1 00:00:00,530 --> 00:00:02,960 The following content is provided under a Creative 2 00:00:02,960 --> 00:00:04,370 Commons license. 3 00:00:04,370 --> 00:00:07,410 Your support will help MIT OpenCourseWare continue to 4 00:00:07,410 --> 00:00:11,060 offer high quality educational resources for free. 5 00:00:11,060 --> 00:00:13,960 To make a donation or view additional materials from 6 00:00:13,960 --> 00:00:19,790 hundreds of MIT courses, visit MIT OpenCourseWare at 7 00:00:19,790 --> 00:00:21,040 ocw.mit.edu. 8 00:00:34,700 --> 00:00:36,110 PROFESSOR: Good afternoon. 9 00:00:36,110 --> 00:00:38,780 Today we're going to go beyond the way we've 10 00:00:38,780 --> 00:00:39,480 spoken about before. 11 00:00:39,480 --> 00:00:41,180 We've talked about how psychologists measure the 12 00:00:41,180 --> 00:00:44,690 mind, how neuroscientists interrogate the brain. 13 00:00:44,690 --> 00:00:47,080 And so now, we'll talk about the first human faculty that 14 00:00:47,080 --> 00:00:48,790 we'll consider in a series of faculties 15 00:00:48,790 --> 00:00:50,460 through the course, Vision-- 16 00:00:50,460 --> 00:00:52,450 How We See. 17 00:00:52,450 --> 00:00:55,015 And for today's lecture, I got help from Melissa Troyer, one 18 00:00:55,015 --> 00:00:56,790 of your teaching assistants. 19 00:00:56,790 --> 00:00:59,060 So, one of the things we can think about is what are the 20 00:00:59,060 --> 00:01:00,180 purposes of vision? 21 00:01:00,180 --> 00:01:03,080 What do we use sight for? 22 00:01:03,080 --> 00:01:04,440 And we'll talk about that. 23 00:01:04,440 --> 00:01:06,695 Problems that the visual system has to overcome. 24 00:01:06,695 --> 00:01:09,530 What are the obstacles for vision to work accurately and 25 00:01:09,530 --> 00:01:10,700 efficiently? 26 00:01:10,700 --> 00:01:13,130 And how does the brain organization of vision serve 27 00:01:13,130 --> 00:01:17,160 the human capacity to see? 28 00:01:17,160 --> 00:01:20,740 So human perceptual abilities are amazing, as are perceptual 29 00:01:20,740 --> 00:01:22,680 abilities of many species in different areas. 30 00:01:22,680 --> 00:01:26,450 But humans can detect a candle 30 miles away on 31 00:01:26,450 --> 00:01:28,190 a dark, clear night. 32 00:01:28,190 --> 00:01:30,120 They can detect cochlear displacements 33 00:01:30,120 --> 00:01:31,400 that's in your ear-- 34 00:01:31,400 --> 00:01:34,500 equal to the width of a hydrogen atom. 35 00:01:34,500 --> 00:01:37,240 You can taste 1 teaspoon of sugar, even when it's mixed in 36 00:01:37,240 --> 00:01:39,200 two gallons of water. 37 00:01:39,200 --> 00:01:41,900 You can smell a drop of perfume diffused into the 38 00:01:41,900 --> 00:01:44,200 space of a three-bedroom apartment. 39 00:01:44,200 --> 00:01:47,260 So the human capacity to perceive the world in a highly 40 00:01:47,260 --> 00:01:49,750 sensitive way is remarkable. 41 00:01:49,750 --> 00:01:52,130 We're going to focus on vision specifically today. 42 00:01:52,130 --> 00:01:55,110 And think, what do we use vision for? 43 00:01:55,110 --> 00:01:59,030 Well, how does it serve us in dealing with the world? 44 00:01:59,030 --> 00:02:02,680 And researchers have thought about two major reasons that 45 00:02:02,680 --> 00:02:04,350 we see, two purposes of vision. 46 00:02:04,350 --> 00:02:06,800 One of them is object recognition. 47 00:02:06,800 --> 00:02:08,550 Recognizing things in the world. 48 00:02:08,550 --> 00:02:11,200 People we know, words we can read, 49 00:02:11,200 --> 00:02:13,750 chairs, tables, animals-- 50 00:02:13,750 --> 00:02:15,300 all kinds of things that we see out there. 51 00:02:15,300 --> 00:02:18,190 And we need to know who they are or what they are in order 52 00:02:18,190 --> 00:02:20,040 to operate in the world. 53 00:02:20,040 --> 00:02:23,050 And the second one is a little bit different, navigation-- 54 00:02:23,050 --> 00:02:24,020 getting around in the world. 55 00:02:24,020 --> 00:02:25,590 When you're running, when you're jumping, when you're 56 00:02:25,590 --> 00:02:28,440 reaching to grab something, you're not usually much 57 00:02:28,440 --> 00:02:31,470 interested in what something is, as where you're moving, 58 00:02:31,470 --> 00:02:33,310 where you need to move to avoid something 59 00:02:33,310 --> 00:02:34,510 being thrown at you. 60 00:02:34,510 --> 00:02:36,160 So two big purposes of vision-- 61 00:02:36,160 --> 00:02:38,930 what things are and where they are, so you can navigate 62 00:02:38,930 --> 00:02:41,540 around quickly in the world. 63 00:02:41,540 --> 00:02:44,570 And when we think about purposes of vision, for what 64 00:02:44,570 --> 00:02:48,720 things are or where they are, psychologists and people 65 00:02:48,720 --> 00:02:51,040 working in computer vision have recognized a series of 66 00:02:51,040 --> 00:02:54,360 problems that our visual system has to solve. 67 00:02:54,360 --> 00:02:55,990 We have to link unique images. 68 00:02:55,990 --> 00:02:58,310 Because when we see something, it'll almost always look 69 00:02:58,310 --> 00:03:01,500 different from one perceptual moment to the next. 70 00:03:01,500 --> 00:03:04,000 We can see the same person, for example, but under 71 00:03:04,000 --> 00:03:06,180 different conditions of illumination, darkness or 72 00:03:06,180 --> 00:03:08,440 lightness or shadows, the angle we see them at, the 73 00:03:08,440 --> 00:03:11,160 distance we see them at, the expression on their face-- a 74 00:03:11,160 --> 00:03:13,140 smile or frown-- 75 00:03:13,140 --> 00:03:15,690 shadows that might be being cast, occlusion, a thing 76 00:03:15,690 --> 00:03:17,380 blocking part of their face. 77 00:03:17,380 --> 00:03:19,760 And yet, we can recognize a person very well under all 78 00:03:19,760 --> 00:03:20,550 these circumstances. 79 00:03:20,550 --> 00:03:23,630 So what arrives in our eye is different from 80 00:03:23,630 --> 00:03:25,150 all of these views. 81 00:03:25,150 --> 00:03:27,600 But what we interpret them is all the same. 82 00:03:27,600 --> 00:03:29,250 That's a person I know. 83 00:03:29,250 --> 00:03:30,230 The same thing with letters. 84 00:03:30,230 --> 00:03:32,550 We can see letters in all kinds of fonts and all kinds 85 00:03:32,550 --> 00:03:35,420 of handwriting, and yet we can interpret what they are in a 86 00:03:35,420 --> 00:03:36,670 uniform way. 87 00:03:36,670 --> 00:03:37,440 Or body. 88 00:03:37,440 --> 00:03:39,810 We can see bodies of all kinds of different positions. 89 00:03:39,810 --> 00:03:41,440 But our visual system lets us know right 90 00:03:41,440 --> 00:03:43,210 away that it's a person. 91 00:03:43,210 --> 00:03:45,530 And so psychologists have tried to formalize these kinds 92 00:03:45,530 --> 00:03:47,990 of problems to ones of equivalents. 93 00:03:47,990 --> 00:03:50,550 So something is the same thing at different views. 94 00:03:50,550 --> 00:03:51,440 Generalization-- 95 00:03:51,440 --> 00:03:53,770 something has a very different shape. 96 00:03:53,770 --> 00:03:56,320 Or impoverished input, where there's poor lighting or 97 00:03:56,320 --> 00:03:57,690 something's complicated. 98 00:03:57,690 --> 00:04:00,210 And you still figure out what it is. 99 00:04:00,210 --> 00:04:02,430 For getting around in the world or where things are in 100 00:04:02,430 --> 00:04:04,640 the world, you're moving through space 101 00:04:04,640 --> 00:04:05,420 and tracking things. 102 00:04:05,420 --> 00:04:07,310 And there's many sources of movement. 103 00:04:07,310 --> 00:04:08,630 Your eyes are constantly moving. 104 00:04:08,630 --> 00:04:09,440 Your head is moving. 105 00:04:09,440 --> 00:04:10,670 Your body is moving. 106 00:04:10,670 --> 00:04:11,880 And the world is moving. 107 00:04:11,880 --> 00:04:13,890 And you have to sort all those out. 108 00:04:13,890 --> 00:04:14,460 Run quickly. 109 00:04:14,460 --> 00:04:17,380 Jump [INAUDIBLE] 110 00:04:17,380 --> 00:04:19,370 world spatially. 111 00:04:19,370 --> 00:04:20,399 And so here's some examples. 112 00:04:20,399 --> 00:04:23,490 Here's a shape constancy problem. 113 00:04:23,490 --> 00:04:25,020 Here's two different versions of a cat. 114 00:04:25,020 --> 00:04:27,320 It's trivial for you to know that even though these things 115 00:04:27,320 --> 00:04:29,790 have different shapes, they represent the same thing. 116 00:04:29,790 --> 00:04:32,310 But that's an achievement of your visual system, to easily 117 00:04:32,310 --> 00:04:35,690 make that equivalence across two very different shapes. 118 00:04:35,690 --> 00:04:38,430 And we know, from computer vision, how hard these 119 00:04:38,430 --> 00:04:40,580 problems are. 120 00:04:40,580 --> 00:04:43,780 No computer vision system sees with anything like the 121 00:04:43,780 --> 00:04:47,110 generalized ability of a very young child. 122 00:04:47,110 --> 00:04:50,820 So our visual systems are so brilliant that psychologists 123 00:04:50,820 --> 00:04:53,990 and vision scientists really interrogate our visual system 124 00:04:53,990 --> 00:04:56,060 to figure out, how do we do it. 125 00:04:56,060 --> 00:04:58,410 If people didn't do this, we'd think it would be impossible 126 00:04:58,410 --> 00:05:01,250 to accomplish at all. 127 00:05:01,250 --> 00:05:03,640 Here's another example that's easily solved by people-- not 128 00:05:03,640 --> 00:05:05,940 so easily solved by machines. 129 00:05:05,940 --> 00:05:09,080 If you see something like a car at different angles, we 130 00:05:09,080 --> 00:05:11,790 know that's the same thing, pretty easily, even though, 131 00:05:11,790 --> 00:05:14,800 again, the information that arrives to our eyes is 132 00:05:14,800 --> 00:05:16,710 radically different. 133 00:05:16,710 --> 00:05:19,170 And the same thing can be said of something like all these 134 00:05:19,170 --> 00:05:21,500 different versions of the letter C. 135 00:05:21,500 --> 00:05:23,220 Radically different shapes. 136 00:05:23,220 --> 00:05:25,550 But we can readily interpret almost all them-- this one 137 00:05:25,550 --> 00:05:26,740 won't be too tough-- 138 00:05:26,740 --> 00:05:28,410 as the letter C. 139 00:05:28,410 --> 00:05:31,260 So our visual system does this brilliantly. 140 00:05:31,260 --> 00:05:34,140 And also you get very impoverished views. 141 00:05:34,140 --> 00:05:36,740 A key might be covered by something, clothes on a hook. 142 00:05:36,740 --> 00:05:38,430 There might be a book over here. 143 00:05:38,430 --> 00:05:41,240 There might be a fire hydrant over here or a person. 144 00:05:41,240 --> 00:05:44,160 And yet, even though you get only partial information, it's 145 00:05:44,160 --> 00:05:46,790 relatively trivial to discover, for you, what it is 146 00:05:46,790 --> 00:05:48,820 you're looking at. 147 00:05:48,820 --> 00:05:51,770 Or you can see things like you've never seen before. 148 00:05:51,770 --> 00:05:56,900 The pink elephant, this odd picture, this striped ... 149 00:05:56,900 --> 00:06:01,960 you've never seen them before but it's not really ... 150 00:06:01,960 --> 00:06:04,610 So again, your visual system is just so brilliant at 151 00:06:04,610 --> 00:06:08,110 matching what it sees with what you know to figure out 152 00:06:08,110 --> 00:06:11,070 what's in front of you. 153 00:06:11,070 --> 00:06:14,960 So today we're going to talk about how our minds and brains 154 00:06:14,960 --> 00:06:16,730 allow us to see the world vision. 155 00:06:16,730 --> 00:06:19,920 And we'll talk about it in three parts; how philosophers 156 00:06:19,920 --> 00:06:23,620 and psychologists have thought about the idea of seeing; how 157 00:06:23,620 --> 00:06:27,320 when information first enters your eye-- your retina-- 158 00:06:27,320 --> 00:06:31,190 which are set of layers in the back of the eye; how that 159 00:06:31,190 --> 00:06:34,110 retina's already organized to help you see the world; and 160 00:06:34,110 --> 00:06:37,940 how that information is then transported into the cortex, 161 00:06:37,940 --> 00:06:40,930 where higher-level processing lets you know where you are 162 00:06:40,930 --> 00:06:43,480 and what you're looking at. 163 00:06:43,480 --> 00:06:46,760 So the ideas about how we learn to see the world have 164 00:06:46,760 --> 00:06:47,840 their roots in philosophy. 165 00:06:47,840 --> 00:06:51,690 For example, John Locke is associated with the idea of 166 00:06:51,690 --> 00:06:54,110 the blank slate or tabula rosa. 167 00:06:54,110 --> 00:06:57,350 He said, "Let us suppose the mind to be a white paper void 168 00:06:57,350 --> 00:06:59,640 of any characters, without any ideas. 169 00:06:59,640 --> 00:07:01,360 How comes it to be furnished? 170 00:07:01,360 --> 00:07:04,050 Whence comes it by that vast store, which the busy and 171 00:07:04,050 --> 00:07:06,920 boundless fancy of man has painted on it with an almost 172 00:07:06,920 --> 00:07:09,960 endless variety?" Where do we get the information for how 173 00:07:09,960 --> 00:07:11,780 the world works when we look at it? 174 00:07:11,780 --> 00:07:13,810 And his answer to that was experience. 175 00:07:13,810 --> 00:07:17,120 That we notice things around us, and experience drives our 176 00:07:17,120 --> 00:07:18,940 understanding of the world. 177 00:07:18,940 --> 00:07:21,870 And that position that there's a blank slate and we learn 178 00:07:21,870 --> 00:07:24,420 objectively from the world around us has been called the 179 00:07:24,420 --> 00:07:25,870 Objectivist view. 180 00:07:25,870 --> 00:07:28,350 And maybe, that we see the world that we build up through 181 00:07:28,350 --> 00:07:31,410 experience, knowledge about how to accurately represent 182 00:07:31,410 --> 00:07:33,190 what's around us. 183 00:07:33,190 --> 00:07:36,330 The alternative you has emphasized how much our mind 184 00:07:36,330 --> 00:07:39,640 organizes what we see, instead of what's out there itself. 185 00:07:39,640 --> 00:07:42,390 And so Gestalt psychologists or Gestalt views on perception 186 00:07:42,390 --> 00:07:44,820 say, it's not what's out there in the world, but the way our 187 00:07:44,820 --> 00:07:48,710 mind organizes and believes what's out there. 188 00:07:48,710 --> 00:07:50,670 And these two things can come together. 189 00:07:50,670 --> 00:07:52,650 So for example, when you think about how you listen to a 190 00:07:52,650 --> 00:07:57,420 piano, music comes from piano the same way a 191 00:07:57,420 --> 00:07:58,880 pianist strikes chords. 192 00:07:58,880 --> 00:08:01,600 There's things out there in the world that you see. 193 00:08:01,600 --> 00:08:03,900 The piano has certain limitations. 194 00:08:03,900 --> 00:08:05,570 It can sound like somethings but not others. 195 00:08:05,570 --> 00:08:06,590 It can't be a clarinet. 196 00:08:06,590 --> 00:08:09,240 There's constraints in the world on what's possible to 197 00:08:09,240 --> 00:08:11,740 see what's out there. 198 00:08:11,740 --> 00:08:14,190 And our percepts are evoked by nature in this way. 199 00:08:14,190 --> 00:08:16,420 But they are personal and not a copy of nature. 200 00:08:16,420 --> 00:08:19,010 In the words, we each, individually, figure out 201 00:08:19,010 --> 00:08:20,950 what's going on there by our own experiences and 202 00:08:20,950 --> 00:08:22,450 interpretation. 203 00:08:22,450 --> 00:08:25,350 And they're exactly that-- an interpretation, not a simple 204 00:08:25,350 --> 00:08:28,770 mirroring or copy of nature. 205 00:08:28,770 --> 00:08:30,560 So I'm going to try and convince you of something that 206 00:08:30,560 --> 00:08:33,190 might not be obvious to people when they first think about 207 00:08:33,190 --> 00:08:35,470 it, that vision is an interpretation of the 208 00:08:35,470 --> 00:08:36,289 world around us. 209 00:08:36,289 --> 00:08:39,940 It's not simply a video camera or mirror, it's an active 210 00:08:39,940 --> 00:08:41,630 calculation of what's out there. 211 00:08:41,630 --> 00:08:45,760 And we see, through inference, what we believe that we see. 212 00:08:45,760 --> 00:08:46,930 And that visual illusions-- 213 00:08:46,930 --> 00:08:49,180 and you've seen some before, and I'll show you some again-- 214 00:08:49,180 --> 00:08:52,810 are not just illusions to trick people and confuse them. 215 00:08:52,810 --> 00:08:55,850 But they're demonstrations of a gap between what's actually 216 00:08:55,850 --> 00:08:59,490 out there in the world and how we interpret it. 217 00:08:59,490 --> 00:09:01,480 Most of the time we don't have illusions. 218 00:09:01,480 --> 00:09:02,770 We don't walk into walls. 219 00:09:02,770 --> 00:09:06,030 We don't misidentify people we know commonly. 220 00:09:06,030 --> 00:09:08,170 Because illusions are rare, because our minds and brains 221 00:09:08,170 --> 00:09:11,110 have evolved to interpret the world that's out there, and 222 00:09:11,110 --> 00:09:14,310 the system works so brilliantly coupled with the 223 00:09:14,310 --> 00:09:15,970 environment that we actually live in, that we 224 00:09:15,970 --> 00:09:16,840 don't think about it. 225 00:09:16,840 --> 00:09:18,690 So the person on the street says, well, what 226 00:09:18,690 --> 00:09:20,500 do I need to see? 227 00:09:20,500 --> 00:09:22,250 I open my eyes. 228 00:09:22,250 --> 00:09:25,200 But that's because our visual system works so brilliantly, 229 00:09:25,200 --> 00:09:27,000 effortlessly, and automatically. 230 00:09:27,000 --> 00:09:29,410 It's such a calculating machine that that's why it 231 00:09:29,410 --> 00:09:31,820 feels so simple. 232 00:09:31,820 --> 00:09:34,820 So here's one, small example. 233 00:09:34,820 --> 00:09:37,960 And I'll show you a number of visual illusions that show us 234 00:09:37,960 --> 00:09:39,960 how we're constantly interpreting even the most 235 00:09:39,960 --> 00:09:41,060 simple thing. 236 00:09:41,060 --> 00:09:43,060 So for example, here's two people. 237 00:09:43,060 --> 00:09:45,390 And we're not worried that this is one of the shortest 238 00:09:45,390 --> 00:09:48,220 men you've ever seen, because we know, through prospective, 239 00:09:48,220 --> 00:09:50,260 this person's standing back there. 240 00:09:50,260 --> 00:09:52,940 Even though, if they were standing right next to 241 00:09:52,940 --> 00:09:55,710 somebody, you'd worry about the size of that person. 242 00:09:55,710 --> 00:09:57,910 So we're constantly interpreting information by 243 00:09:57,910 --> 00:10:00,660 the context in which we see it. 244 00:10:00,660 --> 00:10:03,800 Here's another example of the choice of interpretations. 245 00:10:03,800 --> 00:10:04,900 You can see a vase. 246 00:10:04,900 --> 00:10:07,580 Or you can see two faces here. , Usually it's hard for people 247 00:10:07,580 --> 00:10:08,930 to see them both simultaneously. 248 00:10:08,930 --> 00:10:10,580 They tend to flip back and forth. 249 00:10:10,580 --> 00:10:11,390 But you can choose the 250 00:10:11,390 --> 00:10:14,620 interpretation of what you see. 251 00:10:14,620 --> 00:10:17,190 So let me pick one example where people have studied a 252 00:10:17,190 --> 00:10:20,390 lot, which is the issue of brightness constancy. 253 00:10:20,390 --> 00:10:23,140 So here's the problem for the visual system. 254 00:10:23,140 --> 00:10:26,380 The ambient brightness, the brightness around us, varies 255 00:10:26,380 --> 00:10:28,290 in staggering ratios. 256 00:10:28,290 --> 00:10:30,570 When we're outdoors in the sun-- and even that's not 257 00:10:30,570 --> 00:10:31,110 constant, right? 258 00:10:31,110 --> 00:10:32,150 Sometimes it's a bright day. 259 00:10:32,150 --> 00:10:33,860 Sometimes it's a cloudy day. 260 00:10:33,860 --> 00:10:36,670 Sometimes we're indoors under bright conditions or less 261 00:10:36,670 --> 00:10:37,300 bright conditions. 262 00:10:37,300 --> 00:10:40,330 So huge changes in the ambient illumination. 263 00:10:40,330 --> 00:10:43,520 Or in shadows-- we'll make it even more complicated. 264 00:10:43,520 --> 00:10:45,790 That means in a purely measurement sense, in a purely 265 00:10:45,790 --> 00:10:49,810 physics sense, a piece of coal in sunlight reflects 10 times 266 00:10:49,810 --> 00:10:51,570 as much light as snow in the shade. 267 00:10:51,570 --> 00:10:53,410 So think about that for a moment. 268 00:10:53,410 --> 00:10:55,030 You're not disturbed if you see a piece of 269 00:10:55,030 --> 00:10:56,400 coal inside or outside. 270 00:10:56,400 --> 00:10:59,730 They both look, to you, like black, dark pieces of coal. 271 00:10:59,730 --> 00:11:02,270 And snow looks, pretty much, the same to you if it's inside 272 00:11:02,270 --> 00:11:03,500 or outside. 273 00:11:03,500 --> 00:11:05,990 But if you're visual system worked like a simple, 274 00:11:05,990 --> 00:11:10,100 objective light meter, you would conclude that the piece 275 00:11:10,100 --> 00:11:13,270 of coal in the sunlight is a much brighter thing-- in an 276 00:11:13,270 --> 00:11:14,490 objective sense-- 277 00:11:14,490 --> 00:11:16,200 than snow in the shade. 278 00:11:16,200 --> 00:11:18,770 And yet, you never make that conclusion, because you're 279 00:11:18,770 --> 00:11:22,210 constantly, automatically, without effort, adjusting 280 00:11:22,210 --> 00:11:25,350 mentally for the ambient light condition. 281 00:11:25,350 --> 00:11:27,800 So we use brightness all the time to help us figure out is 282 00:11:27,800 --> 00:11:29,750 that a piece of coal or sunlight? 283 00:11:29,750 --> 00:11:33,410 But that brightness perception is constantly adjusted to 284 00:11:33,410 --> 00:11:36,430 interpret brightness in the context we're sitting in. 285 00:11:36,430 --> 00:11:41,000 And perceptual psychologists have worked on how people 286 00:11:41,000 --> 00:11:42,020 calculate this. 287 00:11:42,020 --> 00:11:46,040 So here's an example of a dark letter T. Again, the black T 288 00:11:46,040 --> 00:11:48,460 would be 10 times brighter outdoors than 289 00:11:48,460 --> 00:11:52,130 white paper is indoors. 290 00:11:52,130 --> 00:11:53,600 And yet, you're never bothered by that. 291 00:11:53,600 --> 00:11:56,500 You can read a newspaper or something else indoors or 292 00:11:56,500 --> 00:11:58,980 outdoors with equal these, because people are calculating 293 00:11:58,980 --> 00:12:00,890 the ratio of the ambient lighting-- 294 00:12:00,890 --> 00:12:04,010 lower inside, much brighter outside, from the sun-- 295 00:12:04,010 --> 00:12:06,780 and using that information to make inferences about the 296 00:12:06,780 --> 00:12:10,630 relative brightness of a stimulus they're looking at. 297 00:12:10,630 --> 00:12:13,870 And there's all kinds of examples or illusions that 298 00:12:13,870 --> 00:12:15,130 show us this. 299 00:12:15,130 --> 00:12:18,965 So for example, here, you detect a white tile in the 300 00:12:18,965 --> 00:12:21,320 shade and a gray tile in brightness. 301 00:12:21,320 --> 00:12:22,970 And that's how you interpret it. 302 00:12:22,970 --> 00:12:25,120 But that's because you're taking already into account-- 303 00:12:25,120 --> 00:12:26,960 without even thinking about it for a moment-- 304 00:12:26,960 --> 00:12:29,560 the fact that this is in the shadow, and this is in the 305 00:12:29,560 --> 00:12:31,340 bright light. 306 00:12:31,340 --> 00:12:33,400 You're right in your interpretation. 307 00:12:33,400 --> 00:12:37,540 But if we show a constant background, you could see that 308 00:12:37,540 --> 00:12:41,340 those two are actually identical in brightness. 309 00:12:41,340 --> 00:12:45,090 The same thing-- you could take a look at this cube and 310 00:12:45,090 --> 00:12:47,670 this cube as part of these Rubik's cubes. 311 00:12:47,670 --> 00:12:48,680 This looks orange. 312 00:12:48,680 --> 00:12:50,370 This looks brown. 313 00:12:50,370 --> 00:12:52,400 You're adjusting for light, as you make those 314 00:12:52,400 --> 00:12:53,640 interpretations. 315 00:12:53,640 --> 00:12:56,110 And if you make the background constant, you can see those 316 00:12:56,110 --> 00:12:58,470 are identical in color and luminance. 317 00:12:58,470 --> 00:13:00,550 So this shows you that we're constantly making these 318 00:13:00,550 --> 00:13:01,770 interpretations. 319 00:13:01,770 --> 00:13:05,470 Only these tricks of visual illusion make us realize that 320 00:13:05,470 --> 00:13:07,790 our inferences are occasionally wrong. 321 00:13:07,790 --> 00:13:10,540 But they're incredibly right almost all the time. 322 00:13:10,540 --> 00:13:13,790 So here's another example of taking a local 323 00:13:13,790 --> 00:13:15,220 luminance into account. 324 00:13:15,220 --> 00:13:18,180 So typically, people see this as a lighter gray. 325 00:13:18,180 --> 00:13:19,570 And this is a darker gray. 326 00:13:19,570 --> 00:13:22,090 Again, they're using the contrast that surrounds it to 327 00:13:22,090 --> 00:13:23,860 make that interpretation. 328 00:13:23,860 --> 00:13:26,330 Because if we give it a constant background, you can 329 00:13:26,330 --> 00:13:28,530 see that in an objective sense the two are identical. 330 00:13:31,730 --> 00:13:34,570 People use edges as a very powerful source of information 331 00:13:34,570 --> 00:13:35,540 to make these inferences. 332 00:13:35,540 --> 00:13:37,240 And you can see that in one moment. 333 00:13:37,240 --> 00:13:40,300 Because this boundary, here, is used to make judgments 334 00:13:40,300 --> 00:13:43,670 about the relative brightness around an object. 335 00:13:43,670 --> 00:13:45,890 So you just do this. 336 00:13:45,890 --> 00:13:48,850 The boundary disappears, and the two things look equally 337 00:13:48,850 --> 00:13:50,920 gray, one above the other. 338 00:13:50,920 --> 00:13:54,960 Or no less spectacular, but measured example, here's two 339 00:13:54,960 --> 00:13:57,530 different shades of gray. 340 00:13:57,530 --> 00:13:59,560 And if you're in a light meter, objectively looking at 341 00:13:59,560 --> 00:14:02,990 how much light is reflected from this, here that changes. 342 00:14:02,990 --> 00:14:05,490 This looks almost the same as this. 343 00:14:05,490 --> 00:14:07,220 But really, there's just a manipulation at the 344 00:14:07,220 --> 00:14:07,750 boundaries. 345 00:14:07,750 --> 00:14:08,780 Again, here's a light meter. 346 00:14:08,780 --> 00:14:09,720 Here's a boundary. 347 00:14:09,720 --> 00:14:12,280 So these two sides are really identical. 348 00:14:12,280 --> 00:14:15,130 It's just been manipulated by the boundary. 349 00:14:15,130 --> 00:14:17,950 And you can see that, because if you get rid of that 350 00:14:17,950 --> 00:14:19,570 boundary, now you can see that these two 351 00:14:19,570 --> 00:14:21,040 sides are equally bright. 352 00:14:23,680 --> 00:14:27,100 Another example, again, of how we're constantly using local 353 00:14:27,100 --> 00:14:28,650 information about brightness to make 354 00:14:28,650 --> 00:14:30,500 judgments about objects. 355 00:14:30,500 --> 00:14:32,610 Here, you see a picture of a woman. 356 00:14:32,610 --> 00:14:33,530 Here's her forehead. 357 00:14:33,530 --> 00:14:35,880 Here's her darker hair. 358 00:14:35,880 --> 00:14:38,980 But in fact, that's a judgment in interpretation. 359 00:14:38,980 --> 00:14:41,920 Because if we cover up the surrounding information, the 360 00:14:41,920 --> 00:14:43,470 hair is identical to the face. 361 00:14:46,020 --> 00:14:48,510 Here's some other illusions, not all about brightness. 362 00:14:48,510 --> 00:14:51,750 But, again, they're just reminders that what we see is 363 00:14:51,750 --> 00:14:53,360 constantly a calculation and an 364 00:14:53,360 --> 00:14:55,730 interpretation of many factors. 365 00:14:55,730 --> 00:14:56,995 So here's a fun spiral. 366 00:14:56,995 --> 00:15:00,510 You can see it zooming around, going from inner tightness and 367 00:15:00,510 --> 00:15:02,860 unwinding peripherally. 368 00:15:02,860 --> 00:15:07,030 And so we can add a dot to that. 369 00:15:07,030 --> 00:15:09,475 And now, we can let this travel around to the middle. 370 00:15:12,510 --> 00:15:15,910 You can see it's not quite making the middle. 371 00:15:15,910 --> 00:15:18,440 And we can try that again. 372 00:15:18,440 --> 00:15:21,560 Because in fact, in this picture, every line, every 373 00:15:21,560 --> 00:15:22,700 circle is a circle. 374 00:15:22,700 --> 00:15:24,310 And none of them are connected, one to the other. 375 00:15:24,310 --> 00:15:26,980 They just appear that way. 376 00:15:26,980 --> 00:15:30,940 This illusion, that Richard Gregory describes, was 377 00:15:30,940 --> 00:15:34,120 inspired because he saw a wall in a cafe that looked like it 378 00:15:34,120 --> 00:15:36,900 had not been well assembled by the person building it. 379 00:15:36,900 --> 00:15:39,870 As if they had been consuming something from the cafe as 380 00:15:39,870 --> 00:15:41,780 they built the wall. 381 00:15:41,780 --> 00:15:46,650 And bizarrely, having all kinds of lines that are not 382 00:15:46,650 --> 00:15:47,980 lined up, as you'd expect in 383 00:15:47,980 --> 00:15:49,480 well-constructed tiles on a wall. 384 00:15:49,480 --> 00:15:53,260 But in fact, every line, here, is perfectly straight. 385 00:15:53,260 --> 00:15:56,830 And you're interpreting the black and white edges to 386 00:15:56,830 --> 00:16:00,770 confuse you about the lining up across the horizontal. 387 00:16:00,770 --> 00:16:02,110 This is very striking. 388 00:16:02,110 --> 00:16:06,260 Here's Rubik's cubes that, again, now they're adding one 389 00:16:06,260 --> 00:16:08,020 more story into this, which is color. 390 00:16:08,020 --> 00:16:10,970 But we use that often in every day sight, of course, to make 391 00:16:10,970 --> 00:16:13,020 conclusions about things. 392 00:16:13,020 --> 00:16:15,670 And here, you can see the difference between, for 393 00:16:15,670 --> 00:16:19,410 example, this yellow cube and this brown cube. 394 00:16:23,320 --> 00:16:26,320 What did I want to do? 395 00:16:26,320 --> 00:16:27,710 You can see the difference between this brown cube-- 396 00:16:27,710 --> 00:16:30,170 sorry, let me get my illusion correct-- 397 00:16:30,170 --> 00:16:34,400 and this darker cube, lighter brown and darker brown. 398 00:16:34,400 --> 00:16:36,480 But if we cover those up, those are identical. 399 00:16:36,480 --> 00:16:39,250 And they're gray. 400 00:16:39,250 --> 00:16:41,710 Here's another one that shows you it's not just the color 401 00:16:41,710 --> 00:16:43,680 itself but the spatial location. 402 00:16:43,680 --> 00:16:48,410 So here's a pink circle, near the middle of this display. 403 00:16:48,410 --> 00:16:51,110 And here's a more orange one. 404 00:16:51,110 --> 00:16:55,780 So we're just simply going to expand those out and get it 405 00:16:55,780 --> 00:16:57,800 bigger and bigger. 406 00:16:57,800 --> 00:17:01,030 And as it's moving across your eye-- 407 00:17:01,030 --> 00:17:03,655 even though, objectively, it remains pink and orange, or 408 00:17:03,655 --> 00:17:05,530 the things will lead you to you interpret that-- 409 00:17:05,530 --> 00:17:08,440 as it gets big enough, the color difference disappears to 410 00:17:08,440 --> 00:17:10,660 your perception. 411 00:17:10,660 --> 00:17:12,400 And sometimes you can get-- and this is a 412 00:17:12,400 --> 00:17:14,430 very complicated one-- 413 00:17:14,430 --> 00:17:15,609 illusions-- 414 00:17:15,609 --> 00:17:17,604 I don't know if it's working for you-- 415 00:17:17,604 --> 00:17:21,200 that these things are actually turning, even though none of 416 00:17:21,200 --> 00:17:22,450 them are moving at all. 417 00:17:26,390 --> 00:17:26,690 OK. 418 00:17:26,690 --> 00:17:29,370 And one last, simple example. 419 00:17:29,370 --> 00:17:31,180 Here's yellow on gray. 420 00:17:31,180 --> 00:17:32,680 Here's light gray on yellow. 421 00:17:32,680 --> 00:17:34,490 But in fact, these X's are identical. 422 00:17:34,490 --> 00:17:36,170 And you can see that when they meet here. 423 00:17:36,170 --> 00:17:41,160 So again, we're constantly interpreting color, shape, 424 00:17:41,160 --> 00:17:43,720 brightness, by things that are surrounding it. 425 00:17:43,720 --> 00:17:47,070 And everything is being the product of an interpretation, 426 00:17:47,070 --> 00:17:50,530 not what's out there in an objective sense of self. 427 00:17:50,530 --> 00:17:52,920 And so, if we think about this Objectivist view that that 428 00:17:52,920 --> 00:17:56,390 can't be right, that the limit of this objective view would 429 00:17:56,390 --> 00:17:58,180 be that everybody sees everything differently all the 430 00:17:58,180 --> 00:18:00,140 time, that would be a confusing world of 431 00:18:00,140 --> 00:18:00,830 hallucinations. 432 00:18:00,830 --> 00:18:02,590 We don't live there either, mostly. 433 00:18:02,590 --> 00:18:05,020 So we think it's the because there's a relationship between 434 00:18:05,020 --> 00:18:06,150 these, or a synthesis. 435 00:18:06,150 --> 00:18:08,330 We perceive only within limits of our nervous system. 436 00:18:08,330 --> 00:18:10,900 There's a way that we see the world. 437 00:18:10,900 --> 00:18:13,750 But the way that our nervous system computes and makes 438 00:18:13,750 --> 00:18:16,590 inferences about the world reflects many properties of 439 00:18:16,590 --> 00:18:19,900 the world that are efficient and accurate. 440 00:18:19,900 --> 00:18:23,460 So how do we get information into our brains to see? 441 00:18:23,460 --> 00:18:26,130 So, if you were to design a system that was hopelessly 442 00:18:26,130 --> 00:18:29,220 failed, the first thing you would do is think the visual 443 00:18:29,220 --> 00:18:31,090 system is going to fail. 444 00:18:31,090 --> 00:18:33,810 The first thing it does is it takes objects in the world and 445 00:18:33,810 --> 00:18:35,800 it flips them upside down-- and how they're perceived in 446 00:18:35,800 --> 00:18:38,070 the eye, the front of the eye, the back of the eye. 447 00:18:38,070 --> 00:18:39,510 Here's the retina. 448 00:18:39,510 --> 00:18:42,860 And the first place in your brain that sees something is 449 00:18:42,860 --> 00:18:44,370 in the back of your eye. 450 00:18:44,370 --> 00:18:46,190 You'd think you would put it in the front of your eye. 451 00:18:46,190 --> 00:18:48,510 And information bounces back into the 452 00:18:48,510 --> 00:18:49,280 deepest part of the eye. 453 00:18:49,280 --> 00:18:52,930 And then it comes out and leads through the optic nerve. 454 00:18:52,930 --> 00:18:54,890 You also know, from prior classes, that the world is 455 00:18:54,890 --> 00:18:58,070 organized by left and right visual fields, instead of the 456 00:18:58,070 --> 00:19:02,090 way we intuitively think about the world. 457 00:19:02,090 --> 00:19:03,840 Let's take a closer look at the retina. 458 00:19:03,840 --> 00:19:07,980 These layers of cells in your eye that begin vision. 459 00:19:07,980 --> 00:19:10,900 So light comes in and bounces through the back. 460 00:19:10,900 --> 00:19:14,350 And here are the rods and cones where vision begins. 461 00:19:14,350 --> 00:19:17,030 The cones are primarily near the fovea. 462 00:19:17,030 --> 00:19:18,655 The rods are primarily peripheral. 463 00:19:21,950 --> 00:19:26,750 And I'm reminding you, again, as the optic nerve leaves, it 464 00:19:26,750 --> 00:19:28,020 goes through the optic chiasm. 465 00:19:28,020 --> 00:19:30,350 The fibers get sorted out by whether they're having 466 00:19:30,350 --> 00:19:33,250 information from the left or right side of the world, and 467 00:19:33,250 --> 00:19:34,520 then enter the cortex that way. 468 00:19:37,020 --> 00:19:42,830 Within the retina, within your eye, here is a graphic of the 469 00:19:42,830 --> 00:19:43,810 different kinds of cells. 470 00:19:43,810 --> 00:19:46,340 Here's what the cells actually look like. 471 00:19:48,840 --> 00:19:54,900 And here's a blow up of these remarkable cones, these larger 472 00:19:54,900 --> 00:20:00,620 entities and the rods that you can see are very small, but 473 00:20:00,620 --> 00:20:02,300 many of them. 474 00:20:02,300 --> 00:20:04,890 And the big difference between rods and cones is that the 475 00:20:04,890 --> 00:20:07,660 rods are receptive to information at only one 476 00:20:07,660 --> 00:20:08,400 wavelength. 477 00:20:08,400 --> 00:20:09,760 They're effectively color blind. 478 00:20:09,760 --> 00:20:10,940 We'll come back to that. 479 00:20:10,940 --> 00:20:13,910 But that's a good wavelength to pick up information, for 480 00:20:13,910 --> 00:20:15,980 example, in low light. 481 00:20:15,980 --> 00:20:19,570 The cones are selective for blue or red or green. 482 00:20:19,570 --> 00:20:22,350 That's the stuff of seeing color. 483 00:20:22,350 --> 00:20:24,990 But it doesn't operate too well, unless you have a pretty 484 00:20:24,990 --> 00:20:28,430 well illuminated situation. 485 00:20:28,430 --> 00:20:32,880 And the rods and cones have a striking physical 486 00:20:32,880 --> 00:20:33,960 organization. 487 00:20:33,960 --> 00:20:36,930 So here's the distribution of cones for color. 488 00:20:36,930 --> 00:20:40,880 Pretty much, all of them in the fovea in the middle part 489 00:20:40,880 --> 00:20:42,280 of the retina. 490 00:20:42,280 --> 00:20:45,450 And then the rods are spread out peripherally. 491 00:20:45,450 --> 00:20:50,050 So there's this big difference in where they're located. 492 00:20:50,050 --> 00:20:53,260 And another big difference is that the cones, the ones that 493 00:20:53,260 --> 00:20:57,860 respond to color, and that are in the middle of the fovea, 494 00:20:57,860 --> 00:21:01,130 have an almost one-to-one relationship between the 495 00:21:01,130 --> 00:21:04,070 receptors for color information and the neurons 496 00:21:04,070 --> 00:21:05,730 that leave the eye with information that 497 00:21:05,730 --> 00:21:07,350 will go into the brain. 498 00:21:07,350 --> 00:21:09,420 The rods have a many-to-one relationship. 499 00:21:09,420 --> 00:21:13,510 Many rods are contributing for a combined calculation of some 500 00:21:13,510 --> 00:21:16,040 kind to a single neuron that leaves the brain. 501 00:21:16,040 --> 00:21:18,620 So you can already see, at the very first moment of reception 502 00:21:18,620 --> 00:21:21,330 from the rods and from the cons, very different kinds of 503 00:21:21,330 --> 00:21:26,430 processes are being begun, as you begin to see. 504 00:21:26,430 --> 00:21:29,180 Two ideas have been very helpful for understanding how 505 00:21:29,180 --> 00:21:30,810 vision is organized in the brain-- 506 00:21:30,810 --> 00:21:33,640 our receptive fields and retinotopy. 507 00:21:33,640 --> 00:21:36,760 Receptive fields are, simply, an area of external space. 508 00:21:36,760 --> 00:21:39,560 It's a physical spot in space in which a 509 00:21:39,560 --> 00:21:40,885 stimulus activates a neuron. 510 00:21:40,885 --> 00:21:44,290 So neurons, early in vision, will respond to specific 511 00:21:44,290 --> 00:21:46,850 locations in space when you're looking straightforward. 512 00:21:46,850 --> 00:21:48,460 One neuron might cover this area. 513 00:21:48,460 --> 00:21:51,880 Another neuron might cover that area and so forth. 514 00:21:51,880 --> 00:21:55,085 Retinotopy refers to topographic or spatial maps of 515 00:21:55,085 --> 00:21:56,540 visual space across a restricted 516 00:21:56,540 --> 00:21:58,100 region of the brain. 517 00:21:58,100 --> 00:22:02,240 And so, retinotopy keeps these receptive fields lined up. 518 00:22:02,240 --> 00:22:05,000 So if the neuron is seeing this spot, this retinotopic 519 00:22:05,000 --> 00:22:07,330 map, and so the next spot stays next to it. 520 00:22:07,330 --> 00:22:08,750 And the next spot stays next to it. 521 00:22:08,750 --> 00:22:11,730 So you can reconstruct what you see, based on a lot of 522 00:22:11,730 --> 00:22:16,480 very local glances at the environment. 523 00:22:16,480 --> 00:22:19,670 And then it travels from the eye, through the optic nerve, 524 00:22:19,670 --> 00:22:22,060 into something called the lateral geniculate nucleus. 525 00:22:22,060 --> 00:22:24,760 The nucleus is simply a collection of cells. 526 00:22:24,760 --> 00:22:26,670 This is part of the thalamus. 527 00:22:26,670 --> 00:22:29,490 And from there, the fibers will travel into the cortex to 528 00:22:29,490 --> 00:22:31,920 begin conscious visual perception. 529 00:22:31,920 --> 00:22:34,200 And you have one on the left and one in the right of the 530 00:22:34,200 --> 00:22:36,930 lateral geniculate nucleon. 531 00:22:36,930 --> 00:22:38,440 And if you look at this blown up-- 532 00:22:38,440 --> 00:22:40,430 this is a sample from a monkey, but the human one 533 00:22:40,430 --> 00:22:41,850 looks very similar. 534 00:22:41,850 --> 00:22:46,960 You can see 1, 2, 3, 4, 5, 6 layers of cells in each 535 00:22:46,960 --> 00:22:49,180 lateral geniculate nucleus. 536 00:22:49,180 --> 00:22:51,060 The first two look a little bit different. 537 00:22:51,060 --> 00:22:55,470 If you look carefully, they're comprised of larger cells. 538 00:22:55,470 --> 00:22:58,870 The other four are comprised of smaller cells. 539 00:22:58,870 --> 00:23:01,350 So people describe the cell layer, made up of larger 540 00:23:01,350 --> 00:23:02,710 cells, as magnocellular-- 541 00:23:02,710 --> 00:23:03,890 big cells. 542 00:23:03,890 --> 00:23:07,620 And the other four layers as parvocellular, or small cells. 543 00:23:07,620 --> 00:23:11,950 The mangocellular layers tend to have large cells. 544 00:23:11,950 --> 00:23:14,190 A lot of their information comes from the rods, the black 545 00:23:14,190 --> 00:23:17,650 and white elements, good for seeing in low vision. 546 00:23:17,650 --> 00:23:19,430 They have large receptive fields. 547 00:23:19,430 --> 00:23:21,380 They cover relatively large patches of the 548 00:23:21,380 --> 00:23:22,460 world for each neuron. 549 00:23:22,460 --> 00:23:25,820 Three times larger than the parvocellular ones, the 550 00:23:25,820 --> 00:23:29,610 neurons fire rapidly, transiently, are color blind. 551 00:23:29,610 --> 00:23:33,460 They're good in low contrast, low lighting. 552 00:23:33,460 --> 00:23:35,430 The parvocellular ones are smaller. 553 00:23:35,430 --> 00:23:37,260 They get a lot of their input from the cones. 554 00:23:37,260 --> 00:23:39,750 They have smaller receptive fields. 555 00:23:39,750 --> 00:23:42,770 They have slow, sustained responses. 556 00:23:42,770 --> 00:23:45,010 They don't turn on and off very quickly. 557 00:23:45,010 --> 00:23:46,150 They're wavelength sensitive. 558 00:23:46,150 --> 00:23:48,170 So they can respond to color. 559 00:23:48,170 --> 00:23:50,690 They operate best when things are very bright. 560 00:23:50,690 --> 00:23:52,240 They're unique to primates, and there are 10 561 00:23:52,240 --> 00:23:53,280 times more of them. 562 00:23:53,280 --> 00:23:55,420 But again, even before you get to the cortex, there's this 563 00:23:55,420 --> 00:23:58,510 huge organization between two modes or pathways of 564 00:23:58,510 --> 00:24:00,040 perception, so you can construct the 565 00:24:00,040 --> 00:24:02,150 world as you see it. 566 00:24:02,150 --> 00:24:04,770 And then we've gone over before that as you 567 00:24:04,770 --> 00:24:06,310 leave the nuclei -- 568 00:24:06,310 --> 00:24:08,310 the lateral geniculate nuclei-- then the next fiber 569 00:24:08,310 --> 00:24:11,570 pathway takes you into the primary visual cortex in the 570 00:24:11,570 --> 00:24:12,820 back of your brain. 571 00:24:14,990 --> 00:24:18,950 Now, vision is so important for primates and people, that 572 00:24:18,950 --> 00:24:22,370 when people estimate how much of the neocortex is devoted to 573 00:24:22,370 --> 00:24:25,520 different modalities, a common estimate is that about half of 574 00:24:25,520 --> 00:24:29,000 the brain is primarily devoted to vision-- 575 00:24:29,000 --> 00:24:31,700 11% to touch, 3% to audition. 576 00:24:31,700 --> 00:24:33,380 These are ballpark estimates. 577 00:24:33,380 --> 00:24:38,430 But it shows you how visual we are with our fellow primates. 578 00:24:38,430 --> 00:24:42,080 There's many specialized areas within the 579 00:24:42,080 --> 00:24:43,260 human visual cortex. 580 00:24:43,260 --> 00:24:47,390 They estimated some years ago it was 32 distinct areas, each 581 00:24:47,390 --> 00:24:50,450 performing a different tasks, lots of specialization. 582 00:24:50,450 --> 00:24:54,610 And another fascinating aspect of vision is proliferation as 583 00:24:54,610 --> 00:24:58,970 we go up into higher stations of visual processing. 584 00:24:58,970 --> 00:25:02,440 So in one lateral geniculate nucleus in your brain-- and 585 00:25:02,440 --> 00:25:03,960 you have two of them, one on the left and one on the 586 00:25:03,960 --> 00:25:06,780 right-- you have about a million neurons. 587 00:25:06,780 --> 00:25:10,210 They will send information that will be interpreted by 588 00:25:10,210 --> 00:25:14,280 about 250 million neurons in your visual cortex. 589 00:25:14,280 --> 00:25:17,460 Those will communicate about 400 million neurons in the 590 00:25:17,460 --> 00:25:19,450 next level of processing. 591 00:25:19,450 --> 00:25:22,120 And finally, there'll be something like 1.3 billion 592 00:25:22,120 --> 00:25:23,580 visual cortical neurons in the brain, 593 00:25:23,580 --> 00:25:26,030 overall, in a gross estimate. 594 00:25:26,030 --> 00:25:28,610 It's as if to discover what you're seeing, you're having 595 00:25:28,610 --> 00:25:31,620 larger and larger populations of neurons unpacking the 596 00:25:31,620 --> 00:25:34,520 mystery of what initially came into the brain very 597 00:25:34,520 --> 00:25:38,010 impoverished information, that's expanded to begin to 598 00:25:38,010 --> 00:25:41,760 understand what you need to perceive a face or a word or a 599 00:25:41,760 --> 00:25:43,160 physical movement. 600 00:25:43,160 --> 00:25:47,230 So a single lateral geniculate neuron, it could be estimated, 601 00:25:47,230 --> 00:25:51,900 will take 600 cortical neurons to interpret what this lateral 602 00:25:51,900 --> 00:25:53,870 geniculate neuron has been exposed to. 603 00:25:56,850 --> 00:25:59,380 Here's this area in the visual cortex, where the information 604 00:25:59,380 --> 00:26:02,185 arrives first into your brain and primary visual cortex. 605 00:26:04,970 --> 00:26:09,150 And when we think about cortex in perception, in several 606 00:26:09,150 --> 00:26:10,900 modalities, one of the striking things is how it's 607 00:26:10,900 --> 00:26:13,670 organized to achieve certain goals. 608 00:26:13,670 --> 00:26:15,910 So here, we're going to switch for a moment to the parts of 609 00:26:15,910 --> 00:26:17,690 your brain that move your body-- 610 00:26:17,690 --> 00:26:21,890 motor cortex or that have your sense of touch. 611 00:26:21,890 --> 00:26:24,740 And when people stimulate these and look at what they're 612 00:26:24,740 --> 00:26:25,380 related to. 613 00:26:25,380 --> 00:26:27,310 For example, in patients undergoing treatment for 614 00:26:27,310 --> 00:26:30,080 epilepsy or an animal study-- support this where you can do 615 00:26:30,080 --> 00:26:32,330 much more expensive experimentation-- 616 00:26:32,330 --> 00:26:36,320 a very striking thing occurs, which is the number of neurons 617 00:26:36,320 --> 00:26:38,880 devoted to different parts of your body are radically 618 00:26:38,880 --> 00:26:41,680 different than your actual body size. 619 00:26:41,680 --> 00:26:44,390 So this funny looking, so-called homunculus 620 00:26:44,390 --> 00:26:46,930 represents how many neurons, for example, in your motor 621 00:26:46,930 --> 00:26:50,590 cortex are devoted to different parts of your body. 622 00:26:50,590 --> 00:26:53,640 So it turns out that we do incredible things with our 623 00:26:53,640 --> 00:26:57,050 hands, so we devote, it seems like, a tremendous amount of 624 00:26:57,050 --> 00:26:57,960 this area to the hand. 625 00:26:57,960 --> 00:26:59,210 That's what that looks so large. 626 00:27:01,850 --> 00:27:04,030 There's a reason we do handshaking and handwriting 627 00:27:04,030 --> 00:27:07,140 and typing with our hands and not with our hips. 628 00:27:07,140 --> 00:27:09,260 Because although our hips are physically larger than our 629 00:27:09,260 --> 00:27:11,750 hand, we devote an incredibly small number of 630 00:27:11,750 --> 00:27:13,320 neurons to do that. 631 00:27:13,320 --> 00:27:16,630 So it's a trick of the brain, given a certain limited size, 632 00:27:16,630 --> 00:27:19,970 to blow up its representation of what needs to be done 633 00:27:19,970 --> 00:27:21,270 brilliantly. 634 00:27:21,270 --> 00:27:23,910 And to reduce its representation of parts of the 635 00:27:23,910 --> 00:27:26,420 body that it doesn't have to do too much with. 636 00:27:26,420 --> 00:27:27,290 So what's blown up? 637 00:27:27,290 --> 00:27:28,840 Your hand? 638 00:27:28,840 --> 00:27:32,130 Things to do with your face, so that you can speak? 639 00:27:32,130 --> 00:27:34,220 Your tongue, because that has to accomplish a lot, in terms 640 00:27:34,220 --> 00:27:36,810 of speech, in terms of eating. 641 00:27:36,810 --> 00:27:39,540 And things like your toes and ankle are gypped of 642 00:27:39,540 --> 00:27:41,900 representation, because you don't do that much that's 643 00:27:41,900 --> 00:27:42,640 brilliance. 644 00:27:42,640 --> 00:27:44,510 There's a very clever strategy, by the brain, to 645 00:27:44,510 --> 00:27:48,760 devote the amount of neural resources to make that part of 646 00:27:48,760 --> 00:27:51,600 your body accomplish either very complicated things or 647 00:27:51,600 --> 00:27:53,850 very simple things. 648 00:27:53,850 --> 00:27:59,440 And if people on the outside looked like this-- 649 00:27:59,440 --> 00:28:01,010 this is what they would look like. 650 00:28:01,010 --> 00:28:04,490 Giant hands, giant head, and a very shriveled up trunk. 651 00:28:04,490 --> 00:28:07,410 So that's not what we look like, but that's the way our 652 00:28:07,410 --> 00:28:10,820 brain represents us, our bodies, so that we could have 653 00:28:10,820 --> 00:28:14,540 fantastically complicated control of our hands and 654 00:28:14,540 --> 00:28:18,960 things having to do with speech around our mouth. 655 00:28:18,960 --> 00:28:20,670 It's different for different modalities. 656 00:28:20,670 --> 00:28:22,900 For hearing, things are organized 657 00:28:22,900 --> 00:28:26,220 by frequency, tonotopy. 658 00:28:26,220 --> 00:28:29,350 For vision, things are organized in two ways. 659 00:28:29,350 --> 00:28:31,050 Spatial relations are maintained. 660 00:28:31,050 --> 00:28:33,650 They have to be, so you know, as different neurons saw 661 00:28:33,650 --> 00:28:36,500 different receptive fields, how's that all kept up? 662 00:28:36,500 --> 00:28:39,520 Here's a kind of a rough experiment, where a monkey 663 00:28:39,520 --> 00:28:42,730 saw-- before it was, as they politely say, sacrificed-- 664 00:28:42,730 --> 00:28:44,480 a display like this. 665 00:28:44,480 --> 00:28:46,520 This is the brain of the monkey, flattened out. 666 00:28:46,520 --> 00:28:49,620 And you can see it has this representation, like this. 667 00:28:49,620 --> 00:28:51,240 Now you could say, well, that's how we see. 668 00:28:51,240 --> 00:28:52,100 We line everything up. 669 00:28:52,100 --> 00:28:54,240 But this is not the part of our brain that we associate 670 00:28:54,240 --> 00:28:56,130 with conscious perception at all. 671 00:28:56,130 --> 00:28:59,880 But if you didn't keep the spatial information aligned 672 00:28:59,880 --> 00:29:02,530 correctly, you could never interpret things, in the end, 673 00:29:02,530 --> 00:29:04,830 correctly that go together, like different parts of a hand 674 00:29:04,830 --> 00:29:07,190 or different letters within a word. 675 00:29:07,190 --> 00:29:10,470 In humans, it's also in the back of the brain. 676 00:29:10,470 --> 00:29:12,000 It also keeps spatial information. 677 00:29:12,000 --> 00:29:14,800 But what do humans do, and other primates? 678 00:29:14,800 --> 00:29:18,710 They greatly expand the central part of vision. 679 00:29:18,710 --> 00:29:21,940 So here's the central part of vision, the so-called fovea, 680 00:29:21,940 --> 00:29:24,560 which is a small part of the visual display. 681 00:29:24,560 --> 00:29:27,240 Here is everything else that's in peripheral vision. 682 00:29:27,240 --> 00:29:28,960 So this is dark green, dark purple. 683 00:29:28,960 --> 00:29:31,690 But when it comes to the representation in the visual 684 00:29:31,690 --> 00:29:34,260 cortex, this small area of what we see 685 00:29:34,260 --> 00:29:35,095 becomes pretty large. 686 00:29:35,095 --> 00:29:37,110 It's way overrepresented. 687 00:29:37,110 --> 00:29:42,010 So just as the motor cortex over represents the hand, the 688 00:29:42,010 --> 00:29:45,750 visual cortex over represents the cone 689 00:29:45,750 --> 00:29:49,380 foveal part of the brain. 690 00:29:49,380 --> 00:29:51,960 So they can do much more examination, much more 691 00:29:51,960 --> 00:29:55,460 computation on that small foveated area. 692 00:29:55,460 --> 00:29:57,980 So that's why it matters, tremendously, where people put 693 00:29:57,980 --> 00:29:58,870 their eyes. 694 00:29:58,870 --> 00:30:01,270 Where they put their eyes is where neuromachinery is 695 00:30:01,270 --> 00:30:04,050 dedicated to do its most brilliant analysis. 696 00:30:04,050 --> 00:30:06,590 And peripheral regions, which are large, in vision, get 697 00:30:06,590 --> 00:30:08,570 relatively small representation. 698 00:30:08,570 --> 00:30:11,640 All we do, in the periphery, is notice if something is 699 00:30:11,640 --> 00:30:13,930 whizzing at our head. 700 00:30:13,930 --> 00:30:15,655 And for that, we don't need to be that sophisticated. 701 00:30:18,580 --> 00:30:21,330 Now, what do neurons communicate in 702 00:30:21,330 --> 00:30:22,410 primary visual cortex? 703 00:30:22,410 --> 00:30:24,510 Well, this is the, sort of, seminal Nobel Prize-winning 704 00:30:24,510 --> 00:30:27,240 work from Hubel and Wiesel, at Harvard, who discovered that 705 00:30:27,240 --> 00:30:32,130 what neurons respond to in primary visual cortex is the 706 00:30:32,130 --> 00:30:34,850 orientation of a piece of something like this. 707 00:30:34,850 --> 00:30:37,955 So here's a neuron that loves a little bit of a stimulus. 708 00:30:37,955 --> 00:30:39,340 You notice that disorientation? 709 00:30:39,340 --> 00:30:43,200 Here's each of these lines as of the neuron firing. 710 00:30:43,200 --> 00:30:46,960 It generalizes if a bar is close in orientation, it will 711 00:30:46,960 --> 00:30:48,820 still like it, somewhat. 712 00:30:48,820 --> 00:30:51,980 And if the bar moves away from its preferred orientation, it 713 00:30:51,980 --> 00:30:53,330 won't respond and all. 714 00:30:53,330 --> 00:30:55,920 So these neurons are coding something about local 715 00:30:55,920 --> 00:30:57,830 orientation of little lines. 716 00:30:57,830 --> 00:30:59,820 Those little lines will be assembled later on in the 717 00:30:59,820 --> 00:31:03,420 brain to represent a letter, a word, a book, a face, a 718 00:31:03,420 --> 00:31:04,540 chair, and so on. 719 00:31:04,540 --> 00:31:07,240 But these neurons simply know they're seeing a line of this 720 00:31:07,240 --> 00:31:10,020 angle or that angle that will be assembled later on for an 721 00:31:10,020 --> 00:31:12,840 entire conscious percept. 722 00:31:12,840 --> 00:31:15,940 And then that information will go from primary visual cortex, 723 00:31:15,940 --> 00:31:19,210 in two pathways, that have two quite different properties, 724 00:31:19,210 --> 00:31:22,020 but turn out to be the super-information processing 725 00:31:22,020 --> 00:31:25,220 pathways of your brain and my brain. 726 00:31:25,220 --> 00:31:27,390 So where do we first learn about this fundamental 727 00:31:27,390 --> 00:31:30,510 organization of how we see into two giant highways of 728 00:31:30,510 --> 00:31:31,880 information processing? 729 00:31:31,880 --> 00:31:34,270 Well, the first studies where we should study's in monkeys. 730 00:31:34,270 --> 00:31:36,600 And I'll show you work in humans as well. 731 00:31:36,600 --> 00:31:39,430 And the major discovery was this, that in our brains, 732 00:31:39,430 --> 00:31:42,220 there's a so-called where pathway that goes up in the 733 00:31:42,220 --> 00:31:46,010 brain into the parietal cortex and a what pathway that goes 734 00:31:46,010 --> 00:31:48,730 into temporal lobe or lower cortex. 735 00:31:48,730 --> 00:31:52,190 And if you make lesions in these different regions, one 736 00:31:52,190 --> 00:31:54,900 form of vision is spared and one is impaired 737 00:31:54,900 --> 00:31:55,910 in selective ways. 738 00:31:55,910 --> 00:31:57,660 So how did they discover this? 739 00:31:57,660 --> 00:31:59,590 So again, here's the big picture. 740 00:31:59,590 --> 00:32:02,960 Early visual processing information a what pathway to 741 00:32:02,960 --> 00:32:06,480 know what objects are a face, a word, a chair. 742 00:32:06,480 --> 00:32:09,400 Or where things are, that you might run to, 743 00:32:09,400 --> 00:32:12,410 grab, or jump over. 744 00:32:12,410 --> 00:32:13,280 So here was the experiment. 745 00:32:13,280 --> 00:32:14,820 It was pretty simple. 746 00:32:14,820 --> 00:32:16,460 Here were two food wells. 747 00:32:16,460 --> 00:32:19,840 And monkeys that were eager to get food would get rewarded, 748 00:32:19,840 --> 00:32:23,260 if they would pick the correct food well. 749 00:32:23,260 --> 00:32:25,790 They didn't see where the food was that was hidden, but they 750 00:32:25,790 --> 00:32:27,930 would get to pick one or the other. 751 00:32:27,930 --> 00:32:32,700 And in one task they would have to pick which food well 752 00:32:32,700 --> 00:32:34,540 is closer to the stimulus. 753 00:32:34,540 --> 00:32:35,680 That's a where task. 754 00:32:35,680 --> 00:32:36,970 Where is this located? 755 00:32:36,970 --> 00:32:38,570 That's the piece of information that will tell me 756 00:32:38,570 --> 00:32:40,140 where to get my food. 757 00:32:40,140 --> 00:32:41,850 And you can see, here's the performance of the animals, 758 00:32:41,850 --> 00:32:42,420 their errors. 759 00:32:42,420 --> 00:32:43,360 So it's good to be low. 760 00:32:43,360 --> 00:32:44,800 They are eager to get their food. 761 00:32:44,800 --> 00:32:48,370 But if the animal had a surgical lesion in the 762 00:32:48,370 --> 00:32:51,180 parietal cortex, the where pathway, they were very poor 763 00:32:51,180 --> 00:32:52,810 at performing this task. 764 00:32:52,810 --> 00:32:55,740 As if they couldn't appreciate the spatial relation between 765 00:32:55,740 --> 00:32:59,360 where the cylinder was located and the food well. 766 00:32:59,360 --> 00:33:01,370 On the other hand, monkeys who had lesions made in the 767 00:33:01,370 --> 00:33:05,720 temporal cortex were poor when they had to make this task. 768 00:33:05,720 --> 00:33:07,270 They would see two different objects. 769 00:33:07,270 --> 00:33:09,760 And they were taught that one always meant that's 770 00:33:09,760 --> 00:33:10,400 where the food is. 771 00:33:10,400 --> 00:33:12,350 Let's pretend it's the cylinder. 772 00:33:12,350 --> 00:33:14,000 So in order to know the correct answer, they would 773 00:33:14,000 --> 00:33:17,200 have to say, OK, is it next to this object or this object? 774 00:33:17,200 --> 00:33:18,480 That's a what discrimination. 775 00:33:18,480 --> 00:33:20,450 Is it a cylinder or a cube? 776 00:33:20,450 --> 00:33:22,670 And now, the temporal lobe lesions were the ones that 777 00:33:22,670 --> 00:33:24,130 affected performance. 778 00:33:24,130 --> 00:33:26,850 So it's as if one part of the brain tells you where things 779 00:33:26,850 --> 00:33:27,740 are, in vision-- 780 00:33:27,740 --> 00:33:28,810 parietal lobe. 781 00:33:28,810 --> 00:33:30,610 And one tells you what they are-- 782 00:33:30,610 --> 00:33:32,030 the temporal lobe. 783 00:33:32,030 --> 00:33:35,170 So that's lesion studies in monkeys. 784 00:33:35,170 --> 00:33:38,170 But there's very interesting things about when we look at 785 00:33:38,170 --> 00:33:41,110 what cells communicate in these two pathways that makes 786 00:33:41,110 --> 00:33:44,140 sense in what we understand to be the goals of these systems. 787 00:33:44,140 --> 00:33:46,710 So for example, in the where pathway, in the parietal 788 00:33:46,710 --> 00:33:50,780 cortex, many neurons pick up information from the fovea, 789 00:33:50,780 --> 00:33:52,580 the central part of vision. 790 00:33:52,580 --> 00:33:57,340 But the majority are sensitive to stimuli in the periphery. 791 00:33:57,340 --> 00:33:59,240 So if you imagine, a good thing to know if you're 792 00:33:59,240 --> 00:34:00,840 running, is in the periphery. 793 00:34:00,840 --> 00:34:04,020 Are there things coming at your head, things to avoid? 794 00:34:04,020 --> 00:34:06,610 You might grab something here, if you need to grab it there. 795 00:34:06,610 --> 00:34:09,030 You would want to have a lot of peripheral information for 796 00:34:09,030 --> 00:34:10,909 spatial things around you. 797 00:34:10,909 --> 00:34:14,679 And in fact, if a monkey was looking at this spot, which is 798 00:34:14,679 --> 00:34:17,610 away, entirely, from the stimulus, it would respond to 799 00:34:17,610 --> 00:34:20,409 both a large and small stimulus in the periphery 800 00:34:20,409 --> 00:34:21,690 quite strongly. 801 00:34:21,690 --> 00:34:24,120 So these neurons are responding to a pretty broad 802 00:34:24,120 --> 00:34:27,480 range of space where things could happen. 803 00:34:27,480 --> 00:34:31,360 Neurons in the what pathway have almost their entire 804 00:34:31,360 --> 00:34:33,870 responsiveness in the fovea. 805 00:34:33,870 --> 00:34:35,750 Because you know what something is, if you're 806 00:34:35,750 --> 00:34:37,469 reading a word and looking at it. 807 00:34:37,469 --> 00:34:39,360 If you're looking at a face, they're figuring out 808 00:34:39,360 --> 00:34:40,460 who the person is. 809 00:34:40,460 --> 00:34:43,586 So it's only responding, these kinds of neurons, to 810 00:34:43,586 --> 00:34:44,940 information in the center. 811 00:34:44,940 --> 00:34:47,500 But it has some very interesting properties, even 812 00:34:47,500 --> 00:34:49,360 at the level of singular neurons. 813 00:34:49,360 --> 00:34:50,650 So here's a depiction of an 814 00:34:50,650 --> 00:34:52,110 experiment, now, from a monkey. 815 00:34:52,110 --> 00:34:53,889 You're looking at a single neuron or 816 00:34:53,889 --> 00:34:55,429 small group of neurons. 817 00:34:55,429 --> 00:34:57,370 And you present something like a hand. 818 00:34:57,370 --> 00:35:00,170 And you can see that those neurons really like the hand. 819 00:35:00,170 --> 00:35:03,850 Now, don't forget, for a primary visual cortex, it just 820 00:35:03,850 --> 00:35:04,390 sees lines. 821 00:35:04,390 --> 00:35:06,430 It doesn't think about the entire objects. 822 00:35:06,430 --> 00:35:08,740 But by the time you get to the higher levels of vision, it's 823 00:35:08,740 --> 00:35:09,840 encoding an entire object. 824 00:35:09,840 --> 00:35:11,870 And then, you can do experiments that basically 825 00:35:11,870 --> 00:35:15,530 interrogate, what does this neuron discover in the world? 826 00:35:15,530 --> 00:35:17,270 What is it interested in? 827 00:35:17,270 --> 00:35:20,890 So if you turn the hand over, it's still very interested. 828 00:35:20,890 --> 00:35:22,900 You show it a less detailed hand. 829 00:35:22,900 --> 00:35:23,790 It's still very interested. 830 00:35:23,790 --> 00:35:26,900 Maybe a bit less, but it's still very interested. 831 00:35:26,900 --> 00:35:29,190 A hand, this way, where all the spatial relations have 832 00:35:29,190 --> 00:35:31,990 changed still recognizes a hand. 833 00:35:31,990 --> 00:35:34,920 It still recognizes a hand with a bit less enthusiasm. 834 00:35:34,920 --> 00:35:38,660 But now, a mitten, which kind of looks like a hand. 835 00:35:38,660 --> 00:35:40,200 That barely counts as a hand at all. 836 00:35:40,200 --> 00:35:43,800 That neuron has lost interest in coding that as a hand. 837 00:35:43,800 --> 00:35:45,170 And you're showed other things that have some 838 00:35:45,170 --> 00:35:47,100 similarity in shape. 839 00:35:47,100 --> 00:35:48,520 Because these all have four elements in 840 00:35:48,520 --> 00:35:49,920 them, like four fingers. 841 00:35:49,920 --> 00:35:51,230 This neuron's not responding at all. 842 00:35:51,230 --> 00:35:53,970 It's not that it has four things or lines. 843 00:35:53,970 --> 00:35:55,290 It's a hand. 844 00:35:55,290 --> 00:35:56,390 And then, you could even worry about things. 845 00:35:56,390 --> 00:35:57,940 Well, maybe this, because it's from a person. 846 00:35:57,940 --> 00:36:01,450 But if they see the face, this neuron doesn't care. 847 00:36:01,450 --> 00:36:03,400 So it's nothing about the gross shape 848 00:36:03,400 --> 00:36:04,540 or that it's a person. 849 00:36:04,540 --> 00:36:08,500 This neuron is specialized, foveally, for spotting a hand. 850 00:36:08,500 --> 00:36:10,580 And not only that, these kinds of neurons already have the 851 00:36:10,580 --> 00:36:14,080 properties we described that are big problems for vision. 852 00:36:14,080 --> 00:36:17,100 So here's a neuron that's responding to an upright face. 853 00:36:19,860 --> 00:36:23,020 And then it's responding if the face is turned on its side 854 00:36:23,020 --> 00:36:24,050 pretty well. 855 00:36:24,050 --> 00:36:26,670 The face is turned upside down, a little less well, but 856 00:36:26,670 --> 00:36:28,680 it's still responding to a face. 857 00:36:28,680 --> 00:36:31,290 If the face is distant, it's still responding. 858 00:36:31,290 --> 00:36:33,120 If the face changes expression, it's still 859 00:36:33,120 --> 00:36:34,704 responding. 860 00:36:34,704 --> 00:36:37,450 And if we put a green filter on it, so you have a Martian 861 00:36:37,450 --> 00:36:38,690 person, it's still responding. 862 00:36:38,690 --> 00:36:41,980 So all kinds of ways that a face changes in the world, 863 00:36:41,980 --> 00:36:43,170 this neuron is still firing. 864 00:36:43,170 --> 00:36:46,520 And interestingly, this neuron was firing for one of the 865 00:36:46,520 --> 00:36:47,790 experimenters. 866 00:36:47,790 --> 00:36:50,430 But it wasn't too interested in the other experimenter. 867 00:36:50,430 --> 00:36:53,170 So it's a neuron that's generalizing all the different 868 00:36:53,170 --> 00:36:55,260 views that you might have of a person. 869 00:36:55,260 --> 00:36:57,930 But it's responding to one neuron versus another. 870 00:36:57,930 --> 00:37:00,940 And there's been a lot of fun in monkey 871 00:37:00,940 --> 00:37:01,840 neurophysiology stuff. 872 00:37:01,840 --> 00:37:05,840 There's a so-called Jennifer Aniston cell, where 873 00:37:05,840 --> 00:37:07,740 researchers have discovered neurons, in monkeys, that seem 874 00:37:07,740 --> 00:37:10,280 to respond to Jennifer Aniston, for whatever reason. 875 00:37:10,280 --> 00:37:14,760 And other specific famous people. 876 00:37:14,760 --> 00:37:17,690 Although, not particularly meaningful to the primates. 877 00:37:17,690 --> 00:37:20,920 And of course, in human brain imaging, you can do tasks that 878 00:37:20,920 --> 00:37:23,045 emphasize what an object is or where it is. 879 00:37:23,045 --> 00:37:25,505 And corresponding to the monkey work, we see activation 880 00:37:25,505 --> 00:37:29,040 in this where pathway towards the parietal cortex. 881 00:37:29,040 --> 00:37:30,450 If you have to make a spatial judgment, 882 00:37:30,450 --> 00:37:32,030 where things are located. 883 00:37:32,030 --> 00:37:34,760 Or this lower temporal lobe, what pathway. 884 00:37:34,760 --> 00:37:37,135 You have to make a judgment about what object you're 885 00:37:37,135 --> 00:37:38,550 looking at. 886 00:37:38,550 --> 00:37:41,880 So that lines up in intact humans very much, with what we 887 00:37:41,880 --> 00:37:46,410 see in primates with invasive studies. 888 00:37:46,410 --> 00:37:49,710 So we're going to end today by discussing two kinds of 889 00:37:49,710 --> 00:37:54,480 lesions in humans and some spectacular impairments in the 890 00:37:54,480 --> 00:37:55,590 what or where pathways. 891 00:37:55,590 --> 00:37:58,360 So let me get you ready for these films for a moment. 892 00:37:58,360 --> 00:38:02,480 So one film you're going to see is the patient who has a 893 00:38:02,480 --> 00:38:05,950 great injury to the parts of the brain in the parietal 894 00:38:05,950 --> 00:38:09,490 cortex that serve our where system. 895 00:38:09,490 --> 00:38:12,470 So this is so-called Balint's syndrome, in the Neurology. 896 00:38:12,470 --> 00:38:14,400 These are bilateral injuries. 897 00:38:14,400 --> 00:38:15,910 That means on both sides of the brain, in 898 00:38:15,910 --> 00:38:17,780 the parietal region. 899 00:38:17,780 --> 00:38:19,830 They're pretty good at knowing what something is, because the 900 00:38:19,830 --> 00:38:22,570 temporal lobe pathway, the what pathway, is intact. 901 00:38:22,570 --> 00:38:24,480 But they have problems in knowing where things are, 902 00:38:24,480 --> 00:38:26,930 reaching for things, where to put their gaze, estimating 903 00:38:26,930 --> 00:38:29,090 distances, and navigation, in general. 904 00:38:29,090 --> 00:38:32,440 So I'll show you an example of a patient like this. 905 00:38:32,440 --> 00:38:35,640 And then the converse, a patient who has big problems 906 00:38:35,640 --> 00:38:36,410 in the what system. 907 00:38:36,410 --> 00:38:39,970 And we'll start with that. 908 00:38:39,970 --> 00:38:42,530 And I'll tell you just one last bit of information to 909 00:38:42,530 --> 00:38:43,100 think about. 910 00:38:43,100 --> 00:38:45,140 That the what and where distinction, sometimes, is not 911 00:38:45,140 --> 00:38:48,940 as complete in every way as you might imagine. 912 00:38:48,940 --> 00:38:51,140 And so, sometimes people have said, really, the where system 913 00:38:51,140 --> 00:38:55,060 is better described as not where things are, but the 914 00:38:55,060 --> 00:38:58,770 information you need for physical action in the world, 915 00:38:58,770 --> 00:38:59,830 which is very close to where. 916 00:38:59,830 --> 00:39:00,720 But it's a little bit different. 917 00:39:00,720 --> 00:39:04,280 And let me give you the kind of what type of information 918 00:39:04,280 --> 00:39:07,320 that the where system still seems to have in it. 919 00:39:07,320 --> 00:39:10,580 So here's a patient with extensive damage in the what 920 00:39:10,580 --> 00:39:12,870 system, in the lower parts of the brain and 921 00:39:12,870 --> 00:39:14,580 posteriorly for vision. 922 00:39:14,580 --> 00:39:17,300 Terrible perception of shapes and orientation of shapes. 923 00:39:17,300 --> 00:39:21,220 Very bad what ability. 924 00:39:21,220 --> 00:39:24,300 And she was asked, then, to reach with an envelope to a 925 00:39:24,300 --> 00:39:27,790 slot at different orientations. 926 00:39:27,790 --> 00:39:30,820 And what's striking is even though if her hand was a 927 00:39:30,820 --> 00:39:33,670 distance away-- so if she would have to think, what is 928 00:39:33,670 --> 00:39:35,260 the angle I would need to approach it with-- 929 00:39:35,260 --> 00:39:36,320 she was terrible. 930 00:39:36,320 --> 00:39:39,550 When she actually moved her hand towards the slot, she was 931 00:39:39,550 --> 00:39:40,910 surprisingly good. 932 00:39:40,910 --> 00:39:43,640 So let me show you the feeling. 933 00:39:43,640 --> 00:39:47,790 So if this patient would have to put her hand so that this 934 00:39:47,790 --> 00:39:50,320 letter would fit into this slot-- 935 00:39:50,320 --> 00:39:56,780 she was very poor at spatial relations and knowing what 936 00:39:56,780 --> 00:39:57,650 things were-- 937 00:39:57,650 --> 00:39:59,450 she couldn't line them up at all. 938 00:39:59,450 --> 00:40:01,370 But now, they said, well, please put your hand out and 939 00:40:01,370 --> 00:40:02,500 just stick it in. 940 00:40:02,500 --> 00:40:05,977 And as her hand approaches it, she changes orientations and 941 00:40:05,977 --> 00:40:07,280 is correct. 942 00:40:07,280 --> 00:40:10,320 Because in order to do things, like to grab something-- 943 00:40:10,320 --> 00:40:10,780 think about it. 944 00:40:10,780 --> 00:40:13,760 When you grab something, like a pencil or a cup, you need a 945 00:40:13,760 --> 00:40:15,260 little bit of knowledge to know what it is. 946 00:40:15,260 --> 00:40:18,230 You wouldn't grab things similarly, whether they could 947 00:40:18,230 --> 00:40:21,320 spill easily or be pointy or painful. 948 00:40:21,320 --> 00:40:24,550 A little bit what information helps you guide even something 949 00:40:24,550 --> 00:40:27,790 like a simple reaching, which is a special task. 950 00:40:27,790 --> 00:40:29,640 So it's not so much, maybe, that the where 951 00:40:29,640 --> 00:40:32,050 system is only where. 952 00:40:32,050 --> 00:40:34,600 But it has just enough information to guide actions 953 00:40:34,600 --> 00:40:37,780 in the world, which has a tiny bit of information about what 954 00:40:37,780 --> 00:40:39,600 things are that you're jumping over. 955 00:40:39,600 --> 00:40:41,240 If you're going to run into something, is it likely to be 956 00:40:41,240 --> 00:40:42,490 soft or hard.