WEBVTT 00:00:01.577 --> 00:00:03.660 The following content is provided under a Creative 00:00:03.660 --> 00:00:05.300 Commons License. 00:00:05.300 --> 00:00:07.920 Your support will help MIT OpenCourseWare 00:00:07.920 --> 00:00:12.280 continue to offer high quality educational resources for free. 00:00:12.280 --> 00:00:14.910 To make a donation, or view additional materials 00:00:14.910 --> 00:00:17.370 from hundreds of MIT courses, visit 00:00:17.370 --> 00:00:19.350 MITOpenCourseWare@OCW.MIT.edu. 00:00:22.420 --> 00:00:23.850 KEN NAKAYAMA: The social mind, let 00:00:23.850 --> 00:00:25.933 me see if I can just go through these slides here. 00:00:28.230 --> 00:00:30.000 One might ask the question on the origin 00:00:30.000 --> 00:00:31.775 of human intelligence. 00:00:31.775 --> 00:00:33.900 OK, I just want to give a little bit of background. 00:00:33.900 --> 00:00:36.360 Nancy summarized my career very nicely. 00:00:36.360 --> 00:00:38.920 I started out recording from retinal ganglion cells 00:00:38.920 --> 00:00:39.930 and animals. 00:00:39.930 --> 00:00:41.770 That was a very long time ago. 00:00:41.770 --> 00:00:43.440 And I was kind of into reductionism, 00:00:43.440 --> 00:00:47.520 trying to understand how retinal ganglion cells determined 00:00:47.520 --> 00:00:50.670 how we see certain psychophysical phenomenon. 00:00:50.670 --> 00:00:53.047 So what am I doing here talking about some 00:00:53.047 --> 00:00:54.130 of that social processing? 00:00:54.130 --> 00:00:56.760 Well, it's been a long time ago, so in your career 00:00:56.760 --> 00:00:58.210 you do different things. 00:00:58.210 --> 00:00:59.850 But as I'll say in a moment, I happened 00:00:59.850 --> 00:01:02.310 to read an interesting paper. 00:01:02.310 --> 00:01:05.220 I was in the University of Tokyo library kind of bored 00:01:05.220 --> 00:01:06.130 and I found a book. 00:01:06.130 --> 00:01:08.280 And I found an article, which I'll talk about that. 00:01:08.280 --> 00:01:10.890 Didn't change my life, exactly, but it reoriented me 00:01:10.890 --> 00:01:14.280 towards this social processing. 00:01:14.280 --> 00:01:16.950 And you'll see there's many different ways of studying. 00:01:16.950 --> 00:01:19.530 I think the realization that social processing 00:01:19.530 --> 00:01:22.860 and the human mind are really very inextricably 00:01:22.860 --> 00:01:26.880 connected opens the door for kinds of investigations 00:01:26.880 --> 00:01:32.310 that I think are sort of really open-ended and somewhat limited 00:01:32.310 --> 00:01:33.960 just by your imagination. 00:01:33.960 --> 00:01:36.740 So I sort of encourage some of you 00:01:36.740 --> 00:01:41.260 with that bent to think about this. 00:01:41.260 --> 00:01:43.680 Since I study vision I will just talk a little bit 00:01:43.680 --> 00:01:46.020 about the visual system as a preamble. 00:01:46.020 --> 00:01:49.900 And I was studying vision about 50 years ago. 00:01:49.900 --> 00:01:52.570 And I think I was sleeping for about 10 years. 00:01:52.570 --> 00:01:54.859 And all of a sudden I found out something interesting. 00:01:54.859 --> 00:01:56.400 Here's a picture of the visual system 00:01:56.400 --> 00:01:58.460 when I started graduate school. 00:01:58.460 --> 00:02:00.900 There was the motor system, the somatosensory system, 00:02:00.900 --> 00:02:02.280 coupled sensory system. 00:02:02.280 --> 00:02:05.980 And vision was that area 17 V1 in the cortex 00:02:05.980 --> 00:02:08.084 I think I slept a little bit and all 00:02:08.084 --> 00:02:11.880 of a sudden I woke up and oops, little bit past 1970. 00:02:11.880 --> 00:02:14.730 Almost the whole half of the brain in the back and the front 00:02:14.730 --> 00:02:16.500 was devoted to vision. 00:02:16.500 --> 00:02:18.520 You probably had lectures by that. 00:02:18.520 --> 00:02:22.259 And so I think that's really an important fact because the fact 00:02:22.259 --> 00:02:23.550 that the brain is very visual-- 00:02:23.550 --> 00:02:24.360 I mean, that's human. 00:02:24.360 --> 00:02:25.700 So maybe in mice it might be different, 00:02:25.700 --> 00:02:27.991 they may be it's old fashioned, or something like that. 00:02:27.991 --> 00:02:29.880 But for primates I think it's very important. 00:02:29.880 --> 00:02:32.010 You probably learned about the different systems. 00:02:32.010 --> 00:02:33.551 That the posterior part of the brain. 00:02:33.551 --> 00:02:36.030 There's a lot of anterior parts. 00:02:36.030 --> 00:02:40.470 David Marr said, anybody heard of David Marr? 00:02:40.470 --> 00:02:44.220 OK, David Marr said vision, where is what by looking, 00:02:44.220 --> 00:02:45.300 what is where by looking? 00:02:45.300 --> 00:02:47.430 I just want to say it's way, way more than that. 00:02:47.430 --> 00:02:50.250 The visual system-- I mean, if vision's half the brain, 00:02:50.250 --> 00:02:52.129 that's just a pretty, puny problem. 00:02:52.129 --> 00:02:53.670 So we have to think of all the things 00:02:53.670 --> 00:02:54.920 that the visual system might do. 00:02:54.920 --> 00:02:56.503 And we really haven't thought of them. 00:02:56.503 --> 00:02:58.630 Well here's some more pictures of a visual system. 00:02:58.630 --> 00:03:03.017 So, it's a little bit like, it started over here on the west 00:03:03.017 --> 00:03:03.683 and you go east. 00:03:03.683 --> 00:03:05.760 It's sort of like Russia going all the way 00:03:05.760 --> 00:03:07.980 to the Pacific Ocean. 00:03:07.980 --> 00:03:09.600 Vision system really expand. 00:03:09.600 --> 00:03:13.380 I think it's kind of slowed down recently. 00:03:13.380 --> 00:03:16.890 I would just say the visual system is very big in primates. 00:03:16.890 --> 00:03:21.870 And if it's half the brain, well all 00:03:21.870 --> 00:03:24.446 the other stuff, like greed, sex, power, music, 00:03:24.446 --> 00:03:25.320 all that other stuff. 00:03:25.320 --> 00:03:27.480 It's about the same. 00:03:27.480 --> 00:03:30.604 So, that means that we just don't really understand. 00:03:30.604 --> 00:03:32.520 That's a lot of stuff over there on the right. 00:03:32.520 --> 00:03:35.610 That means, on the left, we don't know squat. 00:03:35.610 --> 00:03:37.080 So just have that in mind, there's 00:03:37.080 --> 00:03:40.054 plenty for you to do as young scientists. 00:03:40.054 --> 00:03:40.720 So I don't know. 00:03:40.720 --> 00:03:41.660 It must be-- 00:03:41.660 --> 00:03:42.760 This is a very tiny list. 00:03:42.760 --> 00:03:43.650 It's lots of things. 00:03:43.650 --> 00:03:45.962 It's David Marr kind of thing. 00:03:45.962 --> 00:03:46.920 So it's more than that. 00:03:49.740 --> 00:03:52.140 I'm going to talk about visual motor control. 00:03:52.140 --> 00:03:53.130 I'm a psychologist. 00:03:53.130 --> 00:03:58.080 For some reason, I think a lot of our-- 00:03:58.080 --> 00:04:04.260 Roger Sperry said 70 years ago that the ultimate arbiter 00:04:04.260 --> 00:04:06.900 of your mind is action. 00:04:06.900 --> 00:04:09.260 I mean, if you don't do something-- if we can have-- 00:04:09.260 --> 00:04:10.890 I mean, I studied perception most of my life, 00:04:10.890 --> 00:04:12.240 but if you don't do something, you're 00:04:12.240 --> 00:04:14.531 not going to pass your genes on to the next generation. 00:04:14.531 --> 00:04:15.540 So action is it. 00:04:15.540 --> 00:04:17.529 Psychologists, they don't study action at all. 00:04:17.529 --> 00:04:19.029 That's like, they've forgotten that. 00:04:19.029 --> 00:04:22.044 So I just like to put a plug-in for action. 00:04:22.044 --> 00:04:23.460 There's people who study the motor 00:04:23.460 --> 00:04:25.200 system that are kind of picky. 00:04:25.200 --> 00:04:28.375 So and even if they are, and don't let you into the field, 00:04:28.375 --> 00:04:29.250 it's important field. 00:04:29.250 --> 00:04:31.890 I think you should push your way in. 00:04:31.890 --> 00:04:34.500 I think it's extremely important for navigation. 00:04:34.500 --> 00:04:38.820 Most animals live in a widely extended space. 00:04:38.820 --> 00:04:40.710 They know how to go home quickly if they're 00:04:40.710 --> 00:04:42.090 trouble and things like that. 00:04:42.090 --> 00:04:44.610 I'll talk a little bit about some kind of animals like that. 00:04:44.610 --> 00:04:49.100 The range of area that animals go over is astonishing. 00:04:49.100 --> 00:04:52.110 And so I think the understanding of animals in their natural 00:04:52.110 --> 00:04:54.710 environment, there's lots of work on it, 00:04:54.710 --> 00:04:57.960 but I think it's-- a lot of it's quite mysterious. 00:04:57.960 --> 00:05:00.600 But I'm just going to talk mostly about social perception. 00:05:00.600 --> 00:05:03.366 And this is the paper I read a long time ago. 00:05:03.366 --> 00:05:04.240 I never met this guy. 00:05:04.240 --> 00:05:05.790 His name is Nick Humphrey. 00:05:05.790 --> 00:05:09.365 And he wrote this paper, I don't know, maybe 30 years ago, 00:05:09.365 --> 00:05:11.610 and I just happened to come upon it. 00:05:11.610 --> 00:05:14.430 And it was in a book. 00:05:14.430 --> 00:05:16.510 And he said-- he was not very nice. 00:05:16.510 --> 00:05:18.840 He said, experimental psychology in Britain 00:05:18.840 --> 00:05:21.750 have tended to regard social psychology as a poor country 00:05:21.750 --> 00:05:23.670 cousin of their subject. 00:05:23.670 --> 00:05:25.740 So basically, he was putting down-- 00:05:25.740 --> 00:05:27.839 but he really wasn't because he really 00:05:27.839 --> 00:05:28.880 said something different. 00:05:28.880 --> 00:05:30.830 He said, wait a second. 00:05:30.830 --> 00:05:33.615 And he sort of turned psychology upside down. 00:05:33.615 --> 00:05:37.410 And he said, you know, actually, the social part of intelligence 00:05:37.410 --> 00:05:39.120 is the thing that drives everything else. 00:05:39.120 --> 00:05:40.578 I mean, he might be overstating it, 00:05:40.578 --> 00:05:41.960 but let's see what he says, here. 00:05:41.960 --> 00:05:44.140 Oh, I'll come back to that in a minute. 00:05:44.140 --> 00:05:45.780 The intellectual faculties of primates 00:05:45.780 --> 00:05:49.080 have evolved as an adaptation of complexity of social living. 00:05:49.080 --> 00:05:51.000 For better or worse, styles of thinking which 00:05:51.000 --> 00:05:53.450 are primarily suited to social problem- solving 00:05:53.450 --> 00:05:55.380 color the behavior of man and other primates 00:05:55.380 --> 00:05:57.005 even towards the inanimate world. 00:05:57.005 --> 00:05:59.130 So he's sort of making the implication when I said, 00:05:59.130 --> 00:06:00.924 where does intelligence come from? 00:06:00.924 --> 00:06:02.340 One of the sources of intelligence 00:06:02.340 --> 00:06:04.980 are the nature of the social world. 00:06:04.980 --> 00:06:07.150 And it's a quite a complicated world here, 00:06:07.150 --> 00:06:08.420 because this guy, Dunbar. 00:06:08.420 --> 00:06:09.590 You've heard of this guy. 00:06:09.590 --> 00:06:11.970 He sort of-- I'm not sure this is a really good study, 00:06:11.970 --> 00:06:14.220 but what he did was he made-- 00:06:14.220 --> 00:06:16.679 he got the size of the cortex of lots of different primates 00:06:16.679 --> 00:06:18.095 and he made some kind of estimate. 00:06:18.095 --> 00:06:19.530 I have no idea how he did this. 00:06:19.530 --> 00:06:21.912 How many animals were in their troop. 00:06:21.912 --> 00:06:23.370 But you can sort of imagine, if you 00:06:23.370 --> 00:06:25.890 have a different social group, it's not just-- 00:06:25.890 --> 00:06:28.590 it doesn't just go up by N. Because let's say, you know, 00:06:28.590 --> 00:06:30.822 it's not just how many people you have to deal with. 00:06:30.822 --> 00:06:32.280 Those people deal with other people 00:06:32.280 --> 00:06:34.060 and they might be plotting against you. 00:06:34.060 --> 00:06:37.840 So in other words, the number of permutations of social behavior 00:06:37.840 --> 00:06:38.340 really-- 00:06:38.340 --> 00:06:39.830 I mean, it's only one other person, 00:06:39.830 --> 00:06:40.910 it's not too complicated. 00:06:40.910 --> 00:06:42.840 As you get like six, two people might 00:06:42.840 --> 00:06:45.300 be plotting against the third one over there. 00:06:45.300 --> 00:06:47.390 All kinds of possible things happen. 00:06:47.390 --> 00:06:50.070 And as some of the primatologist, 00:06:50.070 --> 00:06:51.590 if you read some of the accounts, 00:06:51.590 --> 00:06:55.530 there's planned murders of all kinds of people in the top. 00:06:55.530 --> 00:06:59.580 It really looks a little bit like the Medici's back 00:06:59.580 --> 00:07:01.500 in the Machiavellian intelligence. 00:07:01.500 --> 00:07:02.711 Doesn't sound so weird. 00:07:02.711 --> 00:07:03.210 OK. 00:07:03.210 --> 00:07:06.600 So basically, the idea is that intelligence-- 00:07:06.600 --> 00:07:09.840 we have to think of intelligence in the social world. 00:07:09.840 --> 00:07:11.470 I'm just scratching the surface here. 00:07:11.470 --> 00:07:14.740 So let me just talk a little bit about prediction. 00:07:14.740 --> 00:07:17.820 That's kind of what we think one of the hallmarks of science 00:07:17.820 --> 00:07:19.260 is, prediction, right? 00:07:19.260 --> 00:07:23.120 So what are-- in what area is prediction better? 00:07:23.120 --> 00:07:24.840 Any thoughts. 00:07:24.840 --> 00:07:29.416 Suppose I took a rock and threw it down the mountainside. 00:07:29.416 --> 00:07:30.540 I couldn't do squat, right? 00:07:30.540 --> 00:07:31.040 I mean-- 00:07:31.040 --> 00:07:33.402 AUDIENCE: Well, you could do all the measuring, right? 00:07:33.402 --> 00:07:35.370 PROFESSOR: Yeah, but nobody's going to do that, right? 00:07:35.370 --> 00:07:36.745 AUDIENCE: That's what I'm saying. 00:07:36.745 --> 00:07:38.490 PROFESSOR: OK. 00:07:38.490 --> 00:07:42.490 Biology, I mean, we know-- for example, 00:07:42.490 --> 00:07:46.242 we know that behavior of atoms, we can't really 00:07:46.242 --> 00:07:48.450 predict the behavior of atoms over long trajectories, 00:07:48.450 --> 00:07:51.640 but we can show that, you know, at a certain date, 00:07:51.640 --> 00:07:55.180 maybe next year, at Woods Hall, people from all over the world, 00:07:55.180 --> 00:07:58.110 including Nancy, are going to show up at 9 o'clock, here. 00:07:58.110 --> 00:07:59.010 We can predict that. 00:08:01.886 --> 00:08:06.250 And we can predict things like my alarm clock 00:08:06.250 --> 00:08:09.810 is going to go off tomorrow at a certain time because I set it. 00:08:09.810 --> 00:08:12.800 So what I'm trying to talk about is 00:08:12.800 --> 00:08:16.680 kind of a point that Dan Dennett made, who is a philosopher. 00:08:16.680 --> 00:08:20.580 Who, I think, has really made really seminal contributions, 00:08:20.580 --> 00:08:23.730 at least to my understanding of psychology to a large extent 00:08:23.730 --> 00:08:26.167 because he really talks about explaining things 00:08:26.167 --> 00:08:27.000 at different levels. 00:08:29.590 --> 00:08:31.800 This is another-- 00:08:31.800 --> 00:08:33.960 I started out as a reductionist in a sense, 00:08:33.960 --> 00:08:38.880 but let me just give you, give you a sort of a thought, here. 00:08:38.880 --> 00:08:41.039 Have you ever noticed that when the moon is 00:08:41.039 --> 00:08:44.460 setting over the Western sky, it's the new moon. 00:08:44.460 --> 00:08:46.729 It sometimes seems kind of the whole moon 00:08:46.729 --> 00:08:48.270 seems to be illuminated a little bit. 00:08:48.270 --> 00:08:49.853 It's not, it's not just that crescent. 00:08:49.853 --> 00:08:53.311 If you think of the, if you think of the sun, 00:08:53.311 --> 00:08:54.810 it should only be a little crescent. 00:08:54.810 --> 00:08:58.110 So why do you see the rest of the moon filled in? 00:08:58.110 --> 00:09:01.140 Well, there's the possibility that the sun reflecting off 00:09:01.140 --> 00:09:04.230 the earth, which then sends light back to the moon. 00:09:04.230 --> 00:09:05.220 That's an explanation. 00:09:05.220 --> 00:09:07.050 Do we need to know about photons? 00:09:07.050 --> 00:09:09.581 Do we need to know about duality of waves and particles? 00:09:09.581 --> 00:09:10.080 No. 00:09:10.080 --> 00:09:11.010 That's an explanation. 00:09:11.010 --> 00:09:12.330 That's all I'm trying to say. 00:09:12.330 --> 00:09:14.580 Some explanations are pretty darn simple. 00:09:14.580 --> 00:09:16.800 And in science, I think we're looking 00:09:16.800 --> 00:09:19.870 for any fun, interesting explanation we can find. 00:09:19.870 --> 00:09:23.090 So I think Dennett has nailed it to some extent. 00:09:23.090 --> 00:09:23.590 OK. 00:09:23.590 --> 00:09:30.660 So he basically has three levels for predicting behavior. 00:09:30.660 --> 00:09:31.770 The physical stance. 00:09:31.770 --> 00:09:34.050 I mean, if we have ideal situation of Galileo dropping 00:09:34.050 --> 00:09:35.550 his balls as a flight that, assuming 00:09:35.550 --> 00:09:37.260 there's no resistance to air and stuff 00:09:37.260 --> 00:09:40.320 like that, we can make nice predictions. 00:09:40.320 --> 00:09:43.162 Biology and engineering, we know that you know, seven minutes, 00:09:43.162 --> 00:09:45.120 the coffee is going to be ready, because that's 00:09:45.120 --> 00:09:47.170 what it was designed for. 00:09:47.170 --> 00:09:50.020 But then he's got something called intentional stance, 00:09:50.020 --> 00:09:53.490 which is in order to really understand what people are 00:09:53.490 --> 00:09:55.432 going to do, Churchland would say, 00:09:55.432 --> 00:09:57.390 well, we go in to look at their nervous system. 00:09:57.390 --> 00:09:59.440 We record from their neurons and stuff like that. 00:09:59.440 --> 00:10:01.190 But Dennett would say, no, we can't really 00:10:01.190 --> 00:10:03.240 use the physics thing or even design, we just use 00:10:03.240 --> 00:10:06.900 some other heuristic, which is very explanatory, 00:10:06.900 --> 00:10:08.820 what people desire and what their beliefs are. 00:10:08.820 --> 00:10:11.289 If we know that, we really can understand 00:10:11.289 --> 00:10:12.330 what they're going to do. 00:10:12.330 --> 00:10:15.420 And of course, your grandma knew that or your great grandmother 00:10:15.420 --> 00:10:16.290 knew that as well. 00:10:16.290 --> 00:10:19.690 So but these are really great ways to understand people. 00:10:19.690 --> 00:10:21.660 And so I think that we can't just 00:10:21.660 --> 00:10:23.310 turn our backs to these things. 00:10:23.310 --> 00:10:24.555 And I think Dennett-- 00:10:24.555 --> 00:10:26.680 the question is, and we might come back to the end. 00:10:26.680 --> 00:10:30.632 What's the sort of scientific sort 00:10:30.632 --> 00:10:33.397 of status of some those types of explanations. 00:10:33.397 --> 00:10:34.230 Are they scientific? 00:10:34.230 --> 00:10:34.938 Or what are they? 00:10:34.938 --> 00:10:37.420 Maybe if we have time, we can talk about that. 00:10:37.420 --> 00:10:38.970 OK. 00:10:38.970 --> 00:10:41.910 I'll skip that part, here. 00:10:41.910 --> 00:10:43.920 So I sort of feel, just as-- what 00:10:43.920 --> 00:10:46.620 I'm saying is it's a non- reductionistic way of looking 00:10:46.620 --> 00:10:48.161 at thing-- you should open your mind. 00:10:48.161 --> 00:10:51.140 And actually, CBMM or brains, minds, and machines 00:10:51.140 --> 00:10:53.010 is kind of your kind of condition to that 00:10:53.010 --> 00:10:54.968 because some people come from computer science. 00:10:54.968 --> 00:10:57.030 I mean, you can't really understand a computer 00:10:57.030 --> 00:10:58.530 in terms of atoms and molecules. 00:10:58.530 --> 00:11:00.321 You really can only understand the computer 00:11:00.321 --> 00:11:02.070 at some kind of a higher level. 00:11:02.070 --> 00:11:04.800 So I'm just telling you, that's fine. 00:11:04.800 --> 00:11:07.710 If you're a neuroscientist and want to reduce it to synapses, 00:11:07.710 --> 00:11:10.950 things like at, good try, but it might be difficult to do. 00:11:10.950 --> 00:11:14.212 There are many other valid things to do. 00:11:14.212 --> 00:11:15.420 But one of the things to do-- 00:11:15.420 --> 00:11:18.600 let's just examine some things about them and explore them. 00:11:18.600 --> 00:11:21.390 I think, as I say, there's so many possible things 00:11:21.390 --> 00:11:22.350 in this realm. 00:11:22.350 --> 00:11:25.330 Doing anything, I mean, people have all kinds of principles. 00:11:25.330 --> 00:11:29.280 My approach in the area, this kind of area of science, 00:11:29.280 --> 00:11:32.270 or any science that I've done, is to develop new tools, 00:11:32.270 --> 00:11:34.690 and then helps you explore. 00:11:34.690 --> 00:11:35.190 OK. 00:11:35.190 --> 00:11:38.310 So there's lots of things that humans do. 00:11:38.310 --> 00:11:40.950 But what I want to stress in the first part of this talk 00:11:40.950 --> 00:11:45.030 is that our human social behavior is really not 00:11:45.030 --> 00:11:46.110 unique in many ways. 00:11:46.110 --> 00:11:48.068 There are very, there are very important things 00:11:48.068 --> 00:11:49.415 that animals do. 00:11:49.415 --> 00:11:51.390 And I'll talk about them shortly. 00:11:51.390 --> 00:11:53.650 Animals, really are very social. 00:11:53.650 --> 00:11:55.530 There are certain animals like octopus, 00:11:55.530 --> 00:11:58.230 apparently is not very intelligent, not very social, 00:11:58.230 --> 00:12:00.610 but by and large many animals are very social. 00:12:00.610 --> 00:12:02.580 Doesn't explain everything but it's-- 00:12:02.580 --> 00:12:05.140 I think we share some core things. 00:12:05.140 --> 00:12:08.010 And if we can understand some of the behavior of animals 00:12:08.010 --> 00:12:11.490 or why they're social and how their social mechanisms work, 00:12:11.490 --> 00:12:13.170 I think we have a treasure. 00:12:13.170 --> 00:12:16.920 So the area of animal behavior is very underfunded area 00:12:16.920 --> 00:12:17.544 right now. 00:12:17.544 --> 00:12:18.210 I sort of feel-- 00:12:18.210 --> 00:12:20.490 I just happen to like to watch nature videos. 00:12:20.490 --> 00:12:21.990 And I'll show you a few, you'll have 00:12:21.990 --> 00:12:23.610 to indulge me a few of these. 00:12:23.610 --> 00:12:25.530 But I sort of feel these can be more 00:12:25.530 --> 00:12:27.690 important than careful psychophysics 00:12:27.690 --> 00:12:29.790 because I think they open your mind to things 00:12:29.790 --> 00:12:31.630 that you may not have thought about. 00:12:31.630 --> 00:12:36.071 So I'm a kind of devoté of these things. 00:12:36.071 --> 00:12:36.570 OK. 00:12:36.570 --> 00:12:40.197 So do animals have an intentional stance, etc., 00:12:40.197 --> 00:12:41.280 kinds of things like that? 00:12:41.280 --> 00:12:42.580 We don't know. 00:12:42.580 --> 00:12:46.255 Here's a fun video I think maybe half of the audience 00:12:46.255 --> 00:12:47.130 have seen this video. 00:12:47.130 --> 00:12:49.230 And maybe I'll show the whole thing 00:12:49.230 --> 00:12:50.790 because I think we have time. 00:12:50.790 --> 00:12:52.330 It was taken just by chance. 00:12:52.330 --> 00:12:54.300 I think it's been a TV program now. 00:12:54.300 --> 00:12:56.820 And I think it's got like 50 million viewers. 00:12:56.820 --> 00:12:59.136 It's about different kinds of animals interacting, 00:12:59.136 --> 00:13:00.430 their social behavior. 00:13:00.430 --> 00:13:03.720 It's about animals that you do not think are that smart 00:13:03.720 --> 00:13:04.900 and things like that. 00:13:04.900 --> 00:13:05.579 Ungulates. 00:13:05.579 --> 00:13:06.120 I don't know. 00:13:06.120 --> 00:13:07.786 I don't know what buffaloes are exactly, 00:13:07.786 --> 00:13:10.980 but we've never thought of them as highly 00:13:10.980 --> 00:13:12.690 intelligent or social. 00:13:24.765 --> 00:13:27.800 AUDIENCE: That is a huge buffalo. 00:13:27.800 --> 00:13:29.285 AUDIENCE: That's a huge buffalo. 00:13:29.285 --> 00:13:30.720 AUDIENCE: That's a huge buffalo. 00:13:37.090 --> 00:13:39.540 AUDIENCE: I love this. 00:13:39.540 --> 00:13:41.010 AUDIENCE: Is it little? 00:13:46.504 --> 00:13:47.670 AUDIENCE: They're crouching. 00:13:47.670 --> 00:13:48.966 AUDIENCE: She's crouching. 00:14:09.798 --> 00:14:11.286 AUDIENCE: She's going to get eaten. 00:14:25.174 --> 00:14:27.158 AUDIENCE: Oh, my god. 00:14:27.158 --> 00:14:29.138 AUDIENCE: Oh, my god. 00:14:29.138 --> 00:14:29.638 Oh, my god. 00:14:29.638 --> 00:14:33.110 [INAUDIBLE] Oh, she's going for him, she's going for him, 00:14:33.110 --> 00:14:34.598 she's going for him. 00:14:34.598 --> 00:14:36.086 She got him. 00:14:36.086 --> 00:14:37.030 AUDIENCE: Jeez. 00:14:37.030 --> 00:14:37.946 AUDIENCE: Oh, she did. 00:14:37.946 --> 00:14:41.046 She got him. 00:14:41.046 --> 00:14:41.790 AUDIENCE: Ladies. 00:14:41.790 --> 00:14:42.706 AUDIENCE: [INAUDIBLE]. 00:14:42.706 --> 00:14:44.330 AUDIENCE: No, the lions have won. 00:14:44.330 --> 00:14:47.324 AUDIENCE: Yeah, but look at all those buffalo. 00:14:53.312 --> 00:14:57.304 AUDIENCE: --Buffalo down. 00:14:57.304 --> 00:14:59.799 AUDIENCE: They're like, go and try chase the lion, 00:14:59.799 --> 00:15:01.795 but I think they're too late. 00:15:05.787 --> 00:15:08.282 AUDIENCE: They're going to chase [INAUDIBLE].. 00:15:12.274 --> 00:15:14.270 AUDIENCE: Look at the teeth, Jay. 00:15:17.264 --> 00:15:19.260 AUDIENCE: You're too late. 00:15:19.260 --> 00:15:20.757 You're too late. 00:15:28.741 --> 00:15:31.170 AUDIENCE: I think because he cannot believe what's going 00:15:31.170 --> 00:15:31.669 here. 00:15:31.669 --> 00:15:33.750 There's a big barrier between lions, crocodiles, 00:15:33.750 --> 00:15:36.200 and buffaloes. 00:15:36.200 --> 00:15:37.670 AUDIENCE: Look at them all. 00:15:40.680 --> 00:15:43.580 AUDIENCE: Whoa. 00:15:43.580 --> 00:15:46.165 AUDIENCE: He swatted at him and kicked at him. 00:15:46.165 --> 00:15:47.482 He's kicking at him, look. 00:15:47.482 --> 00:15:48.958 He's kicking at him. 00:15:54.862 --> 00:15:58.160 AUDIENCE: [INAUDIBLE] I mean, buffaloes is basically 00:15:58.160 --> 00:16:00.785 used to [INAUDIBLE]. 00:16:00.785 --> 00:16:02.338 [INTERPOSING VOICES] 00:16:02.338 --> 00:16:05.006 AUDIENCE: And in deed, [INAUDIBLE].. 00:16:10.352 --> 00:16:14.290 AUDIENCE: Oooh, they got him surrounded. 00:16:14.290 --> 00:16:15.858 AUDIENCE: And that one's-- 00:16:15.858 --> 00:16:18.822 AUDIENCE: Ooh. 00:16:18.822 --> 00:16:22.280 AUDIENCE: Chasing-- go on, go. 00:16:22.280 --> 00:16:26.232 Chasing-- 00:16:28.186 --> 00:16:30.060 AUDIENCE: You got the lion [INAUDIBLE] right. 00:16:30.060 --> 00:16:32.490 AUDIENCE: [INAUDIBLE]. 00:16:32.490 --> 00:16:33.906 Dave, can you get the peace? 00:16:33.906 --> 00:16:35.406 AUDIENCE: The others are doing that. 00:16:35.406 --> 00:16:36.378 [INAUDIBLE] 00:16:36.378 --> 00:16:40.752 [INTERPOSING VOICES] 00:16:40.752 --> 00:16:42.656 AUDIENCE: Never seen that. 00:16:42.656 --> 00:16:43.780 AUDIENCE: I've never seen-- 00:16:43.780 --> 00:16:44.930 AUDIENCE: The calf's still alive. 00:16:44.930 --> 00:16:45.596 AUDIENCE: It is? 00:16:45.596 --> 00:16:46.290 AUDIENCE: Yeah. 00:16:46.290 --> 00:16:48.441 It's trying to get away. 00:16:48.441 --> 00:16:49.420 It's standing up. 00:16:49.420 --> 00:16:50.362 AUDIENCE: It is. 00:16:50.362 --> 00:16:52.717 It's still alive. 00:16:52.717 --> 00:16:54.480 PROFESSOR: There's much more to this. 00:16:54.480 --> 00:16:57.270 There's many other videos about lions and buffalo 00:16:57.270 --> 00:17:02.670 finding a layer of baby lions and the mother is gone. 00:17:02.670 --> 00:17:05.280 And they just kill all the lions in there for revenge, 00:17:05.280 --> 00:17:07.839 or whatever it is, your interpretation, I don't know. 00:17:07.839 --> 00:17:09.720 But anyway, so I'm just trying to tell you. 00:17:09.720 --> 00:17:12.006 I'm not going to show you more, but in the lectures, 00:17:12.006 --> 00:17:13.839 there's going to be a number of videos here. 00:17:13.839 --> 00:17:15.390 I'll show one more I think. 00:17:15.390 --> 00:17:17.936 But I think this is-- 00:17:17.936 --> 00:17:20.190 I don't know, I think you can see this is 00:17:20.190 --> 00:17:22.060 better than an experiment, OK? 00:17:22.060 --> 00:17:25.980 You really get-- to me, it really 00:17:25.980 --> 00:17:29.490 showed me a kind of, a kind of a social organization. 00:17:29.490 --> 00:17:31.940 A kind of-- are these animals conscious? 00:17:31.940 --> 00:17:34.890 All of these kinds of questions here that I think you 00:17:34.890 --> 00:17:36.761 should think about. 00:17:36.761 --> 00:17:37.260 OK. 00:17:37.260 --> 00:17:39.717 So on my website, on the lecture there's 00:17:39.717 --> 00:17:41.800 going to be some other videos that you can look at 00:17:41.800 --> 00:17:42.860 and things like that. 00:17:42.860 --> 00:17:44.100 I won't do this one, here. 00:17:44.100 --> 00:17:45.550 This is a very interesting video. 00:17:45.550 --> 00:17:47.299 Some of you might have seen it as well. 00:17:47.299 --> 00:17:48.840 I don't exactly know what the origin, 00:17:48.840 --> 00:17:50.190 I won't show it here today. 00:17:50.190 --> 00:17:53.480 But it's about a-- 00:17:53.480 --> 00:17:55.560 on the web, it says monkey teases tiger, 00:17:55.560 --> 00:17:57.450 it's a gibbon, or something like that. 00:17:57.450 --> 00:17:59.170 But it's a really amazing show. 00:17:59.170 --> 00:18:00.750 It goes on for about five minutes. 00:18:00.750 --> 00:18:03.840 We're given just swinging around and making 00:18:03.840 --> 00:18:05.062 himself ready for attack. 00:18:05.062 --> 00:18:07.020 He runs along the ground, the guys are chasing, 00:18:07.020 --> 00:18:08.210 it's like playing tag. 00:18:08.210 --> 00:18:10.500 And he runs up a tree and he jumps over 00:18:10.500 --> 00:18:12.580 them and he their tail and he grabs their ears. 00:18:12.580 --> 00:18:14.640 There are two lions, two tigers there. 00:18:14.640 --> 00:18:18.410 And he's just putting his life at risk, but he's doing it. 00:18:18.410 --> 00:18:20.910 Maybe that's that the theory is, maybe that's his territory. 00:18:20.910 --> 00:18:22.660 He doesn't want those people around there. 00:18:22.660 --> 00:18:23.760 But it's pretty amazing. 00:18:23.760 --> 00:18:28.950 So just Google monkey you know, teases tiger. 00:18:28.950 --> 00:18:30.930 You'll see the full video. 00:18:30.930 --> 00:18:33.340 It's a pretty damn amazing video. 00:18:33.340 --> 00:18:36.420 Another video that is fairly interesting and this is just-- 00:18:36.420 --> 00:18:37.110 just shows you. 00:18:37.110 --> 00:18:38.700 This is a guy named Jaak Panksepp. 00:18:38.700 --> 00:18:40.290 I used to be on a study section with this guy. 00:18:40.290 --> 00:18:41.790 I couldn't figure out what this guy's talking about. 00:18:41.790 --> 00:18:43.170 This was many years ago. 00:18:43.170 --> 00:18:47.850 And he's now an expert on laughter in rats. 00:18:47.850 --> 00:18:53.360 And he tickles rats and they follow him around. 00:18:53.360 --> 00:18:55.270 And can you hear them laugh? 00:18:55.270 --> 00:18:56.520 No, you can't hear them laugh. 00:18:56.520 --> 00:19:00.540 So he got this bat detector which takes ultrasonic sounds 00:19:00.540 --> 00:19:04.242 and brings it down to our frequency level. 00:19:04.242 --> 00:19:05.700 And you can hear the rats laughing. 00:19:05.700 --> 00:19:07.120 So try that out as well. 00:19:07.120 --> 00:19:09.490 So you can do that in your spare time. 00:19:09.490 --> 00:19:10.720 So he's got some very-- 00:19:10.720 --> 00:19:11.220 Oh. 00:19:11.220 --> 00:19:13.290 This one is kind of interesting, War. 00:19:13.290 --> 00:19:15.720 One of the interesting things about-- 00:19:15.720 --> 00:19:18.310 We have sort of a whole field called evolutionary psychology. 00:19:18.310 --> 00:19:24.300 And what's the reasons why animals might cooperate a lot? 00:19:24.300 --> 00:19:27.430 That's what we're talking about here. 00:19:27.430 --> 00:19:30.390 These are really-- a lot of these things are up in the air. 00:19:30.390 --> 00:19:33.660 And there's a lot of debates, but one possibility 00:19:33.660 --> 00:19:35.400 is, if animals cooperate, they can 00:19:35.400 --> 00:19:38.100 deal with a rival group of animals who might 00:19:38.100 --> 00:19:39.840 be taking over their territory. 00:19:39.840 --> 00:19:42.671 Animals have private property, you might call it territory. 00:19:42.671 --> 00:19:44.670 They don't have contracts or anything like that, 00:19:44.670 --> 00:19:45.790 but it's pretty close. 00:19:45.790 --> 00:19:48.710 And if they have territory that they have and there's 00:19:48.710 --> 00:19:51.780 another group that has territory, 00:19:51.780 --> 00:19:53.573 there's going to be conflicts. 00:19:53.573 --> 00:19:56.720 And why not call it war? 00:19:56.720 --> 00:20:00.330 And humans have had war for many generations. 00:20:00.330 --> 00:20:03.630 And I just like to show a little bit that-- 00:20:03.630 --> 00:20:06.330 these videos are incredible videos. 00:20:06.330 --> 00:20:08.760 The guy's name is Timothy Clutton- Brock. 00:20:08.760 --> 00:20:11.680 He's a ethologist at the University of Cambridge. 00:20:11.680 --> 00:20:13.750 He studied many different kinds of animals. 00:20:13.750 --> 00:20:15.630 And these meerkats-- there's been a, 00:20:15.630 --> 00:20:17.622 there's actually been a TV show. 00:20:17.622 --> 00:20:19.080 Have you ever watched the BBC show? 00:20:19.080 --> 00:20:19.980 Anybody here? 00:20:19.980 --> 00:20:23.250 It's a BBC show and it's gone for four years. 00:20:23.250 --> 00:20:25.310 I mean, how long did Dynasty go for? 00:20:25.310 --> 00:20:26.910 Or all these ones that you guys watch? 00:20:26.910 --> 00:20:30.010 I mean, four years of this life of these animals here. 00:20:30.010 --> 00:20:32.010 And the really interesting thing about-- they're 00:20:32.010 --> 00:20:36.090 about one foot tall and they run in troops. 00:20:36.090 --> 00:20:39.120 This group of animals is really interesting. 00:20:39.120 --> 00:20:41.310 There's more animals than you think. 00:20:41.310 --> 00:20:44.250 These are female- dominated societies. 00:20:44.250 --> 00:20:46.170 There's a alpha female. 00:20:46.170 --> 00:20:49.530 And she has, she has many children. 00:20:49.530 --> 00:20:51.240 And then her daughters are supposed 00:20:51.240 --> 00:20:54.000 to take care of her kids, not their kids. 00:20:54.000 --> 00:20:56.610 What happens is sometimes the daughter wanders off 00:20:56.610 --> 00:20:58.380 and some Romeo was coming around and they 00:20:58.380 --> 00:20:59.850 had babies and stuff like that. 00:20:59.850 --> 00:21:04.050 That daughter has to really be nice to the mom to stay. 00:21:04.050 --> 00:21:06.210 Usually she's banished and she dies, 00:21:06.210 --> 00:21:08.700 or she joins that other troop. 00:21:08.700 --> 00:21:10.310 There's so many different things. 00:21:10.310 --> 00:21:12.690 And these animals-- you can see these guys, here. 00:21:12.690 --> 00:21:15.900 See the investigators on the BBC show-- 00:21:15.900 --> 00:21:18.750 these animals somehow have been totally inured to humans. 00:21:18.750 --> 00:21:21.510 So they're doing all their thing and people are just walking 00:21:21.510 --> 00:21:23.435 around with their cameras. 00:21:23.435 --> 00:21:25.560 So this is-- you don't have to set up a laboratory, 00:21:25.560 --> 00:21:26.730 you just go out there. 00:21:26.730 --> 00:21:29.550 And you have to go to the Kalahari Desert. 00:21:29.550 --> 00:21:32.790 But I think that this is an incredible research 00:21:32.790 --> 00:21:35.250 enterprise that's been going on for about 10 years. 00:21:38.280 --> 00:21:39.950 One of the things is they-- 00:21:39.950 --> 00:21:42.720 I think, in many ways, it's one of the most studied 00:21:42.720 --> 00:21:46.470 social animals because the Kalahari Desert is just open. 00:21:46.470 --> 00:21:47.940 You can see everything. 00:21:47.940 --> 00:21:52.410 One of the things they do is they dig down into burrows. 00:21:52.410 --> 00:21:55.992 And so when they're digging way down-- they can eat scorpions. 00:21:55.992 --> 00:21:57.700 They're not, they're not bothered if they 00:21:57.700 --> 00:21:59.340 get stung by a scorpion. 00:21:59.340 --> 00:22:01.634 But if they're down there and there's hawks going over, 00:22:01.634 --> 00:22:02.800 they can just pick them off. 00:22:02.800 --> 00:22:04.910 So they have these sentries and things like that. 00:22:04.910 --> 00:22:08.340 So they sort of double up-- and then they have babysitting 00:22:08.340 --> 00:22:10.140 co-ops where they're teenagers that-- 00:22:10.140 --> 00:22:11.640 they have these burrows underground. 00:22:11.640 --> 00:22:14.880 But of course you can't-- if you're growing up as a meerkat. 00:22:14.880 --> 00:22:17.340 By the way, meerkats have nothing to do with cats, 00:22:17.340 --> 00:22:20.890 but they're somehow related to mongoose. 00:22:20.890 --> 00:22:22.860 They're sort of a little more in that category. 00:22:22.860 --> 00:22:25.309 But anyway, they have a baby sitting kind of thing 00:22:25.309 --> 00:22:26.850 where they let the kids out on there. 00:22:26.850 --> 00:22:29.391 They play a little bit and if it gets strange, they run back. 00:22:29.391 --> 00:22:32.160 And so there's a whole babysitting thing 00:22:32.160 --> 00:22:34.740 because every day they have to go out and forage for food. 00:22:34.740 --> 00:22:36.630 And they go very long distances. 00:22:36.630 --> 00:22:39.390 The territory of these meerkats could be 00:22:39.390 --> 00:22:41.370 as much as a square millimeter. 00:22:41.370 --> 00:22:42.030 That's amazing. 00:22:42.030 --> 00:22:43.613 Just think of these animals, this big, 00:22:43.613 --> 00:22:46.170 going over a square millimeter, a kilometer. 00:22:46.170 --> 00:22:48.070 That's territory. 00:22:48.070 --> 00:22:50.734 So what we have, of course, in the human situation-- 00:22:50.734 --> 00:22:52.234 DOCUMENTARY NARRATOR: Moments later, 00:22:52.234 --> 00:22:56.532 the rest of the whiskers return from foraging. 00:22:56.532 --> 00:22:58.030 From the brow of the hill, they see 00:22:58.030 --> 00:23:01.372 that their home has been overrun by the [INAUDIBLE].. 00:23:08.190 --> 00:23:14.060 The rival group spot the owners of the burrow 00:23:14.060 --> 00:23:20.232 and the angry whiskers waste no time in commencing the attack. 00:23:20.232 --> 00:23:22.128 [INTERPOSING VOICES] 00:23:22.128 --> 00:23:24.450 DOCUMENTARY NARRATOR: --Straight towards the whiskers. 00:23:24.450 --> 00:23:26.700 They're going to defend this piece of territory 00:23:26.700 --> 00:23:28.305 as if it's their own. 00:23:28.305 --> 00:23:31.210 A bloody fight is inevitable. 00:23:31.210 --> 00:23:35.114 One of these sworn enemies will have to be in [INAUDIBLE].. 00:23:58.520 --> 00:24:02.015 The [INAUDIBLE] a hasty retreat. 00:24:02.015 --> 00:24:04.010 PROFESSOR: The good guys won. 00:24:04.010 --> 00:24:06.545 What I'm going to tell you is a pale sort 00:24:06.545 --> 00:24:09.020 of version of all that stuff but I 00:24:09.020 --> 00:24:11.370 think we have to study stuff in the laboratory. 00:24:11.370 --> 00:24:14.030 So a lot of my interest comes from the work 00:24:14.030 --> 00:24:16.880 of Nalini Ambady who is a colleague of mine, 00:24:16.880 --> 00:24:19.340 long ago in psychology department. 00:24:19.340 --> 00:24:21.230 And so what-- we're going to go-- 00:24:21.230 --> 00:24:23.687 I'm going to talk about humans now because that's 00:24:23.687 --> 00:24:24.770 what I've been able to do. 00:24:24.770 --> 00:24:27.320 I'm not-- But I do think that we really 00:24:27.320 --> 00:24:30.182 need to look at all different parts of the animal kingdom. 00:24:30.182 --> 00:24:31.640 But she did something that's really 00:24:31.640 --> 00:24:34.249 quite interesting as a social psychologist. 00:24:34.249 --> 00:24:36.790 Mostly people have been handing out questionnaires and people 00:24:36.790 --> 00:24:38.331 how-- what do you think about things? 00:24:38.331 --> 00:24:42.290 Social psychology has mostly been about attitudes 00:24:42.290 --> 00:24:45.350 and had a rightful good history about prejudice. 00:24:45.350 --> 00:24:47.060 And how people who have-- 00:24:47.060 --> 00:24:49.476 a lot of the social psychology came out of the-- 00:24:49.476 --> 00:24:52.960 was stimulated by what happened in the Second World War, 00:24:52.960 --> 00:24:55.249 the atrocities and things like that. 00:24:55.249 --> 00:24:56.790 How could people do things like that? 00:24:56.790 --> 00:24:58.498 And so those are really important things. 00:24:58.498 --> 00:24:59.570 And you read about them. 00:24:59.570 --> 00:25:00.986 But she did something sort of more 00:25:00.986 --> 00:25:03.890 at an everyday level in a sense, and just take 00:25:03.890 --> 00:25:07.880 pictures, very few pictures of different people. 00:25:07.880 --> 00:25:09.970 And just see what people get from these pictures. 00:25:09.970 --> 00:25:11.510 So clip, movie clips. 00:25:11.510 --> 00:25:13.490 And basically, she did one thing and just 00:25:13.490 --> 00:25:17.690 had people rate teachers that they-- 00:25:17.690 --> 00:25:18.530 this is at Harvard. 00:25:18.530 --> 00:25:20.870 We had teaching fellows and they were just 00:25:20.870 --> 00:25:23.020 talking to their class and things like that. 00:25:23.020 --> 00:25:25.492 And then students just rated them, what they thought, 00:25:25.492 --> 00:25:27.950 how good a teacher they were, just on different adjectives. 00:25:27.950 --> 00:25:31.250 And that really predicted the student ratings 00:25:31.250 --> 00:25:33.465 of the whole course. 00:25:33.465 --> 00:25:35.977 In other words, 10 seconds was able to predict the ratings 00:25:35.977 --> 00:25:36.810 of the whole course. 00:25:36.810 --> 00:25:41.054 So remember that when you do teaching. 00:25:41.054 --> 00:25:42.220 I'm hopeless, but who knows. 00:25:42.220 --> 00:25:47.060 Anyway-- Basically, here's another thing they did. 00:25:47.060 --> 00:25:47.560 OK. 00:25:47.560 --> 00:25:49.160 There's that. 00:25:49.160 --> 00:25:51.732 Gaydar, you can tell if somebody is gay or straight 00:25:51.732 --> 00:25:52.690 or something like that. 00:25:52.690 --> 00:25:55.760 It only presenting the thing for about 15 millisecond. 00:25:55.760 --> 00:25:58.310 I find that hard to believe. 00:25:58.310 --> 00:26:00.850 If you look at a video, two people talking together, 00:26:00.850 --> 00:26:03.470 you can tell whether they're friends or strangers. 00:26:06.420 --> 00:26:08.630 There was a really interesting one. 00:26:08.630 --> 00:26:12.750 This is really-- outcome is you have an interview or tapes 00:26:12.750 --> 00:26:14.700 between doctors and patients. 00:26:14.700 --> 00:26:17.400 And you can outcome variables like whether that doctor got 00:26:17.400 --> 00:26:18.100 sued or not. 00:26:18.100 --> 00:26:19.970 You can have people rate, have adjectives 00:26:19.970 --> 00:26:21.190 and it's sort of like, a little bit 00:26:21.190 --> 00:26:22.830 like-- you can think of doing machine learning, 00:26:22.830 --> 00:26:24.538 just taking all those adjectives and then 00:26:24.538 --> 00:26:27.410 sort of predict whether that person is going to be sued. 00:26:27.410 --> 00:26:31.170 You can see just by this a kind of a crowd-sourcing way 00:26:31.170 --> 00:26:34.470 of dealing with all kinds of real world, 00:26:34.470 --> 00:26:39.140 social, significant things. 00:26:39.140 --> 00:26:40.290 OK. 00:26:40.290 --> 00:26:42.160 So I've worked a lot on face recognition. 00:26:42.160 --> 00:26:43.910 That's a very important thing in the sense 00:26:43.910 --> 00:26:47.040 that in order to negotiate our social world, if you 00:26:47.040 --> 00:26:49.030 can't do facial recognition, you're in trouble. 00:26:49.030 --> 00:26:52.980 I work a lot of people with prosopagnosic. 00:26:52.980 --> 00:26:53.970 Oliver Sacks. 00:26:53.970 --> 00:26:55.260 May know who Oliver Sacks is. 00:26:55.260 --> 00:26:57.570 He's definitely prosopagnosic. 00:26:57.570 --> 00:27:00.230 And if he's at a party, he's a little bit lost. 00:27:00.230 --> 00:27:04.219 But he's a-- he has so many other charming characteristics 00:27:04.219 --> 00:27:05.010 that he can manage. 00:27:05.010 --> 00:27:06.301 People come up and talk to him. 00:27:06.301 --> 00:27:07.770 But you can imagine, if you're not 00:27:07.770 --> 00:27:10.404 that charming, witty, funny person 00:27:10.404 --> 00:27:11.820 and you don't recognize people who 00:27:11.820 --> 00:27:13.940 you're supposed to recognize, you're in trouble. 00:27:13.940 --> 00:27:17.650 One of the subject I worked with, she's 00:27:17.650 --> 00:27:20.610 a quite a well-known author. 00:27:20.610 --> 00:27:22.830 And she goes on book tours, she's happy, 00:27:22.830 --> 00:27:25.410 and she is she does very, very well. 00:27:25.410 --> 00:27:27.300 She has a problem with face recognition, 00:27:27.300 --> 00:27:29.370 but she can't really recognize a lot 00:27:29.370 --> 00:27:30.660 of the guys in her departure. 00:27:30.660 --> 00:27:32.670 She teaches-- She's an English literature professor. 00:27:32.670 --> 00:27:34.440 There's about four guys in our department. 00:27:34.440 --> 00:27:36.570 She can't really recognize one from another. 00:27:36.570 --> 00:27:38.860 That makes social interactions very difficult. 00:27:38.860 --> 00:27:42.900 So we all are experts at face recognition 00:27:42.900 --> 00:27:45.895 but I've studied something called face blindness. 00:27:45.895 --> 00:27:48.270 But I just want to step back because the thought occurred 00:27:48.270 --> 00:27:48.960 to me. 00:27:48.960 --> 00:27:50.580 This is one thing that's really-- 00:27:50.580 --> 00:27:53.780 we've just published, not myself, but my collaborators, 00:27:53.780 --> 00:27:54.562 we've-- 00:27:54.562 --> 00:27:56.020 well, I'll come back to that later. 00:27:56.020 --> 00:27:59.090 It's called face recognition under early stress. 00:27:59.090 --> 00:28:00.300 I'll come back to that later. 00:28:00.300 --> 00:28:01.241 Remind me. 00:28:01.241 --> 00:28:01.740 OK. 00:28:01.740 --> 00:28:04.530 So over the last 100 years or so, there's 00:28:04.530 --> 00:28:07.440 something called acquired prosopagnosia which 00:28:07.440 --> 00:28:11.160 means that, I think, the best case was, I think, 00:28:11.160 --> 00:28:13.770 World War II victims, the German soldiers 00:28:13.770 --> 00:28:15.060 in the Second World War. 00:28:15.060 --> 00:28:19.830 And those people, you could tell they had face recognition 00:28:19.830 --> 00:28:24.120 problems because of course, they were able to recognize faces, 00:28:24.120 --> 00:28:26.190 and later, they weren't able to do it. 00:28:26.190 --> 00:28:28.620 And so-- but more recently, and I've 00:28:28.620 --> 00:28:31.200 studied hundreds of these people, 00:28:31.200 --> 00:28:33.900 and we've actually signed up thousands of them. 00:28:33.900 --> 00:28:36.300 People-- we just we put a website out 00:28:36.300 --> 00:28:39.360 and we basically have studied huge numbers of people 00:28:39.360 --> 00:28:42.720 who are just naturally occurring people who can't recognize 00:28:42.720 --> 00:28:44.380 faces very well at all. 00:28:44.380 --> 00:28:46.980 And we've got about 6,000 registrants. 00:28:46.980 --> 00:28:49.247 We really don't test them that much now, 00:28:49.247 --> 00:28:50.580 but we can test them on the web. 00:28:50.580 --> 00:28:53.770 As Nancy mentioned, we do that kind of thing. 00:28:53.770 --> 00:28:56.490 Just some testimonials from these people, one or two, 00:28:56.490 --> 00:28:58.830 just shows the problem they have. 00:28:58.830 --> 00:29:01.540 This week, I went to the wrong baby at my son's daycare 00:29:01.540 --> 00:29:04.260 and only realized he was not my son, blah, blah, blah. 00:29:04.260 --> 00:29:07.980 So in an audience like this, you don't 00:29:07.980 --> 00:29:10.470 have to identify yourself since you got so many people who 00:29:10.470 --> 00:29:11.390 are STEM people. 00:29:11.390 --> 00:29:13.680 STEM people sometimes have these problems. 00:29:13.680 --> 00:29:16.020 Usually if I give a lecture to an audience like that, 00:29:16.020 --> 00:29:18.240 somebody will come up and say that's me. 00:29:18.240 --> 00:29:22.560 So this is a lady I know quite well. 00:29:22.560 --> 00:29:25.390 She's got a PhD in differential geometry. 00:29:25.390 --> 00:29:26.620 She's a mathematician. 00:29:26.620 --> 00:29:28.080 She has lots of problems. 00:29:28.080 --> 00:29:29.910 She is one of the few [INAUDIBLE] 00:29:29.910 --> 00:29:33.360 that I think is kind of mildly on the spectrum. 00:29:33.360 --> 00:29:35.850 But most of the people I've studied are not autistic 00:29:35.850 --> 00:29:39.270 or Asperger's in any way. 00:29:39.270 --> 00:29:41.190 But a lot of people have problem. 00:29:41.190 --> 00:29:42.635 I claim it's a visual problem. 00:29:42.635 --> 00:29:44.760 It's not a psychiatric problem or a social problem, 00:29:44.760 --> 00:29:47.930 but it does lead to social problems. 00:29:47.930 --> 00:29:50.270 And with Brad Duchaine, I've studied 00:29:50.270 --> 00:29:52.050 these people quite extensively. 00:29:52.050 --> 00:29:52.989 And the thing that-- 00:29:52.989 --> 00:29:54.780 I just want to say, in order to study them, 00:29:54.780 --> 00:29:57.450 we developed a face recognition test. 00:29:57.450 --> 00:30:00.030 I got into the testing business in this way 00:30:00.030 --> 00:30:02.640 because the tests that were out there weren't very good. 00:30:02.640 --> 00:30:03.889 They were really bad. 00:30:03.889 --> 00:30:04.680 I mean, they were-- 00:30:04.680 --> 00:30:06.120 I won't go into how bad they were. 00:30:06.120 --> 00:30:08.460 So we spent three or four years just making up 00:30:08.460 --> 00:30:12.420 a test, which is now used widely around the world. 00:30:12.420 --> 00:30:15.300 And I don't want to go into the details of the test 00:30:15.300 --> 00:30:17.235 but it basically enables people to sort 00:30:17.235 --> 00:30:18.780 of over-learn about six faces. 00:30:18.780 --> 00:30:20.890 It's a little bit like natural face recognition. 00:30:20.890 --> 00:30:23.130 It's not-- I think it's a very good test. 00:30:23.130 --> 00:30:25.380 I won't go into why and things like that. 00:30:25.380 --> 00:30:27.300 And it gets harder and harder. 00:30:27.300 --> 00:30:29.500 But I just want to say that we've 00:30:29.500 --> 00:30:32.240 been able to characterize these prosopagnosia people. 00:30:32.240 --> 00:30:34.257 But somebody all of a sudden shows up and says, 00:30:34.257 --> 00:30:35.340 well, I'm not one of them. 00:30:35.340 --> 00:30:36.420 I'm super. 00:30:36.420 --> 00:30:38.160 So we've study those people as well. 00:30:38.160 --> 00:30:39.150 I won't go into detail. 00:30:39.150 --> 00:30:43.090 We made a lot of studies about them. 00:30:43.090 --> 00:30:45.300 Just a couple of testimonials. 00:30:45.300 --> 00:30:46.680 I pretend I don't remember people 00:30:46.680 --> 00:30:48.304 because people think I'm stalking them, 00:30:48.304 --> 00:30:49.360 or something like that. 00:30:49.360 --> 00:30:53.530 So now we've studied half a dozen of them. 00:30:53.530 --> 00:30:56.910 And now we're studying a much larger population of them. 00:30:56.910 --> 00:31:00.270 I would-- All I'd like to say is these people are as 00:31:00.270 --> 00:31:01.937 good as prosopagnosics are bad. 00:31:01.937 --> 00:31:04.270 They're really-- but we haven't found that many of them. 00:31:04.270 --> 00:31:06.640 We've probably identified about 30 of them 00:31:06.640 --> 00:31:07.910 and we've been looking. 00:31:07.910 --> 00:31:10.720 And we give them much harder tests of face recognition. 00:31:10.720 --> 00:31:12.520 Some of you, if you can recognize all four 00:31:12.520 --> 00:31:15.700 of these people before they were famous, I'll come up 00:31:15.700 --> 00:31:18.647 and you might be one of our people, but I doubt it. 00:31:18.647 --> 00:31:20.980 Most people think they're very good at face recognition. 00:31:20.980 --> 00:31:23.360 When we test them, they're not that good. 00:31:23.360 --> 00:31:26.950 So nature versus nurture. 00:31:26.950 --> 00:31:28.810 Make sure I don't go over my time, here. 00:31:32.430 --> 00:31:34.130 You know, most of you are not psychol-- 00:31:34.130 --> 00:31:37.070 how many people are psychologists here? 00:31:37.070 --> 00:31:37.630 OK. 00:31:37.630 --> 00:31:41.580 So you know that there's some experiments 00:31:41.580 --> 00:31:44.910 called twin studies where you study 00:31:44.910 --> 00:31:49.020 some characteristics in monozygotic twins 00:31:49.020 --> 00:31:51.520 and then different, dizygotic, dizygotic twins. 00:31:51.520 --> 00:31:53.734 And so the idea here is that they sort of share 00:31:53.734 --> 00:31:54.900 the same family environment. 00:31:54.900 --> 00:31:56.330 Of course, each person's different, 00:31:56.330 --> 00:31:57.585 and they have a different way of dealing with the family 00:31:57.585 --> 00:31:58.418 and stuff like that. 00:31:58.418 --> 00:32:00.220 But that's the best we can do. 00:32:00.220 --> 00:32:05.310 But if you can show that the correlation between twin 1 00:32:05.310 --> 00:32:08.177 and twin 2 is very, very high, and in our case, 00:32:08.177 --> 00:32:09.510 it's going to be extremely high. 00:32:09.510 --> 00:32:13.200 In fact, it's just as high if twin 1 took the test twice. 00:32:13.200 --> 00:32:15.240 That indicates-- and then if you compare it 00:32:15.240 --> 00:32:19.020 with dizygotic twins, there's a real difference there. 00:32:19.020 --> 00:32:22.110 And that's what we've shown essentially, is that-- 00:32:22.110 --> 00:32:24.460 I'm just saying, the test reliability is very good. 00:32:24.460 --> 00:32:32.940 It's tech has a R of 0.7, which is very good. 00:32:32.940 --> 00:32:34.320 And we had-- 00:32:34.320 --> 00:32:37.680 There's 350 just random people who took the test twice. 00:32:37.680 --> 00:32:41.010 But now what you can do, we have this online way 00:32:41.010 --> 00:32:43.932 of doing research where we can just have people come up and-- 00:32:43.932 --> 00:32:45.390 the real hard thing in twin studies 00:32:45.390 --> 00:32:48.695 is not to get monozygotic twins, it's to get dizygotic twins. 00:32:48.695 --> 00:32:50.820 Dizygotic twins don't really care about being twins 00:32:50.820 --> 00:32:53.860 that much, but monozygotic feel, hey, I'm a twin. 00:32:53.860 --> 00:32:58.140 My wife is a monozygotic twin so I know all about this. 00:32:58.140 --> 00:33:01.440 So we have something called the Austrian twin registry. 00:33:01.440 --> 00:33:04.230 And we pay them and they-- a very nominal fee 00:33:04.230 --> 00:33:05.766 and they do our tests online. 00:33:05.766 --> 00:33:07.140 And what's really interesting, we 00:33:07.140 --> 00:33:09.870 found if you correlate twin 1 and twin 2, 00:33:09.870 --> 00:33:12.040 they have to be obviously the same gender. 00:33:12.040 --> 00:33:15.390 The correlation, you'll see here, is 0.7. 00:33:15.390 --> 00:33:18.120 It's just as good if the twin took the test twice. 00:33:18.120 --> 00:33:19.650 That's pretty darn amazing. 00:33:19.650 --> 00:33:21.630 And the dizygotic is 0.3. 00:33:21.630 --> 00:33:24.170 So in this particular situation, I 00:33:24.170 --> 00:33:26.280 think we've shown to our astonishment 00:33:26.280 --> 00:33:29.730 that the ability of this face-- the ability to learn 00:33:29.730 --> 00:33:34.630 new faces is almost entirely heritable, whatever that means. 00:33:34.630 --> 00:33:36.900 I mean, it's a technical definition, 00:33:36.900 --> 00:33:42.300 but it indicates that your ability to recognize faces 00:33:42.300 --> 00:33:47.240 is strongly controlled by other factors than experience. 00:33:47.240 --> 00:33:49.830 And that's when I want to bring up this childhood adversity 00:33:49.830 --> 00:33:50.820 thing, here. 00:33:50.820 --> 00:33:55.800 Laura Germine has done a very large survey of I think, 00:33:55.800 --> 00:34:00.480 over 1,000 people who were-- 00:34:00.480 --> 00:34:03.240 and these are things-- we had trouble with the IRB here, 00:34:03.240 --> 00:34:06.450 but people talked about all of their horrible things that 00:34:06.450 --> 00:34:10.199 happened to them, childhood sexual abuse, 00:34:10.199 --> 00:34:12.570 neglect by their parents, all kinds 00:34:12.570 --> 00:34:15.719 of things that are really bad when you're growing up. 00:34:15.719 --> 00:34:17.760 And then we did a lot of tests on the web. 00:34:17.760 --> 00:34:22.250 And everything, all our tests showed real deficits 00:34:22.250 --> 00:34:27.150 in cognitive abilities, in even emotion recognition, 00:34:27.150 --> 00:34:30.630 but there was no deficit in ability to learn faces. 00:34:30.630 --> 00:34:32.880 So that's pretty-- so what I would 00:34:32.880 --> 00:34:36.480 like to suggest is that somehow we're sort of topped out. 00:34:36.480 --> 00:34:38.790 We have enough experience seeing faces 00:34:38.790 --> 00:34:40.929 and we've reached our asymptote. 00:34:40.929 --> 00:34:43.199 That's just a point that might be of interest here. 00:34:46.679 --> 00:34:49.199 So another thing we've done recently, 00:34:49.199 --> 00:34:51.900 I don't have any data here, but we just got it paper accepted, 00:34:51.900 --> 00:34:55.469 was to show that using the same sort of paradigm 00:34:55.469 --> 00:34:57.780 of monozygotic and dizygotic twins 00:34:57.780 --> 00:35:02.460 that face attractiveness is not-- 00:35:02.460 --> 00:35:05.560 there's no genetic component at all, basically zero. 00:35:05.560 --> 00:35:09.450 So here's a face attractiveness test that we've cooked up here. 00:35:09.450 --> 00:35:10.502 Here's a bunch of faces. 00:35:10.502 --> 00:35:12.210 You have a bunch of cards, you sort them. 00:35:12.210 --> 00:35:13.680 That's all you do. 00:35:13.680 --> 00:35:16.260 You get the mean ratings for all the faces 00:35:16.260 --> 00:35:17.820 and then you rate the faces. 00:35:17.820 --> 00:35:20.760 And I'm going to say, OK, you're-- 00:35:20.760 --> 00:35:23.490 let's say that upper left- hand corner, 00:35:23.490 --> 00:35:27.190 that's one person's ratings versus the mean ratings. 00:35:27.190 --> 00:35:29.190 That's a correlation of 0.74. 00:35:29.190 --> 00:35:30.970 The next person's 0.91. 00:35:30.970 --> 00:35:34.920 That's really unimaginable to me that, that person 00:35:34.920 --> 00:35:37.287 there's correlation with the mean rating is so high. 00:35:37.287 --> 00:35:38.370 And look at these ratings. 00:35:38.370 --> 00:35:39.600 They're very, very high. 00:35:39.600 --> 00:35:41.912 Of course, these are Harvard undergraduates. 00:35:41.912 --> 00:35:43.620 They all have the same kind of mentality. 00:35:43.620 --> 00:35:45.480 Perhaps that's maybe that's the point. 00:35:45.480 --> 00:35:47.760 But the correlation with the mean, this 00:35:47.760 --> 00:35:49.860 is just from one of my courses. 00:35:49.860 --> 00:35:50.670 It's unbelievable. 00:35:50.670 --> 00:35:53.190 The correlation, the agreement of who's attractive 00:35:53.190 --> 00:35:55.780 and who's not attractive is astonishing. 00:35:55.780 --> 00:36:01.260 So what we did was we had these MZ versus DZ twins 00:36:01.260 --> 00:36:02.640 and had them rate them. 00:36:02.640 --> 00:36:04.320 We have a kind of a jingle. 00:36:04.320 --> 00:36:08.850 We say, how quirky are you when you're a beauty judge, 00:36:08.850 --> 00:36:11.520 or something like that because our web site, 00:36:11.520 --> 00:36:13.230 we don't pay anybody. 00:36:13.230 --> 00:36:16.170 In the twin study, we do, but we like to make the tests fun 00:36:16.170 --> 00:36:17.610 and interesting to the people. 00:36:17.610 --> 00:36:19.290 And we, most of the time, give them 00:36:19.290 --> 00:36:20.850 feedback as to how they done. 00:36:20.850 --> 00:36:22.470 So all I'm trying to tell you there 00:36:22.470 --> 00:36:26.460 is that we don't see any genetic component there at all when 00:36:26.460 --> 00:36:29.870 you do the monozygotic and dizygotic twins, here. 00:36:29.870 --> 00:36:31.270 Gender versus attractiveness. 00:36:31.270 --> 00:36:34.510 Where you give a bunch of pictures to people and you say, 00:36:34.510 --> 00:36:37.420 which looks more feminine, more masculine? 00:36:37.420 --> 00:36:40.240 You do that with girls and then you do it with boys. 00:36:40.240 --> 00:36:42.430 Which was more masculine, feminine? 00:36:42.430 --> 00:36:45.850 Those are very reliable ratings also. 00:36:45.850 --> 00:36:49.480 It's hard to see but female attractiveness correlates very 00:36:49.480 --> 00:36:52.930 well with the gender judgment, male attractiveness doesn't. 00:36:52.930 --> 00:36:55.180 Actually male attractiveness is quite robust. 00:36:55.180 --> 00:36:57.610 People can agree on male attractiveness, 00:36:57.610 --> 00:36:59.540 but it's not the gender axis. 00:36:59.540 --> 00:37:02.820 Maybe you know, 2000 years ago it was machoness or something 00:37:02.820 --> 00:37:03.320 like that. 00:37:03.320 --> 00:37:07.430 But in our society, it's agreed upon but it's a little bit 00:37:07.430 --> 00:37:07.930 mysterious. 00:37:07.930 --> 00:37:10.000 Alex Todorov has published a paper 00:37:10.000 --> 00:37:11.560 on this, which I don't understand. 00:37:11.560 --> 00:37:14.680 But he says he explains it, but I'm not sure. 00:37:14.680 --> 00:37:17.440 But that's an interesting area of what constitutes 00:37:17.440 --> 00:37:19.510 male attractiveness. 00:37:19.510 --> 00:37:23.350 A fellow, who is an MIT student, Richard Russell, 00:37:23.350 --> 00:37:26.470 found one of the things that is related 00:37:26.470 --> 00:37:28.000 to female attractiveness. 00:37:28.000 --> 00:37:30.340 It's basically contrast, facial contrast. 00:37:30.340 --> 00:37:33.340 Not how dark you are, but your facial contrast. 00:37:33.340 --> 00:37:35.200 And he just found that if you just 00:37:35.200 --> 00:37:37.120 measured the contrast of pictures, 00:37:37.120 --> 00:37:39.760 you find the female average contrast is 00:37:39.760 --> 00:37:42.280 higher than the male contrast. 00:37:42.280 --> 00:37:43.840 And if you take a picture like that, 00:37:43.840 --> 00:37:47.790 which is an androgynous person here, and you just change-- 00:37:47.790 --> 00:37:51.280 you have the same picture and you just increase the contrast, 00:37:51.280 --> 00:37:54.640 the one on the left looks more female. 00:37:54.640 --> 00:37:57.070 And maybe that's the reason that Richard thinks that this 00:37:57.070 --> 00:37:58.720 is the basis of some cosmetics. 00:37:58.720 --> 00:38:02.860 And he works with a cosmetic company. 00:38:02.860 --> 00:38:04.311 He's not doing-- 00:38:04.311 --> 00:38:04.810 OK. 00:38:04.810 --> 00:38:07.330 So the final part of this talk, I'd like to talk about 00:38:07.330 --> 00:38:10.590 is that, which I'm more involved in right now. 00:38:10.590 --> 00:38:13.570 Is I sort of feel that most of social psychology 00:38:13.570 --> 00:38:16.000 is involved in kind of asking people 00:38:16.000 --> 00:38:18.619 questions and things like that about sociology. 00:38:18.619 --> 00:38:20.410 But as I said in the beginning of the talk, 00:38:20.410 --> 00:38:22.057 I feel that the realm of human action 00:38:22.057 --> 00:38:23.890 is something that has not been investigated. 00:38:23.890 --> 00:38:26.840 So I just feel it's something that we need to probe. 00:38:26.840 --> 00:38:28.540 And what I would like to argue is 00:38:28.540 --> 00:38:31.150 that by studying our human actions, 00:38:31.150 --> 00:38:33.370 we can actually reveal our social perceptions. 00:38:33.370 --> 00:38:35.060 That's kind of the point here. 00:38:35.060 --> 00:38:39.456 So most of you have been in very crowded places 00:38:39.456 --> 00:38:41.580 and you don't bump into people and stuff like that. 00:38:41.580 --> 00:38:43.070 And there's many reasons for that. 00:38:43.070 --> 00:38:45.580 But basically it's pretty amazing 00:38:45.580 --> 00:38:48.290 that people, pretty much, don't run into each other 00:38:48.290 --> 00:38:51.760 in very crowded and when they're very rushed. 00:38:51.760 --> 00:38:55.450 And there are many situations in which your social interactions 00:38:55.450 --> 00:38:58.900 with people are extremely skilled in a sense, 00:38:58.900 --> 00:39:01.300 in different kinds of dancing or fencing 00:39:01.300 --> 00:39:03.340 or different kinds of athletics. 00:39:03.340 --> 00:39:06.510 So I claim this is the domain of what 00:39:06.510 --> 00:39:11.030 I call rapid social perception. 00:39:11.030 --> 00:39:14.050 So how can we study rapid social perception? 00:39:14.050 --> 00:39:17.732 Well, I'm OK with all these things, 00:39:17.732 --> 00:39:19.190 showing movies and stuff like that. 00:39:19.190 --> 00:39:21.064 But we've sort of put it into the laboratory, 00:39:21.064 --> 00:39:24.800 in a more discrete fashion just because we can study it easily. 00:39:24.800 --> 00:39:26.502 Now one of them is that most of you 00:39:26.502 --> 00:39:28.210 watch the World Cup, is the penalty kick. 00:39:28.210 --> 00:39:30.250 It doesn't happen all the time, but every once in a while, 00:39:30.250 --> 00:39:30.760 it happens. 00:39:30.760 --> 00:39:33.370 And you know, that's a really dramatic moment. 00:39:33.370 --> 00:39:34.890 And of course, you have to predict 00:39:34.890 --> 00:39:38.170 which way the kicker is going to go ahead 00:39:38.170 --> 00:39:40.760 of time if you're the goalie. 00:39:40.760 --> 00:39:43.800 So we've set up something which was sort of parallel to it 00:39:43.800 --> 00:39:46.600 which I call the lab version, here. 00:39:46.600 --> 00:39:48.890 So here's the kicker, here. 00:39:48.890 --> 00:39:54.430 But the job is not to kick the ball into the area, 00:39:54.430 --> 00:39:57.060 but just touch target left or right. 00:39:57.060 --> 00:39:57.880 That's your job. 00:39:57.880 --> 00:39:58.730 That's all you do. 00:39:58.730 --> 00:40:00.020 Go boom, boom. 00:40:00.020 --> 00:40:01.040 That's it. 00:40:01.040 --> 00:40:02.830 That's the trial. 00:40:02.830 --> 00:40:03.850 So it's very simple. 00:40:03.850 --> 00:40:05.560 And we tell people it's a game. 00:40:05.560 --> 00:40:08.740 If you are up to the game-- 00:40:08.740 --> 00:40:11.660 The blocker-- if you get there within 150 mil-- 00:40:11.660 --> 00:40:14.830 we sort of titrate it to match the skill of the two people. 00:40:14.830 --> 00:40:15.550 But it's a game. 00:40:15.550 --> 00:40:17.536 Harvard undergraduates love the game. 00:40:17.536 --> 00:40:18.910 They get tired because a little-- 00:40:18.910 --> 00:40:20.560 but there's no problem. 00:40:20.560 --> 00:40:22.920 Harvard undergraduates don't know each other usually 00:40:22.920 --> 00:40:24.820 so they're not friends or anything like that. 00:40:24.820 --> 00:40:26.750 They just, they just come in there. 00:40:26.750 --> 00:40:29.240 So I think there's a movie here as well. 00:40:29.240 --> 00:40:33.380 Let's see if I can get this going here. 00:40:33.380 --> 00:40:35.760 Oh, here, go this way. 00:40:35.760 --> 00:40:36.380 OK. 00:40:36.380 --> 00:40:37.760 So this is the game, here. 00:40:42.470 --> 00:40:44.719 Is it moving? 00:40:44.719 --> 00:40:45.260 There you go. 00:40:45.260 --> 00:40:47.390 Here. 00:40:47.390 --> 00:40:48.050 That's it. 00:40:48.050 --> 00:40:50.260 That's the game. 00:40:50.260 --> 00:40:52.670 I like pretty simple experiments. 00:40:52.670 --> 00:40:53.250 OK. 00:40:53.250 --> 00:40:56.180 So but then what we do is we just measure the finger 00:40:56.180 --> 00:40:59.940 position of the two players. 00:40:59.940 --> 00:41:01.190 You can just buy these things. 00:41:01.190 --> 00:41:02.290 They are off the shelf. 00:41:02.290 --> 00:41:03.740 They're not very expensive. 00:41:03.740 --> 00:41:06.530 The one I had like you know, a couple of thousand dollars 00:41:06.530 --> 00:41:07.370 these days. 00:41:07.370 --> 00:41:09.020 This is very low tech. 00:41:09.020 --> 00:41:11.000 It measures the position of the fingers 00:41:11.000 --> 00:41:14.810 and at a high precision, very fast, and stuff like that. 00:41:14.810 --> 00:41:16.080 You can buy this thing. 00:41:16.080 --> 00:41:18.480 It takes a while to get it going and stuff like that. 00:41:18.480 --> 00:41:25.580 So basically, we can map the trajectories 00:41:25.580 --> 00:41:26.970 of the various people. 00:41:26.970 --> 00:41:29.135 And you can see that they're well- behaved. 00:41:29.135 --> 00:41:31.040 And just like that. 00:41:31.040 --> 00:41:32.930 So what's interesting is the timing here. 00:41:32.930 --> 00:41:37.170 So the kicker goes like this and the blocker-- 00:41:37.170 --> 00:41:39.180 so you can make little measurements, here. 00:41:39.180 --> 00:41:41.900 And so we can just measure the horizontal positions, here. 00:41:41.900 --> 00:41:45.509 So the blue is the kicker and the red is the blocker. 00:41:45.509 --> 00:41:47.550 And so obviously, they can't do at the same time. 00:41:47.550 --> 00:41:50.340 But the reaction times are interesting. 00:41:50.340 --> 00:41:54.600 What's that difference in the launch point, there. 00:41:54.600 --> 00:42:02.120 And what we found was it was actually very low. 00:42:02.120 --> 00:42:03.710 You can see it there. 00:42:03.710 --> 00:42:08.720 Some were as low as 100 milliseconds but it's 150-- 00:42:08.720 --> 00:42:11.610 it's lower than choice reaction times normally. 00:42:11.610 --> 00:42:13.880 So if you take-- if you could have a button box 00:42:13.880 --> 00:42:16.160 and put a buzzer or something like that, 00:42:16.160 --> 00:42:18.620 choice reaction times are usually 00:42:18.620 --> 00:42:20.390 about 100 milliseconds longer. 00:42:20.390 --> 00:42:23.070 So what's going on here? 00:42:23.070 --> 00:42:27.940 But maybe choice reaction time isn't a really good choice. 00:42:27.940 --> 00:42:29.420 So what we did was we took-- 00:42:29.420 --> 00:42:32.180 we actually can take the position of the finger 00:42:32.180 --> 00:42:33.930 and map it on the screen. 00:42:33.930 --> 00:42:37.250 And we have a little experiment here, where you actually-- 00:42:37.250 --> 00:42:39.090 instead of playing against a person, 00:42:39.090 --> 00:42:41.430 you just, there's a little dot that zooms up there 00:42:41.430 --> 00:42:42.540 and you do that. 00:42:42.540 --> 00:42:45.860 So maybe it's that-- the fact of that. 00:42:45.860 --> 00:42:49.070 So we do this, and that's the experiment. 00:42:49.070 --> 00:42:50.880 And it turns out this experiment, 00:42:50.880 --> 00:42:52.380 we're about 100 milliseconds longer. 00:42:52.380 --> 00:42:54.630 So in other words, if you're playing against the human 00:42:54.630 --> 00:42:57.840 being, that's on average about 100 milliseconds. 00:42:57.840 --> 00:43:00.770 So you are faster when you're dealing with human being. 00:43:03.930 --> 00:43:07.490 I will just skip over some of this stuff, here. 00:43:07.490 --> 00:43:11.210 And again, there's no learning here. 00:43:11.210 --> 00:43:14.270 Basically, it's just, it's flat. 00:43:14.270 --> 00:43:16.340 So what's going on here? 00:43:16.340 --> 00:43:19.370 We think what's happening here, and this is the punch line, 00:43:19.370 --> 00:43:21.050 I'll just elaborate a little bit more. 00:43:21.050 --> 00:43:22.790 You think all through your life you've 00:43:22.790 --> 00:43:25.070 seen people do different kinds of actions. 00:43:25.070 --> 00:43:27.740 And if you do any kind of action, 00:43:27.740 --> 00:43:30.050 there's all kinds of postural adjustments. 00:43:30.050 --> 00:43:32.900 I was just talking to one of the guys in Emilio Bizzi's lab. 00:43:32.900 --> 00:43:35.000 If you're sort of in different kinds of sports, 00:43:35.000 --> 00:43:37.580 if you lunge like that, what's the first muscles 00:43:37.580 --> 00:43:39.900 that contract? 00:43:39.900 --> 00:43:42.600 Anybody want to know? 00:43:42.600 --> 00:43:43.610 Your butt. 00:43:46.310 --> 00:43:50.610 Because that's to maintain your center of gravity. 00:43:50.610 --> 00:43:51.800 So there's all kinds of-- 00:43:51.800 --> 00:43:55.297 I'm not saying people will watch you know, their butts 00:43:55.297 --> 00:43:56.130 and stuff like that. 00:43:56.130 --> 00:43:58.610 But when you make any kind of motion, 00:43:58.610 --> 00:44:00.580 it's your whole body is connected. 00:44:00.580 --> 00:44:04.760 And somehow we have knowledge, implicitly of this. 00:44:04.760 --> 00:44:07.530 So what's going on here? 00:44:07.530 --> 00:44:10.130 We talk about 90 milliseconds or so. 00:44:10.130 --> 00:44:12.260 What section of the body informative? 00:44:12.260 --> 00:44:14.510 Well, in a sense this is kind of-- you just block off 00:44:14.510 --> 00:44:16.051 different pieces and stuff like that. 00:44:16.051 --> 00:44:18.080 And we just, we have all different kinds 00:44:18.080 --> 00:44:19.550 of ways we can just-- 00:44:19.550 --> 00:44:20.220 limited. 00:44:20.220 --> 00:44:22.770 And we do-- we did all kinds of experiments like that. 00:44:22.770 --> 00:44:27.110 And basically, if you show all, there's 00:44:27.110 --> 00:44:29.330 an advantage, just the torso or just the head 00:44:29.330 --> 00:44:32.035 and there's the computer, it's all over the place. 00:44:32.035 --> 00:44:33.410 That the information is all over. 00:44:33.410 --> 00:44:35.694 But that's not unreasonable because when 00:44:35.694 --> 00:44:37.610 you're doing something, all parts of your body 00:44:37.610 --> 00:44:38.510 are connected. 00:44:38.510 --> 00:44:41.780 So there's quite an advantage. 00:44:41.780 --> 00:44:44.801 I think basically, oh, this is your question. 00:44:44.801 --> 00:44:45.800 We're moving this stuff. 00:44:45.800 --> 00:44:48.480 So we have-- we call them cut videos here. 00:44:48.480 --> 00:44:52.380 So what we do is we play a video, here. 00:44:52.380 --> 00:44:54.950 By the way, we now have shown that people 00:44:54.950 --> 00:44:56.720 would behave exactly the same to a video 00:44:56.720 --> 00:44:58.380 than they do to the other person. 00:44:58.380 --> 00:45:00.050 So that makes the life a little simpler. 00:45:00.050 --> 00:45:03.240 So there we go. 00:45:03.240 --> 00:45:06.830 There's-- I don't know if you can tell, but essentially, 00:45:06.830 --> 00:45:09.770 nobody, except a couple of star athletes, 00:45:09.770 --> 00:45:11.880 could tell what it was. 00:45:11.880 --> 00:45:15.060 And it turns out, in the cut videos, in other words, 00:45:15.060 --> 00:45:16.780 you don't have that preparatory stuff. 00:45:16.780 --> 00:45:19.370 You just have all that if the person is still, 00:45:19.370 --> 00:45:21.380 we don't even know what those motions are. 00:45:21.380 --> 00:45:24.660 But basically, they're 100 milliseconds slower 00:45:24.660 --> 00:45:26.720 than the cut videos. 00:45:26.720 --> 00:45:28.445 So another thing is, oh, we even did 00:45:28.445 --> 00:45:30.320 stuff-- we did another series of experiments. 00:45:30.320 --> 00:45:31.794 Well, maybe it's eye movements. 00:45:31.794 --> 00:45:33.710 And basically, we just did some more blocking. 00:45:33.710 --> 00:45:37.140 And shoulders are important, you can see, here. 00:45:37.140 --> 00:45:38.270 Oh, I didn't show that. 00:45:38.270 --> 00:45:39.436 I don't have the data there. 00:45:39.436 --> 00:45:43.770 But basically every-- even the head, there's a little-- 00:45:43.770 --> 00:45:46.270 all of those, the first five, except the last one, 00:45:46.270 --> 00:45:47.460 were very good. 00:45:47.460 --> 00:45:49.914 Even the last one, you can hardly see anything. 00:45:49.914 --> 00:45:51.330 You just see the head then you see 00:45:51.330 --> 00:45:54.734 the eye movement is a little bit of an advantage, even there. 00:46:01.380 --> 00:46:03.780 OK. 00:46:03.780 --> 00:46:08.070 So basically, what I'm trying to say more broadly, 00:46:08.070 --> 00:46:12.330 is that our ability to understand humans probably more 00:46:12.330 --> 00:46:14.400 to predict what they're going to do 00:46:14.400 --> 00:46:18.090 is something that we've learned somewhat unconsciously. 00:46:18.090 --> 00:46:20.340 We're going to do a lot of machine learning of what 00:46:20.340 --> 00:46:21.510 parts of the body it is. 00:46:21.510 --> 00:46:22.885 And all kinds of stuff like that. 00:46:22.885 --> 00:46:24.450 I'm interested in if you people have 00:46:24.450 --> 00:46:26.241 some ideas of what other kinds of things we 00:46:26.241 --> 00:46:28.710 can do to generalize this. 00:46:28.710 --> 00:46:31.140 But I think it's an area where we-- 00:46:31.140 --> 00:46:33.450 it helps us understand all the kind of knowledge 00:46:33.450 --> 00:46:35.880 that we do have of other people that we 00:46:35.880 --> 00:46:42.740 can weed out in ways that are very subtle, but reliable.