1 00:00:00,095 --> 00:00:02,460 The following content is provided under a Creative 2 00:00:02,460 --> 00:00:03,870 Commons license. 3 00:00:03,870 --> 00:00:06,910 Your support will help MIT OpenCourseWare continue to 4 00:00:06,910 --> 00:00:10,560 offer high-quality educational resources for free. 5 00:00:10,560 --> 00:00:13,460 To make a donation or view additional materials from 6 00:00:13,460 --> 00:00:17,390 hundreds of MIT courses, visit MIT OpenCourseWare at 7 00:00:17,390 --> 00:00:18,640 ocw.mit.edu. 8 00:00:28,860 --> 00:00:32,140 PROFESSOR: In a way, I would say most of this course up 9 00:00:32,140 --> 00:00:34,920 until today, or maybe the memory lecture, was about how 10 00:00:34,920 --> 00:00:37,310 brilliant the human mind and brain is. 11 00:00:37,310 --> 00:00:40,520 How the visual system is so brilliant nobody can make a 12 00:00:40,520 --> 00:00:42,910 machine that sees so well. 13 00:00:42,910 --> 00:00:45,260 We didn't talk too much about physical action. 14 00:00:45,260 --> 00:00:47,820 But how we move in the world, how we act upon the world, 15 00:00:47,820 --> 00:00:50,320 there's no robot that can compete with you. 16 00:00:50,320 --> 00:00:53,260 Language is far beyond computers to be nearly as 17 00:00:53,260 --> 00:00:56,410 sophisticated as you are, as a five-year-old is. 18 00:00:56,410 --> 00:01:00,400 So in all those domains, we mostly think about in terms of 19 00:01:00,400 --> 00:01:04,280 psychology or brain function, how is it that we're endowed 20 00:01:04,280 --> 00:01:06,680 with such amazing capacities? 21 00:01:06,680 --> 00:01:09,760 When it comes to thinking, one of the highest forms of human 22 00:01:09,760 --> 00:01:13,200 activity, thinking, hard problem solving, edges of 23 00:01:13,200 --> 00:01:17,120 creativity, that kinds of thing, it's something we have 24 00:01:17,120 --> 00:01:20,690 a huge respect for, something we wish for, something that we 25 00:01:20,690 --> 00:01:22,990 know is a very, very valuable tool. 26 00:01:22,990 --> 00:01:24,650 But psychologists have emphasized the 27 00:01:24,650 --> 00:01:26,400 traps we can fall in. 28 00:01:26,400 --> 00:01:28,510 I mean in a way what's easy for us is what the brain 29 00:01:28,510 --> 00:01:30,010 empowers in us. 30 00:01:30,010 --> 00:01:32,410 It's easy for us to recognize objects or faces 31 00:01:32,410 --> 00:01:34,580 or move in the world. 32 00:01:34,580 --> 00:01:37,270 When we feel something is hard, like thinking hard, for 33 00:01:37,270 --> 00:01:40,840 problem sets or global warming or other things that are hard 34 00:01:40,840 --> 00:01:44,960 to think about, then we know that we're sort of leaving the 35 00:01:44,960 --> 00:01:47,830 apparatus that's easy for us and we're entering into a 36 00:01:47,830 --> 00:01:51,980 hazardous, difficult form of our mental lives. 37 00:01:51,980 --> 00:01:55,520 So, I'm going to show you a video for a moment. 38 00:01:55,520 --> 00:01:57,840 Because the other fun thing is, and maybe this is true of 39 00:01:57,840 --> 00:02:02,430 all of us in different ways, all the time, we often don't 40 00:02:02,430 --> 00:02:05,040 recognize, we often don't recognize when we're thinking 41 00:02:05,040 --> 00:02:07,000 not as well as we might. 42 00:02:07,000 --> 00:02:08,080 So this is a trap. 43 00:02:08,080 --> 00:02:12,590 So I'm going to show you, the original punking TV show, this 44 00:02:12,590 --> 00:02:13,540 was called Candid Camera. 45 00:02:13,540 --> 00:02:14,170 It's pretty old. 46 00:02:14,170 --> 00:02:15,535 So this is a clip from there. 47 00:02:15,535 --> 00:02:19,090 And here's a couple people asked to do a school problem, 48 00:02:19,090 --> 00:02:22,350 7/8 divided by 3/4 and how many square 49 00:02:22,350 --> 00:02:23,840 feet in a square yard? 50 00:02:23,840 --> 00:02:26,940 And what's kind of funny about it in my mind is, not only 51 00:02:26,940 --> 00:02:31,120 that the people are wrong, but how wrong they are and how 52 00:02:31,120 --> 00:02:33,539 much they don't realize sometimes how wrong they are. 53 00:02:40,250 --> 00:02:46,040 I mean what's impressive about this is that not only are the 54 00:02:46,040 --> 00:02:48,580 people wrong, but they don't say like, I forget. 55 00:02:48,580 --> 00:02:51,740 They just start to give answers, throwing out numbers. 56 00:02:51,740 --> 00:02:54,970 And so how different are all of us in that? 57 00:02:54,970 --> 00:02:57,800 So some of these again are on the internet. 58 00:02:57,800 --> 00:03:00,540 And this is an MIT audience, which is about the hardest 59 00:03:00,540 --> 00:03:01,530 audience to pull this off. 60 00:03:01,530 --> 00:03:05,430 And at Stanford, I could pull this off much better. 61 00:03:05,430 --> 00:03:08,440 So some of these won't go as easily as they might for other 62 00:03:08,440 --> 00:03:09,240 typical audiences. 63 00:03:09,240 --> 00:03:10,210 Here we go. 64 00:03:10,210 --> 00:03:12,110 Two train stations are a hundred miles apart. 65 00:03:12,110 --> 00:03:15,130 At 2:00 PM one Saturday afternoon, the two trains 66 00:03:15,130 --> 00:03:16,900 start toward each other, one from each station. 67 00:03:16,900 --> 00:03:18,366 One travels at 60 miles an hour, the other 68 00:03:18,366 --> 00:03:19,080 at 40 miles an hour. 69 00:03:19,080 --> 00:03:20,920 Just as the trains pulls out of their stations, a bird 70 00:03:20,920 --> 00:03:23,260 springs into the air in front of the first train, and flies 71 00:03:23,260 --> 00:03:24,480 ahead to the front of the second. 72 00:03:24,480 --> 00:03:26,530 When the bird reaches the second, it turns back, without 73 00:03:26,530 --> 00:03:28,180 losing any speed, and flies directly 74 00:03:28,180 --> 00:03:28,970 toward the first step. 75 00:03:28,970 --> 00:03:30,630 Doesn't this remind you of all the high school physics 76 00:03:30,630 --> 00:03:32,120 problems you had earlier? 77 00:03:32,120 --> 00:03:33,710 Where's the frictionless pulley? 78 00:03:33,710 --> 00:03:35,720 The bird continues to fly back and forth between the trains 79 00:03:35,720 --> 00:03:36,730 at 80 miles an hour. 80 00:03:36,730 --> 00:03:38,210 How many miles will the bird have flown 81 00:03:38,210 --> 00:03:41,000 before the trains meet? 82 00:03:41,000 --> 00:03:42,230 It's a little bit of a trick. 83 00:03:42,230 --> 00:03:47,000 Because the answer is told to you directly, pretty much. 84 00:03:47,000 --> 00:03:48,450 So 100 miles apart. 85 00:03:48,450 --> 00:03:51,635 How long will it take for the trains to reach each other, if 86 00:03:51,635 --> 00:03:54,630 one is 60 miles an hour and one is 40 miles an hour? 87 00:03:54,630 --> 00:03:55,130 AUDIENCE: Hour. 88 00:03:55,130 --> 00:03:56,720 PROFESSOR: Hour. 89 00:03:56,720 --> 00:03:59,220 So how many miles an hour does the bird fly? 90 00:03:59,220 --> 00:04:01,250 AUDIENCE: 80. 91 00:04:01,250 --> 00:04:02,500 Easier than you think. 92 00:04:05,280 --> 00:04:08,020 So those are the kinds of ones I just showed that people kind 93 00:04:08,020 --> 00:04:09,750 of get themselves into their thinking cap. 94 00:04:09,750 --> 00:04:11,130 And it's a trick. 95 00:04:11,130 --> 00:04:12,900 Here we go. 96 00:04:12,900 --> 00:04:15,010 I need somebody who is willing to, at their chair, 97 00:04:15,010 --> 00:04:16,260 just call out words. 98 00:04:22,120 --> 00:04:23,410 Thank you very much. 99 00:04:23,410 --> 00:04:23,990 Here we go. 100 00:04:23,990 --> 00:04:25,440 In English, many words are pronounced somewhat 101 00:04:25,440 --> 00:04:27,170 differently depending on the way they are spelled. 102 00:04:27,170 --> 00:04:28,330 But here we go. 103 00:04:28,330 --> 00:04:32,270 Can you give me a synonym for people or family, a word that 104 00:04:32,270 --> 00:04:35,630 people would often say for that? 105 00:04:35,630 --> 00:04:37,410 I'll give you one. 106 00:04:37,410 --> 00:04:39,590 Folk. 107 00:04:39,590 --> 00:04:40,810 Now, I know this is a history quiz. 108 00:04:40,810 --> 00:04:42,020 But I'll help you. 109 00:04:42,020 --> 00:04:44,000 I wouldn't know the answer to this, necessarily. 110 00:04:44,000 --> 00:04:45,940 Can you give me the name of an American president at the time 111 00:04:45,940 --> 00:04:46,690 of the Mexican War? 112 00:04:46,690 --> 00:04:47,240 And I'll give you a hint. 113 00:04:47,240 --> 00:04:48,595 It rhymes with folk. 114 00:04:48,595 --> 00:04:49,240 AUDIENCE: Polk. 115 00:04:49,240 --> 00:04:49,940 PROFESSOR: Polk. 116 00:04:49,940 --> 00:04:51,020 Everybody can help. 117 00:04:51,020 --> 00:04:51,650 Everybody help. 118 00:04:51,650 --> 00:04:53,330 One, two, three. 119 00:04:53,330 --> 00:04:55,620 Can you give me the name of the word that means egg white? 120 00:04:55,620 --> 00:04:56,564 AUDIENCE: Yolk. 121 00:04:56,564 --> 00:04:58,520 PROFESSOR: Aha. 122 00:04:58,520 --> 00:04:58,960 No. 123 00:04:58,960 --> 00:05:00,380 Is the yolk, the egg white? 124 00:05:03,050 --> 00:05:03,530 I know. 125 00:05:03,530 --> 00:05:04,580 It's a pretty hilarious course. 126 00:05:04,580 --> 00:05:06,240 Thank you. 127 00:05:06,240 --> 00:05:08,690 So this is an example of what people 128 00:05:08,690 --> 00:05:10,880 call functional fixedness. 129 00:05:10,880 --> 00:05:12,990 Your mind is going a certain way. 130 00:05:12,990 --> 00:05:14,470 And you're on a certain pace. 131 00:05:14,470 --> 00:05:16,280 And all of a sudden when you need to put the mental break 132 00:05:16,280 --> 00:05:19,710 on, your habit of thought takes you a slightly wrong 133 00:05:19,710 --> 00:05:21,190 direction, on a very superficial test. 134 00:05:21,190 --> 00:05:24,110 Thank you very much. 135 00:05:24,110 --> 00:05:26,330 Here's an experiment. 136 00:05:26,330 --> 00:05:29,470 In 10 minutes, only about 39% of people solve this. 137 00:05:29,470 --> 00:05:31,020 You go into a room like this. 138 00:05:31,020 --> 00:05:33,470 The strings are too far apart. 139 00:05:33,470 --> 00:05:34,810 You have stuff around here. 140 00:05:34,810 --> 00:05:36,670 And your job to tie the strings together. 141 00:05:36,670 --> 00:05:37,920 What do you do? 142 00:05:40,870 --> 00:05:42,750 AUDIENCE: Stand on the chair? 143 00:05:42,750 --> 00:05:44,620 PROFESSOR: Standing on the chair is an interesting one. 144 00:05:44,620 --> 00:05:46,830 Let's pretend it doesn't work. 145 00:05:46,830 --> 00:05:47,770 It wouldn't. 146 00:05:47,770 --> 00:05:50,020 It would get you up, but they'd be far apart still. 147 00:05:50,020 --> 00:05:50,660 It could. 148 00:05:50,660 --> 00:05:53,400 Let's pretend it doesn't. 149 00:05:53,400 --> 00:05:54,660 You'd have to be there, to be convinced. 150 00:05:54,660 --> 00:05:54,930 You know what I mean. 151 00:05:54,930 --> 00:05:56,180 It mean depends on the angle. 152 00:05:58,610 --> 00:05:59,790 AUDIENCE: [INAUDIBLE]. 153 00:05:59,790 --> 00:06:00,115 PROFESSOR: Sorry? 154 00:06:00,115 --> 00:06:01,340 AUDIENCE: Tie it to the end. 155 00:06:01,340 --> 00:06:02,930 PROFESSOR: Ah. 156 00:06:02,930 --> 00:06:05,490 So you're one of the 39%. 157 00:06:05,490 --> 00:06:08,850 So the idea is, if you tie for example, the pliers onto the 158 00:06:08,850 --> 00:06:12,680 thing and get it swinging, you could catch it, right? 159 00:06:12,680 --> 00:06:17,770 So why don't people come up with that answer very easily? 160 00:06:17,770 --> 00:06:21,390 PROFESSOR: Because what are pliers for? 161 00:06:21,390 --> 00:06:23,180 Pliering. 162 00:06:23,180 --> 00:06:25,840 They're not for swinging ropes. 163 00:06:25,840 --> 00:06:28,820 So again, the word they use is functional fixedness. 164 00:06:28,820 --> 00:06:30,680 You know what a plier is for. 165 00:06:30,680 --> 00:06:35,390 It doesn't seem like a good candidate to do this job. 166 00:06:35,390 --> 00:06:35,830 Here's another one. 167 00:06:35,830 --> 00:06:38,270 You walk into a room and there's a door that's closed 168 00:06:38,270 --> 00:06:39,180 behind you. 169 00:06:39,180 --> 00:06:40,430 And you see these things on a table. 170 00:06:40,430 --> 00:06:42,310 And you're asked, could you support this 171 00:06:42,310 --> 00:06:45,290 candle on the door? 172 00:06:45,290 --> 00:06:49,780 About 23% of people get this, in 1945. 173 00:06:49,780 --> 00:06:51,590 What do you do? 174 00:06:51,590 --> 00:06:54,220 This is just like all the spy movies. 175 00:06:54,220 --> 00:06:55,944 Yes? 176 00:06:55,944 --> 00:06:57,194 AUDIENCE: [INAUDIBLE]. 177 00:06:59,450 --> 00:06:59,875 PROFESSOR: Sorry? 178 00:06:59,875 --> 00:07:01,515 AUDIENCE: Put the tacks through the width of the door. 179 00:07:01,515 --> 00:07:02,570 PROFESSOR: Yeah, through the width of door 180 00:07:02,570 --> 00:07:03,790 would be pretty good. 181 00:07:03,790 --> 00:07:05,380 It's not really supporting the candle, but it 182 00:07:05,380 --> 00:07:06,230 could get the job done. 183 00:07:06,230 --> 00:07:07,130 That's not a bad thought. 184 00:07:07,130 --> 00:07:08,350 These are all good thoughts. 185 00:07:08,350 --> 00:07:10,090 If it were me, I wouldn't be close. 186 00:07:10,090 --> 00:07:11,300 So that's better than I do. 187 00:07:11,300 --> 00:07:11,980 Yeah? 188 00:07:11,980 --> 00:07:14,740 AUDIENCE: Take out the tacks and then tack the base. 189 00:07:14,740 --> 00:07:16,300 PROFESSOR: Have you see this before? 190 00:07:16,300 --> 00:07:16,690 No. 191 00:07:16,690 --> 00:07:17,410 Excellent. 192 00:07:17,410 --> 00:07:21,060 As I tell you, MIT's audience is not an easy audience. 193 00:07:21,060 --> 00:07:25,230 The idea is that you dump the tacks, and then stick the tack 194 00:07:25,230 --> 00:07:26,900 into the back of the empty box. 195 00:07:26,900 --> 00:07:28,820 Nice platform for a candle. 196 00:07:28,820 --> 00:07:32,520 Why don't most people think about that? 197 00:07:32,520 --> 00:07:36,650 Because what's the job of the box? 198 00:07:36,650 --> 00:07:39,060 To hold the tacks. 199 00:07:39,060 --> 00:07:40,610 My job is to figure out this problem. 200 00:07:40,610 --> 00:07:42,820 You hang on to the tacks. 201 00:07:42,820 --> 00:07:45,330 And in fact if they do the same experiment, but just put 202 00:07:45,330 --> 00:07:48,070 the tacks next to the box, a lot of people 203 00:07:48,070 --> 00:07:49,350 get it right away. 204 00:07:49,350 --> 00:07:52,650 Because now that box, it's yearning to support something. 205 00:07:52,650 --> 00:07:53,570 It no longer has a job. 206 00:07:53,570 --> 00:07:56,360 It's an unemployed box that you can stick onto the door 207 00:07:56,360 --> 00:07:57,530 and support this. 208 00:07:57,530 --> 00:08:01,140 Your mind just gets stuck with thinking the box is a holder. 209 00:08:01,140 --> 00:08:02,390 It's not a platform. 210 00:08:04,870 --> 00:08:06,810 This is the original-- and you've seen this so 211 00:08:06,810 --> 00:08:07,370 many times by now. 212 00:08:07,370 --> 00:08:09,860 This is one of the original, thinking outside of the box 213 00:08:09,860 --> 00:08:12,500 things, where you're supposed to connect the dots with four 214 00:08:12,500 --> 00:08:14,065 straight lines without lifting a pencil. 215 00:08:21,440 --> 00:08:23,140 Some of you know this. 216 00:08:23,140 --> 00:08:24,430 Some of you are figuring out on the spot. 217 00:08:24,430 --> 00:08:26,830 But I'll just show you. 218 00:08:26,830 --> 00:08:28,155 So there's an answer. 219 00:08:28,155 --> 00:08:30,840 The other ones are similar. 220 00:08:30,840 --> 00:08:33,140 Why do so many people get stuck in this 221 00:08:33,140 --> 00:08:34,600 and not get it done? 222 00:08:34,600 --> 00:08:36,980 Because they don't like this part. 223 00:08:36,980 --> 00:08:38,760 And they don't like this part, because it's 224 00:08:38,760 --> 00:08:41,520 drawing outside the box. 225 00:08:41,520 --> 00:08:43,350 They're always looking for the connections within the box. 226 00:08:43,350 --> 00:08:45,870 And it seems cheating in some sense to draw outside the box. 227 00:08:45,870 --> 00:08:48,740 There's an intuition, like with the box with the tacks, 228 00:08:48,740 --> 00:08:50,610 that you're supposed to stay in there. 229 00:08:50,610 --> 00:08:54,450 So if they get no information in this study, practically 230 00:08:54,450 --> 00:08:58,140 nobody solved it, in a given time. 231 00:08:58,140 --> 00:08:59,830 If they get a hint, you can draw outside the 232 00:08:59,830 --> 00:09:01,470 square, that helps. 233 00:09:01,470 --> 00:09:04,080 But then, outside the square puts again, the first line. 234 00:09:04,080 --> 00:09:06,280 They're still only half the time getting it. 235 00:09:06,280 --> 00:09:07,740 Because you've got to go outside a second time. 236 00:09:07,740 --> 00:09:10,420 So it gets a lot of information to get comfortable 237 00:09:10,420 --> 00:09:13,470 with finding a way to do this that's completely within the 238 00:09:13,470 --> 00:09:16,410 rules, but not the way you intuitively feel might be the 239 00:09:16,410 --> 00:09:17,840 right way to do it. 240 00:09:17,840 --> 00:09:21,170 So those are all examples of functional fixedness, where 241 00:09:21,170 --> 00:09:23,620 you have a certain belief about what's going on and it's 242 00:09:23,620 --> 00:09:27,670 hard to overcome that to solve a problem in a novel way. 243 00:09:27,670 --> 00:09:29,950 Another thing that people think about as the way to 244 00:09:29,950 --> 00:09:32,130 overcome some of these problems of thinking about 245 00:09:32,130 --> 00:09:36,790 problems is different representations that let you 246 00:09:36,790 --> 00:09:38,850 take a different look at a problem to be solved. 247 00:09:38,850 --> 00:09:40,700 So picture a large piece of paper, a 248 00:09:40,700 --> 00:09:42,050 hundredth of an inch thick. 249 00:09:42,050 --> 00:09:43,690 Now, in your imagination, fold it once. 250 00:09:43,690 --> 00:09:44,770 You have two layers. 251 00:09:44,770 --> 00:09:46,620 Continue to fold it on itself 50 times. 252 00:09:46,620 --> 00:09:48,120 It's true that's it's impossible to fold it in 253 00:09:48,120 --> 00:09:50,270 place, such a piece of paper, 50 times. 254 00:09:50,270 --> 00:09:52,350 But for the sake of the problem, imagining that you 255 00:09:52,350 --> 00:09:55,712 can, about how thick would the 50-times-folded paper be? 256 00:09:55,712 --> 00:09:57,640 And everybody's first intuition is, well, how thick 257 00:09:57,640 --> 00:09:58,430 can a piece of paper be? 258 00:09:58,430 --> 00:10:00,370 Of course, this is a hypothetical problem, not an 259 00:10:00,370 --> 00:10:02,130 actual paper problem. 260 00:10:02,130 --> 00:10:03,660 But if you do the math, it's big. 261 00:10:08,800 --> 00:10:10,150 So here's another one. 262 00:10:14,160 --> 00:10:16,620 One morning, exactly at sunrise, a Buddhist monk began 263 00:10:16,620 --> 00:10:17,980 to climb a tall mountain. 264 00:10:17,980 --> 00:10:20,075 A narrow path no more than a foot or two wide, spiraled 265 00:10:20,075 --> 00:10:21,430 around the mountain to a glittering 266 00:10:21,430 --> 00:10:22,520 temple at the summit. 267 00:10:22,520 --> 00:10:24,690 The monk ascended at various rates of speed, stopping many 268 00:10:24,690 --> 00:10:27,240 times along the way to rest and eat dried fruit he 269 00:10:27,240 --> 00:10:27,710 carried with him. 270 00:10:27,710 --> 00:10:29,370 He reached the temple shortly before sunset. 271 00:10:29,370 --> 00:10:31,350 After several days of fasting and meditation, he began his 272 00:10:31,350 --> 00:10:33,920 journey back along the same path, starting it at sunrise 273 00:10:33,920 --> 00:10:35,630 and again walking at variable speeds, with many 274 00:10:35,630 --> 00:10:36,910 pauses along the way. 275 00:10:36,910 --> 00:10:38,710 His average speed descending was, of course, greater than 276 00:10:38,710 --> 00:10:40,280 his average climbing speed. 277 00:10:40,280 --> 00:10:42,700 Show that there's a spot along the path that the monk will 278 00:10:42,700 --> 00:10:44,320 occupy on both trips at precisely the 279 00:10:44,320 --> 00:10:46,020 same time of day. 280 00:10:46,020 --> 00:10:48,050 So that sounds moderately hard. 281 00:10:48,050 --> 00:10:50,610 But if you draw the picture, so we 282 00:10:50,610 --> 00:10:51,630 went and get an equation. 283 00:10:51,630 --> 00:10:53,860 Now, we have a pictorial representation. 284 00:10:53,860 --> 00:10:57,010 You can see there has to be some moment of the ascending 285 00:10:57,010 --> 00:10:59,450 and descending path that cross in time. 286 00:10:59,450 --> 00:11:00,820 You don't know what it's going to be. 287 00:11:00,820 --> 00:11:02,600 But there has to be some moment in time. 288 00:11:02,600 --> 00:11:03,700 And maybe that's obvious. 289 00:11:03,700 --> 00:11:05,840 But for a lot of people it seems stunningly complicated 290 00:11:05,840 --> 00:11:07,025 that you could even demonstrate that 291 00:11:07,025 --> 00:11:09,040 in any way at all. 292 00:11:09,040 --> 00:11:10,880 Here's one you could think about. 293 00:11:10,880 --> 00:11:13,620 Suppose you are a doctor faced with a patient who has a 294 00:11:13,620 --> 00:11:15,190 malignant tumor in his stomach. 295 00:11:15,190 --> 00:11:17,340 It's impossible to operate on the tumor, but unless the 296 00:11:17,340 --> 00:11:19,440 tumor is destroyed the patient will die. 297 00:11:19,440 --> 00:11:21,660 There's a kind of ray that can be used to destroy the tumor. 298 00:11:21,660 --> 00:11:24,860 If the rays reach the tumor all at once, with sufficiently 299 00:11:24,860 --> 00:11:26,680 high intensity, the tumor will be destroyed. 300 00:11:26,680 --> 00:11:30,350 Unfortunately, at this high intensity the healthy tissue 301 00:11:30,350 --> 00:11:33,240 that the rays pass through, will also be destroyed. 302 00:11:33,240 --> 00:11:35,860 At lower intensity, the rays are harmless to healthy 303 00:11:35,860 --> 00:11:38,570 tissue, but they don't affect the tumor either. 304 00:11:38,570 --> 00:11:40,860 What type of procedure might be used to destroy the tumor 305 00:11:40,860 --> 00:11:43,910 with the rays, and at the same time avoid destroying the 306 00:11:43,910 --> 00:11:44,820 healthy tissue? 307 00:11:44,820 --> 00:11:47,050 Now, if you know the answer to this, don't jump right up. 308 00:11:47,050 --> 00:11:48,820 Give other people a few moments to puzzle. 309 00:11:53,370 --> 00:11:57,760 About 10% of people solve this in a small unit of time, 310 00:11:57,760 --> 00:11:59,010 before the Internet. 311 00:12:02,800 --> 00:12:06,180 And so, what's the answer though, for those of you? 312 00:12:06,180 --> 00:12:07,950 Yeah, I see some hands, I think. 313 00:12:07,950 --> 00:12:08,270 Go ahead. 314 00:12:08,270 --> 00:12:09,920 Did you want to say? 315 00:12:09,920 --> 00:12:10,410 AUDIENCE: [INAUDIBLE]. 316 00:12:10,410 --> 00:12:10,530 PROFESSOR: No. 317 00:12:10,530 --> 00:12:11,385 Exactly at the spot, sir. 318 00:12:11,385 --> 00:12:12,780 AUDIENCE: [INAUDIBLE]. 319 00:12:12,780 --> 00:12:13,430 PROFESSOR: Yeah. 320 00:12:13,430 --> 00:12:17,350 Have several weak ones aimed to converge at the tumor. 321 00:12:17,350 --> 00:12:19,290 I saw your hands. 322 00:12:19,290 --> 00:12:21,170 So the idea is to have several different rays, each of which 323 00:12:21,170 --> 00:12:22,740 are weak enough not to destroy the tissue. 324 00:12:22,740 --> 00:12:24,930 But when they converge spatially at the tumor, they 325 00:12:24,930 --> 00:12:27,400 would sum enough to destroy the tumor. 326 00:12:27,400 --> 00:12:29,840 That's the problem-solving answer to this. 327 00:12:29,840 --> 00:12:33,990 So knowing that, here's another kind of a problem. 328 00:12:33,990 --> 00:12:36,390 A dictator ruled a small country from a fortress. 329 00:12:36,390 --> 00:12:38,750 The fortress was situated in the middle of the country and 330 00:12:38,750 --> 00:12:40,420 many roads radiated outward from it, 331 00:12:40,420 --> 00:12:42,670 like spokes in a wheel. 332 00:12:42,670 --> 00:12:44,740 A great general vowed to capture the fortress and free 333 00:12:44,740 --> 00:12:46,080 the country of the dictator. 334 00:12:46,080 --> 00:12:47,810 The general knew that if the entire army could attack the 335 00:12:47,810 --> 00:12:49,320 fortress at once, it could be captured. 336 00:12:49,320 --> 00:12:52,300 But a spy reported that the dictator had planted mines on 337 00:12:52,300 --> 00:12:53,380 each of the roads. 338 00:12:53,380 --> 00:12:55,630 The mines were set so that small bodies of men could pass 339 00:12:55,630 --> 00:12:58,180 over them safely, since dictator needed to be able to 340 00:12:58,180 --> 00:13:00,000 move troops and workers about. 341 00:13:00,000 --> 00:13:02,610 However, any large force would detonate the mines. 342 00:13:02,610 --> 00:13:04,320 Not only would this blow up the road, but the dictator 343 00:13:04,320 --> 00:13:06,860 would destroy many villages in retaliation. 344 00:13:06,860 --> 00:13:09,050 A full-scale, direct attack on the fortress seemed 345 00:13:09,050 --> 00:13:11,250 impossible. 346 00:13:11,250 --> 00:13:13,270 Does this problem remind you of the other problem, I mean 347 00:13:13,270 --> 00:13:15,190 back-to-back, like this? 348 00:13:15,190 --> 00:13:17,130 He divided his army up into small groups. 349 00:13:17,130 --> 00:13:20,520 Now, they're small enough per path into the castle. 350 00:13:20,520 --> 00:13:22,320 And they converge on the castle, like the X-rays 351 00:13:22,320 --> 00:13:24,260 converged on the tumor. 352 00:13:24,260 --> 00:13:26,000 So this is reasoning by analogy. 353 00:13:26,000 --> 00:13:27,170 We have problem solving approach. 354 00:13:27,170 --> 00:13:29,000 Divide your resources spatially. 355 00:13:29,000 --> 00:13:31,270 Have them meet at the same time, and converge in a way 356 00:13:31,270 --> 00:13:32,310 that's effective. 357 00:13:32,310 --> 00:13:34,750 So what you might call the deep structure of the problem 358 00:13:34,750 --> 00:13:36,760 is the same, with the surface story being different. 359 00:13:36,760 --> 00:13:40,280 One is tumors and treatments. 360 00:13:40,280 --> 00:13:41,690 But there's a strict analogy. 361 00:13:41,690 --> 00:13:44,400 Capturing the castle is like destroying the tumor. 362 00:13:44,400 --> 00:13:47,290 The fortress is like the tumor that you want to get to. 363 00:13:47,290 --> 00:13:48,710 The army is like the rays and the 364 00:13:48,710 --> 00:13:50,250 general is like the doctor. 365 00:13:50,250 --> 00:13:53,650 So if it is back-to-back like this, where I just said, 366 00:13:53,650 --> 00:13:55,280 here's a problem. 367 00:13:55,280 --> 00:13:57,430 And here's a problem just like that. 368 00:13:57,430 --> 00:14:01,300 Everybody says, well, send the troops on the different paths 369 00:14:01,300 --> 00:14:03,660 and have them converge at the same time. 370 00:14:03,660 --> 00:14:06,590 If you don't say that though, if you don't tell people this 371 00:14:06,590 --> 00:14:08,010 is the same, use the same rule. 372 00:14:08,010 --> 00:14:11,160 If you don't tell people that, only about a third come up 373 00:14:11,160 --> 00:14:12,460 with it spontaneously. 374 00:14:12,460 --> 00:14:14,920 If you just make a little bit of an effort not to make it 375 00:14:14,920 --> 00:14:17,610 completely obvious that it's the same problem. 376 00:14:17,610 --> 00:14:18,860 You just wait a little bit. 377 00:14:20,990 --> 00:14:25,030 So the surface differences, the war story versus the 378 00:14:25,030 --> 00:14:29,120 medical story, will mostly throw people from recognizing 379 00:14:29,120 --> 00:14:30,220 the analogies. 380 00:14:30,220 --> 00:14:33,320 People recognize surface analogies all the time. 381 00:14:33,320 --> 00:14:35,690 If we have war in the Middle East now, from the US 382 00:14:35,690 --> 00:14:40,240 perspective there's analogies to Vietnam, a war and a war. 383 00:14:40,240 --> 00:14:42,540 But other kinds of analogies across situations, people have 384 00:14:42,540 --> 00:14:46,740 a hard time mapping, if the surfaces look different. 385 00:14:46,740 --> 00:14:50,140 And here's an example where they gave people two problems 386 00:14:50,140 --> 00:14:53,410 of a certain kind. 387 00:14:53,410 --> 00:14:55,720 And they said as part of the experiment, and here's a 388 00:14:55,720 --> 00:14:56,800 classroom demonstration. 389 00:14:56,800 --> 00:14:59,620 If it's immediate, they sort of get it. 390 00:14:59,620 --> 00:15:03,390 But if they wait just a little bit, and they move from one 391 00:15:03,390 --> 00:15:07,640 room to another room, they don't apply the rule at all. 392 00:15:07,640 --> 00:15:09,230 So it's very tricky. 393 00:15:09,230 --> 00:15:10,970 We would like to think well, gee, we know all kinds of 394 00:15:10,970 --> 00:15:13,040 things about the world, that we can bring in a mental 395 00:15:13,040 --> 00:15:16,380 toolkit to solve problems, all kinds of ideas. 396 00:15:16,380 --> 00:15:19,690 But if the problem doesn't look the same as the prior 397 00:15:19,690 --> 00:15:22,070 problem, people have a hard time spontaneously saying, 398 00:15:22,070 --> 00:15:23,900 there's an answer I know, and I can apply it to the 399 00:15:23,900 --> 00:15:26,520 situation, unless it's really, really obvious. 400 00:15:26,520 --> 00:15:30,230 People don't transfer deep solutions very easily across 401 00:15:30,230 --> 00:15:31,480 what looks like different situations. 402 00:15:34,150 --> 00:15:35,020 Something different. 403 00:15:35,020 --> 00:15:37,530 The instructors in a flight school adopted a policy of 404 00:15:37,530 --> 00:15:39,990 consistent, positive reinforcement, recommended by 405 00:15:39,990 --> 00:15:40,780 psychologists. 406 00:15:40,780 --> 00:15:42,150 And we know we're very suspicious about 407 00:15:42,150 --> 00:15:44,320 psychologists, right? 408 00:15:44,320 --> 00:15:47,580 But you have to imagine the instructor went to some 409 00:15:47,580 --> 00:15:49,350 classroom and somebody said, positive reinforcement 410 00:15:49,350 --> 00:15:50,800 is the way to go. 411 00:15:50,800 --> 00:15:53,480 They verbally reinforced each successful execution of a 412 00:15:53,480 --> 00:15:54,820 flight maneuver. 413 00:15:54,820 --> 00:15:56,710 After some experience with this training approach, the 414 00:15:56,710 --> 00:15:59,020 instructors claimed that contrary to psychological 415 00:15:59,020 --> 00:16:03,980 doctrine, high praise for good execution of complex maneuvers 416 00:16:03,980 --> 00:16:06,480 typically result in a decrement of performance on 417 00:16:06,480 --> 00:16:07,880 the next try. 418 00:16:07,880 --> 00:16:09,920 Are they correct? 419 00:16:09,920 --> 00:16:12,360 So you understand, you have to have in your mind all the 420 00:16:12,360 --> 00:16:13,550 military movies where there's a really 421 00:16:13,550 --> 00:16:15,140 tough sergeant training. 422 00:16:15,140 --> 00:16:17,860 The other one that yells at you to make you succeed. 423 00:16:17,860 --> 00:16:19,830 But for half the movie, you don't know if the sergeant 424 00:16:19,830 --> 00:16:21,770 will break you or make you into a better woman or a 425 00:16:21,770 --> 00:16:23,000 better man. 426 00:16:23,000 --> 00:16:23,970 So he's barking orders. 427 00:16:23,970 --> 00:16:25,940 And some psychologist says, well, you should really think 428 00:16:25,940 --> 00:16:27,520 about saying nice things to these people, because that's 429 00:16:27,520 --> 00:16:28,150 encouraging. 430 00:16:28,150 --> 00:16:29,760 Errgh. 431 00:16:29,760 --> 00:16:31,820 Somebody does a flight maneuver and does 432 00:16:31,820 --> 00:16:32,427 a really good job. 433 00:16:32,427 --> 00:16:33,870 And he goes, that's awesome. 434 00:16:33,870 --> 00:16:35,400 And then the person goes up and does worse. 435 00:16:35,400 --> 00:16:37,990 And he goes aargh, you're all terrible. 436 00:16:37,990 --> 00:16:39,170 So what's happening? 437 00:16:39,170 --> 00:16:40,420 Yeah. 438 00:16:49,470 --> 00:16:53,110 AUDIENCE: It must-- the person typically does really well. 439 00:16:53,110 --> 00:16:56,470 Doing really well one time isn't indicative of really 440 00:16:56,470 --> 00:16:56,950 well the next time. 441 00:16:56,950 --> 00:16:58,100 PROFESSOR: It's even worse than that, right. 442 00:16:58,100 --> 00:17:00,040 So all of us have in us a range of performance. 443 00:17:00,040 --> 00:17:00,660 We know that. 444 00:17:00,660 --> 00:17:05,430 If you do sports, if you do research, if you do anything, 445 00:17:05,430 --> 00:17:07,680 some days we're at our average. 446 00:17:07,680 --> 00:17:08,920 Some days, we're not so good. 447 00:17:08,920 --> 00:17:11,480 Some days, we go wow, that was pretty good. 448 00:17:11,480 --> 00:17:13,910 We all have a range of performance that's possible, 449 00:17:13,910 --> 00:17:16,970 our average performance, our best days, our worst days. 450 00:17:16,970 --> 00:17:19,700 So you just went up and you did an 451 00:17:19,700 --> 00:17:21,700 awesome flight maneuver. 452 00:17:21,700 --> 00:17:24,919 Is that your average one or is that a peak example? 453 00:17:28,530 --> 00:17:30,330 It's a peak example. 454 00:17:30,330 --> 00:17:32,070 Statistically, what's the likelihood of your next one 455 00:17:32,070 --> 00:17:35,020 going to be, better or worse? 456 00:17:35,020 --> 00:17:36,270 Worse. 457 00:17:38,210 --> 00:17:40,930 You can't always be at your best, because you'd sort of go 458 00:17:40,930 --> 00:17:43,440 into infinite excellence. 459 00:17:43,440 --> 00:17:44,840 You have a range of performance. 460 00:17:48,500 --> 00:17:49,800 In the NBA, what's a really good score? 461 00:17:49,800 --> 00:17:52,090 30 points is really good. 462 00:17:52,090 --> 00:17:53,700 That's the average though, of the best players. 463 00:17:53,700 --> 00:17:55,530 They don't go out and go, I got 30 tonight. 464 00:17:55,530 --> 00:17:56,600 And tomorrow, I'll get 32. 465 00:17:56,600 --> 00:17:58,770 And by the end of the season, I have like 180 466 00:17:58,770 --> 00:18:01,320 points every game. 467 00:18:01,320 --> 00:18:02,690 Even if you're on the Miami Heat, you have 468 00:18:02,690 --> 00:18:05,190 to do a good job. 469 00:18:05,190 --> 00:18:08,800 So do you understand? 470 00:18:08,800 --> 00:18:10,920 So here's another example. 471 00:18:10,920 --> 00:18:14,340 Parents of very famous, successful kids, are they 472 00:18:14,340 --> 00:18:17,680 mostly super-famous and super-successful? 473 00:18:17,680 --> 00:18:20,800 Or do we mostly read about them and go whoa, that's a 474 00:18:20,800 --> 00:18:22,050 tough deal, what happened there? 475 00:18:22,050 --> 00:18:23,365 What's your impression? 476 00:18:27,200 --> 00:18:28,630 Let's ask this question-- 477 00:18:28,630 --> 00:18:30,950 and if you are facing these burdens. 478 00:18:30,950 --> 00:18:34,155 If your parent wins a Nobel Prize, what's the odds that 479 00:18:34,155 --> 00:18:36,450 you win a Nobel Prize. 480 00:18:36,450 --> 00:18:36,785 Small. 481 00:18:36,785 --> 00:18:39,530 And they go oh, the underachieving kid. 482 00:18:39,530 --> 00:18:42,110 All the pressure from the parent to do really well, with 483 00:18:42,110 --> 00:18:43,710 a Nobel Laureate as your parent. 484 00:18:43,710 --> 00:18:44,960 Well, maybe. 485 00:18:44,960 --> 00:18:47,360 Maybe your parent is berating from kindergarten on saying, 486 00:18:47,360 --> 00:18:49,820 if you don't eat your peas and carrots, you won't win a Nobel 487 00:18:49,820 --> 00:18:51,380 Prize, like I. That's a possibility. 488 00:18:51,380 --> 00:18:53,180 We can't rule it out. 489 00:18:53,180 --> 00:18:55,490 But if somebody in your family wins a Nobel Prize, what's the 490 00:18:55,490 --> 00:18:58,630 odds that other people in your family will win that. 491 00:18:58,630 --> 00:18:58,950 PROFESSOR: Yeah. 492 00:18:58,950 --> 00:19:01,320 So we call that regression to the mean. 493 00:19:01,320 --> 00:19:04,380 If you have an outstanding example, a person who wins a 494 00:19:04,380 --> 00:19:09,210 Nobel Prize, everybody around them is not likely to win. 495 00:19:09,210 --> 00:19:12,870 If you have an outstanding day at what you do, like a really 496 00:19:12,870 --> 00:19:17,120 good day, the next day is likely to be worse, just 497 00:19:17,120 --> 00:19:18,080 statistically. 498 00:19:18,080 --> 00:19:20,000 So what's happening with this flight instructor? 499 00:19:20,000 --> 00:19:22,160 This is a very powerful and important idea, that 500 00:19:22,160 --> 00:19:23,830 intuitively often, what's happening with the flight 501 00:19:23,830 --> 00:19:24,440 instructor? 502 00:19:24,440 --> 00:19:27,050 Of course if the guy does awesome, he's going to do 503 00:19:27,050 --> 00:19:30,700 worse the next time, every single time, practically, 504 00:19:30,700 --> 00:19:31,620 statistically. 505 00:19:31,620 --> 00:19:35,200 So we can think about this, regression to the mean. 506 00:19:35,200 --> 00:19:35,770 Here's a standard 507 00:19:35,770 --> 00:19:37,640 distribution, bell-shaped curve. 508 00:19:37,640 --> 00:19:39,560 Here's the mean of whatever you do, how well you shoot 509 00:19:39,560 --> 00:19:41,785 baskets, how well you study for a test, how well you wrote 510 00:19:41,785 --> 00:19:45,680 a paper, how well you helped a friend who needed consoling. 511 00:19:45,680 --> 00:19:47,930 You pick your thing. 512 00:19:47,930 --> 00:19:49,610 Some days you do a medium job. 513 00:19:49,610 --> 00:19:50,540 You're average. 514 00:19:50,540 --> 00:19:54,150 Some days you really impress yourself with how you did. 515 00:19:54,150 --> 00:19:57,270 And some days, you're pretty shocked at what a miserable 516 00:19:57,270 --> 00:19:58,630 day you had. 517 00:19:58,630 --> 00:19:59,710 Does that makes sense? 518 00:19:59,710 --> 00:20:02,230 So if you had an awesome outing, by average, the next 519 00:20:02,230 --> 00:20:06,220 outing is going to be less good, regression to the mean. 520 00:20:08,790 --> 00:20:12,570 So one place that this has been done over and over and 521 00:20:12,570 --> 00:20:14,290 over again, and somebody help me out here. 522 00:20:14,290 --> 00:20:15,570 This could happen in any sport. 523 00:20:15,570 --> 00:20:19,270 But especially in basketball, people talk about a hot hand. 524 00:20:19,270 --> 00:20:21,050 So some of you are sports enthusiasts. 525 00:20:21,050 --> 00:20:22,130 Probably some of you are not. 526 00:20:22,130 --> 00:20:25,250 So a sports enthusiast, will you please help me describe 527 00:20:25,250 --> 00:20:30,410 what a hot hand is in basketball? 528 00:20:30,410 --> 00:20:31,320 Help the rest of the class. 529 00:20:31,320 --> 00:20:31,812 Oh, thanks. 530 00:20:31,812 --> 00:20:34,887 AUDIENCE: You think you're on a hot streak just because you 531 00:20:34,887 --> 00:20:36,240 made a couple baskets in row. 532 00:20:36,240 --> 00:20:38,536 But they do the statistics and then it ends up being that 533 00:20:38,536 --> 00:20:41,652 they're just overconfident in their shots, since they are 534 00:20:41,652 --> 00:20:45,550 shooting more shots in the end. 535 00:20:45,550 --> 00:20:45,641 PROFESSOR: So people have a saying -- 536 00:20:45,641 --> 00:20:47,920 If you watch a basketball game, it's incredibly how 537 00:20:47,920 --> 00:20:50,790 often the announcer will say, oh, player x, he 538 00:20:50,790 --> 00:20:53,370 or she is on a tear. 539 00:20:53,370 --> 00:20:54,350 They're so hot. 540 00:20:54,350 --> 00:20:55,570 Pass that ball to that person. 541 00:20:55,570 --> 00:20:56,600 They're so hot. 542 00:20:56,600 --> 00:20:57,510 They shoot a three-pointer. 543 00:20:57,510 --> 00:20:58,540 And they come down and they shoot a three-pointer. 544 00:20:58,540 --> 00:20:59,970 And they say, why are you passing the 545 00:20:59,970 --> 00:21:00,620 ball to that person? 546 00:21:00,620 --> 00:21:03,100 They're so hot. 547 00:21:03,100 --> 00:21:06,610 And some players are known as streaky players, who go out 548 00:21:06,610 --> 00:21:09,160 and have just awesome nights. 549 00:21:09,160 --> 00:21:10,650 You hear this in the sports all the time. 550 00:21:10,650 --> 00:21:12,790 I can't tell you, the end of a close March Madness game, 551 00:21:12,790 --> 00:21:14,400 they're always saying, pass it to this guy. 552 00:21:14,400 --> 00:21:17,580 Because this guy is on a streak in this game. 553 00:21:20,180 --> 00:21:21,550 So they can do the statistics. 554 00:21:21,550 --> 00:21:23,020 How would you do the statistic to know 555 00:21:23,020 --> 00:21:24,700 whether streaks actually-- 556 00:21:24,700 --> 00:21:27,170 streaks exist. 557 00:21:27,170 --> 00:21:29,610 Sometimes you do two baskets in a row or five. 558 00:21:29,610 --> 00:21:30,870 Or you miss five in a row. 559 00:21:30,870 --> 00:21:33,520 There are runs, like heads and tails. 560 00:21:33,520 --> 00:21:36,380 But the question we're asking is, if you made a shot, does 561 00:21:36,380 --> 00:21:39,460 that an increase in any short run, the probability that 562 00:21:39,460 --> 00:21:40,870 you'll make the next shot? 563 00:21:40,870 --> 00:21:41,660 That would be the streak. 564 00:21:41,660 --> 00:21:42,780 That would be the hot shooter. 565 00:21:42,780 --> 00:21:44,180 So you can go do statistics. 566 00:21:44,180 --> 00:21:44,930 And they did it. 567 00:21:44,930 --> 00:21:47,540 Psychologists have done it on many teams, in many leagues. 568 00:21:47,540 --> 00:21:50,440 They did every single shot of a Cornell basketball team, the 569 00:21:50,440 --> 00:21:51,070 entire season. 570 00:21:51,070 --> 00:21:52,500 But they've done it on many other teams. 571 00:21:52,500 --> 00:21:54,670 There is no such thing as a hot streak. 572 00:21:54,670 --> 00:21:55,920 There is no such thing as a hot streak. 573 00:21:55,920 --> 00:21:59,090 Of course, you have small runs, where people have a lot 574 00:21:59,090 --> 00:22:00,500 of good shots. 575 00:22:00,500 --> 00:22:02,280 Another day, they'll have a lot of bad shots. 576 00:22:02,280 --> 00:22:04,740 But the probability of making any one shot is no higher or 577 00:22:04,740 --> 00:22:07,140 lower, given the probability that you made the prior shot. 578 00:22:07,140 --> 00:22:08,850 They're completely independent. 579 00:22:08,850 --> 00:22:11,430 And nobody can find statistical support for any 580 00:22:11,430 --> 00:22:13,900 kind of streaks in sports, except of course you have huge 581 00:22:13,900 --> 00:22:14,420 distributions. 582 00:22:14,420 --> 00:22:15,910 So sometimes a person will have an 583 00:22:15,910 --> 00:22:17,775 exceptionally good or bad night. 584 00:22:20,570 --> 00:22:21,910 So this is kind of like that. 585 00:22:21,910 --> 00:22:23,140 And you will get this. 586 00:22:23,140 --> 00:22:25,290 Suppose that you have a normal penny with a head and a tail. 587 00:22:25,290 --> 00:22:26,130 You toss it six times. 588 00:22:26,130 --> 00:22:27,460 Which is these outcomes is most likely? 589 00:22:30,860 --> 00:22:32,690 All the same. 590 00:22:32,690 --> 00:22:34,350 You're absolutely right. 591 00:22:34,350 --> 00:22:42,980 But why do people tend not to like a), mostly, who haven't 592 00:22:42,980 --> 00:22:45,280 studied probability much? 593 00:22:45,280 --> 00:22:45,950 They don't like a). 594 00:22:45,950 --> 00:22:47,200 Why don't they like a)? 595 00:22:49,400 --> 00:22:52,500 Most people, most of the time, as a random thing? 596 00:22:52,500 --> 00:22:52,980 Yeah? 597 00:22:52,980 --> 00:22:53,960 AUDIENCE: It doesn't look random. 598 00:22:53,960 --> 00:22:56,360 PROFESSOR: It doesn't look random, man. 599 00:22:56,360 --> 00:22:57,540 A) can't be random, right? 600 00:22:57,540 --> 00:22:59,610 But we know that we have short runs of anything. 601 00:22:59,610 --> 00:23:01,010 And each of these are independent outcomes. 602 00:23:01,010 --> 00:23:02,260 That's exactly what you got. 603 00:23:04,690 --> 00:23:05,850 We did this example before. 604 00:23:05,850 --> 00:23:07,650 But there are 30 people in the group. 605 00:23:07,650 --> 00:23:09,470 You get the month and date of each person's birthday. 606 00:23:09,470 --> 00:23:11,330 What is the approximate probability that two people 607 00:23:11,330 --> 00:23:12,670 have the exact same birthday? 608 00:23:12,670 --> 00:23:15,490 And most people will say, oh, I haven't met that many people 609 00:23:15,490 --> 00:23:16,020 on my birthday. 610 00:23:16,020 --> 00:23:17,090 Not that high. 611 00:23:17,090 --> 00:23:19,290 But it's actually 70%. 612 00:23:19,290 --> 00:23:20,420 Because it's any two in the group. 613 00:23:20,420 --> 00:23:21,580 It's not you. 614 00:23:21,580 --> 00:23:22,650 It's any two in the group. 615 00:23:22,650 --> 00:23:26,080 And that increases it to 70%. 616 00:23:26,080 --> 00:23:27,010 Here's one we haven't done. 617 00:23:27,010 --> 00:23:28,997 Imagine a college in which the average height of men is 175 618 00:23:28,997 --> 00:23:30,040 centimeters. 619 00:23:30,040 --> 00:23:31,980 You randomly measure the height of three men. 620 00:23:31,980 --> 00:23:33,620 Which of these two outcomes is more likely? 621 00:23:33,620 --> 00:23:36,540 And I'll let you think about this for a moment. 622 00:23:36,540 --> 00:23:40,610 John at 178, Mike at 170, and Bob at 176; or John at 177, 623 00:23:40,610 --> 00:23:43,400 Mike at 177, and Bob at 177? 624 00:23:43,400 --> 00:23:46,174 Which is more likely, or can't tell? 625 00:23:46,174 --> 00:23:47,424 AUDIENCE: [INAUDIBLE]. 626 00:23:52,820 --> 00:24:01,510 PROFESSOR: I heard a B. Is it obvious? 627 00:24:01,510 --> 00:24:02,045 Let me tell you. 628 00:24:02,045 --> 00:24:04,160 It's the same concept we've been talking about, about 629 00:24:04,160 --> 00:24:05,810 distributions and means. 630 00:24:05,810 --> 00:24:10,610 So here's the average, which you are given as 175. 631 00:24:10,610 --> 00:24:14,000 So these guys are closer to the average. 632 00:24:14,000 --> 00:24:17,550 Height is distributed in a normalized way. 633 00:24:17,550 --> 00:24:21,320 So here's the three people, who are red dots, the equal 634 00:24:21,320 --> 00:24:23,680 heights, 177, and 177, and 177. 635 00:24:23,680 --> 00:24:25,150 Here are the heights of the blue. 636 00:24:25,150 --> 00:24:26,940 There's more people in this part of the distribution. 637 00:24:26,940 --> 00:24:30,100 So it's actually more likely to get this than this, about 638 00:24:30,100 --> 00:24:31,480 40% more likely. 639 00:24:31,480 --> 00:24:34,140 Because you're sampling from closer to the mean. 640 00:24:34,140 --> 00:24:36,820 And there are more people near the mean. 641 00:24:36,820 --> 00:24:38,420 Does that make sense? 642 00:24:38,420 --> 00:24:39,530 But people don't like that. 643 00:24:39,530 --> 00:24:42,380 Overwhelmingly, if you ask people, they like the top 644 00:24:42,380 --> 00:24:45,160 answer, because it just doesn't feel right to get 645 00:24:45,160 --> 00:24:48,430 three people of equal height, in a random way. 646 00:24:48,430 --> 00:24:51,990 And you could say, what do you mean it doesn't feel right? 647 00:24:51,990 --> 00:24:54,490 We can do the math or we can show you the picture. 648 00:24:54,490 --> 00:24:57,390 And we can all agree, I think, that that's correct. 649 00:24:57,390 --> 00:24:59,660 What do you mean it doesn't feel right? 650 00:24:59,660 --> 00:25:02,540 And that's what people mean by the difference between 651 00:25:02,540 --> 00:25:04,220 algorithms and heuristics. 652 00:25:04,220 --> 00:25:06,230 This is work from Kahneman and Tversky, that won a Noble 653 00:25:06,230 --> 00:25:07,410 Prize for Danny Kahneman. 654 00:25:07,410 --> 00:25:08,690 Tversky passed away. 655 00:25:08,690 --> 00:25:11,370 Which is that humans could figure out many things 656 00:25:11,370 --> 00:25:12,330 algorithmically. 657 00:25:12,330 --> 00:25:14,060 You can do the probability. 658 00:25:14,060 --> 00:25:17,440 But as an intuition, for many, many, many people, even 659 00:25:17,440 --> 00:25:20,280 educated people who know a lot about probability. 660 00:25:20,280 --> 00:25:22,590 There's an intuition that we have about things, heuristics, 661 00:25:22,590 --> 00:25:24,700 a feeling we have about things. 662 00:25:24,700 --> 00:25:28,370 Like the odds of this are almost nil. 663 00:25:28,370 --> 00:25:30,590 And we go by that feeling, instead of by being the 664 00:25:30,590 --> 00:25:36,790 rational analyst that we might be. 665 00:25:36,790 --> 00:25:37,160 Here's one. 666 00:25:37,160 --> 00:25:38,240 You'll get this one, I think. 667 00:25:38,240 --> 00:25:40,220 A nearby town is served by two hospitals. 668 00:25:40,220 --> 00:25:42,910 About 45 babies are born each day in the larger, about 15 in 669 00:25:42,910 --> 00:25:43,910 the smaller. 670 00:25:43,910 --> 00:25:46,010 Approximately half of the babies are boys. 671 00:25:46,010 --> 00:25:47,600 However, the exact percentage varies day to 672 00:25:47,600 --> 00:25:48,610 day, of boys and girls. 673 00:25:48,610 --> 00:25:50,120 Some days, it's higher than 50%. 674 00:25:50,120 --> 00:25:51,350 Some days, lower. 675 00:25:51,350 --> 00:25:53,490 For a period of a year, both the larger and the smaller 676 00:25:53,490 --> 00:25:56,350 hospital recorded the number of days in which more than 60% 677 00:25:56,350 --> 00:25:57,850 of the babies were boys. 678 00:25:57,850 --> 00:25:59,860 So a disproportionate number were boys. 679 00:25:59,860 --> 00:26:01,870 Which hospital do you think recorded more such days? 680 00:26:10,360 --> 00:26:16,090 The answer is about the same. 681 00:26:16,090 --> 00:26:18,040 The most common answer is about the same. 682 00:26:18,040 --> 00:26:21,003 But because of the law of small numbers, you're going to 683 00:26:21,003 --> 00:26:23,115 get more weird samples in the smaller hospital. 684 00:26:25,930 --> 00:26:27,070 Because it'll be a less of a distribution. 685 00:26:27,070 --> 00:26:29,970 You'll get more weird stuff. 686 00:26:29,970 --> 00:26:31,130 Here's one. 687 00:26:31,130 --> 00:26:34,040 It's a little dated, in terms of the language used. 688 00:26:34,040 --> 00:26:35,460 But they would give people an example of this. 689 00:26:35,460 --> 00:26:36,860 Linda is a 31-year-old, single, 690 00:26:36,860 --> 00:26:37,840 outspoken, very bright. 691 00:26:37,840 --> 00:26:39,580 She majored in philosophy as a student. 692 00:26:39,580 --> 00:26:41,165 She was deeply concerned with the issue of discrimination 693 00:26:41,165 --> 00:26:43,690 and social justice and also participated in antinuclear 694 00:26:43,690 --> 00:26:44,420 demonstrations. 695 00:26:44,420 --> 00:26:46,690 And then rank the options in terms of their likelihood. 696 00:26:46,690 --> 00:26:48,840 In describing Linda, I'll give a ranking of 1 to the most 697 00:26:48,840 --> 00:26:51,660 likely option and a ranking of 8 to the least likely option. 698 00:26:51,660 --> 00:26:53,350 So people go through this. 699 00:26:53,350 --> 00:26:54,980 I mean you're sort of just making up stuff. 700 00:26:54,980 --> 00:26:58,090 Having said this, I think people go around all the time 701 00:26:58,090 --> 00:27:00,280 having impressions of people in their head, what are they 702 00:27:00,280 --> 00:27:01,930 really like, what are they really about. 703 00:27:01,930 --> 00:27:03,280 But they do this kind of thing. 704 00:27:03,280 --> 00:27:06,770 And here's the interesting element to the result. 705 00:27:06,770 --> 00:27:09,320 People on average, are more likely to endorse the 706 00:27:09,320 --> 00:27:11,990 statement, Linda is a bank teller and active in the 707 00:27:11,990 --> 00:27:15,060 feminist movement, than Linda is a bank teller. 708 00:27:15,060 --> 00:27:21,060 Now, why logically is that a bad idea, as you're guessing 709 00:27:21,060 --> 00:27:22,790 about Linda and what you might do? 710 00:27:22,790 --> 00:27:25,890 Why is it logically a bad idea to pick h, more than f? 711 00:27:25,890 --> 00:27:26,940 AUDIENCE: [INAUDIBLE]. 712 00:27:26,940 --> 00:27:28,360 PROFESSOR: Right. 713 00:27:28,360 --> 00:27:28,630 Yeah. 714 00:27:28,630 --> 00:27:29,860 Yeah. 715 00:27:29,860 --> 00:27:33,500 Just logically, heuristically, algorithmically, right, sorry, 716 00:27:33,500 --> 00:27:35,253 it's more likely you'd be correct saying 717 00:27:35,253 --> 00:27:35,430 she's a bank teller. 718 00:27:35,430 --> 00:27:36,970 She's a bank teller and shes-- 719 00:27:36,970 --> 00:27:38,660 But why do you think more people pick this one? 720 00:27:42,790 --> 00:27:43,570 Because of your intuition. 721 00:27:43,570 --> 00:27:45,000 So people think, well this is a person who 722 00:27:45,000 --> 00:27:46,330 might be like this? 723 00:27:46,330 --> 00:27:48,310 So they go for the bigger description. 724 00:27:48,310 --> 00:27:51,020 And not just unsuspecting undergraduates, but also 725 00:27:51,020 --> 00:27:52,960 first-year grad students in stats courses, and also 726 00:27:52,960 --> 00:27:54,630 doctoral students in decision science programs 727 00:27:54,630 --> 00:27:55,840 and business school. 728 00:27:55,840 --> 00:27:59,460 It's not, with more training you can avert these errors. 729 00:27:59,460 --> 00:28:04,060 But everybody has it in them pretty easily to be seduced by 730 00:28:04,060 --> 00:28:06,940 intuitive heuristics of reasoning, rather than 731 00:28:06,940 --> 00:28:10,380 algorithmic analyses. 732 00:28:10,380 --> 00:28:11,560 Availability is kind of fun. 733 00:28:11,560 --> 00:28:13,030 So this is a slow one. 734 00:28:15,930 --> 00:28:18,460 This example is a little work, for an example. 735 00:28:18,460 --> 00:28:20,040 Some experts studied the frequency of appearance of 736 00:28:20,040 --> 00:28:21,440 various letters in the English language. 737 00:28:21,440 --> 00:28:23,360 They studied a typical passage in English and recorded the 738 00:28:23,360 --> 00:28:25,250 relative frequency with which various letters of the 739 00:28:25,250 --> 00:28:26,840 alphabet appeared in the first and the third 740 00:28:26,840 --> 00:28:27,400 positions of words. 741 00:28:27,400 --> 00:28:28,840 For example, in the word "language," appears "L" 742 00:28:28,840 --> 00:28:31,990 appears in the first position and "N" appears in the third. 743 00:28:31,990 --> 00:28:33,530 In this study, words with less than three 744 00:28:33,530 --> 00:28:34,310 letters were not examined. 745 00:28:34,310 --> 00:28:36,730 Consider the letter "K." Do you think that the K is more 746 00:28:36,730 --> 00:28:38,210 likely to appear in the first position 747 00:28:38,210 --> 00:28:39,480 or the third position? 748 00:28:39,480 --> 00:28:42,310 Now estimate the ratio for the number of times it appears in 749 00:28:42,310 --> 00:28:45,490 the first versus the third. 750 00:28:45,490 --> 00:28:49,010 So it could be even, 1:1; it could be more often in the 751 00:28:49,010 --> 00:28:51,840 third position, like 1:2; or it could twice as often in the 752 00:28:51,840 --> 00:28:54,160 first position. 753 00:28:54,160 --> 00:28:55,410 Any feelings? 754 00:28:58,190 --> 00:29:01,110 Just for fun, who thinks K appears more often in the 755 00:29:01,110 --> 00:29:03,700 first word than the third? 756 00:29:03,700 --> 00:29:03,980 Just for fun. 757 00:29:03,980 --> 00:29:05,080 Help me out here. 758 00:29:05,080 --> 00:29:05,430 Hands up. 759 00:29:05,430 --> 00:29:06,520 If you have to pick one or the other. 760 00:29:06,520 --> 00:29:08,180 You have to pick one or the other. 761 00:29:08,180 --> 00:29:10,920 How many people like in the third position? 762 00:29:10,920 --> 00:29:14,265 Anyway, so here's what happens mostly. 763 00:29:14,265 --> 00:29:16,660 Not your estimate, but the estimate of 764 00:29:16,660 --> 00:29:17,950 students in larger samples. 765 00:29:17,950 --> 00:29:20,090 2:1, they like K in the first position. 766 00:29:20,090 --> 00:29:22,140 It's really 1:2 in the dictionary. 767 00:29:22,140 --> 00:29:24,760 Why do you think on average, people tend to go for K in the 768 00:29:24,760 --> 00:29:26,220 first position? 769 00:29:26,220 --> 00:29:29,710 So now we need somebody who's willing to say something, give 770 00:29:29,710 --> 00:29:30,740 some answers out there. 771 00:29:30,740 --> 00:29:32,050 Yeah? 772 00:29:32,050 --> 00:29:34,504 AUDIENCE: Because it's easier to think of words that start 773 00:29:34,504 --> 00:29:35,380 with K than it is-- 774 00:29:35,380 --> 00:29:36,560 PROFESSOR: Yes. 775 00:29:36,560 --> 00:29:37,250 So help me out. 776 00:29:37,250 --> 00:29:39,040 Let's do this for one second. 777 00:29:39,040 --> 00:29:40,540 Somebody call out a K word, any word? 778 00:29:40,540 --> 00:29:41,360 AUDIENCE: Knight. 779 00:29:41,360 --> 00:29:41,820 PROFESSOR: Knight. 780 00:29:41,820 --> 00:29:42,770 Another word? 781 00:29:42,770 --> 00:29:43,570 AUDIENCE: Kind. 782 00:29:43,570 --> 00:29:44,400 PROFESSOR: Kind. 783 00:29:44,400 --> 00:29:46,000 Another word? 784 00:29:46,000 --> 00:29:46,910 AUDIENCE: Kangaroo. 785 00:29:46,910 --> 00:29:47,080 PROFESSOR: Kangaroo. 786 00:29:47,080 --> 00:29:47,780 Excellent. 787 00:29:47,780 --> 00:29:48,510 OK, very good. 788 00:29:48,510 --> 00:29:50,305 OK, quick, a K in the third position? 789 00:29:50,305 --> 00:29:51,460 AUDIENCE: [INAUDIBLE]. 790 00:29:51,460 --> 00:29:52,710 PROFESSOR: Oh, you guys are-- 791 00:29:55,090 --> 00:29:58,350 I should have done it differently. 792 00:29:58,350 --> 00:30:01,340 So now we're talking about the heuristic of availability, 793 00:30:01,340 --> 00:30:05,450 that we sort of think that how common something is and then 794 00:30:05,450 --> 00:30:08,650 in some ways, how important something is, by the ease with 795 00:30:08,650 --> 00:30:09,520 which it comes to mind. 796 00:30:09,520 --> 00:30:12,510 It's easier really to think of words beginning with K, then 797 00:30:12,510 --> 00:30:14,100 having K in the third position. 798 00:30:14,100 --> 00:30:16,420 Here's a question. 799 00:30:16,420 --> 00:30:18,720 How much more likely are you, and I don't know the answer to 800 00:30:18,720 --> 00:30:20,405 this, but you can figure it out, to drown in the bathtub 801 00:30:20,405 --> 00:30:22,510 than to die from a terrorist attack in the United States? 802 00:30:22,510 --> 00:30:25,980 Obviously since 9/11, terrorism has been a a huge 803 00:30:25,980 --> 00:30:28,490 issue in our county, a huge issue. 804 00:30:28,490 --> 00:30:31,820 But in the US, what do you think? 805 00:30:31,820 --> 00:30:36,730 Much more likely for people to die drowning in a bathtub than 806 00:30:36,730 --> 00:30:39,880 terrorist attacks, including 9/11, for the 2000s. 807 00:30:39,880 --> 00:30:43,600 So why is it, why is it that you have constant discussion 808 00:30:43,600 --> 00:30:44,990 about terrorism? 809 00:30:44,990 --> 00:30:48,430 Why is it that you take off your shoes and can't take much 810 00:30:48,430 --> 00:30:50,220 of your toothpaste on the airplane? 811 00:30:50,220 --> 00:30:54,280 And yet nobody is talking to you, saying, please exercise 812 00:30:54,280 --> 00:30:55,580 caution when you go into the bathroom. 813 00:30:55,580 --> 00:30:57,930 And please have those things, those sticky things that make 814 00:30:57,930 --> 00:30:59,740 it less likely to slip. 815 00:30:59,740 --> 00:31:01,540 Do you get much of that? 816 00:31:01,540 --> 00:31:04,050 Do you go to the airport and they say, those shoes look a 817 00:31:04,050 --> 00:31:07,440 little slippery, in case you go into a shower. 818 00:31:07,440 --> 00:31:09,950 So think about this, which is the real danger, just in a 819 00:31:09,950 --> 00:31:13,500 practical way, and why do people worry so much, and not 820 00:31:13,500 --> 00:31:15,520 for bad reasons, about terrorism? 821 00:31:15,520 --> 00:31:18,830 What's the difference between them? 822 00:31:18,830 --> 00:31:21,410 And you can think of more than one. 823 00:31:21,410 --> 00:31:21,900 Yeah? 824 00:31:21,900 --> 00:31:24,104 AUDIENCE: You hear about terrorism on the news, but you 825 00:31:24,104 --> 00:31:25,830 don't hear about anyone drowning in the bathtub. 826 00:31:25,830 --> 00:31:26,280 PROFESSOR: That's huge. 827 00:31:26,280 --> 00:31:27,930 Availability is huge. 828 00:31:27,930 --> 00:31:31,400 The news is a very powerful way in which we think that's 829 00:31:31,400 --> 00:31:33,560 what the world is full of, terrorism in the US. 830 00:31:33,560 --> 00:31:35,770 We hear about terror groups that are found, terror cells 831 00:31:35,770 --> 00:31:37,750 that are found. 832 00:31:37,750 --> 00:31:42,330 We'll hear about homicides a little bit, in the local news. 833 00:31:42,330 --> 00:31:44,370 But you just never hear about, unless it unfortunately 834 00:31:44,370 --> 00:31:46,600 happens to somebody you know personally, somebody slipping 835 00:31:46,600 --> 00:31:47,690 and dying in the bathtub. 836 00:31:47,690 --> 00:31:48,730 And it does happen. 837 00:31:48,730 --> 00:31:49,990 But you don't hear about it much. 838 00:31:49,990 --> 00:31:50,720 It's not available to you. 839 00:31:50,720 --> 00:31:51,080 Yeah? 840 00:31:51,080 --> 00:31:53,972 AUDIENCE: We're most scared of things that are foreign to us 841 00:31:53,972 --> 00:31:54,936 even if they are less dangerous. 842 00:31:54,936 --> 00:31:58,068 Like we'd be more likely to drink a clear fluid that was 843 00:31:58,068 --> 00:32:00,250 more dangerous than something that looks scary. 844 00:32:00,250 --> 00:32:02,080 PROFESSOR: Part of it is, what you're saying, there's a sense 845 00:32:02,080 --> 00:32:04,420 of foreignness to the terrorism that we don't 846 00:32:04,420 --> 00:32:04,940 understand. 847 00:32:04,940 --> 00:32:06,440 Maybe there's a sense of passivity. 848 00:32:06,440 --> 00:32:08,430 We feel like we could do something about the bathroom. 849 00:32:08,430 --> 00:32:10,470 We can't do something about an airplane being 850 00:32:10,470 --> 00:32:11,860 driven into a building. 851 00:32:11,860 --> 00:32:12,960 These things are complicated. 852 00:32:12,960 --> 00:32:17,450 But it's fascinating what people think is scary versus 853 00:32:17,450 --> 00:32:19,750 what by any rational analysis, is dangerous. 854 00:32:22,480 --> 00:32:26,640 Here's another kind of way that people easily struggle 855 00:32:26,640 --> 00:32:27,900 with numbers and thinking about what's 856 00:32:27,900 --> 00:32:29,030 dangerous and not. 857 00:32:29,030 --> 00:32:31,100 So imagine you're a physician and you're presented with this 858 00:32:31,100 --> 00:32:31,380 information. 859 00:32:31,380 --> 00:32:34,090 And let's hypothesize that it's true. 860 00:32:34,090 --> 00:32:35,230 Somebody comes in with dizziness. 861 00:32:35,230 --> 00:32:37,390 And they say, I'm worried about a brain tumor. 862 00:32:37,390 --> 00:32:38,700 They go to your neurologist. 863 00:32:38,700 --> 00:32:40,630 And the neurologist says well, here we have some statistics 864 00:32:40,630 --> 00:32:43,360 over the last two years, whether somebody turned out to 865 00:32:43,360 --> 00:32:46,300 have a brain tumor or not, present or absent; and whether 866 00:32:46,300 --> 00:32:47,990 they were dizzy or not, present or absent. 867 00:32:47,990 --> 00:32:52,320 So you can make a 2 x 2 cell, about they had a tumor and 868 00:32:52,320 --> 00:32:56,040 they reported dizziness; they reported dizziness, but it 869 00:32:56,040 --> 00:32:58,280 turns out they had the tumor; and so on. 870 00:32:58,280 --> 00:33:00,330 So here's the question for you. 871 00:33:00,330 --> 00:33:03,760 Is being dizzy something to worry about in 872 00:33:03,760 --> 00:33:05,480 terms of having a tumor? 873 00:33:05,480 --> 00:33:07,680 Should you be worried if you're dizzy, that you might 874 00:33:07,680 --> 00:33:11,870 have a brain tumor, by these numbers? 875 00:33:11,870 --> 00:33:14,020 So what do you think most people think? 876 00:33:14,020 --> 00:33:16,900 You have to make a quick answer. 877 00:33:16,900 --> 00:33:19,170 You're one of those doctors on House, who's going to get 878 00:33:19,170 --> 00:33:21,320 berated in a moment. 879 00:33:21,320 --> 00:33:27,620 And you go, whoa, 160, bad. 880 00:33:27,620 --> 00:33:29,240 That's what a lot of people feel. 881 00:33:29,240 --> 00:33:32,270 And by the way, there's a lot of concern in medicine about 882 00:33:32,270 --> 00:33:36,140 how doctors convey risk to patients, as patients pick 883 00:33:36,140 --> 00:33:37,740 what kind of treatment they want. 884 00:33:37,740 --> 00:33:39,510 Because it's exactly what we're talking about now. 885 00:33:39,510 --> 00:33:42,790 It's the exact way that you convey to a patient, the risk 886 00:33:42,790 --> 00:33:45,540 they're facing or their family member is facing among 887 00:33:45,540 --> 00:33:48,250 different treatment options. 888 00:33:48,250 --> 00:33:49,740 So is dizziness associated with a brain tumor? 889 00:33:49,740 --> 00:33:53,820 And the answer is no, because if you do the probabilities, 890 00:33:53,820 --> 00:33:56,170 so the probability of a tumor is 4:1. 891 00:33:56,170 --> 00:34:00,680 So here's whether you have the tumor, brain tumor. 892 00:34:00,680 --> 00:34:03,450 And it's one out of four, and it's one out of four. 893 00:34:03,450 --> 00:34:04,980 The odds are identical. 894 00:34:04,980 --> 00:34:07,460 But you're impressed by this absolute, big number. 895 00:34:07,460 --> 00:34:12,440 Intuitively, almost everybody is, almost all the time. 896 00:34:12,440 --> 00:34:13,739 Here's another one that's a bit more subtle. 897 00:34:13,739 --> 00:34:15,489 It comes up all the time in medical testing. 898 00:34:15,489 --> 00:34:18,150 It's come up in things like discussions about how often we 899 00:34:18,150 --> 00:34:21,940 should test for, if you're a male, for prostate cancer when 900 00:34:21,940 --> 00:34:26,060 you get older; if you're a female, for breast cancer. 901 00:34:26,060 --> 00:34:28,340 So these kinds of things come up all the time in decisions 902 00:34:28,340 --> 00:34:30,820 about should you get the tests or not, and so on. 903 00:34:30,820 --> 00:34:33,130 So imagine now, a hypothetical syndrome, is a serious 904 00:34:33,130 --> 00:34:35,889 condition that affects one person in 1,000. 905 00:34:35,889 --> 00:34:38,760 Imagine also that the test to diagnose the disease always 906 00:34:38,760 --> 00:34:41,170 indicates correctly that a person who has it, 907 00:34:41,170 --> 00:34:42,899 actually has it. 908 00:34:42,899 --> 00:34:46,300 So if you actually have the disorder and you get a yes, 909 00:34:46,300 --> 00:34:47,580 you will always be identified. 910 00:34:47,580 --> 00:34:49,630 So that's a good test in that way. 911 00:34:49,630 --> 00:34:51,880 Finally, suppose the test occasionally misidentifies a 912 00:34:51,880 --> 00:34:54,620 healthy individual as having that disorder. 913 00:34:54,620 --> 00:34:56,370 So that's a false positive. 914 00:34:56,370 --> 00:34:58,340 The test is a false positive at a rate of 5%. 915 00:34:58,340 --> 00:35:00,670 Meaning the test wrongly indicates that the virus is 916 00:35:00,670 --> 00:35:02,640 present in 5% of cases where the person does 917 00:35:02,640 --> 00:35:04,380 not have the virus. 918 00:35:04,380 --> 00:35:07,280 So if you have it, it's always correct. 919 00:35:07,280 --> 00:35:10,040 But 5% of healthy people will be misidentified on the 920 00:35:10,040 --> 00:35:11,470 initial test. 921 00:35:11,470 --> 00:35:15,170 Choose a person at random, administer the test, and the 922 00:35:15,170 --> 00:35:17,950 person tests positive for that syndrome. 923 00:35:17,950 --> 00:35:21,630 What is the probability the person really has that? 924 00:35:21,630 --> 00:35:24,760 This is the kind of medical testing topic that goes on all 925 00:35:24,760 --> 00:35:26,050 the time, all the time. 926 00:35:30,840 --> 00:35:32,200 What do you think most people are going to say? 927 00:35:35,984 --> 00:35:36,930 AUDIENCE: 95%. 928 00:35:36,930 --> 00:35:38,515 PROFESSOR: Yeah, 95% exactly. 929 00:35:38,515 --> 00:35:40,950 Because he said well, 5% of the time it's wrong. 930 00:35:40,950 --> 00:35:42,840 But that ignores the base rate of this order. 931 00:35:42,840 --> 00:35:46,110 Because if there's 1/1000, then if the other 999 are 932 00:35:46,110 --> 00:35:51,140 tested, then you have about 0.5 times 999. 933 00:35:51,140 --> 00:35:53,580 That means for every 51 who are tested, only one has it. 934 00:35:53,580 --> 00:35:55,610 So the answer is only about 2%. 935 00:35:55,610 --> 00:35:58,070 Because the odds, the prior odds, that you have 936 00:35:58,070 --> 00:36:00,890 it are so, so low. 937 00:36:00,890 --> 00:36:03,470 So that comes into the likelihood that it's an 938 00:36:03,470 --> 00:36:05,710 accurate test and the false positive rate. 939 00:36:05,710 --> 00:36:07,840 So again, people's intuition when they hear, their 940 00:36:07,840 --> 00:36:11,560 intuition is there's a 95% probability that the person 941 00:36:11,560 --> 00:36:12,780 has the disorder. 942 00:36:12,780 --> 00:36:18,400 It's really only about 2%, given the low known base rate. 943 00:36:18,400 --> 00:36:20,650 And if all this is whizzing by you a bit, I know I'm going 944 00:36:20,650 --> 00:36:22,050 through it pretty fast. 945 00:36:22,050 --> 00:36:23,730 But you can think in two ways. 946 00:36:23,730 --> 00:36:25,970 You can also think you're a patient being told this. 947 00:36:25,970 --> 00:36:27,420 You're not going to get an hour long lecture on 948 00:36:27,420 --> 00:36:28,730 probabilities either. 949 00:36:28,730 --> 00:36:31,790 So how this is communicated to physicians, for example in the 950 00:36:31,790 --> 00:36:36,120 medical area and families, is a really big challenge. 951 00:36:36,120 --> 00:36:38,500 So we talked about two heuristics, that people liked 952 00:36:38,500 --> 00:36:42,850 things to look random, that we tend to go for what we often 953 00:36:42,850 --> 00:36:44,970 hear about and imagine that's how frequent or dangerous 954 00:36:44,970 --> 00:36:48,920 something is, in a very broad, intuitive way. 955 00:36:48,920 --> 00:36:50,880 Anchoring and adjustment, here's a fun one. 956 00:36:50,880 --> 00:36:52,080 So here's the experiment they did. 957 00:36:52,080 --> 00:36:54,926 They went to people, like in courtyards around things that 958 00:36:54,926 --> 00:36:56,970 are around Stanford, actually mostly. 959 00:36:56,970 --> 00:36:58,230 And they had them do the following. 960 00:36:58,230 --> 00:37:02,850 They gave them either this problem or this problem. 961 00:37:02,850 --> 00:37:05,230 And here's what happened. 962 00:37:05,230 --> 00:37:07,270 If they got this problem, and they quickly had to guess the 963 00:37:07,270 --> 00:37:10,220 answer, they came up with this many. 964 00:37:10,220 --> 00:37:12,130 This was their average estimate and this was the 965 00:37:12,130 --> 00:37:13,090 average estimate. 966 00:37:13,090 --> 00:37:14,990 So the first thing we can agree upon, regardless of the 967 00:37:14,990 --> 00:37:17,070 fact that everybody is completely underestimating the 968 00:37:17,070 --> 00:37:18,530 actual answer-- 969 00:37:18,530 --> 00:37:21,020 that's almost not the point-- 970 00:37:21,020 --> 00:37:23,810 regardless of that, look at this estimate. 971 00:37:23,810 --> 00:37:26,840 This rapid estimate is four times greater than that. 972 00:37:26,840 --> 00:37:29,260 Why is that happening? 973 00:37:29,260 --> 00:37:30,890 You're only getting one or the other. 974 00:37:30,890 --> 00:37:32,700 But why is that happening? 975 00:37:32,700 --> 00:37:35,960 This is the heuristic of anchoring, how people come to 976 00:37:35,960 --> 00:37:37,210 quick decisions. 977 00:37:41,000 --> 00:37:44,540 If we go, 8, that's a pretty big number. 978 00:37:44,540 --> 00:37:47,000 8, we're talking some big numbers here, right now. 979 00:37:47,000 --> 00:37:50,300 They don't even get that they're going to get to here. 980 00:37:50,300 --> 00:37:51,890 They start with 1 and 2. 981 00:37:51,890 --> 00:37:54,340 They go, we're talking about a pretty small thing. 982 00:37:54,340 --> 00:37:56,630 But I can't quite calculate it out, because it's too hard to 983 00:37:56,630 --> 00:37:58,930 calculate it out for most of us. 984 00:37:58,930 --> 00:38:01,110 This is going to be pretty small by the time-- so they 985 00:38:01,110 --> 00:38:02,180 are already thinking small. 986 00:38:02,180 --> 00:38:04,860 They are already thinking big. 987 00:38:04,860 --> 00:38:08,600 So now you know why, when you go negotiate for your salary 988 00:38:08,600 --> 00:38:11,490 somewhere in the future, if you have any negotiating 989 00:38:11,490 --> 00:38:13,680 power, start with a big number. 990 00:38:16,400 --> 00:38:18,250 And the person who is negotiating with you knows 991 00:38:18,250 --> 00:38:20,800 that too, and in fact is often more practiced than you are. 992 00:38:20,800 --> 00:38:22,080 So here's a practical clue. 993 00:38:22,080 --> 00:38:23,470 Start with a big number. 994 00:38:23,470 --> 00:38:24,960 Because that's the number where people's minds are. 995 00:38:24,960 --> 00:38:26,520 And I'll show you several more examples of how 996 00:38:26,520 --> 00:38:27,780 ridiculous this is. 997 00:38:27,780 --> 00:38:30,250 But when there's no true, exact number that you can 998 00:38:30,250 --> 00:38:32,590 quickly come up with, what's the fair salary for you to 999 00:38:32,590 --> 00:38:35,770 get, the first year out of MIT in a job or something? 1000 00:38:35,770 --> 00:38:39,100 First number out there, has a lot of power. 1001 00:38:39,100 --> 00:38:41,540 So here they made it ridiculous. 1002 00:38:41,540 --> 00:38:44,630 This anchoring, that my mind start somewhere and I kind of 1003 00:38:44,630 --> 00:38:45,810 stay in that neighborhood. 1004 00:38:45,810 --> 00:38:47,750 I don't recalibrate myself. 1005 00:38:47,750 --> 00:38:50,420 So they took a wheel of fortune, a spinning wheel of 1006 00:38:50,420 --> 00:38:52,100 fortune, with the numbers 1 to 100. 1007 00:38:52,100 --> 00:38:53,590 It's clearly random. 1008 00:38:53,590 --> 00:38:56,120 Can't make it more obviously random than that. 1009 00:38:56,120 --> 00:38:57,070 It stops somewhere. 1010 00:38:57,070 --> 00:38:59,270 And they say, we want you to estimate the number of African 1011 00:38:59,270 --> 00:39:00,130 nations in the UN? 1012 00:39:00,130 --> 00:39:01,320 And most people don't really know. 1013 00:39:01,320 --> 00:39:03,520 And there's some vague sense that it's more than a couple. 1014 00:39:03,520 --> 00:39:06,170 But who knows how big it is exactly. 1015 00:39:06,170 --> 00:39:08,210 So the number stops somewhere. 1016 00:39:08,210 --> 00:39:10,400 And here's the kind of thing that happens. 1017 00:39:10,400 --> 00:39:12,880 First you say, is it more or less than the number it 1018 00:39:12,880 --> 00:39:13,780 randomly stopped at? 1019 00:39:13,780 --> 00:39:16,170 Let's pretend it stopped at 65. 1020 00:39:16,170 --> 00:39:20,190 Then average estimate was 45 African nations. 1021 00:39:20,190 --> 00:39:22,110 Let's say it randomly stopped at 10. 1022 00:39:22,110 --> 00:39:26,170 Then the average estimate was 25 nations. 1023 00:39:26,170 --> 00:39:27,100 What's happening? 1024 00:39:27,100 --> 00:39:28,390 Anchoring. 1025 00:39:28,390 --> 00:39:29,890 You say well, it's more than 10. 1026 00:39:29,890 --> 00:39:33,280 But 10, I'm going to go way up from 10. 1027 00:39:33,280 --> 00:39:34,820 Because that's way too low. 1028 00:39:34,820 --> 00:39:36,250 And I'll split-- 1029 00:39:36,250 --> 00:39:38,980 going beyond 25 seems hazardous. 1030 00:39:38,980 --> 00:39:39,810 You start at 65. 1031 00:39:39,810 --> 00:39:41,650 You go, whoa, that's way too many. 1032 00:39:41,650 --> 00:39:42,900 But you're anchored. 1033 00:39:42,900 --> 00:39:45,310 And then you use don't go down that much. 1034 00:39:45,310 --> 00:39:47,870 So that first number, when you don't really know what the 1035 00:39:47,870 --> 00:39:50,970 right answer is, has an incredible power over how far 1036 00:39:50,970 --> 00:39:54,300 you range from that, for your best estimate. 1037 00:39:54,300 --> 00:39:55,450 Same idea. 1038 00:39:55,450 --> 00:39:57,340 Is the Mississippi River longer or 1039 00:39:57,340 --> 00:39:58,610 shorter than 500 miles? 1040 00:39:58,610 --> 00:40:00,120 So they're giving you the anchor. 1041 00:40:00,120 --> 00:40:01,910 How long is it actually do you think? 1042 00:40:01,910 --> 00:40:04,290 Or another person is asked, is it longer or shorter than 1043 00:40:04,290 --> 00:40:05,550 5,000 miles? 1044 00:40:05,550 --> 00:40:06,520 There's the anchor. 1045 00:40:06,520 --> 00:40:07,750 How long is it, do you think? 1046 00:40:07,750 --> 00:40:09,440 And here's the estimates. 1047 00:40:09,440 --> 00:40:11,100 They're both underestimating. 1048 00:40:11,100 --> 00:40:13,600 But they're moving towards that number that was 1049 00:40:13,600 --> 00:40:15,260 completely randomly thrown in. 1050 00:40:15,260 --> 00:40:17,360 But it's not random to the human mind. 1051 00:40:17,360 --> 00:40:19,280 We don't know what the right number is, but we're going to 1052 00:40:19,280 --> 00:40:22,240 start in the neighborhood that we first have any number 1053 00:40:22,240 --> 00:40:24,470 presented to us. 1054 00:40:24,470 --> 00:40:25,110 Framing. 1055 00:40:25,110 --> 00:40:28,900 Framing is arguably, together with availability, maybe the 1056 00:40:28,900 --> 00:40:30,910 one that's the most interesting in terms of how 1057 00:40:30,910 --> 00:40:34,715 powerful it is and how often people use it to convince you 1058 00:40:34,715 --> 00:40:37,550 that their political position, their corporate position, 1059 00:40:37,550 --> 00:40:40,540 their anything, is the way to go. 1060 00:40:40,540 --> 00:40:43,300 And once you know framing, you're empowered a bit to 1061 00:40:43,300 --> 00:40:45,560 think about that, because you hear arguments made to you 1062 00:40:45,560 --> 00:40:47,460 about what to believe in or what to support. 1063 00:40:47,460 --> 00:40:48,970 So here comes the framing. 1064 00:40:48,970 --> 00:40:50,955 Imagining the United States is preparing for the outbreak of 1065 00:40:50,955 --> 00:40:53,230 an unusual South American disease, which is expected to 1066 00:40:53,230 --> 00:40:54,870 kill 600 people. 1067 00:40:54,870 --> 00:40:56,330 Two alternative programs to combat the 1068 00:40:56,330 --> 00:40:57,960 disease have been proposed. 1069 00:40:57,960 --> 00:41:00,290 Assume that the exact scientific estimate of the 1070 00:41:00,290 --> 00:41:01,960 consequences of the programs were as follows. 1071 00:41:01,960 --> 00:41:04,580 If Program A is adopted, 200 people will be saved. 1072 00:41:04,580 --> 00:41:06,690 If Program B is adopted, there's a one-third 1073 00:41:06,690 --> 00:41:09,300 probability the 600 people will be saved and a two-thirds 1074 00:41:09,300 --> 00:41:10,590 probability that no people will be saved. 1075 00:41:10,590 --> 00:41:12,640 Which program do you favor? 1076 00:41:12,640 --> 00:41:14,590 So B is pretty complicated. 1077 00:41:14,590 --> 00:41:17,200 But A is pretty simple, 200 people might be 1078 00:41:17,200 --> 00:41:19,740 saved, will be saved. 1079 00:41:19,740 --> 00:41:26,140 So here's the exact same scenario by lives and deaths, 1080 00:41:26,140 --> 00:41:30,600 given to a second group of research participants. 1081 00:41:30,600 --> 00:41:33,190 If Program A is adopted, 400 people will die. 1082 00:41:33,190 --> 00:41:36,460 Do you see that if A is adopted, 200 people are saved, 1083 00:41:36,460 --> 00:41:40,110 equals 400 people will die. 1084 00:41:40,110 --> 00:41:40,680 It's the same thing. 1085 00:41:40,680 --> 00:41:43,040 There's 600. 1086 00:41:43,040 --> 00:41:46,300 You're really saying the same outcome. 1087 00:41:46,300 --> 00:41:50,450 But that numerical identity is treated by humans as 1088 00:41:50,450 --> 00:41:53,370 incredibly different information on which to base 1089 00:41:53,370 --> 00:41:54,220 your decision. 1090 00:41:54,220 --> 00:41:55,740 And here's what happens. 1091 00:41:55,740 --> 00:41:58,000 If people are given this scenario, which is explained 1092 00:41:58,000 --> 00:42:01,665 in terms of lives saved, 200 lives saved out of 600, 3/4 of 1093 00:42:01,665 --> 00:42:02,875 the time they pick it. 1094 00:42:02,875 --> 00:42:06,560 If people are given this scenario, 3/4 of the time they 1095 00:42:06,560 --> 00:42:07,420 don't pick it. 1096 00:42:07,420 --> 00:42:11,190 It's the exact same choice. 1097 00:42:11,190 --> 00:42:16,220 But to be human, is to gravitate towards something 1098 00:42:16,220 --> 00:42:18,650 where you save lives and avoid something 1099 00:42:18,650 --> 00:42:20,460 where you lose lives. 1100 00:42:20,460 --> 00:42:23,510 Even though numerically, it's the exact same outcome. 1101 00:42:23,510 --> 00:42:26,130 But people radically switch which one they think is the 1102 00:42:26,130 --> 00:42:27,670 correct one, by the framing of it. 1103 00:42:27,670 --> 00:42:29,610 By simply the way it's described in 1104 00:42:29,610 --> 00:42:31,880 this most simple sense. 1105 00:42:31,880 --> 00:42:33,760 So does that makes sense? 1106 00:42:33,760 --> 00:42:34,745 I mean it's very powerful. 1107 00:42:34,745 --> 00:42:37,200 When people present to you stuff, how do they present it? 1108 00:42:37,200 --> 00:42:40,700 Lives saved, lives lost; jobs made, jobs lost; whatever you 1109 00:42:40,700 --> 00:42:43,690 want, money made, money lost. 1110 00:42:43,690 --> 00:42:45,060 We don't treat them as two bits of 1111 00:42:45,060 --> 00:42:46,260 information that are equal. 1112 00:42:46,260 --> 00:42:47,280 We should. 1113 00:42:47,280 --> 00:42:51,390 That's the algorithm, out of 600 people, 1114 00:42:51,390 --> 00:42:53,430 200 people make it. 1115 00:42:53,430 --> 00:42:55,680 But that's not how we psychologically interpret it. 1116 00:42:55,680 --> 00:42:57,940 We go tremendously by, we're drawn-- 1117 00:42:57,940 --> 00:43:01,210 any answer that uses the word "saves", is a positive thing. 1118 00:43:01,210 --> 00:43:04,560 And we're averse from any answer that talks about bad 1119 00:43:04,560 --> 00:43:06,090 things, how many people will die. 1120 00:43:06,090 --> 00:43:07,730 Even though they're identical in the 1121 00:43:07,730 --> 00:43:09,310 consequence of life and death. 1122 00:43:09,310 --> 00:43:11,200 Is that clear? 1123 00:43:11,200 --> 00:43:13,040 Very powerful. 1124 00:43:13,040 --> 00:43:14,790 Here's another version of it now, moving to money. 1125 00:43:14,790 --> 00:43:16,480 And it gives you another feeling of this. 1126 00:43:16,480 --> 00:43:18,650 So you could think about the top one. 1127 00:43:18,650 --> 00:43:21,040 Would you rather have a sure gain of $75. 1128 00:43:21,040 --> 00:43:22,680 Somebody said, I'll give you $75 right now. 1129 00:43:22,680 --> 00:43:25,690 Or you can have a lottery where you have a 75% chance of 1130 00:43:25,690 --> 00:43:29,430 winning $100, so that's even more, or a 25% chance of 1131 00:43:29,430 --> 00:43:30,680 winning nothing at all. 1132 00:43:33,970 --> 00:43:35,630 Another group of people get this question. 1133 00:43:35,630 --> 00:43:37,540 We're going to take from you $75. 1134 00:43:37,540 --> 00:43:40,781 It's a rough experiment to sign up for. 1135 00:43:40,781 --> 00:43:44,220 Or you're going to be in a lottery with a 75% chance of 1136 00:43:44,220 --> 00:43:48,060 losing $100, which is even a worse loss, and 25% chance of 1137 00:43:48,060 --> 00:43:49,460 losing nothing at all. 1138 00:43:49,460 --> 00:43:52,260 So these are meant to be kind of symmetric stories. 1139 00:43:52,260 --> 00:43:56,370 But one is expressed all in terms of gain and one all in 1140 00:43:56,370 --> 00:43:57,590 terms of losses. 1141 00:43:57,590 --> 00:44:02,380 And when people are thinking about gains, 1142 00:44:02,380 --> 00:44:03,890 they're risk averse. 1143 00:44:03,890 --> 00:44:06,590 I'm not going to give up what's in my hands. 1144 00:44:06,590 --> 00:44:10,520 So 84% of the time, they take that. 1145 00:44:10,520 --> 00:44:13,110 Even though this is sort of formally similar because it's 1146 00:44:13,110 --> 00:44:17,350 a loss, now when it's losses, people are risk taking. 1147 00:44:17,350 --> 00:44:19,470 Humans are very asymmetric. 1148 00:44:19,470 --> 00:44:21,750 If things are expressed in gains, we're risk averse. 1149 00:44:21,750 --> 00:44:22,970 Let's do the safe thing. 1150 00:44:22,970 --> 00:44:25,740 I'm going to hang on to what's already in my grasp. 1151 00:44:25,740 --> 00:44:28,730 If it's in terms of loss, it's like, let's bet the house, 1152 00:44:28,730 --> 00:44:30,670 let's do any crazy, wild thing. 1153 00:44:30,670 --> 00:44:33,910 Because losses are so bad that I'll take any 1154 00:44:33,910 --> 00:44:35,900 chance to avoid a loss. 1155 00:44:35,900 --> 00:44:38,980 Even though rationally, the choices should be about the 1156 00:44:38,980 --> 00:44:42,460 same, if we were machines making choices. 1157 00:44:42,460 --> 00:44:44,060 Very big difference. 1158 00:44:44,060 --> 00:44:45,820 I mean when I first heard about these 1159 00:44:45,820 --> 00:44:46,730 things, I was stunned. 1160 00:44:46,730 --> 00:44:48,650 Because it's huge. 1161 00:44:48,650 --> 00:44:51,120 Everything that we vote for, everything that we support, 1162 00:44:51,120 --> 00:44:54,730 every decision we make about careers, and policies that we 1163 00:44:54,730 --> 00:44:57,960 are for or against, are always being expressed to us in a 1164 00:44:57,960 --> 00:44:59,350 framework like this. 1165 00:44:59,350 --> 00:45:01,920 Things that we can gain or lose in our own lives, in the 1166 00:45:01,920 --> 00:45:03,870 world, in societies. 1167 00:45:03,870 --> 00:45:07,790 And you spin around so easily by how they're framed. 1168 00:45:07,790 --> 00:45:08,980 Although now you know something. 1169 00:45:08,980 --> 00:45:10,230 And you can think about them. 1170 00:45:12,570 --> 00:45:14,830 Here's just two odds and ends things, that I thought were 1171 00:45:14,830 --> 00:45:17,470 kind of fun. 1172 00:45:17,470 --> 00:45:19,800 Other things that people find kind of challenging to think 1173 00:45:19,800 --> 00:45:22,320 their way through, just because of the way our minds 1174 00:45:22,320 --> 00:45:22,920 are constructed. 1175 00:45:22,920 --> 00:45:24,390 So here's a problem. 1176 00:45:24,390 --> 00:45:27,480 Jack is looking at Anne, but Anne is looking at George. 1177 00:45:27,480 --> 00:45:31,260 Jack is married, but George is not. 1178 00:45:31,260 --> 00:45:32,750 Here's your problem to think about. 1179 00:45:32,750 --> 00:45:39,040 Is a married person looking at an unmarried person, yes, no, 1180 00:45:39,040 --> 00:45:40,290 or cannot be determined? 1181 00:45:46,970 --> 00:45:48,560 The only reason I know the answers to these is because I 1182 00:45:48,560 --> 00:45:49,830 have the answers to these. 1183 00:45:49,830 --> 00:45:52,480 The first time I see these, I'm like, oh no. 1184 00:45:52,480 --> 00:45:57,500 So just for fun. 1185 00:45:57,500 --> 00:45:59,970 You can go by intuition and maybe you worked it out or you 1186 00:45:59,970 --> 00:46:01,170 looked ahead. 1187 00:46:01,170 --> 00:46:05,420 How many people who haven't looked ahead, say yes? 1188 00:46:05,420 --> 00:46:07,360 OK, there's some hands, 10. 1189 00:46:07,360 --> 00:46:09,360 How many say no? 1190 00:46:09,360 --> 00:46:09,580 Nobody. 1191 00:46:09,580 --> 00:46:11,750 How many say, cannot be determined? 1192 00:46:11,750 --> 00:46:14,680 The answer is, that's good. 1193 00:46:14,680 --> 00:46:18,046 Most answer, 80% say cannot be determined. 1194 00:46:18,046 --> 00:46:20,800 But the answer is this. 1195 00:46:20,800 --> 00:46:21,390 Think about this. 1196 00:46:21,390 --> 00:46:22,160 The answer is yes. 1197 00:46:22,160 --> 00:46:23,340 And here's why. 1198 00:46:23,340 --> 00:46:24,510 We don't know Anne's marital status. 1199 00:46:24,510 --> 00:46:25,200 The other two are given. 1200 00:46:25,200 --> 00:46:27,230 So we have to figure out Anne's situation. 1201 00:46:27,230 --> 00:46:28,860 Let's say she's married. 1202 00:46:28,860 --> 00:46:31,150 Then she's looking at unmarried George. 1203 00:46:31,150 --> 00:46:31,960 Let's say she's not married. 1204 00:46:31,960 --> 00:46:33,070 Then Jack is looking at unmarried Anne. 1205 00:46:33,070 --> 00:46:34,420 Is that OK? 1206 00:46:34,420 --> 00:46:36,560 This is what people in logic, call fully disjunctive 1207 00:46:36,560 --> 00:46:40,610 reasoning, where if you work your way through the 1208 00:46:40,610 --> 00:46:43,140 possibilities, you see it has to work out. 1209 00:46:43,140 --> 00:46:46,930 But that's not our intuitions. 1210 00:46:46,930 --> 00:46:51,250 I mean you're a very smart group. 1211 00:46:51,250 --> 00:46:54,620 So if it's hard for you, imagine the rest of the world 1212 00:46:54,620 --> 00:46:57,250 is not as suspicious about how to figure their problems out. 1213 00:46:57,250 --> 00:47:00,010 So it's just fantastically interesting how the human mind 1214 00:47:00,010 --> 00:47:01,410 is brilliant at some things. 1215 00:47:01,410 --> 00:47:05,850 And for other things, it could solve, it could solve, 1216 00:47:05,850 --> 00:47:07,970 intuitively, it's not often going to happen. 1217 00:47:07,970 --> 00:47:10,450 We go by these shortcuts where either we don't get the answer 1218 00:47:10,450 --> 00:47:12,690 at all or we come up with a completely wrong one, because 1219 00:47:12,690 --> 00:47:15,450 of availability or framing. 1220 00:47:15,450 --> 00:47:15,970 Here's a quick one. 1221 00:47:15,970 --> 00:47:17,100 You'll get this fast. 1222 00:47:17,100 --> 00:47:17,740 It's fun. 1223 00:47:17,740 --> 00:47:20,810 A ball and a bat cost $1.10 in total. 1224 00:47:20,810 --> 00:47:22,100 The bat cost a dollar more than the ball. 1225 00:47:22,100 --> 00:47:25,330 How much did the ball cost? 1226 00:47:25,330 --> 00:47:26,780 All right, you guys are now, you're very suspicious. 1227 00:47:26,780 --> 00:47:28,150 You're thinking it through. 1228 00:47:28,150 --> 00:47:30,330 Most people love $0.10. 1229 00:47:30,330 --> 00:47:33,330 But you can tell that's not going to work? 1230 00:47:33,330 --> 00:47:35,520 Because then the bat would have to cost $1.10 and the 1231 00:47:35,520 --> 00:47:38,700 total would be $1.20. 1232 00:47:38,700 --> 00:47:44,230 But a lot you were pulled to, most of us to, having a $1.10, 1233 00:47:44,230 --> 00:47:45,490 because we don't quite think it through. 1234 00:47:45,490 --> 00:47:46,030 There's $1.00. 1235 00:47:46,030 --> 00:47:47,060 There's $0.10. 1236 00:47:47,060 --> 00:47:49,400 And we do these shortcuts, these heuristics, instead of 1237 00:47:49,400 --> 00:47:52,250 logically working our way through. 1238 00:47:52,250 --> 00:47:54,390 One more example of this kind of thing. 1239 00:47:54,390 --> 00:47:55,950 Because now we're going to add one more 1240 00:47:55,950 --> 00:47:56,940 element just for this. 1241 00:47:56,940 --> 00:47:58,950 Imagine that the US Department of Transportation has found 1242 00:47:58,950 --> 00:48:00,860 that a particular German car is eight times more likely 1243 00:48:00,860 --> 00:48:03,370 than a typical family car to kill occupants of another car 1244 00:48:03,370 --> 00:48:04,500 in a crash. 1245 00:48:04,500 --> 00:48:06,260 The federal government is considering restricting sale 1246 00:48:06,260 --> 00:48:07,230 and use of this German car. 1247 00:48:07,230 --> 00:48:09,090 Do you think sales of the German car should be banned in 1248 00:48:09,090 --> 00:48:12,300 the US or do you think the German car should be banned 1249 00:48:12,300 --> 00:48:13,670 from being driven on the US streets? 1250 00:48:13,670 --> 00:48:18,860 So here's one, given in this study to US participants. 1251 00:48:18,860 --> 00:48:20,600 Or to do exactly the same thing, but it's 1252 00:48:20,600 --> 00:48:21,690 not a German car. 1253 00:48:21,690 --> 00:48:22,940 It's a Ford Explorer. 1254 00:48:25,960 --> 00:48:29,600 So American participants, if it's a German car, 73%, get 1255 00:48:29,600 --> 00:48:31,610 that car off the road. 1256 00:48:31,610 --> 00:48:35,230 If it's a Ford Explorer, sorry. 1257 00:48:35,230 --> 00:48:37,300 If a German car, off the streets, you get 73%, If it's 1258 00:48:37,300 --> 00:48:40,110 the American car, and Germany is making the decision, only 1259 00:48:40,110 --> 00:48:43,560 40% of the people think that Germany should do that. 1260 00:48:43,560 --> 00:48:45,400 Now, that's not purely logic. 1261 00:48:45,400 --> 00:48:47,280 What is that? 1262 00:48:47,280 --> 00:48:48,130 That's bias, right? 1263 00:48:48,130 --> 00:48:51,140 That's like, yeah, on my streets, we don't want these 1264 00:48:51,140 --> 00:48:53,720 threatening machines from overseas. 1265 00:48:53,720 --> 00:48:55,730 Germany, we've got to keep that economy going. 1266 00:48:55,730 --> 00:48:58,310 Those Ford workers are really doing a good job. 1267 00:48:58,310 --> 00:49:02,340 So of course most decisions aren't hyperrational in the 1268 00:49:02,340 --> 00:49:03,860 way we've been talking about, unknown people. 1269 00:49:03,860 --> 00:49:06,800 Of course they involve things in the world that we already 1270 00:49:06,800 --> 00:49:07,650 have feelings about. 1271 00:49:07,650 --> 00:49:10,500 So you add that in and you can see that making a truly 1272 00:49:10,500 --> 00:49:13,690 logical judgment is shockingly hard, if anything is a little 1273 00:49:13,690 --> 00:49:15,750 complicated. 1274 00:49:15,750 --> 00:49:19,370 So let me turn for the last little bit to what we think of 1275 00:49:19,370 --> 00:49:22,260 as some of the core parts of the brain for higher level 1276 00:49:22,260 --> 00:49:25,770 thought, the struggle we have between the best we have in 1277 00:49:25,770 --> 00:49:28,730 our mind and the traps we can fall into. 1278 00:49:28,730 --> 00:49:32,650 And when we talk about that, we tend to focus on sort of 1279 00:49:32,650 --> 00:49:33,820 three areas of the frontal lobe. 1280 00:49:33,820 --> 00:49:36,380 So let me just remind you of this. 1281 00:49:36,380 --> 00:49:39,370 So here's the frontal lobes. 1282 00:49:39,370 --> 00:49:41,210 And you could think of the frontal lobe I think as the 1283 00:49:41,210 --> 00:49:43,210 business end of the brain. 1284 00:49:43,210 --> 00:49:45,510 Here comes visual information coming in, auditory 1285 00:49:45,510 --> 00:49:47,800 information coming in, touch coming in. 1286 00:49:47,800 --> 00:49:51,760 Somewhere back here, would be taste. 1287 00:49:51,760 --> 00:49:54,750 So that's getting stuck in and figuring out what's out there. 1288 00:49:54,750 --> 00:49:57,350 So the front half is the business end of the brain, 1289 00:49:57,350 --> 00:49:58,360 acting upon the world. 1290 00:49:58,360 --> 00:50:01,020 Moving your hands, moving your body, making decisions about 1291 00:50:01,020 --> 00:50:02,580 what you do. 1292 00:50:02,580 --> 00:50:06,100 Motor cortex, premotor cortex, a guy's motor cortex. 1293 00:50:06,100 --> 00:50:08,340 This so-called lateral prefrontal cortex, that's 1294 00:50:08,340 --> 00:50:10,030 higher levels of thought. 1295 00:50:10,030 --> 00:50:12,900 And this bottom part of ventromedial or orbitofrontal 1296 00:50:12,900 --> 00:50:15,920 cortex, that we talked about with Phineas Gage. 1297 00:50:15,920 --> 00:50:20,260 So Oliver Sacks, in Chapter 13, talks about one example of 1298 00:50:20,260 --> 00:50:25,330 a woman who has a large tumor in this ventromedial area. 1299 00:50:25,330 --> 00:50:29,610 So sometimes I think there's a Roman philosopher who said 1300 00:50:29,610 --> 00:50:33,130 that life is composed of desires and decisions, desires 1301 00:50:33,130 --> 00:50:34,070 and decisions. 1302 00:50:34,070 --> 00:50:36,400 So this part of the brain is involved in desires and this 1303 00:50:36,400 --> 00:50:37,210 part in decisions. 1304 00:50:37,210 --> 00:50:39,210 And I'll show that, roughly speaking. 1305 00:50:39,210 --> 00:50:42,620 So here's a woman who is a former research chemist. 1306 00:50:42,620 --> 00:50:44,830 And she has a rapid personality change, becoming 1307 00:50:44,830 --> 00:50:47,450 facetious, impulsive, superficial. 1308 00:50:47,450 --> 00:50:49,430 And she a cerebral tumor in this 1309 00:50:49,430 --> 00:50:51,540 ventromedial part of the brain. 1310 00:50:51,540 --> 00:50:53,540 When I see her, she's full of quips and cracks, 1311 00:50:53,540 --> 00:50:55,020 often clever and funny. 1312 00:50:55,020 --> 00:50:57,425 Yes, father, she says to me on one occasion; yes, sister, on 1313 00:50:57,425 --> 00:50:59,230 another; yes, doctor, on a third. 1314 00:50:59,230 --> 00:50:59,960 What am I? 1315 00:50:59,960 --> 00:51:01,620 And then she make some jokes. 1316 00:51:01,620 --> 00:51:03,260 So she's constantly joking. 1317 00:51:03,260 --> 00:51:08,030 And she says, everything means nothing. 1318 00:51:08,030 --> 00:51:10,090 Nothing at all, she says with a bright smile and a tone of 1319 00:51:10,090 --> 00:51:13,020 one who makes a joke, wins an argument, or wins at poker. 1320 00:51:13,020 --> 00:51:17,090 So she just finds that sort of there are facts of the world, 1321 00:51:17,090 --> 00:51:20,010 but they just don't feel like they mean anything. 1322 00:51:20,010 --> 00:51:22,460 Because a tumor is influencing a part of the brain that we 1323 00:51:22,460 --> 00:51:26,450 understand to organize the relationship between thoughts 1324 00:51:26,450 --> 00:51:28,450 and feelings, between thoughts and feelings. 1325 00:51:28,450 --> 00:51:31,150 And that's a huge way about how we go about the world, 1326 00:51:31,150 --> 00:51:32,650 what we think, what we feel. 1327 00:51:32,650 --> 00:51:34,360 We've put it into a formula of some sense. 1328 00:51:34,360 --> 00:51:36,590 And that's how we act upon the world, whether we help 1329 00:51:36,590 --> 00:51:38,390 somebody, or don't help somebody, like somebody, or 1330 00:51:38,390 --> 00:51:39,350 don't like somebody. 1331 00:51:39,350 --> 00:51:40,860 All those sorts of things. 1332 00:51:40,860 --> 00:51:47,690 So when we think about behavior, we normally think of 1333 00:51:47,690 --> 00:51:49,830 the challenge of getting it going. 1334 00:51:49,830 --> 00:51:52,020 You get in your car and you turn on and you accelerate 1335 00:51:52,020 --> 00:51:53,190 from 0 to 60. 1336 00:51:53,190 --> 00:51:55,680 But a really interesting thing about the human brain is that 1337 00:51:55,680 --> 00:51:59,030 we have lots of habits, things we acquire about how to deal 1338 00:51:59,030 --> 00:51:59,640 with the world. 1339 00:51:59,640 --> 00:52:03,540 So just as big a problem for our brains is stopping the 1340 00:52:03,540 --> 00:52:07,490 course of action that's no longer relevant. 1341 00:52:07,490 --> 00:52:10,520 So the frontal cortex both has the job of initiating things, 1342 00:52:10,520 --> 00:52:11,870 but also stopping things. 1343 00:52:11,870 --> 00:52:14,280 And you can see it in a couple of ways, I'll show you. 1344 00:52:14,280 --> 00:52:15,860 So here's an example of a patient with a 1345 00:52:15,860 --> 00:52:17,880 frontal lobe injury. 1346 00:52:17,880 --> 00:52:21,090 They're drawn this scratchily, by a physician. 1347 00:52:21,090 --> 00:52:22,480 And here's their exact copy. 1348 00:52:22,480 --> 00:52:24,950 Do you see they can't stop? 1349 00:52:24,950 --> 00:52:26,590 Here's again the model, 1350 00:52:26,590 --> 00:52:27,750 scratched out by the physician. 1351 00:52:27,750 --> 00:52:28,460 Look at the patient. 1352 00:52:28,460 --> 00:52:30,360 The patient can't stop. 1353 00:52:30,360 --> 00:52:32,000 So the problem's not starting. 1354 00:52:32,000 --> 00:52:35,060 The problem is stopping the mental operation once it's 1355 00:52:35,060 --> 00:52:36,550 going and repeating. 1356 00:52:36,550 --> 00:52:39,250 And it's hard to put on the mental breaks. 1357 00:52:39,250 --> 00:52:42,860 Here's an example of a behavioral test of that kind. 1358 00:52:42,860 --> 00:52:45,650 And I remember this, when I was testing patients in E17 1359 00:52:45,650 --> 00:52:48,170 and E18 here, I did this a fair bit. 1360 00:52:48,170 --> 00:52:50,770 It's called the Wisconsin Card Sorting Test. 1361 00:52:50,770 --> 00:52:54,330 And what happens is, you put on four cards like these. 1362 00:52:54,330 --> 00:52:54,930 These are the key cards. 1363 00:52:54,930 --> 00:52:56,310 It's very mysterious when you give the test. 1364 00:52:56,310 --> 00:52:58,400 You just put them down. 1365 00:52:58,400 --> 00:53:00,860 And you have a pile of cards over here. 1366 00:53:00,860 --> 00:53:03,410 And you tell the person, your job is to pick a card from 1367 00:53:03,410 --> 00:53:07,090 this pile and put it below one of these cards. 1368 00:53:07,090 --> 00:53:10,370 And I'll tell you if you're right or wrong. 1369 00:53:10,370 --> 00:53:12,120 And then use that information to know where to 1370 00:53:12,120 --> 00:53:13,840 put the next card. 1371 00:53:13,840 --> 00:53:15,800 So imagine you are doing this experiment, and if you're 1372 00:53:15,800 --> 00:53:17,440 color blind, there's an extra issue. 1373 00:53:17,440 --> 00:53:17,960 I'll just warn you. 1374 00:53:17,960 --> 00:53:20,610 But you get this first card. 1375 00:53:20,610 --> 00:53:21,960 Where would you put it below? 1376 00:53:21,960 --> 00:53:23,516 Where could you put it below? 1377 00:53:23,516 --> 00:53:25,500 AUDIENCE: [INAUDIBLE]. 1378 00:53:25,500 --> 00:53:26,140 PROFESSOR: OK. 1379 00:53:26,140 --> 00:53:27,090 That has an equal number. 1380 00:53:27,090 --> 00:53:27,840 That's one possibility. 1381 00:53:27,840 --> 00:53:30,430 What's another possibility, as the rule by which you might 1382 00:53:30,430 --> 00:53:31,510 sort the card? 1383 00:53:31,510 --> 00:53:31,980 AUDIENCE: Color. 1384 00:53:31,980 --> 00:53:32,510 PROFESSOR: Color. 1385 00:53:32,510 --> 00:53:33,110 Yeah, that's green. 1386 00:53:33,110 --> 00:53:33,650 That goes with that. 1387 00:53:33,650 --> 00:53:35,340 What's another possibility that you can see? 1388 00:53:35,340 --> 00:53:35,635 Shape. 1389 00:53:35,635 --> 00:53:37,320 You can say that's a plus sign, that's a plus sign. 1390 00:53:37,320 --> 00:53:38,770 They all seem reasonable. 1391 00:53:38,770 --> 00:53:40,915 So people usually pick one of those three. 1392 00:53:40,915 --> 00:53:44,510 And if they happen to pick the one that I as a tester know 1393 00:53:44,510 --> 00:53:47,070 from my instruction sheet, it's my job to reinforce. 1394 00:53:47,070 --> 00:53:49,770 Let's pretend it starts with color. 1395 00:53:49,770 --> 00:53:51,920 If they do this, I go wrong. 1396 00:53:51,920 --> 00:53:54,020 I go, hmm. 1397 00:53:54,020 --> 00:53:55,930 And they pick another card and they try something else. 1398 00:53:55,930 --> 00:53:58,160 They pick another card and they try something else. 1399 00:53:58,160 --> 00:54:00,200 And after three or four cards, people pretty 1400 00:54:00,200 --> 00:54:02,830 much get the color. 1401 00:54:02,830 --> 00:54:04,240 And then you go get another card out. 1402 00:54:04,240 --> 00:54:07,840 You go right, right, right, right right, wrong. 1403 00:54:07,840 --> 00:54:10,980 And when I did this, I was approximately your age. 1404 00:54:10,980 --> 00:54:12,430 And most of the patients I tested were older. 1405 00:54:12,430 --> 00:54:15,180 And they go like, sonny, look back on your score sheet 1406 00:54:15,180 --> 00:54:16,140 because it's correct. 1407 00:54:16,140 --> 00:54:17,580 And I go no ma'am, no sir. 1408 00:54:17,580 --> 00:54:18,170 I'm sorry about that. 1409 00:54:18,170 --> 00:54:19,520 But it's wrong. 1410 00:54:19,520 --> 00:54:22,050 Because what you do is after 10 consecutive correct 1411 00:54:22,050 --> 00:54:26,910 responses, you switch the rule from color to shape. 1412 00:54:26,910 --> 00:54:29,510 After 10 correct shape, you switch to number. 1413 00:54:29,510 --> 00:54:30,360 And then you do that again. 1414 00:54:30,360 --> 00:54:32,400 So you switch the rule after 10. 1415 00:54:32,400 --> 00:54:35,500 So it's a test of mental flexibility, in a fairly 1416 00:54:35,500 --> 00:54:36,090 simple way. 1417 00:54:36,090 --> 00:54:37,300 Does that make sense? 1418 00:54:37,300 --> 00:54:39,040 They have to figure out what the answer is. 1419 00:54:39,040 --> 00:54:39,960 Then they're zooming along. 1420 00:54:39,960 --> 00:54:41,806 And you go, no stop. 1421 00:54:41,806 --> 00:54:44,160 And you have to come up with the next answer. 1422 00:54:44,160 --> 00:54:47,790 So here's what happens for patients with frontal lobe 1423 00:54:47,790 --> 00:54:50,490 lesions, they make a huge amount of what's called 1424 00:54:50,490 --> 00:54:52,260 perseverative errors. 1425 00:54:52,260 --> 00:54:55,550 So this is now the intellectual analog of the too 1426 00:54:55,550 --> 00:54:57,320 many squiggles. 1427 00:54:57,320 --> 00:55:01,870 Once they get that first rule, they can't stop that rule. 1428 00:55:01,870 --> 00:55:04,150 So they go color, color, color, color, color. 1429 00:55:04,150 --> 00:55:05,740 They get about 10. 1430 00:55:05,740 --> 00:55:06,640 So you switch. 1431 00:55:06,640 --> 00:55:07,730 And they just keep doing it. 1432 00:55:07,730 --> 00:55:11,150 You go no, wrong, wrong, wrong, wrong, wrong. 1433 00:55:11,150 --> 00:55:14,310 But they can't stop the rule that worked in the first 1434 00:55:14,310 --> 00:55:17,950 place, without the prefrontal cortex being intact. 1435 00:55:17,950 --> 00:55:20,800 So our mental flexibility seems to depend tremendously 1436 00:55:20,800 --> 00:55:22,470 on prefrontal cortex. 1437 00:55:22,470 --> 00:55:24,510 And you can do the same kind of thing in imaging, just to 1438 00:55:24,510 --> 00:55:25,740 make sure it's not just a patient thing. 1439 00:55:25,740 --> 00:55:29,090 And again, when you have to do this very same kind of task, 1440 00:55:29,090 --> 00:55:32,410 typical people activate prefrontal cortex to do the 1441 00:55:32,410 --> 00:55:36,490 mental manipulation of the simple problem solving task. 1442 00:55:36,490 --> 00:55:38,640 So we talked about perserveration and its hard to 1443 00:55:38,640 --> 00:55:41,000 stop and mental flexibility. 1444 00:55:41,000 --> 00:55:43,240 With ventromedial lesions, you get some weird things. 1445 00:55:43,240 --> 00:55:44,960 You get what they call utilization behaviors. 1446 00:55:44,960 --> 00:55:47,720 So again, we're having this idea what's the good thing 1447 00:55:47,720 --> 00:55:49,820 about having very powerful habits? 1448 00:55:53,400 --> 00:55:55,980 Is it very good to have very powerful habits, and we use a 1449 00:55:55,980 --> 00:55:59,470 different word for this, when you read? 1450 00:55:59,470 --> 00:55:59,930 Yeah. 1451 00:55:59,930 --> 00:56:00,820 Because that's how you're a fast reader. 1452 00:56:00,820 --> 00:56:02,990 You know how to read words really fast. 1453 00:56:02,990 --> 00:56:07,126 Is it good to have very powerful habits when you walk? 1454 00:56:07,126 --> 00:56:07,710 Yeah. 1455 00:56:07,710 --> 00:56:10,050 You don't want to go like, OK, I'm moving the leg now. 1456 00:56:10,050 --> 00:56:10,860 Everybody get ready. 1457 00:56:10,860 --> 00:56:11,780 We're moving the other leg. 1458 00:56:11,780 --> 00:56:13,540 Because you could never get out of the room very 1459 00:56:13,540 --> 00:56:14,340 efficiently. 1460 00:56:14,340 --> 00:56:17,090 So for many, many things, having very powerful habits 1461 00:56:17,090 --> 00:56:17,840 are very strong. 1462 00:56:17,840 --> 00:56:21,650 But for other things, they're not as good and you have to 1463 00:56:21,650 --> 00:56:22,270 put the brakes on. 1464 00:56:22,270 --> 00:56:24,480 So Lhermitte described patients with ventromedial 1465 00:56:24,480 --> 00:56:27,150 lesions who came in, saw in his office. 1466 00:56:27,150 --> 00:56:28,300 This was not a set-up experiment. 1467 00:56:28,300 --> 00:56:29,440 He didn't expect this. 1468 00:56:29,440 --> 00:56:30,694 There was a hammer, a nail, and a picture. 1469 00:56:30,694 --> 00:56:32,550 And the patient just goes over, up. 1470 00:56:32,550 --> 00:56:34,770 And you could imagine, if you go into the doctor's office 1471 00:56:34,770 --> 00:56:37,710 and you see a hammer, a nail, and a picture, would you go 1472 00:56:37,710 --> 00:56:39,630 over and put up the picture? 1473 00:56:39,630 --> 00:56:40,740 No. 1474 00:56:40,740 --> 00:56:42,610 I mean you know that's what you do with that stuff. 1475 00:56:42,610 --> 00:56:44,140 You know that's what's going on. 1476 00:56:44,140 --> 00:56:45,730 But you go, that's not my place to do it, even though 1477 00:56:45,730 --> 00:56:46,750 it's attempted to do it. 1478 00:56:46,750 --> 00:56:48,450 The patient could not help himself but go do that, 1479 00:56:48,450 --> 00:56:49,740 literally go do that. 1480 00:56:49,740 --> 00:56:52,090 The most dramatic example was, the patient walks in and sees 1481 00:56:52,090 --> 00:56:54,080 a hypodermic needle, pulls down his pants, and injects 1482 00:56:54,080 --> 00:56:56,810 himself in the derriere. 1483 00:56:56,810 --> 00:56:59,740 So again, he knew that was going to happen. 1484 00:56:59,740 --> 00:57:01,060 He knew that's the habit. 1485 00:57:01,060 --> 00:57:02,950 But you wouldn't go into a doctor's office and start 1486 00:57:02,950 --> 00:57:05,160 using their stuff and perform on yourself. 1487 00:57:05,160 --> 00:57:05,680 You understand. 1488 00:57:05,680 --> 00:57:08,330 The habits are completely running the show. 1489 00:57:08,330 --> 00:57:11,010 And any kind of frontal cortical control of those 1490 00:57:11,010 --> 00:57:14,030 habits has been eliminated in these patients. 1491 00:57:14,030 --> 00:57:15,115 And here's a couple more things that 1492 00:57:15,115 --> 00:57:16,920 are along that line. 1493 00:57:16,920 --> 00:57:20,550 So I need a volunteer, in your seats, to try something with 1494 00:57:20,550 --> 00:57:21,800 me for a minute. 1495 00:57:23,730 --> 00:57:25,080 It's not bad. 1496 00:57:25,080 --> 00:57:27,420 I think I've traumatized you in these examples. 1497 00:57:27,420 --> 00:57:29,250 OK, thank you. 1498 00:57:29,250 --> 00:57:30,970 If you had to answer this question, and there's no 1499 00:57:30,970 --> 00:57:31,530 correct answer. 1500 00:57:31,530 --> 00:57:33,110 But let's just talk about how you might do it. 1501 00:57:33,110 --> 00:57:35,577 How long is a man's spine, on average? 1502 00:57:35,577 --> 00:57:37,020 AUDIENCE: About two feet? 1503 00:57:37,020 --> 00:57:38,220 PROFESSOR: And how did you kind of come to that? 1504 00:57:38,220 --> 00:57:39,548 And that might be right or not. 1505 00:57:39,548 --> 00:57:42,416 AUDIENCE: My back is roughly two feet long. 1506 00:57:42,416 --> 00:57:45,290 My spine is-- 1507 00:57:45,290 --> 00:57:45,660 PROFESSOR: Yeah. 1508 00:57:45,660 --> 00:57:47,280 It's got to be less seven feet. 1509 00:57:47,280 --> 00:57:49,750 Because that's like the tallest basketball players. 1510 00:57:49,750 --> 00:57:51,950 On average, it's got to be more than a couple feet. 1511 00:57:51,950 --> 00:57:52,890 Something in that range. 1512 00:57:52,890 --> 00:57:54,680 OK, you just guessed which it was. 1513 00:57:54,680 --> 00:57:56,860 Or how fast does a horse in a race gallop? 1514 00:57:56,860 --> 00:57:59,720 Or what's the largest object normally found in the house? 1515 00:57:59,720 --> 00:58:01,854 What do you think that might be? 1516 00:58:01,854 --> 00:58:02,730 AUDIENCE: Fridge? 1517 00:58:02,730 --> 00:58:03,500 PROFESSOR: Help me out here. 1518 00:58:03,500 --> 00:58:03,830 AUDIENCE: A refrigerator. 1519 00:58:03,830 --> 00:58:04,970 PROFESSOR: A refrigerator is a good one. 1520 00:58:04,970 --> 00:58:06,052 Anybody else? 1521 00:58:06,052 --> 00:58:07,290 AUDIENCE: [INAUDIBLE]. 1522 00:58:07,290 --> 00:58:08,110 PROFESSOR: All those are good. 1523 00:58:08,110 --> 00:58:09,600 Is toaster a good answer? 1524 00:58:09,600 --> 00:58:11,310 No. 1525 00:58:11,310 --> 00:58:11,880 One more person. 1526 00:58:11,880 --> 00:58:12,650 Who's going to help me out here? 1527 00:58:12,650 --> 00:58:13,900 One more example. 1528 00:58:21,160 --> 00:58:21,780 Come on. 1529 00:58:21,780 --> 00:58:22,100 One more example. 1530 00:58:22,100 --> 00:58:22,340 It's easy. 1531 00:58:22,340 --> 00:58:23,580 OK, thank you, Rich. 1532 00:58:23,580 --> 00:58:26,090 How many camels are there in the Netherlands? 1533 00:58:26,090 --> 00:58:27,310 Now, nobody knows. 1534 00:58:27,310 --> 00:58:30,010 But tell me a thought process roughly, so you would come up 1535 00:58:30,010 --> 00:58:31,063 with an estimate. 1536 00:58:31,063 --> 00:58:32,050 AUDIENCE: 100. 1537 00:58:32,050 --> 00:58:32,600 PROFESSOR: Yeah. 1538 00:58:32,600 --> 00:58:34,830 Because are you going to figure there's a lot in the 1539 00:58:34,830 --> 00:58:35,785 Netherlands? 1540 00:58:35,785 --> 00:58:36,140 No. 1541 00:58:36,140 --> 00:58:37,850 Because there's no desert, right? 1542 00:58:37,850 --> 00:58:40,410 Where might you find a few? 1543 00:58:40,410 --> 00:58:42,610 AUDIENCE: [INAUDIBLE] 1544 00:58:42,610 --> 00:58:43,976 PROFESSOR: On a ranch. 1545 00:58:43,976 --> 00:58:46,660 I haven't been to Amsterdam for a while, so perhaps 1546 00:58:46,660 --> 00:58:50,395 there's a few loping around in the canals. 1547 00:58:50,395 --> 00:58:54,130 And some zoo, some kids' petting thing, who knows? 1548 00:58:54,130 --> 00:58:56,030 So, yeah, 100 sounds good to me. 1549 00:58:56,030 --> 00:58:58,860 But frontal patients will give really bad answers, 1550 00:58:58,860 --> 00:59:00,620 thousands or zero. 1551 00:59:00,620 --> 00:59:02,560 Or 12 feet for a spinal cord. 1552 00:59:02,560 --> 00:59:03,600 Or just something that you go like, where 1553 00:59:03,600 --> 00:59:05,250 did you come up that? 1554 00:59:05,250 --> 00:59:06,760 You can do the Price Is Right game with them. 1555 00:59:06,760 --> 00:59:08,920 They'll come up with more ridiculous answers. 1556 00:59:08,920 --> 00:59:09,950 Here's just a graph. 1557 00:59:09,950 --> 00:59:12,770 These are bad answers for how expensive something is, just 1558 00:59:12,770 --> 00:59:14,280 unreasonable answers within these sort 1559 00:59:14,280 --> 00:59:15,760 of open-ended questions. 1560 00:59:15,760 --> 00:59:18,330 Or you can do more formal problems. 1561 00:59:18,330 --> 00:59:20,640 The kind of problem where you're given 1562 00:59:20,640 --> 00:59:21,380 a puzzle like this. 1563 00:59:21,380 --> 00:59:24,900 And your job is to move these into a target state. 1564 00:59:24,900 --> 00:59:27,730 And so to get over to here, you've got to move the blue 1565 00:59:27,730 --> 00:59:32,160 guy over here and whatever, in a different number of moves. 1566 00:59:32,160 --> 00:59:34,310 That's planning ahead and problem solving. 1567 00:59:34,310 --> 00:59:36,790 Patients with frontal lobe lesions make many mistakes on 1568 00:59:36,790 --> 00:59:39,470 these kinds of tasks. 1569 00:59:39,470 --> 00:59:41,210 So the last thing I want to talk is a little bit about 1570 00:59:41,210 --> 00:59:44,390 Phineas Gage kind of lesions. 1571 00:59:44,390 --> 00:59:49,120 And we'll get in a moment to a comment about serial killers. 1572 00:59:49,120 --> 00:59:51,120 So Phineas Gage had this huge injury. 1573 00:59:51,120 --> 00:59:52,320 We talked about him already in the 1574 00:59:52,320 --> 00:59:55,730 ventromedial prefrontal cortex. 1575 00:59:55,730 --> 00:59:58,090 There's some other patients who have orbitofrontal 1576 00:59:58,090 --> 00:59:59,030 lesions, who have been studied. 1577 00:59:59,030 --> 01:00:00,210 And let me tell you a little bit about 1578 01:00:00,210 --> 01:00:02,050 what's known about them. 1579 01:00:02,050 --> 01:00:03,590 And I can tell you this is the part of the brain that's been 1580 01:00:03,590 --> 01:00:07,180 most implicated in psychopaths, some of whom turn 1581 01:00:07,180 --> 01:00:10,110 out to be these serial killers. 1582 01:00:10,110 --> 01:00:12,360 One measure they've used in these experiments is galvanic 1583 01:00:12,360 --> 01:00:13,250 skin response. 1584 01:00:13,250 --> 01:00:14,360 That's the kind of stuff they use in 1585 01:00:14,360 --> 01:00:15,730 polygraphs, lie detectors. 1586 01:00:15,730 --> 01:00:19,220 They put them on your skin and what they measure is responses 1587 01:00:19,220 --> 01:00:22,190 that are related to sweat glands. 1588 01:00:22,190 --> 01:00:26,430 So it's a measure of emotional reactivity, basically. 1589 01:00:26,430 --> 01:00:28,020 Here's a really interesting thing. 1590 01:00:28,020 --> 01:00:30,820 If we measure your galvanic skin response and we show you 1591 01:00:30,820 --> 01:00:33,320 faces of people you know, family members, friends, and 1592 01:00:33,320 --> 01:00:36,730 so on, versus people you don't know, you get a galvanic skin 1593 01:00:36,730 --> 01:00:38,510 response for the people you know, because 1594 01:00:38,510 --> 01:00:39,780 they matter to you. 1595 01:00:39,780 --> 01:00:44,030 It's an emotional response, versus faces you don't know. 1596 01:00:44,030 --> 01:00:46,350 You even get that kind of strikingly in several studies 1597 01:00:46,350 --> 01:00:47,850 in patients with prosopagnosia. 1598 01:00:47,850 --> 01:00:50,280 So we talked about those patients who can't recognize 1599 01:00:50,280 --> 01:00:51,890 people by their faces. 1600 01:00:51,890 --> 01:00:54,620 But something in them still recognizes who's 1601 01:00:54,620 --> 01:00:57,120 familiar and who's not. 1602 01:00:57,120 --> 01:01:00,030 Even if they can't tell you, that's my mother or that's my 1603 01:01:00,030 --> 01:01:02,980 father or that's my brother, their galvanic skin response 1604 01:01:02,980 --> 01:01:06,210 is a pretty good response. 1605 01:01:06,210 --> 01:01:07,520 Patients with orbitofrontal lesions 1606 01:01:07,520 --> 01:01:09,140 are exactly the opposite. 1607 01:01:09,140 --> 01:01:11,830 When they're shown pictures of family members and friends, 1608 01:01:11,830 --> 01:01:15,280 they have no more of a response in the sweat glands, 1609 01:01:15,280 --> 01:01:17,300 in the autonomic nervous system, than they do for 1610 01:01:17,300 --> 01:01:19,120 absolute strangers. 1611 01:01:19,120 --> 01:01:22,220 And you might intuitively imagine that it's pretty easy 1612 01:01:22,220 --> 01:01:25,730 to lie and deceive your friends and family, if they 1613 01:01:25,730 --> 01:01:27,420 don't mean to you anything more 1614 01:01:27,420 --> 01:01:28,670 than an absolute stranger. 1615 01:01:30,890 --> 01:01:32,640 No galvanic response to emotional 1616 01:01:32,640 --> 01:01:33,990 versus neutral pictures. 1617 01:01:33,990 --> 01:01:36,420 And so these patients seem to have this disconnection 1618 01:01:36,420 --> 01:01:37,660 between thought and emotion. 1619 01:01:37,660 --> 01:01:39,160 They know who the person is. 1620 01:01:39,160 --> 01:01:41,620 But they don't have an emotional response of any kind 1621 01:01:41,620 --> 01:01:43,460 to that person. 1622 01:01:43,460 --> 01:01:46,020 And here's one clever experiment that shows some of 1623 01:01:46,020 --> 01:01:47,560 the consequence of that. 1624 01:01:47,560 --> 01:01:49,780 So this is work from Damasio and his colleagues. 1625 01:01:49,780 --> 01:01:51,670 So they took a couple of these patients. 1626 01:01:51,670 --> 01:01:53,020 There's not that many of them. 1627 01:01:53,020 --> 01:01:55,160 And they put them into an experiment where you could 1628 01:01:55,160 --> 01:01:56,410 pick from two decks. 1629 01:01:56,410 --> 01:01:58,100 One deck has a low immediate reward, but 1630 01:01:58,100 --> 01:01:59,860 positive long-term rewards. 1631 01:01:59,860 --> 01:02:02,410 It's a little bit like studying a lot, and hoping it 1632 01:02:02,410 --> 01:02:03,930 all pays off. 1633 01:02:03,930 --> 01:02:06,490 And another one is high immediate rewards, but higher 1634 01:02:06,490 --> 01:02:07,270 long-term losses. 1635 01:02:07,270 --> 01:02:10,860 Like not studying has some unpleasant consequences. 1636 01:02:10,860 --> 01:02:12,040 So two decks of cards. 1637 01:02:12,040 --> 01:02:15,570 And you don't know this, but you just quickly discover that 1638 01:02:15,570 --> 01:02:21,120 Pile A, you get the occasional $50, but you often lose $100. 1639 01:02:21,120 --> 01:02:25,460 But Pile B, you get the thrill of the $100 loss, but you 1640 01:02:25,460 --> 01:02:27,730 often lose $1,000. 1641 01:02:27,730 --> 01:02:30,310 So they're kind of pitting against each other, the thrill 1642 01:02:30,310 --> 01:02:36,500 of the $100 win, versus the certain doom of the quick 1643 01:02:36,500 --> 01:02:38,450 thrill pile. 1644 01:02:38,450 --> 01:02:41,510 You get the quick thrill, but it will doom you. 1645 01:02:41,510 --> 01:02:43,270 So what do typical people do? 1646 01:02:43,270 --> 01:02:45,480 After a little while, they figure out well, Pile B is 1647 01:02:45,480 --> 01:02:46,680 pretty fun when I get the $100. 1648 01:02:46,680 --> 01:02:49,270 But it beats me up a lot in the long term, so they go for 1649 01:02:49,270 --> 01:02:50,180 Pile A. 1650 01:02:50,180 --> 01:02:51,650 Orbitofrontal patients don't. 1651 01:02:51,650 --> 01:02:54,470 They go for that one-time thrill, over any long-term 1652 01:02:54,470 --> 01:02:56,720 consequences. 1653 01:02:56,720 --> 01:02:59,830 As interesting as that, if you measure the galvanic skin 1654 01:02:59,830 --> 01:03:05,650 response, for control people, typical people, when they go 1655 01:03:05,650 --> 01:03:10,040 for the risky pile, their heart is pounding, their hands 1656 01:03:10,040 --> 01:03:10,230 are sweating. 1657 01:03:10,230 --> 01:03:13,390 And they go, I'm going to walk on the wild side. 1658 01:03:13,390 --> 01:03:15,320 Back to the save file, mostly. 1659 01:03:15,320 --> 01:03:17,340 No galvanic skin response for the patients with 1660 01:03:17,340 --> 01:03:17,690 orbitofrontal lesions. 1661 01:03:17,690 --> 01:03:18,117 Yeah? 1662 01:03:18,117 --> 01:03:20,370 AUDIENCE: Do people get the lesions 1663 01:03:20,370 --> 01:03:22,930 after reaching adulthood? 1664 01:03:22,930 --> 01:03:23,640 PROFESSOR: So this is interesting. 1665 01:03:23,640 --> 01:03:26,620 I'm going to come to this in a moment. 1666 01:03:26,620 --> 01:03:29,330 Actually literally, absolutely right now. 1667 01:03:29,330 --> 01:03:31,680 Same pattern for people who have these injuries at 15 1668 01:03:31,680 --> 01:03:34,500 months or three months, as happens in adulthood. 1669 01:03:34,500 --> 01:03:36,590 Now, we don't know if that's everybody. 1670 01:03:36,590 --> 01:03:39,290 But these are patients who came forth with problems. 1671 01:03:39,290 --> 01:03:40,800 So this is a little bit like, we don't know if there are a 1672 01:03:40,800 --> 01:03:42,860 lot of people with damage there, who are not doing this. 1673 01:03:42,860 --> 01:03:47,160 But these two patients who they studied, had that. 1674 01:03:47,160 --> 01:03:48,380 And they got in lots of trouble in 1675 01:03:48,380 --> 01:03:49,220 school, all the time. 1676 01:03:49,220 --> 01:03:51,770 They did all the things you might imagine a 1677 01:03:51,770 --> 01:03:53,660 three-year-old, and for the rest of us, like 1678 01:03:53,660 --> 01:03:55,700 Phineas Gage would do. 1679 01:03:55,700 --> 01:03:57,890 So that raises the question about whether the kinds of 1680 01:03:57,890 --> 01:04:02,020 people who behave in this most disturbing, psychopathic, 1681 01:04:02,020 --> 01:04:06,540 serial killer fashion have either an injury or something 1682 01:04:06,540 --> 01:04:11,310 like that, that makes them predisposed to being very 1683 01:04:11,310 --> 01:04:12,810 inhumane in the way they think about people. 1684 01:04:15,480 --> 01:04:18,010 And just in case you, as careful MIT scientists, worry 1685 01:04:18,010 --> 01:04:20,210 about well, maybe they're not having a GSR that works at 1686 01:04:20,210 --> 01:04:22,220 all, what does that mean? 1687 01:04:22,220 --> 01:04:25,720 If these patients who had the damage early in childhood just 1688 01:04:25,720 --> 01:04:30,780 get a loud tone, a big boat horn, like ooh, there's a 1689 01:04:30,780 --> 01:04:32,620 galvanic skin response that looks pretty good. 1690 01:04:32,620 --> 01:04:35,880 So it's not their autonomic system won't respond to 1691 01:04:35,880 --> 01:04:38,430 emotionally provocative things, like a big sound. 1692 01:04:38,430 --> 01:04:40,980 It just doesn't respond to emotionally provocative things 1693 01:04:40,980 --> 01:04:44,600 like risk and people you should care about. 1694 01:04:48,250 --> 01:04:49,810 And I'll just end in this one minute. 1695 01:04:49,810 --> 01:04:54,650 So this is an area where a guy, Kent Khiel, at the 1696 01:04:54,650 --> 01:04:55,350 University of New Mexico. 1697 01:04:55,350 --> 01:04:56,150 You can go look this up. 1698 01:04:56,150 --> 01:04:57,850 It's an amazing story. 1699 01:04:57,850 --> 01:05:01,830 He's very interested in these brain differences in 1700 01:05:01,830 --> 01:05:02,640 psychopaths in prison. 1701 01:05:02,640 --> 01:05:05,280 Many are in prison because they're scary people. 1702 01:05:05,280 --> 01:05:07,150 So you can't get them out to do research very easily. 1703 01:05:07,150 --> 01:05:09,070 So he has a truck with an MR scanner. 1704 01:05:09,070 --> 01:05:12,030 And he drives from penitentiary to penitentiary 1705 01:05:12,030 --> 01:05:14,240 and does brain scanning with these people. 1706 01:05:14,240 --> 01:05:16,650 And is getting on average, some differences 1707 01:05:16,650 --> 01:05:18,480 in this brain region. 1708 01:05:18,480 --> 01:05:22,600 So I have a question for you, just for one minute. 1709 01:05:22,600 --> 01:05:24,935 Do you think that should be introduced in a court case for 1710 01:05:24,935 --> 01:05:26,185 a serial killer? 1711 01:05:30,840 --> 01:05:34,510 If he gets up there and shows you brain pictures, should a 1712 01:05:34,510 --> 01:05:38,550 jury hear about that or should they not hear about that, for 1713 01:05:38,550 --> 01:05:41,416 somebody who's done an awful murder or an 1714 01:05:41,416 --> 01:05:42,060 awful string murders? 1715 01:05:42,060 --> 01:05:43,330 AUDIENCE: I'd say no. 1716 01:05:43,330 --> 01:05:44,430 PROFESSOR: Sorry. 1717 01:05:44,430 --> 01:05:45,000 You say no. 1718 01:05:45,000 --> 01:05:45,695 Because what? 1719 01:05:45,695 --> 01:05:49,102 AUDIENCE: I think that the risk is too great that you 1720 01:05:49,102 --> 01:05:51,587 will have innocent people be shown. 1721 01:05:51,587 --> 01:05:54,072 And the science is not fully resolved yet in that respect. 1722 01:05:54,072 --> 01:05:56,805 There are also a lot of salient factors and 1723 01:05:56,805 --> 01:05:59,539 environmental factors that have been constrained by 1724 01:05:59,539 --> 01:06:03,515 genetics, and say, oh, if you have this huge issue, this 1725 01:06:03,515 --> 01:06:07,550 condition of the brain, you're automatically [INAUDIBLE]. 1726 01:06:07,550 --> 01:06:08,260 PROFESSOR: OK. 1727 01:06:08,260 --> 01:06:09,330 Let me tell one sentence. 1728 01:06:09,330 --> 01:06:10,230 Yeah, go ahead. 1729 01:06:10,230 --> 01:06:13,530 AUDIENCE: You know the correlation in one direction-- 1730 01:06:13,530 --> 01:06:15,780 PROFESSOR: We don't know. 1731 01:06:15,780 --> 01:06:16,430 These are good points. 1732 01:06:16,430 --> 01:06:18,480 Let me just say, so here's where we stand now, because 1733 01:06:18,480 --> 01:06:20,320 this will be your lives in the future, in a lot 1734 01:06:20,320 --> 01:06:21,940 of different ways. 1735 01:06:21,940 --> 01:06:25,580 Where we stand now is, for the first time ever, brain imaging 1736 01:06:25,580 --> 01:06:29,180 evidence was introduced in the court case about a year ago of 1737 01:06:29,180 --> 01:06:31,300 a serial killer. 1738 01:06:31,300 --> 01:06:33,120 They were only allowed to do it-- 1739 01:06:33,120 --> 01:06:34,820 court cases like this go through two phases. 1740 01:06:34,820 --> 01:06:36,520 You see these in TV shows or movies. 1741 01:06:36,520 --> 01:06:38,300 There's the initial trial. 1742 01:06:38,300 --> 01:06:40,440 And if the person is found guilty, there's a second phase 1743 01:06:40,440 --> 01:06:42,280 where they decide what the appropriate penalty is. 1744 01:06:42,280 --> 01:06:46,520 The judge said they could only be reported in the penalty 1745 01:06:46,520 --> 01:06:48,350 phase, when people were thinking about what's the 1746 01:06:48,350 --> 01:06:51,610 right punishment for a person, not before the conviction. 1747 01:06:51,610 --> 01:06:54,640 And they said they couldn't show any color pictures of 1748 01:06:54,640 --> 01:06:55,660 brain stuff, because that would 1749 01:06:55,660 --> 01:06:58,510 over-influence the jurors. 1750 01:06:58,510 --> 01:07:00,330 And so you could only talk about this stuff. 1751 01:07:00,330 --> 01:07:02,040 Because if you showed a brain picture, sort of like you 1752 01:07:02,040 --> 01:07:04,600 said, it would look so scientific that they were 1753 01:07:04,600 --> 01:07:08,330 afraid it overwhelm the jurors' judgment. 1754 01:07:08,330 --> 01:07:11,690 So this is all coming up, about what the brain maybe do 1755 01:07:11,690 --> 01:07:12,570 in your lifetimes. 1756 01:07:12,570 --> 01:07:13,820 Thanks.