1 00:00:01,577 --> 00:00:03,660 The following content is provided under a Creative 2 00:00:03,660 --> 00:00:05,300 Commons License. 3 00:00:05,300 --> 00:00:07,920 Your support will help MIT OpenCourseWare 4 00:00:07,920 --> 00:00:12,280 continue to offer high quality educational resources for free. 5 00:00:12,280 --> 00:00:14,910 To make a donation, or view additional materials 6 00:00:14,910 --> 00:00:17,370 from hundreds of MIT courses, visit 7 00:00:17,370 --> 00:00:19,350 MITOpenCourseWare@OCW.MIT.edu. 8 00:00:22,420 --> 00:00:23,850 KEN NAKAYAMA: The social mind, let 9 00:00:23,850 --> 00:00:25,933 me see if I can just go through these slides here. 10 00:00:28,230 --> 00:00:30,000 One might ask the question on the origin 11 00:00:30,000 --> 00:00:31,775 of human intelligence. 12 00:00:31,775 --> 00:00:33,900 OK, I just want to give a little bit of background. 13 00:00:33,900 --> 00:00:36,360 Nancy summarized my career very nicely. 14 00:00:36,360 --> 00:00:38,920 I started out recording from retinal ganglion cells 15 00:00:38,920 --> 00:00:39,930 and animals. 16 00:00:39,930 --> 00:00:41,770 That was a very long time ago. 17 00:00:41,770 --> 00:00:43,440 And I was kind of into reductionism, 18 00:00:43,440 --> 00:00:47,520 trying to understand how retinal ganglion cells determined 19 00:00:47,520 --> 00:00:50,670 how we see certain psychophysical phenomenon. 20 00:00:50,670 --> 00:00:53,047 So what am I doing here talking about some 21 00:00:53,047 --> 00:00:54,130 of that social processing? 22 00:00:54,130 --> 00:00:56,760 Well, it's been a long time ago, so in your career 23 00:00:56,760 --> 00:00:58,210 you do different things. 24 00:00:58,210 --> 00:00:59,850 But as I'll say in a moment, I happened 25 00:00:59,850 --> 00:01:02,310 to read an interesting paper. 26 00:01:02,310 --> 00:01:05,220 I was in the University of Tokyo library kind of bored 27 00:01:05,220 --> 00:01:06,130 and I found a book. 28 00:01:06,130 --> 00:01:08,280 And I found an article, which I'll talk about that. 29 00:01:08,280 --> 00:01:10,890 Didn't change my life, exactly, but it reoriented me 30 00:01:10,890 --> 00:01:14,280 towards this social processing. 31 00:01:14,280 --> 00:01:16,950 And you'll see there's many different ways of studying. 32 00:01:16,950 --> 00:01:19,530 I think the realization that social processing 33 00:01:19,530 --> 00:01:22,860 and the human mind are really very inextricably 34 00:01:22,860 --> 00:01:26,880 connected opens the door for kinds of investigations 35 00:01:26,880 --> 00:01:32,310 that I think are sort of really open-ended and somewhat limited 36 00:01:32,310 --> 00:01:33,960 just by your imagination. 37 00:01:33,960 --> 00:01:36,740 So I sort of encourage some of you 38 00:01:36,740 --> 00:01:41,260 with that bent to think about this. 39 00:01:41,260 --> 00:01:43,680 Since I study vision I will just talk a little bit 40 00:01:43,680 --> 00:01:46,020 about the visual system as a preamble. 41 00:01:46,020 --> 00:01:49,900 And I was studying vision about 50 years ago. 42 00:01:49,900 --> 00:01:52,570 And I think I was sleeping for about 10 years. 43 00:01:52,570 --> 00:01:54,859 And all of a sudden I found out something interesting. 44 00:01:54,859 --> 00:01:56,400 Here's a picture of the visual system 45 00:01:56,400 --> 00:01:58,460 when I started graduate school. 46 00:01:58,460 --> 00:02:00,900 There was the motor system, the somatosensory system, 47 00:02:00,900 --> 00:02:02,280 coupled sensory system. 48 00:02:02,280 --> 00:02:05,980 And vision was that area 17 V1 in the cortex 49 00:02:05,980 --> 00:02:08,084 I think I slept a little bit and all 50 00:02:08,084 --> 00:02:11,880 of a sudden I woke up and oops, little bit past 1970. 51 00:02:11,880 --> 00:02:14,730 Almost the whole half of the brain in the back and the front 52 00:02:14,730 --> 00:02:16,500 was devoted to vision. 53 00:02:16,500 --> 00:02:18,520 You probably had lectures by that. 54 00:02:18,520 --> 00:02:22,259 And so I think that's really an important fact because the fact 55 00:02:22,259 --> 00:02:23,550 that the brain is very visual-- 56 00:02:23,550 --> 00:02:24,360 I mean, that's human. 57 00:02:24,360 --> 00:02:25,700 So maybe in mice it might be different, 58 00:02:25,700 --> 00:02:27,991 they may be it's old fashioned, or something like that. 59 00:02:27,991 --> 00:02:29,880 But for primates I think it's very important. 60 00:02:29,880 --> 00:02:32,010 You probably learned about the different systems. 61 00:02:32,010 --> 00:02:33,551 That the posterior part of the brain. 62 00:02:33,551 --> 00:02:36,030 There's a lot of anterior parts. 63 00:02:36,030 --> 00:02:40,470 David Marr said, anybody heard of David Marr? 64 00:02:40,470 --> 00:02:44,220 OK, David Marr said vision, where is what by looking, 65 00:02:44,220 --> 00:02:45,300 what is where by looking? 66 00:02:45,300 --> 00:02:47,430 I just want to say it's way, way more than that. 67 00:02:47,430 --> 00:02:50,250 The visual system-- I mean, if vision's half the brain, 68 00:02:50,250 --> 00:02:52,129 that's just a pretty, puny problem. 69 00:02:52,129 --> 00:02:53,670 So we have to think of all the things 70 00:02:53,670 --> 00:02:54,920 that the visual system might do. 71 00:02:54,920 --> 00:02:56,503 And we really haven't thought of them. 72 00:02:56,503 --> 00:02:58,630 Well here's some more pictures of a visual system. 73 00:02:58,630 --> 00:03:03,017 So, it's a little bit like, it started over here on the west 74 00:03:03,017 --> 00:03:03,683 and you go east. 75 00:03:03,683 --> 00:03:05,760 It's sort of like Russia going all the way 76 00:03:05,760 --> 00:03:07,980 to the Pacific Ocean. 77 00:03:07,980 --> 00:03:09,600 Vision system really expand. 78 00:03:09,600 --> 00:03:13,380 I think it's kind of slowed down recently. 79 00:03:13,380 --> 00:03:16,890 I would just say the visual system is very big in primates. 80 00:03:16,890 --> 00:03:21,870 And if it's half the brain, well all 81 00:03:21,870 --> 00:03:24,446 the other stuff, like greed, sex, power, music, 82 00:03:24,446 --> 00:03:25,320 all that other stuff. 83 00:03:25,320 --> 00:03:27,480 It's about the same. 84 00:03:27,480 --> 00:03:30,604 So, that means that we just don't really understand. 85 00:03:30,604 --> 00:03:32,520 That's a lot of stuff over there on the right. 86 00:03:32,520 --> 00:03:35,610 That means, on the left, we don't know squat. 87 00:03:35,610 --> 00:03:37,080 So just have that in mind, there's 88 00:03:37,080 --> 00:03:40,054 plenty for you to do as young scientists. 89 00:03:40,054 --> 00:03:40,720 So I don't know. 90 00:03:40,720 --> 00:03:41,660 It must be-- 91 00:03:41,660 --> 00:03:42,760 This is a very tiny list. 92 00:03:42,760 --> 00:03:43,650 It's lots of things. 93 00:03:43,650 --> 00:03:45,962 It's David Marr kind of thing. 94 00:03:45,962 --> 00:03:46,920 So it's more than that. 95 00:03:49,740 --> 00:03:52,140 I'm going to talk about visual motor control. 96 00:03:52,140 --> 00:03:53,130 I'm a psychologist. 97 00:03:53,130 --> 00:03:58,080 For some reason, I think a lot of our-- 98 00:03:58,080 --> 00:04:04,260 Roger Sperry said 70 years ago that the ultimate arbiter 99 00:04:04,260 --> 00:04:06,900 of your mind is action. 100 00:04:06,900 --> 00:04:09,260 I mean, if you don't do something-- if we can have-- 101 00:04:09,260 --> 00:04:10,890 I mean, I studied perception most of my life, 102 00:04:10,890 --> 00:04:12,240 but if you don't do something, you're 103 00:04:12,240 --> 00:04:14,531 not going to pass your genes on to the next generation. 104 00:04:14,531 --> 00:04:15,540 So action is it. 105 00:04:15,540 --> 00:04:17,529 Psychologists, they don't study action at all. 106 00:04:17,529 --> 00:04:19,029 That's like, they've forgotten that. 107 00:04:19,029 --> 00:04:22,044 So I just like to put a plug-in for action. 108 00:04:22,044 --> 00:04:23,460 There's people who study the motor 109 00:04:23,460 --> 00:04:25,200 system that are kind of picky. 110 00:04:25,200 --> 00:04:28,375 So and even if they are, and don't let you into the field, 111 00:04:28,375 --> 00:04:29,250 it's important field. 112 00:04:29,250 --> 00:04:31,890 I think you should push your way in. 113 00:04:31,890 --> 00:04:34,500 I think it's extremely important for navigation. 114 00:04:34,500 --> 00:04:38,820 Most animals live in a widely extended space. 115 00:04:38,820 --> 00:04:40,710 They know how to go home quickly if they're 116 00:04:40,710 --> 00:04:42,090 trouble and things like that. 117 00:04:42,090 --> 00:04:44,610 I'll talk a little bit about some kind of animals like that. 118 00:04:44,610 --> 00:04:49,100 The range of area that animals go over is astonishing. 119 00:04:49,100 --> 00:04:52,110 And so I think the understanding of animals in their natural 120 00:04:52,110 --> 00:04:54,710 environment, there's lots of work on it, 121 00:04:54,710 --> 00:04:57,960 but I think it's-- a lot of it's quite mysterious. 122 00:04:57,960 --> 00:05:00,600 But I'm just going to talk mostly about social perception. 123 00:05:00,600 --> 00:05:03,366 And this is the paper I read a long time ago. 124 00:05:03,366 --> 00:05:04,240 I never met this guy. 125 00:05:04,240 --> 00:05:05,790 His name is Nick Humphrey. 126 00:05:05,790 --> 00:05:09,365 And he wrote this paper, I don't know, maybe 30 years ago, 127 00:05:09,365 --> 00:05:11,610 and I just happened to come upon it. 128 00:05:11,610 --> 00:05:14,430 And it was in a book. 129 00:05:14,430 --> 00:05:16,510 And he said-- he was not very nice. 130 00:05:16,510 --> 00:05:18,840 He said, experimental psychology in Britain 131 00:05:18,840 --> 00:05:21,750 have tended to regard social psychology as a poor country 132 00:05:21,750 --> 00:05:23,670 cousin of their subject. 133 00:05:23,670 --> 00:05:25,740 So basically, he was putting down-- 134 00:05:25,740 --> 00:05:27,839 but he really wasn't because he really 135 00:05:27,839 --> 00:05:28,880 said something different. 136 00:05:28,880 --> 00:05:30,830 He said, wait a second. 137 00:05:30,830 --> 00:05:33,615 And he sort of turned psychology upside down. 138 00:05:33,615 --> 00:05:37,410 And he said, you know, actually, the social part of intelligence 139 00:05:37,410 --> 00:05:39,120 is the thing that drives everything else. 140 00:05:39,120 --> 00:05:40,578 I mean, he might be overstating it, 141 00:05:40,578 --> 00:05:41,960 but let's see what he says, here. 142 00:05:41,960 --> 00:05:44,140 Oh, I'll come back to that in a minute. 143 00:05:44,140 --> 00:05:45,780 The intellectual faculties of primates 144 00:05:45,780 --> 00:05:49,080 have evolved as an adaptation of complexity of social living. 145 00:05:49,080 --> 00:05:51,000 For better or worse, styles of thinking which 146 00:05:51,000 --> 00:05:53,450 are primarily suited to social problem- solving 147 00:05:53,450 --> 00:05:55,380 color the behavior of man and other primates 148 00:05:55,380 --> 00:05:57,005 even towards the inanimate world. 149 00:05:57,005 --> 00:05:59,130 So he's sort of making the implication when I said, 150 00:05:59,130 --> 00:06:00,924 where does intelligence come from? 151 00:06:00,924 --> 00:06:02,340 One of the sources of intelligence 152 00:06:02,340 --> 00:06:04,980 are the nature of the social world. 153 00:06:04,980 --> 00:06:07,150 And it's a quite a complicated world here, 154 00:06:07,150 --> 00:06:08,420 because this guy, Dunbar. 155 00:06:08,420 --> 00:06:09,590 You've heard of this guy. 156 00:06:09,590 --> 00:06:11,970 He sort of-- I'm not sure this is a really good study, 157 00:06:11,970 --> 00:06:14,220 but what he did was he made-- 158 00:06:14,220 --> 00:06:16,679 he got the size of the cortex of lots of different primates 159 00:06:16,679 --> 00:06:18,095 and he made some kind of estimate. 160 00:06:18,095 --> 00:06:19,530 I have no idea how he did this. 161 00:06:19,530 --> 00:06:21,912 How many animals were in their troop. 162 00:06:21,912 --> 00:06:23,370 But you can sort of imagine, if you 163 00:06:23,370 --> 00:06:25,890 have a different social group, it's not just-- 164 00:06:25,890 --> 00:06:28,590 it doesn't just go up by N. Because let's say, you know, 165 00:06:28,590 --> 00:06:30,822 it's not just how many people you have to deal with. 166 00:06:30,822 --> 00:06:32,280 Those people deal with other people 167 00:06:32,280 --> 00:06:34,060 and they might be plotting against you. 168 00:06:34,060 --> 00:06:37,840 So in other words, the number of permutations of social behavior 169 00:06:37,840 --> 00:06:38,340 really-- 170 00:06:38,340 --> 00:06:39,830 I mean, it's only one other person, 171 00:06:39,830 --> 00:06:40,910 it's not too complicated. 172 00:06:40,910 --> 00:06:42,840 As you get like six, two people might 173 00:06:42,840 --> 00:06:45,300 be plotting against the third one over there. 174 00:06:45,300 --> 00:06:47,390 All kinds of possible things happen. 175 00:06:47,390 --> 00:06:50,070 And as some of the primatologist, 176 00:06:50,070 --> 00:06:51,590 if you read some of the accounts, 177 00:06:51,590 --> 00:06:55,530 there's planned murders of all kinds of people in the top. 178 00:06:55,530 --> 00:06:59,580 It really looks a little bit like the Medici's back 179 00:06:59,580 --> 00:07:01,500 in the Machiavellian intelligence. 180 00:07:01,500 --> 00:07:02,711 Doesn't sound so weird. 181 00:07:02,711 --> 00:07:03,210 OK. 182 00:07:03,210 --> 00:07:06,600 So basically, the idea is that intelligence-- 183 00:07:06,600 --> 00:07:09,840 we have to think of intelligence in the social world. 184 00:07:09,840 --> 00:07:11,470 I'm just scratching the surface here. 185 00:07:11,470 --> 00:07:14,740 So let me just talk a little bit about prediction. 186 00:07:14,740 --> 00:07:17,820 That's kind of what we think one of the hallmarks of science 187 00:07:17,820 --> 00:07:19,260 is, prediction, right? 188 00:07:19,260 --> 00:07:23,120 So what are-- in what area is prediction better? 189 00:07:23,120 --> 00:07:24,840 Any thoughts. 190 00:07:24,840 --> 00:07:29,416 Suppose I took a rock and threw it down the mountainside. 191 00:07:29,416 --> 00:07:30,540 I couldn't do squat, right? 192 00:07:30,540 --> 00:07:31,040 I mean-- 193 00:07:31,040 --> 00:07:33,402 AUDIENCE: Well, you could do all the measuring, right? 194 00:07:33,402 --> 00:07:35,370 PROFESSOR: Yeah, but nobody's going to do that, right? 195 00:07:35,370 --> 00:07:36,745 AUDIENCE: That's what I'm saying. 196 00:07:36,745 --> 00:07:38,490 PROFESSOR: OK. 197 00:07:38,490 --> 00:07:42,490 Biology, I mean, we know-- for example, 198 00:07:42,490 --> 00:07:46,242 we know that behavior of atoms, we can't really 199 00:07:46,242 --> 00:07:48,450 predict the behavior of atoms over long trajectories, 200 00:07:48,450 --> 00:07:51,640 but we can show that, you know, at a certain date, 201 00:07:51,640 --> 00:07:55,180 maybe next year, at Woods Hall, people from all over the world, 202 00:07:55,180 --> 00:07:58,110 including Nancy, are going to show up at 9 o'clock, here. 203 00:07:58,110 --> 00:07:59,010 We can predict that. 204 00:08:01,886 --> 00:08:06,250 And we can predict things like my alarm clock 205 00:08:06,250 --> 00:08:09,810 is going to go off tomorrow at a certain time because I set it. 206 00:08:09,810 --> 00:08:12,800 So what I'm trying to talk about is 207 00:08:12,800 --> 00:08:16,680 kind of a point that Dan Dennett made, who is a philosopher. 208 00:08:16,680 --> 00:08:20,580 Who, I think, has really made really seminal contributions, 209 00:08:20,580 --> 00:08:23,730 at least to my understanding of psychology to a large extent 210 00:08:23,730 --> 00:08:26,167 because he really talks about explaining things 211 00:08:26,167 --> 00:08:27,000 at different levels. 212 00:08:29,590 --> 00:08:31,800 This is another-- 213 00:08:31,800 --> 00:08:33,960 I started out as a reductionist in a sense, 214 00:08:33,960 --> 00:08:38,880 but let me just give you, give you a sort of a thought, here. 215 00:08:38,880 --> 00:08:41,039 Have you ever noticed that when the moon is 216 00:08:41,039 --> 00:08:44,460 setting over the Western sky, it's the new moon. 217 00:08:44,460 --> 00:08:46,729 It sometimes seems kind of the whole moon 218 00:08:46,729 --> 00:08:48,270 seems to be illuminated a little bit. 219 00:08:48,270 --> 00:08:49,853 It's not, it's not just that crescent. 220 00:08:49,853 --> 00:08:53,311 If you think of the, if you think of the sun, 221 00:08:53,311 --> 00:08:54,810 it should only be a little crescent. 222 00:08:54,810 --> 00:08:58,110 So why do you see the rest of the moon filled in? 223 00:08:58,110 --> 00:09:01,140 Well, there's the possibility that the sun reflecting off 224 00:09:01,140 --> 00:09:04,230 the earth, which then sends light back to the moon. 225 00:09:04,230 --> 00:09:05,220 That's an explanation. 226 00:09:05,220 --> 00:09:07,050 Do we need to know about photons? 227 00:09:07,050 --> 00:09:09,581 Do we need to know about duality of waves and particles? 228 00:09:09,581 --> 00:09:10,080 No. 229 00:09:10,080 --> 00:09:11,010 That's an explanation. 230 00:09:11,010 --> 00:09:12,330 That's all I'm trying to say. 231 00:09:12,330 --> 00:09:14,580 Some explanations are pretty darn simple. 232 00:09:14,580 --> 00:09:16,800 And in science, I think we're looking 233 00:09:16,800 --> 00:09:19,870 for any fun, interesting explanation we can find. 234 00:09:19,870 --> 00:09:23,090 So I think Dennett has nailed it to some extent. 235 00:09:23,090 --> 00:09:23,590 OK. 236 00:09:23,590 --> 00:09:30,660 So he basically has three levels for predicting behavior. 237 00:09:30,660 --> 00:09:31,770 The physical stance. 238 00:09:31,770 --> 00:09:34,050 I mean, if we have ideal situation of Galileo dropping 239 00:09:34,050 --> 00:09:35,550 his balls as a flight that, assuming 240 00:09:35,550 --> 00:09:37,260 there's no resistance to air and stuff 241 00:09:37,260 --> 00:09:40,320 like that, we can make nice predictions. 242 00:09:40,320 --> 00:09:43,162 Biology and engineering, we know that you know, seven minutes, 243 00:09:43,162 --> 00:09:45,120 the coffee is going to be ready, because that's 244 00:09:45,120 --> 00:09:47,170 what it was designed for. 245 00:09:47,170 --> 00:09:50,020 But then he's got something called intentional stance, 246 00:09:50,020 --> 00:09:53,490 which is in order to really understand what people are 247 00:09:53,490 --> 00:09:55,432 going to do, Churchland would say, 248 00:09:55,432 --> 00:09:57,390 well, we go in to look at their nervous system. 249 00:09:57,390 --> 00:09:59,440 We record from their neurons and stuff like that. 250 00:09:59,440 --> 00:10:01,190 But Dennett would say, no, we can't really 251 00:10:01,190 --> 00:10:03,240 use the physics thing or even design, we just use 252 00:10:03,240 --> 00:10:06,900 some other heuristic, which is very explanatory, 253 00:10:06,900 --> 00:10:08,820 what people desire and what their beliefs are. 254 00:10:08,820 --> 00:10:11,289 If we know that, we really can understand 255 00:10:11,289 --> 00:10:12,330 what they're going to do. 256 00:10:12,330 --> 00:10:15,420 And of course, your grandma knew that or your great grandmother 257 00:10:15,420 --> 00:10:16,290 knew that as well. 258 00:10:16,290 --> 00:10:19,690 So but these are really great ways to understand people. 259 00:10:19,690 --> 00:10:21,660 And so I think that we can't just 260 00:10:21,660 --> 00:10:23,310 turn our backs to these things. 261 00:10:23,310 --> 00:10:24,555 And I think Dennett-- 262 00:10:24,555 --> 00:10:26,680 the question is, and we might come back to the end. 263 00:10:26,680 --> 00:10:30,632 What's the sort of scientific sort 264 00:10:30,632 --> 00:10:33,397 of status of some those types of explanations. 265 00:10:33,397 --> 00:10:34,230 Are they scientific? 266 00:10:34,230 --> 00:10:34,938 Or what are they? 267 00:10:34,938 --> 00:10:37,420 Maybe if we have time, we can talk about that. 268 00:10:37,420 --> 00:10:38,970 OK. 269 00:10:38,970 --> 00:10:41,910 I'll skip that part, here. 270 00:10:41,910 --> 00:10:43,920 So I sort of feel, just as-- what 271 00:10:43,920 --> 00:10:46,620 I'm saying is it's a non- reductionistic way of looking 272 00:10:46,620 --> 00:10:48,161 at thing-- you should open your mind. 273 00:10:48,161 --> 00:10:51,140 And actually, CBMM or brains, minds, and machines 274 00:10:51,140 --> 00:10:53,010 is kind of your kind of condition to that 275 00:10:53,010 --> 00:10:54,968 because some people come from computer science. 276 00:10:54,968 --> 00:10:57,030 I mean, you can't really understand a computer 277 00:10:57,030 --> 00:10:58,530 in terms of atoms and molecules. 278 00:10:58,530 --> 00:11:00,321 You really can only understand the computer 279 00:11:00,321 --> 00:11:02,070 at some kind of a higher level. 280 00:11:02,070 --> 00:11:04,800 So I'm just telling you, that's fine. 281 00:11:04,800 --> 00:11:07,710 If you're a neuroscientist and want to reduce it to synapses, 282 00:11:07,710 --> 00:11:10,950 things like at, good try, but it might be difficult to do. 283 00:11:10,950 --> 00:11:14,212 There are many other valid things to do. 284 00:11:14,212 --> 00:11:15,420 But one of the things to do-- 285 00:11:15,420 --> 00:11:18,600 let's just examine some things about them and explore them. 286 00:11:18,600 --> 00:11:21,390 I think, as I say, there's so many possible things 287 00:11:21,390 --> 00:11:22,350 in this realm. 288 00:11:22,350 --> 00:11:25,330 Doing anything, I mean, people have all kinds of principles. 289 00:11:25,330 --> 00:11:29,280 My approach in the area, this kind of area of science, 290 00:11:29,280 --> 00:11:32,270 or any science that I've done, is to develop new tools, 291 00:11:32,270 --> 00:11:34,690 and then helps you explore. 292 00:11:34,690 --> 00:11:35,190 OK. 293 00:11:35,190 --> 00:11:38,310 So there's lots of things that humans do. 294 00:11:38,310 --> 00:11:40,950 But what I want to stress in the first part of this talk 295 00:11:40,950 --> 00:11:45,030 is that our human social behavior is really not 296 00:11:45,030 --> 00:11:46,110 unique in many ways. 297 00:11:46,110 --> 00:11:48,068 There are very, there are very important things 298 00:11:48,068 --> 00:11:49,415 that animals do. 299 00:11:49,415 --> 00:11:51,390 And I'll talk about them shortly. 300 00:11:51,390 --> 00:11:53,650 Animals, really are very social. 301 00:11:53,650 --> 00:11:55,530 There are certain animals like octopus, 302 00:11:55,530 --> 00:11:58,230 apparently is not very intelligent, not very social, 303 00:11:58,230 --> 00:12:00,610 but by and large many animals are very social. 304 00:12:00,610 --> 00:12:02,580 Doesn't explain everything but it's-- 305 00:12:02,580 --> 00:12:05,140 I think we share some core things. 306 00:12:05,140 --> 00:12:08,010 And if we can understand some of the behavior of animals 307 00:12:08,010 --> 00:12:11,490 or why they're social and how their social mechanisms work, 308 00:12:11,490 --> 00:12:13,170 I think we have a treasure. 309 00:12:13,170 --> 00:12:16,920 So the area of animal behavior is very underfunded area 310 00:12:16,920 --> 00:12:17,544 right now. 311 00:12:17,544 --> 00:12:18,210 I sort of feel-- 312 00:12:18,210 --> 00:12:20,490 I just happen to like to watch nature videos. 313 00:12:20,490 --> 00:12:21,990 And I'll show you a few, you'll have 314 00:12:21,990 --> 00:12:23,610 to indulge me a few of these. 315 00:12:23,610 --> 00:12:25,530 But I sort of feel these can be more 316 00:12:25,530 --> 00:12:27,690 important than careful psychophysics 317 00:12:27,690 --> 00:12:29,790 because I think they open your mind to things 318 00:12:29,790 --> 00:12:31,630 that you may not have thought about. 319 00:12:31,630 --> 00:12:36,071 So I'm a kind of devoté of these things. 320 00:12:36,071 --> 00:12:36,570 OK. 321 00:12:36,570 --> 00:12:40,197 So do animals have an intentional stance, etc., 322 00:12:40,197 --> 00:12:41,280 kinds of things like that? 323 00:12:41,280 --> 00:12:42,580 We don't know. 324 00:12:42,580 --> 00:12:46,255 Here's a fun video I think maybe half of the audience 325 00:12:46,255 --> 00:12:47,130 have seen this video. 326 00:12:47,130 --> 00:12:49,230 And maybe I'll show the whole thing 327 00:12:49,230 --> 00:12:50,790 because I think we have time. 328 00:12:50,790 --> 00:12:52,330 It was taken just by chance. 329 00:12:52,330 --> 00:12:54,300 I think it's been a TV program now. 330 00:12:54,300 --> 00:12:56,820 And I think it's got like 50 million viewers. 331 00:12:56,820 --> 00:12:59,136 It's about different kinds of animals interacting, 332 00:12:59,136 --> 00:13:00,430 their social behavior. 333 00:13:00,430 --> 00:13:03,720 It's about animals that you do not think are that smart 334 00:13:03,720 --> 00:13:04,900 and things like that. 335 00:13:04,900 --> 00:13:05,579 Ungulates. 336 00:13:05,579 --> 00:13:06,120 I don't know. 337 00:13:06,120 --> 00:13:07,786 I don't know what buffaloes are exactly, 338 00:13:07,786 --> 00:13:10,980 but we've never thought of them as highly 339 00:13:10,980 --> 00:13:12,690 intelligent or social. 340 00:13:24,765 --> 00:13:27,800 AUDIENCE: That is a huge buffalo. 341 00:13:27,800 --> 00:13:29,285 AUDIENCE: That's a huge buffalo. 342 00:13:29,285 --> 00:13:30,720 AUDIENCE: That's a huge buffalo. 343 00:13:37,090 --> 00:13:39,540 AUDIENCE: I love this. 344 00:13:39,540 --> 00:13:41,010 AUDIENCE: Is it little? 345 00:13:46,504 --> 00:13:47,670 AUDIENCE: They're crouching. 346 00:13:47,670 --> 00:13:48,966 AUDIENCE: She's crouching. 347 00:14:09,798 --> 00:14:11,286 AUDIENCE: She's going to get eaten. 348 00:14:25,174 --> 00:14:27,158 AUDIENCE: Oh, my god. 349 00:14:27,158 --> 00:14:29,138 AUDIENCE: Oh, my god. 350 00:14:29,138 --> 00:14:29,638 Oh, my god. 351 00:14:29,638 --> 00:14:33,110 [INAUDIBLE] Oh, she's going for him, she's going for him, 352 00:14:33,110 --> 00:14:34,598 she's going for him. 353 00:14:34,598 --> 00:14:36,086 She got him. 354 00:14:36,086 --> 00:14:37,030 AUDIENCE: Jeez. 355 00:14:37,030 --> 00:14:37,946 AUDIENCE: Oh, she did. 356 00:14:37,946 --> 00:14:41,046 She got him. 357 00:14:41,046 --> 00:14:41,790 AUDIENCE: Ladies. 358 00:14:41,790 --> 00:14:42,706 AUDIENCE: [INAUDIBLE]. 359 00:14:42,706 --> 00:14:44,330 AUDIENCE: No, the lions have won. 360 00:14:44,330 --> 00:14:47,324 AUDIENCE: Yeah, but look at all those buffalo. 361 00:14:53,312 --> 00:14:57,304 AUDIENCE: --Buffalo down. 362 00:14:57,304 --> 00:14:59,799 AUDIENCE: They're like, go and try chase the lion, 363 00:14:59,799 --> 00:15:01,795 but I think they're too late. 364 00:15:05,787 --> 00:15:08,282 AUDIENCE: They're going to chase [INAUDIBLE].. 365 00:15:12,274 --> 00:15:14,270 AUDIENCE: Look at the teeth, Jay. 366 00:15:17,264 --> 00:15:19,260 AUDIENCE: You're too late. 367 00:15:19,260 --> 00:15:20,757 You're too late. 368 00:15:28,741 --> 00:15:31,170 AUDIENCE: I think because he cannot believe what's going 369 00:15:31,170 --> 00:15:31,669 here. 370 00:15:31,669 --> 00:15:33,750 There's a big barrier between lions, crocodiles, 371 00:15:33,750 --> 00:15:36,200 and buffaloes. 372 00:15:36,200 --> 00:15:37,670 AUDIENCE: Look at them all. 373 00:15:40,680 --> 00:15:43,580 AUDIENCE: Whoa. 374 00:15:43,580 --> 00:15:46,165 AUDIENCE: He swatted at him and kicked at him. 375 00:15:46,165 --> 00:15:47,482 He's kicking at him, look. 376 00:15:47,482 --> 00:15:48,958 He's kicking at him. 377 00:15:54,862 --> 00:15:58,160 AUDIENCE: [INAUDIBLE] I mean, buffaloes is basically 378 00:15:58,160 --> 00:16:00,785 used to [INAUDIBLE]. 379 00:16:00,785 --> 00:16:02,338 [INTERPOSING VOICES] 380 00:16:02,338 --> 00:16:05,006 AUDIENCE: And in deed, [INAUDIBLE].. 381 00:16:10,352 --> 00:16:14,290 AUDIENCE: Oooh, they got him surrounded. 382 00:16:14,290 --> 00:16:15,858 AUDIENCE: And that one's-- 383 00:16:15,858 --> 00:16:18,822 AUDIENCE: Ooh. 384 00:16:18,822 --> 00:16:22,280 AUDIENCE: Chasing-- go on, go. 385 00:16:22,280 --> 00:16:26,232 Chasing-- 386 00:16:28,186 --> 00:16:30,060 AUDIENCE: You got the lion [INAUDIBLE] right. 387 00:16:30,060 --> 00:16:32,490 AUDIENCE: [INAUDIBLE]. 388 00:16:32,490 --> 00:16:33,906 Dave, can you get the peace? 389 00:16:33,906 --> 00:16:35,406 AUDIENCE: The others are doing that. 390 00:16:35,406 --> 00:16:36,378 [INAUDIBLE] 391 00:16:36,378 --> 00:16:40,752 [INTERPOSING VOICES] 392 00:16:40,752 --> 00:16:42,656 AUDIENCE: Never seen that. 393 00:16:42,656 --> 00:16:43,780 AUDIENCE: I've never seen-- 394 00:16:43,780 --> 00:16:44,930 AUDIENCE: The calf's still alive. 395 00:16:44,930 --> 00:16:45,596 AUDIENCE: It is? 396 00:16:45,596 --> 00:16:46,290 AUDIENCE: Yeah. 397 00:16:46,290 --> 00:16:48,441 It's trying to get away. 398 00:16:48,441 --> 00:16:49,420 It's standing up. 399 00:16:49,420 --> 00:16:50,362 AUDIENCE: It is. 400 00:16:50,362 --> 00:16:52,717 It's still alive. 401 00:16:52,717 --> 00:16:54,480 PROFESSOR: There's much more to this. 402 00:16:54,480 --> 00:16:57,270 There's many other videos about lions and buffalo 403 00:16:57,270 --> 00:17:02,670 finding a layer of baby lions and the mother is gone. 404 00:17:02,670 --> 00:17:05,280 And they just kill all the lions in there for revenge, 405 00:17:05,280 --> 00:17:07,839 or whatever it is, your interpretation, I don't know. 406 00:17:07,839 --> 00:17:09,720 But anyway, so I'm just trying to tell you. 407 00:17:09,720 --> 00:17:12,006 I'm not going to show you more, but in the lectures, 408 00:17:12,006 --> 00:17:13,839 there's going to be a number of videos here. 409 00:17:13,839 --> 00:17:15,390 I'll show one more I think. 410 00:17:15,390 --> 00:17:17,936 But I think this is-- 411 00:17:17,936 --> 00:17:20,190 I don't know, I think you can see this is 412 00:17:20,190 --> 00:17:22,060 better than an experiment, OK? 413 00:17:22,060 --> 00:17:25,980 You really get-- to me, it really 414 00:17:25,980 --> 00:17:29,490 showed me a kind of, a kind of a social organization. 415 00:17:29,490 --> 00:17:31,940 A kind of-- are these animals conscious? 416 00:17:31,940 --> 00:17:34,890 All of these kinds of questions here that I think you 417 00:17:34,890 --> 00:17:36,761 should think about. 418 00:17:36,761 --> 00:17:37,260 OK. 419 00:17:37,260 --> 00:17:39,717 So on my website, on the lecture there's 420 00:17:39,717 --> 00:17:41,800 going to be some other videos that you can look at 421 00:17:41,800 --> 00:17:42,860 and things like that. 422 00:17:42,860 --> 00:17:44,100 I won't do this one, here. 423 00:17:44,100 --> 00:17:45,550 This is a very interesting video. 424 00:17:45,550 --> 00:17:47,299 Some of you might have seen it as well. 425 00:17:47,299 --> 00:17:48,840 I don't exactly know what the origin, 426 00:17:48,840 --> 00:17:50,190 I won't show it here today. 427 00:17:50,190 --> 00:17:53,480 But it's about a-- 428 00:17:53,480 --> 00:17:55,560 on the web, it says monkey teases tiger, 429 00:17:55,560 --> 00:17:57,450 it's a gibbon, or something like that. 430 00:17:57,450 --> 00:17:59,170 But it's a really amazing show. 431 00:17:59,170 --> 00:18:00,750 It goes on for about five minutes. 432 00:18:00,750 --> 00:18:03,840 We're given just swinging around and making 433 00:18:03,840 --> 00:18:05,062 himself ready for attack. 434 00:18:05,062 --> 00:18:07,020 He runs along the ground, the guys are chasing, 435 00:18:07,020 --> 00:18:08,210 it's like playing tag. 436 00:18:08,210 --> 00:18:10,500 And he runs up a tree and he jumps over 437 00:18:10,500 --> 00:18:12,580 them and he their tail and he grabs their ears. 438 00:18:12,580 --> 00:18:14,640 There are two lions, two tigers there. 439 00:18:14,640 --> 00:18:18,410 And he's just putting his life at risk, but he's doing it. 440 00:18:18,410 --> 00:18:20,910 Maybe that's that the theory is, maybe that's his territory. 441 00:18:20,910 --> 00:18:22,660 He doesn't want those people around there. 442 00:18:22,660 --> 00:18:23,760 But it's pretty amazing. 443 00:18:23,760 --> 00:18:28,950 So just Google monkey you know, teases tiger. 444 00:18:28,950 --> 00:18:30,930 You'll see the full video. 445 00:18:30,930 --> 00:18:33,340 It's a pretty damn amazing video. 446 00:18:33,340 --> 00:18:36,420 Another video that is fairly interesting and this is just-- 447 00:18:36,420 --> 00:18:37,110 just shows you. 448 00:18:37,110 --> 00:18:38,700 This is a guy named Jaak Panksepp. 449 00:18:38,700 --> 00:18:40,290 I used to be on a study section with this guy. 450 00:18:40,290 --> 00:18:41,790 I couldn't figure out what this guy's talking about. 451 00:18:41,790 --> 00:18:43,170 This was many years ago. 452 00:18:43,170 --> 00:18:47,850 And he's now an expert on laughter in rats. 453 00:18:47,850 --> 00:18:53,360 And he tickles rats and they follow him around. 454 00:18:53,360 --> 00:18:55,270 And can you hear them laugh? 455 00:18:55,270 --> 00:18:56,520 No, you can't hear them laugh. 456 00:18:56,520 --> 00:19:00,540 So he got this bat detector which takes ultrasonic sounds 457 00:19:00,540 --> 00:19:04,242 and brings it down to our frequency level. 458 00:19:04,242 --> 00:19:05,700 And you can hear the rats laughing. 459 00:19:05,700 --> 00:19:07,120 So try that out as well. 460 00:19:07,120 --> 00:19:09,490 So you can do that in your spare time. 461 00:19:09,490 --> 00:19:10,720 So he's got some very-- 462 00:19:10,720 --> 00:19:11,220 Oh. 463 00:19:11,220 --> 00:19:13,290 This one is kind of interesting, War. 464 00:19:13,290 --> 00:19:15,720 One of the interesting things about-- 465 00:19:15,720 --> 00:19:18,310 We have sort of a whole field called evolutionary psychology. 466 00:19:18,310 --> 00:19:24,300 And what's the reasons why animals might cooperate a lot? 467 00:19:24,300 --> 00:19:27,430 That's what we're talking about here. 468 00:19:27,430 --> 00:19:30,390 These are really-- a lot of these things are up in the air. 469 00:19:30,390 --> 00:19:33,660 And there's a lot of debates, but one possibility 470 00:19:33,660 --> 00:19:35,400 is, if animals cooperate, they can 471 00:19:35,400 --> 00:19:38,100 deal with a rival group of animals who might 472 00:19:38,100 --> 00:19:39,840 be taking over their territory. 473 00:19:39,840 --> 00:19:42,671 Animals have private property, you might call it territory. 474 00:19:42,671 --> 00:19:44,670 They don't have contracts or anything like that, 475 00:19:44,670 --> 00:19:45,790 but it's pretty close. 476 00:19:45,790 --> 00:19:48,710 And if they have territory that they have and there's 477 00:19:48,710 --> 00:19:51,780 another group that has territory, 478 00:19:51,780 --> 00:19:53,573 there's going to be conflicts. 479 00:19:53,573 --> 00:19:56,720 And why not call it war? 480 00:19:56,720 --> 00:20:00,330 And humans have had war for many generations. 481 00:20:00,330 --> 00:20:03,630 And I just like to show a little bit that-- 482 00:20:03,630 --> 00:20:06,330 these videos are incredible videos. 483 00:20:06,330 --> 00:20:08,760 The guy's name is Timothy Clutton- Brock. 484 00:20:08,760 --> 00:20:11,680 He's a ethologist at the University of Cambridge. 485 00:20:11,680 --> 00:20:13,750 He studied many different kinds of animals. 486 00:20:13,750 --> 00:20:15,630 And these meerkats-- there's been a, 487 00:20:15,630 --> 00:20:17,622 there's actually been a TV show. 488 00:20:17,622 --> 00:20:19,080 Have you ever watched the BBC show? 489 00:20:19,080 --> 00:20:19,980 Anybody here? 490 00:20:19,980 --> 00:20:23,250 It's a BBC show and it's gone for four years. 491 00:20:23,250 --> 00:20:25,310 I mean, how long did Dynasty go for? 492 00:20:25,310 --> 00:20:26,910 Or all these ones that you guys watch? 493 00:20:26,910 --> 00:20:30,010 I mean, four years of this life of these animals here. 494 00:20:30,010 --> 00:20:32,010 And the really interesting thing about-- they're 495 00:20:32,010 --> 00:20:36,090 about one foot tall and they run in troops. 496 00:20:36,090 --> 00:20:39,120 This group of animals is really interesting. 497 00:20:39,120 --> 00:20:41,310 There's more animals than you think. 498 00:20:41,310 --> 00:20:44,250 These are female- dominated societies. 499 00:20:44,250 --> 00:20:46,170 There's a alpha female. 500 00:20:46,170 --> 00:20:49,530 And she has, she has many children. 501 00:20:49,530 --> 00:20:51,240 And then her daughters are supposed 502 00:20:51,240 --> 00:20:54,000 to take care of her kids, not their kids. 503 00:20:54,000 --> 00:20:56,610 What happens is sometimes the daughter wanders off 504 00:20:56,610 --> 00:20:58,380 and some Romeo was coming around and they 505 00:20:58,380 --> 00:20:59,850 had babies and stuff like that. 506 00:20:59,850 --> 00:21:04,050 That daughter has to really be nice to the mom to stay. 507 00:21:04,050 --> 00:21:06,210 Usually she's banished and she dies, 508 00:21:06,210 --> 00:21:08,700 or she joins that other troop. 509 00:21:08,700 --> 00:21:10,310 There's so many different things. 510 00:21:10,310 --> 00:21:12,690 And these animals-- you can see these guys, here. 511 00:21:12,690 --> 00:21:15,900 See the investigators on the BBC show-- 512 00:21:15,900 --> 00:21:18,750 these animals somehow have been totally inured to humans. 513 00:21:18,750 --> 00:21:21,510 So they're doing all their thing and people are just walking 514 00:21:21,510 --> 00:21:23,435 around with their cameras. 515 00:21:23,435 --> 00:21:25,560 So this is-- you don't have to set up a laboratory, 516 00:21:25,560 --> 00:21:26,730 you just go out there. 517 00:21:26,730 --> 00:21:29,550 And you have to go to the Kalahari Desert. 518 00:21:29,550 --> 00:21:32,790 But I think that this is an incredible research 519 00:21:32,790 --> 00:21:35,250 enterprise that's been going on for about 10 years. 520 00:21:38,280 --> 00:21:39,950 One of the things is they-- 521 00:21:39,950 --> 00:21:42,720 I think, in many ways, it's one of the most studied 522 00:21:42,720 --> 00:21:46,470 social animals because the Kalahari Desert is just open. 523 00:21:46,470 --> 00:21:47,940 You can see everything. 524 00:21:47,940 --> 00:21:52,410 One of the things they do is they dig down into burrows. 525 00:21:52,410 --> 00:21:55,992 And so when they're digging way down-- they can eat scorpions. 526 00:21:55,992 --> 00:21:57,700 They're not, they're not bothered if they 527 00:21:57,700 --> 00:21:59,340 get stung by a scorpion. 528 00:21:59,340 --> 00:22:01,634 But if they're down there and there's hawks going over, 529 00:22:01,634 --> 00:22:02,800 they can just pick them off. 530 00:22:02,800 --> 00:22:04,910 So they have these sentries and things like that. 531 00:22:04,910 --> 00:22:08,340 So they sort of double up-- and then they have babysitting 532 00:22:08,340 --> 00:22:10,140 co-ops where they're teenagers that-- 533 00:22:10,140 --> 00:22:11,640 they have these burrows underground. 534 00:22:11,640 --> 00:22:14,880 But of course you can't-- if you're growing up as a meerkat. 535 00:22:14,880 --> 00:22:17,340 By the way, meerkats have nothing to do with cats, 536 00:22:17,340 --> 00:22:20,890 but they're somehow related to mongoose. 537 00:22:20,890 --> 00:22:22,860 They're sort of a little more in that category. 538 00:22:22,860 --> 00:22:25,309 But anyway, they have a baby sitting kind of thing 539 00:22:25,309 --> 00:22:26,850 where they let the kids out on there. 540 00:22:26,850 --> 00:22:29,391 They play a little bit and if it gets strange, they run back. 541 00:22:29,391 --> 00:22:32,160 And so there's a whole babysitting thing 542 00:22:32,160 --> 00:22:34,740 because every day they have to go out and forage for food. 543 00:22:34,740 --> 00:22:36,630 And they go very long distances. 544 00:22:36,630 --> 00:22:39,390 The territory of these meerkats could be 545 00:22:39,390 --> 00:22:41,370 as much as a square millimeter. 546 00:22:41,370 --> 00:22:42,030 That's amazing. 547 00:22:42,030 --> 00:22:43,613 Just think of these animals, this big, 548 00:22:43,613 --> 00:22:46,170 going over a square millimeter, a kilometer. 549 00:22:46,170 --> 00:22:48,070 That's territory. 550 00:22:48,070 --> 00:22:50,734 So what we have, of course, in the human situation-- 551 00:22:50,734 --> 00:22:52,234 DOCUMENTARY NARRATOR: Moments later, 552 00:22:52,234 --> 00:22:56,532 the rest of the whiskers return from foraging. 553 00:22:56,532 --> 00:22:58,030 From the brow of the hill, they see 554 00:22:58,030 --> 00:23:01,372 that their home has been overrun by the [INAUDIBLE].. 555 00:23:08,190 --> 00:23:14,060 The rival group spot the owners of the burrow 556 00:23:14,060 --> 00:23:20,232 and the angry whiskers waste no time in commencing the attack. 557 00:23:20,232 --> 00:23:22,128 [INTERPOSING VOICES] 558 00:23:22,128 --> 00:23:24,450 DOCUMENTARY NARRATOR: --Straight towards the whiskers. 559 00:23:24,450 --> 00:23:26,700 They're going to defend this piece of territory 560 00:23:26,700 --> 00:23:28,305 as if it's their own. 561 00:23:28,305 --> 00:23:31,210 A bloody fight is inevitable. 562 00:23:31,210 --> 00:23:35,114 One of these sworn enemies will have to be in [INAUDIBLE].. 563 00:23:58,520 --> 00:24:02,015 The [INAUDIBLE] a hasty retreat. 564 00:24:02,015 --> 00:24:04,010 PROFESSOR: The good guys won. 565 00:24:04,010 --> 00:24:06,545 What I'm going to tell you is a pale sort 566 00:24:06,545 --> 00:24:09,020 of version of all that stuff but I 567 00:24:09,020 --> 00:24:11,370 think we have to study stuff in the laboratory. 568 00:24:11,370 --> 00:24:14,030 So a lot of my interest comes from the work 569 00:24:14,030 --> 00:24:16,880 of Nalini Ambady who is a colleague of mine, 570 00:24:16,880 --> 00:24:19,340 long ago in psychology department. 571 00:24:19,340 --> 00:24:21,230 And so what-- we're going to go-- 572 00:24:21,230 --> 00:24:23,687 I'm going to talk about humans now because that's 573 00:24:23,687 --> 00:24:24,770 what I've been able to do. 574 00:24:24,770 --> 00:24:27,320 I'm not-- But I do think that we really 575 00:24:27,320 --> 00:24:30,182 need to look at all different parts of the animal kingdom. 576 00:24:30,182 --> 00:24:31,640 But she did something that's really 577 00:24:31,640 --> 00:24:34,249 quite interesting as a social psychologist. 578 00:24:34,249 --> 00:24:36,790 Mostly people have been handing out questionnaires and people 579 00:24:36,790 --> 00:24:38,331 how-- what do you think about things? 580 00:24:38,331 --> 00:24:42,290 Social psychology has mostly been about attitudes 581 00:24:42,290 --> 00:24:45,350 and had a rightful good history about prejudice. 582 00:24:45,350 --> 00:24:47,060 And how people who have-- 583 00:24:47,060 --> 00:24:49,476 a lot of the social psychology came out of the-- 584 00:24:49,476 --> 00:24:52,960 was stimulated by what happened in the Second World War, 585 00:24:52,960 --> 00:24:55,249 the atrocities and things like that. 586 00:24:55,249 --> 00:24:56,790 How could people do things like that? 587 00:24:56,790 --> 00:24:58,498 And so those are really important things. 588 00:24:58,498 --> 00:24:59,570 And you read about them. 589 00:24:59,570 --> 00:25:00,986 But she did something sort of more 590 00:25:00,986 --> 00:25:03,890 at an everyday level in a sense, and just take 591 00:25:03,890 --> 00:25:07,880 pictures, very few pictures of different people. 592 00:25:07,880 --> 00:25:09,970 And just see what people get from these pictures. 593 00:25:09,970 --> 00:25:11,510 So clip, movie clips. 594 00:25:11,510 --> 00:25:13,490 And basically, she did one thing and just 595 00:25:13,490 --> 00:25:17,690 had people rate teachers that they-- 596 00:25:17,690 --> 00:25:18,530 this is at Harvard. 597 00:25:18,530 --> 00:25:20,870 We had teaching fellows and they were just 598 00:25:20,870 --> 00:25:23,020 talking to their class and things like that. 599 00:25:23,020 --> 00:25:25,492 And then students just rated them, what they thought, 600 00:25:25,492 --> 00:25:27,950 how good a teacher they were, just on different adjectives. 601 00:25:27,950 --> 00:25:31,250 And that really predicted the student ratings 602 00:25:31,250 --> 00:25:33,465 of the whole course. 603 00:25:33,465 --> 00:25:35,977 In other words, 10 seconds was able to predict the ratings 604 00:25:35,977 --> 00:25:36,810 of the whole course. 605 00:25:36,810 --> 00:25:41,054 So remember that when you do teaching. 606 00:25:41,054 --> 00:25:42,220 I'm hopeless, but who knows. 607 00:25:42,220 --> 00:25:47,060 Anyway-- Basically, here's another thing they did. 608 00:25:47,060 --> 00:25:47,560 OK. 609 00:25:47,560 --> 00:25:49,160 There's that. 610 00:25:49,160 --> 00:25:51,732 Gaydar, you can tell if somebody is gay or straight 611 00:25:51,732 --> 00:25:52,690 or something like that. 612 00:25:52,690 --> 00:25:55,760 It only presenting the thing for about 15 millisecond. 613 00:25:55,760 --> 00:25:58,310 I find that hard to believe. 614 00:25:58,310 --> 00:26:00,850 If you look at a video, two people talking together, 615 00:26:00,850 --> 00:26:03,470 you can tell whether they're friends or strangers. 616 00:26:06,420 --> 00:26:08,630 There was a really interesting one. 617 00:26:08,630 --> 00:26:12,750 This is really-- outcome is you have an interview or tapes 618 00:26:12,750 --> 00:26:14,700 between doctors and patients. 619 00:26:14,700 --> 00:26:17,400 And you can outcome variables like whether that doctor got 620 00:26:17,400 --> 00:26:18,100 sued or not. 621 00:26:18,100 --> 00:26:19,970 You can have people rate, have adjectives 622 00:26:19,970 --> 00:26:21,190 and it's sort of like, a little bit 623 00:26:21,190 --> 00:26:22,830 like-- you can think of doing machine learning, 624 00:26:22,830 --> 00:26:24,538 just taking all those adjectives and then 625 00:26:24,538 --> 00:26:27,410 sort of predict whether that person is going to be sued. 626 00:26:27,410 --> 00:26:31,170 You can see just by this a kind of a crowd-sourcing way 627 00:26:31,170 --> 00:26:34,470 of dealing with all kinds of real world, 628 00:26:34,470 --> 00:26:39,140 social, significant things. 629 00:26:39,140 --> 00:26:40,290 OK. 630 00:26:40,290 --> 00:26:42,160 So I've worked a lot on face recognition. 631 00:26:42,160 --> 00:26:43,910 That's a very important thing in the sense 632 00:26:43,910 --> 00:26:47,040 that in order to negotiate our social world, if you 633 00:26:47,040 --> 00:26:49,030 can't do facial recognition, you're in trouble. 634 00:26:49,030 --> 00:26:52,980 I work a lot of people with prosopagnosic. 635 00:26:52,980 --> 00:26:53,970 Oliver Sacks. 636 00:26:53,970 --> 00:26:55,260 May know who Oliver Sacks is. 637 00:26:55,260 --> 00:26:57,570 He's definitely prosopagnosic. 638 00:26:57,570 --> 00:27:00,230 And if he's at a party, he's a little bit lost. 639 00:27:00,230 --> 00:27:04,219 But he's a-- he has so many other charming characteristics 640 00:27:04,219 --> 00:27:05,010 that he can manage. 641 00:27:05,010 --> 00:27:06,301 People come up and talk to him. 642 00:27:06,301 --> 00:27:07,770 But you can imagine, if you're not 643 00:27:07,770 --> 00:27:10,404 that charming, witty, funny person 644 00:27:10,404 --> 00:27:11,820 and you don't recognize people who 645 00:27:11,820 --> 00:27:13,940 you're supposed to recognize, you're in trouble. 646 00:27:13,940 --> 00:27:17,650 One of the subject I worked with, she's 647 00:27:17,650 --> 00:27:20,610 a quite a well-known author. 648 00:27:20,610 --> 00:27:22,830 And she goes on book tours, she's happy, 649 00:27:22,830 --> 00:27:25,410 and she is she does very, very well. 650 00:27:25,410 --> 00:27:27,300 She has a problem with face recognition, 651 00:27:27,300 --> 00:27:29,370 but she can't really recognize a lot 652 00:27:29,370 --> 00:27:30,660 of the guys in her departure. 653 00:27:30,660 --> 00:27:32,670 She teaches-- She's an English literature professor. 654 00:27:32,670 --> 00:27:34,440 There's about four guys in our department. 655 00:27:34,440 --> 00:27:36,570 She can't really recognize one from another. 656 00:27:36,570 --> 00:27:38,860 That makes social interactions very difficult. 657 00:27:38,860 --> 00:27:42,900 So we all are experts at face recognition 658 00:27:42,900 --> 00:27:45,895 but I've studied something called face blindness. 659 00:27:45,895 --> 00:27:48,270 But I just want to step back because the thought occurred 660 00:27:48,270 --> 00:27:48,960 to me. 661 00:27:48,960 --> 00:27:50,580 This is one thing that's really-- 662 00:27:50,580 --> 00:27:53,780 we've just published, not myself, but my collaborators, 663 00:27:53,780 --> 00:27:54,562 we've-- 664 00:27:54,562 --> 00:27:56,020 well, I'll come back to that later. 665 00:27:56,020 --> 00:27:59,090 It's called face recognition under early stress. 666 00:27:59,090 --> 00:28:00,300 I'll come back to that later. 667 00:28:00,300 --> 00:28:01,241 Remind me. 668 00:28:01,241 --> 00:28:01,740 OK. 669 00:28:01,740 --> 00:28:04,530 So over the last 100 years or so, there's 670 00:28:04,530 --> 00:28:07,440 something called acquired prosopagnosia which 671 00:28:07,440 --> 00:28:11,160 means that, I think, the best case was, I think, 672 00:28:11,160 --> 00:28:13,770 World War II victims, the German soldiers 673 00:28:13,770 --> 00:28:15,060 in the Second World War. 674 00:28:15,060 --> 00:28:19,830 And those people, you could tell they had face recognition 675 00:28:19,830 --> 00:28:24,120 problems because of course, they were able to recognize faces, 676 00:28:24,120 --> 00:28:26,190 and later, they weren't able to do it. 677 00:28:26,190 --> 00:28:28,620 And so-- but more recently, and I've 678 00:28:28,620 --> 00:28:31,200 studied hundreds of these people, 679 00:28:31,200 --> 00:28:33,900 and we've actually signed up thousands of them. 680 00:28:33,900 --> 00:28:36,300 People-- we just we put a website out 681 00:28:36,300 --> 00:28:39,360 and we basically have studied huge numbers of people 682 00:28:39,360 --> 00:28:42,720 who are just naturally occurring people who can't recognize 683 00:28:42,720 --> 00:28:44,380 faces very well at all. 684 00:28:44,380 --> 00:28:46,980 And we've got about 6,000 registrants. 685 00:28:46,980 --> 00:28:49,247 We really don't test them that much now, 686 00:28:49,247 --> 00:28:50,580 but we can test them on the web. 687 00:28:50,580 --> 00:28:53,770 As Nancy mentioned, we do that kind of thing. 688 00:28:53,770 --> 00:28:56,490 Just some testimonials from these people, one or two, 689 00:28:56,490 --> 00:28:58,830 just shows the problem they have. 690 00:28:58,830 --> 00:29:01,540 This week, I went to the wrong baby at my son's daycare 691 00:29:01,540 --> 00:29:04,260 and only realized he was not my son, blah, blah, blah. 692 00:29:04,260 --> 00:29:07,980 So in an audience like this, you don't 693 00:29:07,980 --> 00:29:10,470 have to identify yourself since you got so many people who 694 00:29:10,470 --> 00:29:11,390 are STEM people. 695 00:29:11,390 --> 00:29:13,680 STEM people sometimes have these problems. 696 00:29:13,680 --> 00:29:16,020 Usually if I give a lecture to an audience like that, 697 00:29:16,020 --> 00:29:18,240 somebody will come up and say that's me. 698 00:29:18,240 --> 00:29:22,560 So this is a lady I know quite well. 699 00:29:22,560 --> 00:29:25,390 She's got a PhD in differential geometry. 700 00:29:25,390 --> 00:29:26,620 She's a mathematician. 701 00:29:26,620 --> 00:29:28,080 She has lots of problems. 702 00:29:28,080 --> 00:29:29,910 She is one of the few [INAUDIBLE] 703 00:29:29,910 --> 00:29:33,360 that I think is kind of mildly on the spectrum. 704 00:29:33,360 --> 00:29:35,850 But most of the people I've studied are not autistic 705 00:29:35,850 --> 00:29:39,270 or Asperger's in any way. 706 00:29:39,270 --> 00:29:41,190 But a lot of people have problem. 707 00:29:41,190 --> 00:29:42,635 I claim it's a visual problem. 708 00:29:42,635 --> 00:29:44,760 It's not a psychiatric problem or a social problem, 709 00:29:44,760 --> 00:29:47,930 but it does lead to social problems. 710 00:29:47,930 --> 00:29:50,270 And with Brad Duchaine, I've studied 711 00:29:50,270 --> 00:29:52,050 these people quite extensively. 712 00:29:52,050 --> 00:29:52,989 And the thing that-- 713 00:29:52,989 --> 00:29:54,780 I just want to say, in order to study them, 714 00:29:54,780 --> 00:29:57,450 we developed a face recognition test. 715 00:29:57,450 --> 00:30:00,030 I got into the testing business in this way 716 00:30:00,030 --> 00:30:02,640 because the tests that were out there weren't very good. 717 00:30:02,640 --> 00:30:03,889 They were really bad. 718 00:30:03,889 --> 00:30:04,680 I mean, they were-- 719 00:30:04,680 --> 00:30:06,120 I won't go into how bad they were. 720 00:30:06,120 --> 00:30:08,460 So we spent three or four years just making up 721 00:30:08,460 --> 00:30:12,420 a test, which is now used widely around the world. 722 00:30:12,420 --> 00:30:15,300 And I don't want to go into the details of the test 723 00:30:15,300 --> 00:30:17,235 but it basically enables people to sort 724 00:30:17,235 --> 00:30:18,780 of over-learn about six faces. 725 00:30:18,780 --> 00:30:20,890 It's a little bit like natural face recognition. 726 00:30:20,890 --> 00:30:23,130 It's not-- I think it's a very good test. 727 00:30:23,130 --> 00:30:25,380 I won't go into why and things like that. 728 00:30:25,380 --> 00:30:27,300 And it gets harder and harder. 729 00:30:27,300 --> 00:30:29,500 But I just want to say that we've 730 00:30:29,500 --> 00:30:32,240 been able to characterize these prosopagnosia people. 731 00:30:32,240 --> 00:30:34,257 But somebody all of a sudden shows up and says, 732 00:30:34,257 --> 00:30:35,340 well, I'm not one of them. 733 00:30:35,340 --> 00:30:36,420 I'm super. 734 00:30:36,420 --> 00:30:38,160 So we've study those people as well. 735 00:30:38,160 --> 00:30:39,150 I won't go into detail. 736 00:30:39,150 --> 00:30:43,090 We made a lot of studies about them. 737 00:30:43,090 --> 00:30:45,300 Just a couple of testimonials. 738 00:30:45,300 --> 00:30:46,680 I pretend I don't remember people 739 00:30:46,680 --> 00:30:48,304 because people think I'm stalking them, 740 00:30:48,304 --> 00:30:49,360 or something like that. 741 00:30:49,360 --> 00:30:53,530 So now we've studied half a dozen of them. 742 00:30:53,530 --> 00:30:56,910 And now we're studying a much larger population of them. 743 00:30:56,910 --> 00:31:00,270 I would-- All I'd like to say is these people are as 744 00:31:00,270 --> 00:31:01,937 good as prosopagnosics are bad. 745 00:31:01,937 --> 00:31:04,270 They're really-- but we haven't found that many of them. 746 00:31:04,270 --> 00:31:06,640 We've probably identified about 30 of them 747 00:31:06,640 --> 00:31:07,910 and we've been looking. 748 00:31:07,910 --> 00:31:10,720 And we give them much harder tests of face recognition. 749 00:31:10,720 --> 00:31:12,520 Some of you, if you can recognize all four 750 00:31:12,520 --> 00:31:15,700 of these people before they were famous, I'll come up 751 00:31:15,700 --> 00:31:18,647 and you might be one of our people, but I doubt it. 752 00:31:18,647 --> 00:31:20,980 Most people think they're very good at face recognition. 753 00:31:20,980 --> 00:31:23,360 When we test them, they're not that good. 754 00:31:23,360 --> 00:31:26,950 So nature versus nurture. 755 00:31:26,950 --> 00:31:28,810 Make sure I don't go over my time, here. 756 00:31:32,430 --> 00:31:34,130 You know, most of you are not psychol-- 757 00:31:34,130 --> 00:31:37,070 how many people are psychologists here? 758 00:31:37,070 --> 00:31:37,630 OK. 759 00:31:37,630 --> 00:31:41,580 So you know that there's some experiments 760 00:31:41,580 --> 00:31:44,910 called twin studies where you study 761 00:31:44,910 --> 00:31:49,020 some characteristics in monozygotic twins 762 00:31:49,020 --> 00:31:51,520 and then different, dizygotic, dizygotic twins. 763 00:31:51,520 --> 00:31:53,734 And so the idea here is that they sort of share 764 00:31:53,734 --> 00:31:54,900 the same family environment. 765 00:31:54,900 --> 00:31:56,330 Of course, each person's different, 766 00:31:56,330 --> 00:31:57,585 and they have a different way of dealing with the family 767 00:31:57,585 --> 00:31:58,418 and stuff like that. 768 00:31:58,418 --> 00:32:00,220 But that's the best we can do. 769 00:32:00,220 --> 00:32:05,310 But if you can show that the correlation between twin 1 770 00:32:05,310 --> 00:32:08,177 and twin 2 is very, very high, and in our case, 771 00:32:08,177 --> 00:32:09,510 it's going to be extremely high. 772 00:32:09,510 --> 00:32:13,200 In fact, it's just as high if twin 1 took the test twice. 773 00:32:13,200 --> 00:32:15,240 That indicates-- and then if you compare it 774 00:32:15,240 --> 00:32:19,020 with dizygotic twins, there's a real difference there. 775 00:32:19,020 --> 00:32:22,110 And that's what we've shown essentially, is that-- 776 00:32:22,110 --> 00:32:24,460 I'm just saying, the test reliability is very good. 777 00:32:24,460 --> 00:32:32,940 It's tech has a R of 0.7, which is very good. 778 00:32:32,940 --> 00:32:34,320 And we had-- 779 00:32:34,320 --> 00:32:37,680 There's 350 just random people who took the test twice. 780 00:32:37,680 --> 00:32:41,010 But now what you can do, we have this online way 781 00:32:41,010 --> 00:32:43,932 of doing research where we can just have people come up and-- 782 00:32:43,932 --> 00:32:45,390 the real hard thing in twin studies 783 00:32:45,390 --> 00:32:48,695 is not to get monozygotic twins, it's to get dizygotic twins. 784 00:32:48,695 --> 00:32:50,820 Dizygotic twins don't really care about being twins 785 00:32:50,820 --> 00:32:53,860 that much, but monozygotic feel, hey, I'm a twin. 786 00:32:53,860 --> 00:32:58,140 My wife is a monozygotic twin so I know all about this. 787 00:32:58,140 --> 00:33:01,440 So we have something called the Austrian twin registry. 788 00:33:01,440 --> 00:33:04,230 And we pay them and they-- a very nominal fee 789 00:33:04,230 --> 00:33:05,766 and they do our tests online. 790 00:33:05,766 --> 00:33:07,140 And what's really interesting, we 791 00:33:07,140 --> 00:33:09,870 found if you correlate twin 1 and twin 2, 792 00:33:09,870 --> 00:33:12,040 they have to be obviously the same gender. 793 00:33:12,040 --> 00:33:15,390 The correlation, you'll see here, is 0.7. 794 00:33:15,390 --> 00:33:18,120 It's just as good if the twin took the test twice. 795 00:33:18,120 --> 00:33:19,650 That's pretty darn amazing. 796 00:33:19,650 --> 00:33:21,630 And the dizygotic is 0.3. 797 00:33:21,630 --> 00:33:24,170 So in this particular situation, I 798 00:33:24,170 --> 00:33:26,280 think we've shown to our astonishment 799 00:33:26,280 --> 00:33:29,730 that the ability of this face-- the ability to learn 800 00:33:29,730 --> 00:33:34,630 new faces is almost entirely heritable, whatever that means. 801 00:33:34,630 --> 00:33:36,900 I mean, it's a technical definition, 802 00:33:36,900 --> 00:33:42,300 but it indicates that your ability to recognize faces 803 00:33:42,300 --> 00:33:47,240 is strongly controlled by other factors than experience. 804 00:33:47,240 --> 00:33:49,830 And that's when I want to bring up this childhood adversity 805 00:33:49,830 --> 00:33:50,820 thing, here. 806 00:33:50,820 --> 00:33:55,800 Laura Germine has done a very large survey of I think, 807 00:33:55,800 --> 00:34:00,480 over 1,000 people who were-- 808 00:34:00,480 --> 00:34:03,240 and these are things-- we had trouble with the IRB here, 809 00:34:03,240 --> 00:34:06,450 but people talked about all of their horrible things that 810 00:34:06,450 --> 00:34:10,199 happened to them, childhood sexual abuse, 811 00:34:10,199 --> 00:34:12,570 neglect by their parents, all kinds 812 00:34:12,570 --> 00:34:15,719 of things that are really bad when you're growing up. 813 00:34:15,719 --> 00:34:17,760 And then we did a lot of tests on the web. 814 00:34:17,760 --> 00:34:22,250 And everything, all our tests showed real deficits 815 00:34:22,250 --> 00:34:27,150 in cognitive abilities, in even emotion recognition, 816 00:34:27,150 --> 00:34:30,630 but there was no deficit in ability to learn faces. 817 00:34:30,630 --> 00:34:32,880 So that's pretty-- so what I would 818 00:34:32,880 --> 00:34:36,480 like to suggest is that somehow we're sort of topped out. 819 00:34:36,480 --> 00:34:38,790 We have enough experience seeing faces 820 00:34:38,790 --> 00:34:40,929 and we've reached our asymptote. 821 00:34:40,929 --> 00:34:43,199 That's just a point that might be of interest here. 822 00:34:46,679 --> 00:34:49,199 So another thing we've done recently, 823 00:34:49,199 --> 00:34:51,900 I don't have any data here, but we just got it paper accepted, 824 00:34:51,900 --> 00:34:55,469 was to show that using the same sort of paradigm 825 00:34:55,469 --> 00:34:57,780 of monozygotic and dizygotic twins 826 00:34:57,780 --> 00:35:02,460 that face attractiveness is not-- 827 00:35:02,460 --> 00:35:05,560 there's no genetic component at all, basically zero. 828 00:35:05,560 --> 00:35:09,450 So here's a face attractiveness test that we've cooked up here. 829 00:35:09,450 --> 00:35:10,502 Here's a bunch of faces. 830 00:35:10,502 --> 00:35:12,210 You have a bunch of cards, you sort them. 831 00:35:12,210 --> 00:35:13,680 That's all you do. 832 00:35:13,680 --> 00:35:16,260 You get the mean ratings for all the faces 833 00:35:16,260 --> 00:35:17,820 and then you rate the faces. 834 00:35:17,820 --> 00:35:20,760 And I'm going to say, OK, you're-- 835 00:35:20,760 --> 00:35:23,490 let's say that upper left- hand corner, 836 00:35:23,490 --> 00:35:27,190 that's one person's ratings versus the mean ratings. 837 00:35:27,190 --> 00:35:29,190 That's a correlation of 0.74. 838 00:35:29,190 --> 00:35:30,970 The next person's 0.91. 839 00:35:30,970 --> 00:35:34,920 That's really unimaginable to me that, that person 840 00:35:34,920 --> 00:35:37,287 there's correlation with the mean rating is so high. 841 00:35:37,287 --> 00:35:38,370 And look at these ratings. 842 00:35:38,370 --> 00:35:39,600 They're very, very high. 843 00:35:39,600 --> 00:35:41,912 Of course, these are Harvard undergraduates. 844 00:35:41,912 --> 00:35:43,620 They all have the same kind of mentality. 845 00:35:43,620 --> 00:35:45,480 Perhaps that's maybe that's the point. 846 00:35:45,480 --> 00:35:47,760 But the correlation with the mean, this 847 00:35:47,760 --> 00:35:49,860 is just from one of my courses. 848 00:35:49,860 --> 00:35:50,670 It's unbelievable. 849 00:35:50,670 --> 00:35:53,190 The correlation, the agreement of who's attractive 850 00:35:53,190 --> 00:35:55,780 and who's not attractive is astonishing. 851 00:35:55,780 --> 00:36:01,260 So what we did was we had these MZ versus DZ twins 852 00:36:01,260 --> 00:36:02,640 and had them rate them. 853 00:36:02,640 --> 00:36:04,320 We have a kind of a jingle. 854 00:36:04,320 --> 00:36:08,850 We say, how quirky are you when you're a beauty judge, 855 00:36:08,850 --> 00:36:11,520 or something like that because our web site, 856 00:36:11,520 --> 00:36:13,230 we don't pay anybody. 857 00:36:13,230 --> 00:36:16,170 In the twin study, we do, but we like to make the tests fun 858 00:36:16,170 --> 00:36:17,610 and interesting to the people. 859 00:36:17,610 --> 00:36:19,290 And we, most of the time, give them 860 00:36:19,290 --> 00:36:20,850 feedback as to how they done. 861 00:36:20,850 --> 00:36:22,470 So all I'm trying to tell you there 862 00:36:22,470 --> 00:36:26,460 is that we don't see any genetic component there at all when 863 00:36:26,460 --> 00:36:29,870 you do the monozygotic and dizygotic twins, here. 864 00:36:29,870 --> 00:36:31,270 Gender versus attractiveness. 865 00:36:31,270 --> 00:36:34,510 Where you give a bunch of pictures to people and you say, 866 00:36:34,510 --> 00:36:37,420 which looks more feminine, more masculine? 867 00:36:37,420 --> 00:36:40,240 You do that with girls and then you do it with boys. 868 00:36:40,240 --> 00:36:42,430 Which was more masculine, feminine? 869 00:36:42,430 --> 00:36:45,850 Those are very reliable ratings also. 870 00:36:45,850 --> 00:36:49,480 It's hard to see but female attractiveness correlates very 871 00:36:49,480 --> 00:36:52,930 well with the gender judgment, male attractiveness doesn't. 872 00:36:52,930 --> 00:36:55,180 Actually male attractiveness is quite robust. 873 00:36:55,180 --> 00:36:57,610 People can agree on male attractiveness, 874 00:36:57,610 --> 00:36:59,540 but it's not the gender axis. 875 00:36:59,540 --> 00:37:02,820 Maybe you know, 2000 years ago it was machoness or something 876 00:37:02,820 --> 00:37:03,320 like that. 877 00:37:03,320 --> 00:37:07,430 But in our society, it's agreed upon but it's a little bit 878 00:37:07,430 --> 00:37:07,930 mysterious. 879 00:37:07,930 --> 00:37:10,000 Alex Todorov has published a paper 880 00:37:10,000 --> 00:37:11,560 on this, which I don't understand. 881 00:37:11,560 --> 00:37:14,680 But he says he explains it, but I'm not sure. 882 00:37:14,680 --> 00:37:17,440 But that's an interesting area of what constitutes 883 00:37:17,440 --> 00:37:19,510 male attractiveness. 884 00:37:19,510 --> 00:37:23,350 A fellow, who is an MIT student, Richard Russell, 885 00:37:23,350 --> 00:37:26,470 found one of the things that is related 886 00:37:26,470 --> 00:37:28,000 to female attractiveness. 887 00:37:28,000 --> 00:37:30,340 It's basically contrast, facial contrast. 888 00:37:30,340 --> 00:37:33,340 Not how dark you are, but your facial contrast. 889 00:37:33,340 --> 00:37:35,200 And he just found that if you just 890 00:37:35,200 --> 00:37:37,120 measured the contrast of pictures, 891 00:37:37,120 --> 00:37:39,760 you find the female average contrast is 892 00:37:39,760 --> 00:37:42,280 higher than the male contrast. 893 00:37:42,280 --> 00:37:43,840 And if you take a picture like that, 894 00:37:43,840 --> 00:37:47,790 which is an androgynous person here, and you just change-- 895 00:37:47,790 --> 00:37:51,280 you have the same picture and you just increase the contrast, 896 00:37:51,280 --> 00:37:54,640 the one on the left looks more female. 897 00:37:54,640 --> 00:37:57,070 And maybe that's the reason that Richard thinks that this 898 00:37:57,070 --> 00:37:58,720 is the basis of some cosmetics. 899 00:37:58,720 --> 00:38:02,860 And he works with a cosmetic company. 900 00:38:02,860 --> 00:38:04,311 He's not doing-- 901 00:38:04,311 --> 00:38:04,810 OK. 902 00:38:04,810 --> 00:38:07,330 So the final part of this talk, I'd like to talk about 903 00:38:07,330 --> 00:38:10,590 is that, which I'm more involved in right now. 904 00:38:10,590 --> 00:38:13,570 Is I sort of feel that most of social psychology 905 00:38:13,570 --> 00:38:16,000 is involved in kind of asking people 906 00:38:16,000 --> 00:38:18,619 questions and things like that about sociology. 907 00:38:18,619 --> 00:38:20,410 But as I said in the beginning of the talk, 908 00:38:20,410 --> 00:38:22,057 I feel that the realm of human action 909 00:38:22,057 --> 00:38:23,890 is something that has not been investigated. 910 00:38:23,890 --> 00:38:26,840 So I just feel it's something that we need to probe. 911 00:38:26,840 --> 00:38:28,540 And what I would like to argue is 912 00:38:28,540 --> 00:38:31,150 that by studying our human actions, 913 00:38:31,150 --> 00:38:33,370 we can actually reveal our social perceptions. 914 00:38:33,370 --> 00:38:35,060 That's kind of the point here. 915 00:38:35,060 --> 00:38:39,456 So most of you have been in very crowded places 916 00:38:39,456 --> 00:38:41,580 and you don't bump into people and stuff like that. 917 00:38:41,580 --> 00:38:43,070 And there's many reasons for that. 918 00:38:43,070 --> 00:38:45,580 But basically it's pretty amazing 919 00:38:45,580 --> 00:38:48,290 that people, pretty much, don't run into each other 920 00:38:48,290 --> 00:38:51,760 in very crowded and when they're very rushed. 921 00:38:51,760 --> 00:38:55,450 And there are many situations in which your social interactions 922 00:38:55,450 --> 00:38:58,900 with people are extremely skilled in a sense, 923 00:38:58,900 --> 00:39:01,300 in different kinds of dancing or fencing 924 00:39:01,300 --> 00:39:03,340 or different kinds of athletics. 925 00:39:03,340 --> 00:39:06,510 So I claim this is the domain of what 926 00:39:06,510 --> 00:39:11,030 I call rapid social perception. 927 00:39:11,030 --> 00:39:14,050 So how can we study rapid social perception? 928 00:39:14,050 --> 00:39:17,732 Well, I'm OK with all these things, 929 00:39:17,732 --> 00:39:19,190 showing movies and stuff like that. 930 00:39:19,190 --> 00:39:21,064 But we've sort of put it into the laboratory, 931 00:39:21,064 --> 00:39:24,800 in a more discrete fashion just because we can study it easily. 932 00:39:24,800 --> 00:39:26,502 Now one of them is that most of you 933 00:39:26,502 --> 00:39:28,210 watch the World Cup, is the penalty kick. 934 00:39:28,210 --> 00:39:30,250 It doesn't happen all the time, but every once in a while, 935 00:39:30,250 --> 00:39:30,760 it happens. 936 00:39:30,760 --> 00:39:33,370 And you know, that's a really dramatic moment. 937 00:39:33,370 --> 00:39:34,890 And of course, you have to predict 938 00:39:34,890 --> 00:39:38,170 which way the kicker is going to go ahead 939 00:39:38,170 --> 00:39:40,760 of time if you're the goalie. 940 00:39:40,760 --> 00:39:43,800 So we've set up something which was sort of parallel to it 941 00:39:43,800 --> 00:39:46,600 which I call the lab version, here. 942 00:39:46,600 --> 00:39:48,890 So here's the kicker, here. 943 00:39:48,890 --> 00:39:54,430 But the job is not to kick the ball into the area, 944 00:39:54,430 --> 00:39:57,060 but just touch target left or right. 945 00:39:57,060 --> 00:39:57,880 That's your job. 946 00:39:57,880 --> 00:39:58,730 That's all you do. 947 00:39:58,730 --> 00:40:00,020 Go boom, boom. 948 00:40:00,020 --> 00:40:01,040 That's it. 949 00:40:01,040 --> 00:40:02,830 That's the trial. 950 00:40:02,830 --> 00:40:03,850 So it's very simple. 951 00:40:03,850 --> 00:40:05,560 And we tell people it's a game. 952 00:40:05,560 --> 00:40:08,740 If you are up to the game-- 953 00:40:08,740 --> 00:40:11,660 The blocker-- if you get there within 150 mil-- 954 00:40:11,660 --> 00:40:14,830 we sort of titrate it to match the skill of the two people. 955 00:40:14,830 --> 00:40:15,550 But it's a game. 956 00:40:15,550 --> 00:40:17,536 Harvard undergraduates love the game. 957 00:40:17,536 --> 00:40:18,910 They get tired because a little-- 958 00:40:18,910 --> 00:40:20,560 but there's no problem. 959 00:40:20,560 --> 00:40:22,920 Harvard undergraduates don't know each other usually 960 00:40:22,920 --> 00:40:24,820 so they're not friends or anything like that. 961 00:40:24,820 --> 00:40:26,750 They just, they just come in there. 962 00:40:26,750 --> 00:40:29,240 So I think there's a movie here as well. 963 00:40:29,240 --> 00:40:33,380 Let's see if I can get this going here. 964 00:40:33,380 --> 00:40:35,760 Oh, here, go this way. 965 00:40:35,760 --> 00:40:36,380 OK. 966 00:40:36,380 --> 00:40:37,760 So this is the game, here. 967 00:40:42,470 --> 00:40:44,719 Is it moving? 968 00:40:44,719 --> 00:40:45,260 There you go. 969 00:40:45,260 --> 00:40:47,390 Here. 970 00:40:47,390 --> 00:40:48,050 That's it. 971 00:40:48,050 --> 00:40:50,260 That's the game. 972 00:40:50,260 --> 00:40:52,670 I like pretty simple experiments. 973 00:40:52,670 --> 00:40:53,250 OK. 974 00:40:53,250 --> 00:40:56,180 So but then what we do is we just measure the finger 975 00:40:56,180 --> 00:40:59,940 position of the two players. 976 00:40:59,940 --> 00:41:01,190 You can just buy these things. 977 00:41:01,190 --> 00:41:02,290 They are off the shelf. 978 00:41:02,290 --> 00:41:03,740 They're not very expensive. 979 00:41:03,740 --> 00:41:06,530 The one I had like you know, a couple of thousand dollars 980 00:41:06,530 --> 00:41:07,370 these days. 981 00:41:07,370 --> 00:41:09,020 This is very low tech. 982 00:41:09,020 --> 00:41:11,000 It measures the position of the fingers 983 00:41:11,000 --> 00:41:14,810 and at a high precision, very fast, and stuff like that. 984 00:41:14,810 --> 00:41:16,080 You can buy this thing. 985 00:41:16,080 --> 00:41:18,480 It takes a while to get it going and stuff like that. 986 00:41:18,480 --> 00:41:25,580 So basically, we can map the trajectories 987 00:41:25,580 --> 00:41:26,970 of the various people. 988 00:41:26,970 --> 00:41:29,135 And you can see that they're well- behaved. 989 00:41:29,135 --> 00:41:31,040 And just like that. 990 00:41:31,040 --> 00:41:32,930 So what's interesting is the timing here. 991 00:41:32,930 --> 00:41:37,170 So the kicker goes like this and the blocker-- 992 00:41:37,170 --> 00:41:39,180 so you can make little measurements, here. 993 00:41:39,180 --> 00:41:41,900 And so we can just measure the horizontal positions, here. 994 00:41:41,900 --> 00:41:45,509 So the blue is the kicker and the red is the blocker. 995 00:41:45,509 --> 00:41:47,550 And so obviously, they can't do at the same time. 996 00:41:47,550 --> 00:41:50,340 But the reaction times are interesting. 997 00:41:50,340 --> 00:41:54,600 What's that difference in the launch point, there. 998 00:41:54,600 --> 00:42:02,120 And what we found was it was actually very low. 999 00:42:02,120 --> 00:42:03,710 You can see it there. 1000 00:42:03,710 --> 00:42:08,720 Some were as low as 100 milliseconds but it's 150-- 1001 00:42:08,720 --> 00:42:11,610 it's lower than choice reaction times normally. 1002 00:42:11,610 --> 00:42:13,880 So if you take-- if you could have a button box 1003 00:42:13,880 --> 00:42:16,160 and put a buzzer or something like that, 1004 00:42:16,160 --> 00:42:18,620 choice reaction times are usually 1005 00:42:18,620 --> 00:42:20,390 about 100 milliseconds longer. 1006 00:42:20,390 --> 00:42:23,070 So what's going on here? 1007 00:42:23,070 --> 00:42:27,940 But maybe choice reaction time isn't a really good choice. 1008 00:42:27,940 --> 00:42:29,420 So what we did was we took-- 1009 00:42:29,420 --> 00:42:32,180 we actually can take the position of the finger 1010 00:42:32,180 --> 00:42:33,930 and map it on the screen. 1011 00:42:33,930 --> 00:42:37,250 And we have a little experiment here, where you actually-- 1012 00:42:37,250 --> 00:42:39,090 instead of playing against a person, 1013 00:42:39,090 --> 00:42:41,430 you just, there's a little dot that zooms up there 1014 00:42:41,430 --> 00:42:42,540 and you do that. 1015 00:42:42,540 --> 00:42:45,860 So maybe it's that-- the fact of that. 1016 00:42:45,860 --> 00:42:49,070 So we do this, and that's the experiment. 1017 00:42:49,070 --> 00:42:50,880 And it turns out this experiment, 1018 00:42:50,880 --> 00:42:52,380 we're about 100 milliseconds longer. 1019 00:42:52,380 --> 00:42:54,630 So in other words, if you're playing against the human 1020 00:42:54,630 --> 00:42:57,840 being, that's on average about 100 milliseconds. 1021 00:42:57,840 --> 00:43:00,770 So you are faster when you're dealing with human being. 1022 00:43:03,930 --> 00:43:07,490 I will just skip over some of this stuff, here. 1023 00:43:07,490 --> 00:43:11,210 And again, there's no learning here. 1024 00:43:11,210 --> 00:43:14,270 Basically, it's just, it's flat. 1025 00:43:14,270 --> 00:43:16,340 So what's going on here? 1026 00:43:16,340 --> 00:43:19,370 We think what's happening here, and this is the punch line, 1027 00:43:19,370 --> 00:43:21,050 I'll just elaborate a little bit more. 1028 00:43:21,050 --> 00:43:22,790 You think all through your life you've 1029 00:43:22,790 --> 00:43:25,070 seen people do different kinds of actions. 1030 00:43:25,070 --> 00:43:27,740 And if you do any kind of action, 1031 00:43:27,740 --> 00:43:30,050 there's all kinds of postural adjustments. 1032 00:43:30,050 --> 00:43:32,900 I was just talking to one of the guys in Emilio Bizzi's lab. 1033 00:43:32,900 --> 00:43:35,000 If you're sort of in different kinds of sports, 1034 00:43:35,000 --> 00:43:37,580 if you lunge like that, what's the first muscles 1035 00:43:37,580 --> 00:43:39,900 that contract? 1036 00:43:39,900 --> 00:43:42,600 Anybody want to know? 1037 00:43:42,600 --> 00:43:43,610 Your butt. 1038 00:43:46,310 --> 00:43:50,610 Because that's to maintain your center of gravity. 1039 00:43:50,610 --> 00:43:51,800 So there's all kinds of-- 1040 00:43:51,800 --> 00:43:55,297 I'm not saying people will watch you know, their butts 1041 00:43:55,297 --> 00:43:56,130 and stuff like that. 1042 00:43:56,130 --> 00:43:58,610 But when you make any kind of motion, 1043 00:43:58,610 --> 00:44:00,580 it's your whole body is connected. 1044 00:44:00,580 --> 00:44:04,760 And somehow we have knowledge, implicitly of this. 1045 00:44:04,760 --> 00:44:07,530 So what's going on here? 1046 00:44:07,530 --> 00:44:10,130 We talk about 90 milliseconds or so. 1047 00:44:10,130 --> 00:44:12,260 What section of the body informative? 1048 00:44:12,260 --> 00:44:14,510 Well, in a sense this is kind of-- you just block off 1049 00:44:14,510 --> 00:44:16,051 different pieces and stuff like that. 1050 00:44:16,051 --> 00:44:18,080 And we just, we have all different kinds 1051 00:44:18,080 --> 00:44:19,550 of ways we can just-- 1052 00:44:19,550 --> 00:44:20,220 limited. 1053 00:44:20,220 --> 00:44:22,770 And we do-- we did all kinds of experiments like that. 1054 00:44:22,770 --> 00:44:27,110 And basically, if you show all, there's 1055 00:44:27,110 --> 00:44:29,330 an advantage, just the torso or just the head 1056 00:44:29,330 --> 00:44:32,035 and there's the computer, it's all over the place. 1057 00:44:32,035 --> 00:44:33,410 That the information is all over. 1058 00:44:33,410 --> 00:44:35,694 But that's not unreasonable because when 1059 00:44:35,694 --> 00:44:37,610 you're doing something, all parts of your body 1060 00:44:37,610 --> 00:44:38,510 are connected. 1061 00:44:38,510 --> 00:44:41,780 So there's quite an advantage. 1062 00:44:41,780 --> 00:44:44,801 I think basically, oh, this is your question. 1063 00:44:44,801 --> 00:44:45,800 We're moving this stuff. 1064 00:44:45,800 --> 00:44:48,480 So we have-- we call them cut videos here. 1065 00:44:48,480 --> 00:44:52,380 So what we do is we play a video, here. 1066 00:44:52,380 --> 00:44:54,950 By the way, we now have shown that people 1067 00:44:54,950 --> 00:44:56,720 would behave exactly the same to a video 1068 00:44:56,720 --> 00:44:58,380 than they do to the other person. 1069 00:44:58,380 --> 00:45:00,050 So that makes the life a little simpler. 1070 00:45:00,050 --> 00:45:03,240 So there we go. 1071 00:45:03,240 --> 00:45:06,830 There's-- I don't know if you can tell, but essentially, 1072 00:45:06,830 --> 00:45:09,770 nobody, except a couple of star athletes, 1073 00:45:09,770 --> 00:45:11,880 could tell what it was. 1074 00:45:11,880 --> 00:45:15,060 And it turns out, in the cut videos, in other words, 1075 00:45:15,060 --> 00:45:16,780 you don't have that preparatory stuff. 1076 00:45:16,780 --> 00:45:19,370 You just have all that if the person is still, 1077 00:45:19,370 --> 00:45:21,380 we don't even know what those motions are. 1078 00:45:21,380 --> 00:45:24,660 But basically, they're 100 milliseconds slower 1079 00:45:24,660 --> 00:45:26,720 than the cut videos. 1080 00:45:26,720 --> 00:45:28,445 So another thing is, oh, we even did 1081 00:45:28,445 --> 00:45:30,320 stuff-- we did another series of experiments. 1082 00:45:30,320 --> 00:45:31,794 Well, maybe it's eye movements. 1083 00:45:31,794 --> 00:45:33,710 And basically, we just did some more blocking. 1084 00:45:33,710 --> 00:45:37,140 And shoulders are important, you can see, here. 1085 00:45:37,140 --> 00:45:38,270 Oh, I didn't show that. 1086 00:45:38,270 --> 00:45:39,436 I don't have the data there. 1087 00:45:39,436 --> 00:45:43,770 But basically every-- even the head, there's a little-- 1088 00:45:43,770 --> 00:45:46,270 all of those, the first five, except the last one, 1089 00:45:46,270 --> 00:45:47,460 were very good. 1090 00:45:47,460 --> 00:45:49,914 Even the last one, you can hardly see anything. 1091 00:45:49,914 --> 00:45:51,330 You just see the head then you see 1092 00:45:51,330 --> 00:45:54,734 the eye movement is a little bit of an advantage, even there. 1093 00:46:01,380 --> 00:46:03,780 OK. 1094 00:46:03,780 --> 00:46:08,070 So basically, what I'm trying to say more broadly, 1095 00:46:08,070 --> 00:46:12,330 is that our ability to understand humans probably more 1096 00:46:12,330 --> 00:46:14,400 to predict what they're going to do 1097 00:46:14,400 --> 00:46:18,090 is something that we've learned somewhat unconsciously. 1098 00:46:18,090 --> 00:46:20,340 We're going to do a lot of machine learning of what 1099 00:46:20,340 --> 00:46:21,510 parts of the body it is. 1100 00:46:21,510 --> 00:46:22,885 And all kinds of stuff like that. 1101 00:46:22,885 --> 00:46:24,450 I'm interested in if you people have 1102 00:46:24,450 --> 00:46:26,241 some ideas of what other kinds of things we 1103 00:46:26,241 --> 00:46:28,710 can do to generalize this. 1104 00:46:28,710 --> 00:46:31,140 But I think it's an area where we-- 1105 00:46:31,140 --> 00:46:33,450 it helps us understand all the kind of knowledge 1106 00:46:33,450 --> 00:46:35,880 that we do have of other people that we 1107 00:46:35,880 --> 00:46:42,740 can weed out in ways that are very subtle, but reliable.