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These are the user uploaded subtitles that are being translated: 1 00:00:01,100 --> 00:00:03,136 ♪ ♪ 2 00:00:04,970 --> 00:00:07,874 ♪ ♪ 3 00:00:07,907 --> 00:00:10,109 ANIL SETH: The brain is one of the most complex objects 4 00:00:10,142 --> 00:00:13,246 that we know of in the universe. 5 00:00:13,279 --> 00:00:16,382 BOBBY KASTHURI: There are more connections in your brain 6 00:00:16,415 --> 00:00:20,420 than there are stars in the Milky Way galaxy. 7 00:00:20,453 --> 00:00:23,823 So, we literally walk around with about 10,000 galaxies' 8 00:00:23,856 --> 00:00:25,758 worth of neuronal connections 9 00:00:25,791 --> 00:00:27,260 in one of our brains. 10 00:00:27,293 --> 00:00:29,262 HEATHER BERLIN: That vast web of connections creates you. 11 00:00:29,295 --> 00:00:30,963 But how? 12 00:00:30,996 --> 00:00:34,300 NANCY KANWISHER: Figuring out how the brain implements the mind 13 00:00:34,333 --> 00:00:37,103 is a massive challenge. 14 00:00:37,136 --> 00:00:38,404 ♪ ♪ 15 00:00:38,437 --> 00:00:39,672 SETH: It seems as though 16 00:00:39,705 --> 00:00:41,841 the world just pours itself into the mind 17 00:00:41,874 --> 00:00:44,644 through the transparent windows of the eyes 18 00:00:44,677 --> 00:00:46,813 and the ears and, and all our other senses. 19 00:00:49,381 --> 00:00:51,717 ♪ ♪ 20 00:00:51,750 --> 00:00:53,252 BERLIN: But is what we see, 21 00:00:53,285 --> 00:00:54,987 hear, 22 00:00:55,020 --> 00:00:57,824 and feel real? 23 00:00:57,857 --> 00:01:00,059 You might think that the reality 24 00:01:00,092 --> 00:01:02,829 outside is actually what you're perceiving. 25 00:01:02,862 --> 00:01:05,865 And the answer is no, it really isn't. 26 00:01:05,898 --> 00:01:07,733 Almost at the very first moment, 27 00:01:07,766 --> 00:01:09,268 we are transforming reality. 28 00:01:09,301 --> 00:01:10,770 It feels so real 29 00:01:10,803 --> 00:01:12,405 because we don't know better. 30 00:01:12,438 --> 00:01:14,273 Think about illusions. 31 00:01:14,306 --> 00:01:15,875 ROSA LAFER-SOUSA: Do you remember the dress? 32 00:01:15,908 --> 00:01:18,344 Of course-- it's like a celebrity, the dress. 33 00:01:18,377 --> 00:01:19,946 A polarizing debate 34 00:01:19,979 --> 00:01:21,981 that took over the internet. 35 00:01:22,014 --> 00:01:23,516 White and gold. 36 00:01:22,014 --> 00:01:23,516 Blue and black. 37 00:01:23,549 --> 00:01:26,152 SETH: Illusions are fascinating; 38 00:01:26,185 --> 00:01:27,286 they're like fractures in the matrix. 39 00:01:27,319 --> 00:01:28,287 BERLIN: Ow! 40 00:01:28,320 --> 00:01:30,456 Isn't that interesting? 41 00:01:28,320 --> 00:01:30,456 Whoa! 42 00:01:30,489 --> 00:01:32,291 They reveal to us that the way 43 00:01:32,324 --> 00:01:35,695 we perceive things isn't necessarily the way they are. 44 00:01:35,728 --> 00:01:39,031 BERLIN: Could you be the biggest illusion of all? 45 00:01:39,064 --> 00:01:42,835 SUSANA MARTINEZ-CONDE: Your sense of who you are is an illusion 46 00:01:42,868 --> 00:01:45,105 as everything else; you're no exception. 47 00:01:46,106 --> 00:01:49,542 BERLIN: "Your Brain: Perception Deception." 48 00:01:49,575 --> 00:01:51,511 Right now, on "NOVA." 49 00:01:51,544 --> 00:01:56,182 ♪ ♪ 50 00:02:03,756 --> 00:02:05,391 MAN: Okay, rolling. 51 00:02:05,424 --> 00:02:11,030 ♪ ♪ 52 00:02:26,679 --> 00:02:29,082 Take three. 53 00:02:32,384 --> 00:02:35,254 BERLIN: Have you ever thought about what's real? 54 00:02:35,287 --> 00:02:37,624 (echoing) 55 00:02:44,029 --> 00:02:46,832 Somehow the whole world out there gets inside my head. 56 00:02:46,865 --> 00:02:50,570 How do I know what I see, what I hear, 57 00:02:50,603 --> 00:02:53,306 what I feel is right? 58 00:02:53,339 --> 00:02:55,541 It's a question that's fascinated me 59 00:02:55,574 --> 00:02:58,711 ever since I was a little girl. 60 00:02:58,744 --> 00:03:01,147 I couldn't sleep one night, 61 00:03:01,180 --> 00:03:04,551 and I had this thought for the first time: 62 00:03:08,520 --> 00:03:10,890 And then I thought, well, even if I don't have a body, 63 00:03:10,923 --> 00:03:14,360 can I at least keep my own inner thoughts? 64 00:03:14,393 --> 00:03:16,629 So I asked my dad the next day, 65 00:03:16,662 --> 00:03:19,532 "Dad, where do my thoughts come from?" 66 00:03:19,565 --> 00:03:23,169 And he said, "They come from your brain." 67 00:03:23,202 --> 00:03:24,570 (explosion echoes) 68 00:03:24,603 --> 00:03:26,405 ♪ ♪ 69 00:03:26,438 --> 00:03:29,309 "Your brain." I was hooked. 70 00:03:30,342 --> 00:03:32,278 This bag of jelly between my ears, 71 00:03:32,311 --> 00:03:34,013 how does it work? 72 00:03:34,046 --> 00:03:35,982 STANISLAS DEHAENE: I think it's one of the ultimate mysteries. 73 00:03:36,015 --> 00:03:38,718 How matter becomes thought. 74 00:03:40,286 --> 00:03:42,922 ANDRÉ FENTON: To answer that question would be perhaps 75 00:03:42,955 --> 00:03:44,958 the highest human achievement to date. 76 00:03:46,425 --> 00:03:47,893 DANIELA SCHILLER: I mean, forget about scientific quest. 77 00:03:47,926 --> 00:03:49,729 It's a human quest. 78 00:04:03,542 --> 00:04:06,779 ♪ ♪ 79 00:04:06,812 --> 00:04:09,548 BERLIN: To find answers, 80 00:04:09,581 --> 00:04:12,719 I became a neuroscientist and a psychologist. 81 00:04:16,922 --> 00:04:19,025 I'm Heather Berlin, 82 00:04:19,058 --> 00:04:22,028 and my journey to understand my brain begins with a question. 83 00:04:23,529 --> 00:04:25,531 How does the world out there, 84 00:04:25,564 --> 00:04:28,200 with all its beauty and complexity, 85 00:04:28,233 --> 00:04:30,503 get inside our heads? 86 00:04:30,536 --> 00:04:33,739 ♪ ♪ 87 00:04:33,772 --> 00:04:35,041 Think about it. 88 00:04:35,074 --> 00:04:37,143 Imagine for a second you're a brain, 89 00:04:37,176 --> 00:04:39,578 sealed inside your skull. 90 00:04:39,611 --> 00:04:42,882 There's no light, no sound. 91 00:04:44,383 --> 00:04:45,985 KASTHURI: You're a massive collection 92 00:04:46,018 --> 00:04:48,788 of billions and billions of cells that are living 93 00:04:48,821 --> 00:04:51,490 in this weird pond that is entirely devoid 94 00:04:51,523 --> 00:04:54,260 of all of the sensations, and that somehow, 95 00:04:54,293 --> 00:04:57,663 through chemistry and electricity, all of these 96 00:04:57,696 --> 00:05:00,032 perceptions and memories of the world 97 00:05:00,065 --> 00:05:01,701 originate in our brains. 98 00:05:03,335 --> 00:05:05,037 BERLIN: All brains-- from the tiny fish 99 00:05:05,070 --> 00:05:08,341 to the enormous elephant-- contain microscopic cells 100 00:05:08,374 --> 00:05:12,345 called neurons, and one of their jobs is to translate input 101 00:05:12,378 --> 00:05:14,580 from the external world, whether that's light, 102 00:05:14,613 --> 00:05:17,249 heat, sound, or pressure, for instance, 103 00:05:17,282 --> 00:05:21,220 into electrochemical signals the organism can use to act. 104 00:05:21,253 --> 00:05:23,489 What might be surprising to you is that 105 00:05:23,522 --> 00:05:26,058 as neurons process sensory signals, 106 00:05:26,091 --> 00:05:28,527 they create an edited version of reality, 107 00:05:28,560 --> 00:05:32,031 even on the most basic level. 108 00:05:32,064 --> 00:05:35,768 KASTHURI: We're deciding to throw away 99% of the world. 109 00:05:35,801 --> 00:05:38,838 Almost at the very first moment, we are transforming reality 110 00:05:38,871 --> 00:05:40,940 into something we could use. 111 00:05:40,973 --> 00:05:44,777 BERLIN: Neurons transform reality by competing with each other. 112 00:05:44,810 --> 00:05:47,980 When a creature touches, smells, sees, or hears something, 113 00:05:48,013 --> 00:05:51,517 its sensory neurons fire; some a little, some a lot, 114 00:05:51,550 --> 00:05:54,186 depending on where the physical signal is strongest. 115 00:05:54,219 --> 00:05:56,822 But follow those signals down towards its brain, 116 00:05:56,855 --> 00:05:59,859 you'll see that the weaker ones get stamped out. 117 00:06:01,193 --> 00:06:03,028 For simple brains, say, the brain of a crab, 118 00:06:03,061 --> 00:06:07,266 a diffuse light to the eye becomes a sharp beam. 119 00:06:07,299 --> 00:06:10,336 For more complex brains like ours, 120 00:06:10,369 --> 00:06:12,171 it's in part what makes you think 121 00:06:12,204 --> 00:06:15,741 that these two squares are completely different colors, 122 00:06:15,774 --> 00:06:18,944 but actually, they're identical. 123 00:06:18,977 --> 00:06:24,283 ♪ ♪ 124 00:06:24,316 --> 00:06:26,585 MARTINEZ-CONDE: Think about illusions. 125 00:06:26,618 --> 00:06:28,921 First, they're a lot of fun, but as neuroscientists... 126 00:06:28,954 --> 00:06:31,357 Whoa... 127 00:06:31,390 --> 00:06:33,826 MARTINEZ-CONDE: ...illusions are very important to us. 128 00:06:33,859 --> 00:06:38,164 Because of this discrepancy between objective reality 129 00:06:38,197 --> 00:06:41,367 and subjective perception, we can use these illusions 130 00:06:41,400 --> 00:06:45,571 as a handle to try to understand what the brain 131 00:06:45,604 --> 00:06:48,507 is doing all the time. 132 00:06:48,540 --> 00:06:51,143 BERLIN: Susana Martinez-Conde, along with her partner 133 00:06:51,176 --> 00:06:52,511 and collaborator Stephen Macknik, 134 00:06:52,544 --> 00:06:55,181 are among the world's preeminent experts 135 00:06:55,214 --> 00:06:57,149 on illusions and perception, 136 00:06:57,182 --> 00:07:00,152 and what they tell us about how the brain works. 137 00:07:00,185 --> 00:07:02,221 Ha, now what? 138 00:07:02,254 --> 00:07:03,522 (both laughing) 139 00:07:03,555 --> 00:07:05,024 MARTINEZ-CONDE: To give a different example, 140 00:07:05,057 --> 00:07:09,395 Adelson's checkerboard illusion, this is so striking because 141 00:07:09,428 --> 00:07:14,300 you see some of the checks as dark and others as bright, 142 00:07:14,333 --> 00:07:18,904 but you realize that it is exactly the same shade of gray. 143 00:07:18,937 --> 00:07:20,506 BERLIN: Don't believe it? 144 00:07:20,539 --> 00:07:23,642 Look at the squares labeled A and B. 145 00:07:23,675 --> 00:07:26,645 A looks darker, right? 146 00:07:26,678 --> 00:07:29,048 Wrong-- that's the illusion. 147 00:07:29,081 --> 00:07:32,251 That's because your brain is adjusting for the shadow. 148 00:07:32,284 --> 00:07:34,887 MARTINEZ-CONDE: What's happening is that your brain is considering 149 00:07:34,920 --> 00:07:39,391 the light source and basically subtracting that light source 150 00:07:39,424 --> 00:07:41,594 from your resulting perception. 151 00:07:41,627 --> 00:07:44,497 Your brain is performing an interpretation, 152 00:07:44,530 --> 00:07:47,700 a shortcut, if you will, to arrive 153 00:07:47,733 --> 00:07:49,635 at a perception. 154 00:07:49,668 --> 00:07:53,038 BERLIN: If the brain's shortcuts distort reality this much, 155 00:07:53,071 --> 00:07:56,876 how much of the world are we really seeing? 156 00:07:56,909 --> 00:07:58,511 STEPHEN MACKNIK: What you need to understand 157 00:07:58,544 --> 00:08:02,448 is that we really can't see most of the world around us. 158 00:08:02,481 --> 00:08:04,350 We're effectively blind 159 00:08:04,383 --> 00:08:06,886 to 99.9% of the world around us at any given time. 160 00:08:06,919 --> 00:08:09,455 If you hold out your thumb at arm's length... 161 00:08:09,488 --> 00:08:11,724 Mm-hmm. 162 00:08:09,488 --> 00:08:11,724 And you straighten your elbow and you look at your thumbnail, 163 00:08:11,757 --> 00:08:14,660 your thumbnail is about one degree of visual angle here, 164 00:08:14,693 --> 00:08:17,663 and it turns out that that's the only place 165 00:08:17,696 --> 00:08:19,398 we can actually see with 20/20 vision. 166 00:08:19,431 --> 00:08:21,967 Wow. 167 00:08:19,431 --> 00:08:21,967 And everywhere else, we're legally blind. 168 00:08:22,000 --> 00:08:24,904 BERLIN: It might sound hard to believe, 169 00:08:24,937 --> 00:08:27,473 but human vision is really like this. 170 00:08:27,506 --> 00:08:29,408 You actually only see detail 171 00:08:29,441 --> 00:08:31,610 in about one percent of your visual field. 172 00:08:31,643 --> 00:08:34,346 That's because only a tiny portion of the world 173 00:08:34,379 --> 00:08:37,616 can be processed in detail by the retina. 174 00:08:37,649 --> 00:08:40,753 It feels like I'm seeing the whole world in 20/20 vision. 175 00:08:40,786 --> 00:08:44,123 And it's almost all completely made up in your brain, 176 00:08:44,156 --> 00:08:47,526 based on assumptions and models of how the world works 177 00:08:47,559 --> 00:08:50,129 and just a tiny bit of high-quality visual information. 178 00:08:50,162 --> 00:08:51,830 Let me demonstrate this to you. 179 00:08:51,863 --> 00:08:53,766 I know it's kind of hard to believe... 180 00:08:51,863 --> 00:08:53,766 Mm-hmm. 181 00:08:53,799 --> 00:08:55,568 ...because you've been having your whole life 182 00:08:55,601 --> 00:08:56,602 where you feel like everything's continuous. 183 00:08:55,601 --> 00:08:56,602 Yeah, show me the data. 184 00:08:56,635 --> 00:08:58,070 (laughs) 185 00:08:56,635 --> 00:08:58,070 Show me the evidence. 186 00:08:58,103 --> 00:09:00,506 Let's look at an eye-tracker and look at your eyes 187 00:09:00,539 --> 00:09:01,874 and how they actually work. 188 00:09:01,907 --> 00:09:04,677 And if you put your head in this headrest... 189 00:09:04,710 --> 00:09:07,313 Mm-hmm. 190 00:09:04,710 --> 00:09:07,313 ...we'll point the camera at your eyeballs 191 00:09:07,346 --> 00:09:08,714 and we'll actually be able to see 192 00:09:08,747 --> 00:09:11,417 where your eyeballs point during this demonstration. 193 00:09:11,450 --> 00:09:14,253 Feels like "Clockwork Orange." (chuckles) 194 00:09:14,286 --> 00:09:16,589 "Buck lived at a big house 195 00:09:16,622 --> 00:09:18,991 in the sun-kissed Santa Clara Valley." 196 00:09:20,058 --> 00:09:22,461 BERLIN: First up, a reading demo. 197 00:09:22,494 --> 00:09:24,830 Though most of your screen may be filled with Xs, 198 00:09:24,863 --> 00:09:27,499 to me, it just feels like normal reading. 199 00:09:27,532 --> 00:09:29,301 I barely see the Xs, 200 00:09:29,334 --> 00:09:31,103 and that's because the display of letters 201 00:09:31,136 --> 00:09:33,872 is tied to my eye movements. 202 00:09:33,905 --> 00:09:35,207 BERLIN: Well, it's just, the words are being 203 00:09:35,240 --> 00:09:37,042 revealed depending on where I look. 204 00:09:37,075 --> 00:09:39,178 That's right, so as you move your eyes... 205 00:09:39,211 --> 00:09:40,980 So weird. 206 00:09:39,211 --> 00:09:40,980 ...the words are revealed to you. 207 00:09:41,013 --> 00:09:43,649 But we don't move our eyes in the same way you do. 208 00:09:43,682 --> 00:09:46,585 So we just see a bunch of Xs most of the time. 209 00:09:46,618 --> 00:09:49,521 BERLIN: What turns out to be critical is my eye movements. 210 00:09:49,554 --> 00:09:53,525 MACKNIK: So our eye movements program what part of this 211 00:09:53,558 --> 00:09:55,327 high-quality piece of visual real estate 212 00:09:55,360 --> 00:09:57,129 we're going to put where and at what time. 213 00:09:57,162 --> 00:10:00,399 BERLIN: The human eye moves about three times per second. 214 00:10:00,432 --> 00:10:03,569 We take it for granted, but without these movements, 215 00:10:03,602 --> 00:10:05,237 we'd be basically blind, 216 00:10:05,270 --> 00:10:07,272 as Steve is about to show me. 217 00:10:07,305 --> 00:10:09,475 MACKNIK: In this demonstration, it's the opposite. 218 00:10:09,508 --> 00:10:12,511 Here we're blocking what you can possibly see, right? 219 00:10:12,544 --> 00:10:15,447 BERLIN: Though you may see a whole scene with a square moving around, 220 00:10:15,480 --> 00:10:17,716 all I see is the square! 221 00:10:17,749 --> 00:10:20,185 I can tell something is around the edges, but it's blurry. 222 00:10:20,218 --> 00:10:23,656 Whenever I try to look, the square moves with my eyes 223 00:10:23,689 --> 00:10:25,290 and it's blocked. 224 00:10:25,323 --> 00:10:27,526 BERLIN: This is so frustrating, this one. 225 00:10:27,559 --> 00:10:30,696 Who has their hand up in this image? 226 00:10:32,030 --> 00:10:35,334 BERLIN: Uh... I think that guy down there? 227 00:10:35,367 --> 00:10:37,603 MACKNIK: That's right, but it's very hard for you to see, 228 00:10:37,636 --> 00:10:39,538 right? 229 00:10:37,636 --> 00:10:39,538 Every time I look at him, it, yeah. 230 00:10:39,571 --> 00:10:43,342 It disappears because this block, it blocks it. 231 00:10:43,375 --> 00:10:45,611 MACKNIK: This actually is interesting 232 00:10:45,644 --> 00:10:48,480 because it's in high-quality vision wherever you look, 233 00:10:48,513 --> 00:10:51,083 but it's blurry in the surround. 234 00:10:51,116 --> 00:10:53,786 BERLIN: Now the scene looks normal to me, 235 00:10:53,819 --> 00:10:55,521 but mostly blurry to you 236 00:10:55,554 --> 00:10:57,690 because your eye movements don't match mine. 237 00:10:57,723 --> 00:10:59,291 MACKNIK: Wherever you happen to look, 238 00:10:59,324 --> 00:11:02,761 you have high-quality image processing happening 239 00:11:02,794 --> 00:11:05,297 and the surround is completely blurry. 240 00:11:05,330 --> 00:11:09,068 This kind of represents exactly what your visual system 241 00:11:09,101 --> 00:11:11,904 looks like all the time anyway. 242 00:11:11,937 --> 00:11:14,273 So why would our brains be built this way? 243 00:11:14,306 --> 00:11:16,008 Well, think about what the alternative is. 244 00:11:16,041 --> 00:11:17,276 What if we didn't have eye movements? 245 00:11:17,309 --> 00:11:18,577 Well, if we didn't have eye movements, 246 00:11:18,610 --> 00:11:20,612 and we just wanted to see the entire world, 247 00:11:20,645 --> 00:11:21,914 we'd need to have our retinas 248 00:11:21,947 --> 00:11:23,682 see everything in very high quality. 249 00:11:23,715 --> 00:11:25,918 Our brains would be 600 times bigger, 250 00:11:25,951 --> 00:11:28,721 and you gotta remember, the visual system's our best sense. 251 00:11:28,754 --> 00:11:30,355 This is our richest sense. 252 00:11:30,388 --> 00:11:33,292 So our other senses are, are even more impoverished. 253 00:11:33,325 --> 00:11:36,962 BERLIN: Here's how your brain really sees the world. 254 00:11:36,995 --> 00:11:38,697 It's easy to think it's like this. 255 00:11:38,730 --> 00:11:41,600 You open your eyes and the whole world pours in. 256 00:11:41,633 --> 00:11:43,569 But really, it's like this. 257 00:11:45,904 --> 00:11:48,474 Your eyes sample tiny pieces of the world 258 00:11:48,507 --> 00:11:52,511 and the brain fills in the rest-- constantly, all the time. 259 00:11:52,544 --> 00:11:53,912 KANWISHER: We feel like we have 260 00:11:53,945 --> 00:11:57,549 this incredibly rich, wide, full, detailed percept 261 00:11:57,582 --> 00:11:59,284 of what's going on moment to moment, 262 00:11:59,317 --> 00:12:01,553 and that's probably pretty illusory. 263 00:12:01,586 --> 00:12:04,089 What we're actually aware of is a tiny subset 264 00:12:04,122 --> 00:12:06,225 of the information that comes in through our eyes. 265 00:12:06,258 --> 00:12:08,227 BERLIN: Don't believe it? 266 00:12:08,260 --> 00:12:09,728 Consider this: 267 00:12:09,761 --> 00:12:12,564 your optic nerve is what connects your eye to your brain, 268 00:12:12,597 --> 00:12:15,200 and its location near the center of your retina 269 00:12:15,233 --> 00:12:17,136 effectively creates a blind spot 270 00:12:17,169 --> 00:12:19,304 near the center of your visual field. 271 00:12:19,337 --> 00:12:23,275 And yet, you don't experience the blind spot-- why? 272 00:12:23,308 --> 00:12:26,011 The brain samples the area near the blind spot 273 00:12:26,044 --> 00:12:29,014 and fills in the gap with its best guess. 274 00:12:29,047 --> 00:12:30,682 KASTHURI: It's probably not fair 275 00:12:30,715 --> 00:12:32,985 to say that we completely confabulate the world, 276 00:12:33,018 --> 00:12:35,154 it's just that we probably represent 277 00:12:35,187 --> 00:12:38,056 one percent of it at any particular moment in time. 278 00:12:38,089 --> 00:12:41,393 So, it's a constant updating between what I see with, 279 00:12:41,426 --> 00:12:44,630 versus what I remember, versus what I expect. 280 00:12:44,663 --> 00:12:47,299 And it's that dance between those three 281 00:12:47,332 --> 00:12:49,835 that actually gives us our sense of reality. 282 00:12:49,868 --> 00:12:53,172 BERLIN: And amazingly, that edited reality-- 283 00:12:53,205 --> 00:12:54,873 despite its limitations-- 284 00:12:54,906 --> 00:12:56,742 serves us quite well. 285 00:12:56,775 --> 00:12:58,811 KASTHURI: You might ask, "If I'm just keeping track 286 00:12:58,844 --> 00:13:00,345 "of one percent of the information in the world, 287 00:13:00,378 --> 00:13:02,581 how can I drive a car?" 288 00:13:02,614 --> 00:13:05,150 And it turns out that first one percent 289 00:13:05,183 --> 00:13:06,819 of the information that comes in from the world 290 00:13:06,852 --> 00:13:08,687 is actually an enormous amount of information. (chuckling) 291 00:13:08,720 --> 00:13:11,423 If we had to actually pay attention 292 00:13:11,456 --> 00:13:14,960 to everything on the road at the, at one particular time, 293 00:13:14,993 --> 00:13:17,396 it would take minutes, maybe even longer, 294 00:13:17,429 --> 00:13:19,198 before I decide to turn the wheel right 295 00:13:19,231 --> 00:13:21,633 or to turn the wheel left. 296 00:13:21,666 --> 00:13:23,168 ♪ ♪ 297 00:13:23,201 --> 00:13:25,871 BERLIN: By understanding how my senses really work, 298 00:13:25,904 --> 00:13:27,739 I'm getting a peek behind the curtain: 299 00:13:27,772 --> 00:13:31,911 what my brain is really up to outside of my awareness. 300 00:13:33,612 --> 00:13:37,449 MARTINEZ-CONDE: Based on this very tiny amount of information, 301 00:13:37,482 --> 00:13:41,053 we construct this grand simulation 302 00:13:41,086 --> 00:13:44,857 of the visual world around us. 303 00:13:44,890 --> 00:13:48,160 It feels so real because we don't know better. 304 00:13:49,294 --> 00:13:52,264 BERLIN: And most of the time, we all agree on that simulation. 305 00:13:52,297 --> 00:13:56,401 It's when we don't that we can learn something. 306 00:13:56,434 --> 00:13:58,871 So do you remember the dress? 307 00:13:56,434 --> 00:13:58,871 Of course. 308 00:13:58,904 --> 00:14:02,307 Did you see this dress or this one? 309 00:14:02,340 --> 00:14:03,909 It's a simple question, 310 00:14:03,942 --> 00:14:06,345 but the answer has divided friends and family. 311 00:14:06,378 --> 00:14:08,180 White and gold. 312 00:14:06,378 --> 00:14:08,180 Blue and black. 313 00:14:08,213 --> 00:14:10,015 I remember it caused quite the stir, right? 314 00:14:10,048 --> 00:14:11,383 Massive stir. 315 00:14:11,416 --> 00:14:12,684 A polarizing debate 316 00:14:12,717 --> 00:14:14,586 that took over the internet. 317 00:14:14,619 --> 00:14:16,188 ♪ ♪ 318 00:14:16,221 --> 00:14:19,992 LAFER-SOUSA: People had existential crises over this image. 319 00:14:20,025 --> 00:14:21,560 People tweeted things like, 320 00:14:21,593 --> 00:14:23,262 "If that's not white and gold, 321 00:14:23,295 --> 00:14:24,897 my life has been a lie." 322 00:14:24,930 --> 00:14:26,865 Swear on your mother's grave. 323 00:14:26,898 --> 00:14:28,467 Because that dress is white and gold. 324 00:14:28,500 --> 00:14:29,635 Out of her (bleep) mind. 325 00:14:29,668 --> 00:14:31,236 LAFER-SOUSA: Massive arguments. 326 00:14:31,269 --> 00:14:33,839 I watched videos of people screaming at each other. 327 00:14:33,872 --> 00:14:35,240 GRAYSON DOLAN: This is white, dude! 328 00:14:35,273 --> 00:14:37,309 ETHAN DOLAN: White? That is dark blue! 329 00:14:37,342 --> 00:14:38,777 It's purple-blue! 330 00:14:38,810 --> 00:14:40,479 LAFER-SOUSA: I bet there was a divorce here or there 331 00:14:40,512 --> 00:14:43,181 over this image. 332 00:14:43,214 --> 00:14:45,150 BERLIN: So when you first saw that dress, 333 00:14:45,183 --> 00:14:47,953 as a, as a vision scientist, what did you think? 334 00:14:47,986 --> 00:14:50,222 Well, when I first saw the dress, 335 00:14:50,255 --> 00:14:52,224 I thought it was blue and black. 336 00:14:52,257 --> 00:14:54,660 And I thought that the internet was yanking my chain. 337 00:14:54,693 --> 00:14:57,362 Right-- to get the goat of vision neuroscientists. 338 00:14:57,395 --> 00:14:59,097 Sure. 339 00:14:57,395 --> 00:14:59,097 (chuckles) 340 00:14:59,130 --> 00:15:01,166 But in the morning, when I looked at my phone, 341 00:15:01,199 --> 00:15:03,268 I saw white and gold. 342 00:15:03,301 --> 00:15:05,304 And now, of course, I was obsessed. 343 00:15:05,337 --> 00:15:07,539 So I said, "Well, if this is an ambiguous image, 344 00:15:07,572 --> 00:15:09,841 all I have to do is disambiguate it." 345 00:15:09,874 --> 00:15:12,010 So I set to work, I got into Photoshop, 346 00:15:12,043 --> 00:15:16,048 cut out the dress, put it into a scene with lots of rich cues. 347 00:15:16,081 --> 00:15:17,282 BERLIN: Hm. 348 00:15:17,315 --> 00:15:18,850 LAFER-SOUSA: And all of a sudden, boom: 349 00:15:18,883 --> 00:15:20,786 you can see the dress is white and gold. 350 00:15:20,819 --> 00:15:22,487 Wow. 351 00:15:20,819 --> 00:15:22,487 Now, the pixels, 352 00:15:22,520 --> 00:15:23,989 the pixels that make up the dress there, 353 00:15:24,022 --> 00:15:26,358 are identical to the original image. 354 00:15:24,022 --> 00:15:26,358 Okay. 355 00:15:26,391 --> 00:15:28,927 BERLIN: Now, this doesn't work for everybody, 356 00:15:28,960 --> 00:15:31,563 but for most, the visual context 357 00:15:31,596 --> 00:15:33,799 can make all the difference. 358 00:15:33,832 --> 00:15:36,201 LAFER-SOUSA: What's different here is, her skin is tinted blue, 359 00:15:36,234 --> 00:15:39,338 the background has blue light cast on it, 360 00:15:39,371 --> 00:15:42,040 she's standing in the shadow of that cube, 361 00:15:42,073 --> 00:15:45,177 and so your brain says, "Aha, I need to ignore 362 00:15:45,210 --> 00:15:46,812 "some amount of blue light 363 00:15:46,845 --> 00:15:49,481 "that is in this signal that's hitting my eye 364 00:15:49,514 --> 00:15:51,650 and render this as white and gold." 365 00:15:51,683 --> 00:15:54,353 BERLIN: And if we flip things around? 366 00:15:54,386 --> 00:15:57,990 LAFER-SOUSA: Same dress, pasted it into this other scene. 367 00:15:58,023 --> 00:16:01,393 Her skin is tinted yellow, the background has a yellow cast, 368 00:16:01,426 --> 00:16:03,996 she's standing no longer in the shadow but in the light. 369 00:16:04,029 --> 00:16:05,163 Boom! Blue and black. 370 00:16:05,196 --> 00:16:07,199 Amazing, that's really amazing. 371 00:16:07,232 --> 00:16:08,500 So again, the dress, 372 00:16:08,533 --> 00:16:09,868 the pixels are exactly the same. 373 00:16:09,901 --> 00:16:11,303 LAFER-SOUSA: Identical. 374 00:16:11,336 --> 00:16:13,305 BERLIN: The dress is a powerful example 375 00:16:13,338 --> 00:16:15,574 of how color really works in the brain. 376 00:16:15,607 --> 00:16:17,809 Does that mean we're creating color 377 00:16:17,842 --> 00:16:21,313 in our mind, or does color actually exist in the world? 378 00:16:21,346 --> 00:16:23,281 Color takes place in the brain, 379 00:16:23,314 --> 00:16:25,717 and I've prepared a little illusion for you 380 00:16:25,750 --> 00:16:27,753 that should convince you of this. 381 00:16:27,786 --> 00:16:30,756 LAFER-SOUSA: So I have a picture of four cars here. 382 00:16:30,789 --> 00:16:33,859 I want you to tell me, what color are these cars? 383 00:16:33,892 --> 00:16:36,161 Let's start on the top left. 384 00:16:33,892 --> 00:16:36,161 BERLIN: Okay, so the one on the left looks red, 385 00:16:36,194 --> 00:16:38,830 then the one next to it looks blue. 386 00:16:38,863 --> 00:16:40,499 I'd say the one, the bottom there, 387 00:16:40,532 --> 00:16:41,767 bottom left looks green, 388 00:16:41,800 --> 00:16:44,669 and then the one next to it looks orange. 389 00:16:44,702 --> 00:16:45,971 LAFER-SOUSA: Okay. 390 00:16:44,702 --> 00:16:45,971 Mm-hmm. 391 00:16:46,004 --> 00:16:47,105 What if I told you 392 00:16:47,138 --> 00:16:50,108 that all of those pixels are not only gray, 393 00:16:50,141 --> 00:16:52,511 they are the same gray? 394 00:16:52,544 --> 00:16:54,212 BERLIN: How is this possible? 395 00:16:54,245 --> 00:16:57,682 It's because the light that enters your eyes, 396 00:16:57,715 --> 00:17:01,486 contrary to what you might have learned in school, is not color. 397 00:17:01,519 --> 00:17:04,556 Color is an interpretation of your brain. 398 00:17:04,589 --> 00:17:06,458 Here's how it works. 399 00:17:06,491 --> 00:17:08,627 Light shines on the world 400 00:17:08,660 --> 00:17:11,863 and bounces off objects-- this part you know. 401 00:17:11,896 --> 00:17:13,098 And light comes in 402 00:17:13,131 --> 00:17:14,366 different wavelengths, 403 00:17:14,399 --> 00:17:16,735 each corresponding to a different color. 404 00:17:16,768 --> 00:17:18,437 What you might not have heard in school 405 00:17:18,470 --> 00:17:20,372 is how those wavelengths change 406 00:17:20,405 --> 00:17:22,307 when they hit different surfaces-- 407 00:17:22,340 --> 00:17:24,576 rough, smooth, wet, et cetera. 408 00:17:24,609 --> 00:17:26,878 This signal that gets into your eye 409 00:17:26,911 --> 00:17:30,115 is actually a product of the reflective properties 410 00:17:30,148 --> 00:17:33,085 of the object and the wavelength of light hitting it. 411 00:17:33,118 --> 00:17:35,287 Then that signal is focused on the retina, 412 00:17:35,320 --> 00:17:37,055 the back of the eye, 413 00:17:37,088 --> 00:17:40,759 where we have about 130 million light-sensitive cells. 414 00:17:40,792 --> 00:17:43,462 Three types called cones are involved in color, 415 00:17:43,495 --> 00:17:46,398 each sensitive to different wavelengths of light: 416 00:17:46,431 --> 00:17:48,467 long, medium, and short. 417 00:17:48,500 --> 00:17:51,036 But that light still isn't color. 418 00:17:51,069 --> 00:17:53,271 For that to happen, our brain has to take 419 00:17:53,304 --> 00:17:55,340 that three-piece code from the retina 420 00:17:55,373 --> 00:17:59,945 and use the relative response of the cones to encode color. 421 00:17:59,978 --> 00:18:03,982 It's not until that signal gets to an area called V4 422 00:18:04,015 --> 00:18:06,718 that we get a neural representation of color 423 00:18:06,751 --> 00:18:09,921 that corresponds to our perceptual experience. 424 00:18:09,954 --> 00:18:12,424 So why would our brains be built this way? 425 00:18:12,457 --> 00:18:14,893 Well, if our brains weren't built this way, 426 00:18:14,926 --> 00:18:19,364 objects would appear to change in color all the time, 427 00:18:19,397 --> 00:18:21,900 and that would render color 428 00:18:21,933 --> 00:18:24,469 a pretty useless signal in the world. 429 00:18:24,502 --> 00:18:26,204 ♪ ♪ 430 00:18:26,237 --> 00:18:28,907 BERLIN: That's because objects reflect different wavelengths 431 00:18:28,940 --> 00:18:32,177 into your eye depending on the lighting conditions. 432 00:18:32,210 --> 00:18:34,412 If your brain didn't compensate for this, 433 00:18:34,445 --> 00:18:36,781 a red berry would appear gray in a cave, 434 00:18:36,814 --> 00:18:39,885 blue at dawn, and orange at dusk. 435 00:18:39,918 --> 00:18:42,420 But instead, your brain carefully calibrates 436 00:18:42,453 --> 00:18:45,423 your experience to hold color constant. 437 00:18:45,456 --> 00:18:48,226 Similarly, color vision in other animals 438 00:18:48,259 --> 00:18:50,662 is tuned to their needs. 439 00:18:50,695 --> 00:18:54,166 SETH: Different species have very different kinds of color vision 440 00:18:54,199 --> 00:18:57,102 that are suited to their particular environments 441 00:18:57,135 --> 00:19:00,038 and their particular challenges for staying alive. 442 00:19:00,071 --> 00:19:03,975 BERLIN: Dogs rely on smell, so they have fewer types of cones, 443 00:19:04,008 --> 00:19:06,311 and thus see the world like this. 444 00:19:06,344 --> 00:19:09,147 Birds need to recognize tiny color differences 445 00:19:09,180 --> 00:19:10,715 from great distances. 446 00:19:10,748 --> 00:19:13,251 so they have an extra type of cone that allows them 447 00:19:13,284 --> 00:19:14,719 to see more colors than we do. 448 00:19:14,752 --> 00:19:17,923 And bees need to find flowers rich in nectar, 449 00:19:17,956 --> 00:19:21,960 so they see ultraviolet light that's invisible to us. 450 00:19:21,993 --> 00:19:24,396 LAFER-SOUSA: Color provides a lot of valuable information 451 00:19:24,429 --> 00:19:26,031 about the world, 452 00:19:26,064 --> 00:19:28,967 but only if we can faithfully extract 453 00:19:29,000 --> 00:19:32,037 something about the object. 454 00:19:32,070 --> 00:19:35,073 So we don't actually see color as it is in the real world, 455 00:19:35,106 --> 00:19:39,177 we just see it in terms of how it's useful for us? 456 00:19:39,210 --> 00:19:40,845 Absolutely. 457 00:19:40,878 --> 00:19:43,148 And the dress is probably the best example of that. 458 00:19:43,181 --> 00:19:45,917 It's a really powerful demonstration 459 00:19:45,950 --> 00:19:48,220 of how our color machinery works. 460 00:19:48,253 --> 00:19:51,122 BERLIN: So why do people see this image 461 00:19:51,155 --> 00:19:52,891 of the dress differently? 462 00:19:52,924 --> 00:19:54,960 It comes down to your brain's assumptions 463 00:19:54,993 --> 00:19:56,995 about the lighting conditions. 464 00:19:57,028 --> 00:19:59,631 It seems that the more time you spend working indoors 465 00:19:59,664 --> 00:20:02,500 under artificial light, which is predominantly yellow, 466 00:20:02,533 --> 00:20:05,804 the more likely you are to say the dress is black and blue, 467 00:20:05,837 --> 00:20:08,907 because your brain assumes it is lit by artificial light 468 00:20:08,940 --> 00:20:11,209 and subtracts out the yellow. 469 00:20:11,242 --> 00:20:13,111 Conversely, if you spend more time 470 00:20:13,144 --> 00:20:15,280 in natural light, which is bluer, 471 00:20:15,313 --> 00:20:18,950 you are more likely to see it as white and gold. 472 00:20:18,983 --> 00:20:21,987 So then what is the actual color of the dress? 473 00:20:22,020 --> 00:20:26,091 Well, Heather, I happen to have brought it with me. 474 00:20:27,792 --> 00:20:29,894 (chuckles): Wow. 475 00:20:27,792 --> 00:20:29,894 So what color is it? 476 00:20:29,927 --> 00:20:32,264 Uh, it's obviously, I was right, blue and black. 477 00:20:32,297 --> 00:20:34,532 Team blue and black for the win. 478 00:20:32,297 --> 00:20:34,532 Yes, yes. 479 00:20:34,565 --> 00:20:36,801 I can't believe this is the actual dress. 480 00:20:36,834 --> 00:20:38,036 BERLIN: I feel like I'm holding, like... 481 00:20:38,069 --> 00:20:40,438 It's like a celebrity, the dress. 482 00:20:40,471 --> 00:20:42,540 I know, it should be in a museum, not in my closet. 483 00:20:40,471 --> 00:20:42,540 Yes! 484 00:20:42,573 --> 00:20:44,009 (both laugh) 485 00:20:44,042 --> 00:20:46,611 Before the dress, people hadn't really realized 486 00:20:46,644 --> 00:20:48,913 we differ so much between individuals. 487 00:20:48,946 --> 00:20:50,682 We're now quite used to the idea 488 00:20:50,715 --> 00:20:52,250 that we all differ on the outside. 489 00:20:52,283 --> 00:20:55,353 We all have differences in skin color, in height, in shape. 490 00:20:55,386 --> 00:20:57,622 But just as we all differ on the outside, 491 00:20:57,655 --> 00:21:00,125 we all differ on the inside, too. 492 00:21:00,158 --> 00:21:03,395 And this inner diversity is very important. 493 00:21:03,428 --> 00:21:06,598 It gives us a certain humility about our own ways of seeing. 494 00:21:06,631 --> 00:21:09,868 BERLIN: Illusions give us a ringside seat 495 00:21:09,901 --> 00:21:12,103 to watch how the brain creates our world. 496 00:21:12,136 --> 00:21:14,806 And it's not just the visual domain. 497 00:21:14,839 --> 00:21:17,008 Try listening to this. 498 00:21:17,041 --> 00:21:18,576 (staticky computerized voice playing) 499 00:21:18,609 --> 00:21:20,111 Brainstorm, right? 500 00:21:20,144 --> 00:21:21,613 Simple enough. 501 00:21:21,646 --> 00:21:23,014 Now, listen to this. 502 00:21:23,047 --> 00:21:24,616 (staticky computerized voice playing) 503 00:21:24,649 --> 00:21:25,950 Green needle. 504 00:21:25,983 --> 00:21:28,553 Okay, so you're thinking, "What's the big deal?" 505 00:21:28,586 --> 00:21:32,023 But, what if I tell you that the two audio clips I just played 506 00:21:32,056 --> 00:21:34,392 were exactly identical? 507 00:21:34,425 --> 00:21:38,129 For most people, what you hear depends on which label you read. 508 00:21:38,162 --> 00:21:39,531 Sounds unbelievable? 509 00:21:39,564 --> 00:21:44,069 Here, try it again, but this time, just read one. 510 00:21:44,102 --> 00:21:46,004 (staticky computerized voice playing) 511 00:21:46,037 --> 00:21:49,808 Okay, now read the other and listen again. 512 00:21:49,841 --> 00:21:52,310 (staticky computerized voice playing) 513 00:21:52,343 --> 00:21:53,978 Now, when I first encountered this, 514 00:21:54,011 --> 00:21:55,747 I was floored, too. 515 00:21:55,780 --> 00:21:57,949 Even though I know what's going on. 516 00:21:57,982 --> 00:22:00,185 When your brain encounters uncertainty, 517 00:22:00,218 --> 00:22:02,921 it fills in the gaps with its best guess. 518 00:22:02,954 --> 00:22:05,557 In this case, we have a degraded audio clip, 519 00:22:05,590 --> 00:22:08,293 and when you're primed with a certain word to go with it, 520 00:22:08,326 --> 00:22:11,296 your brain automatically jumps to the best fit. 521 00:22:11,329 --> 00:22:14,899 For most of us, we literally hear what we want to hear. 522 00:22:14,932 --> 00:22:18,903 And it gets even worse-- let's try one more. 523 00:22:18,936 --> 00:22:21,406 Another internet sensation 524 00:22:21,439 --> 00:22:23,908 that lit up debates across the country. 525 00:22:23,941 --> 00:22:28,713 (cleaner computerized voice playing) 526 00:22:28,746 --> 00:22:30,615 Once and for all, is it yanny, is it laurel? 527 00:22:30,648 --> 00:22:33,618 It's not yanny, it's laurel. 528 00:22:30,648 --> 00:22:33,618 It's yanny! 529 00:22:33,651 --> 00:22:35,653 Did you hear yanny? 530 00:22:35,686 --> 00:22:36,721 (cheering and applauding) 531 00:22:36,754 --> 00:22:38,056 Who heard laurel? 532 00:22:38,089 --> 00:22:39,357 (cheering more loudly) 533 00:22:39,390 --> 00:22:41,993 It is laurel and not yanny. 534 00:22:42,026 --> 00:22:44,329 (remix of computerized voice playing) 535 00:22:44,362 --> 00:22:46,464 It's like that stupid dress again 536 00:22:46,497 --> 00:22:48,700 all over, but in audio form! 537 00:22:48,733 --> 00:22:51,503 This is not saying "laurel," this is only saying "yanny." 538 00:22:51,536 --> 00:22:54,606 Exactly, it's laurel! 539 00:22:54,639 --> 00:22:57,642 Now, about half of you hear yanny, 540 00:22:57,675 --> 00:22:59,511 and the other laurel, 541 00:22:59,544 --> 00:23:02,814 and unlike the first illusion, I can't get most of you 542 00:23:02,847 --> 00:23:05,784 to experience this one any other way. 543 00:23:05,817 --> 00:23:08,253 You're locked into your version of reality. 544 00:23:08,286 --> 00:23:11,856 Experts aren't exactly sure why, but some of us 545 00:23:11,889 --> 00:23:15,460 seem to pay more attention to the low frequencies, laurel, 546 00:23:15,493 --> 00:23:17,495 and others to the high, yanny. 547 00:23:17,528 --> 00:23:20,465 The divide stems from the fact that the audio file 548 00:23:20,498 --> 00:23:22,267 is an ambiguous signal made up 549 00:23:22,300 --> 00:23:24,335 of both high and low frequencies. 550 00:23:24,368 --> 00:23:26,571 But by manipulating the frequencies, 551 00:23:26,604 --> 00:23:29,140 I might be able to change what you hear. 552 00:23:29,173 --> 00:23:31,376 (computerized voice playing at mid frequency) 553 00:23:31,409 --> 00:23:32,377 High... 554 00:23:32,410 --> 00:23:34,779 (voice plays at high frequency) 555 00:23:34,812 --> 00:23:38,383 (voice plays at low frequency) 556 00:23:34,812 --> 00:23:38,383 Low. 557 00:23:38,416 --> 00:23:39,984 All of this goes to show 558 00:23:40,017 --> 00:23:41,252 how much the brain 559 00:23:41,285 --> 00:23:43,721 is an active interpreter of sensory input. 560 00:23:43,754 --> 00:23:45,924 Our perception of the external world 561 00:23:45,957 --> 00:23:49,561 is actually much less objective than we'd like to believe. 562 00:23:49,594 --> 00:23:51,663 Most of the world around us is very real, 563 00:23:51,696 --> 00:23:54,632 but you just never lived there, okay? 564 00:23:54,665 --> 00:23:56,701 You lived in your mind, 565 00:23:56,734 --> 00:24:00,472 which is a perception of that world that's being filtered 566 00:24:00,505 --> 00:24:03,374 through a bunch of salt water sacks of proteins 567 00:24:03,407 --> 00:24:07,312 and electrochemical signals, which can't possibly be making 568 00:24:07,345 --> 00:24:10,281 completely accurate determinations 569 00:24:10,314 --> 00:24:13,184 of what's actually in the outside world. 570 00:24:13,217 --> 00:24:16,521 Not convinced? Or maybe you're just asking, "Why?" 571 00:24:16,554 --> 00:24:19,658 Well, try watching for when the green dot flashes. 572 00:24:21,058 --> 00:24:23,161 Does it line up with the red dot? 573 00:24:23,194 --> 00:24:24,562 If you are like most people, 574 00:24:24,595 --> 00:24:27,298 the red one always seems just a little bit ahead. 575 00:24:27,331 --> 00:24:29,801 Now try again. 576 00:24:32,169 --> 00:24:36,074 The red dot and the green dot are actually perfectly aligned. 577 00:24:36,107 --> 00:24:39,043 That's because some neuroscientists would say 578 00:24:39,076 --> 00:24:40,712 that it's not your brain's job 579 00:24:40,745 --> 00:24:42,614 to perceive the world accurately. 580 00:24:42,647 --> 00:24:45,717 Rather, its job is to predict what happens next. 581 00:24:45,750 --> 00:24:49,554 To a certain extent, you see what you expect to see: 582 00:24:49,587 --> 00:24:51,689 a predicted path of motion. 583 00:24:51,722 --> 00:24:54,158 And this is what helps us hit a home run 584 00:24:54,191 --> 00:24:58,363 or flinch from a punch at just the right moment. 585 00:25:00,665 --> 00:25:02,901 The brain is a predicting machine. 586 00:25:02,934 --> 00:25:07,605 Given a set of circumstances in this story at this moment, 587 00:25:07,638 --> 00:25:11,442 what are the likely plausible 588 00:25:11,475 --> 00:25:15,013 next events in the story? 589 00:25:15,046 --> 00:25:17,682 SETH: The brain is using sensory information 590 00:25:17,715 --> 00:25:19,450 to calibrate, 591 00:25:19,483 --> 00:25:21,853 update, to fine-tune these predictions 592 00:25:21,886 --> 00:25:23,621 so they remain tied 593 00:25:23,654 --> 00:25:27,292 to reality in ways that are not constrained by accuracy, 594 00:25:27,325 --> 00:25:30,061 but that are constrained by how useful 595 00:25:30,094 --> 00:25:32,163 the brain's perceptual predictions are 596 00:25:32,196 --> 00:25:34,299 in the business of staying alive. 597 00:25:41,339 --> 00:25:43,808 BERLIN: And to keep us alive, the brain has evolved 598 00:25:43,841 --> 00:25:46,311 to look for signals of potential danger. 599 00:25:46,344 --> 00:25:48,713 One of the most important is pain, 600 00:25:48,746 --> 00:25:51,983 and as neuroscientist Theanne Griffith is about to show me, 601 00:25:52,016 --> 00:25:54,886 sometimes that can be a kind of illusion, as well. 602 00:25:54,919 --> 00:25:56,621 BERLIN: So what is this? 603 00:25:56,654 --> 00:26:00,024 GRIFFITH: This is a thermal grill. 604 00:25:56,654 --> 00:26:00,024 Okay. 605 00:26:00,057 --> 00:26:02,460 This is a machine that could give us some insight 606 00:26:02,493 --> 00:26:05,496 as to how pain works in your brain. 607 00:26:05,529 --> 00:26:07,365 All right, this is making me nervous already 608 00:26:07,398 --> 00:26:08,900 as I'm getting strapped in! 609 00:26:07,398 --> 00:26:08,900 (Griffith laughs) 610 00:26:08,933 --> 00:26:11,536 GRIFFITH: Don't worry, it's all an illusion, actually. 611 00:26:11,569 --> 00:26:13,404 Okay. 612 00:26:13,437 --> 00:26:16,274 And it's comprised of these different metal bars 613 00:26:16,307 --> 00:26:18,309 that are either set to a cold or warm temperature. 614 00:26:18,342 --> 00:26:21,212 So why don't you go ahead and touch that first bar? 615 00:26:21,245 --> 00:26:23,381 It's warm, right? 616 00:26:23,414 --> 00:26:25,016 And then the next bar? 617 00:26:25,049 --> 00:26:26,751 Cold. 618 00:26:25,049 --> 00:26:26,751 Mm-hmm. 619 00:26:26,784 --> 00:26:28,453 GRIFFITH: And then the next one, warm. 620 00:26:28,486 --> 00:26:30,188 You see? So they're alternating cold, warm, cold, warm. 621 00:26:28,486 --> 00:26:30,188 BERLIN: Mm-hmm. 622 00:26:30,221 --> 00:26:31,789 Now, you want to see what happens 623 00:26:31,822 --> 00:26:33,391 when you put your hand down? 624 00:26:33,424 --> 00:26:35,193 Not necessarily. 625 00:26:33,424 --> 00:26:35,193 (both laugh) 626 00:26:35,226 --> 00:26:37,662 Go ahead. 627 00:26:35,226 --> 00:26:37,662 Okay. 628 00:26:37,695 --> 00:26:39,030 Okay, here we go. 629 00:26:39,063 --> 00:26:40,565 BERLIN: Ow! 630 00:26:40,598 --> 00:26:42,266 GRIFFITH: Right? Isn't that interesting? 631 00:26:40,598 --> 00:26:42,266 Whoa! 632 00:26:42,299 --> 00:26:44,202 Yeah, what is going on there? 633 00:26:44,235 --> 00:26:45,937 It sort of feels cold at first, but then... 634 00:26:45,970 --> 00:26:48,272 Then it gets this kind of burning sensation, right? 635 00:26:48,305 --> 00:26:49,941 Yes, very much so. 636 00:26:49,974 --> 00:26:52,910 BERLIN: It feels super-hot, like I'm getting burnt. 637 00:26:52,943 --> 00:26:56,147 So it's not 100% clear exactly how this is happening. 638 00:26:56,180 --> 00:26:58,383 But what we think might be going on 639 00:26:58,416 --> 00:27:00,151 is that, basically, your brain 640 00:27:00,184 --> 00:27:02,020 is getting a little bit confused. 641 00:27:02,053 --> 00:27:03,087 Okay. 642 00:27:02,053 --> 00:27:03,087 It's feeling cold, 643 00:27:03,120 --> 00:27:05,356 and it's also feeling warmth. 644 00:27:05,389 --> 00:27:09,027 And somehow, it's interpreting these two signals as pain. 645 00:27:09,060 --> 00:27:13,264 BERLIN: Here's what neuroscientists think is going on. 646 00:27:13,297 --> 00:27:16,067 In your hands, you have separate sensors for heat, 647 00:27:16,100 --> 00:27:18,269 cold, and pain. 648 00:27:18,302 --> 00:27:21,339 Normally, when you touch something slightly cold, 649 00:27:21,372 --> 00:27:24,242 both your cold and pain sensors are activated, 650 00:27:24,275 --> 00:27:26,344 but the cold ones override the signals 651 00:27:26,377 --> 00:27:27,845 from the pain sensors, 652 00:27:27,878 --> 00:27:30,281 telling your brain there's nothing to worry about. 653 00:27:30,314 --> 00:27:32,517 Unless, in this very unnatural scenario 654 00:27:32,550 --> 00:27:35,119 with the thermal grill, you happen to be touching 655 00:27:35,152 --> 00:27:37,789 something warm at the same time. 656 00:27:37,822 --> 00:27:40,892 Here, the heat signals cancel out the cold ones, 657 00:27:40,925 --> 00:27:43,594 leaving you with just the pain ones activated, 658 00:27:43,627 --> 00:27:47,065 telling your brain, "Ouch!" 659 00:27:47,098 --> 00:27:49,300 So in that respect, is pain real? 660 00:27:49,333 --> 00:27:51,169 Mm-hmm. 661 00:27:49,333 --> 00:27:51,169 Or is it just an illusion 662 00:27:51,202 --> 00:27:52,437 or a construct of the brain? 663 00:27:52,470 --> 00:27:54,038 That's a really good question. 664 00:27:54,071 --> 00:27:57,542 So noxious stimuli is, is a real thing, right? 665 00:27:57,575 --> 00:27:59,377 If you stick your hand in boiling water, 666 00:27:59,410 --> 00:28:00,978 that's an aversive stimulus. 667 00:28:01,011 --> 00:28:06,184 The perception of a noxious stimuli is real. 668 00:28:01,011 --> 00:28:06,184 Mm-hmm. 669 00:28:06,217 --> 00:28:08,419 Pain is more of a construct, right? 670 00:28:06,217 --> 00:28:08,419 Mm-hmm. 671 00:28:08,452 --> 00:28:10,755 And it can vary from individual to individual. 672 00:28:10,788 --> 00:28:14,625 EMERY BROWN: Pain is a construct of the brain. 673 00:28:14,658 --> 00:28:16,494 How do we know that? 674 00:28:16,527 --> 00:28:19,130 You touch a needle, right? And prick your finger. 675 00:28:19,163 --> 00:28:21,933 We can draw the anatomy of what just happened. 676 00:28:21,966 --> 00:28:25,303 We have very well-defined pathways 677 00:28:25,336 --> 00:28:27,538 saying, "This is pain information." 678 00:28:27,571 --> 00:28:33,077 We don't interpret it as pain until it hits your brain. 679 00:28:33,110 --> 00:28:35,413 BERLIN: Pain, not unlike the experience of color, 680 00:28:35,446 --> 00:28:37,949 is a construct of the mind. 681 00:28:37,982 --> 00:28:39,250 Mama! Mama! 682 00:28:39,283 --> 00:28:41,052 BERLIN: But just because pain is in your brain 683 00:28:41,085 --> 00:28:43,988 doesn't make it any less critical for survival. 684 00:28:44,021 --> 00:28:45,623 GRIFFITH: Pain is a very important... 685 00:28:45,656 --> 00:28:49,293 (gasps) 686 00:28:45,656 --> 00:28:49,293 ...learning mechanism for children. 687 00:28:49,326 --> 00:28:51,896 They learn what behaviors they can engage in that are safe 688 00:28:51,929 --> 00:28:54,665 and what behaviors, well, they should not engage in 689 00:28:54,698 --> 00:28:57,468 because they could cause them bodily harm. 690 00:28:57,501 --> 00:29:00,605 And there's, um, uh, different mutations that people can have 691 00:29:00,638 --> 00:29:02,673 in certain proteins that make them 692 00:29:02,706 --> 00:29:04,442 completely insensitive to pain. 693 00:29:04,475 --> 00:29:06,644 And so kids do things like bite on their lips 694 00:29:06,677 --> 00:29:09,213 or on their fingers when they're very young, 695 00:29:09,246 --> 00:29:12,283 and as they get older, can engage in risky behavior. 696 00:29:12,316 --> 00:29:16,821 So pain is extremely important for us to feel. 697 00:29:16,854 --> 00:29:20,324 SETH: Illusions are fascinating. 698 00:29:20,357 --> 00:29:22,493 They're like fractures in the matrix. 699 00:29:22,526 --> 00:29:25,596 They reveal to us that the way we perceive things 700 00:29:25,629 --> 00:29:28,232 isn't necessarily the way they are. 701 00:29:28,265 --> 00:29:31,736 MACKNIK: Illusions help us find the cracks in the mortar 702 00:29:31,769 --> 00:29:34,105 of that world we've built for ourselves, 703 00:29:34,138 --> 00:29:37,575 and understand what it is our world is actually made out of 704 00:29:37,608 --> 00:29:39,443 and what the brain is actually doing. 705 00:29:39,476 --> 00:29:41,913 So most people think of the brain 706 00:29:41,946 --> 00:29:45,449 reconstructing the world more or less verbatim. 707 00:29:45,482 --> 00:29:47,485 (dog growling) 708 00:29:45,482 --> 00:29:47,485 But that's just not true. 709 00:29:47,518 --> 00:29:49,220 What it's actually doing is, 710 00:29:49,253 --> 00:29:50,621 it's getting very little information 711 00:29:50,654 --> 00:29:53,624 and it's using that very little information 712 00:29:53,657 --> 00:29:56,361 to make a big, grand model of the world. 713 00:29:57,395 --> 00:30:00,998 MARTINEZ-CONDE: We cannot process the vast amount of information 714 00:30:01,031 --> 00:30:05,937 that is constantly bombarding our senses. 715 00:30:05,970 --> 00:30:08,306 Illusions, you can think of them as shortcuts. 716 00:30:08,339 --> 00:30:11,809 Shortcuts make us faster, 717 00:30:11,842 --> 00:30:15,346 more efficient with less resources. 718 00:30:15,379 --> 00:30:18,049 Based on these snippets of information, 719 00:30:18,082 --> 00:30:23,521 we build this more complex simulation of reality. 720 00:30:23,554 --> 00:30:25,923 And that simulation of the world is 721 00:30:25,956 --> 00:30:27,091 what we call consciousness. 722 00:30:29,393 --> 00:30:31,195 BERLIN: Consciousness. 723 00:30:31,228 --> 00:30:34,832 (alarm buzzing) 724 00:30:31,228 --> 00:30:34,832 We take it for granted, but every time you wake up, 725 00:30:34,865 --> 00:30:38,870 (alarm stops) 726 00:30:34,865 --> 00:30:38,870 your brain stitches together all your sensory inputs-- 727 00:30:38,903 --> 00:30:40,771 the sound of a distant train... 728 00:30:40,804 --> 00:30:42,540 (train whistle blowing) 729 00:30:42,573 --> 00:30:44,775 ...the smell of coffee, 730 00:30:44,808 --> 00:30:47,678 the warmth of the sun-- 731 00:30:47,711 --> 00:30:50,214 into an experience of the world. 732 00:30:50,247 --> 00:30:52,049 And that experience, 733 00:30:52,082 --> 00:30:54,285 that awareness of the world, 734 00:30:54,318 --> 00:30:57,622 is what scientists call consciousness. 735 00:30:57,655 --> 00:31:00,958 In neuroscience, consciousness is the Holy Grail. 736 00:31:00,991 --> 00:31:03,761 Humans have been fascinated by consciousness 737 00:31:03,794 --> 00:31:06,631 for thousands of years, probably much longer than that. (chuckling) 738 00:31:06,664 --> 00:31:08,432 Take three. 739 00:31:08,465 --> 00:31:10,534 Now, of course, the word "consciousness" 740 00:31:10,567 --> 00:31:12,470 means a lot of things to different people. 741 00:31:12,503 --> 00:31:14,639 To some, consciousness means being awake, 742 00:31:14,672 --> 00:31:16,073 as opposed to asleep. 743 00:31:16,106 --> 00:31:19,644 Or self-aware, or the contents of my thoughts. 744 00:31:19,677 --> 00:31:22,780 But that's not how we neuroscientists think about it. 745 00:31:22,813 --> 00:31:27,685 We think of it as something much more basic-- 746 00:31:27,718 --> 00:31:30,288 it's just internal experience. 747 00:31:30,321 --> 00:31:33,591 It feels like something to see the color red. 748 00:31:33,624 --> 00:31:35,860 To taste a strawberry. 749 00:31:35,893 --> 00:31:37,194 (thunder rumbling) 750 00:31:37,227 --> 00:31:39,997 To hear the crack of thunder. 751 00:31:40,030 --> 00:31:43,501 (thunder crashing) 752 00:31:43,534 --> 00:31:46,904 SETH: We are complicated biological creatures, 753 00:31:46,937 --> 00:31:49,206 but the most central feature of our lives 754 00:31:49,239 --> 00:31:51,475 is that we are conscious creatures, too. 755 00:31:51,508 --> 00:31:53,210 When I open my eyes, 756 00:31:53,243 --> 00:31:56,447 it's not just that my brain does some sophisticated processing 757 00:31:56,480 --> 00:31:57,548 of the visual information. 758 00:31:57,581 --> 00:32:00,284 I have an experience. 759 00:32:00,317 --> 00:32:02,320 BERLIN: During the course of my journey, 760 00:32:02,353 --> 00:32:04,221 I've seen how our experience of reality 761 00:32:04,254 --> 00:32:05,856 is not what it seems. 762 00:32:05,889 --> 00:32:07,291 If my conscious awareness 763 00:32:07,324 --> 00:32:10,828 is built from my perceptions, flawed as they may be, 764 00:32:10,861 --> 00:32:15,333 how does that work and what does it mean? 765 00:32:15,366 --> 00:32:18,302 ♪ ♪ 766 00:32:18,335 --> 00:32:20,171 So please take a seat. 767 00:32:20,204 --> 00:32:23,507 BERLIN: Some of the first clues trickled in from people like this. 768 00:32:23,540 --> 00:32:25,343 LORELLA BATTELLI: Put your chin on the chin rest. 769 00:32:25,376 --> 00:32:26,911 BROWN: A lot of very valuable information 770 00:32:26,944 --> 00:32:30,681 comes from patients who've had, part of the brain's damaged. 771 00:32:30,714 --> 00:32:34,352 By piecing these various parts together, seeing what was lost, 772 00:32:34,385 --> 00:32:36,287 we've come to appreciate the role 773 00:32:36,320 --> 00:32:37,955 that these various brain regions play 774 00:32:37,988 --> 00:32:39,857 in the creation of consciousness. 775 00:32:39,890 --> 00:32:41,826 We're going to calibrate your eyes first. 776 00:32:41,859 --> 00:32:44,095 SETH: A powerful example of this 777 00:32:44,128 --> 00:32:46,230 is the phenomenon of blindsight. 778 00:32:46,263 --> 00:32:49,633 BATTELLI: This is a patient who had a stroke 779 00:32:49,666 --> 00:32:51,268 in her visual areas, 780 00:32:51,301 --> 00:32:52,970 in the back of the brain. 781 00:32:53,003 --> 00:32:55,406 And this stroke is affecting her visual field. 782 00:32:55,439 --> 00:32:59,043 BERLIN: Three years ago, she felt a pounding in her head. 783 00:32:59,076 --> 00:33:01,345 WOMAN: I had what I thought was a migraine. 784 00:33:01,378 --> 00:33:04,181 I actually went to the emergency room 785 00:33:04,214 --> 00:33:06,617 because I'm walking around with this area where I can't see. 786 00:33:06,650 --> 00:33:09,153 BERLIN: The stroke damaged a piece of the brain 787 00:33:09,186 --> 00:33:11,756 devoted to vision, leaving her with an apparent 788 00:33:11,789 --> 00:33:13,491 total blind spot. 789 00:33:13,524 --> 00:33:16,594 WOMAN: That blind spot, it's enough that if you're driving, 790 00:33:16,627 --> 00:33:20,965 an oncoming car disappears into it. 791 00:33:20,998 --> 00:33:24,802 It's a little anxiety-producing and, and things like that. 792 00:33:24,835 --> 00:33:26,570 BERLIN: In everyday tasks, 793 00:33:26,603 --> 00:33:28,973 her eye movements make up the difference. 794 00:33:29,006 --> 00:33:32,309 But what happens when she doesn't move her eyes? 795 00:33:32,342 --> 00:33:34,578 Neuroscientist Lorella Battelli wants to find out, 796 00:33:34,611 --> 00:33:38,716 so she developed a clever series of experiments to pin down, 797 00:33:38,749 --> 00:33:40,785 just how blind is she really in that spot? 798 00:33:42,152 --> 00:33:43,654 BATTELLI: We're using EyeLink, 799 00:33:43,687 --> 00:33:46,057 which is the eye-tracking system 800 00:33:46,090 --> 00:33:47,892 to make sure she doesn't move the eyes. 801 00:33:47,925 --> 00:33:50,261 BERLIN: She keeps her eyes focused on the center spot. 802 00:33:50,294 --> 00:33:52,763 Every time she hears a beep, 803 00:33:52,796 --> 00:33:54,665 she has to say if those little dots inside the circle 804 00:33:54,698 --> 00:33:56,901 are moving to the left or to the right. 805 00:33:56,934 --> 00:33:58,836 Left. 806 00:33:58,869 --> 00:34:01,939 BERLIN: The eye tracker checks that she's not shifting her gaze. 807 00:34:03,140 --> 00:34:04,575 WOMAN: Right. 808 00:34:04,608 --> 00:34:06,477 When you're doing tests like this, 809 00:34:06,510 --> 00:34:09,480 that blind area, what does that kind of look like for you? 810 00:34:09,513 --> 00:34:11,348 What does it feel like for you? 811 00:34:11,381 --> 00:34:15,753 When that target pops up in my blind area, 812 00:34:15,786 --> 00:34:17,388 I don't see it. 813 00:34:17,421 --> 00:34:19,657 (machine beeps) 814 00:34:19,690 --> 00:34:21,559 Left. 815 00:34:21,592 --> 00:34:23,494 BERLIN: Strangely, even though she says she doesn't see 816 00:34:23,527 --> 00:34:25,329 anything in the blind spot, 817 00:34:25,362 --> 00:34:26,897 she gets it right more often than not. 818 00:34:26,930 --> 00:34:28,933 WOMAN: Right. 819 00:34:28,966 --> 00:34:30,201 BERLIN: So that some information 820 00:34:30,234 --> 00:34:31,268 is getting in. 821 00:34:30,234 --> 00:34:31,268 Yeah, so... 822 00:34:31,301 --> 00:34:33,737 But they're not consciously seeing it, 823 00:34:33,770 --> 00:34:35,639 but they can respond to it in different ways. 824 00:34:33,770 --> 00:34:35,639 Exactly. 825 00:34:35,672 --> 00:34:38,776 BATTELLI: Even if they say, "I didn't see anything." 826 00:34:38,809 --> 00:34:40,945 Left. 827 00:34:38,809 --> 00:34:40,945 But you tell them, "Please, just tell me 828 00:34:40,978 --> 00:34:42,947 whether you saw it or not," 829 00:34:42,980 --> 00:34:44,882 then their response would be above chance. 830 00:34:44,915 --> 00:34:47,718 BERLIN: So just keep your eyes closed, okay? 831 00:34:47,751 --> 00:34:49,720 And... 832 00:34:49,753 --> 00:34:51,755 BERLIN: What's going on? 833 00:34:51,788 --> 00:34:54,125 To probe deeper, Lorella lets me give the patient 834 00:34:54,158 --> 00:34:56,994 a different version of the challenge. 835 00:34:57,027 --> 00:34:59,630 I put a miniature screwdriver in her blind spot. 836 00:34:59,663 --> 00:35:02,867 BERLIN: Okay, I'm going to have you open your eyes and fixate. 837 00:35:02,900 --> 00:35:04,936 Okay. 838 00:35:02,900 --> 00:35:04,936 Okay. 839 00:35:07,037 --> 00:35:08,239 I see nothing. 840 00:35:08,272 --> 00:35:09,240 You see nothing. 841 00:35:09,273 --> 00:35:10,474 Nothing. 842 00:35:09,273 --> 00:35:10,474 Okay. 843 00:35:10,507 --> 00:35:11,775 BERLIN: Even though she says 844 00:35:11,808 --> 00:35:14,445 she sees nothing, look at which tool she picks. 845 00:35:14,478 --> 00:35:16,380 Now turn over there and look at the objects. 846 00:35:16,413 --> 00:35:17,414 Tell me what you think you saw. 847 00:35:17,447 --> 00:35:20,151 WOMAN: The screwdriver. 848 00:35:17,447 --> 00:35:20,151 BERLIN: Yep. 849 00:35:20,184 --> 00:35:22,219 BERLIN: Now try another one. 850 00:35:22,252 --> 00:35:25,322 BERLIN: Next, I display a tiny wrench. 851 00:35:25,355 --> 00:35:29,760 ♪ ♪ 852 00:35:29,793 --> 00:35:31,328 Did you see anything? 853 00:35:31,361 --> 00:35:33,097 No. 854 00:35:31,361 --> 00:35:33,097 No? Okay. 855 00:35:33,130 --> 00:35:37,067 Look over there and guess what you think you, was there. 856 00:35:37,100 --> 00:35:40,271 I think the wrench? 857 00:35:37,100 --> 00:35:40,271 Yep. 858 00:35:40,304 --> 00:35:42,506 WOMAN: I'm going to guess the scissors. 859 00:35:42,539 --> 00:35:44,675 Yeah, good job, great, scissors. 860 00:35:44,708 --> 00:35:46,010 BERLIN: Time and time again, 861 00:35:46,043 --> 00:35:47,878 she makes the right choice. 862 00:35:47,911 --> 00:35:49,813 Amazing, so, you know, it seems 863 00:35:49,846 --> 00:35:52,016 to me that you're saying you're not seeing anything, 864 00:35:52,049 --> 00:35:54,885 yet when I'm asking you to choose, 865 00:35:54,918 --> 00:35:56,954 you're pretty much getting it correct, 866 00:35:56,987 --> 00:35:58,455 so something is getting in. 867 00:35:58,488 --> 00:36:01,091 BERLIN: How is this possible? 868 00:36:01,124 --> 00:36:04,028 It's as if she sees the tools, but doesn't know it. 869 00:36:04,061 --> 00:36:05,863 BATTELLI: They actually saw something. 870 00:36:05,896 --> 00:36:08,399 Mm-hmm, certainly. 871 00:36:05,896 --> 00:36:08,399 But they're not entirely aware of it. 872 00:36:08,432 --> 00:36:10,568 Information is getting in, affecting our behavior 873 00:36:10,601 --> 00:36:12,369 and how we're responding to the world around us, 874 00:36:12,402 --> 00:36:14,338 without there being a conscious perception 875 00:36:14,371 --> 00:36:16,707 of that piece of visual information. 876 00:36:14,371 --> 00:36:16,707 Correct. 877 00:36:16,740 --> 00:36:18,742 WOMAN: I'm gonna go with the hammer again. 878 00:36:18,775 --> 00:36:23,480 BERLIN: Until patients like this, we scientists had never seen 879 00:36:23,513 --> 00:36:26,116 perception separate from conscious experience. 880 00:36:26,149 --> 00:36:28,219 And this tells us that perception 881 00:36:28,252 --> 00:36:31,522 and consciousness are separate things in the brain. 882 00:36:31,555 --> 00:36:33,424 But it also has me wondering, 883 00:36:33,457 --> 00:36:35,759 if someone can still use visual information 884 00:36:35,792 --> 00:36:37,695 without awareness of it, 885 00:36:37,728 --> 00:36:39,763 why do we have consciousness at all? 886 00:36:39,796 --> 00:36:42,132 What is consciousness for? 887 00:36:42,165 --> 00:36:44,368 A clue might come from babies. 888 00:36:44,401 --> 00:36:46,370 (cooing) 889 00:36:46,403 --> 00:36:48,305 ALISON GOPNIK: Babies-- everything we know suggests 890 00:36:48,338 --> 00:36:50,174 that they're born conscious. 891 00:36:50,207 --> 00:36:51,442 They're certainly taking in information 892 00:36:51,475 --> 00:36:52,710 from the time they're born. 893 00:36:52,743 --> 00:36:55,112 REBECCA SAXE: They are making rational choices 894 00:36:55,145 --> 00:36:58,649 about what they learn from extremely early on. 895 00:36:58,682 --> 00:37:02,319 And they are forming memories of their specific surroundings, 896 00:37:02,352 --> 00:37:03,687 of their parents, 897 00:37:03,720 --> 00:37:05,756 of their important relationships. 898 00:37:05,789 --> 00:37:08,125 We can see that in their behavior. 899 00:37:08,158 --> 00:37:11,996 BERLIN: And all that behavior burns a lot of fuel. 900 00:37:12,029 --> 00:37:14,832 GOPNIK: Brains are expensive computing gadgets. 901 00:37:14,865 --> 00:37:16,300 So while you're just sitting here, 902 00:37:16,333 --> 00:37:19,036 your brain is using up about 20% of all the calories 903 00:37:19,069 --> 00:37:21,071 that you have, so it's using up quite a bit. 904 00:37:21,104 --> 00:37:23,240 But if you think about a two-year-old, 905 00:37:23,273 --> 00:37:27,144 his brain is using 60% of his calories. 906 00:37:27,177 --> 00:37:32,783 So almost all that food is just going to keep his brain going. 907 00:37:32,816 --> 00:37:36,020 BERLIN: To understand why young brains might need so much more fuel, 908 00:37:36,053 --> 00:37:38,789 check out the connections in a toddler's brain 909 00:37:38,822 --> 00:37:40,591 versus an adult's. 910 00:37:40,624 --> 00:37:44,595 A two-year-old's brain has about two quadrillion synapses. 911 00:37:44,628 --> 00:37:49,533 By the time they hit adulthood, that number is cut in half. 912 00:37:49,566 --> 00:37:51,602 So if you think about the difference between 913 00:37:51,635 --> 00:37:54,038 the baby brain, the child's brain, and the adult brain, 914 00:37:54,071 --> 00:37:58,075 the child's brain is more like back country roads 915 00:37:58,108 --> 00:38:00,010 where you have little, tiny roads 916 00:38:00,043 --> 00:38:02,713 that are going from one village to the next. 917 00:38:02,746 --> 00:38:04,815 None of them are very efficient. 918 00:38:04,848 --> 00:38:05,983 There's not a lot of traffic, 919 00:38:06,016 --> 00:38:07,885 and the traffic doesn't go very quickly, 920 00:38:07,918 --> 00:38:09,853 but they connect lots and lots of different places. 921 00:38:09,886 --> 00:38:12,556 And the adult brain is more like superhighways 922 00:38:12,589 --> 00:38:15,326 that get you from one place to another very quickly, 923 00:38:15,359 --> 00:38:17,061 and take a lot of traffic, 924 00:38:17,094 --> 00:38:19,763 but don't connect as many different places. 925 00:38:19,796 --> 00:38:22,266 BERLIN: As we age, in the interest of efficiency, 926 00:38:22,299 --> 00:38:25,069 we strengthen the connections that are useful to us 927 00:38:25,102 --> 00:38:27,204 and prune the rest. 928 00:38:27,237 --> 00:38:30,374 MACKNIK: You basically take neurons you don't need, you get rid of them. 929 00:38:30,407 --> 00:38:33,477 And what you have now is a very lean machine 930 00:38:33,510 --> 00:38:38,416 that does certain things and it does them very well. 931 00:38:39,483 --> 00:38:42,052 GOPNIK: We see this early brain that's very exploratory, 932 00:38:42,085 --> 00:38:43,854 that has lots and lots of potential, 933 00:38:43,887 --> 00:38:45,055 lots of possibilities. 934 00:38:45,088 --> 00:38:47,358 Not very good at putting on your jacket 935 00:38:47,391 --> 00:38:49,426 and getting out to preschool in the morning. 936 00:38:49,459 --> 00:38:50,961 And then we have this later brain 937 00:38:50,994 --> 00:38:52,730 that's very good at doing things. 938 00:38:52,763 --> 00:38:54,098 Not so good at changing, 939 00:38:54,131 --> 00:38:55,699 not so good at taking in new information, 940 00:38:55,732 --> 00:38:56,967 not so good at doing something new. 941 00:38:57,000 --> 00:38:59,670 BERLIN: What this suggests is that maybe 942 00:38:59,703 --> 00:39:01,171 what consciousness is for 943 00:39:01,204 --> 00:39:03,507 is choosing what's important for us to be aware of 944 00:39:03,540 --> 00:39:05,042 at any given moment. 945 00:39:05,075 --> 00:39:07,544 Kind of like a spotlight. 946 00:39:07,577 --> 00:39:10,047 GOPNICK: For adults, it's as if consciousness is 947 00:39:10,080 --> 00:39:11,548 this bright spotlight in one place 948 00:39:11,581 --> 00:39:12,950 and everything around it is dark. 949 00:39:14,951 --> 00:39:16,587 BERLIN: While for children and babies, 950 00:39:16,620 --> 00:39:18,255 it would be more like a flood light, 951 00:39:18,288 --> 00:39:20,557 where nearly everything is illuminated. 952 00:39:20,590 --> 00:39:24,228 GOPNIK: You're conscious of a lot more that's going on. 953 00:39:24,261 --> 00:39:27,097 BERLIN: Consciousness may be like an amplifier, 954 00:39:27,130 --> 00:39:29,666 boosting the important signals over the noise. 955 00:39:29,699 --> 00:39:31,736 Is there any evidence? 956 00:39:33,136 --> 00:39:35,372 This is where FMRI comes in, 957 00:39:35,405 --> 00:39:38,008 a special tool in neuroscience that takes pictures of the brain 958 00:39:38,041 --> 00:39:40,677 while it's doing something to map where blood flow 959 00:39:40,710 --> 00:39:41,912 is in high demand. 960 00:39:41,945 --> 00:39:44,582 The result is a map of brain activity. 961 00:39:45,749 --> 00:39:48,552 So where is consciousness in the brain 962 00:39:48,585 --> 00:39:51,522 and how does it work? 963 00:39:51,555 --> 00:39:53,390 To find out, neuroscientists designed 964 00:39:53,423 --> 00:39:56,460 a clever series of experiments that go like this. 965 00:39:56,493 --> 00:39:58,729 They start by flashing a word 966 00:39:58,762 --> 00:40:01,198 on a screen for about 30 milliseconds. 967 00:40:01,231 --> 00:40:02,199 (computer beeps) 968 00:40:02,232 --> 00:40:03,600 DEHAENE: You flash this word, 969 00:40:03,633 --> 00:40:06,170 and the person is not able to see the word at all. 970 00:40:06,203 --> 00:40:07,871 She says, "There was no word." 971 00:40:07,904 --> 00:40:12,075 BERLIN: But in the FMRI scanner, the visual cortex is activated, 972 00:40:12,108 --> 00:40:15,612 even though people say they don't see anything. 973 00:40:15,645 --> 00:40:18,682 So the trick here is to find the threshold. 974 00:40:18,715 --> 00:40:21,518 Find the timing where sometimes 975 00:40:21,551 --> 00:40:24,188 people consciously see the image. 976 00:40:24,221 --> 00:40:28,892 DEHAENE: So if you make now the word a little bit longer, 977 00:40:28,925 --> 00:40:30,461 suddenly, the person says, 978 00:40:30,494 --> 00:40:32,129 "Oh, well, there is a word, obviously." 979 00:40:32,162 --> 00:40:34,364 And it's completely visible. 980 00:40:34,397 --> 00:40:36,767 There is really a sort of all or none phenomenon. 981 00:40:36,800 --> 00:40:38,569 Either you see it or you don't. 982 00:40:38,602 --> 00:40:40,804 And once you've done that, 983 00:40:40,837 --> 00:40:42,439 you've got a really powerful window 984 00:40:42,472 --> 00:40:45,209 onto the neural correlates of conscious perception. 985 00:40:45,242 --> 00:40:47,678 BERLIN: When that happens, 986 00:40:47,711 --> 00:40:49,646 suddenly, a suite of different parts of the brain 987 00:40:49,679 --> 00:40:52,983 shows a surge in activity: the parietal cortex, 988 00:40:53,016 --> 00:40:54,751 which integrates the senses, 989 00:40:54,784 --> 00:40:56,386 the anterior cingulate, 990 00:40:56,419 --> 00:40:58,388 which modulates drive and decision-making, 991 00:40:58,421 --> 00:41:00,023 and the prefrontal cortex, 992 00:41:00,056 --> 00:41:03,961 which handles reasoning and higher-order cognition. 993 00:41:03,994 --> 00:41:06,663 An ignition of distributed brain areas 994 00:41:06,696 --> 00:41:09,600 that come online together, speak to each other, 995 00:41:09,633 --> 00:41:12,769 and broadcast this information to the rest of the brain. 996 00:41:12,802 --> 00:41:14,104 And this is what we think is occurring 997 00:41:14,137 --> 00:41:15,639 during conscious perception. 998 00:41:17,407 --> 00:41:19,076 BERLIN: According to some experts, 999 00:41:19,109 --> 00:41:21,211 this communication between brain regions 1000 00:41:21,244 --> 00:41:23,481 is the signature of consciousness. 1001 00:41:24,848 --> 00:41:27,751 This discovery could have real-world applications 1002 00:41:27,784 --> 00:41:29,386 in matters of life and death. 1003 00:41:29,419 --> 00:41:31,255 But that would take one more step: 1004 00:41:31,288 --> 00:41:34,858 figuring out how to measure consciousness. 1005 00:41:34,891 --> 00:41:36,727 ♪ ♪ 1006 00:41:36,760 --> 00:41:38,795 SETH: In science, when we struggle to understand 1007 00:41:38,828 --> 00:41:40,597 a phenomenon that seems quite mysterious, 1008 00:41:40,630 --> 00:41:44,234 it's often really important to be able to measure it. 1009 00:41:44,267 --> 00:41:46,537 So a few hundred years ago, 1010 00:41:46,570 --> 00:41:49,139 this happened with heat-- you know, it was the development 1011 00:41:49,172 --> 00:41:53,077 of thermometers that catalyzed our understanding. 1012 00:41:53,911 --> 00:41:56,079 Could something work for consciousness the same way? 1013 00:41:56,112 --> 00:41:58,248 Could we have a consciousness-ometer 1014 00:41:58,281 --> 00:41:59,650 that will lead us 1015 00:41:59,683 --> 00:42:02,886 to a deeper understanding of what consciousness is? 1016 00:42:02,919 --> 00:42:06,256 BERLIN: That deeper understanding could transform 1017 00:42:06,289 --> 00:42:08,625 the treatment of people with brain injuries. 1018 00:42:08,658 --> 00:42:11,595 BRIAN EDLOW: So every year, over a million people worldwide 1019 00:42:11,628 --> 00:42:15,265 will come into an intensive care unit unresponsive, comatose. 1020 00:42:15,298 --> 00:42:18,135 The challenge that we face is that our bedside exam-- 1021 00:42:18,168 --> 00:42:20,070 asking the person to open their eyes, 1022 00:42:20,103 --> 00:42:22,506 pinching them and seeing if they'll respond, 1023 00:42:22,539 --> 00:42:24,474 seeing if they move their arms and their legs-- 1024 00:42:24,507 --> 00:42:27,578 that bedside exam is fundamentally limited. 1025 00:42:27,611 --> 00:42:30,380 BERLIN: Limited because it often misses people 1026 00:42:30,413 --> 00:42:32,182 who are actually conscious. 1027 00:42:32,215 --> 00:42:34,918 In this case, we have a healthy volunteer 1028 00:42:34,951 --> 00:42:36,720 from the "NOVA" team, 1029 00:42:36,753 --> 00:42:38,322 but what if she were unresponsive? 1030 00:42:38,355 --> 00:42:41,058 How would we ever know if she was conscious? 1031 00:42:41,091 --> 00:42:43,126 Brian Edlow's team at Mass. General Hospital 1032 00:42:43,159 --> 00:42:46,163 is testing a new technique to find out. 1033 00:42:46,196 --> 00:42:48,298 So tell me, what are you doing here? 1034 00:42:48,331 --> 00:42:51,001 EDLOW: We are pinging the brain with a magnetic pulse 1035 00:42:51,034 --> 00:42:53,503 and looking for an electrical echo. 1036 00:42:53,536 --> 00:42:57,207 The ping is transcranial magnetic stimulation, 1037 00:42:57,240 --> 00:42:58,842 or TMS. 1038 00:42:58,875 --> 00:43:01,812 The echo is the key; if it dies out quickly, 1039 00:43:01,845 --> 00:43:04,114 the patient is unconscious-- they might be in a coma, 1040 00:43:04,147 --> 00:43:06,149 deep sleep, or under anesthesia. 1041 00:43:06,182 --> 00:43:09,720 If instead the echo rings out across the brain 1042 00:43:09,753 --> 00:43:11,622 and becomes more complex, 1043 00:43:11,655 --> 00:43:14,558 the patient is likely to be conscious and aware, 1044 00:43:14,591 --> 00:43:17,160 even if they appear unresponsive. 1045 00:43:17,193 --> 00:43:18,228 EDLOW: The analogy that we like to use 1046 00:43:18,261 --> 00:43:20,964 is throwing a pebble in a lake. 1047 00:43:20,997 --> 00:43:24,368 So the pebble represents the TMS pulse to stimulate the brain, 1048 00:43:24,401 --> 00:43:27,137 and the brain waves, the electrical ripples 1049 00:43:27,170 --> 00:43:32,209 that emanate from that pulse, represent the waves in the lake. 1050 00:43:32,242 --> 00:43:34,411 The more complex those waves are, 1051 00:43:34,444 --> 00:43:36,446 and the longer duration, 1052 00:43:36,479 --> 00:43:38,549 the more likely that person is to be conscious. 1053 00:43:40,050 --> 00:43:43,453 BERLIN: To detect those brain waves, Edlow's team uses E.E.G., 1054 00:43:43,486 --> 00:43:45,956 a tool that measures electrical activity 1055 00:43:45,989 --> 00:43:48,792 in the brain, to quantify the amount of complexity 1056 00:43:48,825 --> 00:43:51,128 a patient's brain bounces back. 1057 00:43:51,161 --> 00:43:52,462 Here's how it works. 1058 00:43:52,495 --> 00:43:53,697 ♪ ♪ 1059 00:43:53,730 --> 00:43:55,832 All neurons, when poked by a magnet, 1060 00:43:55,865 --> 00:43:59,036 will kick back an electrical signal that looks like this: 1061 00:43:59,069 --> 00:44:00,671 a brain wave. 1062 00:44:00,704 --> 00:44:02,773 But if the surrounding neurons aren't healthy, 1063 00:44:02,806 --> 00:44:06,176 those brain waves won't get very far. 1064 00:44:06,209 --> 00:44:08,412 It turns out that in conscious people, 1065 00:44:08,445 --> 00:44:10,647 even those who appear unresponsive, 1066 00:44:10,680 --> 00:44:13,917 not only do those brain waves spread all over the brain, 1067 00:44:13,950 --> 00:44:17,320 but they become more complex too-- it's as if, 1068 00:44:17,353 --> 00:44:19,423 to use music as the analogy, 1069 00:44:19,456 --> 00:44:22,893 what starts as a single repeated note by a few neurons 1070 00:44:22,926 --> 00:44:26,697 eventually turns into a coordinated symphony 1071 00:44:26,730 --> 00:44:28,465 of millions. 1072 00:44:28,498 --> 00:44:30,901 ♪ ♪ 1073 00:44:30,934 --> 00:44:34,871 DEHAENE: We find is that there is this explosion of complexity 1074 00:44:34,904 --> 00:44:36,473 only when the person is conscious. 1075 00:44:36,506 --> 00:44:38,008 This complexity, 1076 00:44:38,041 --> 00:44:40,143 the way different brain areas speak to each other, 1077 00:44:40,176 --> 00:44:43,046 is a signature, a marker of consciousness. 1078 00:44:43,079 --> 00:44:46,349 BERLIN: Studies of hundreds of patients in various states-- 1079 00:44:46,382 --> 00:44:48,985 from deep sleep to anesthesia to coma-- 1080 00:44:49,018 --> 00:44:52,723 have enabled scientists to develop a complexity scale. 1081 00:44:52,756 --> 00:44:54,691 A score above a certain threshold 1082 00:44:54,724 --> 00:44:59,262 means you are conscious or have the capacity for consciousness. 1083 00:44:59,295 --> 00:45:00,931 EDLOW: Multiple studies have now shown 1084 00:45:00,964 --> 00:45:04,735 that 15% to 20% of patients who appear unresponsive, 1085 00:45:04,768 --> 00:45:07,704 they don't express themselves on our behavioral exam, 1086 00:45:07,737 --> 00:45:09,473 they are actually conscious. 1087 00:45:09,506 --> 00:45:11,341 So, you know, will this be able 1088 00:45:11,374 --> 00:45:13,510 to help people in those states? 1089 00:45:13,543 --> 00:45:15,112 When we speak to families 1090 00:45:15,145 --> 00:45:16,980 about what matters to them most, 1091 00:45:17,013 --> 00:45:20,217 it is that patient's current level of consciousness 1092 00:45:20,250 --> 00:45:24,221 and their potential for future recovery of consciousness. 1093 00:45:24,254 --> 00:45:25,789 If families were to have 1094 00:45:25,822 --> 00:45:28,358 that information, it could fundamentally affect 1095 00:45:28,391 --> 00:45:29,626 the decisions they make about 1096 00:45:29,659 --> 00:45:31,361 whether to continue life-sustaining therapy. 1097 00:45:33,129 --> 00:45:37,534 DEHAENE: Progress in the clinic is extremely real and fast, 1098 00:45:37,567 --> 00:45:39,836 and people realize that the problem of consciousness 1099 00:45:39,869 --> 00:45:41,404 is starting to be solved. 1100 00:45:41,437 --> 00:45:44,307 ♪ ♪ 1101 00:45:44,340 --> 00:45:46,209 BERLIN: Starting to be solved, 1102 00:45:46,242 --> 00:45:47,978 because while we have several clues 1103 00:45:48,011 --> 00:45:50,213 about how conscious awareness might work in the brain, 1104 00:45:50,246 --> 00:45:52,616 this is only the beginning. 1105 00:45:52,649 --> 00:45:55,519 How all of those pieces of brain activation add up 1106 00:45:55,552 --> 00:45:58,789 to you, a distinct individual with a sense of self, 1107 00:45:58,822 --> 00:46:00,190 is still a mystery. 1108 00:46:00,223 --> 00:46:02,793 I know my brain creates an internal experience 1109 00:46:02,826 --> 00:46:05,295 by knitting together bits of sensory information, 1110 00:46:05,328 --> 00:46:07,264 filling in the gaps with its best guess 1111 00:46:07,297 --> 00:46:09,099 of what's out there in the world, 1112 00:46:09,132 --> 00:46:12,269 but what are those guesses based on? 1113 00:46:12,302 --> 00:46:14,805 Memory. 1114 00:46:14,838 --> 00:46:18,708 Each of us has a life rich with experiences to draw from. 1115 00:46:18,741 --> 00:46:21,111 Where we were born, went to school, 1116 00:46:21,144 --> 00:46:23,246 who we fell in love with. 1117 00:46:23,279 --> 00:46:25,982 Memories are the cornerstone of our identities, 1118 00:46:26,015 --> 00:46:27,450 but as it turns out, 1119 00:46:27,483 --> 00:46:30,487 they have a very shaky foundation. 1120 00:46:30,520 --> 00:46:32,923 I could swear by it, 1121 00:46:32,956 --> 00:46:35,192 and would pass every lie detector test, that... 1122 00:46:35,225 --> 00:46:36,693 I had met Mother Teresa. 1123 00:46:36,726 --> 00:46:37,794 But I hadn't. 1124 00:46:37,827 --> 00:46:39,362 Something that I wanted to happen 1125 00:46:39,395 --> 00:46:40,631 but it never did happen. 1126 00:46:43,199 --> 00:46:44,734 SCHILLER: The stories we tell 1127 00:46:44,767 --> 00:46:48,405 ourselves, or what we consider our memory, is a construction. 1128 00:46:48,438 --> 00:46:50,640 We create these representations. 1129 00:46:50,673 --> 00:46:53,710 And they're very dynamic, they constantly change. 1130 00:46:53,743 --> 00:46:55,912 You're kind of living a revision 1131 00:46:55,945 --> 00:46:58,348 of the story of your life, constantly. 1132 00:46:59,349 --> 00:47:01,351 SETH: The more often we recall things, 1133 00:47:01,384 --> 00:47:03,820 the less objectively accurate 1134 00:47:03,853 --> 00:47:05,189 our memories become. 1135 00:47:06,190 --> 00:47:08,525 BERLIN: It turns out that every time you a recall a memory-- 1136 00:47:08,558 --> 00:47:10,660 your first kiss, 1137 00:47:10,693 --> 00:47:12,429 graduating from college, 1138 00:47:12,462 --> 00:47:14,464 the death of a loved one-- 1139 00:47:14,497 --> 00:47:19,069 the very act of recollection makes it vulnerable to change. 1140 00:47:19,102 --> 00:47:20,937 SCHILLER: So when you experience a new event, 1141 00:47:20,970 --> 00:47:23,473 it has to be stored in the brain. 1142 00:47:23,506 --> 00:47:25,008 And then, we used to think that 1143 00:47:25,041 --> 00:47:26,676 whenever you think about that event, 1144 00:47:26,709 --> 00:47:30,447 you retrieve the same original memory. 1145 00:47:30,480 --> 00:47:34,184 But what we got to realize in the last few decades 1146 00:47:34,217 --> 00:47:36,353 is that whenever you retrieve a memory, 1147 00:47:36,386 --> 00:47:38,588 it goes back to an unstable state. 1148 00:47:38,621 --> 00:47:44,027 BERLIN: In 2000, memory scientist Eric Kandel won the Nobel Prize 1149 00:47:44,060 --> 00:47:47,497 for showing that each memory creates new synapses, 1150 00:47:47,530 --> 00:47:49,332 connections that store the memory. 1151 00:47:49,365 --> 00:47:53,036 But what happens when you recall it? 1152 00:47:53,069 --> 00:47:54,838 Every time you remember it, 1153 00:47:54,871 --> 00:47:56,539 you bring it up into your working memory 1154 00:47:56,572 --> 00:47:57,774 and you perceive it, 1155 00:47:57,807 --> 00:47:59,943 and you destroy the long-term memory. 1156 00:47:59,976 --> 00:48:02,913 And you actually have 1157 00:48:02,946 --> 00:48:04,948 to recast it into long-term memory 1158 00:48:04,981 --> 00:48:06,683 when you re-remember it. 1159 00:48:06,716 --> 00:48:08,685 So every single time you remember something, 1160 00:48:08,718 --> 00:48:10,320 you actually add more noise to it, 1161 00:48:10,353 --> 00:48:12,689 so that it's more and more and more false 1162 00:48:12,722 --> 00:48:14,624 throughout time. 1163 00:48:14,657 --> 00:48:17,627 BERLIN: This mechanism, called reconsolidation, 1164 00:48:17,660 --> 00:48:19,296 was first discovered in rodents, 1165 00:48:19,329 --> 00:48:22,265 where neuroscientists witnessed what happens 1166 00:48:22,298 --> 00:48:24,301 when a memory gets recollected: 1167 00:48:24,334 --> 00:48:26,937 for the memory to return to long-term storage, 1168 00:48:26,970 --> 00:48:31,474 the connections between neurons actually have to get rebuilt. 1169 00:48:31,507 --> 00:48:34,044 Recent experiments have suggested 1170 00:48:34,077 --> 00:48:37,113 this is likely a mechanism in human brains, as well, 1171 00:48:37,146 --> 00:48:40,650 because certain drugs known to disrupt reconsolidation 1172 00:48:40,683 --> 00:48:43,587 have been shown to alter human memories. 1173 00:48:44,721 --> 00:48:48,758 FENTON: We're stuck with the problem of, how do we know what is true? 1174 00:48:48,791 --> 00:48:51,027 How do we know what's real? 1175 00:48:51,060 --> 00:48:52,896 And maybe 1176 00:48:52,929 --> 00:48:55,665 part of the recognition is, some of those things 1177 00:48:55,698 --> 00:48:58,835 don't matter as much as we think they do. 1178 00:49:01,471 --> 00:49:04,341 SCHILLER: If we think about the fact that maybe our memories are not 1179 00:49:04,374 --> 00:49:05,942 as they originally happened, 1180 00:49:05,975 --> 00:49:09,212 it could be a scary thought, because then, who are we? 1181 00:49:09,245 --> 00:49:12,382 I think you need to think about it as something 1182 00:49:12,415 --> 00:49:15,618 more liberating, because if you're stuck 1183 00:49:15,651 --> 00:49:17,687 with original representations, 1184 00:49:17,720 --> 00:49:19,423 you're kind of stuck in the past. 1185 00:49:20,524 --> 00:49:24,294 BERLIN: Just like our perceptions, our sense of self is dynamic, 1186 00:49:24,327 --> 00:49:26,863 built to serve us in the present. 1187 00:49:26,896 --> 00:49:28,732 SETH: Our experience of, of self 1188 00:49:28,765 --> 00:49:31,034 is a construction at all sorts of different levels. 1189 00:49:31,067 --> 00:49:33,503 What the brain is doing, is interested in, 1190 00:49:33,536 --> 00:49:36,973 is weaving together a kind of story. 1191 00:49:37,006 --> 00:49:40,643 FENTON: The brain is a storytelling machine, right? 1192 00:49:40,676 --> 00:49:43,980 It's a machine that's designed to make predictions. 1193 00:49:44,013 --> 00:49:47,751 KASTHURI: The narratives that we tell ourselves 1194 00:49:47,784 --> 00:49:49,819 are the biggest illusions that we ever participate in. 1195 00:49:49,852 --> 00:49:52,255 Your sense of who you are is an illusion, 1196 00:49:52,288 --> 00:49:55,325 as everything else-- you're no exception. 1197 00:49:56,492 --> 00:49:59,262 BERLIN: But if even our sense of self is an illusion, 1198 00:49:59,295 --> 00:50:01,564 where does that leave us? 1199 00:50:01,597 --> 00:50:04,601 MARTINEZ-CONDE (chuckles): Trust the illusion, that's the only thing 1200 00:50:04,634 --> 00:50:07,137 that we can be sure of, 1201 00:50:07,170 --> 00:50:12,142 that what we perceive is not what's there. 1202 00:50:17,447 --> 00:50:19,516 BERLIN: So all these years later, 1203 00:50:19,549 --> 00:50:22,552 in my quest to understand where my thoughts come from 1204 00:50:22,585 --> 00:50:24,387 and how my brain works, 1205 00:50:24,420 --> 00:50:27,457 I've learned that my brain is an exquisite machine 1206 00:50:27,490 --> 00:50:31,061 that perceives reality in the service of survival, 1207 00:50:31,094 --> 00:50:33,029 not accuracy. 1208 00:50:33,062 --> 00:50:35,732 The world I carry inside of my head 1209 00:50:35,765 --> 00:50:37,333 is a construction of my brain 1210 00:50:37,366 --> 00:50:40,003 built on bits of sensory information 1211 00:50:40,036 --> 00:50:41,905 woven together with memory 1212 00:50:41,938 --> 00:50:44,974 to create a conscious experience. 1213 00:50:45,007 --> 00:50:47,410 Now, to some this might sound scary, 1214 00:50:47,443 --> 00:50:50,146 but to me, it's inspiring. 1215 00:50:50,179 --> 00:50:53,817 ♪ ♪ 1216 00:50:53,850 --> 00:50:55,819 SETH: The simple act of opening our eyes 1217 00:50:55,852 --> 00:50:57,620 and seeing a world, 1218 00:50:57,653 --> 00:50:59,923 we should not take that for granted. 1219 00:50:59,956 --> 00:51:01,858 And in realizing 1220 00:51:01,891 --> 00:51:05,695 what a miracle of neural computation 1221 00:51:05,728 --> 00:51:07,263 is going on under the hood, 1222 00:51:07,296 --> 00:51:09,699 to give us even the simplest experiences, 1223 00:51:09,732 --> 00:51:11,267 I think this adds value, 1224 00:51:11,300 --> 00:51:13,970 it adds meaning, it adds depth to our lives. 1225 00:51:14,003 --> 00:51:15,772 ♪ ♪ 1226 00:51:15,805 --> 00:51:18,308 DEHAENE: I think it's liberating to understand 1227 00:51:18,341 --> 00:51:23,113 that we rise from this organization of matter. 1228 00:51:23,146 --> 00:51:25,815 It means that we can be a little bit more humble. 1229 00:51:25,848 --> 00:51:29,385 We are gorgeous machines designed by evolution 1230 00:51:29,418 --> 00:51:34,524 as well as by our environment, education, friends, families. 1231 00:51:34,557 --> 00:51:36,560 All of that is inscribed in our brains. 1232 00:51:39,195 --> 00:51:42,832 SAXE: Sometimes when I wonder what I'm doing with my life, 1233 00:51:42,865 --> 00:51:46,035 I think how is it that a spatial 1234 00:51:46,068 --> 00:51:48,271 and temporal pattern of electrical signals 1235 00:51:48,304 --> 00:51:52,041 passing between cells in our brains makes us who we are? 1236 00:51:52,074 --> 00:51:55,478 That just being a part of the team 1237 00:51:55,511 --> 00:52:00,250 asking that question is worth keeping going for. 1238 00:52:00,283 --> 00:52:04,020 ♪ ♪ 1239 00:52:13,396 --> 00:52:20,937 ♪ ♪ 1240 00:52:24,774 --> 00:52:32,315 ♪ ♪ 1241 00:52:33,950 --> 00:52:41,491 ♪ ♪ 1242 00:52:43,125 --> 00:52:50,667 ♪ ♪ 1243 00:52:56,405 --> 00:53:03,580 ♪ ♪ 90605

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