 --> 00:21:54,040 Speaker 1: so strange is because nothing else works that way. Institutions, 341 00:21:54,080 --> 00:21:56,800 Speaker 1: for example, don't remember their older stuff better than they 342 00:21:56,880 --> 00:22:00,960 Speaker 1: remember their newer stuff. But brain works this way. 343 00:22:01,280 --> 00:22:02,280 Speaker 3: That is true. 344 00:22:02,359 --> 00:22:04,879 Speaker 1: So my question is, I mean, when when it comes 345 00:22:04,920 --> 00:22:09,760 Speaker 1: to music, presumably most of these great musicians have been 346 00:22:09,800 --> 00:22:13,280 Speaker 1: playing since they were a little kids, and these particular 347 00:22:13,320 --> 00:22:17,200 Speaker 1: songs are overlearned, as you mentioned. Is this an expression 348 00:22:17,240 --> 00:22:20,240 Speaker 1: of simply of Ribo's law, which is that it's an 349 00:22:20,280 --> 00:22:21,879 Speaker 1: older memory and that's why they're able to do it? 350 00:22:22,000 --> 00:22:24,520 Speaker 1: Or is there something different about music than if I 351 00:22:24,600 --> 00:22:28,160 Speaker 1: ask them something about their you know, their their childhood home. 352 00:22:28,840 --> 00:22:29,720 Speaker 3: What a great question. 353 00:22:29,960 --> 00:22:34,120 Speaker 2: Well, there is something different about music to invoke Claude Shannon. 354 00:22:34,880 --> 00:22:42,440 Speaker 2: It's a highly organized and structured stimulus like language, and 355 00:22:42,920 --> 00:22:49,720 Speaker 2: so in an information theory perspective, which is Shannon, your 356 00:22:49,760 --> 00:22:53,280 Speaker 2: ability to predict what will come next in any sequence 357 00:22:54,119 --> 00:22:58,119 Speaker 2: defines how structured highly structured it is. Even more so 358 00:22:58,280 --> 00:23:01,920 Speaker 2: than language. Music is highly constrained. So I could say 359 00:23:01,920 --> 00:23:04,439 Speaker 2: a sentence to you like this, Let's try this, the 360 00:23:04,480 --> 00:23:08,119 Speaker 2: pizza was too hot to blank? What comes to mind? 361 00:23:08,320 --> 00:23:15,359 Speaker 2: Eat yeah or touch yeah? I would not be likely 362 00:23:15,400 --> 00:23:19,400 Speaker 2: to say the pizza was too hot to sleep. Once 363 00:23:19,440 --> 00:23:21,399 Speaker 2: I say it, you understand what I meant, and you 364 00:23:21,480 --> 00:23:23,320 Speaker 2: understand that I use the correct part of speech. I 365 00:23:23,359 --> 00:23:26,760 Speaker 2: put a verb at the end, But it's an unlikely outcome. 366 00:23:28,040 --> 00:23:30,800 Speaker 2: Music is even more constrained because there are only twelve 367 00:23:30,800 --> 00:23:34,280 Speaker 2: notes in our scale, and there are I wouldn't call 368 00:23:34,320 --> 00:23:37,560 Speaker 2: them laws of music theory, but customs of music theory. 369 00:23:38,000 --> 00:23:40,800 Speaker 2: And there are rhythmic rules or customs. 370 00:23:41,440 --> 00:23:43,160 Speaker 3: And so once you. 371 00:23:43,160 --> 00:23:47,280 Speaker 2: Get going on a piece of music, its own structure 372 00:23:48,320 --> 00:23:52,439 Speaker 2: constrains what the possible completions are, making it easier to 373 00:23:52,640 --> 00:23:56,439 Speaker 2: remember and then easier to recollect, easier to store and 374 00:23:56,480 --> 00:24:01,640 Speaker 2: easier to retrieve, and then moreover, as its own internal tempo. 375 00:24:02,600 --> 00:24:06,200 Speaker 2: Once the beat is going, it's carrying you along, whether 376 00:24:06,240 --> 00:24:08,600 Speaker 2: you're ready to go along with it or not. And 377 00:24:08,680 --> 00:24:11,760 Speaker 2: so you're going to fill those slots with what needs 378 00:24:11,800 --> 00:24:15,040 Speaker 2: to go there or your best approximation for it. And 379 00:24:15,080 --> 00:24:18,720 Speaker 2: when you look at performing musicians, like typically the ones 380 00:24:18,760 --> 00:24:21,920 Speaker 2: that work holiday ins on Friday night lounge bands and stuff, 381 00:24:22,680 --> 00:24:26,159 Speaker 2: they might know two thousand songs where I would use 382 00:24:26,200 --> 00:24:29,960 Speaker 2: the word no in quotes, they probably don't know every 383 00:24:30,040 --> 00:24:32,240 Speaker 2: single note and every single rhythm. 384 00:24:32,720 --> 00:24:34,080 Speaker 3: But they can approximate it. 385 00:24:34,119 --> 00:24:37,160 Speaker 2: They can improvise and estimate it so that you don't 386 00:24:37,160 --> 00:24:38,280 Speaker 2: really notice the difference. 387 00:24:38,720 --> 00:24:42,360 Speaker 1: Well, that's actually a good segue into Parkinson's disease. 388 00:24:42,760 --> 00:24:46,280 Speaker 2: How is music used there? So in all these cases 389 00:24:47,359 --> 00:24:50,439 Speaker 2: of music is medicine. We're not talking about like a 390 00:24:50,520 --> 00:24:55,320 Speaker 2: music module in the brain or a music medicine prescription 391 00:24:55,520 --> 00:24:59,080 Speaker 2: that's straightforward, because music is doing different things in different 392 00:24:59,080 --> 00:25:01,879 Speaker 2: parts of the brain, different aspects of the music are 393 00:25:01,920 --> 00:25:04,480 Speaker 2: doing it, and Parkinson's is I'm so glad you brought 394 00:25:04,480 --> 00:25:07,480 Speaker 2: this up. It's actually the best case for understanding this 395 00:25:07,560 --> 00:25:14,080 Speaker 2: differentiation in Parkinson's disease. At some point, most patients will 396 00:25:14,119 --> 00:25:19,159 Speaker 2: experience difficulty walking movement disorders in general, but walking in particular, 397 00:25:19,680 --> 00:25:24,280 Speaker 2: and it's because the disease degrade circuits in the basal 398 00:25:24,320 --> 00:25:27,439 Speaker 2: ganglia that are required to maintain a smooth and steady 399 00:25:27,480 --> 00:25:30,640 Speaker 2: gait and to orchestrate the movements of one foot has 400 00:25:30,680 --> 00:25:32,160 Speaker 2: to go after the other and you have to put 401 00:25:32,160 --> 00:25:33,679 Speaker 2: it down at a certain time where you end up 402 00:25:33,680 --> 00:25:35,280 Speaker 2: with both feet in the air at the same time, 403 00:25:35,760 --> 00:25:38,720 Speaker 2: and that's not good for walking. And the circuits that 404 00:25:38,800 --> 00:25:42,840 Speaker 2: allow you to walk rely on an internal intrinsic timer 405 00:25:43,040 --> 00:25:47,120 Speaker 2: in the brain, a clock, and that's what gets degraded. 406 00:25:47,640 --> 00:25:50,159 Speaker 2: If you listen to music that has the tempo of 407 00:25:50,200 --> 00:25:58,560 Speaker 2: your natural gait, you have neurons, neuronal clusters that were 408 00:25:58,560 --> 00:26:02,600 Speaker 2: not damaged that synchronize to that pulse, and then they 409 00:26:02,600 --> 00:26:04,960 Speaker 2: can act as an external clock that allows you to 410 00:26:05,000 --> 00:26:06,560 Speaker 2: walk smoothly and continuously. 411 00:26:07,600 --> 00:26:11,080 Speaker 1: What else besides dementia and Parkinson's, where else do we 412 00:26:11,119 --> 00:26:12,520 Speaker 1: see therapeutic effects? 413 00:26:13,080 --> 00:26:16,359 Speaker 2: Well, I think one of the big ones is an anxiety. 414 00:26:16,800 --> 00:26:21,359 Speaker 2: Dentists figured this out a long time ago. They play 415 00:26:21,400 --> 00:26:25,040 Speaker 2: you what's supposed to be relaxing music to reduce your anxiety, 416 00:26:25,880 --> 00:26:27,879 Speaker 2: which reduces swelling and inflammation. 417 00:26:28,040 --> 00:26:29,879 Speaker 1: And why does that work? 418 00:26:30,640 --> 00:26:35,040 Speaker 2: Some music we find to be relaxing, some we find 419 00:26:35,080 --> 00:26:38,239 Speaker 2: to be stimulating, some we find to be inspiring. And 420 00:26:38,960 --> 00:26:42,639 Speaker 2: the difficulty is, there's no one music that will do 421 00:26:42,760 --> 00:26:45,440 Speaker 2: those things for everybody. In fact, there's no one song 422 00:26:45,480 --> 00:26:50,440 Speaker 2: everybody likes. There's no one song everybody hates. It's subjective, 423 00:26:50,920 --> 00:26:54,320 Speaker 2: like your taste for food. You know, why doesn't everybody 424 00:26:54,400 --> 00:26:58,400 Speaker 2: like Indian food? I love food, Not everybody does. You 425 00:26:58,440 --> 00:27:00,680 Speaker 2: have our own taste. It seems as though we have 426 00:27:00,720 --> 00:27:04,720 Speaker 2: an esthetic module each of us that governs things like 427 00:27:04,800 --> 00:27:08,919 Speaker 2: our taste and colors and people and tastes and music. 428 00:27:09,280 --> 00:27:12,560 Speaker 2: And we actually showed in a PNAS paper that a 429 00:27:12,640 --> 00:27:17,560 Speaker 2: person's preferences for certain musical combinations is correlated with their 430 00:27:17,600 --> 00:27:21,879 Speaker 2: preferences for certain color combinations, which seemed completely crazy to me. 431 00:27:22,520 --> 00:27:25,400 Speaker 2: It just seems so far fetched. But there's this underlying 432 00:27:25,440 --> 00:27:27,920 Speaker 2: aesthetic module that we can't account for yet. 433 00:27:29,080 --> 00:27:32,120 Speaker 1: What was the argument there, did you guys forward a hypothesis? 434 00:27:32,160 --> 00:27:33,200 Speaker 1: I read no? 435 00:27:33,200 --> 00:27:37,840 Speaker 2: Oh well, I mean there was some hand waving. Yes, 436 00:27:37,880 --> 00:27:41,399 Speaker 2: there's an aesthetics module, and yeah, I mean to some extent, 437 00:27:41,480 --> 00:27:44,359 Speaker 2: it had to do with consonants and dissonance. Do you 438 00:27:44,440 --> 00:27:47,639 Speaker 2: like to see contrasting colors and contrasting chords? Do you 439 00:27:47,720 --> 00:27:51,240 Speaker 2: like to see things that are more consonant and harmonious? 440 00:27:51,800 --> 00:27:56,280 Speaker 2: But sharp edges and the metaphorical sharp edges and music. 441 00:27:56,320 --> 00:27:58,720 Speaker 2: But apart from that, it was pretty speculative. 442 00:27:59,119 --> 00:28:02,040 Speaker 1: So let me double click on this issue about individual differences, 443 00:28:02,160 --> 00:28:05,640 Speaker 1: because one thing that's clear is across the population, some 444 00:28:05,640 --> 00:28:08,760 Speaker 1: people don't really like music that much. Other people love music, 445 00:28:08,840 --> 00:28:10,840 Speaker 1: it's a big part of their lives. But on one 446 00:28:10,880 --> 00:28:13,639 Speaker 1: on the spectrum, you just it's sort of meaningless to 447 00:28:13,680 --> 00:28:15,199 Speaker 1: many people. So how do you interpret that? 448 00:28:15,720 --> 00:28:18,880 Speaker 2: Well, I just look at this as as a necessity 449 00:28:19,040 --> 00:28:24,080 Speaker 2: of Darwinian theory, which is that we can't all be alike, 450 00:28:24,760 --> 00:28:28,520 Speaker 2: or we'd you know, genetically or behaviorally or we would 451 00:28:28,520 --> 00:28:32,040 Speaker 2: all be wiped out by a single opportunistic virus. So, 452 00:28:32,359 --> 00:28:35,400 Speaker 2: you know, a cornerstone of Darwinian theory, as you teach it, 453 00:28:35,600 --> 00:28:39,280 Speaker 2: as I teach it, is descent with modification and random mutation. 454 00:28:39,920 --> 00:28:44,520 Speaker 2: And so you know, most random mutations end up being unobservable. 455 00:28:45,080 --> 00:28:48,440 Speaker 2: Some of them we see a phenotypic variation, that is 456 00:28:48,440 --> 00:28:51,560 Speaker 2: a behavioral variation. And in that case, yeah, ten percent 457 00:28:51,600 --> 00:28:54,800 Speaker 2: of the population probably don't like music, and they don't 458 00:28:54,880 --> 00:28:57,560 Speaker 2: understand why the rest of us spend so much money 459 00:28:57,560 --> 00:29:00,520 Speaker 2: and time on it. Ten percent of the population probably 460 00:29:00,520 --> 00:29:04,680 Speaker 2: don't like chocolate. I find that so impossible to believe, 461 00:29:04,760 --> 00:29:07,800 Speaker 2: but they don't. And then there are you know, some 462 00:29:07,840 --> 00:29:11,080 Speaker 2: percentage of the population don't like sex. They tend not 463 00:29:11,160 --> 00:29:14,760 Speaker 2: to pass that on through reproduction, but that trait, but 464 00:29:15,160 --> 00:29:16,200 Speaker 2: it's the way it works. 465 00:29:16,560 --> 00:29:20,600 Speaker 1: How do you think music fits into the story of 466 00:29:21,080 --> 00:29:23,400 Speaker 1: evolution and human evolution in particular. 467 00:29:24,240 --> 00:29:29,480 Speaker 2: So Stephen Pinker famously threw down a gauntlet in nineteen 468 00:29:29,600 --> 00:29:32,400 Speaker 2: ninety seven when he said that he thinks that music 469 00:29:32,440 --> 00:29:36,360 Speaker 2: has nothing to do with evolution, that it was just 470 00:29:36,440 --> 00:29:39,800 Speaker 2: sort of a byproduct of other things that we developed, 471 00:29:39,880 --> 00:29:45,760 Speaker 2: like language. He called music auditory cheesecake, And what he 472 00:29:45,840 --> 00:29:50,960 Speaker 2: was saying was that, well, we didn't really evolve to 473 00:29:51,160 --> 00:29:54,720 Speaker 2: like cheesecake. We evolved to like sweets and fats because 474 00:29:54,760 --> 00:29:58,960 Speaker 2: in the very small amounts they were available across evolutionary 475 00:29:59,000 --> 00:30:02,440 Speaker 2: time periods to it was adaptive to seek them out. Now, 476 00:30:02,480 --> 00:30:06,479 Speaker 2: if you get cheesecake, it'll just you know, you know, 477 00:30:06,600 --> 00:30:10,520 Speaker 2: spike your blood sugar levels and be bad for your health. 478 00:30:10,600 --> 00:30:12,479 Speaker 1: I'm curious if you have a different view on it, 479 00:30:13,000 --> 00:30:15,400 Speaker 1: whether there's a role in evolution for music. 480 00:30:15,680 --> 00:30:17,600 Speaker 2: We do have some data and I'd like to share 481 00:30:17,600 --> 00:30:20,320 Speaker 2: that with you, our and our listeners. So to begin with, 482 00:30:20,800 --> 00:30:23,000 Speaker 2: I mean, just as a resource, I would mention Stephen 483 00:30:23,080 --> 00:30:26,680 Speaker 2: Mithen's book The Singing Neanderthals, where he makes the case 484 00:30:26,760 --> 00:30:30,800 Speaker 2: that music was a proto language that preceded, you know, 485 00:30:30,880 --> 00:30:36,560 Speaker 2: linguistic language speech. I would say the evidence that we 486 00:30:36,720 --> 00:30:40,520 Speaker 2: have from neuroanatomy is that, from the work that Vanode 487 00:30:40,520 --> 00:30:44,880 Speaker 2: Menon and I have done in others, those circuits that 488 00:30:44,960 --> 00:30:48,920 Speaker 2: are engaged with music, listening and performing are phylogenetically older 489 00:30:49,440 --> 00:30:52,360 Speaker 2: than the speech circuits. And that's the reason why Gabby 490 00:30:52,400 --> 00:30:55,760 Speaker 2: Giffords was able to recover speech after she was shot 491 00:30:55,760 --> 00:30:58,320 Speaker 2: in the head and lost the ability to speak. It's 492 00:30:58,320 --> 00:31:02,080 Speaker 2: called melodic intonation therapy. We can take somebody who's lost 493 00:31:02,080 --> 00:31:06,040 Speaker 2: speech ephasic expressive aphasia and teach them to sing what 494 00:31:06,080 --> 00:31:07,440 Speaker 2: they need to communicate. 495 00:31:08,280 --> 00:31:10,800 Speaker 1: So give us an example of that. What Gabby Gifference does. 496 00:31:11,320 --> 00:31:11,560 Speaker 3: Well. 497 00:31:11,640 --> 00:31:14,440 Speaker 2: She could not speak, but she might have been taught 498 00:31:14,480 --> 00:31:17,960 Speaker 2: things like I need a glass of water, show me 499 00:31:18,040 --> 00:31:22,560 Speaker 2: to the bathroom, I'm ready for bed now. She could 500 00:31:22,560 --> 00:31:27,280 Speaker 2: sing those things perfectly, and through neuroplasticity, the brain rewired 501 00:31:27,320 --> 00:31:32,320 Speaker 2: itself by passing those damaged circuits using the intact music circuits. 502 00:31:32,400 --> 00:31:37,640 Speaker 2: They are phylogenetically, that is, evolutionarily older. That's one piece 503 00:31:37,640 --> 00:31:41,880 Speaker 2: of evidence. Another is if we look at contemporary hunter 504 00:31:42,040 --> 00:31:46,120 Speaker 2: gatherer preliterate societies or all around the world that have 505 00:31:46,200 --> 00:31:50,040 Speaker 2: been cut off from Western civilization, and we make the assumption, David, 506 00:31:50,080 --> 00:31:52,239 Speaker 2: that they're living life now pretty much as they have 507 00:31:52,400 --> 00:31:55,440 Speaker 2: for ten or twenty thousand years, and all of them 508 00:31:55,520 --> 00:31:58,640 Speaker 2: use music for a number of things, not just a 509 00:31:58,640 --> 00:32:02,880 Speaker 2: single thing, but the emerging ideas that music evolved as 510 00:32:02,920 --> 00:32:05,800 Speaker 2: many things did, not for a single to solve a 511 00:32:05,800 --> 00:32:09,320 Speaker 2: single adaptive problem, but multiple problems, and one of them 512 00:32:09,480 --> 00:32:13,640 Speaker 2: was how do you encode knowledge in a pre literate society. 513 00:32:13,720 --> 00:32:16,640 Speaker 2: We've only had a written language for five thousand years. 514 00:32:16,720 --> 00:32:18,959 Speaker 2: We've been on the planet ten or twenty times. As 515 00:32:19,000 --> 00:32:22,040 Speaker 2: long as that, we still had to remember things like, oh, 516 00:32:22,600 --> 00:32:26,360 Speaker 2: don't go over that hill there, because my grandfather went 517 00:32:26,400 --> 00:32:29,400 Speaker 2: over there and they killed him because they're very vicious 518 00:32:29,800 --> 00:32:32,840 Speaker 2: and warring, and so you know, don't go there. And 519 00:32:33,280 --> 00:32:36,080 Speaker 2: when this water well runs dry, this is a route 520 00:32:36,120 --> 00:32:39,479 Speaker 2: to the other well, the supplementary one. Don't eat that 521 00:32:39,560 --> 00:32:42,480 Speaker 2: plant unless you boil it in this particular way. This 522 00:32:42,600 --> 00:32:46,520 Speaker 2: kind of knowledge is embedded in song in pre literate 523 00:32:46,560 --> 00:32:50,080 Speaker 2: hunter gatherer tribes and probably has been for a long time. 524 00:32:50,680 --> 00:32:54,640 Speaker 2: Mothers soothing their infants to imprint their infant on their 525 00:32:54,760 --> 00:32:58,480 Speaker 2: voice so that if they become separated, the infant will 526 00:32:59,200 --> 00:33:02,280 Speaker 2: know the sound the mother's voice. So we've got knowledge, 527 00:33:02,320 --> 00:33:06,120 Speaker 2: we've got bonding between mother and infant. We have social bonding. 528 00:33:06,200 --> 00:33:10,000 Speaker 2: Singing around a campfire to ward off a neighboring tribe 529 00:33:10,080 --> 00:33:14,960 Speaker 2: or predators as a way to defuse interpersonal tensions within 530 00:33:15,040 --> 00:33:18,360 Speaker 2: a tribe and to protect you from outside invaders. 531 00:33:18,920 --> 00:33:20,040 Speaker 3: Lots of different uses. 532 00:33:22,400 --> 00:33:23,880 Speaker 1: Let me double click on this for a second. Do 533 00:33:23,960 --> 00:33:29,520 Speaker 1: you feel that music is universal in terms of when 534 00:33:29,560 --> 00:33:31,600 Speaker 1: you compare across culture around the world, or are there 535 00:33:31,600 --> 00:33:35,120 Speaker 1: important differences locally both. 536 00:33:35,440 --> 00:33:39,320 Speaker 2: Music is a cultural universal. There is no known culture 537 00:33:39,360 --> 00:33:42,080 Speaker 2: now or any time in the past that lacked it. 538 00:33:42,760 --> 00:33:47,520 Speaker 2: In David Huron's words, music is marked by its ubiquity 539 00:33:47,720 --> 00:33:54,000 Speaker 2: and its antiquity, and there are huge local variations. One 540 00:33:54,040 --> 00:33:57,480 Speaker 2: thing in common is we all have the octave, which 541 00:33:57,520 --> 00:34:01,160 Speaker 2: is defined by frequencies that are double or having of 542 00:34:01,200 --> 00:34:04,680 Speaker 2: one another hundred hertz, two hundred hertz, fifty hertz, all 543 00:34:04,680 --> 00:34:09,279 Speaker 2: the same. We perceive them as perceptually very similar um 544 00:34:10,200 --> 00:34:13,880 Speaker 2: similar notes. We give them the same name. In Western system, 545 00:34:14,440 --> 00:34:16,880 Speaker 2: there's middle C and there's high C, and there's low C, 546 00:34:17,200 --> 00:34:17,839 Speaker 2: things like that. 547 00:34:18,160 --> 00:34:19,680 Speaker 3: But the way we divide up the. 548 00:34:19,640 --> 00:34:24,600 Speaker 2: Octave into pieces is different across cultures. We divide the 549 00:34:24,600 --> 00:34:27,319 Speaker 2: octave up into twelve pieces, and we tend to use 550 00:34:27,360 --> 00:34:29,360 Speaker 2: only five or seven of the notes at a time. 551 00:34:29,920 --> 00:34:32,840 Speaker 2: The patterns that we make once we've divided the octave, 552 00:34:33,440 --> 00:34:37,800 Speaker 2: the way we combine them, either sequentially or simultaneously melodies 553 00:34:37,840 --> 00:34:41,800 Speaker 2: and chords are different. And that's why Chinese opera and 554 00:34:41,920 --> 00:34:46,439 Speaker 2: the music of Sub Saharan Africa sounds so different to us. 555 00:34:47,200 --> 00:34:50,759 Speaker 2: It's based on different customs and a different system. And 556 00:34:50,840 --> 00:34:54,360 Speaker 2: it's not the case that our music is better or 557 00:34:54,400 --> 00:34:58,400 Speaker 2: that if only you went into the Amazon and played 558 00:34:58,400 --> 00:35:01,200 Speaker 2: the indigenous people their mozart, they would feel that they 559 00:35:01,200 --> 00:35:04,640 Speaker 2: had suddenly heard from God himself. It doesn't work that way. 560 00:35:04,840 --> 00:35:06,000 Speaker 2: We've done the experiment. 561 00:35:06,760 --> 00:35:08,040 Speaker 1: What is their impression of it? 562 00:35:08,480 --> 00:35:10,759 Speaker 3: Eh? Meh? Yeah. 563 00:35:10,800 --> 00:35:16,280 Speaker 1: Presumably whatever you've grown up with culturally affects your enjoyment 564 00:35:16,480 --> 00:35:17,720 Speaker 1: of what you're hearing, right. 565 00:35:18,040 --> 00:35:18,399 Speaker 3: Very much. 566 00:35:18,440 --> 00:35:21,040 Speaker 2: So. Yeah, we imprint on the music we're raised on, 567 00:35:21,760 --> 00:35:27,640 Speaker 2: and so we implicitly learn the grammar of our music 568 00:35:27,719 --> 00:35:30,720 Speaker 2: the way we implicitly learn the grammar of our language. 569 00:35:30,719 --> 00:35:35,799 Speaker 2: As Chomsky had said, here's a little test. Hang on 570 00:35:35,800 --> 00:35:39,000 Speaker 2: a second. Okay for the listener, Dan has des grabbed 571 00:35:39,040 --> 00:35:45,839 Speaker 2: his guitar, So we all know implicitly, uh scales, even 572 00:35:45,840 --> 00:35:53,920 Speaker 2: if we don't realize we do. Now you're expecting another note. 573 00:35:54,480 --> 00:35:55,799 Speaker 2: You're probably expecting this one. 574 00:35:57,960 --> 00:36:01,600 Speaker 1: Thank God about that? Yeah, otherwise. 575 00:36:06,440 --> 00:36:14,560 Speaker 3: That doesn't sound bad. But that sounds quite not quite right. 576 00:36:15,520 --> 00:36:18,040 Speaker 2: And we know chords. We know that if I go 577 00:36:26,600 --> 00:36:27,800 Speaker 2: it wants to go somewhere. 578 00:36:28,080 --> 00:36:30,799 Speaker 3: I can't just stop I've got to come back to. 579 00:36:32,400 --> 00:36:35,880 Speaker 2: Or or. 580 00:36:37,840 --> 00:36:42,040 Speaker 3: There's some options, but not unlimited options, right. 581 00:36:42,120 --> 00:36:45,000 Speaker 1: And I've talked in many episodes about how fundamentally the 582 00:36:45,040 --> 00:36:47,960 Speaker 1: brain is a prediction machine. It's just it is trying 583 00:36:48,000 --> 00:36:50,799 Speaker 1: to guess ahead at what's going on. It's got an 584 00:36:50,880 --> 00:36:54,200 Speaker 1: internal model of what's coming next. So in those cases, 585 00:36:54,280 --> 00:36:56,840 Speaker 1: let's say, with the chords that you were playing, is 586 00:36:56,840 --> 00:37:00,319 Speaker 1: it the case that I had certain predictions because of 587 00:37:00,320 --> 00:37:02,520 Speaker 1: the culture I've grown up in. To what extent would 588 00:37:02,520 --> 00:37:04,439 Speaker 1: that be universal about what chorn comes next? 589 00:37:04,840 --> 00:37:08,680 Speaker 2: Absolutely, and something I very much appreciate about you, because 590 00:37:09,200 --> 00:37:13,000 Speaker 2: this is not something that all cognitive scientists cognitive scientists 591 00:37:13,040 --> 00:37:15,560 Speaker 2: talk about, but you and I, I think, have been 592 00:37:15,600 --> 00:37:17,920 Speaker 2: big proponents for this idea that if the brain is 593 00:37:18,320 --> 00:37:21,040 Speaker 2: if the brain is nothing else, it is a giant 594 00:37:21,040 --> 00:37:25,160 Speaker 2: prediction machine. That is its job. It's just based on 595 00:37:25,280 --> 00:37:28,839 Speaker 2: what we've heard over and over and over again. And 596 00:37:29,680 --> 00:37:33,440 Speaker 2: the job of the composer and the performer is to 597 00:37:33,600 --> 00:37:37,520 Speaker 2: reward those expectations just enough of the time that you 598 00:37:37,600 --> 00:37:41,680 Speaker 2: feel like you're following along with the story as it unfolds. 599 00:37:42,239 --> 00:37:44,319 Speaker 2: But they have to surprise you just enough of the 600 00:37:44,360 --> 00:37:47,479 Speaker 2: time that you've learned something, or you get that little 601 00:37:47,600 --> 00:37:51,200 Speaker 2: hit of oh, isn't that interesting? As much as I 602 00:37:51,239 --> 00:37:54,120 Speaker 2: was trying to predict what comes next, they came up 603 00:37:54,160 --> 00:37:56,840 Speaker 2: with something even better than I could have imagined. 604 00:37:57,360 --> 00:38:00,000 Speaker 1: Just like with everything in life, there's a spectrum between 605 00:38:00,080 --> 00:38:04,040 Speaker 1: novelty and familiarity and all the sweet spot is in 606 00:38:04,120 --> 00:38:21,880 Speaker 1: between that. Somewhere on that note, what is the reason 607 00:38:22,000 --> 00:38:26,800 Speaker 1: that we care for rhythm so much? Do you feel 608 00:38:26,840 --> 00:38:28,880 Speaker 1: as I do that it has to do with predictability, 609 00:38:29,160 --> 00:38:32,239 Speaker 1: where the brain says, oh, now I know what's coming next. 610 00:38:32,280 --> 00:38:34,120 Speaker 1: There's the next beat, in the next beat, and there's 611 00:38:34,160 --> 00:38:39,600 Speaker 1: something very satisfying about having some structure of prediction, and 612 00:38:39,640 --> 00:38:41,719 Speaker 1: then there's surprises thrown on top of that. 613 00:38:42,200 --> 00:38:45,719 Speaker 2: It's because the brain's a prediction machine that rhythm is 614 00:38:45,840 --> 00:38:49,760 Speaker 2: meaningful to us. There are populations of neurons that fire 615 00:38:49,800 --> 00:38:53,880 Speaker 2: in synchrony with the beat, with the tempo that sets 616 00:38:54,000 --> 00:39:01,120 Speaker 2: us up for movement. And I mean that metaphor and literally, 617 00:39:02,200 --> 00:39:05,000 Speaker 2: music is possibly the only art form that makes you 618 00:39:05,000 --> 00:39:07,319 Speaker 2: want to wiggle your body in response to it. People 619 00:39:07,360 --> 00:39:10,720 Speaker 2: aren't standing in front of the Mona Lisa and dancing. 620 00:39:10,480 --> 00:39:12,920 Speaker 3: Although they do, they. 621 00:39:12,280 --> 00:39:14,120 Speaker 2: Seem weird and they might get kicked out of the louver, 622 00:39:14,520 --> 00:39:17,799 Speaker 2: but we dance to music because we can't help it. 623 00:39:17,840 --> 00:39:20,359 Speaker 2: And in the neuroimaging studies I've done all of them, 624 00:39:20,640 --> 00:39:24,080 Speaker 2: where we ask people to stay still, we still see 625 00:39:24,120 --> 00:39:27,160 Speaker 2: activity in their premotor cortex and their motor cortex that 626 00:39:27,160 --> 00:39:30,799 Speaker 2: they're trying to suppress. And then on the metaphorical movement, yes, 627 00:39:31,440 --> 00:39:35,000 Speaker 2: rhythm is important because it's telling us that there is 628 00:39:35,040 --> 00:39:37,640 Speaker 2: more to come and we want to know what that is. 629 00:39:38,040 --> 00:39:41,840 Speaker 2: It sets up a narrative momentum and when a musician 630 00:39:41,880 --> 00:39:44,560 Speaker 2: could play around with that rhythm. If you listen to 631 00:39:44,560 --> 00:39:48,759 Speaker 2: what Stevie Wonder does and the opening to Superstition, I 632 00:39:48,760 --> 00:39:52,080 Speaker 2: can't replicate this exactly, but he's playing around on the 633 00:39:52,160 --> 00:39:54,400 Speaker 2: high hat. He's setting up a beat. The high hat's 634 00:39:54,400 --> 00:40:00,200 Speaker 2: that little symbol and ordinarily a normal drummer would just go, 635 00:40:02,600 --> 00:40:10,920 Speaker 2: but Stevie doesn't goes. He's playing around. No, two times 636 00:40:10,960 --> 00:40:13,720 Speaker 2: are the same, and we may not notice it because 637 00:40:13,719 --> 00:40:17,880 Speaker 2: he keeps the underlying pulse there, but it is ear 638 00:40:17,960 --> 00:40:22,160 Speaker 2: Candy Man. It is just so interesting for the brain 639 00:40:23,120 --> 00:40:25,720 Speaker 2: to take all that in, even at a subliminal level, 640 00:40:26,440 --> 00:40:28,480 Speaker 2: that there's so much going on, and not only is 641 00:40:28,520 --> 00:40:31,280 Speaker 2: he changing in the rhythm. He's moving his stick around 642 00:40:31,880 --> 00:40:33,919 Speaker 2: so he gets the bell of the symbol, he gets 643 00:40:33,960 --> 00:40:36,280 Speaker 2: the edge, he gets the middle, he gets different sounds 644 00:40:36,320 --> 00:40:36,759 Speaker 2: out of it. 645 00:40:38,680 --> 00:40:40,799 Speaker 1: So this is a good segue into something I've been 646 00:40:40,800 --> 00:40:43,080 Speaker 1: wanting to ask you, which is, what is your take 647 00:40:44,120 --> 00:40:48,279 Speaker 1: on artificial intelligence and music the future of And there 648 00:40:48,280 --> 00:40:49,879 Speaker 1: are two ways to think about this, of course. One 649 00:40:49,960 --> 00:40:55,400 Speaker 1: is AI composing music. Another is AI finding the perfect 650 00:40:55,480 --> 00:40:57,520 Speaker 1: music for you. Maybe there are other ways to think 651 00:40:57,520 --> 00:40:59,480 Speaker 1: about it as well, But tell us your take on 652 00:40:59,520 --> 00:41:00,680 Speaker 1: the future. 653 00:41:00,880 --> 00:41:05,239 Speaker 2: AI composing music. I look at it this way. I 654 00:41:05,239 --> 00:41:09,280 Speaker 2: have a friend, a novelist, Gail Jones, wonderful novelist, who 655 00:41:09,320 --> 00:41:13,520 Speaker 2: says that AI music now is like those artificial flowers 656 00:41:13,560 --> 00:41:16,319 Speaker 2: at a holiday inn. You walk in the lobby, you 657 00:41:16,320 --> 00:41:18,839 Speaker 2: see this big display and you go, wow, isn't that nice? 658 00:41:18,880 --> 00:41:21,160 Speaker 2: And you get up close and you realize they have 659 00:41:21,280 --> 00:41:25,120 Speaker 2: no nice odor and they're plastic, And so AI music 660 00:41:25,160 --> 00:41:29,280 Speaker 2: at a distance probably sounds just fine. And it's already 661 00:41:29,280 --> 00:41:33,160 Speaker 2: crept into advertising on social media. It's being used in 662 00:41:33,200 --> 00:41:36,840 Speaker 2: the background. It's sonic wallpaper. It's kind of like the 663 00:41:36,920 --> 00:41:39,799 Speaker 2: painting in the bedroom of that same holiday in room 664 00:41:39,840 --> 00:41:43,080 Speaker 2: it's like, Okay, it's covering the wall. It's kind of nice, 665 00:41:43,080 --> 00:41:44,719 Speaker 2: but I'm not going to sit there and stare at 666 00:41:44,760 --> 00:41:49,840 Speaker 2: it for hours. I think that you and I subscribe 667 00:41:50,080 --> 00:41:56,080 Speaker 2: to Dan Dennett's functionalism, the idea that in theory, all 668 00:41:56,080 --> 00:41:58,560 Speaker 2: of our thoughts, hopes, desires, and beliefs, all of our 669 00:41:58,600 --> 00:42:01,960 Speaker 2: mental activity can be redo to brain activity, and that 670 00:42:02,040 --> 00:42:05,400 Speaker 2: brain activity can be characterized by patterns of neural firings 671 00:42:05,440 --> 00:42:08,799 Speaker 2: and connections if you could replicate a human brain and 672 00:42:09,000 --> 00:42:11,960 Speaker 2: add in the right amount of random factors. I think 673 00:42:11,960 --> 00:42:15,320 Speaker 2: in theory, yes, AI music could be great. I don't 674 00:42:15,400 --> 00:42:17,920 Speaker 2: like the thought of it, but I mean that's the reality. 675 00:42:19,960 --> 00:42:22,680 Speaker 2: But for now, I don't think AI music is going 676 00:42:22,760 --> 00:42:25,240 Speaker 2: to be a threat to real music and real feeling. 677 00:42:25,719 --> 00:42:29,440 Speaker 2: But you nailed it on the head. Where I think 678 00:42:29,480 --> 00:42:32,840 Speaker 2: AI can be the most use is helping us to 679 00:42:32,960 --> 00:42:36,319 Speaker 2: find music that we like. Most of us listen to 680 00:42:36,360 --> 00:42:39,440 Speaker 2: a couple of thousand songs maximum, over and over and again, 681 00:42:39,960 --> 00:42:43,360 Speaker 2: and occasionally let in a few new ones, and those 682 00:42:43,440 --> 00:42:47,400 Speaker 2: can be comforting and rewarding, but they can dig deep 683 00:42:47,520 --> 00:42:50,400 Speaker 2: neural ruts such that we get tired of them and 684 00:42:50,440 --> 00:42:54,840 Speaker 2: we want something new. There are now two hundred million 685 00:42:55,480 --> 00:43:00,840 Speaker 2: songs across the streaming services, with a one hundred thousand 686 00:43:00,920 --> 00:43:04,520 Speaker 2: new ones being uploaded every day. So how do you 687 00:43:04,560 --> 00:43:08,880 Speaker 2: find what you like? The major streaming services have recommendation systems. 688 00:43:08,920 --> 00:43:14,160 Speaker 2: They don't work particularly well for me, But AI in 689 00:43:14,280 --> 00:43:19,520 Speaker 2: principle can extract hundreds of features latent features for music 690 00:43:19,920 --> 00:43:23,840 Speaker 2: and build a multi dimensional model, a higher dimensional manifold 691 00:43:23,880 --> 00:43:27,919 Speaker 2: of musical structure and DNA that might have one hundred 692 00:43:27,960 --> 00:43:31,279 Speaker 2: and fifty orthogonal dimensions, and set up a kind of 693 00:43:31,440 --> 00:43:35,319 Speaker 2: universe of music where things that are where each song 694 00:43:35,400 --> 00:43:37,359 Speaker 2: is like a planet, and planets that are near each 695 00:43:37,400 --> 00:43:40,640 Speaker 2: other are going to be similar, not just structurally, but 696 00:43:40,800 --> 00:43:43,440 Speaker 2: in an emotional space where they're likely to cause a 697 00:43:43,480 --> 00:43:48,640 Speaker 2: similar emotional reaction in a given listener, knowing what your 698 00:43:48,840 --> 00:43:52,400 Speaker 2: own personal space is, right, It's not going to be 699 00:43:52,440 --> 00:43:57,319 Speaker 2: something that is entirely objective and prescriptive and one one 700 00:43:57,400 --> 00:44:00,279 Speaker 2: hundred and fifty dimensional map for everybody. They'll have to 701 00:44:00,280 --> 00:44:02,440 Speaker 2: be to maps for each person, because your tastes are 702 00:44:02,480 --> 00:44:06,120 Speaker 2: different than mine. As my grandfather used to say, if 703 00:44:06,160 --> 00:44:08,799 Speaker 2: everybody liked the same things, they'd all want to get 704 00:44:08,840 --> 00:44:12,839 Speaker 2: with your grandma, I'm going. 705 00:44:12,840 --> 00:44:14,920 Speaker 1: To steal that line if you don't mind, when I'm 706 00:44:14,960 --> 00:44:17,880 Speaker 1: a grandfather. What have I not asked you that I 707 00:44:17,880 --> 00:44:18,640 Speaker 1: should ask you? 708 00:44:19,160 --> 00:44:23,160 Speaker 2: I wanted to ask you of the many successful books 709 00:44:23,800 --> 00:44:27,359 Speaker 2: and highly regarded books you've written, did you write them 710 00:44:27,560 --> 00:44:30,200 Speaker 2: because you wanted to read them and you couldn't find them? 711 00:44:30,280 --> 00:44:32,600 Speaker 2: Or did you write them because you just thought that 712 00:44:32,719 --> 00:44:34,720 Speaker 2: you had a different take on something than others? 713 00:44:35,160 --> 00:44:38,640 Speaker 1: Oh? No, I actually know precisely why I write my 714 00:44:38,680 --> 00:44:40,399 Speaker 1: books and who I'm writing them for, which is I'm 715 00:44:40,440 --> 00:44:44,200 Speaker 1: writing them for the younger version of me that didn't 716 00:44:44,280 --> 00:44:48,959 Speaker 1: know those particular facts when he was younger, but would 717 00:44:49,040 --> 00:44:51,839 Speaker 1: have loved that. That's who I'm always writing for. 718 00:44:52,560 --> 00:44:55,480 Speaker 2: You know, Joni Mitchell taught me about something about songwriting 719 00:44:55,520 --> 00:44:59,640 Speaker 2: that was really transformative and my songs I've been writing 720 00:44:59,640 --> 00:45:01,920 Speaker 2: since I I was eighteen, but since Joni told me 721 00:45:01,960 --> 00:45:04,919 Speaker 2: this in two thousand and five, I not only did 722 00:45:04,920 --> 00:45:07,279 Speaker 2: my current songs get better, but I went back to 723 00:45:07,360 --> 00:45:10,680 Speaker 2: the old ones and started rewriting them. And what she 724 00:45:10,760 --> 00:45:13,440 Speaker 2: said was, you don't write a song because you figured 725 00:45:13,480 --> 00:45:17,080 Speaker 2: something out. You write it in order to figure something out. 726 00:45:24,960 --> 00:45:30,280 Speaker 1: That was my friend and colleague Dan Levitton, neuroscientist, cognitive psychologist, writer, musician, 727 00:45:30,400 --> 00:45:33,600 Speaker 1: and record producer. So what has emerged from a couple 728 00:45:33,600 --> 00:45:36,680 Speaker 1: of decades of the study of music in the brain 729 00:45:37,400 --> 00:45:41,880 Speaker 1: is that music can't be understood simply as different pitches 730 00:45:41,960 --> 00:45:46,920 Speaker 1: hitting the ear. The sounds of music trigger a neural 731 00:45:47,360 --> 00:45:51,279 Speaker 1: hurricane of spikes on the inside of the skull, and 732 00:45:51,280 --> 00:45:54,040 Speaker 1: this correlates with the pitch in rhythm and timbre, but 733 00:45:54,120 --> 00:45:59,440 Speaker 1: it jins up a silent neural symphony, and that activity 734 00:45:59,600 --> 00:46:04,040 Speaker 1: is closely tied in with our emotions and it pulls 735 00:46:04,320 --> 00:46:08,600 Speaker 1: strings to our memories. We don't just hear music, we 736 00:46:08,800 --> 00:46:13,319 Speaker 1: actually resonate with it neurally. So the brain doesn't just 737 00:46:13,600 --> 00:46:18,560 Speaker 1: process music, it magnifies it, which shows us once again 738 00:46:19,000 --> 00:46:22,240 Speaker 1: that the ultimate instrument is not the piano or the guitar, 739 00:46:22,800 --> 00:46:29,520 Speaker 1: but the one between our ears. Go to eagleman dot 740 00:46:29,560 --> 00:46:32,279 Speaker 1: com slash podcast for more information and to find for