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These are the user uploaded subtitles that are being translated: 1 00:00:01,100 --> 00:00:03,133 ♪ ♪ 2 00:00:04,966 --> 00:00:07,900 ♪ ♪ 3 00:00:07,900 --> 00:00:10,133 ANIL SETH: The brain is one of the most complex objects 4 00:00:10,133 --> 00:00:13,266 that we know of in the universe. 5 00:00:13,266 --> 00:00:16,400 BOBBY KASTHURI: There are more connections in your brain 6 00:00:16,400 --> 00:00:20,433 than there are stars in the Milky Way galaxy. 7 00:00:20,433 --> 00:00:23,833 So, we literally walk around with about 10,000 galaxies' 8 00:00:23,833 --> 00:00:25,766 worth of neuronal connections 9 00:00:25,766 --> 00:00:27,266 in one of our brains. 10 00:00:27,266 --> 00:00:29,266 HEATHER BERLIN: That vast web of connections creates you. 11 00:00:29,266 --> 00:00:30,966 But how? 12 00:00:30,966 --> 00:00:34,300 NANCY KANWISHER: Figuring out how the brain implements the mind 13 00:00:34,300 --> 00:00:37,100 is a massive challenge. 14 00:00:37,100 --> 00:00:38,400 ♪ ♪ 15 00:00:38,400 --> 00:00:39,666 SETH: It seems as though 16 00:00:39,666 --> 00:00:41,833 the world just pours itself into the mind 17 00:00:41,833 --> 00:00:44,633 through the transparent windows of the eyes 18 00:00:44,633 --> 00:00:46,766 and the ears and, and all our other senses. 19 00:00:49,333 --> 00:00:51,700 ♪ ♪ 20 00:00:51,700 --> 00:00:53,233 BERLIN: But is what we see, 21 00:00:53,233 --> 00:00:54,966 hear, 22 00:00:54,966 --> 00:00:57,800 and feel real? 23 00:00:57,800 --> 00:01:00,033 You might think that the reality 24 00:01:00,033 --> 00:01:02,866 outside is actually what you're perceiving. 25 00:01:02,866 --> 00:01:05,900 And the answer is no, it really isn't. 26 00:01:05,900 --> 00:01:07,766 Almost at the very first moment, 27 00:01:07,766 --> 00:01:09,300 we are transforming reality. 28 00:01:09,300 --> 00:01:10,800 It feels so real 29 00:01:10,800 --> 00:01:12,433 because we don't know better. 30 00:01:12,433 --> 00:01:14,300 Think about illusions. 31 00:01:14,300 --> 00:01:15,900 ROSA LAFER-SOUSA: Do you remember the dress? 32 00:01:15,900 --> 00:01:18,366 Of course-- it's like a celebrity, the dress. 33 00:01:18,366 --> 00:01:19,966 A polarizing debate 34 00:01:19,966 --> 00:01:22,000 that took over the internet. 35 00:01:22,000 --> 00:01:23,533 White and gold. Blue and black. 36 00:01:23,533 --> 00:01:26,166 SETH: Illusions are fascinating; 37 00:01:26,166 --> 00:01:27,300 they're like fractures in the matrix. 38 00:01:27,300 --> 00:01:28,300 BERLIN: Ow! 39 00:01:28,300 --> 00:01:30,466 Isn't that interesting? Whoa! 40 00:01:30,466 --> 00:01:32,300 They reveal to us that the way 41 00:01:32,300 --> 00:01:35,700 we perceive things isn't necessarily the way they are. 42 00:01:35,700 --> 00:01:39,033 BERLIN: Could you be the biggest illusion of all? 43 00:01:39,033 --> 00:01:42,833 SUSANA MARTINEZ-CONDE: Your sense of who you are is an illusion 44 00:01:42,833 --> 00:01:45,066 as everything else; you're no exception. 45 00:01:46,066 --> 00:01:49,533 BERLIN: "Your Brain: Perception Deception." 46 00:01:49,533 --> 00:01:51,500 Right now, on "NOVA." 47 00:01:51,500 --> 00:01:56,133 ♪ ♪ 48 00:02:12,766 --> 00:02:14,433 MAN: Okay, rolling. 49 00:02:14,433 --> 00:02:20,033 ♪ ♪ 50 00:02:35,666 --> 00:02:38,066 Take three. 51 00:02:41,366 --> 00:02:44,266 BERLIN: Have you ever thought about what's real? 52 00:02:44,266 --> 00:02:46,600 (echoing) 53 00:02:53,000 --> 00:02:55,833 Somehow the whole world out there gets inside my head. 54 00:02:55,833 --> 00:02:59,566 How do I know what I see, what I hear, 55 00:02:59,566 --> 00:03:02,366 what I feel is right? 56 00:03:02,366 --> 00:03:04,600 It's a question that's fascinated me 57 00:03:04,600 --> 00:03:07,766 ever since I was a little girl. 58 00:03:07,766 --> 00:03:10,200 I couldn't sleep one night, 59 00:03:10,200 --> 00:03:13,566 and I had this thought for the first time: 60 00:03:17,533 --> 00:03:19,933 And then I thought, well, even if I don't have a body, 61 00:03:19,933 --> 00:03:23,400 can I at least keep my own inner thoughts? 62 00:03:23,400 --> 00:03:25,666 So I asked my dad the next day, 63 00:03:25,666 --> 00:03:28,566 "Dad, where do my thoughts come from?" 64 00:03:28,566 --> 00:03:32,200 And he said, "They come from your brain." 65 00:03:32,200 --> 00:03:33,600 (explosion echoes) 66 00:03:33,600 --> 00:03:35,433 ♪ ♪ 67 00:03:35,433 --> 00:03:38,300 "Your brain." I was hooked. 68 00:03:39,333 --> 00:03:41,300 This bag of jelly between my ears, 69 00:03:41,300 --> 00:03:43,033 how does it work? 70 00:03:43,033 --> 00:03:45,000 STANISLAS DEHAENE: I think it's one of the ultimate mysteries. 71 00:03:45,000 --> 00:03:47,700 How matter becomes thought. 72 00:03:49,266 --> 00:03:51,933 ANDRÉ FENTON: To answer that question would be perhaps 73 00:03:51,933 --> 00:03:53,933 the highest human achievement to date. 74 00:03:55,400 --> 00:03:56,900 DANIELA SCHILLER: I mean, forget about scientific quest. 75 00:03:56,900 --> 00:03:58,700 It's a human quest. 76 00:04:12,566 --> 00:04:15,833 ♪ ♪ 77 00:04:15,833 --> 00:04:18,600 BERLIN: To find answers, 78 00:04:18,600 --> 00:04:21,733 I became a neuroscientist and a psychologist. 79 00:04:25,933 --> 00:04:28,066 I'm Heather Berlin, 80 00:04:28,066 --> 00:04:31,033 and my journey to understand my brain begins with a question. 81 00:04:32,533 --> 00:04:34,566 How does the world out there, 82 00:04:34,566 --> 00:04:37,233 with all its beauty and complexity, 83 00:04:37,233 --> 00:04:39,533 get inside our heads? 84 00:04:39,533 --> 00:04:42,766 ♪ ♪ 85 00:04:42,766 --> 00:04:44,066 Think about it. 86 00:04:44,066 --> 00:04:46,166 Imagine for a second you're a brain, 87 00:04:46,166 --> 00:04:48,600 sealed inside your skull. 88 00:04:48,600 --> 00:04:51,866 There's no light, no sound. 89 00:04:53,366 --> 00:04:55,000 KASTHURI: You're a massive collection 90 00:04:55,000 --> 00:04:57,800 of billions and billions of cells that are living 91 00:04:57,800 --> 00:05:00,566 in this weird pond that is entirely devoid 92 00:05:00,566 --> 00:05:03,333 of all of the sensations, and that somehow, 93 00:05:03,333 --> 00:05:06,733 through chemistry and electricity, all of these 94 00:05:06,733 --> 00:05:09,100 perceptions and memories of the world 95 00:05:09,100 --> 00:05:10,733 originate in our brains. 96 00:05:12,366 --> 00:05:14,100 BERLIN: All brains-- from the tiny fish 97 00:05:14,100 --> 00:05:17,400 to the enormous elephant-- contain microscopic cells 98 00:05:17,400 --> 00:05:21,400 called neurons, and one of their jobs is to translate input 99 00:05:21,400 --> 00:05:23,633 from the external world, whether that's light, 100 00:05:23,633 --> 00:05:26,300 heat, sound, or pressure, for instance, 101 00:05:26,300 --> 00:05:30,266 into electrochemical signals the organism can use to act. 102 00:05:30,266 --> 00:05:32,533 What might be surprising to you is that 103 00:05:32,533 --> 00:05:35,100 as neurons process sensory signals, 104 00:05:35,100 --> 00:05:37,566 they create an edited version of reality, 105 00:05:37,566 --> 00:05:41,066 even on the most basic level. 106 00:05:41,066 --> 00:05:44,800 KASTHURI: We're deciding to throw away 99% of the world. 107 00:05:44,800 --> 00:05:47,866 Almost at the very first moment, we are transforming reality 108 00:05:47,866 --> 00:05:49,966 into something we could use. 109 00:05:49,966 --> 00:05:53,800 BERLIN: Neurons transform reality by competing with each other. 110 00:05:53,800 --> 00:05:57,000 When a creature touches, smells, sees, or hears something, 111 00:05:57,000 --> 00:06:00,600 its sensory neurons fire; some a little, some a lot, 112 00:06:00,600 --> 00:06:03,266 depending on where the physical signal is strongest. 113 00:06:03,266 --> 00:06:05,900 But follow those signals down towards its brain, 114 00:06:05,900 --> 00:06:08,900 you'll see that the weaker ones get stamped out. 115 00:06:10,233 --> 00:06:12,100 For simple brains, say, the brain of a crab, 116 00:06:12,100 --> 00:06:16,333 a diffuse light to the eye becomes a sharp beam. 117 00:06:16,333 --> 00:06:19,400 For more complex brains like ours, 118 00:06:19,400 --> 00:06:21,233 it's in part what makes you think 119 00:06:21,233 --> 00:06:24,800 that these two squares are completely different colors, 120 00:06:24,800 --> 00:06:28,000 but actually, they're identical. 121 00:06:28,000 --> 00:06:33,333 ♪ ♪ 122 00:06:33,333 --> 00:06:35,633 MARTINEZ-CONDE: Think about illusions. 123 00:06:35,633 --> 00:06:37,966 First, they're a lot of fun, but as neuroscientists... 124 00:06:37,966 --> 00:06:40,400 Whoa... 125 00:06:40,400 --> 00:06:42,866 MARTINEZ-CONDE: ...illusions are very important to us. 126 00:06:42,866 --> 00:06:47,200 Because of this discrepancy between objective reality 127 00:06:47,200 --> 00:06:50,400 and subjective perception, we can use these illusions 128 00:06:50,400 --> 00:06:54,600 as a handle to try to understand what the brain 129 00:06:54,600 --> 00:06:57,533 is doing all the time. 130 00:06:57,533 --> 00:07:00,233 BERLIN: Susana Martinez-Conde, along with her partner 131 00:07:00,233 --> 00:07:01,600 and collaborator Stephen Macknik, 132 00:07:01,600 --> 00:07:04,266 are among the world's preeminent experts 133 00:07:04,266 --> 00:07:06,233 on illusions and perception, 134 00:07:06,233 --> 00:07:09,233 and what they tell us about how the brain works. 135 00:07:09,233 --> 00:07:11,300 Ha, now what? 136 00:07:11,300 --> 00:07:12,600 (both laughing) 137 00:07:12,600 --> 00:07:14,100 MARTINEZ-CONDE: To give a different example, 138 00:07:14,100 --> 00:07:18,466 Adelson's checkerboard illusion, this is so striking because 139 00:07:18,466 --> 00:07:23,366 you see some of the checks as dark and others as bright, 140 00:07:23,366 --> 00:07:27,966 but you realize that it is exactly the same shade of gray. 141 00:07:27,966 --> 00:07:29,566 BERLIN: Don't believe it? 142 00:07:29,566 --> 00:07:32,700 Look at the squares labeled A and B. 143 00:07:32,700 --> 00:07:35,700 A looks darker, right? 144 00:07:35,700 --> 00:07:38,100 Wrong-- that's the illusion. 145 00:07:38,100 --> 00:07:41,300 That's because your brain is adjusting for the shadow. 146 00:07:41,300 --> 00:07:43,933 MARTINEZ-CONDE: What's happening is that your brain is considering 147 00:07:43,933 --> 00:07:48,433 the light source and basically subtracting that light source 148 00:07:48,433 --> 00:07:50,633 from your resulting perception. 149 00:07:50,633 --> 00:07:53,533 Your brain is performing an interpretation, 150 00:07:53,533 --> 00:07:56,733 a shortcut, if you will, to arrive 151 00:07:56,733 --> 00:07:58,666 at a perception. 152 00:07:58,666 --> 00:08:02,133 BERLIN: If the brain's shortcuts distort reality this much, 153 00:08:02,133 --> 00:08:05,966 how much of the world are we really seeing? 154 00:08:05,966 --> 00:08:07,600 STEPHEN MACKNIK: What you need to understand 155 00:08:07,600 --> 00:08:11,533 is that we really can't see most of the world around us. 156 00:08:11,533 --> 00:08:13,433 We're effectively blind 157 00:08:13,433 --> 00:08:15,966 to 99.9% of the world around us at any given time. 158 00:08:15,966 --> 00:08:18,533 If you hold out your thumb at arm's length... 159 00:08:18,533 --> 00:08:20,800 Mm-hmm. And you straighten your elbow and you look at your thumbnail, 160 00:08:20,800 --> 00:08:23,733 your thumbnail is about one degree of visual angle here, 161 00:08:23,733 --> 00:08:26,733 and it turns out that that's the only place 162 00:08:26,733 --> 00:08:28,466 we can actually see with 20/20 vision. 163 00:08:28,466 --> 00:08:31,033 Wow. And everywhere else, we're legally blind. 164 00:08:31,033 --> 00:08:33,966 BERLIN: It might sound hard to believe, 165 00:08:33,966 --> 00:08:36,533 but human vision is really like this. 166 00:08:36,533 --> 00:08:38,466 You actually only see detail 167 00:08:38,466 --> 00:08:40,666 in about one percent of your visual field. 168 00:08:40,666 --> 00:08:43,400 That's because only a tiny portion of the world 169 00:08:43,400 --> 00:08:46,666 can be processed in detail by the retina. 170 00:08:46,666 --> 00:08:49,800 It feels like I'm seeing the whole world in 20/20 vision. 171 00:08:49,800 --> 00:08:53,166 And it's almost all completely made up in your brain, 172 00:08:53,166 --> 00:08:56,566 based on assumptions and models of how the world works 173 00:08:56,566 --> 00:08:59,166 and just a tiny bit of high-quality visual information. 174 00:08:59,166 --> 00:09:00,933 Let me demonstrate this to you. 175 00:09:00,933 --> 00:09:02,866 I know it's kind of hard to believe... Mm-hmm. 176 00:09:02,866 --> 00:09:04,666 ...because you've been having your whole life 177 00:09:04,666 --> 00:09:05,700 where you feel like everything's continuous. Yeah, show me the data. 178 00:09:05,700 --> 00:09:07,166 (laughs) Show me the evidence. 179 00:09:07,166 --> 00:09:09,600 Let's look at an eye-tracker and look at your eyes 180 00:09:09,600 --> 00:09:10,966 and how they actually work. 181 00:09:10,966 --> 00:09:13,766 And if you put your head in this headrest... 182 00:09:13,766 --> 00:09:16,400 Mm-hmm. ...we'll point the camera at your eyeballs 183 00:09:16,400 --> 00:09:17,800 and we'll actually be able to see 184 00:09:17,800 --> 00:09:20,500 where your eyeballs point during this demonstration. 185 00:09:20,500 --> 00:09:23,333 Feels like "Clockwork Orange." (chuckles) 186 00:09:23,333 --> 00:09:25,666 "Buck lived at a big house 187 00:09:25,666 --> 00:09:28,033 in the sun-kissed Santa Clara Valley." 188 00:09:29,100 --> 00:09:31,533 BERLIN: First up, a reading demo. 189 00:09:31,533 --> 00:09:33,900 Though most of your screen may be filled with Xs, 190 00:09:33,900 --> 00:09:36,566 to me, it just feels like normal reading. 191 00:09:36,566 --> 00:09:38,366 I barely see the Xs, 192 00:09:38,366 --> 00:09:40,166 and that's because the display of letters 193 00:09:40,166 --> 00:09:42,933 is tied to my eye movements. 194 00:09:42,933 --> 00:09:44,266 BERLIN: Well, it's just, the words are being 195 00:09:44,266 --> 00:09:46,100 revealed depending on where I look. 196 00:09:46,100 --> 00:09:48,233 That's right, so as you move your eyes... 197 00:09:48,233 --> 00:09:50,033 So weird. ...the words are revealed to you. 198 00:09:50,033 --> 00:09:52,700 But we don't move our eyes in the same way you do. 199 00:09:52,700 --> 00:09:55,633 So we just see a bunch of Xs most of the time. 200 00:09:55,633 --> 00:09:58,566 BERLIN: What turns out to be critical is my eye movements. 201 00:09:58,566 --> 00:10:02,566 MACKNIK: So our eye movements program what part of this 202 00:10:02,566 --> 00:10:04,366 high-quality piece of visual real estate 203 00:10:04,366 --> 00:10:06,166 we're going to put where and at what time. 204 00:10:06,166 --> 00:10:09,433 BERLIN: The human eye moves about three times per second. 205 00:10:09,433 --> 00:10:12,600 We take it for granted, but without these movements, 206 00:10:12,600 --> 00:10:14,266 we'd be basically blind, 207 00:10:14,266 --> 00:10:16,300 as Steve is about to show me. 208 00:10:16,300 --> 00:10:18,500 MACKNIK: In this demonstration, it's the opposite. 209 00:10:18,500 --> 00:10:21,533 Here we're blocking what you can possibly see, right? 210 00:10:21,533 --> 00:10:24,466 BERLIN: Though you may see a whole scene with a square moving around, 211 00:10:24,466 --> 00:10:26,733 all I see is the square! 212 00:10:26,733 --> 00:10:29,200 I can tell something is around the edges, but it's blurry. 213 00:10:29,200 --> 00:10:32,666 Whenever I try to look, the square moves with my eyes 214 00:10:32,666 --> 00:10:34,300 and it's blocked. 215 00:10:34,300 --> 00:10:36,533 BERLIN: This is so frustrating, this one. 216 00:10:36,533 --> 00:10:39,666 Who has their hand up in this image? 217 00:10:41,000 --> 00:10:44,333 BERLIN: Uh... I think that guy down there? 218 00:10:44,333 --> 00:10:46,600 MACKNIK: That's right, but it's very hard for you to see, 219 00:10:46,600 --> 00:10:48,533 right? Every time I look at him, it, yeah. 220 00:10:48,533 --> 00:10:52,333 It disappears because this block, it blocks it. 221 00:10:52,333 --> 00:10:54,600 MACKNIK: This actually is interesting 222 00:10:54,600 --> 00:10:57,466 because it's in high-quality vision wherever you look, 223 00:10:57,466 --> 00:11:00,133 but it's blurry in the surround. 224 00:11:00,133 --> 00:11:02,833 BERLIN: Now the scene looks normal to me, 225 00:11:02,833 --> 00:11:04,566 but mostly blurry to you 226 00:11:04,566 --> 00:11:06,733 because your eye movements don't match mine. 227 00:11:06,733 --> 00:11:08,333 MACKNIK: Wherever you happen to look, 228 00:11:08,333 --> 00:11:11,800 you have high-quality image processing happening 229 00:11:11,800 --> 00:11:14,333 and the surround is completely blurry. 230 00:11:14,333 --> 00:11:18,100 This kind of represents exactly what your visual system 231 00:11:18,100 --> 00:11:20,933 looks like all the time anyway. 232 00:11:20,933 --> 00:11:23,300 So why would our brains be built this way? 233 00:11:23,300 --> 00:11:25,033 Well, think about what the alternative is. 234 00:11:25,033 --> 00:11:26,300 What if we didn't have eye movements? 235 00:11:26,300 --> 00:11:27,600 Well, if we didn't have eye movements, 236 00:11:27,600 --> 00:11:29,633 and we just wanted to see the entire world, 237 00:11:29,633 --> 00:11:30,933 we'd need to have our retinas 238 00:11:30,933 --> 00:11:32,700 see everything in very high quality. 239 00:11:32,700 --> 00:11:34,933 Our brains would be 600 times bigger, 240 00:11:34,933 --> 00:11:37,733 and you gotta remember, the visual system's our best sense. 241 00:11:37,733 --> 00:11:39,366 This is our richest sense. 242 00:11:39,366 --> 00:11:42,300 So our other senses are, are even more impoverished. 243 00:11:42,300 --> 00:11:45,966 BERLIN: Here's how your brain really sees the world. 244 00:11:45,966 --> 00:11:47,700 It's easy to think it's like this. 245 00:11:47,700 --> 00:11:50,600 You open your eyes and the whole world pours in. 246 00:11:50,600 --> 00:11:52,533 But really, it's like this. 247 00:11:54,866 --> 00:11:57,466 Your eyes sample tiny pieces of the world 248 00:11:57,466 --> 00:12:01,566 and the brain fills in the rest-- constantly, all the time. 249 00:12:01,566 --> 00:12:02,966 KANWISHER: We feel like we have 250 00:12:02,966 --> 00:12:06,600 this incredibly rich, wide, full, detailed percept 251 00:12:06,600 --> 00:12:08,333 of what's going on moment to moment, 252 00:12:08,333 --> 00:12:10,600 and that's probably pretty illusory. 253 00:12:10,600 --> 00:12:13,133   What we're actually aware of is a tiny subset 254 00:12:13,133 --> 00:12:15,266 of the information that comes in through our eyes. 255 00:12:15,266 --> 00:12:17,266 BERLIN: Don't believe it? 256 00:12:17,266 --> 00:12:18,766 Consider this: 257 00:12:18,766 --> 00:12:21,600 your optic nerve is what connects your eye to your brain, 258 00:12:21,600 --> 00:12:24,233 and its location near the center of your retina 259 00:12:24,233 --> 00:12:26,166 effectively creates a blind spot 260 00:12:26,166 --> 00:12:28,333 near the center of your visual field. 261 00:12:28,333 --> 00:12:32,300 And yet, you don't experience the blind spot-- why? 262 00:12:32,300 --> 00:12:35,033 The brain samples the area near the blind spot 263 00:12:35,033 --> 00:12:38,033 and fills in the gap with its best guess. 264 00:12:38,033 --> 00:12:39,700 KASTHURI: It's probably not fair 265 00:12:39,700 --> 00:12:42,000 to say that we completely confabulate the world, 266 00:12:42,000 --> 00:12:44,166 it's just that we probably represent 267 00:12:44,166 --> 00:12:47,066 one percent of it at any particular moment in time. 268 00:12:47,066 --> 00:12:50,400 So, it's a constant updating between what I see with, 269 00:12:50,400 --> 00:12:53,633 versus what I remember, versus what I expect. 270 00:12:53,633 --> 00:12:56,300 And it's that dance between those three 271 00:12:56,300 --> 00:12:58,833 that actually gives us our sense of reality. 272 00:12:58,833 --> 00:13:02,233 BERLIN: And amazingly, that edited reality-- 273 00:13:02,233 --> 00:13:03,933 despite its limitations-- 274 00:13:03,933 --> 00:13:05,800 serves us quite well. 275 00:13:05,800 --> 00:13:07,866 KASTHURI: You might ask, "If I'm just keeping track 276 00:13:07,866 --> 00:13:09,400 "of one percent of the information in the world, 277 00:13:09,400 --> 00:13:11,633 how can I drive a car?" 278 00:13:11,633 --> 00:13:14,200 And it turns out that first one percent 279 00:13:14,200 --> 00:13:15,866 of the information that comes in from the world 280 00:13:15,866 --> 00:13:17,733 is actually an enormous amount of information. (chuckling) 281 00:13:17,733 --> 00:13:20,466 If we had to actually pay attention 282 00:13:20,466 --> 00:13:24,000 to everything on the road at the, at one particular time, 283 00:13:24,000 --> 00:13:26,433 it would take minutes, maybe even longer, 284 00:13:26,433 --> 00:13:28,233 before I decide to turn the wheel right 285 00:13:28,233 --> 00:13:30,666 or to turn the wheel left. 286 00:13:30,666 --> 00:13:32,200 ♪ ♪ 287 00:13:32,200 --> 00:13:34,900 BERLIN: By understanding how my senses really work, 288 00:13:34,900 --> 00:13:36,766 I'm getting a peek behind the curtain: 289 00:13:36,766 --> 00:13:40,900 what my brain is really up to outside of my awareness. 290 00:13:42,600 --> 00:13:46,466 MARTINEZ-CONDE: Based on this very tiny amount of information, 291 00:13:46,466 --> 00:13:50,066 we construct this grand simulation 292 00:13:50,066 --> 00:13:53,866 of the visual world around us. 293 00:13:53,866 --> 00:13:57,133 It feels so real because we don't know better. 294 00:13:58,266 --> 00:14:01,333 BERLIN: And most of the time, we all agree on that simulation. 295 00:14:01,333 --> 00:14:05,466 It's when we don't that we can learn something. 296 00:14:05,466 --> 00:14:07,933 So do you remember the dress? Of course. 297 00:14:07,933 --> 00:14:11,366 Did you see this dress or this one? 298 00:14:11,366 --> 00:14:12,966 It's a simple question, 299 00:14:12,966 --> 00:14:15,400 but the answer has divided friends and family. 300 00:14:15,400 --> 00:14:17,233 White and gold. Blue and black. 301 00:14:17,233 --> 00:14:19,066 I remember it caused quite the stir, right? 302 00:14:19,066 --> 00:14:20,433 Massive stir. 303 00:14:20,433 --> 00:14:21,733 A polarizing debate 304 00:14:21,733 --> 00:14:23,633 that took over the internet. 305 00:14:23,633 --> 00:14:25,233 ♪ ♪ 306 00:14:25,233 --> 00:14:29,033 LAFER-SOUSA: People had existential crises over this image. 307 00:14:29,033 --> 00:14:30,600 People tweeted things like, 308 00:14:30,600 --> 00:14:32,300 "If that's not white and gold, 309 00:14:32,300 --> 00:14:33,933 my life has been a lie." 310 00:14:33,933 --> 00:14:35,900 Swear on your mother's grave. 311 00:14:35,900 --> 00:14:37,500 Because that dress is white and gold. 312 00:14:37,500 --> 00:14:38,666 Out of her (bleep) mind. 313 00:14:38,666 --> 00:14:40,266 LAFER-SOUSA: Massive arguments. 314 00:14:40,266 --> 00:14:42,866 I watched videos of people screaming at each other. 315 00:14:42,866 --> 00:14:44,266 GRAYSON DOLAN: This is white, dude! 316 00:14:44,266 --> 00:14:46,333 ETHAN DOLAN: White? That is dark blue! 317 00:14:46,333 --> 00:14:47,800 It's purple-blue! 318 00:14:47,800 --> 00:14:49,500 LAFER-SOUSA: I bet there was a divorce here or there 319 00:14:49,500 --> 00:14:52,200 over this image. 320 00:14:52,200 --> 00:14:54,166 BERLIN: So when you first saw that dress, 321 00:14:54,166 --> 00:14:56,966 as a, as a vision scientist, what did you think? 322 00:14:56,966 --> 00:14:59,233 Well, when I first saw the dress, 323 00:14:59,233 --> 00:15:01,300 I thought it was blue and black. 324 00:15:01,300 --> 00:15:03,733 And I thought that the internet was yanking my chain. 325 00:15:03,733 --> 00:15:06,433 Right-- to get the goat of vision neuroscientists. 326 00:15:06,433 --> 00:15:08,166 Sure. (chuckles) 327 00:15:08,166 --> 00:15:10,233 But in the morning, when I looked at my phone, 328 00:15:10,233 --> 00:15:12,333 I saw white and gold. 329 00:15:12,333 --> 00:15:14,366 And now, of course, I was obsessed. 330 00:15:14,366 --> 00:15:16,600 So I said, "Well, if this is an ambiguous image, 331 00:15:16,600 --> 00:15:18,900 all I have to do is disambiguate it." 332 00:15:18,900 --> 00:15:21,066 So I set to work, I got into Photoshop, 333 00:15:21,066 --> 00:15:25,100 cut out the dress, put it into a scene with lots of rich cues. 334 00:15:25,100 --> 00:15:26,333 BERLIN: Hm. 335 00:15:26,333 --> 00:15:27,900 LAFER-SOUSA: And all of a sudden, boom: 336 00:15:27,900 --> 00:15:29,833 you can see the dress is white and gold. 337 00:15:29,833 --> 00:15:31,533 Wow. Now, the pixels, 338 00:15:31,533 --> 00:15:33,033 the pixels that make up the dress there, 339 00:15:33,033 --> 00:15:35,400 are identical to the original image. Okay. 340 00:15:35,400 --> 00:15:37,966 BERLIN: Now, this doesn't work for everybody, 341 00:15:37,966 --> 00:15:40,600 but for most, the visual context 342 00:15:40,600 --> 00:15:42,833 can make all the difference. 343 00:15:42,833 --> 00:15:45,233 LAFER-SOUSA: What's different here is, her skin is tinted blue, 344 00:15:45,233 --> 00:15:48,366 the background has blue light cast on it, 345 00:15:48,366 --> 00:15:51,066 she's standing in the shadow of that cube, 346 00:15:51,066 --> 00:15:54,200 and so your brain says, "Aha, I need to ignore 347 00:15:54,200 --> 00:15:55,833 "some amount of blue light 348 00:15:55,833 --> 00:15:58,500 "that is in this signal that's hitting my eye 349 00:15:58,500 --> 00:16:00,733 and render this as white and gold." 350 00:16:00,733 --> 00:16:03,433 BERLIN: And if we flip things around? 351 00:16:03,433 --> 00:16:07,066 LAFER-SOUSA: Same dress, pasted it into this other scene. 352 00:16:07,066 --> 00:16:10,466 Her skin is tinted yellow, the background has a yellow cast, 353 00:16:10,466 --> 00:16:13,066 she's standing no longer in the shadow but in the light. 354 00:16:13,066 --> 00:16:14,233 Boom! Blue and black. 355 00:16:14,233 --> 00:16:16,266 Amazing, that's really amazing. 356 00:16:16,266 --> 00:16:17,566 So again, the dress, 357 00:16:17,566 --> 00:16:18,933 the pixels are exactly the same. 358 00:16:18,933 --> 00:16:20,366 LAFER-SOUSA: Identical. 359 00:16:20,366 --> 00:16:22,366 BERLIN: The dress is a powerful example 360 00:16:22,366 --> 00:16:24,633 of how color really works in the brain. 361 00:16:24,633 --> 00:16:26,866 Does that mean we're creating color 362 00:16:26,866 --> 00:16:30,366 in our mind, or does color actually exist in the world? 363 00:16:30,366 --> 00:16:32,333 Color takes place in the brain, 364 00:16:32,333 --> 00:16:34,766 and I've prepared a little illusion for you 365 00:16:34,766 --> 00:16:36,800 that should convince you of this. 366 00:16:36,800 --> 00:16:39,800 LAFER-SOUSA: So I have a picture of four cars here. 367 00:16:39,800 --> 00:16:42,900 I want you to tell me, what color are these cars? 368 00:16:42,900 --> 00:16:45,200 Let's start on the top left. BERLIN: Okay, so the one on the left looks red, 369 00:16:45,200 --> 00:16:47,866 then the one next to it looks blue. 370 00:16:47,866 --> 00:16:49,533 I'd say the one, the bottom there, 371 00:16:49,533 --> 00:16:50,800 bottom left looks green, 372 00:16:50,800 --> 00:16:53,700 and then the one next to it looks orange. 373 00:16:53,700 --> 00:16:55,000 LAFER-SOUSA: Okay. Mm-hmm. 374 00:16:55,000 --> 00:16:56,133 What if I told you 375 00:16:56,133 --> 00:16:59,133 that all of those pixels are not only gray, 376 00:16:59,133 --> 00:17:01,600 they are the same gray? 377 00:17:01,600 --> 00:17:03,300 BERLIN: How is this possible? 378 00:17:03,300 --> 00:17:06,766 It's because the light that enters your eyes, 379 00:17:06,766 --> 00:17:10,566 contrary to what you might have learned in school, is not color. 380 00:17:10,566 --> 00:17:13,633 Color is an interpretation of your brain. 381 00:17:13,633 --> 00:17:15,533 Here's how it works. 382 00:17:15,533 --> 00:17:17,700 Light shines on the world 383 00:17:17,700 --> 00:17:20,933 and bounces off objects-- this part you know. 384 00:17:20,933 --> 00:17:22,166 And light comes in 385 00:17:22,166 --> 00:17:23,433 different wavelengths, 386 00:17:23,433 --> 00:17:25,800 each corresponding to a different color. 387 00:17:25,800 --> 00:17:27,500 What you might not have heard in school 388 00:17:27,500 --> 00:17:29,433 is how those wavelengths change 389 00:17:29,433 --> 00:17:31,366 when they hit different surfaces-- 390 00:17:31,366 --> 00:17:33,633 rough, smooth, wet, et cetera. 391 00:17:33,633 --> 00:17:35,933 This signal that gets into your eye 392 00:17:35,933 --> 00:17:39,166 is actually a product of the reflective properties 393 00:17:39,166 --> 00:17:42,133 of the object and the wavelength of light hitting it. 394 00:17:42,133 --> 00:17:44,333 Then that signal is focused on the retina, 395 00:17:44,333 --> 00:17:46,100 the back of the eye, 396 00:17:46,100 --> 00:17:49,800 where we have about 130 million light-sensitive cells. 397 00:17:49,800 --> 00:17:52,500 Three types called cones are involved in color, 398 00:17:52,500 --> 00:17:55,433 each sensitive to different wavelengths of light: 399 00:17:55,433 --> 00:17:57,500 long, medium, and short. 400 00:17:57,500 --> 00:18:00,133 But that light still isn't color. 401 00:18:00,133 --> 00:18:02,366 For that to happen, our brain has to take 402 00:18:02,366 --> 00:18:04,433 that three-piece code from the retina 403 00:18:04,433 --> 00:18:09,033 and use the relative response of the cones to encode color. 404 00:18:09,033 --> 00:18:13,066 It's not until that signal gets to an area called V4 405 00:18:13,066 --> 00:18:15,800 that we get a neural representation of color 406 00:18:15,800 --> 00:18:19,000 that corresponds to our perceptual experience. 407 00:18:19,000 --> 00:18:21,500 So why would our brains be built this way? 408 00:18:21,500 --> 00:18:23,966 Well, if our brains weren't built this way, 409 00:18:23,966 --> 00:18:28,433 objects would appear to change in color all the time, 410 00:18:28,433 --> 00:18:30,966 and that would render color 411 00:18:30,966 --> 00:18:33,533 a pretty useless signal in the world. 412 00:18:33,533 --> 00:18:35,266 ♪ ♪ 413 00:18:35,266 --> 00:18:37,966 BERLIN: That's because objects reflect different wavelengths 414 00:18:37,966 --> 00:18:41,233 into your eye depending on the lighting conditions. 415 00:18:41,233 --> 00:18:43,466 If your brain didn't compensate for this, 416 00:18:43,466 --> 00:18:45,833 a red berry would appear gray in a cave, 417 00:18:45,833 --> 00:18:48,933 blue at dawn, and orange at dusk. 418 00:18:48,933 --> 00:18:51,466 But instead, your brain carefully calibrates 419 00:18:51,466 --> 00:18:54,466 your experience to hold color constant. 420 00:18:54,466 --> 00:18:57,266 Similarly, color vision in other animals 421 00:18:57,266 --> 00:18:59,700 is tuned to their needs. 422 00:18:59,700 --> 00:19:03,266 SETH: Different species have very different kinds of color vision 423 00:19:03,266 --> 00:19:06,200 that are suited to their particular environments 424 00:19:06,200 --> 00:19:09,133 and their particular challenges for staying alive. 425 00:19:09,133 --> 00:19:13,066 BERLIN: Dogs rely on smell, so they have fewer types of cones, 426 00:19:13,066 --> 00:19:15,400 and thus see the world like this. 427 00:19:15,400 --> 00:19:18,233 Birds need to recognize tiny color differences 428 00:19:18,233 --> 00:19:19,800 from great distances. 429 00:19:19,800 --> 00:19:22,333 so they have an extra type of cone that allows them 430 00:19:22,333 --> 00:19:23,800 to see more colors than we do. 431 00:19:23,800 --> 00:19:27,000 And bees need to find flowers rich in nectar, 432 00:19:27,000 --> 00:19:31,033 so they see ultraviolet light that's invisible to us. 433 00:19:31,033 --> 00:19:33,466 LAFER-SOUSA: Color provides a lot of valuable information 434 00:19:33,466 --> 00:19:35,100 about the world, 435 00:19:35,100 --> 00:19:38,033 but only if we can faithfully extract 436 00:19:38,033 --> 00:19:41,100 something about the object. 437 00:19:41,100 --> 00:19:44,133 So we don't actually see color as it is in the real world, 438 00:19:44,133 --> 00:19:48,233 we just see it in terms of how it's useful for us? 439 00:19:48,233 --> 00:19:49,900 Absolutely. 440 00:19:49,900 --> 00:19:52,200 And the dress is probably the best example of that. 441 00:19:52,200 --> 00:19:54,966 It's a really powerful demonstration 442 00:19:54,966 --> 00:19:57,266 of how our color machinery works. 443 00:19:57,266 --> 00:20:00,166 BERLIN: So why do people see this image 444 00:20:00,166 --> 00:20:01,933 of the dress differently? 445 00:20:01,933 --> 00:20:04,000 It comes down to your brain's assumptions 446 00:20:04,000 --> 00:20:06,033 about the lighting conditions. 447 00:20:06,033 --> 00:20:08,666 It seems that the more time you spend working indoors 448 00:20:08,666 --> 00:20:11,533 under artificial light, which is predominantly yellow, 449 00:20:11,533 --> 00:20:14,833 the more likely you are to say the dress is black and blue, 450 00:20:14,833 --> 00:20:17,933 because your brain assumes it is lit by artificial light 451 00:20:17,933 --> 00:20:20,233 and subtracts out the yellow. 452 00:20:20,233 --> 00:20:22,133 Conversely, if you spend more time 453 00:20:22,133 --> 00:20:24,300 in natural light, which is bluer, 454 00:20:24,300 --> 00:20:27,966 you are more likely to see it as white and gold. 455 00:20:27,966 --> 00:20:31,000 So then what is the actual color of the dress? 456 00:20:31,000 --> 00:20:35,066 Well, Heather, I happen to have brought it with me. 457 00:20:36,766 --> 00:20:38,900 (chuckles): Wow. So what color is it? 458 00:20:38,900 --> 00:20:41,266 Uh, it's obviously, I was right, blue and black. 459 00:20:41,266 --> 00:20:43,533 Team blue and black for the win. Yes, yes. 460 00:20:43,533 --> 00:20:45,800 I can't believe this is the actual dress. 461 00:20:45,800 --> 00:20:47,033 BERLIN: I feel like I'm holding, like... 462 00:20:47,033 --> 00:20:49,433 It's like a celebrity, the dress. 463 00:20:49,433 --> 00:20:51,533 I know, it should be in a museum, not in my closet. Yes! 464 00:20:51,533 --> 00:20:53,000 (both laugh) 465 00:20:53,000 --> 00:20:55,600 Before the dress, people hadn't really realized 466 00:20:55,600 --> 00:20:57,900 we differ so much between individuals. 467 00:20:57,900 --> 00:20:59,666 We're now quite used to the idea 468 00:20:59,666 --> 00:21:01,300 that we all differ on the outside. 469 00:21:01,300 --> 00:21:04,400 We all have differences in skin color, in height, in shape. 470 00:21:04,400 --> 00:21:06,666 But just as we all differ on the outside, 471 00:21:06,666 --> 00:21:09,166 we all differ on the inside, too. 472 00:21:09,166 --> 00:21:12,433 And this inner diversity is very important. 473 00:21:12,433 --> 00:21:15,633 It gives us a certain humility about our own ways of seeing. 474 00:21:15,633 --> 00:21:18,900 BERLIN: Illusions give us a ringside seat 475 00:21:18,900 --> 00:21:21,133 to watch how the brain creates our world. 476 00:21:21,133 --> 00:21:23,833 And it's not just the visual domain. 477 00:21:23,833 --> 00:21:26,033 Try listening to this. 478 00:21:26,033 --> 00:21:27,600 (staticky computerized voice playing) 479 00:21:27,600 --> 00:21:29,133 Brainstorm, right? 480 00:21:29,133 --> 00:21:30,633 Simple enough. 481 00:21:30,633 --> 00:21:32,033 Now, listen to this. 482 00:21:32,033 --> 00:21:33,633 (staticky computerized voice playing) 483 00:21:33,633 --> 00:21:34,966 Green needle. 484 00:21:34,966 --> 00:21:37,566 Okay, so you're thinking, "What's the big deal?" 485 00:21:37,566 --> 00:21:41,033 But, what if I tell you that the two audio clips I just played 486 00:21:41,033 --> 00:21:43,400 were exactly identical? 487 00:21:43,400 --> 00:21:47,133 For most people, what you hear depends on which label you read. 488 00:21:47,133 --> 00:21:48,533 Sounds unbelievable? 489 00:21:48,533 --> 00:21:53,066 Here, try it again, but this time, just read one. 490 00:21:53,066 --> 00:21:55,000 (staticky computerized voice playing) 491 00:21:55,000 --> 00:21:58,800 Okay, now read the other and listen again. 492 00:21:58,800 --> 00:22:01,366 (staticky computerized voice playing) 493 00:22:01,366 --> 00:22:03,033 Now, when I first encountered this, 494 00:22:03,033 --> 00:22:04,800 I was floored, too. 495 00:22:04,800 --> 00:22:07,000 Even though I know what's going on. 496 00:22:07,000 --> 00:22:09,233 When your brain encounters uncertainty, 497 00:22:09,233 --> 00:22:11,966 it fills in the gaps with its best guess. 498 00:22:11,966 --> 00:22:14,600 In this case, we have a degraded audio clip, 499 00:22:14,600 --> 00:22:17,333 and when you're primed with a certain word to go with it, 500 00:22:17,333 --> 00:22:20,333 your brain automatically jumps to the best fit. 501 00:22:20,333 --> 00:22:23,933 For most of us, we literally hear what we want to hear. 502 00:22:23,933 --> 00:22:27,933 And it gets even worse-- let's try one more. 503 00:22:27,933 --> 00:22:30,433 Another internet sensation 504 00:22:30,433 --> 00:22:32,933 that lit up debates across the country. 505 00:22:32,933 --> 00:22:37,733 (cleaner computerized voice playing) 506 00:22:37,733 --> 00:22:39,633 Once and for all, is it yanny, is it laurel? 507 00:22:39,633 --> 00:22:42,633 It's not yanny, it's laurel. It's yanny! 508 00:22:42,633 --> 00:22:44,666 Did you hear yanny? 509 00:22:44,666 --> 00:22:45,733 (cheering and applauding) 510 00:22:45,733 --> 00:22:47,066 Who heard laurel? 511 00:22:47,066 --> 00:22:48,366 (cheering more loudly) 512 00:22:48,366 --> 00:22:51,000 It is laurel and not yanny. 513 00:22:51,000 --> 00:22:53,333 (remix of computerized voice playing) 514 00:22:53,333 --> 00:22:55,466 It's like that stupid dress again 515 00:22:55,466 --> 00:22:57,700 all over, but in audio form! 516 00:22:57,700 --> 00:23:00,566 This is not saying "laurel," this is only saying "yanny." 517 00:23:00,566 --> 00:23:03,666 Exactly, it's laurel! 518 00:23:03,666 --> 00:23:06,700 Now, about half of you hear yanny, 519 00:23:06,700 --> 00:23:08,566 and the other laurel, 520 00:23:08,566 --> 00:23:11,866 and unlike the first illusion, I can't get most of you 521 00:23:11,866 --> 00:23:14,833 to experience this one any other way. 522 00:23:14,833 --> 00:23:17,300 You're locked into your version of reality. 523 00:23:17,300 --> 00:23:20,900 Experts aren't exactly sure why, but some of us 524 00:23:20,900 --> 00:23:24,500 seem to pay more attention to the low frequencies, laurel, 525 00:23:24,500 --> 00:23:26,533 and others to the high, yanny. 526 00:23:26,533 --> 00:23:29,500 The divide stems from the fact that the audio file 527 00:23:29,500 --> 00:23:31,300 is an ambiguous signal made up 528 00:23:31,300 --> 00:23:33,366 of both high and low frequencies. 529 00:23:33,366 --> 00:23:35,600 But by manipulating the frequencies, 530 00:23:35,600 --> 00:23:38,166 I might be able to change what you hear. 531 00:23:38,166 --> 00:23:40,400 (computerized voice playing at mid frequency) 532 00:23:40,400 --> 00:23:41,400 High... 533 00:23:41,400 --> 00:23:43,800 (voice plays at high frequency) 534 00:23:43,800 --> 00:23:47,400 (voice plays at low frequency) Low. 535 00:23:47,400 --> 00:23:49,000 All of this goes to show 536 00:23:49,000 --> 00:23:50,266 how much the brain 537 00:23:50,266 --> 00:23:52,733 is an active interpreter of sensory input. 538 00:23:52,733 --> 00:23:54,933 Our perception of the external world 539 00:23:54,933 --> 00:23:58,566 is actually much less objective than we'd like to believe. 540 00:23:58,566 --> 00:24:00,733 Most of the world around us is very real, 541 00:24:00,733 --> 00:24:03,700 but you just never lived there, okay? 542 00:24:03,700 --> 00:24:05,766 You lived in your mind, 543 00:24:05,766 --> 00:24:09,533 which is a perception of that world that's being filtered 544 00:24:09,533 --> 00:24:12,433 through a bunch of salt water sacks of proteins 545 00:24:12,433 --> 00:24:16,366 and electrochemical signals, which can't possibly be making 546 00:24:16,366 --> 00:24:19,333 completely accurate determinations 547 00:24:19,333 --> 00:24:22,233 of what's actually in the outside world. 548 00:24:22,233 --> 00:24:25,566 Not convinced? Or maybe you're just asking, "Why?" 549 00:24:25,566 --> 00:24:28,666 Well, try watching for when the green dot flashes. 550 00:24:30,066 --> 00:24:32,200 Does it line up with the red dot? 551 00:24:32,200 --> 00:24:33,600 If you are like most people, 552 00:24:33,600 --> 00:24:36,333 the red one always seems just a little bit ahead. 553 00:24:36,333 --> 00:24:38,800 Now try again. 554 00:24:41,166 --> 00:24:45,100 The red dot and the green dot are actually perfectly aligned. 555 00:24:45,100 --> 00:24:48,066 That's because some neuroscientists would say 556 00:24:48,066 --> 00:24:49,733 that it's not your brain's job 557 00:24:49,733 --> 00:24:51,633 to perceive the world accurately. 558 00:24:51,633 --> 00:24:54,733 Rather, its job is to predict what happens next. 559 00:24:54,733 --> 00:24:58,566 To a certain extent, you see what you expect to see: 560 00:24:58,566 --> 00:25:00,766 a predicted path of motion. 561 00:25:00,766 --> 00:25:03,233 And this is what helps us hit a home run 562 00:25:03,233 --> 00:25:07,400 or flinch from a punch at just the right moment. 563 00:25:09,700 --> 00:25:11,966 The brain is a predicting machine. 564 00:25:11,966 --> 00:25:16,666 Given a set of circumstances in this story at this moment, 565 00:25:16,666 --> 00:25:20,500 what are the likely plausible 566 00:25:20,500 --> 00:25:24,066 next events in the story? 567 00:25:24,066 --> 00:25:26,733 SETH: The brain is using sensory information 568 00:25:26,733 --> 00:25:28,500 to calibrate, 569 00:25:28,500 --> 00:25:30,900 update, to fine-tune these predictions 570 00:25:30,900 --> 00:25:32,666 so they remain tied 571 00:25:32,666 --> 00:25:36,333 to reality in ways that are not constrained by accuracy, 572 00:25:36,333 --> 00:25:39,100 but that are constrained by how useful 573 00:25:39,100 --> 00:25:41,200 the brain's perceptual predictions are 574 00:25:41,200 --> 00:25:43,300 in the business of staying alive. 575 00:25:50,333 --> 00:25:52,833 BERLIN: And to keep us alive, the brain has evolved 576 00:25:52,833 --> 00:25:55,333 to look for signals of potential danger. 577 00:25:55,333 --> 00:25:57,733 One of the most important is pain, 578 00:25:57,733 --> 00:26:01,066 and as neuroscientist Theanne Griffith is about to show me, 579 00:26:01,066 --> 00:26:03,966 sometimes that can be a kind of illusion, as well. 580 00:26:03,966 --> 00:26:05,700 BERLIN: So what is this? 581 00:26:05,700 --> 00:26:09,100 GRIFFITH: This is a thermal grill. Okay. 582 00:26:09,100 --> 00:26:11,533 This is a machine that could give us some insight 583 00:26:11,533 --> 00:26:14,566 as to how pain works in your brain. 584 00:26:14,566 --> 00:26:16,433 All right, this is making me nervous already 585 00:26:16,433 --> 00:26:17,966 as I'm getting strapped in! (Griffith laughs) 586 00:26:17,966 --> 00:26:20,600 GRIFFITH: Don't worry, it's all an illusion, actually. 587 00:26:20,600 --> 00:26:22,466 Okay. 588 00:26:22,466 --> 00:26:25,333 And it's comprised of these different metal bars 589 00:26:25,333 --> 00:26:27,366 that are either set to a cold or warm temperature. 590 00:26:27,366 --> 00:26:30,266 So why don't you go ahead and touch that first bar? 591 00:26:30,266 --> 00:26:32,433 It's warm, right? 592 00:26:32,433 --> 00:26:34,066 And then the next bar? 593 00:26:34,066 --> 00:26:35,800 Cold. Mm-hmm. 594 00:26:35,800 --> 00:26:37,500 GRIFFITH: And then the next one, warm. 595 00:26:37,500 --> 00:26:39,233 You see? So they're alternating cold, warm, cold, warm. BERLIN: Mm-hmm. 596 00:26:39,233 --> 00:26:40,833 Now, you want to see what happens 597 00:26:40,833 --> 00:26:42,433 when you put your hand down? 598 00:26:42,433 --> 00:26:44,233 Not necessarily. (both laugh) 599 00:26:44,233 --> 00:26:46,700 Go ahead. Okay. 600 00:26:46,700 --> 00:26:48,066 Okay, here we go. 601 00:26:48,066 --> 00:26:49,600 BERLIN: Ow! 602 00:26:49,600 --> 00:26:51,300 GRIFFITH: Right? Isn't that interesting? Whoa! 603 00:26:51,300 --> 00:26:53,233 Yeah, what is going on there? 604 00:26:53,233 --> 00:26:54,966 It sort of feels cold at first, but then... 605 00:26:54,966 --> 00:26:57,300 Then it gets this kind of burning sensation, right? 606 00:26:57,300 --> 00:26:58,966 Yes, very much so. 607 00:26:58,966 --> 00:27:02,000 BERLIN: It feels super-hot, like I'm getting burnt. 608 00:27:02,000 --> 00:27:05,233 So it's not 100% clear exactly how this is happening. 609 00:27:05,233 --> 00:27:07,466 But what we think might be going on 610 00:27:07,466 --> 00:27:09,233 is that, basically, your brain 611 00:27:09,233 --> 00:27:11,100 is getting a little bit confused. 612 00:27:11,100 --> 00:27:12,166 Okay. It's feeling cold, 613 00:27:12,166 --> 00:27:14,433 and it's also feeling warmth. 614 00:27:14,433 --> 00:27:18,100 And somehow, it's interpreting these two signals as pain. 615 00:27:18,100 --> 00:27:22,333 BERLIN: Here's what neuroscientists think is going on. 616 00:27:22,333 --> 00:27:25,133 In your hands, you have separate sensors for heat, 617 00:27:25,133 --> 00:27:27,333 cold, and pain. 618 00:27:27,333 --> 00:27:30,400 Normally, when you touch something slightly cold, 619 00:27:30,400 --> 00:27:33,300 both your cold and pain sensors are activated, 620 00:27:33,300 --> 00:27:35,400 but the cold ones override the signals 621 00:27:35,400 --> 00:27:36,900 from the pain sensors, 622 00:27:36,900 --> 00:27:39,333 telling your brain there's nothing to worry about. 623 00:27:39,333 --> 00:27:41,566 Unless, in this very unnatural scenario 624 00:27:41,566 --> 00:27:44,166 with the thermal grill, you happen to be touching 625 00:27:44,166 --> 00:27:46,833 something warm at the same time. 626 00:27:46,833 --> 00:27:49,933 Here, the heat signals cancel out the cold ones, 627 00:27:49,933 --> 00:27:52,633 leaving you with just the pain ones activated, 628 00:27:52,633 --> 00:27:56,100 telling your brain, "Ouch!" 629 00:27:56,100 --> 00:27:58,333 So in that respect, is pain real? 630 00:27:58,333 --> 00:28:00,266 Mm-hmm. Or is it just an illusion 631 00:28:00,266 --> 00:28:01,533 or a construct of the brain? 632 00:28:01,533 --> 00:28:03,133 That's a really good question. 633 00:28:03,133 --> 00:28:06,633 So noxious stimuli is, is a real thing, right? 634 00:28:06,633 --> 00:28:08,466 If you stick your hand in boiling water, 635 00:28:08,466 --> 00:28:10,066 that's an aversive stimulus. 636 00:28:10,066 --> 00:28:15,266 The perception of a noxious stimuli is real. Mm-hmm. 637 00:28:15,266 --> 00:28:17,500 Pain is more of a construct, right? Mm-hmm. 638 00:28:17,500 --> 00:28:19,833 And it can vary from individual to individual. 639 00:28:19,833 --> 00:28:23,700 EMERY BROWN: Pain is a construct of the brain. 640 00:28:23,700 --> 00:28:25,566 How do we know that? 641 00:28:25,566 --> 00:28:28,200 You touch a needle, right? And prick your finger. 642 00:28:28,200 --> 00:28:31,000 We can draw the anatomy of what just happened. 643 00:28:31,000 --> 00:28:34,366 We have very well-defined pathways 644 00:28:34,366 --> 00:28:36,600 saying, "This is pain information." 645 00:28:36,600 --> 00:28:42,133 We don't interpret it as pain until it hits your brain. 646 00:28:42,133 --> 00:28:44,466 BERLIN: Pain, not unlike the experience of color, 647 00:28:44,466 --> 00:28:47,000 is a construct of the mind. 648 00:28:47,000 --> 00:28:48,300 Mama! Mama! 649 00:28:48,300 --> 00:28:50,100 BERLIN: But just because pain is in your brain 650 00:28:50,100 --> 00:28:53,033 doesn't make it any less critical for survival. 651 00:28:53,033 --> 00:28:54,666 GRIFFITH: Pain is a very important... 652 00:28:54,666 --> 00:28:58,333 (gasps) ...learning mechanism for children. 653 00:28:58,333 --> 00:29:01,000 They learn what behaviors they can engage in that are safe 654 00:29:01,000 --> 00:29:03,766 and what behaviors, well, they should not engage in 655 00:29:03,766 --> 00:29:06,566 because they could cause them bodily harm. 656 00:29:06,566 --> 00:29:09,700 And there's, um, uh, different mutations that people can have 657 00:29:09,700 --> 00:29:11,766 in certain proteins that make them 658 00:29:11,766 --> 00:29:13,533 completely insensitive to pain. 659 00:29:13,533 --> 00:29:15,733 And so kids do things like bite on their lips 660 00:29:15,733 --> 00:29:18,300 or on their fingers when they're very young, 661 00:29:18,300 --> 00:29:21,366 and as they get older, can engage in risky behavior. 662 00:29:21,366 --> 00:29:25,900 So pain is extremely important for us to feel. 663 00:29:25,900 --> 00:29:29,400 SETH: Illusions are fascinating. 664 00:29:29,400 --> 00:29:31,566 They're like fractures in the matrix. 665 00:29:31,566 --> 00:29:34,666 They reveal to us that the way we perceive things 666 00:29:34,666 --> 00:29:37,300 isn't necessarily the way they are. 667 00:29:37,300 --> 00:29:40,800 MACKNIK: Illusions help us find the cracks in the mortar 668 00:29:40,800 --> 00:29:43,166 of that world we've built for ourselves, 669 00:29:43,166 --> 00:29:46,633 and understand what it is our world is actually made out of 670 00:29:46,633 --> 00:29:48,500 and what the brain is actually doing. 671 00:29:48,500 --> 00:29:50,966 So most people think of the brain 672 00:29:50,966 --> 00:29:54,500 reconstructing the world more or less verbatim. 673 00:29:54,500 --> 00:29:56,533 (dog growling) But that's just not true. 674 00:29:56,533 --> 00:29:58,266 What it's actually doing is, 675 00:29:58,266 --> 00:29:59,666 it's getting very little information 676 00:29:59,666 --> 00:30:02,666 and it's using that very little information 677 00:30:02,666 --> 00:30:05,366 to make a big, grand model of the world. 678 00:30:06,400 --> 00:30:10,033 MARTINEZ-CONDE: We cannot process the vast amount of information 679 00:30:10,033 --> 00:30:14,966 that is constantly bombarding our senses. 680 00:30:14,966 --> 00:30:17,333 Illusions, you can think of them as shortcuts. 681 00:30:17,333 --> 00:30:20,833 Shortcuts make us faster, 682 00:30:20,833 --> 00:30:24,366 more efficient with less resources. 683 00:30:24,366 --> 00:30:27,066   Based on these snippets of information, 684 00:30:27,066 --> 00:30:32,533 we build this more complex simulation of reality. 685 00:30:32,533 --> 00:30:34,933 And that simulation of the world is 686 00:30:34,933 --> 00:30:36,066 what we call consciousness. 687 00:30:38,366 --> 00:30:40,200 BERLIN: Consciousness. 688 00:30:40,200 --> 00:30:43,833 (alarm buzzing) We take it for granted, but every time you wake up, 689 00:30:43,833 --> 00:30:47,866 (alarm stops) your brain stitches together all your sensory inputs-- 690 00:30:47,866 --> 00:30:49,766 the sound of a distant train... 691 00:30:49,766 --> 00:30:51,533 (train whistle blowing) 692 00:30:51,533 --> 00:30:53,766 ...the smell of coffee, 693 00:30:53,766 --> 00:30:56,666 the warmth of the sun-- 694 00:30:56,666 --> 00:30:59,200 into an experience of the world. 695 00:30:59,200 --> 00:31:01,100 And that experience, 696 00:31:01,100 --> 00:31:03,333 that awareness of the world, 697 00:31:03,333 --> 00:31:06,666 is what scientists call consciousness. 698 00:31:06,666 --> 00:31:10,000 In neuroscience, consciousness is the Holy Grail. 699 00:31:10,000 --> 00:31:12,800 Humans have been fascinated by consciousness 700 00:31:12,800 --> 00:31:15,666 for thousands of years, probably much longer than that. (chuckling) 701 00:31:15,666 --> 00:31:17,466 Take three. 702 00:31:17,466 --> 00:31:19,566 Now, of course, the word "consciousness" 703 00:31:19,566 --> 00:31:21,500 means a lot of things to different people. 704 00:31:21,500 --> 00:31:23,666 To some, consciousness means being awake, 705 00:31:23,666 --> 00:31:25,100 as opposed to asleep. 706 00:31:25,100 --> 00:31:28,666 Or self-aware, or the contents of my thoughts. 707 00:31:28,666 --> 00:31:31,800 But that's not how we neuroscientists think about it. 708 00:31:31,800 --> 00:31:36,700 We think of it as something much more basic-- 709 00:31:36,700 --> 00:31:39,300 it's just internal experience. 710 00:31:39,300 --> 00:31:42,600 It feels like something to see the color red. 711 00:31:42,600 --> 00:31:44,866 To taste a strawberry. 712 00:31:44,866 --> 00:31:46,200 (thunder rumbling) 713 00:31:46,200 --> 00:31:49,000 To hear the crack of thunder. 714 00:31:49,000 --> 00:31:52,500 (thunder crashing) 715 00:31:52,500 --> 00:31:55,900 SETH: We are complicated biological creatures, 716 00:31:55,900 --> 00:31:58,200 but the most central feature of our lives 717 00:31:58,200 --> 00:32:00,533 is that we are conscious creatures, too. 718 00:32:00,533 --> 00:32:02,266 When I open my eyes, 719 00:32:02,266 --> 00:32:05,500 it's not just that my brain does some sophisticated processing 720 00:32:05,500 --> 00:32:06,600 of the visual information. 721 00:32:06,600 --> 00:32:09,333 I have an experience. 722 00:32:09,333 --> 00:32:11,366 BERLIN: During the course of my journey, 723 00:32:11,366 --> 00:32:13,266 I've seen how our experience of reality 724 00:32:13,266 --> 00:32:14,900 is not what it seems. 725 00:32:14,900 --> 00:32:16,333 If my conscious awareness 726 00:32:16,333 --> 00:32:19,866 is built from my perceptions, flawed as they may be, 727 00:32:19,866 --> 00:32:24,366 how does that work and what does it mean? 728 00:32:24,366 --> 00:32:27,333 ♪ ♪ 729 00:32:27,333 --> 00:32:29,200 So please take a seat. 730 00:32:29,200 --> 00:32:32,533 BERLIN: Some of the first clues trickled in from people like this. 731 00:32:32,533 --> 00:32:34,366 LORELLA BATTELLI: Put your chin on the chin rest. 732 00:32:34,366 --> 00:32:35,933 BROWN: A lot of very valuable information 733 00:32:35,933 --> 00:32:39,700 comes from patients who've had, part of the brain's damaged. 734 00:32:39,700 --> 00:32:43,366 By piecing these various parts together, seeing what was lost, 735 00:32:43,366 --> 00:32:45,300 we've come to appreciate the role 736 00:32:45,300 --> 00:32:46,966 that these various brain regions play 737 00:32:46,966 --> 00:32:48,866 in the creation of consciousness. 738 00:32:48,866 --> 00:32:50,833 We're going to calibrate your eyes first. 739 00:32:50,833 --> 00:32:53,100 SETH: A powerful example of this 740 00:32:53,100 --> 00:32:55,233 is the phenomenon of blindsight. 741 00:32:55,233 --> 00:32:58,633 BATTELLI: This is a patient who had a stroke 742 00:32:58,633 --> 00:33:00,333 in her visual areas, 743 00:33:00,333 --> 00:33:02,033 in the back of the brain. 744 00:33:02,033 --> 00:33:04,466 And this stroke is affecting her visual field. 745 00:33:04,466 --> 00:33:08,100 BERLIN: Three years ago, she felt a pounding in her head. 746 00:33:08,100 --> 00:33:10,400 WOMAN: I had what I thought was a migraine. 747 00:33:10,400 --> 00:33:13,233 I actually went to the emergency room 748 00:33:13,233 --> 00:33:15,666 because I'm walking around with this area where I can't see. 749 00:33:15,666 --> 00:33:18,200 BERLIN: The stroke damaged a piece of the brain 750 00:33:18,200 --> 00:33:20,800 devoted to vision, leaving her with an apparent 751 00:33:20,800 --> 00:33:22,533 total blind spot. 752 00:33:22,533 --> 00:33:25,633 WOMAN: That blind spot, it's enough that if you're driving, 753 00:33:25,633 --> 00:33:30,000 an oncoming car disappears into it. 754 00:33:30,000 --> 00:33:33,833 It's a little anxiety-producing and, and things like that. 755 00:33:33,833 --> 00:33:35,600 BERLIN: In everyday tasks, 756 00:33:35,600 --> 00:33:38,000 her eye movements make up the difference. 757 00:33:38,000 --> 00:33:41,333 But what happens when she doesn't move her eyes? 758 00:33:41,333 --> 00:33:43,600 Neuroscientist Lorella Battelli wants to find out, 759 00:33:43,600 --> 00:33:47,733 so she developed a clever series of experiments to pin down, 760 00:33:47,733 --> 00:33:49,766 just how blind is she really in that spot? 761 00:33:51,133 --> 00:33:52,666 BATTELLI: We're using EyeLink, 762 00:33:52,666 --> 00:33:55,066 which is the eye-tracking system 763 00:33:55,066 --> 00:33:56,900 to make sure she doesn't move the eyes. 764 00:33:56,900 --> 00:33:59,266 BERLIN: She keeps her eyes focused on the center spot. 765 00:33:59,266 --> 00:34:01,833 Every time she hears a beep, 766 00:34:01,833 --> 00:34:03,733 she has to say if those little dots inside the circle 767 00:34:03,733 --> 00:34:05,966 are moving to the left or to the right. 768 00:34:05,966 --> 00:34:07,900 Left. 769 00:34:07,900 --> 00:34:10,966 BERLIN: The eye tracker checks that she's not shifting her gaze. 770 00:34:12,166 --> 00:34:13,633 WOMAN: Right. 771 00:34:13,633 --> 00:34:15,533 When you're doing tests like this, 772 00:34:15,533 --> 00:34:18,533 that blind area, what does that kind of look like for you? 773 00:34:18,533 --> 00:34:20,400 What does it feel like for you? 774 00:34:20,400 --> 00:34:24,800 When that target pops up in my blind area, 775 00:34:24,800 --> 00:34:26,433 I don't see it. 776 00:34:26,433 --> 00:34:28,700 (machine beeps) 777 00:34:28,700 --> 00:34:30,600 Left. 778 00:34:30,600 --> 00:34:32,533 BERLIN: Strangely, even though she says she doesn't see 779 00:34:32,533 --> 00:34:34,366 anything in the blind spot, 780 00:34:34,366 --> 00:34:35,933 she gets it right more often than not. 781 00:34:35,933 --> 00:34:37,966 WOMAN: Right. 782 00:34:37,966 --> 00:34:39,233 BERLIN: So that some information 783 00:34:39,233 --> 00:34:40,300 is getting in. Yeah, so... 784 00:34:40,300 --> 00:34:42,766 But they're not consciously seeing it, 785 00:34:42,766 --> 00:34:44,666 but they can respond to it in different ways. Exactly. 786 00:34:44,666 --> 00:34:47,800 BATTELLI: Even if they say, "I didn't see anything." 787 00:34:47,800 --> 00:34:49,966 Left. But you tell them, "Please, just tell me 788 00:34:49,966 --> 00:34:51,966 whether you saw it or not," 789 00:34:51,966 --> 00:34:53,900 then their response would be above chance. 790 00:34:53,900 --> 00:34:56,733 BERLIN: So just keep your eyes closed, okay? 791 00:34:56,733 --> 00:34:58,733 And... 792 00:34:58,733 --> 00:35:00,833 BERLIN: What's going on? 793 00:35:00,833 --> 00:35:03,200 To probe deeper, Lorella lets me give the patient 794 00:35:03,200 --> 00:35:06,066 a different version of the challenge. 795 00:35:06,066 --> 00:35:08,700 I put a miniature screwdriver in her blind spot. 796 00:35:08,700 --> 00:35:11,933 BERLIN: Okay, I'm going to have you open your eyes and fixate. 797 00:35:11,933 --> 00:35:13,966 Okay. Okay. 798 00:35:16,066 --> 00:35:17,300 I see nothing. 799 00:35:17,300 --> 00:35:18,300 You see nothing. 800 00:35:18,300 --> 00:35:19,533 Nothing. Okay. 801 00:35:19,533 --> 00:35:20,833 BERLIN: Even though she says 802 00:35:20,833 --> 00:35:23,500 she sees nothing, look at which tool she picks. 803 00:35:23,500 --> 00:35:25,433 Now turn over there and look at the objects. 804 00:35:25,433 --> 00:35:26,466 Tell me what you think you saw. 805 00:35:26,466 --> 00:35:29,200 WOMAN: The screwdriver. BERLIN: Yep. 806 00:35:29,200 --> 00:35:31,266 BERLIN: Now try another one. 807 00:35:31,266 --> 00:35:34,366 BERLIN: Next, I display a tiny wrench. 808 00:35:34,366 --> 00:35:38,800 ♪ ♪ 809 00:35:38,800 --> 00:35:40,366 Did you see anything? 810 00:35:40,366 --> 00:35:42,133 No. No? Okay. 811 00:35:42,133 --> 00:35:46,100 Look over there and guess what you think you, was there. 812 00:35:46,100 --> 00:35:49,300 I think the wrench? Yep. 813 00:35:49,300 --> 00:35:51,533 WOMAN: I'm going to guess the scissors. 814 00:35:51,533 --> 00:35:53,700 Yeah, good job, great, scissors. 815 00:35:53,700 --> 00:35:55,033 BERLIN: Time and time again, 816 00:35:55,033 --> 00:35:56,900 she makes the right choice. 817 00:35:56,900 --> 00:35:58,833 Amazing, so, you know, it seems 818 00:35:58,833 --> 00:36:01,100 to me that you're saying you're not seeing anything, 819 00:36:01,100 --> 00:36:03,966 yet when I'm asking you to choose, 820 00:36:03,966 --> 00:36:06,033 you're pretty much getting it correct, 821 00:36:06,033 --> 00:36:07,533 so something is getting in. 822 00:36:07,533 --> 00:36:10,166 BERLIN: How is this possible? 823 00:36:10,166 --> 00:36:13,100 It's as if she sees the tools, but doesn't know it. 824 00:36:13,100 --> 00:36:14,933 BATTELLI: They actually saw something. 825 00:36:14,933 --> 00:36:17,466 Mm-hmm, certainly. But they're not entirely aware of it. 826 00:36:17,466 --> 00:36:19,633 Information is getting in, affecting our behavior 827 00:36:19,633 --> 00:36:21,433 and how we're responding to the world around us, 828 00:36:21,433 --> 00:36:23,400 without there being a conscious perception 829 00:36:23,400 --> 00:36:25,766 of that piece of visual information. Correct. 830 00:36:25,766 --> 00:36:27,800 WOMAN: I'm gonna go with the hammer again. 831 00:36:27,800 --> 00:36:32,533 BERLIN: Until patients like this, we scientists had never seen 832 00:36:32,533 --> 00:36:35,166 perception separate from conscious experience. 833 00:36:35,166 --> 00:36:37,266 And this tells us that perception 834 00:36:37,266 --> 00:36:40,566 and consciousness are separate things in the brain. 835 00:36:40,566 --> 00:36:42,466 But it also has me wondering, 836 00:36:42,466 --> 00:36:44,800 if someone can still use visual information 837 00:36:44,800 --> 00:36:46,733 without awareness of it, 838 00:36:46,733 --> 00:36:48,800 why do we have consciousness at all? 839 00:36:48,800 --> 00:36:51,166 What is consciousness for? 840 00:36:51,166 --> 00:36:53,400 A clue might come from babies. 841 00:36:53,400 --> 00:36:55,400 (cooing) 842 00:36:55,400 --> 00:36:57,333 ALISON GOPNIK: Babies-- everything we know suggests 843 00:36:57,333 --> 00:36:59,200 that they're born conscious. 844 00:36:59,200 --> 00:37:00,533 They're certainly taking in information 845 00:37:00,533 --> 00:37:01,800 from the time they're born. 846 00:37:01,800 --> 00:37:04,200 REBECCA SAXE: They are making rational choices 847 00:37:04,200 --> 00:37:07,733 about what they learn from extremely early on. 848 00:37:07,733 --> 00:37:11,400 And they are forming memories of their specific surroundings, 849 00:37:11,400 --> 00:37:12,766 of their parents, 850 00:37:12,766 --> 00:37:14,833 of their important relationships. 851 00:37:14,833 --> 00:37:17,200 We can see that in their behavior. 852 00:37:17,200 --> 00:37:21,066 BERLIN: And all that behavior burns a lot of fuel. 853 00:37:21,066 --> 00:37:23,900 GOPNIK: Brains are expensive computing gadgets. 854 00:37:23,900 --> 00:37:25,366 So while you're just sitting here, 855 00:37:25,366 --> 00:37:28,100 your brain is using up about 20% of all the calories 856 00:37:28,100 --> 00:37:30,133 that you have, so it's using up quite a bit. 857 00:37:30,133 --> 00:37:32,300 But if you think about a two-year-old, 858 00:37:32,300 --> 00:37:36,200 his brain is using 60% of his calories. 859 00:37:36,200 --> 00:37:41,833 So almost all that food is just going to keep his brain going. 860 00:37:41,833 --> 00:37:45,066 BERLIN: To understand why young brains might need so much more fuel, 861 00:37:45,066 --> 00:37:47,833 check out the connections in a toddler's brain 862 00:37:47,833 --> 00:37:49,633 versus an adult's. 863 00:37:49,633 --> 00:37:53,633 A two-year-old's brain has about two quadrillion synapses. 864 00:37:53,633 --> 00:37:58,566 By the time they hit adulthood, that number is cut in half. 865 00:37:58,566 --> 00:38:00,700 So if you think about the difference between 866 00:38:00,700 --> 00:38:03,133 the baby brain, the child's brain, and the adult brain, 867 00:38:03,133 --> 00:38:07,166 the child's brain is more like back country roads 868 00:38:07,166 --> 00:38:09,100 where you have little, tiny roads 869 00:38:09,100 --> 00:38:11,800 that are going from one village to the next. 870 00:38:11,800 --> 00:38:13,900 None of them are very efficient. 871 00:38:13,900 --> 00:38:15,066 There's not a lot of traffic, 872 00:38:15,066 --> 00:38:16,966 and the traffic doesn't go very quickly, 873 00:38:16,966 --> 00:38:18,933 but they connect lots and lots of different places. 874 00:38:18,933 --> 00:38:21,633 And the adult brain is more like superhighways 875 00:38:21,633 --> 00:38:24,400 that get you from one place to another very quickly, 876 00:38:24,400 --> 00:38:26,133 and take a lot of traffic, 877 00:38:26,133 --> 00:38:28,833 but don't connect as many different places. 878 00:38:28,833 --> 00:38:31,333 BERLIN: As we age, in the interest of efficiency, 879 00:38:31,333 --> 00:38:34,133 we strengthen the connections that are useful to us 880 00:38:34,133 --> 00:38:36,266 and prune the rest. 881 00:38:36,266 --> 00:38:39,433 MACKNIK: You basically take neurons you don't need, you get rid of them. 882 00:38:39,433 --> 00:38:42,533 And what you have now is a very lean machine 883 00:38:42,533 --> 00:38:47,433 that does certain things and it does them very well. 884 00:38:48,500 --> 00:38:51,100 GOPNIK: We see this early brain that's very exploratory, 885 00:38:51,100 --> 00:38:52,900 that has lots and lots of potential, 886 00:38:52,900 --> 00:38:54,100 lots of possibilities. 887 00:38:54,100 --> 00:38:56,400 Not very good at putting on your jacket 888 00:38:56,400 --> 00:38:58,466 and getting out to preschool in the morning. 889 00:38:58,466 --> 00:39:00,000 And then we have this later brain 890 00:39:00,000 --> 00:39:01,833 that's very good at doing things. 891 00:39:01,833 --> 00:39:03,200 Not so good at changing, 892 00:39:03,200 --> 00:39:04,800 not so good at taking in new information, 893 00:39:04,800 --> 00:39:06,066 not so good at doing something new. 894 00:39:06,066 --> 00:39:08,766 BERLIN: What this suggests is that maybe 895 00:39:08,766 --> 00:39:10,266 what consciousness is for 896 00:39:10,266 --> 00:39:12,600 is choosing what's important for us to be aware of 897 00:39:12,600 --> 00:39:14,133 at any given moment. 898 00:39:14,133 --> 00:39:16,633 Kind of like a spotlight. 899 00:39:16,633 --> 00:39:19,133 GOPNICK: For adults, it's as if consciousness is 900 00:39:19,133 --> 00:39:20,633 this bright spotlight in one place 901 00:39:20,633 --> 00:39:22,000 and everything around it is dark. 902 00:39:24,000 --> 00:39:25,666 BERLIN: While for children and babies, 903 00:39:25,666 --> 00:39:27,333 it would be more like a flood light, 904 00:39:27,333 --> 00:39:29,633 where nearly everything is illuminated. 905 00:39:29,633 --> 00:39:33,300 GOPNIK: You're conscious of a lot more that's going on. 906 00:39:33,300 --> 00:39:36,166 BERLIN: Consciousness may be like an amplifier, 907 00:39:36,166 --> 00:39:38,733 boosting the important signals over the noise. 908 00:39:38,733 --> 00:39:40,766 Is there any evidence? 909 00:39:42,166 --> 00:39:44,433 This is where FMRI comes in, 910 00:39:44,433 --> 00:39:47,066 a special tool in neuroscience that takes pictures of the brain 911 00:39:47,066 --> 00:39:49,733 while it's doing something to map where blood flow 912 00:39:49,733 --> 00:39:50,966 is in high demand. 913 00:39:50,966 --> 00:39:53,600 The result is a map of brain activity. 914 00:39:54,766 --> 00:39:57,600 So where is consciousness in the brain 915 00:39:57,600 --> 00:40:00,566 and how does it work? 916 00:40:00,566 --> 00:40:02,433 To find out, neuroscientists designed 917 00:40:02,433 --> 00:40:05,500 a clever series of experiments that go like this. 918 00:40:05,500 --> 00:40:07,766 They start by flashing a word 919 00:40:07,766 --> 00:40:10,233 on a screen for about 30 milliseconds. 920 00:40:10,233 --> 00:40:11,233 (computer beeps) 921 00:40:11,233 --> 00:40:12,633 DEHAENE: You flash this word, 922 00:40:12,633 --> 00:40:15,200 and the person is not able to see the word at all. 923 00:40:15,200 --> 00:40:16,900 She says, "There was no word." 924 00:40:16,900 --> 00:40:21,100   BERLIN: But in the FMRI scanner, the visual cortex is activated, 925 00:40:21,100 --> 00:40:24,633 even though people say they don't see anything. 926 00:40:24,633 --> 00:40:27,700 So the trick here is to find the threshold. 927 00:40:27,700 --> 00:40:30,533 Find the timing where sometimes 928 00:40:30,533 --> 00:40:33,200 people consciously see the image. 929 00:40:33,200 --> 00:40:37,900 DEHAENE: So if you make now the word a little bit longer, 930 00:40:37,900 --> 00:40:39,466 suddenly, the person says, 931 00:40:39,466 --> 00:40:41,133 "Oh, well, there is a word, obviously." 932 00:40:41,133 --> 00:40:43,366 And it's completely visible. 933 00:40:43,366 --> 00:40:45,766 There is really a sort of all or none phenomenon. 934 00:40:45,766 --> 00:40:47,566 Either you see it or you don't. 935 00:40:47,566 --> 00:40:49,800 And once you've done that, 936 00:40:49,800 --> 00:40:51,433 you've got a really powerful window 937 00:40:51,433 --> 00:40:54,200 onto the neural correlates of conscious perception. 938 00:40:54,200 --> 00:40:56,666 BERLIN: When that happens, 939 00:40:56,666 --> 00:40:58,633 suddenly, a suite of different parts of the brain 940 00:40:58,633 --> 00:41:02,033 shows a surge in activity: the parietal cortex, 941 00:41:02,033 --> 00:41:03,800 which integrates the senses, 942 00:41:03,800 --> 00:41:05,433 the anterior cingulate, 943 00:41:05,433 --> 00:41:07,433 which modulates drive and decision-making, 944 00:41:07,433 --> 00:41:09,066 and the prefrontal cortex, 945 00:41:09,066 --> 00:41:13,000 which handles reasoning and higher-order cognition. 946 00:41:13,000 --> 00:41:15,700 An ignition of distributed brain areas 947 00:41:15,700 --> 00:41:18,633 that come online together, speak to each other, 948 00:41:18,633 --> 00:41:21,800 and broadcast this information to the rest of the brain. 949 00:41:21,800 --> 00:41:23,133 And this is what we think is occurring 950 00:41:23,133 --> 00:41:24,633 during conscious perception. 951 00:41:26,400 --> 00:41:28,100 BERLIN: According to some experts, 952 00:41:28,100 --> 00:41:30,233 this communication between brain regions 953 00:41:30,233 --> 00:41:32,466 is the signature of consciousness. 954 00:41:33,833 --> 00:41:36,766 This discovery could have real-world applications 955 00:41:36,766 --> 00:41:38,400 in matters of life and death. 956 00:41:38,400 --> 00:41:40,266 But that would take one more step: 957 00:41:40,266 --> 00:41:43,866 figuring out how to measure consciousness. 958 00:41:43,866 --> 00:41:45,733 ♪ ♪ 959 00:41:45,733 --> 00:41:47,800 SETH: In science, when we struggle to understand 960 00:41:47,800 --> 00:41:49,600 a phenomenon that seems quite mysterious, 961 00:41:49,600 --> 00:41:53,233 it's often really important to be able to measure it. 962 00:41:53,233 --> 00:41:55,533 So a few hundred years ago, 963 00:41:55,533 --> 00:41:58,133 this happened with heat-- you know, it was the development 964 00:41:58,133 --> 00:42:02,100 of thermometers that catalyzed our understanding. 965 00:42:02,933 --> 00:42:05,133 Could something work for consciousness the same way? 966 00:42:05,133 --> 00:42:07,300 Could we have a consciousness-ometer 967 00:42:07,300 --> 00:42:08,700 that will lead us 968 00:42:08,700 --> 00:42:11,933 to a deeper understanding of what consciousness is? 969 00:42:11,933 --> 00:42:15,300 BERLIN: That deeper understanding could transform 970 00:42:15,300 --> 00:42:17,666 the treatment of people with brain injuries. 971 00:42:17,666 --> 00:42:20,633 BRIAN EDLOW: So every year, over a million people worldwide 972 00:42:20,633 --> 00:42:24,300 will come into an intensive care unit unresponsive, comatose. 973 00:42:24,300 --> 00:42:27,166 The challenge that we face is that our bedside exam-- 974 00:42:27,166 --> 00:42:29,100 asking the person to open their eyes, 975 00:42:29,100 --> 00:42:31,533 pinching them and seeing if they'll respond, 976 00:42:31,533 --> 00:42:33,500 seeing if they move their arms and their legs-- 977 00:42:33,500 --> 00:42:36,600 that bedside exam is fundamentally limited. 978 00:42:36,600 --> 00:42:39,400 BERLIN: Limited because it often misses people 979 00:42:39,400 --> 00:42:41,200 who are actually conscious. 980 00:42:41,200 --> 00:42:43,933 In this case, we have a healthy volunteer 981 00:42:43,933 --> 00:42:45,733 from the "NOVA" team, 982 00:42:45,733 --> 00:42:47,333 but what if she were unresponsive? 983 00:42:47,333 --> 00:42:50,066 How would we ever know if she was conscious? 984 00:42:50,066 --> 00:42:52,133 Brian Edlow's team at Mass. General Hospital 985 00:42:52,133 --> 00:42:55,166 is testing a new technique to find out. 986 00:42:55,166 --> 00:42:57,300 So tell me, what are you doing here? 987 00:42:57,300 --> 00:43:00,000 EDLOW: We are pinging the brain with a magnetic pulse 988 00:43:00,000 --> 00:43:02,566 and looking for an electrical echo. 989 00:43:02,566 --> 00:43:06,266 The ping is transcranial magnetic stimulation, 990 00:43:06,266 --> 00:43:07,900 or TMS. 991 00:43:07,900 --> 00:43:10,866 The echo is the key; if it dies out quickly, 992 00:43:10,866 --> 00:43:13,166 the patient is unconscious-- they might be in a coma, 993 00:43:13,166 --> 00:43:15,200 deep sleep, or under anesthesia. 994 00:43:15,200 --> 00:43:18,766 If instead the echo rings out across the brain 995 00:43:18,766 --> 00:43:20,666 and becomes more complex, 996 00:43:20,666 --> 00:43:23,600 the patient is likely to be conscious and aware, 997 00:43:23,600 --> 00:43:26,200 even if they appear unresponsive. 998 00:43:26,200 --> 00:43:27,266 EDLOW: The analogy that we like to use 999 00:43:27,266 --> 00:43:30,000 is throwing a pebble in a lake. 1000 00:43:30,000 --> 00:43:33,400 So the pebble represents the TMS pulse to stimulate the brain, 1001 00:43:33,400 --> 00:43:36,166 and the brain waves, the electrical ripples 1002 00:43:36,166 --> 00:43:41,233 that emanate from that pulse, represent the waves in the lake. 1003 00:43:41,233 --> 00:43:43,433 The more complex those waves are, 1004 00:43:43,433 --> 00:43:45,466 and the longer duration, 1005 00:43:45,466 --> 00:43:47,533 the more likely that person is to be conscious. 1006 00:43:49,033 --> 00:43:52,466 BERLIN: To detect those brain waves, Edlow's team uses E.E.G., 1007 00:43:52,466 --> 00:43:54,966 a tool that measures electrical activity 1008 00:43:54,966 --> 00:43:57,800 in the brain, to quantify the amount of complexity 1009 00:43:57,800 --> 00:44:00,200 a patient's brain bounces back. 1010 00:44:00,200 --> 00:44:01,533 Here's how it works. 1011 00:44:01,533 --> 00:44:02,766 ♪ ♪ 1012 00:44:02,766 --> 00:44:04,900 All neurons, when poked by a magnet, 1013 00:44:04,900 --> 00:44:08,100 will kick back an electrical signal that looks like this: 1014 00:44:08,100 --> 00:44:09,733 a brain wave. 1015 00:44:09,733 --> 00:44:11,833 But if the surrounding neurons aren't healthy, 1016 00:44:11,833 --> 00:44:15,233 those brain waves won't get very far. 1017 00:44:15,233 --> 00:44:17,466 It turns out that in conscious people, 1018 00:44:17,466 --> 00:44:19,700 even those who appear unresponsive, 1019 00:44:19,700 --> 00:44:22,966 not only do those brain waves spread all over the brain, 1020 00:44:22,966 --> 00:44:26,366 but they become more complex too-- it's as if, 1021 00:44:26,366 --> 00:44:28,466 to use music as the analogy, 1022 00:44:28,466 --> 00:44:31,933 what starts as a single repeated note by a few neurons 1023 00:44:31,933 --> 00:44:35,733 eventually turns into a coordinated symphony 1024 00:44:35,733 --> 00:44:37,500 of millions. 1025 00:44:37,500 --> 00:44:39,933 ♪ ♪ 1026 00:44:39,933 --> 00:44:43,900 DEHAENE: We find is that there is this explosion of complexity 1027 00:44:43,900 --> 00:44:45,500 only when the person is conscious. 1028 00:44:45,500 --> 00:44:47,033 This complexity, 1029 00:44:47,033 --> 00:44:49,166 the way different brain areas speak to each other, 1030 00:44:49,166 --> 00:44:52,066 is a signature, a marker of consciousness. 1031 00:44:52,066 --> 00:44:55,366 BERLIN: Studies of hundreds of patients in various states-- 1032 00:44:55,366 --> 00:44:58,000 from deep sleep to anesthesia to coma-- 1033 00:44:58,000 --> 00:45:01,800 have enabled scientists to develop a complexity scale. 1034 00:45:01,800 --> 00:45:03,766 A score above a certain threshold 1035 00:45:03,766 --> 00:45:08,333 means you are conscious or have the capacity for consciousness. 1036 00:45:08,333 --> 00:45:10,000 EDLOW: Multiple studies have now shown 1037 00:45:10,000 --> 00:45:13,800 that 15% to 20% of patients who appear unresponsive, 1038 00:45:13,800 --> 00:45:16,766 they don't express themselves on our behavioral exam, 1039 00:45:16,766 --> 00:45:18,533 they are actually conscious. 1040 00:45:18,533 --> 00:45:20,400 So, you know, will this be able 1041 00:45:20,400 --> 00:45:22,566 to help people in those states? 1042 00:45:22,566 --> 00:45:24,166 When we speak to families 1043 00:45:24,166 --> 00:45:26,033 about what matters to them most, 1044 00:45:26,033 --> 00:45:29,266 it is that patient's current level of consciousness 1045 00:45:29,266 --> 00:45:33,266 and their potential for future recovery of consciousness. 1046 00:45:33,266 --> 00:45:34,833 If families were to have 1047 00:45:34,833 --> 00:45:37,400 that information, it could fundamentally affect 1048 00:45:37,400 --> 00:45:38,666 the decisions they make about 1049 00:45:38,666 --> 00:45:40,366 whether to continue life-sustaining therapy. 1050 00:45:42,133 --> 00:45:46,566 DEHAENE: Progress in the clinic is extremely real and fast, 1051 00:45:46,566 --> 00:45:48,866 and people realize that the problem of consciousness 1052 00:45:48,866 --> 00:45:50,433 is starting to be solved. 1053 00:45:50,433 --> 00:45:53,333 ♪ ♪ 1054 00:45:53,333 --> 00:45:55,233 BERLIN: Starting to be solved, 1055 00:45:55,233 --> 00:45:57,000 because while we have several clues 1056 00:45:57,000 --> 00:45:59,233 about how conscious awareness might work in the brain, 1057 00:45:59,233 --> 00:46:01,700 this is only the beginning. 1058 00:46:01,700 --> 00:46:04,600 How all of those pieces of brain activation add up 1059 00:46:04,600 --> 00:46:07,866 to you, a distinct individual with a sense of self, 1060 00:46:07,866 --> 00:46:09,266 is still a mystery. 1061 00:46:09,266 --> 00:46:11,866 I know my brain creates an internal experience 1062 00:46:11,866 --> 00:46:14,366 by knitting together bits of sensory information, 1063 00:46:14,366 --> 00:46:16,333 filling in the gaps with its best guess 1064 00:46:16,333 --> 00:46:18,166 of what's out there in the world, 1065 00:46:18,166 --> 00:46:21,333 but what are those guesses based on? 1066 00:46:21,333 --> 00:46:23,866 Memory. 1067 00:46:23,866 --> 00:46:27,766 Each of us has a life rich with experiences to draw from. 1068 00:46:27,766 --> 00:46:30,166 Where we were born, went to school, 1069 00:46:30,166 --> 00:46:32,300 who we fell in love with. 1070 00:46:32,300 --> 00:46:35,033 Memories are the cornerstone of our identities, 1071 00:46:35,033 --> 00:46:36,500 but as it turns out, 1072 00:46:36,500 --> 00:46:39,533 they have a very shaky foundation. 1073 00:46:39,533 --> 00:46:41,966 I could swear by it, 1074 00:46:41,966 --> 00:46:44,233 and would pass every lie detector test, that... 1075 00:46:44,233 --> 00:46:45,733 I had met Mother Teresa. 1076 00:46:45,733 --> 00:46:46,833 But I hadn't. 1077 00:46:46,833 --> 00:46:48,400 Something that I wanted to happen 1078 00:46:48,400 --> 00:46:49,633 but it never did happen. 1079 00:46:52,200 --> 00:46:53,766 SCHILLER: The stories we tell 1080 00:46:53,766 --> 00:46:57,433 ourselves, or what we consider our memory, is a construction. 1081 00:46:57,433 --> 00:46:59,666 We create these representations. 1082 00:46:59,666 --> 00:47:02,800 And they're very dynamic, they constantly change. 1083 00:47:02,800 --> 00:47:05,000 You're kind of living a revision 1084 00:47:05,000 --> 00:47:07,400 of the story of your life, constantly. 1085 00:47:08,400 --> 00:47:10,433 SETH: The more often we recall things, 1086 00:47:10,433 --> 00:47:12,900 the less objectively accurate 1087 00:47:12,900 --> 00:47:14,233 our memories become. 1088 00:47:15,233 --> 00:47:17,600 BERLIN: It turns out that every time you a recall a memory-- 1089 00:47:17,600 --> 00:47:19,733 your first kiss, 1090 00:47:19,733 --> 00:47:21,500 graduating from college, 1091 00:47:21,500 --> 00:47:23,533 the death of a loved one-- 1092 00:47:23,533 --> 00:47:28,133 the very act of recollection makes it vulnerable to change. 1093 00:47:28,133 --> 00:47:30,000 SCHILLER: So when you experience a new event, 1094 00:47:30,000 --> 00:47:32,533 it has to be stored in the brain. 1095 00:47:32,533 --> 00:47:34,066 And then, we used to think that 1096 00:47:34,066 --> 00:47:35,733 whenever you think about that event, 1097 00:47:35,733 --> 00:47:39,500 you retrieve the same original memory. 1098 00:47:39,500 --> 00:47:43,233 But what we got to realize in the last few decades 1099 00:47:43,233 --> 00:47:45,400 is that whenever you retrieve a memory, 1100 00:47:45,400 --> 00:47:47,633 it goes back to an unstable state. 1101 00:47:47,633 --> 00:47:53,066 BERLIN: In 2000, memory scientist Eric Kandel won the Nobel Prize 1102 00:47:53,066 --> 00:47:56,533 for showing that each memory creates new synapses, 1103 00:47:56,533 --> 00:47:58,366 connections that store the memory. 1104 00:47:58,366 --> 00:48:02,133 But what happens when you recall it? 1105 00:48:02,133 --> 00:48:03,933 Every time you remember it, 1106 00:48:03,933 --> 00:48:05,633 you bring it up into your working memory 1107 00:48:05,633 --> 00:48:06,866 and you perceive it, 1108 00:48:06,866 --> 00:48:09,033 and you destroy the long-term memory. 1109 00:48:09,033 --> 00:48:12,000 And you actually have 1110 00:48:12,000 --> 00:48:14,033 to recast it into long-term memory 1111 00:48:14,033 --> 00:48:15,766 when you re-remember it. 1112 00:48:15,766 --> 00:48:17,766 So every single time you remember something, 1113 00:48:17,766 --> 00:48:19,400 you actually add more noise to it, 1114 00:48:19,400 --> 00:48:21,766 so that it's more and more and more false 1115 00:48:21,766 --> 00:48:23,700 throughout time. 1116 00:48:23,700 --> 00:48:26,700 BERLIN: This mechanism, called reconsolidation, 1117 00:48:26,700 --> 00:48:28,366 was first discovered in rodents, 1118 00:48:28,366 --> 00:48:31,333 where neuroscientists witnessed what happens 1119 00:48:31,333 --> 00:48:33,366 when a memory gets recollected: 1120 00:48:33,366 --> 00:48:36,000 for the memory to return to long-term storage, 1121 00:48:36,000 --> 00:48:40,533 the connections between neurons actually have to get rebuilt. 1122 00:48:40,533 --> 00:48:43,100 Recent experiments have suggested 1123 00:48:43,100 --> 00:48:46,166 this is likely a mechanism in human brains, as well, 1124 00:48:46,166 --> 00:48:49,700 because certain drugs known to disrupt reconsolidation 1125 00:48:49,700 --> 00:48:52,600 have been shown to alter human memories. 1126 00:48:53,733 --> 00:48:57,800 FENTON: We're stuck with the problem of, how do we know what is true? 1127 00:48:57,800 --> 00:49:00,133 How do we know what's real? 1128 00:49:00,133 --> 00:49:02,000 And maybe 1129 00:49:02,000 --> 00:49:04,766 part of the recognition is, some of those things 1130 00:49:04,766 --> 00:49:07,900 don't matter as much as we think they do. 1131 00:49:10,533 --> 00:49:13,433 SCHILLER: If we think about the fact that maybe our memories are not 1132 00:49:13,433 --> 00:49:15,033 as they originally happened, 1133 00:49:15,033 --> 00:49:18,300 it could be a scary thought, because then, who are we? 1134 00:49:18,300 --> 00:49:21,466 I think you need to think about it as something 1135 00:49:21,466 --> 00:49:24,700 more liberating, because if you're stuck 1136 00:49:24,700 --> 00:49:26,766 with original representations, 1137 00:49:26,766 --> 00:49:28,466 you're kind of stuck in the past. 1138 00:49:29,566 --> 00:49:33,366 BERLIN: Just like our perceptions, our sense of self is dynamic, 1139 00:49:33,366 --> 00:49:35,933 built to serve us in the present. 1140 00:49:35,933 --> 00:49:37,800 SETH: Our experience of, of self 1141 00:49:37,800 --> 00:49:40,100 is a construction at all sorts of different levels. 1142 00:49:40,100 --> 00:49:42,566 What the brain is doing, is interested in, 1143 00:49:42,566 --> 00:49:46,033 is weaving together a kind of story. 1144 00:49:46,033 --> 00:49:49,700 FENTON: The brain is a storytelling machine, right? 1145 00:49:49,700 --> 00:49:53,033 It's a machine that's designed to make predictions. 1146 00:49:53,033 --> 00:49:56,800 KASTHURI: The narratives that we tell ourselves 1147 00:49:56,800 --> 00:49:58,866 are the biggest illusions that we ever participate in. 1148 00:49:58,866 --> 00:50:01,300 Your sense of who you are is an illusion, 1149 00:50:01,300 --> 00:50:04,333 as everything else-- you're no exception. 1150 00:50:05,500 --> 00:50:08,300 BERLIN: But if even our sense of self is an illusion, 1151 00:50:08,300 --> 00:50:10,600 where does that leave us? 1152 00:50:10,600 --> 00:50:13,633 MARTINEZ-CONDE (chuckles): Trust the illusion, that's the only thing 1153 00:50:13,633 --> 00:50:16,166 that we can be sure of, 1154 00:50:16,166 --> 00:50:21,133 that what we perceive is not what's there. 1155 00:50:26,433 --> 00:50:28,533 BERLIN: So all these years later, 1156 00:50:28,533 --> 00:50:31,566 in my quest to understand where my thoughts come from 1157 00:50:31,566 --> 00:50:33,400 and how my brain works, 1158 00:50:33,400 --> 00:50:36,466 I've learned that my brain is an exquisite machine 1159 00:50:36,466 --> 00:50:40,066 that perceives reality in the service of survival, 1160 00:50:40,066 --> 00:50:42,033 not accuracy. 1161 00:50:42,033 --> 00:50:44,733 The world I carry inside of my head 1162 00:50:44,733 --> 00:50:46,333 is a construction of my brain 1163 00:50:46,333 --> 00:50:49,000 built on bits of sensory information 1164 00:50:49,000 --> 00:50:50,900 woven together with memory 1165 00:50:50,900 --> 00:50:53,966 to create a conscious experience. 1166 00:50:53,966 --> 00:50:56,400 Now, to some this might sound scary, 1167 00:50:56,400 --> 00:50:59,133 but to me, it's inspiring. 1168 00:50:59,133 --> 00:51:02,866 ♪ ♪ 1169 00:51:02,866 --> 00:51:04,866 SETH: The simple act of opening our eyes 1170 00:51:04,866 --> 00:51:06,666 and seeing a world, 1171 00:51:06,666 --> 00:51:08,966 we should not take that for granted. 1172 00:51:08,966 --> 00:51:10,900 And in realizing 1173 00:51:10,900 --> 00:51:14,733 what a miracle of neural computation 1174 00:51:14,733 --> 00:51:16,300 is going on under the hood, 1175 00:51:16,300 --> 00:51:18,733 to give us even the simplest experiences, 1176 00:51:18,733 --> 00:51:20,300 I think this adds value, 1177 00:51:20,300 --> 00:51:23,000 it adds meaning, it adds depth to our lives. 1178 00:51:23,000 --> 00:51:24,800 ♪ ♪ 1179 00:51:24,800 --> 00:51:27,333 DEHAENE: I think it's liberating to understand 1180 00:51:27,333 --> 00:51:32,133 that we rise from this organization of matter. 1181 00:51:32,133 --> 00:51:34,833 It means that we can be a little bit more humble. 1182 00:51:34,833 --> 00:51:38,400 We are gorgeous machines designed by evolution 1183 00:51:38,400 --> 00:51:43,533 as well as by our environment, education, friends, families. 1184 00:51:43,533 --> 00:51:45,533   All of that is inscribed in our brains. 1185 00:51:48,166 --> 00:51:51,833 SAXE: Sometimes when I wonder what I'm doing with my life, 1186 00:51:51,833 --> 00:51:55,033 I think how is it that a spatial 1187 00:51:55,033 --> 00:51:57,266 and temporal pattern of electrical signals 1188 00:51:57,266 --> 00:52:01,100 passing between cells in our brains makes us who we are? 1189 00:52:01,100 --> 00:52:04,533 That just being a part of the team 1190 00:52:04,533 --> 00:52:09,300 asking that question is worth keeping going for. 1191 00:52:09,300 --> 00:52:13,033 ♪ ♪ 1192 00:52:31,400 --> 00:52:38,933 ♪ ♪ 1193 00:52:42,766 --> 00:52:50,300 ♪ ♪ 1194 00:52:51,933 --> 00:52:59,466 ♪ ♪ 1195 00:53:01,166 --> 00:53:08,700 ♪ ♪ 1196 00:53:14,433 --> 00:53:21,600 ♪ ♪ 94727

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