1 00:00:00,500 --> 00:00:04,006 The following content is provided by MIT OpenCourseWare 2 00:00:04,006 --> 00:00:07,011 under a Creative Commons license. 3 00:00:07,011 --> 00:00:10,516 Additional information about our license at MIT 4 00:00:10,516 --> 00:00:12,520 OpenCourseWare in general is available at ocw.mit.edu. 5 00:01:40,160 --> 00:01:42,260 PROFESSOR: The midterm marks is sort of an interesting 6 00:01:42,260 --> 00:01:47,090 dividing point in the subject material of the course. 7 00:01:47,090 --> 00:01:49,450 It's not an absolute division by any stretch of the 8 00:01:49,450 --> 00:01:52,430 imagination, but it's a division. 9 00:01:52,430 --> 00:01:59,140 Up to this point we've been talking about things that we 10 00:01:59,140 --> 00:02:01,230 could measure of various varieties 11 00:02:01,230 --> 00:02:06,150 like your memory span. 12 00:02:11,150 --> 00:02:16,510 You can remember 7 plus or minus 2, color names, you can 13 00:02:16,510 --> 00:02:20,640 see these colors, you can do this, you can do that-- 14 00:02:20,640 --> 00:02:26,800 in these cases the you has been a sort of a plural you 15 00:02:26,800 --> 00:02:32,560 and the data point of interest has been the average data 16 00:02:32,560 --> 00:02:34,160 point, the mean data point, what's 17 00:02:34,160 --> 00:02:38,330 normal, what's standard. 18 00:02:38,330 --> 00:02:46,630 We measure people's ability to memorize color names, the 19 00:02:46,630 --> 00:02:50,710 average of that we've decided is going to be 7. 20 00:02:50,710 --> 00:02:54,080 They'll be some variation around that, but it's the 7 21 00:02:54,080 --> 00:02:56,140 that's been interesting. 22 00:02:56,140 --> 00:03:00,710 That turns out not to be the case in the same way when 23 00:03:00,710 --> 00:03:04,900 you're talking about something like intelligence. 24 00:03:04,900 --> 00:03:07,880 Particularly, intelligence testing-- announcing that on 25 00:03:07,880 --> 00:03:10,340 average people have average 26 00:03:10,340 --> 00:03:13,850 intelligence is kind of boring. 27 00:03:13,850 --> 00:03:17,050 What's interesting about intelligence and what is 28 00:03:17,050 --> 00:03:23,250 interesting about things like personality is the variation 29 00:03:23,250 --> 00:03:28,010 around the mean and the explanation of that variation. 30 00:03:28,010 --> 00:03:31,550 And that's what we need to start talking about now. 31 00:03:31,550 --> 00:03:36,890 Oh by the way, while it is of course, by definition the case 32 00:03:36,890 --> 00:03:40,470 that on average people have average intelligence, they 33 00:03:40,470 --> 00:03:42,130 don't believe it. 34 00:03:42,130 --> 00:03:47,940 If you ask people, do you think that you are more 35 00:03:47,940 --> 00:03:50,780 intelligent, less intelligent or about of average 36 00:03:50,780 --> 00:03:53,700 intelligence compared to the rest of the population you'll 37 00:03:53,700 --> 00:03:55,780 get some distribution around that, too. 38 00:03:55,780 --> 00:03:59,880 But you'll find out that the average perception of 39 00:03:59,880 --> 00:04:05,060 intelligence is that we're all above average. 40 00:04:05,060 --> 00:04:09,570 This goes for a variety of other questions like, how good 41 00:04:09,570 --> 00:04:11,270 looking are you? 42 00:04:11,270 --> 00:04:14,040 Above average, below average, or average? 43 00:04:14,040 --> 00:04:18,970 Well, we're all a little above average there, too. 44 00:04:18,970 --> 00:04:23,760 It turns out that there is one group that gets the answer 45 00:04:23,760 --> 00:04:28,790 correct, that if you ask this group of people on average-- 46 00:04:28,790 --> 00:04:31,710 you brighter or dumber than average?-- they'll come out in 47 00:04:31,710 --> 00:04:32,030 the middle. 48 00:04:32,030 --> 00:04:36,550 You ask them, are you cuter or uglier than average-- they'll 49 00:04:36,550 --> 00:04:38,070 come out average. 50 00:04:38,070 --> 00:04:41,820 Anybody know who that group is? 51 00:04:41,820 --> 00:04:43,360 [UNINTELLIGIBLE] 52 00:04:43,360 --> 00:04:45,830 Hand, we need a hand. 53 00:04:45,830 --> 00:04:49,090 No hands, nobody who cares to speculate-- yeah, yeah. 54 00:04:49,090 --> 00:04:51,690 Yes, you with the computer there. 55 00:04:51,690 --> 00:04:52,060 AUDIENCE: Kids. 56 00:04:52,060 --> 00:04:53,750 PROFESSOR: No, I don't think so. 57 00:04:53,750 --> 00:04:55,480 I don't know what the answer is. 58 00:04:55,480 --> 00:04:57,810 Certainly it's not true for parents of kids-- all of whom 59 00:04:57,810 --> 00:05:01,190 know their children are above average. 60 00:05:01,190 --> 00:05:02,790 AUDIENCE: Depressed people? 61 00:05:02,790 --> 00:05:04,930 PROFESSOR: Yes, it's the depressed. 62 00:05:04,930 --> 00:05:07,580 Depressed people have an accurate assessment of their 63 00:05:07,580 --> 00:05:09,010 own intelligence and good looks. 64 00:05:12,180 --> 00:05:20,740 In fact, it has been seriously argued that part of what keeps 65 00:05:20,740 --> 00:05:23,300 us undepressed is an unrealistic 66 00:05:23,300 --> 00:05:25,020 assessment of the world. 67 00:05:25,020 --> 00:05:28,590 We're smart, we're good looking, we're going places. 68 00:05:28,590 --> 00:05:33,980 If you knew the truth we'd all be depressed. 69 00:05:33,980 --> 00:05:38,710 But that's the topic for another day. 70 00:05:38,710 --> 00:05:41,610 Like when you get the midterm back. 71 00:05:41,610 --> 00:05:44,380 Oh, I shouldn't have said that. 72 00:05:47,240 --> 00:05:50,100 But the midterm doesn't depress us actually, it 73 00:05:50,100 --> 00:05:54,350 provides a certain amount of lighthearted merriment for us 74 00:05:54,350 --> 00:05:59,570 you'll be happy to know because people do come up with 75 00:05:59,570 --> 00:06:01,090 some great answers to stuff. 76 00:06:05,800 --> 00:06:13,570 So, as I say, we've been interested mostly in the mean 77 00:06:13,570 --> 00:06:18,170 value of measurements to this point. 78 00:06:18,170 --> 00:06:21,920 Now we're interested in the variation, the distribution of 79 00:06:21,920 --> 00:06:25,460 those points for a wide range of measures. 80 00:06:25,460 --> 00:06:30,410 If you measure a whole population of people and you 81 00:06:30,410 --> 00:06:38,250 count the number of people-- whoops-- who fall into each 82 00:06:38,250 --> 00:06:41,840 bin, you know, have a bunch of bins here-- different scores 83 00:06:41,840 --> 00:06:43,460 on a test, let's say. 84 00:06:43,460 --> 00:06:47,070 You'll get one of these bell shaped or normal curved 85 00:06:47,070 --> 00:06:50,460 distributions. 86 00:06:50,460 --> 00:06:52,280 The questions that we're interested in here are 87 00:06:52,280 --> 00:06:56,650 questions about, where does that variability come from? 88 00:06:56,650 --> 00:07:03,300 The variance of a distribution is the sum of the squares of 89 00:07:03,300 --> 00:07:06,570 the difference between the mean and the data points. 90 00:07:06,570 --> 00:07:10,560 So, got the mean, got a data point-- take that distance, 91 00:07:10,560 --> 00:07:13,410 square it, sum all those up and divide by the number of 92 00:07:13,410 --> 00:07:14,650 observations. 93 00:07:14,650 --> 00:07:19,760 That's the variance and the square root of that is one 94 00:07:19,760 --> 00:07:28,540 standard deviation away from the mean, so this would be-- 95 00:07:28,540 --> 00:07:33,400 and those units of standard deviation are sort of the yard 96 00:07:33,400 --> 00:07:36,950 stick for how far away you are from the mean of a 97 00:07:36,950 --> 00:07:37,850 distribution. 98 00:07:37,850 --> 00:07:43,490 So if you take something like the SAT, for example, the SAT 99 00:07:43,490 --> 00:07:48,620 is scaled in such a way that the mean is intended 100 00:07:48,620 --> 00:07:51,740 to be at about 500. 101 00:07:51,740 --> 00:07:55,870 At least that's where they started and each hundred 102 00:07:55,870 --> 00:08:00,720 points is one standard deviation away. 103 00:08:00,720 --> 00:08:06,620 What that means, for example, is that if you've got 700 or 104 00:08:06,620 --> 00:08:10,260 above that the number of people getting 700 or above on 105 00:08:10,260 --> 00:08:18,770 a properly normed SAT type test is about 2.5% of the 106 00:08:18,770 --> 00:08:21,880 population-- actually, that line would be at 1.96 as it 107 00:08:21,880 --> 00:08:24,450 shows on the handout if you want the 2.5% point. 108 00:08:24,450 --> 00:08:27,370 But roughly speaking two standard deviations above the 109 00:08:27,370 --> 00:08:33,160 mean gives you about 2.5% of the population. 110 00:08:33,160 --> 00:08:36,150 Three standard deviations above the mean, so that 800 111 00:08:36,150 --> 00:08:37,790 score gives you-- 112 00:08:37,790 --> 00:08:41,820 I can't remember-- it's a very small percentage of a normal 113 00:08:41,820 --> 00:08:44,610 population above the mean. 114 00:08:44,610 --> 00:08:48,900 That's at least the ideal for something like an SAT test. 115 00:08:48,900 --> 00:08:53,810 Works well for the SAT 1 Math and Verbal kinds of things. 116 00:08:53,810 --> 00:08:56,280 They are more or less normally distributed. 117 00:08:56,280 --> 00:08:59,100 The place where it's a disaster is things 118 00:08:59,100 --> 00:09:01,090 like the SAT 2. 119 00:09:01,090 --> 00:09:03,000 How many of you took the SAT 2? 120 00:09:03,000 --> 00:09:04,150 Their are two versions of it, right? 121 00:09:04,150 --> 00:09:06,070 There's the hard version, the easy version. 122 00:09:06,070 --> 00:09:09,140 How many of you took the hard version of the SAT 2? 123 00:09:09,140 --> 00:09:11,660 INTERPOSING VOICES] 124 00:09:11,660 --> 00:09:12,410 PROFESSOR: What? 125 00:09:12,410 --> 00:09:14,390 Oh, sorry, the math. 126 00:09:14,390 --> 00:09:17,780 There are two versions of the math. 127 00:09:17,780 --> 00:09:20,820 I got my jargon wrong because they used to be called 128 00:09:20,820 --> 00:09:24,640 achievement tests, they're now SAT 2s, right? 129 00:09:24,640 --> 00:09:26,840 Is it still AB and BC or something? 130 00:09:26,840 --> 00:09:29,410 [INTERPOSING VOICES] 131 00:09:29,410 --> 00:09:30,670 PROFESSOR: Well, whatever it is-- 132 00:09:30,670 --> 00:09:33,090 I don't care what it is. 133 00:09:33,090 --> 00:09:35,710 This is-- whoa, I'm losing my glasses now. 134 00:09:35,710 --> 00:09:37,020 Getting too excited here. 135 00:09:37,020 --> 00:09:39,490 The problem is I took the easy version. 136 00:09:39,490 --> 00:09:43,050 I took the easy version of it because I looked at this 137 00:09:43,050 --> 00:09:48,520 distribution and I realized that the whatever it is-- the 138 00:09:48,520 --> 00:09:53,580 fancy version of the SAT 2 actually has a distribution 139 00:09:53,580 --> 00:09:58,390 that looks like this, which is to say everybody who takes it 140 00:09:58,390 --> 00:10:03,610 gets an 800 on it and those of us, mathematical incompetents 141 00:10:03,610 --> 00:10:04,680 who were going to score-- 142 00:10:04,680 --> 00:10:08,090 I don't know, 760 or something, we were going to 143 00:10:08,090 --> 00:10:10,940 come out in the fifth percentile and not from the 144 00:10:10,940 --> 00:10:12,740 good side of the distribution. 145 00:10:12,740 --> 00:10:18,800 So I took what was then the achievement test because I 146 00:10:18,800 --> 00:10:21,070 knew that-- 147 00:10:21,070 --> 00:10:24,520 the math 1 achievement test-- because I knew that I could 148 00:10:24,520 --> 00:10:26,660 score in the upper end of the distribution. 149 00:10:26,660 --> 00:10:28,810 The other people taking that were people who couldn't add 150 00:10:28,810 --> 00:10:30,330 and subtract. 151 00:10:30,330 --> 00:10:34,800 So anyway, it worked for me. 152 00:10:34,800 --> 00:10:41,820 Where does variability on tests like this come from? 153 00:10:41,820 --> 00:10:46,880 There are lots and lots of potential sources of 154 00:10:46,880 --> 00:10:47,900 variability. 155 00:10:47,900 --> 00:10:55,670 One important point to make at the moment is to note that 156 00:10:55,670 --> 00:10:59,330 things that cause variability across groups are not 157 00:10:59,330 --> 00:11:01,570 necessarily the same things the cause 158 00:11:01,570 --> 00:11:03,350 variation within groups. 159 00:11:03,350 --> 00:11:07,890 Let's give a silly example, if you grow a bunch of tomato 160 00:11:07,890 --> 00:11:12,680 plants and you vary the amount of fertilizer you put on 161 00:11:12,680 --> 00:11:15,640 them-- more fertilizer, bigger plants, right? 162 00:11:15,640 --> 00:11:22,870 So you can have something that shows you that you can account 163 00:11:22,870 --> 00:11:25,050 for some of the variability in the size of the plants from 164 00:11:25,050 --> 00:11:26,140 the fertilizer. 165 00:11:26,140 --> 00:11:30,000 Now if you grow some tomato plants and some redwood trees 166 00:11:30,000 --> 00:11:33,570 you get very different heights too, but the source of 167 00:11:33,570 --> 00:11:36,630 variability between tomatoes and redwoods is different than 168 00:11:36,630 --> 00:11:41,380 the source of variability within tomato plants. 169 00:11:41,380 --> 00:11:45,420 This becomes relevant in more subtle and interesting ways 170 00:11:45,420 --> 00:11:51,220 when you start talking about variation within population 171 00:11:51,220 --> 00:11:56,880 groups and across population groups on a measure like IQ or 172 00:11:56,880 --> 00:11:57,720 intelligence. 173 00:11:57,720 --> 00:12:01,910 So IQ is just a number on a test, but it's supposed to be 174 00:12:01,910 --> 00:12:03,780 a measure of intelligence. 175 00:12:03,780 --> 00:12:07,730 What is it actually measuring? 176 00:12:07,730 --> 00:12:12,350 Well there's a certain circularity that's popular in 177 00:12:12,350 --> 00:12:15,630 the field, which is to say that intelligence is what IQ 178 00:12:15,630 --> 00:12:19,270 tests measure and what do IQ tests-- well, they measure 179 00:12:19,270 --> 00:12:22,910 intelligence, but it'd be nice to be a little more 180 00:12:22,910 --> 00:12:23,950 interesting than that. 181 00:12:23,950 --> 00:12:28,320 A little more interesting than that is to note that you can 182 00:12:28,320 --> 00:12:33,830 divide up this sort of intuitive idea of intelligence 183 00:12:33,830 --> 00:12:34,810 in various ways. 184 00:12:34,810 --> 00:12:37,950 One of the interesting ways to divide it up is into so-called 185 00:12:37,950 --> 00:12:41,740 fluid and crystallized intelligence. 186 00:12:41,740 --> 00:12:48,200 With IQ tests being a punitive measure of fluid intelligence, 187 00:12:48,200 --> 00:12:53,540 fluid intelligence is the sort of set of reasoning abilities 188 00:12:53,540 --> 00:12:55,010 that let you deal with something 189 00:12:55,010 --> 00:13:00,230 like abstract relations. 190 00:13:00,230 --> 00:13:06,390 It is said to have reached its mature state, its adults state 191 00:13:06,390 --> 00:13:09,080 by about age 16 or so. 192 00:13:09,080 --> 00:13:14,270 So you guys have pretty much leveled out on that. 193 00:13:14,270 --> 00:13:22,140 Crystallized intelligence is more the application of 194 00:13:22,140 --> 00:13:25,450 knowledge to particular tasks and can continue growing 195 00:13:25,450 --> 00:13:26,770 throughout the life span. 196 00:13:26,770 --> 00:13:30,240 A particular example would be something like vocabulary. 197 00:13:30,240 --> 00:13:35,490 Your vocabulary is not fixed and with luck will continue to 198 00:13:35,490 --> 00:13:42,500 grow as you age and the idea that the IQ is picking up 199 00:13:42,500 --> 00:13:43,210 intelligence. 200 00:13:43,210 --> 00:13:50,020 Tests that are IQ like are more tests of fluid 201 00:13:50,020 --> 00:13:53,260 intelligence than they are of this crystallized 202 00:13:53,260 --> 00:13:56,080 intelligence, at least that's the intent. 203 00:13:56,080 --> 00:14:03,020 What is it that is this fluid intelligence? 204 00:14:03,020 --> 00:14:08,190 One possibility is that when you talk about somebody being 205 00:14:08,190 --> 00:14:11,490 mentally quick that that's literally what 206 00:14:11,490 --> 00:14:13,670 you're talking about. 207 00:14:13,670 --> 00:14:17,730 That what differs between people who score high on these 208 00:14:17,730 --> 00:14:19,970 sort of tests and people who don't is 209 00:14:19,970 --> 00:14:23,510 simple speed of response. 210 00:14:23,510 --> 00:14:28,170 And it is in fact the case that simple reaction time is 211 00:14:28,170 --> 00:14:32,950 related to measures of IQ. 212 00:14:32,950 --> 00:14:36,280 People who bang a response key more quickly in a reaction 213 00:14:36,280 --> 00:14:38,240 time experiment are also people who score 214 00:14:38,240 --> 00:14:41,550 higher on IQ tests. 215 00:14:41,550 --> 00:14:44,320 Not a perfect relationship, not even a hugely strong 216 00:14:44,320 --> 00:14:45,190 relationship. 217 00:14:45,190 --> 00:14:47,810 And in fact, we probably want to look for something a little 218 00:14:47,810 --> 00:14:53,130 more subtle than that in understanding what 219 00:14:53,130 --> 00:14:54,930 intelligence might be. 220 00:14:54,930 --> 00:15:00,140 More interesting are claims that it has something to do 221 00:15:00,140 --> 00:15:03,440 with the constellation of operations we were talking 222 00:15:03,440 --> 00:15:05,720 about in the context of working memory 223 00:15:05,720 --> 00:15:07,180 earlier in the course. 224 00:15:07,180 --> 00:15:10,370 These sort of executive function-- the desktop of the 225 00:15:10,370 --> 00:15:13,230 computer of your mind kind of functions. 226 00:15:13,230 --> 00:15:18,720 How much stuff can you have up there and how effectively can 227 00:15:18,720 --> 00:15:19,830 you manipulate it? 228 00:15:19,830 --> 00:15:24,490 So one possible correlate would be something as simple 229 00:15:24,490 --> 00:15:27,830 as digit span or in this class, color span. 230 00:15:27,830 --> 00:15:29,290 How many color names can you name? 231 00:15:29,290 --> 00:15:30,950 I go red, green, blue. 232 00:15:30,950 --> 00:15:34,670 You say, red, green, blue, that sort of thing. 233 00:15:34,670 --> 00:15:37,110 Again, related. 234 00:15:37,110 --> 00:15:39,270 It tracks along with intelligence, but 235 00:15:39,270 --> 00:15:42,050 not all that well. 236 00:15:42,050 --> 00:15:48,260 People like Randy Engle in Georgia have worked on 237 00:15:48,260 --> 00:15:52,950 creating tasks that they think capture this working memory 238 00:15:52,950 --> 00:15:57,160 executive function aspect of it better and that's-- 239 00:15:57,160 --> 00:16:00,700 I think, I put on the handout this notion 240 00:16:00,700 --> 00:16:02,920 of active span tasks. 241 00:16:02,920 --> 00:16:09,660 These are tasks that are like the color name task, but a 242 00:16:09,660 --> 00:16:11,160 little more complicated. 243 00:16:11,160 --> 00:16:13,655 So the sort of thing that you might do if you were in Randy 244 00:16:13,655 --> 00:16:17,350 Engle's lab would be something like this-- 245 00:16:17,350 --> 00:16:19,360 what I'm going to get you to do is I'm going to read you a 246 00:16:19,360 --> 00:16:21,290 list in words and you're going to spit them 247 00:16:21,290 --> 00:16:23,430 back to me in order. 248 00:16:23,430 --> 00:16:27,990 And the measure that's going to relate to something like an 249 00:16:27,990 --> 00:16:30,130 IQ test score is going to be how many you 250 00:16:30,130 --> 00:16:31,380 can get back in order. 251 00:16:31,380 --> 00:16:33,480 But I'm not just going to read you the names. 252 00:16:33,480 --> 00:16:35,500 What I'm going to do is give you a pair-- 253 00:16:38,730 --> 00:16:43,330 an equation and a word on each presentation. 254 00:16:43,330 --> 00:16:45,460 Either on a computer screen or I can do this orally. 255 00:16:45,460 --> 00:16:51,940 So I might ask you to verify, is it true that 2 plus 3 minus 256 00:16:51,940 --> 00:16:53,880 1 equals 4? 257 00:16:53,880 --> 00:16:59,480 And at the same time you see the word uncle. 258 00:16:59,480 --> 00:17:02,520 And so your job at the time is store uncle 259 00:17:02,520 --> 00:17:05,340 and verify the equation. 260 00:17:05,340 --> 00:17:09,990 OK, next one would be 4 minus 3 plus 5 equals 6 and the word 261 00:17:09,990 --> 00:17:12,000 would be fish. 262 00:17:12,000 --> 00:17:14,780 So-- no, that one doesn't sound right. 263 00:17:14,780 --> 00:17:18,410 OK, now what were the two words? 264 00:17:18,410 --> 00:17:19,420 Uncle and fish. 265 00:17:19,420 --> 00:17:21,520 That's good. 266 00:17:21,520 --> 00:17:25,630 But if I was to do this without the long song and 267 00:17:25,630 --> 00:17:31,080 dance and go up to 4, 5, 6 of these, you would discover that 268 00:17:31,080 --> 00:17:32,870 you started losing them. 269 00:17:32,870 --> 00:17:38,570 And the number that you can hold while doing these 270 00:17:38,570 --> 00:17:44,560 calculations at the same time turns out to be a more 271 00:17:44,560 --> 00:17:48,670 powerful predictor of things like IQ scores. 272 00:17:48,670 --> 00:17:52,200 Again, not perfect, but getting closer, perhaps, to 273 00:17:52,200 --> 00:17:54,660 what the underlying substrate of what we mean by 274 00:17:54,660 --> 00:17:56,760 intelligence might be. 275 00:17:56,760 --> 00:18:01,860 That it might have something to do with how well you move 276 00:18:01,860 --> 00:18:07,290 things around in that mental space that we were calling 277 00:18:07,290 --> 00:18:10,140 working memory. 278 00:18:10,140 --> 00:18:12,610 Another possibility, not necessarily unrelated, but 279 00:18:12,610 --> 00:18:15,270 another possibility is that it has to do with the degree to 280 00:18:15,270 --> 00:18:20,720 which your brain is plastic-- not in the plastic kind of 281 00:18:20,720 --> 00:18:23,300 sense, but plastic in the modifiable sense. 282 00:18:23,300 --> 00:18:28,730 That maybe the ability to change and modify the 283 00:18:28,730 --> 00:18:35,370 structure of your brain is the neural substrate of 284 00:18:35,370 --> 00:18:38,120 intelligence. 285 00:18:38,120 --> 00:18:46,960 In any case, whatever it is, it is a useful predictor of a 286 00:18:46,960 --> 00:18:48,220 variety of things. 287 00:18:48,220 --> 00:18:52,540 It's a usable predictor of performance in school, which 288 00:18:52,540 --> 00:18:54,010 is in fact where it started. 289 00:18:54,010 --> 00:18:56,100 Binet in France-- 290 00:18:56,100 --> 00:19:01,350 that's B-I-N-E-T, but it's French, so it's Binet, in 291 00:19:01,350 --> 00:19:04,940 France, started intelligence testing as a way of seeing 292 00:19:04,940 --> 00:19:08,020 which kids might need help in school. 293 00:19:08,020 --> 00:19:11,580 It is a predictor of a variety of a things. 294 00:19:11,580 --> 00:19:17,870 So more IQ points, higher lifetime salary. 295 00:19:17,870 --> 00:19:21,410 More IQ points less of a chance of a criminal 296 00:19:21,410 --> 00:19:24,590 conviction, less of a chance of teen pregnancy. 297 00:19:24,590 --> 00:19:27,900 So it's related to stuff that you'd like to know something 298 00:19:27,900 --> 00:19:34,040 about and you'd like to know as a result where it is that 299 00:19:34,040 --> 00:19:36,650 the variability comes from. 300 00:19:40,270 --> 00:19:47,710 Part of the class of statistical tests for where 301 00:19:47,710 --> 00:19:51,360 variances come from, the effort to parcel it out you 302 00:19:51,360 --> 00:19:53,460 will have seen in a variety of the papers that you read, 303 00:19:53,460 --> 00:19:55,580 probably-- statistical tests called ANOVA. 304 00:19:55,580 --> 00:19:59,690 It stands for Analysis of Variance. 305 00:19:59,690 --> 00:20:04,840 It's part of the statistical armamentarium that allows you 306 00:20:04,840 --> 00:20:08,510 to take the variance apart and say, some of due to this and 307 00:20:08,510 --> 00:20:09,390 some of it's due to that. 308 00:20:09,390 --> 00:20:12,350 Now this is made simpler by the fact that 309 00:20:12,350 --> 00:20:14,730 variances are additive. 310 00:20:14,730 --> 00:20:18,240 So if you have two sources of variability that they add in a 311 00:20:18,240 --> 00:20:19,060 nice direct way. 312 00:20:19,060 --> 00:20:22,690 So if we've got the total variation and we are in a 313 00:20:22,690 --> 00:20:28,620 psychology course one way to divide up the variance, at 314 00:20:28,620 --> 00:20:31,810 least theoretically, is into variance that's 315 00:20:31,810 --> 00:20:34,850 due to genetic factors-- 316 00:20:34,850 --> 00:20:39,990 our nice nativist component and variance that's due to 317 00:20:39,990 --> 00:20:42,640 environmental factors. 318 00:20:46,420 --> 00:20:50,070 In principle, if this is a good way to think about the 319 00:20:50,070 --> 00:20:52,770 variance we should be able to go in and do experiments that 320 00:20:52,770 --> 00:20:56,150 allow us to see the genetic variance and the environmental 321 00:20:56,150 --> 00:21:01,700 variance and see how they add up to make the total variance. 322 00:21:01,700 --> 00:21:08,100 What you will often see in papers on intelligence and 323 00:21:08,100 --> 00:21:14,010 actually, widely in the popular literature on the 324 00:21:14,010 --> 00:21:18,280 genetic component of pick your favorite function. 325 00:21:18,280 --> 00:21:24,370 Chance [UNINTELLIGIBLE], what is the genetic component to 326 00:21:24,370 --> 00:21:26,450 male fidelity or something like that. 327 00:21:26,450 --> 00:21:32,930 You'll often see discussions of so-called heritability 328 00:21:32,930 --> 00:21:38,300 usually written with a big H. Heritability is simply the-- 329 00:21:38,300 --> 00:21:40,880 whoops-- 330 00:21:40,880 --> 00:21:46,580 the genetic variance over the total variance, which in this 331 00:21:46,580 --> 00:21:49,990 story would be the environmental variance plus 332 00:21:49,990 --> 00:21:51,860 the genetic variance. 333 00:21:51,860 --> 00:21:57,500 There are difficulties with thinking about-- 334 00:21:57,500 --> 00:22:03,060 we're withdrawing conclusions from that that we will come to 335 00:22:03,060 --> 00:22:08,790 shortly, but the first thing I want to do before going on to 336 00:22:08,790 --> 00:22:13,520 that is to talk about this sort of data that are brought 337 00:22:13,520 --> 00:22:16,720 to bear to understand variability. 338 00:22:16,720 --> 00:22:21,270 Because a lot of this is so-called correlational data 339 00:22:21,270 --> 00:22:26,550 and it is important to turn you into educated consumers of 340 00:22:26,550 --> 00:22:30,640 correlational data, so that's why you have this lovely 341 00:22:30,640 --> 00:22:32,190 second page of the handout. 342 00:22:35,260 --> 00:22:38,010 Correlation is simply a way of describing the relationship 343 00:22:38,010 --> 00:22:45,770 between two variables measured on the same subject. 344 00:22:45,770 --> 00:22:47,730 So let's take the silly example in 345 00:22:47,730 --> 00:22:49,800 the upper left there. 346 00:22:49,800 --> 00:22:55,580 If I took everybody here and measured your height in inches 347 00:22:55,580 --> 00:23:02,140 and measured your height in centimeters and for each 348 00:23:02,140 --> 00:23:05,290 person-- now each person generates a data point-- those 349 00:23:05,290 --> 00:23:11,270 data points had better lie on a straight line, right? 350 00:23:11,270 --> 00:23:13,860 Those are two measures that are really seriously 351 00:23:13,860 --> 00:23:16,930 correlated; one with the other. 352 00:23:16,930 --> 00:23:20,650 If I know inches, I know centimeters. 353 00:23:20,650 --> 00:23:24,680 Correlation coefficients are calculated, so you calculate 354 00:23:24,680 --> 00:23:28,070 the line that fits the data. 355 00:23:28,070 --> 00:23:32,290 And then you calculate how strong your correlation is by 356 00:23:32,290 --> 00:23:35,640 looking at the distances of data points 357 00:23:35,640 --> 00:23:37,650 away from that line. 358 00:23:37,650 --> 00:23:40,860 In this case, all the data points lie on the line and 359 00:23:40,860 --> 00:23:45,080 that leads to a regression value, regression 360 00:23:45,080 --> 00:23:46,190 coefficient-- 361 00:23:46,190 --> 00:23:51,050 usually written as little r of 1. 362 00:23:51,050 --> 00:23:56,210 A correlation coefficient of 1 means if I know this variable 363 00:23:56,210 --> 00:23:58,640 then I know this variable perfectly. 364 00:24:01,980 --> 00:24:05,420 Now those are really boring data. 365 00:24:05,420 --> 00:24:07,590 Nobody spends a lot of time working out correlation 366 00:24:07,590 --> 00:24:08,750 coefficients for that. 367 00:24:08,750 --> 00:24:11,850 You work out correlation coefficients for data where 368 00:24:11,850 --> 00:24:13,010 there's some variability. 369 00:24:13,010 --> 00:24:14,400 That's the whole point here. 370 00:24:14,400 --> 00:24:17,700 So the second one is height and weight-- 371 00:24:17,700 --> 00:24:23,440 actually, taken from a subset of a 900 class some years ago. 372 00:24:23,440 --> 00:24:27,260 If you measure height and weight you now get data points 373 00:24:27,260 --> 00:24:30,710 that are clustered in a broken hunk of 374 00:24:30,710 --> 00:24:36,690 chalk around some line. 375 00:24:36,690 --> 00:24:39,890 If you calculate the regression coefficient now 376 00:24:39,890 --> 00:24:41,640 it's going to be something less than 377 00:24:41,640 --> 00:24:45,830 1, but still positive. 378 00:24:45,830 --> 00:24:51,130 So greater than zero, less than 1. 379 00:24:51,130 --> 00:24:52,850 What is it actually on the handout? 380 00:24:52,850 --> 00:24:55,520 Like 0.78 or something? 381 00:24:55,520 --> 00:24:56,830 The relationship between height and 382 00:24:56,830 --> 00:25:00,200 weight is pretty strong. 383 00:25:00,200 --> 00:25:08,790 So if I know that your 6 foot 5 I don't know your weight 384 00:25:08,790 --> 00:25:12,000 exactly, but I can make a nonrandom 385 00:25:12,000 --> 00:25:16,290 guess about that weight. 386 00:25:16,290 --> 00:25:20,610 On the other hand, if I know your height-- 387 00:25:24,160 --> 00:25:27,590 let's plot, OK, we still have height there. 388 00:25:27,590 --> 00:25:33,680 So let's plot last two digits of social security number 389 00:25:33,680 --> 00:25:35,290 against height. 390 00:25:35,290 --> 00:25:36,920 It is my firm belief-- 391 00:25:36,920 --> 00:25:40,620 I don't know this to be true not having collected the data, 392 00:25:40,620 --> 00:25:47,070 but it is my firm belief that those data are just a random 393 00:25:47,070 --> 00:25:48,860 cloud of spots, right? 394 00:25:48,860 --> 00:25:50,800 I don't think there's anything about social security number 395 00:25:50,800 --> 00:25:53,900 that's related to your height, I hope not. 396 00:25:58,640 --> 00:26:01,800 So, if I know your height I know squat about your social 397 00:26:01,800 --> 00:26:03,650 security number. 398 00:26:03,650 --> 00:26:05,480 That is a correlation of zero. 399 00:26:08,720 --> 00:26:13,810 Correlation can go below zero, but below zero is not worse 400 00:26:13,810 --> 00:26:17,390 than zero, it just tells you the direction of the 401 00:26:17,390 --> 00:26:18,120 correlation. 402 00:26:18,120 --> 00:26:21,240 So let's go back to the inches-- 403 00:26:21,240 --> 00:26:22,500 OK, we'll stick with height. 404 00:26:22,500 --> 00:26:26,450 So this time I'm going to measure-- get everybody here-- 405 00:26:26,450 --> 00:26:28,400 I'm going to measure their height and I'm going to 406 00:26:28,400 --> 00:26:30,910 measure the distance from the top of their head to the 407 00:26:30,910 --> 00:26:34,540 ceiling, very exciting data collection. 408 00:26:34,540 --> 00:26:37,400 I'm going to get orderly looking data, but this time 409 00:26:37,400 --> 00:26:39,460 it's going to look like this right? 410 00:26:39,460 --> 00:26:41,040 I've just changed the slope. 411 00:26:41,040 --> 00:26:44,570 That's going to give me a correlation in this silly 412 00:26:44,570 --> 00:26:46,380 example of minus 1. 413 00:26:46,380 --> 00:26:49,960 It's a perfect correlation, but the direction of 414 00:26:49,960 --> 00:26:51,500 relationship is the other way. 415 00:26:51,500 --> 00:26:55,760 If I know distance from the floor I absolutely know 416 00:26:55,760 --> 00:26:57,270 distance from the ceiling. 417 00:26:57,270 --> 00:27:00,490 It's just that as one goes up the other one goes down. 418 00:27:00,490 --> 00:27:03,460 Again, that's a really boring example. 419 00:27:03,460 --> 00:27:08,080 A more interesting example is on the handout, which is if 420 00:27:08,080 --> 00:27:14,870 you come into my lab and we do a reaction time experiment and 421 00:27:14,870 --> 00:27:21,940 I plot your average reaction time in whatever the task is 422 00:27:21,940 --> 00:27:26,870 and I plot your error rate, what I will find across 423 00:27:26,870 --> 00:27:35,790 individuals is data that are noisy, but look like this. 424 00:27:35,790 --> 00:27:40,060 The faster you go the more errors you make. 425 00:27:40,060 --> 00:27:46,230 It's known as a speed accuracy trade-off in dull literature, 426 00:27:46,230 --> 00:27:52,970 which you will notice has an interesting set of initials, 427 00:27:52,970 --> 00:27:56,640 so SAT gets written about in my trade all the time, but has 428 00:27:56,640 --> 00:27:58,390 nothing to do with standardized tests, it's a 429 00:27:58,390 --> 00:27:59,870 speed accuracy trade-off. 430 00:27:59,870 --> 00:28:02,000 But it will produce a negative correlation. 431 00:28:02,000 --> 00:28:04,450 I think I put one of those on the handout, too. 432 00:28:04,450 --> 00:28:06,467 What's the correlation there? 433 00:28:06,467 --> 00:28:08,340 AUDIENCE: Negative 0.52. 434 00:28:08,340 --> 00:28:12,890 PROFESSOR: OK, so negative 0.52 meaning a pretty good 435 00:28:12,890 --> 00:28:17,780 relationship, but going in this negative direction. 436 00:28:17,780 --> 00:28:24,280 You will often see in papers r squared rather than r. 437 00:28:24,280 --> 00:28:28,960 r squared of course is always positive. 438 00:28:28,960 --> 00:28:34,360 So r squared here would be what, about 0.26 or something? 439 00:28:34,360 --> 00:28:37,120 The reason people use r squared is it turns out that 440 00:28:37,120 --> 00:28:39,890 that gives you the percentage of the variance that you're 441 00:28:39,890 --> 00:28:41,070 explaining. 442 00:28:41,070 --> 00:28:47,830 So if you've got a correlation of 0.5 you can explain a 443 00:28:47,830 --> 00:28:49,330 quarter of the variability. 444 00:28:53,930 --> 00:28:57,080 If you know one you know about where a quarter of the 445 00:28:57,080 --> 00:28:58,930 variability would come from in the other 446 00:28:58,930 --> 00:29:00,270 variable in this case. 447 00:29:00,270 --> 00:29:02,940 Now it should say on the handout, oh actually I think I 448 00:29:02,940 --> 00:29:05,330 put the answer on the handout this time too. 449 00:29:08,040 --> 00:29:09,980 I'm not sure it's really the most important thing to know 450 00:29:09,980 --> 00:29:13,330 about correlation, but the thing that gets lost in 451 00:29:13,330 --> 00:29:17,310 discussions in psychology all the time, certainly in 452 00:29:17,310 --> 00:29:21,330 discussions of intelligence all the time about correlation 453 00:29:21,330 --> 00:29:25,420 is that correlation does not tell you about causality. 454 00:29:25,420 --> 00:29:29,000 It is extremely tempting, it's not extremely tempting in 455 00:29:29,000 --> 00:29:33,520 these examples, right? 456 00:29:33,520 --> 00:29:38,210 Does inches tells you about centimeter? 457 00:29:38,210 --> 00:29:43,000 Yeah, but not because inches cause centimeters-- 458 00:29:43,000 --> 00:29:45,080 that's stupid. 459 00:29:45,080 --> 00:29:53,170 But it's really tempting to infer directly from nothing 460 00:29:53,170 --> 00:29:58,160 but correlational data, to infer causality. 461 00:29:58,160 --> 00:30:02,900 I put fertilizer on the field, the tomato plants grow bigger, 462 00:30:02,900 --> 00:30:14,980 so it I plot amount of fertilizer in yellow now-- 463 00:30:14,980 --> 00:30:16,320 cool-- 464 00:30:16,320 --> 00:30:22,060 against height of tomato plants I presumably get data 465 00:30:22,060 --> 00:30:26,770 that look like this, some nice positive correlation. 466 00:30:26,770 --> 00:30:31,320 I may have a notion, I may even have a correct notion 467 00:30:31,320 --> 00:30:35,710 that the fertilizer is the cause of this change in the 468 00:30:35,710 --> 00:30:39,950 height of the tomato plants, but just these data 469 00:30:39,950 --> 00:30:40,930 don't tell me that. 470 00:30:40,930 --> 00:30:42,430 I would need more. 471 00:30:42,430 --> 00:30:47,320 I'd get exactly the same data if I flipped the axes, right? 472 00:30:47,320 --> 00:30:50,130 Height of tomato plants, fertilizer. 473 00:30:50,130 --> 00:30:51,590 Get exactly the same data. 474 00:30:51,590 --> 00:30:54,170 The height of the tomato plant causes how much 475 00:30:54,170 --> 00:30:56,010 fertilizer I put on it. 476 00:30:56,010 --> 00:30:59,820 No, that would be stupid. 477 00:30:59,820 --> 00:31:04,740 You have to impose a theory on your correlational data. 478 00:31:04,740 --> 00:31:07,210 The correlation's just math. 479 00:31:07,210 --> 00:31:08,630 It's not by itself a theory. 480 00:31:08,630 --> 00:31:12,360 It can be used to support causal theories, but it is not 481 00:31:12,360 --> 00:31:15,550 by itself a causal theory. 482 00:31:15,550 --> 00:31:21,970 I'll try to point out later where this runs into trouble. 483 00:31:21,970 --> 00:31:26,900 OK, so we can get correlational data. 484 00:31:26,900 --> 00:31:33,250 Correlational data are very important data in the study of 485 00:31:33,250 --> 00:31:38,210 variability in intelligence, so you might get data like the 486 00:31:38,210 --> 00:31:52,520 following: let's plot parent's IQ against child's IQ. 487 00:31:55,980 --> 00:32:02,010 What you'll get is another one of these clouds-- 488 00:32:02,010 --> 00:32:04,485 I think it actually says on the handout that the r value 489 00:32:04,485 --> 00:32:06,970 is about 0.5 for that. 490 00:32:06,970 --> 00:32:09,750 So it is the case that if you know the parents IQ you know 491 00:32:09,750 --> 00:32:14,730 something about the child's IQ. 492 00:32:14,730 --> 00:32:25,810 And the goal is to figure out why, where's that relationship 493 00:32:25,810 --> 00:32:27,560 coming from? 494 00:32:27,560 --> 00:32:30,150 The obvious temptation of course is to think that the 495 00:32:30,150 --> 00:32:37,760 answer is, well, the parents have good genes or bad genes-- 496 00:32:37,760 --> 00:32:39,760 they have some amount of intelligence coded into their 497 00:32:39,760 --> 00:32:42,040 genes, they pass it onto their kids. 498 00:32:42,040 --> 00:32:46,010 To understand why it's not completely trivial recognize 499 00:32:46,010 --> 00:32:52,570 that parental wealth is correlated with child's wealth 500 00:32:52,570 --> 00:32:55,940 not because there's money coded into the genes. 501 00:32:55,940 --> 00:32:58,240 It is true that the richer your parents are the richer 502 00:32:58,240 --> 00:33:06,250 you're likely to be, but the causes-- 503 00:33:06,250 --> 00:33:10,400 you do inherit that, but in a different kind of non-genetic 504 00:33:10,400 --> 00:33:11,670 sort of way. 505 00:33:16,190 --> 00:33:19,860 As I say, the simple story has been to try to partition the 506 00:33:19,860 --> 00:33:25,250 variance into a genetic component and a 507 00:33:25,250 --> 00:33:29,280 environmental component. 508 00:33:29,280 --> 00:33:34,990 The simple version of that leaves out an important piece 509 00:33:34,990 --> 00:33:39,720 of it, which is that if you do an analysis of variance-- 510 00:33:39,720 --> 00:33:41,710 everybody should do an analysis of variance sometime 511 00:33:41,710 --> 00:33:44,710 because the computer will do it for you on your data now. 512 00:33:44,710 --> 00:33:48,480 Everybody should really do one by hand-- 513 00:33:48,480 --> 00:33:52,660 back when we were young we had to do them by hand, which took 514 00:33:52,660 --> 00:33:53,430 a long time. 515 00:33:53,430 --> 00:33:56,900 But anyway, when you do that, suppose you've got two 516 00:33:56,900 --> 00:33:59,890 variables, like a genetic component, an 517 00:33:59,890 --> 00:34:01,400 environmental component. 518 00:34:01,400 --> 00:34:04,820 An analysis of variance will tell you-- 519 00:34:04,820 --> 00:34:07,476 this statistical test, think this much of the variance is 520 00:34:07,476 --> 00:34:09,250 due to this and this much is due to this. 521 00:34:09,250 --> 00:34:13,860 But it'll also give you an interaction term to say that 522 00:34:13,860 --> 00:34:19,380 the genetics and the environment might interact in 523 00:34:19,380 --> 00:34:20,310 some fashion. 524 00:34:20,310 --> 00:34:23,350 Interestingly that term never gets introduced into these 525 00:34:23,350 --> 00:34:25,490 calculations-- 526 00:34:25,490 --> 00:34:27,770 doesn't quite get introduced in these calculations of 527 00:34:27,770 --> 00:34:31,280 heritability, but let me try to give you a feeling for why 528 00:34:31,280 --> 00:34:32,640 that's important. 529 00:34:32,640 --> 00:34:36,890 Let's move off of intelligence and ask about a sort of a 530 00:34:36,890 --> 00:34:38,710 personality variable. 531 00:34:38,710 --> 00:34:45,250 How anxious do tests make you? 532 00:34:45,250 --> 00:34:47,990 You've now taken a bunch of MIT tests, how 533 00:34:47,990 --> 00:34:49,250 anxious do you get? 534 00:34:49,250 --> 00:34:53,760 Well, let's develop a little variance 535 00:34:53,760 --> 00:34:56,620 partitioning theory here. 536 00:34:56,620 --> 00:34:59,280 Let's not develop that theory, let's develop this theory. 537 00:35:02,450 --> 00:35:06,080 Whoops-- oh this looks like a great theory. 538 00:35:06,080 --> 00:35:10,640 Get rid of that theory, that's got too much 539 00:35:10,640 --> 00:35:12,190 fancy stuff in it. 540 00:35:15,090 --> 00:35:17,410 We will have a simpler theory. 541 00:35:17,410 --> 00:35:23,480 So the simple theory might be that the total variance, the 542 00:35:23,480 --> 00:35:31,050 total anxiety is a function of the variance due to you-- 543 00:35:31,050 --> 00:35:33,250 to your personality-- 544 00:35:33,250 --> 00:35:36,520 are you an anxious person or an unanxious person? 545 00:35:36,520 --> 00:35:39,610 And the variance due to the class. 546 00:35:42,160 --> 00:35:45,290 You know, if you're taking 547 00:35:45,290 --> 00:35:51,170 introduction to clay ashtrays-- 548 00:35:51,170 --> 00:35:56,530 make you that anxious and if you're taking advanced 549 00:35:56,530 --> 00:36:02,740 thermonuclear chemical integral thermodynamical 550 00:36:02,740 --> 00:36:06,650 bio-something or other and you skipped all the prereqs- yeah, 551 00:36:06,650 --> 00:36:09,970 it makes you a little anxious. 552 00:36:09,970 --> 00:36:14,810 And you could try partitioning your variance into these two 553 00:36:14,810 --> 00:36:15,670 components. 554 00:36:15,670 --> 00:36:19,430 But it turns out that's not where the action is. 555 00:36:19,430 --> 00:36:24,840 The action in anxiety about tests is all in the 556 00:36:24,840 --> 00:36:30,430 interaction term, which we can call you crossed with class 557 00:36:30,430 --> 00:36:31,690 interaction term. 558 00:36:31,690 --> 00:36:39,500 Because how anxious you are depends on how you are doing 559 00:36:39,500 --> 00:36:42,370 in this class. 560 00:36:42,370 --> 00:36:45,780 You-- know I'm sure it matters if you're basically a nervous 561 00:36:45,780 --> 00:36:49,350 person or not, but you're going to be nervous in intro 562 00:36:49,350 --> 00:36:51,530 psych if the midterm's coming up and you 563 00:36:51,530 --> 00:36:53,990 never cracked the book. 564 00:36:53,990 --> 00:37:01,250 You might be less nervous in calculus because you're good. 565 00:37:01,250 --> 00:37:06,040 Your neighbor might have memorized Gleitman and 566 00:37:06,040 --> 00:37:09,720 forgotten to show up in calculus and have the flip 567 00:37:09,720 --> 00:37:13,150 anxiety, the anxiety is dependant on the interaction. 568 00:37:13,150 --> 00:37:16,490 And those interaction term, I mean, you will almost always 569 00:37:16,490 --> 00:37:21,330 get a nice nod to the notion that of course we know in 570 00:37:21,330 --> 00:37:26,780 something like intelligent that the environment and 571 00:37:26,780 --> 00:37:32,880 genetics interact, but we will typically not get much more 572 00:37:32,880 --> 00:37:34,730 than a nod at that. 573 00:37:34,730 --> 00:37:37,310 You then get simple minded statements about just how 574 00:37:37,310 --> 00:37:38,730 heritable something is. 575 00:37:38,730 --> 00:37:40,670 Let me give you an example from the intelligence 576 00:37:40,670 --> 00:37:43,930 literature from a new and I gather somewhat 577 00:37:43,930 --> 00:37:46,060 controversial study-- 578 00:37:46,060 --> 00:37:48,100 controversial in the sense that there are other people 579 00:37:48,100 --> 00:37:54,860 who claim that the methodology is flawed and that they don't 580 00:37:54,860 --> 00:37:56,350 believe the results. 581 00:37:56,350 --> 00:37:59,530 But this is new enough that we don't know how it shakes out, 582 00:37:59,530 --> 00:38:03,080 but it makes the point about the importance of interaction 583 00:38:03,080 --> 00:38:05,180 terms here. 584 00:38:05,180 --> 00:38:13,540 Suppose you calculate how heritable intelligence is, but 585 00:38:13,540 --> 00:38:17,750 we're going to do it separately as a function of, 586 00:38:17,750 --> 00:38:19,680 oh here, let's introduce a little more jargon. 587 00:38:19,680 --> 00:38:23,800 SES stands for socioeconomic status. 588 00:38:23,800 --> 00:38:26,120 It's the fancy way of asking how much money and other 589 00:38:26,120 --> 00:38:28,600 resources you've got. 590 00:38:28,600 --> 00:38:39,670 If you look at high SES kids and ask, how much of a genetic 591 00:38:39,670 --> 00:38:43,100 component is there to IQ? 592 00:38:43,100 --> 00:38:47,910 You get estimates, as I recall, it's something like 593 00:38:47,910 --> 00:38:49,670 0.6, 0.7 something like that. 594 00:38:49,670 --> 00:38:53,460 A relatively high number for that H, that 595 00:38:53,460 --> 00:38:54,660 heritability number. 596 00:38:54,660 --> 00:39:02,340 If you look at low SES kids, in this particular study it 597 00:39:02,340 --> 00:39:05,340 was dramatically lower, about 0.1. 598 00:39:05,340 --> 00:39:06,990 What's going on there? 599 00:39:06,990 --> 00:39:12,560 Could it possibly be the case that the genetics are 600 00:39:12,560 --> 00:39:17,300 operating differently at the low end of the economic scale 601 00:39:17,300 --> 00:39:18,970 than at the high end of the economic scale? 602 00:39:18,970 --> 00:39:23,850 I mean, this implies that if you plot something like 603 00:39:23,850 --> 00:39:28,210 parent's IQ against kid's IQ-- 604 00:39:28,210 --> 00:39:32,510 one of the ways of looking for a genetic contribution-- that 605 00:39:32,510 --> 00:39:37,810 at the low end of the economic scale that the data look like 606 00:39:37,810 --> 00:39:46,890 a cloud and at the high end they look closer to the line 607 00:39:46,890 --> 00:39:48,510 kind of data. 608 00:39:48,510 --> 00:39:50,400 That's very odd. 609 00:39:50,400 --> 00:39:53,400 What really seems to be going on is an interaction between 610 00:39:53,400 --> 00:39:57,650 environment and genetics influencing this 611 00:39:57,650 --> 00:40:00,210 heritability thing. 612 00:40:00,210 --> 00:40:02,300 What's going on? 613 00:40:02,300 --> 00:40:07,620 Well, here look at this equation and ask yourself what 614 00:40:07,620 --> 00:40:11,020 happens if you drive the 615 00:40:11,020 --> 00:40:14,450 environmental variance to zero? 616 00:40:14,450 --> 00:40:20,080 Well, you're going to drive this up towards 1. 617 00:40:20,080 --> 00:40:21,760 It's just going to be the genetic variance over the 618 00:40:21,760 --> 00:40:24,370 genetic variance. 619 00:40:24,370 --> 00:40:32,000 It may be that for the point of view of intelligence or 620 00:40:32,000 --> 00:40:35,820 what's measured on IQ tests, that by the time you get into 621 00:40:35,820 --> 00:40:39,160 the middle class the environment is largely 622 00:40:39,160 --> 00:40:40,760 homogeneous. 623 00:40:40,760 --> 00:40:45,200 Everybody gets fed, everybody goes to school, everybody has 624 00:40:45,200 --> 00:40:51,130 books, and everybody has access to medical care. 625 00:40:51,130 --> 00:40:54,860 Most people come from family situations that are at least 626 00:40:54,860 --> 00:40:57,810 not vastly problematic. 627 00:40:57,810 --> 00:41:00,820 At the lowest end of the socioeconomic 628 00:41:00,820 --> 00:41:02,750 scale that's not true. 629 00:41:02,750 --> 00:41:05,590 It's not true that everybody has the same access to 630 00:41:05,590 --> 00:41:09,310 resources and this environmental factor makes it 631 00:41:09,310 --> 00:41:13,920 much larger with the result that this measure of the 632 00:41:13,920 --> 00:41:17,790 apparent heritability, the genetic component if you like, 633 00:41:17,790 --> 00:41:19,910 seems to get bigger. 634 00:41:19,910 --> 00:41:27,820 So you see how the interaction can end up being important. 635 00:41:32,770 --> 00:41:35,180 All right, let's go back to this example of parents and 636 00:41:35,180 --> 00:41:39,900 kids across the population as a whole that relationship 637 00:41:39,900 --> 00:41:42,650 gives you an r squared value of about 0.5. 638 00:41:42,650 --> 00:41:44,660 What does that mean? 639 00:41:44,660 --> 00:41:50,210 Well, we don't know really because while you share some 640 00:41:50,210 --> 00:41:56,840 genetic material with your parents, assuming you're not 641 00:41:56,840 --> 00:41:58,390 an adopted child-- 642 00:41:58,390 --> 00:42:01,900 we'll come back to that in a minute-- 643 00:42:01,900 --> 00:42:05,710 if you share genetic material with your biological parents, 644 00:42:05,710 --> 00:42:08,340 if you were raised with your biological parents you also 645 00:42:08,340 --> 00:42:13,210 share a lot of environment with them. 646 00:42:13,210 --> 00:42:16,240 The two factors co-vary. 647 00:42:16,240 --> 00:42:19,120 In order to sort of separate this out what you want to do 648 00:42:19,120 --> 00:42:22,140 is you want to get the two factors to vary at least 649 00:42:22,140 --> 00:42:23,300 somewhat independently. 650 00:42:23,300 --> 00:42:27,450 Now the little table on page 3 of the handout gives you one 651 00:42:27,450 --> 00:42:28,610 effort to do that. 652 00:42:28,610 --> 00:42:33,190 Let's look at sibling pairs who vary systematically in the 653 00:42:33,190 --> 00:42:35,740 amount of genetic material they share. 654 00:42:35,740 --> 00:42:42,190 So identical twins are genetically identical and they 655 00:42:42,190 --> 00:42:48,000 have very high correlations between their IQs, a 656 00:42:48,000 --> 00:42:50,860 correlation of around 0.9. 657 00:42:50,860 --> 00:42:53,540 Fraternal twins-- same sex fraternal 658 00:42:53,540 --> 00:42:56,520 twins I should've added-- 659 00:42:56,520 --> 00:43:00,470 have a correlation that drops to about 0.6, which tells you 660 00:43:00,470 --> 00:43:10,110 that environment, well, they are only as related as 661 00:43:10,110 --> 00:43:11,940 standard siblings. 662 00:43:11,940 --> 00:43:14,590 And so you reduce that genetic component, you reduce the 663 00:43:14,590 --> 00:43:16,740 correlation. 664 00:43:16,740 --> 00:43:20,290 Siblings, age different siblings- standard brother 665 00:43:20,290 --> 00:43:23,930 sister, brother brother, kind of pairs have a correlation of 666 00:43:23,930 --> 00:43:29,310 0.5 and if you have unrelated children raised in the same 667 00:43:29,310 --> 00:43:34,610 family that correlation drops to about 0.2. 668 00:43:34,610 --> 00:43:43,350 So that's certainly indicates a genetic contribution to IQ, 669 00:43:43,350 --> 00:43:49,720 but it's not entirely clear what's going on because again, 670 00:43:49,720 --> 00:43:52,600 environment and genetics are co-varying; 671 00:43:52,600 --> 00:43:53,720 one with the other. 672 00:43:53,720 --> 00:43:57,070 Identical twins get treated more similarly, they have a 673 00:43:57,070 --> 00:43:59,860 more similar environment than fraternal twins. 674 00:43:59,860 --> 00:44:06,170 Fraternal twins by virtue of being the same age are treated 675 00:44:06,170 --> 00:44:11,070 more similarly than age separated siblings. 676 00:44:11,070 --> 00:44:15,710 And typically, adopted children are treated somewhat 677 00:44:15,710 --> 00:44:17,610 differently. 678 00:44:17,610 --> 00:44:23,680 Children who have been adopted into a family have experiences 679 00:44:23,680 --> 00:44:26,450 that kids born within the same family don't 680 00:44:26,450 --> 00:44:28,340 have and vice versa. 681 00:44:28,340 --> 00:44:30,150 So again, there's more of a difference there. 682 00:44:30,150 --> 00:44:34,390 So environment and genetics are co-varying, so it's not a 683 00:44:34,390 --> 00:44:37,000 clean experiment. 684 00:44:37,000 --> 00:44:41,610 One way to do the clean experiment is to take a jumbo 685 00:44:41,610 --> 00:44:46,540 jet full of identical twins at birth, put little parachutes 686 00:44:46,540 --> 00:44:52,170 on them, fly around the world and push them out at random. 687 00:44:52,170 --> 00:44:56,450 Come back 18 years later after fluid intelligence has reached 688 00:44:56,450 --> 00:45:00,190 its asymptotic level, collect all your twins and see what 689 00:45:00,190 --> 00:45:02,300 the correlation is. 690 00:45:02,300 --> 00:45:04,690 This is, for a variety of reasons, a difficult 691 00:45:04,690 --> 00:45:07,810 experiment to actually do and so we don't 692 00:45:07,810 --> 00:45:09,280 have the data on that. 693 00:45:09,280 --> 00:45:16,700 But there is a literature on identical twins reared apart. 694 00:45:16,700 --> 00:45:19,520 It's not common, but it does happen. 695 00:45:19,520 --> 00:45:24,630 It happens when either both twins or one of a pair of 696 00:45:24,630 --> 00:45:28,270 twins gets put up for adoption. 697 00:45:28,270 --> 00:45:31,000 The one of a pair of twins thing may sound a little 698 00:45:31,000 --> 00:45:34,640 strange, but this happens for instance, typically at the 699 00:45:34,640 --> 00:45:38,880 lower end of the socioeconomic scale when you're thinking, oh 700 00:45:38,880 --> 00:45:39,690 my goodness. 701 00:45:39,690 --> 00:45:44,320 I think we can just barely manage this kid when he or she 702 00:45:44,320 --> 00:45:48,250 is born and there's two of them. 703 00:45:48,250 --> 00:45:51,450 And so one of them gets put up for adoption. 704 00:45:51,450 --> 00:45:54,960 It's rare, these things are rare. 705 00:45:54,960 --> 00:45:57,920 But it does happen, and a variety of efforts have been 706 00:45:57,920 --> 00:46:03,330 made over the years to collect data on such. 707 00:46:03,330 --> 00:46:07,430 Some of this has less to do with IQ than with the sort of 708 00:46:07,430 --> 00:46:08,900 general personality variables. 709 00:46:08,900 --> 00:46:12,250 There's a whole lot of amusing, though god knows what 710 00:46:12,250 --> 00:46:16,540 to make of it, literature on identical twins who only meet 711 00:46:16,540 --> 00:46:20,500 their twin-- didn't realize they had a twin until they're 712 00:46:20,500 --> 00:46:23,820 adults and then they meet and oh my god, they both married 713 00:46:23,820 --> 00:46:29,040 women named Gladys and they both have dogs named Gerald 714 00:46:29,040 --> 00:46:30,440 Ford or something like that. 715 00:46:33,750 --> 00:46:34,780 That's a little strange. 716 00:46:34,780 --> 00:46:37,330 It's a little hard to believe that [? Gladys-ness ?] 717 00:46:37,330 --> 00:46:40,500 was wired into the genes, but weird 718 00:46:40,500 --> 00:46:42,310 coincidental things happen. 719 00:46:44,900 --> 00:46:49,020 The identical twins reared apart stuff is again, not 720 00:46:49,020 --> 00:46:50,240 perfect data. 721 00:46:50,240 --> 00:46:53,866 Because for instance, twins that are separated at birth 722 00:46:53,866 --> 00:46:59,113 are sometimes separated by going off to live with aunt so 723 00:46:59,113 --> 00:47:01,680 and so, who lives in the next valley or something like that. 724 00:47:01,680 --> 00:47:05,000 Is that really separated? 725 00:47:05,000 --> 00:47:06,430 Hard hard to know. 726 00:47:06,430 --> 00:47:08,950 Oh, the place where you do these studies-- well, there's 727 00:47:08,950 --> 00:47:12,230 one thing on the handout about the Minnesota twins study 728 00:47:12,230 --> 00:47:14,130 because those guys are at Minnesota, but the place you 729 00:47:14,130 --> 00:47:17,540 want to do these studies is Scandinavia. 730 00:47:17,540 --> 00:47:19,390 Not because they take your twins apart a lot in 731 00:47:19,390 --> 00:47:24,330 Scandinavia, but because boy, do those guys keep records. 732 00:47:24,330 --> 00:47:28,420 You get yourself some idea that you want to know where 733 00:47:28,420 --> 00:47:31,410 all the twins who were separated at birth are in 734 00:47:31,410 --> 00:47:35,140 Denmark and there's somebody in the Danish bureaucracy who 735 00:47:35,140 --> 00:47:37,660 could get that answer for you. 736 00:47:37,660 --> 00:47:39,850 I mean, in the U.S. bureaucracy you're lucky if 737 00:47:39,850 --> 00:47:41,980 they can figure out-- oh, I don't know-- where your 300 738 00:47:41,980 --> 00:47:45,210 tons of the Iraqi explosives are or something like that, 739 00:47:45,210 --> 00:47:47,570 but in Denmark they'll know where your stuff is. 740 00:47:50,570 --> 00:47:54,230 The previous remark should not be construed as being paid for 741 00:47:54,230 --> 00:47:57,320 by any particular political campaign. 742 00:47:57,320 --> 00:48:01,750 It's just what bubbled into my fevered brain here. 743 00:48:01,750 --> 00:48:06,210 But in any case, when you do the identical twin reared 744 00:48:06,210 --> 00:48:12,760 apart study, people yell and holler about these, but the 745 00:48:12,760 --> 00:48:16,580 best of these data look like a correlation of 746 00:48:16,580 --> 00:48:19,360 around 0.7 or so. 747 00:48:19,360 --> 00:48:23,240 Significantly lower than the 0.9 for identical twins reared 748 00:48:23,240 --> 00:48:29,450 in the same family, but clearly indicating some sort 749 00:48:29,450 --> 00:48:39,140 of a genetic component to what is being measured by IQ. 750 00:48:39,140 --> 00:48:46,980 So I think it's been a very hot topic for a variety of 751 00:48:46,980 --> 00:48:52,950 reasons, many of them not well formed to ask how much of a 752 00:48:52,950 --> 00:48:59,550 genetic contribution there is to IQ. 753 00:48:59,550 --> 00:49:03,410 And it's been an answer where the answer that you're looking 754 00:49:03,410 --> 00:49:05,640 for is heavily driven by your politics as 755 00:49:05,640 --> 00:49:08,970 much as by your science. 756 00:49:08,970 --> 00:49:12,600 In part, well look on the handout. 757 00:49:12,600 --> 00:49:15,030 There are a bunch of pitfalls to this idea of heritability. 758 00:49:15,030 --> 00:49:17,400 Jumps down to number 3. 759 00:49:17,400 --> 00:49:20,850 Number 3 is perhaps the reason that it's been most 760 00:49:20,850 --> 00:49:22,750 politically loaded. 761 00:49:22,750 --> 00:49:28,430 There are people and there are groups whose IQ, on average, 762 00:49:28,430 --> 00:49:34,510 are lower than other groups or other people. 763 00:49:34,510 --> 00:49:41,220 If you believe that there's a strong genetic component to IQ 764 00:49:41,220 --> 00:49:49,880 and you believe that this item 3 here, that things that have 765 00:49:49,880 --> 00:49:55,860 strong heritable components are largely unmodifiable then 766 00:49:55,860 --> 00:50:04,000 what you're saying is that if you've got low IQ that's sort 767 00:50:04,000 --> 00:50:05,460 of your tough luck. 768 00:50:05,460 --> 00:50:07,690 There's nothing much that we as a society 769 00:50:07,690 --> 00:50:09,180 could do about it. 770 00:50:09,180 --> 00:50:12,400 This was the thesis of-- 771 00:50:12,400 --> 00:50:14,850 actually, it's been a thesis of a repeated series of books. 772 00:50:14,850 --> 00:50:18,730 The most famous recent one is a book called The Bell Curve 773 00:50:18,730 --> 00:50:25,480 by Herrnstein and Murray and the basic argument ran like 774 00:50:25,480 --> 00:50:31,990 this, there's clear evidence for heritability of IQ. 775 00:50:31,990 --> 00:50:37,020 IQ is not very modifiable. 776 00:50:37,020 --> 00:50:42,210 IQ is correlated positively with good stuff and correlated 777 00:50:42,210 --> 00:50:43,750 negatively with bad stuff. 778 00:50:46,410 --> 00:50:52,300 And that this country is an IQ stratified country. 779 00:50:52,300 --> 00:50:54,880 Another way of putting it is a meritocracy. 780 00:50:54,880 --> 00:51:00,670 It's not that you get born the Duke of Cambridge or something 781 00:51:00,670 --> 00:51:05,410 like that, you get to rise to the top because you've got 782 00:51:05,410 --> 00:51:09,550 this great IQ that gets you to MIT, that gets you the good 783 00:51:09,550 --> 00:51:12,180 job and then you become President of the United States 784 00:51:12,180 --> 00:51:13,430 or something like that. 785 00:51:17,450 --> 00:51:18,700 Interesting. 786 00:51:22,300 --> 00:51:26,040 Or you get this great IQ and you come to MIT and you hang 787 00:51:26,040 --> 00:51:27,950 around in the infinite corridor making [? boingy ?] 788 00:51:27,950 --> 00:51:29,960 noises. 789 00:51:29,960 --> 00:51:31,650 That's different. 790 00:51:31,650 --> 00:51:33,920 Anyway, if you take-- 791 00:51:33,920 --> 00:51:36,270 I don't know, that 4 points or something-- you take those 4 792 00:51:36,270 --> 00:51:40,260 points and you add them up, what you get to-- 793 00:51:43,420 --> 00:51:46,220 somebody over there take a little walk down the hall and 794 00:51:46,220 --> 00:51:48,560 ask [? boingy ?] to cool it. 795 00:51:48,560 --> 00:51:51,490 Thank you. 796 00:51:51,490 --> 00:51:55,485 What you get to is the notion that there are some people who 797 00:51:55,485 --> 00:51:59,470 are going to be at the bottom of the stack in America. 798 00:51:59,470 --> 00:52:02,330 And that's just the way it's going to be, you know? 799 00:52:02,330 --> 00:52:03,920 They're going to be the criminals, they're going to 800 00:52:03,920 --> 00:52:06,570 get pregnant, they're not going to make any money and 801 00:52:06,570 --> 00:52:08,230 that's just tough. 802 00:52:08,230 --> 00:52:09,810 You know, there's nothing much to be done about it. 803 00:52:09,810 --> 00:52:11,920 I'm oversimplifying the book. 804 00:52:11,920 --> 00:52:15,330 It ought to be fairly obvious that I disagree strongly with 805 00:52:15,330 --> 00:52:17,040 that thesis. 806 00:52:17,040 --> 00:52:19,710 But I should say, it's not a stupid book. 807 00:52:19,710 --> 00:52:23,070 If you're interested in these issues it's worth reading the 808 00:52:23,070 --> 00:52:27,920 book in the way that you know, if you're on the left 809 00:52:27,920 --> 00:52:30,590 politically it's worth listening to right-wing talk 810 00:52:30,590 --> 00:52:33,290 radio to kind of sharpen your brain and if you're on the 811 00:52:33,290 --> 00:52:35,890 right politically it's worth listening to left-wing talk 812 00:52:35,890 --> 00:52:40,780 radio to sharpen your brain as long as it-- well actually 813 00:52:40,780 --> 00:52:43,060 talk radio's probably a wrong example because it really is 814 00:52:43,060 --> 00:52:44,750 pretty stupid; most of it. 815 00:52:44,750 --> 00:52:46,450 Herrnstein and Murray aren't stupid. 816 00:52:46,450 --> 00:52:48,980 I think they're wrong, but they're not stupid. 817 00:52:48,980 --> 00:52:58,620 In any case, let me give a non-intelligence example for 818 00:52:58,620 --> 00:53:02,980 why this pitfall of thinking that these things are fixed 819 00:53:02,980 --> 00:53:06,460 and unalterable just because they're inherited. 820 00:53:06,460 --> 00:53:08,520 Did it work? 821 00:53:08,520 --> 00:53:11,300 Oh, you weren't the person looking for-- it's 822 00:53:11,300 --> 00:53:12,310 not the same guy. 823 00:53:12,310 --> 00:53:15,980 Oh, this is a lovely change blindness thing, right? 824 00:53:15,980 --> 00:53:17,980 It's perfect. 825 00:53:17,980 --> 00:53:20,330 A guy went out, the [? boingy ?] 826 00:53:20,330 --> 00:53:21,800 stopped, the guy came back. 827 00:53:24,380 --> 00:53:25,840 Unfortunately I guess, the [? boingy ?] 828 00:53:25,840 --> 00:53:28,920 guy has killed the guy who went out. 829 00:53:28,920 --> 00:53:29,990 This is really sad. 830 00:53:29,990 --> 00:53:36,970 We could send another one out, but where was I? 831 00:53:36,970 --> 00:53:42,750 OK, so things can be inherited, things can be 832 00:53:42,750 --> 00:53:45,310 inherited related to intelligence and nevertheless 833 00:53:45,310 --> 00:53:45,980 quite changeable. 834 00:53:45,980 --> 00:53:55,410 PKU is a disorder where you are unable to metabolize one 835 00:53:55,410 --> 00:54:00,980 of the basic amino acids and the result is it 836 00:54:00,980 --> 00:54:02,120 produces zero toxins. 837 00:54:02,120 --> 00:54:04,500 It munches up your brain-- kids with this disorder, it's 838 00:54:04,500 --> 00:54:08,390 a genetic birth defect type of disorder-- 839 00:54:08,390 --> 00:54:13,230 kids with this disorder before it was understood were 840 00:54:13,230 --> 00:54:15,640 condemned to severe mental retardation. 841 00:54:15,640 --> 00:54:19,050 Once it was figured out what the problem was you could 842 00:54:19,050 --> 00:54:23,230 prevent the severe mental retardation 843 00:54:23,230 --> 00:54:24,550 by controlling diet. 844 00:54:24,550 --> 00:54:27,130 Basically, by controlling an environmental factor. 845 00:54:27,130 --> 00:54:30,730 You take the precursor out of the diet, you don't produce 846 00:54:30,730 --> 00:54:32,590 the neurotoxins, you don't produce the mental 847 00:54:32,590 --> 00:54:33,750 retardation. 848 00:54:33,750 --> 00:54:35,370 It doesn't mean that the disorder 849 00:54:35,370 --> 00:54:38,660 was any less heritable. 850 00:54:38,660 --> 00:54:39,550 It's a birth defect. 851 00:54:39,550 --> 00:54:40,320 Are you the guy? 852 00:54:40,320 --> 00:54:41,630 Is he the guy? 853 00:54:41,630 --> 00:54:43,990 Thank you. 854 00:54:43,990 --> 00:54:44,600 What was it? 855 00:54:44,600 --> 00:54:47,720 AUDIENCE: It was just someone walking through the hall 856 00:54:47,720 --> 00:54:48,240 making noise. 857 00:54:48,240 --> 00:54:49,800 He left. 858 00:54:49,800 --> 00:54:53,830 PROFESSOR: OK, he's not going to need medical care or 859 00:54:53,830 --> 00:54:54,610 nothing, right? 860 00:54:54,610 --> 00:54:55,510 AUDIENCE: He's OK. 861 00:54:55,510 --> 00:54:56,070 PROFESSOR: OK, good. 862 00:54:56,070 --> 00:54:57,440 No, I was worried that you might have 863 00:54:57,440 --> 00:55:00,450 worked him over or something. 864 00:55:00,450 --> 00:55:03,300 Thank you for taking care of that in any case. 865 00:55:06,660 --> 00:55:10,160 So you can have something that's clearly genetic in 866 00:55:10,160 --> 00:55:15,650 origin, where an environmental change might make a 867 00:55:15,650 --> 00:55:16,070 difference. 868 00:55:16,070 --> 00:55:19,670 PKU is simply a dramatic example of that. 869 00:55:19,670 --> 00:55:20,730 Let me just take a look. 870 00:55:20,730 --> 00:55:27,170 OK, so I've already hit the pitfall 2, the notion that the 871 00:55:27,170 --> 00:55:30,200 interaction term might be important. 872 00:55:30,200 --> 00:55:34,790 The evidence for that might be this low socioeconomic status, 873 00:55:34,790 --> 00:55:40,430 high socioeconomic status influence on the apparent 874 00:55:40,430 --> 00:55:42,250 heritability of it. 875 00:55:42,250 --> 00:55:47,940 And I really also already hit that first pitfall there. 876 00:55:47,940 --> 00:55:53,070 High heritability might just mean that there wasn't much 877 00:55:53,070 --> 00:55:56,470 variation in the environment in your particular experiment. 878 00:55:56,470 --> 00:56:00,020 If you grow all the tomato plants in exactly the same 879 00:56:00,020 --> 00:56:02,180 field with the same water, the same sun and the same 880 00:56:02,180 --> 00:56:05,850 fertilizer, there will still be some variability of course. 881 00:56:05,850 --> 00:56:09,720 You'll get a heritability score that will be near 1, but 882 00:56:09,720 --> 00:56:11,700 that's because you've driven this term down to 883 00:56:11,700 --> 00:56:14,210 zero or near zero. 884 00:56:14,210 --> 00:56:20,120 And that doesn't prove that the environment is 885 00:56:20,120 --> 00:56:20,780 unimportant. 886 00:56:20,780 --> 00:56:25,390 So the fact that there is a significant genetic component 887 00:56:25,390 --> 00:56:29,870 doesn't mean there is not a significant 888 00:56:29,870 --> 00:56:32,070 environmental component. 889 00:56:32,070 --> 00:56:36,920 OK, take a quick stretch, make [? boingy ?] noises. 890 00:56:36,920 --> 00:56:38,320 AUDIENCE: [UNINTELLIGIBLE] 891 00:56:38,320 --> 00:56:38,810 PROFESSOR: That's good. 892 00:56:38,810 --> 00:56:40,060 Thank you. 893 00:56:46,410 --> 00:56:50,980 AUDIENCE: Hi, we actually decided that we were going to 894 00:56:50,980 --> 00:56:52,370 go over the midterms in class. 895 00:56:52,370 --> 00:56:52,460 PROFESSOR: Yes, of course. 896 00:56:52,460 --> 00:56:53,300 Go ahead. 897 00:56:53,300 --> 00:56:55,800 AUDIENCE: OK, but my class is right after this. 898 00:56:55,800 --> 00:56:56,620 PROFESSOR: No, go ahead. 899 00:56:56,620 --> 00:56:57,100 Absolutely. 900 00:56:57,100 --> 00:57:00,210 No, this is in case there are all those society for 901 00:57:00,210 --> 00:57:02,200 neuroscience TAs. 902 00:57:02,200 --> 00:57:03,210 No, by all means. 903 00:57:03,210 --> 00:57:14,490 I want to cover for them, but not to discourage you. 904 00:57:40,830 --> 00:57:44,050 Looking at the time and looking at my notes I realize 905 00:57:44,050 --> 00:57:46,520 I'd better get cooking here. 906 00:57:46,520 --> 00:57:49,520 So let me cook here. 907 00:57:49,520 --> 00:57:56,860 What I want to do is to tell you a little bit, well, let me 908 00:57:56,860 --> 00:57:59,280 frame this. 909 00:57:59,280 --> 00:58:03,680 Since I've already introduced the Herrnstein and Murray 910 00:58:03,680 --> 00:58:11,370 book, the most controversial piece of the Herrnstein and 911 00:58:11,370 --> 00:58:14,140 Murray kind of story is when you start getting to group 912 00:58:14,140 --> 00:58:18,890 differences and you say, look, in America at the present time 913 00:58:18,890 --> 00:58:23,840 the average African American IQ score is about 10 points 914 00:58:23,840 --> 00:58:28,560 lower than the average white American score. 915 00:58:28,560 --> 00:58:30,530 That's data. 916 00:58:30,530 --> 00:58:35,830 And what you care to make of those data is very important 917 00:58:35,830 --> 00:58:38,200 from a policy point of view. 918 00:58:38,200 --> 00:58:42,480 Herrnstein and Murray were making the argument that since 919 00:58:42,480 --> 00:58:46,540 genetics has this big role and these are things that are 920 00:58:46,540 --> 00:58:52,240 unalterable that the fact that blacks in the U.S. also, on 921 00:58:52,240 --> 00:58:54,360 average, make less money and so on. 922 00:58:54,360 --> 00:58:59,460 You know, hey, man, that's just science and genetics. 923 00:58:59,460 --> 00:59:02,770 And the fact that we're a meritocracy and good things 924 00:59:02,770 --> 00:59:05,530 like that, live with it. 925 00:59:05,530 --> 00:59:10,190 What I want to do first is to describe a couple of what are 926 00:59:10,190 --> 00:59:13,880 really sort of amusing historical anecdotes about the 927 00:59:13,880 --> 00:59:16,920 history of intelligence testing drawn from Stephen 928 00:59:16,920 --> 00:59:20,220 Gould's book called The Mismeasure of Man, I will 929 00:59:20,220 --> 00:59:25,000 answer the question on the handout already. 930 00:59:25,000 --> 00:59:27,050 What's the point of these amusing stories? 931 00:59:27,050 --> 00:59:31,200 The point is not to make fun of folks operating 100 932 00:59:31,200 --> 00:59:33,220 or 150 years ago. 933 00:59:33,220 --> 00:59:41,620 The point is to make us take with caution our understanding 934 00:59:41,620 --> 00:59:45,720 of similar answers that we're getting today. 935 00:59:45,720 --> 00:59:49,350 So for example, who has a higher 936 00:59:49,350 --> 00:59:52,720 average IQ: men or women? 937 00:59:52,720 --> 00:59:55,410 How many vote that it's men? 938 00:59:55,410 --> 00:59:57,720 How many vote that it's women? 939 00:59:57,720 --> 01:00:00,570 How many vote that it's equal? 940 01:00:00,570 --> 01:00:03,025 How many vote that I ain't touching this because I smell 941 01:00:03,025 --> 01:00:06,760 a political question when I can-- how many 942 01:00:06,760 --> 01:00:09,440 vote, I don't vote? 943 01:00:09,440 --> 01:00:10,650 That's the wrong answer. 944 01:00:10,650 --> 01:00:12,480 At least, next Tuesday. 945 01:00:12,480 --> 01:00:15,570 This ad paid for you by the league of women voters. 946 01:00:20,380 --> 01:00:25,530 Just stepping aside here, it's sort of a pain often to vote 947 01:00:25,530 --> 01:00:28,100 if you're an undergraduate, right? 948 01:00:28,100 --> 01:00:30,840 Because your way away from whatever district you supposed 949 01:00:30,840 --> 01:00:32,850 to vote in, but go and vote. 950 01:00:32,850 --> 01:00:36,530 If for no other reason than nobody polled you and you can 951 01:00:36,530 --> 01:00:39,820 screw up all those polls that claimed that they knew the 952 01:00:39,820 --> 01:00:42,650 answer-- which they don't know-- to the election one way 953 01:00:42,650 --> 01:00:46,390 or the other by voting because when they called your parents 954 01:00:46,390 --> 01:00:47,360 you weren't home. 955 01:00:47,360 --> 01:00:50,280 So anyway, but you should vote. 956 01:00:50,280 --> 01:00:54,660 Oh, yeah, so men and women-- the answer is that men and 957 01:00:54,660 --> 01:00:58,700 women have the same average IQ. 958 01:00:58,700 --> 01:01:03,070 But the interesting question is why that's the case? 959 01:01:03,070 --> 01:01:03,990 Why is it the case? 960 01:01:03,990 --> 01:01:07,140 Well, back at the beginning of the 20th century when IQ tests 961 01:01:07,140 --> 01:01:12,900 came from France to America, how do you build an IQ test? 962 01:01:12,900 --> 01:01:16,050 What you do is you make up some questions that you think 963 01:01:16,050 --> 01:01:19,960 might be reasonable measures of intelligence, you give them 964 01:01:19,960 --> 01:01:22,230 to a bunch of kids because this was originally a school 965 01:01:22,230 --> 01:01:27,520 testing kind of thing, and then you sort of ask, are 966 01:01:27,520 --> 01:01:28,670 these sensible questions? 967 01:01:28,670 --> 01:01:32,010 Like, do the kids who the teacher thinks is smart, do 968 01:01:32,010 --> 01:01:33,730 well on them? 969 01:01:33,730 --> 01:01:34,980 And you work from that. 970 01:01:34,980 --> 01:01:37,520 And so they're working on standardizing the original 971 01:01:37,520 --> 01:01:41,550 tests and on the original drafts of the test women, 972 01:01:41,550 --> 01:01:45,260 girls were scoring about 10 points higher than men. 973 01:01:48,140 --> 01:01:51,440 The guys literally, who made up the test knew 974 01:01:51,440 --> 01:01:52,690 that this was wrong. 975 01:01:55,310 --> 01:01:59,590 Now it is to their credit in the early 20th century that 976 01:01:59,590 --> 01:02:03,140 they knew a priori that the correct answer was that men 977 01:02:03,140 --> 01:02:04,650 and women were equal. 978 01:02:04,650 --> 01:02:07,120 It would have been no big surprise at that point to have 979 01:02:07,120 --> 01:02:09,380 them figure out that men should have been scoring 980 01:02:09,380 --> 01:02:12,610 higher, but they knew that the answer was that men and women 981 01:02:12,610 --> 01:02:13,340 were equal. 982 01:02:13,340 --> 01:02:17,250 And here's what they did, you do what's known as an item 983 01:02:17,250 --> 01:02:18,410 analysis on your test. 984 01:02:18,410 --> 01:02:22,940 You look at all the questions, you say, oh look, guys did 985 01:02:22,940 --> 01:02:27,260 much better on this question than women did. 986 01:02:27,260 --> 01:02:29,270 Oh look, women did much better on this 987 01:02:29,270 --> 01:02:31,150 question than guys did. 988 01:02:31,150 --> 01:02:32,760 You know what we're going to do with this question? 989 01:02:32,760 --> 01:02:35,620 We're going to throw it off the exam. 990 01:02:35,620 --> 01:02:37,870 You know, we're making up the exam as we go along and so 991 01:02:37,870 --> 01:02:42,390 they tooled the exam to get rid of that 10 point 992 01:02:42,390 --> 01:02:46,060 difference and subsequent tests are standardized against 993 01:02:46,060 --> 01:02:47,130 the older tests. 994 01:02:47,130 --> 01:02:50,090 That's why men and women have the same IQ. 995 01:02:50,090 --> 01:02:50,460 Yes? 996 01:02:50,460 --> 01:02:53,400 AUDIENCE: How do you factor motivation into this? 997 01:02:53,400 --> 01:02:55,360 Aren't little girls more focused in general-- 998 01:02:55,360 --> 01:02:56,950 PROFESSOR: Oh, there's a whole lot of-- 999 01:02:56,950 --> 01:02:59,220 you know, I don't know when it is that 1000 01:02:59,220 --> 01:03:02,670 guys finally get focused. 1001 01:03:07,250 --> 01:03:10,650 And I don't have much direct experience with this having 1002 01:03:10,650 --> 01:03:15,890 nearly had 3 unfocused guys myself in my family. 1003 01:03:15,890 --> 01:03:18,430 I always wanted one of those focused girls who just-- 1004 01:03:18,430 --> 01:03:20,770 anyway. 1005 01:03:20,770 --> 01:03:24,310 There's all sorts that stuff like that. 1006 01:03:27,620 --> 01:03:29,920 That's a whole other course. 1007 01:03:29,920 --> 01:03:34,980 But what they did was they made the difference go away by 1008 01:03:34,980 --> 01:03:37,990 manipulating the test. 1009 01:03:37,990 --> 01:03:42,450 Now these tests were hardly the first efforts to study 1010 01:03:42,450 --> 01:03:43,730 intelligence. 1011 01:03:43,730 --> 01:03:49,630 Actually, in an interesting circular movement, these days 1012 01:03:49,630 --> 01:03:53,340 there's great interest again in looking at the brain and 1013 01:03:53,340 --> 01:03:55,780 saying, is there something about smart brains that's 1014 01:03:55,780 --> 01:03:59,160 different from less smart brains? 1015 01:03:59,160 --> 01:04:01,880 We can do that with the renewed interest comes from 1016 01:04:01,880 --> 01:04:07,450 the fact that you can now go and look at brains in walking, 1017 01:04:07,450 --> 01:04:09,330 talking people. 1018 01:04:09,330 --> 01:04:12,570 The previous boom in looking at brains 1019 01:04:12,570 --> 01:04:15,650 was in the 19th century. 1020 01:04:15,650 --> 01:04:19,090 The reason Binet developed his test was that looking at 1021 01:04:19,090 --> 01:04:21,440 brains was only good at autopsy. 1022 01:04:21,440 --> 01:04:23,530 You're not going to figure out if your kid needs help in 1023 01:04:23,530 --> 01:04:25,220 school by cutting his head open and looking at 1024 01:04:25,220 --> 01:04:26,290 his brains any good. 1025 01:04:26,290 --> 01:04:28,730 In the 19th century it's just not a really practical 1026 01:04:28,730 --> 01:04:33,650 solution, but people like Paul Broca of Broca's area-- famous 1027 01:04:33,650 --> 01:04:37,870 French neurologist, spent a lot of time looking at the 1028 01:04:37,870 --> 01:04:41,080 brains of their deceased colleagues and others to see 1029 01:04:41,080 --> 01:04:43,740 what made people talented. 1030 01:04:43,740 --> 01:04:48,620 Broca's doctrine, translated from the French, but quoting 1031 01:04:48,620 --> 01:04:51,860 is that all other things being equal, there's a remarkable 1032 01:04:51,860 --> 01:04:54,150 relationship between the development of intelligence 1033 01:04:54,150 --> 01:04:55,710 and the volume of the brain. 1034 01:04:55,710 --> 01:05:00,080 He was interested in the size of the brain. 1035 01:05:00,080 --> 01:05:02,540 To a first approximation he's got to be right. 1036 01:05:02,540 --> 01:05:07,720 One of the reasons there are no chickens enrolled as MIT 1037 01:05:07,720 --> 01:05:11,800 undergraduates is because little chicken brains just 1038 01:05:11,800 --> 01:05:18,740 don't cut it when it comes to surviving at MIT. 1039 01:05:18,740 --> 01:05:23,940 So having quantity of brain really does make some 1040 01:05:23,940 --> 01:05:26,400 difference. 1041 01:05:26,400 --> 01:05:33,490 So what he did was to look at the sizes of brains. 1042 01:05:33,490 --> 01:05:40,100 And his conclusion was that in general, the brain is larger 1043 01:05:40,100 --> 01:05:44,550 in mature adults than in the elderly, in man than in women, 1044 01:05:44,550 --> 01:05:48,100 in eminent men than in men of mediocre talent, and in 1045 01:05:48,100 --> 01:05:52,910 superior races than in inferior races. 1046 01:05:52,910 --> 01:05:57,050 This reflects very much the biases of 19th century 1047 01:05:57,050 --> 01:06:00,980 European, but in fact that's sort of the point. 1048 01:06:00,980 --> 01:06:04,090 It's not to say ooh, Broca was an evil, nasty man, but you 1049 01:06:04,090 --> 01:06:07,780 should be a little suspicious when your science places you 1050 01:06:07,780 --> 01:06:10,710 at the pinnacle of creation. 1051 01:06:10,710 --> 01:06:13,910 That should be a little warning bell that goes, oh, 1052 01:06:13,910 --> 01:06:18,730 the inferior races by the way were for Broca, were the 1053 01:06:18,730 --> 01:06:21,480 Chinese, the Hindus, that's 1054 01:06:21,480 --> 01:06:25,090 subcontinental India, and blacks. 1055 01:06:25,090 --> 01:06:28,220 I can't remember if Jews made it onto his list or not. 1056 01:06:28,220 --> 01:06:32,470 But in any case, it was sort of the 19th century collection 1057 01:06:32,470 --> 01:06:35,850 of not white males. 1058 01:06:35,850 --> 01:06:38,190 But how did Broca get to this conclusion? 1059 01:06:38,190 --> 01:06:41,760 Well, he weighed a lot of brains and what he discovered 1060 01:06:41,760 --> 01:06:43,860 was in fact, it's true. 1061 01:06:43,860 --> 01:06:48,900 That female brains are on average a little lighter than 1062 01:06:48,900 --> 01:06:51,750 male brain, on average. 1063 01:06:51,750 --> 01:06:54,250 All right, so? 1064 01:06:54,250 --> 01:06:56,640 Chicken brains are lighter than your brain, chickens 1065 01:06:56,640 --> 01:07:02,690 don't go to MIT; female brains are lighter than male brains, 1066 01:07:02,690 --> 01:07:07,360 but he also found that French brains were lighter than 1067 01:07:07,360 --> 01:07:10,340 German brains. 1068 01:07:10,340 --> 01:07:13,500 Broca was French. 1069 01:07:13,500 --> 01:07:15,390 So he wrote, 1070 01:07:15,390 --> 01:07:20,070 "Germans ingest a quantity of solid food and drink far 1071 01:07:20,070 --> 01:07:22,720 greater than that which satisfies us. 1072 01:07:25,270 --> 01:07:28,830 This, joined with his consumption of beer makes the 1073 01:07:28,830 --> 01:07:32,280 German so much more fleshy than the Frenchman. 1074 01:07:32,280 --> 01:07:34,610 So much more so that the relation of brain size to 1075 01:07:34,610 --> 01:07:37,860 total mass far from being superior to ours seems to me, 1076 01:07:37,860 --> 01:07:42,380 on the contrary, to be inferior." 1077 01:07:42,380 --> 01:07:47,280 Well, what he's doing is not stupid. 1078 01:07:47,280 --> 01:07:51,740 He's correcting for body size and there's a very interesting 1079 01:07:51,740 --> 01:07:56,080 graph that you may have seen someplace or other. 1080 01:07:56,080 --> 01:08:07,080 If you plot body size weight against brain weight-- 1081 01:08:07,080 --> 01:08:10,350 I can't remember if it works just for mammals or for sort 1082 01:08:10,350 --> 01:08:11,740 of everybody, but anyway-- 1083 01:08:11,740 --> 01:08:17,700 let's claim it's mammals, you get a very tight correlation. 1084 01:08:17,700 --> 01:08:22,640 All the way up from mouse to whale. 1085 01:08:25,520 --> 01:08:30,110 Except that humans are up here somewhere. 1086 01:08:30,110 --> 01:08:32,740 They're off the curve. 1087 01:08:32,740 --> 01:08:35,190 So the argument is people always-- 1088 01:08:35,190 --> 01:08:37,020 a lot of people think-- whales, they must be these 1089 01:08:37,020 --> 01:08:39,790 philosophers of the deep, they got a huge brain. 1090 01:08:39,790 --> 01:08:42,650 But nobody quite understands what they're doing with that 1091 01:08:42,650 --> 01:08:46,130 great big brain, but they do lie on this function. 1092 01:08:46,130 --> 01:08:50,990 Humans have a very big brain relative to their body size. 1093 01:08:50,990 --> 01:08:54,630 So this is the point at which some-- typically, woman-- in 1094 01:08:54,630 --> 01:08:57,740 the class is supposed to raise their hand and say? 1095 01:08:57,740 --> 01:08:59,820 AUDIENCE: Females are smaller. 1096 01:08:59,820 --> 01:09:00,710 PROFESSOR: Yeah. 1097 01:09:00,710 --> 01:09:04,050 Females are smaller than males, so if we correct for 1098 01:09:04,050 --> 01:09:07,390 body weight-- well, Broca wasn't stupid, he knew that. 1099 01:09:07,390 --> 01:09:11,460 And he wrote, "we might ask, if the small size of the 1100 01:09:11,460 --> 01:09:14,220 female brain depends exclusively on the small size 1101 01:09:14,220 --> 01:09:15,450 of her body. 1102 01:09:15,450 --> 01:09:18,090 We must not forget, says Broca, that women are on 1103 01:09:18,090 --> 01:09:20,670 average, a little less intelligent than men. 1104 01:09:20,670 --> 01:09:23,900 We are therefore permitted to suppose that the relatively 1105 01:09:23,900 --> 01:09:26,640 small size of the female brain depends in part on her 1106 01:09:26,640 --> 01:09:29,600 physical inferiority and in part, on her intellectual 1107 01:09:29,600 --> 01:09:36,690 inferiority." Now those 2 quotes from Broca are taken 1108 01:09:36,690 --> 01:09:38,600 from completely different publications. 1109 01:09:38,600 --> 01:09:40,970 Even Broca might have noticed if you put them right next to 1110 01:09:40,970 --> 01:09:44,620 each other that there's a certain conflict there. 1111 01:09:44,620 --> 01:09:47,230 Again, the point isn't to say, Broca was a mean, nasty, 1112 01:09:47,230 --> 01:09:48,480 stupid man. 1113 01:09:48,480 --> 01:09:50,670 There's no evidence for any of that, but there's clear 1114 01:09:50,670 --> 01:09:54,420 evidence that what he thought he knew ahead of time was 1115 01:09:54,420 --> 01:10:01,090 influencing how he was reading his data. 1116 01:10:01,090 --> 01:10:04,660 The brain weighing endeavor sort of fell apart because it 1117 01:10:04,660 --> 01:10:05,890 didn't work all that well. 1118 01:10:05,890 --> 01:10:09,070 You know, people like the scientist who gives his name 1119 01:10:09,070 --> 01:10:12,680 to that unit of magnetic whatever it is and Gaussian 1120 01:10:12,680 --> 01:10:14,390 curves and stuff like that turned out to 1121 01:10:14,390 --> 01:10:15,550 have a small brain. 1122 01:10:15,550 --> 01:10:18,250 Had a lot of wrinkles in it though. 1123 01:10:18,250 --> 01:10:20,770 Lenin, the Communists were always big on carving up the 1124 01:10:20,770 --> 01:10:22,250 brains of their ex-leaders. 1125 01:10:22,250 --> 01:10:26,080 Lenin was reputed to have a cortex-- 1126 01:10:26,080 --> 01:10:29,370 we all have a cortex, its got 6 cell layers in it-- 1127 01:10:29,370 --> 01:10:31,340 Lenin's allegedly had 7. 1128 01:10:38,130 --> 01:10:42,440 Again, the point of these stories is not to say that 1129 01:10:42,440 --> 01:10:45,840 we're smart and they were all stupid and silly people, the 1130 01:10:45,840 --> 01:10:49,790 point is to say that people tend to impose their ideas on 1131 01:10:49,790 --> 01:10:53,120 their data as well as imposing their data on their ideas and 1132 01:10:53,120 --> 01:10:56,030 you've got to keep an eye open for that. 1133 01:10:56,030 --> 01:10:59,780 In the remaining time let's ask a bit about this question 1134 01:10:59,780 --> 01:11:04,930 about, suppose we really do think that more IQ is better. 1135 01:11:04,930 --> 01:11:09,400 Is there any evidence that we can get more? 1136 01:11:09,400 --> 01:11:12,450 You know, is there a way to get more IQ? 1137 01:11:12,450 --> 01:11:16,930 The answer appears to be yes, though we're not-- 1138 01:11:16,930 --> 01:11:18,900 some of it's kind of mysterious. 1139 01:11:18,900 --> 01:11:21,950 The mysterious bit is on the handout on 1140 01:11:21,950 --> 01:11:23,480 the back page I see. 1141 01:11:23,480 --> 01:11:27,910 Well, on page 4 at least as the Flynn effect. 1142 01:11:27,910 --> 01:11:30,240 Flynn is a political scientist, James Flynn is a 1143 01:11:30,240 --> 01:11:33,380 political scientist sitting down at the University of 1144 01:11:33,380 --> 01:11:38,490 Otago at the bottom of New Zealand, which nobody had ever 1145 01:11:38,490 --> 01:11:42,030 heard of until the "Lord of the Rings" movies, but you 1146 01:11:42,030 --> 01:11:45,450 don't get further away unless you go to Antarctica. 1147 01:11:45,450 --> 01:11:50,050 But what he did was he took a look just at raw IQ 1148 01:11:50,050 --> 01:11:57,300 statistics, initially in the Pacific Rim countries in the 1149 01:11:57,300 --> 01:12:01,840 period shortly before and since World War 2-- couple of 1150 01:12:01,840 --> 01:12:02,950 generations worth. 1151 01:12:02,950 --> 01:12:04,730 And what he found-- well, it's summed up in the 1152 01:12:04,730 --> 01:12:06,070 title of his paper-- 1153 01:12:06,070 --> 01:12:09,770 massive IQ gains in these countries. 1154 01:12:09,770 --> 01:12:15,890 So for example, back a decade or two ago when the Japanese 1155 01:12:15,890 --> 01:12:20,340 economy was booming, people liked to point out-- people 1156 01:12:20,340 --> 01:12:24,200 given to these group IQ arguments liked to point out 1157 01:12:24,200 --> 01:12:27,590 that the average Japanese IQ was about 10 points higher 1158 01:12:27,590 --> 01:12:31,490 than the average American IQ and the reasons they're eating 1159 01:12:31,490 --> 01:12:36,740 our lunch is because they've been breeding with each other 1160 01:12:36,740 --> 01:12:37,950 for years, you know? 1161 01:12:37,950 --> 01:12:41,730 And they're just smarter people than we are and we're 1162 01:12:41,730 --> 01:12:44,630 all doomed to serve the Japanese forever. 1163 01:12:44,630 --> 01:12:46,810 Well, that argument has kind of gone away since they went 1164 01:12:46,810 --> 01:12:49,460 into a 10-year recession or something. 1165 01:12:49,460 --> 01:12:52,600 But the more interesting point, from our point of view, 1166 01:12:52,600 --> 01:12:58,570 is that apparently they got smart really fast because 1167 01:12:58,570 --> 01:13:01,920 before World War 2 the average Japanese IQ was about 10 1168 01:13:01,920 --> 01:13:04,590 points lower than the U.S. IQ. 1169 01:13:04,590 --> 01:13:10,730 There is no genetic story that makes that work. 1170 01:13:10,730 --> 01:13:13,070 And this happens in Pacific Rim country 1171 01:13:13,070 --> 01:13:16,710 after Pacific Rim country. 1172 01:13:16,710 --> 01:13:21,470 That after World War 2 IQs just go way up and then 1173 01:13:21,470 --> 01:13:23,410 subsequently, it turns out what's happening all over the 1174 01:13:23,410 --> 01:13:29,980 world in fact, it had been mapped in the U.S. because IQ 1175 01:13:29,980 --> 01:13:32,630 tests keep getting renormed. 1176 01:13:32,630 --> 01:13:37,830 The average IQ in the U.S. keeps drifting higher and then 1177 01:13:37,830 --> 01:13:42,990 because average IQ is defined as 100 you renorm the test so 1178 01:13:42,990 --> 01:13:46,550 that the average is 100 again. 1179 01:13:46,550 --> 01:13:49,420 Flynn wrote a wonderful paper, which I didn't cite on here, 1180 01:13:49,420 --> 01:13:52,460 but if anybody wants send me an e-mail, where he was having 1181 01:13:52,460 --> 01:13:57,880 fun with these statistics proving that if you used these 1182 01:13:57,880 --> 01:14:01,680 statistics literally you would conclude that our founding 1183 01:14:01,680 --> 01:14:06,870 fathers were all congenital idiots and were probably too 1184 01:14:06,870 --> 01:14:10,030 dumb to walk across the street if you just extrapolate back. 1185 01:14:10,030 --> 01:14:12,170 Something very odd is going on. 1186 01:14:12,170 --> 01:14:13,480 It's not clear what it is. 1187 01:14:13,480 --> 01:14:16,140 One of the thoughts had been that it was simply nutrition. 1188 01:14:16,140 --> 01:14:19,410 Pacific Rim nutrition got a lot better after World War 2 1189 01:14:19,410 --> 01:14:22,120 and maybe better food makes better brains. 1190 01:14:22,120 --> 01:14:24,810 That seems to be true, but there's counter evidence. 1191 01:14:24,810 --> 01:14:30,160 For example, there's no dip in the Dutch average IQ for the 1192 01:14:30,160 --> 01:14:34,230 cohort that was in the vulnerable part of early 1193 01:14:34,230 --> 01:14:37,280 childhood during World War 2 when the Germans 1194 01:14:37,280 --> 01:14:40,870 systematically starved the Netherlands. 1195 01:14:40,870 --> 01:14:43,470 There's no dip in the IQ, so it's not clear that the food 1196 01:14:43,470 --> 01:14:44,530 story works. 1197 01:14:44,530 --> 01:14:49,870 There are other stories that say that what's going on is 1198 01:14:49,870 --> 01:14:53,120 it's something about a change in the culture. 1199 01:14:53,120 --> 01:14:55,420 That the world culture has become this much more 1200 01:14:55,420 --> 01:15:01,520 information intensive culture that supports and nourishes 1201 01:15:01,520 --> 01:15:04,910 the sorts of things that are measured by IQ. 1202 01:15:04,910 --> 01:15:08,600 That the sorts of talents that you needed when you were a 1203 01:15:08,600 --> 01:15:13,250 subsistence farmer, you needed some smarts to make yourself 1204 01:15:13,250 --> 01:15:15,450 survive, but they weren't the sort of thing that were 1205 01:15:15,450 --> 01:15:21,060 getting picked up on IQ tests and that now your life builds 1206 01:15:21,060 --> 01:15:23,580 IQ points and something-- it's not clear what's going on. 1207 01:15:23,580 --> 01:15:26,350 What is clear is it's not genetic. 1208 01:15:26,350 --> 01:15:30,570 It's just too fast to be a genetic change. 1209 01:15:30,570 --> 01:15:33,650 Now that's at the level of groups, can you change 1210 01:15:33,650 --> 01:15:36,490 individual IQs? 1211 01:15:36,490 --> 01:15:39,570 And the evidence there is yeah, you can do that, too. 1212 01:15:39,570 --> 01:15:46,760 And the classic experiments come from the 60s. 1213 01:15:46,760 --> 01:15:47,930 Oh, what is he today? 1214 01:15:47,930 --> 01:15:50,580 He's a rat. 1215 01:15:50,580 --> 01:15:52,950 All right, so we're going to take rats. 1216 01:15:52,950 --> 01:15:55,780 Sort of a homogeneous population of rats and we're 1217 01:15:55,780 --> 01:15:58,910 going to randomized them into 3 populations. 1218 01:15:58,910 --> 01:16:03,600 Population 1 goes into-- well, let's stick with socioeconomic 1219 01:16:03,600 --> 01:16:06,480 status because that's what they were trying to model. 1220 01:16:06,480 --> 01:16:11,650 This is the low SES group, which meant in rat case that 1221 01:16:11,650 --> 01:16:15,650 they lived-- 1222 01:16:15,650 --> 01:16:17,570 looking like ducks or something-- they lived all 1223 01:16:17,570 --> 01:16:19,930 alone in an isolated cage with nothing to play with. 1224 01:16:22,560 --> 01:16:29,410 The medium group lived in what was sort of the standard lab 1225 01:16:29,410 --> 01:16:36,820 cage of a couple of cockroaches. 1226 01:16:36,820 --> 01:16:38,880 A couple of rats, not much action. 1227 01:16:38,880 --> 01:16:44,630 And then there was the high SES rat group or the enriched 1228 01:16:44,630 --> 01:16:46,970 group, who lived in a sort of a big 1229 01:16:46,970 --> 01:16:51,250 group rat daycare center. 1230 01:16:51,250 --> 01:16:56,150 You know, with lots of cool toys to play with and the 1231 01:16:56,150 --> 01:16:59,010 Habitrail thingy and stuff. 1232 01:16:59,010 --> 01:17:02,020 You know, a lot of good, cool stuff there. 1233 01:17:02,020 --> 01:17:06,010 Let them grow up in these environments, test them on 1234 01:17:06,010 --> 01:17:09,990 little rat IQ tests, cognitive tests for rats. 1235 01:17:09,990 --> 01:17:12,810 These guys do not do as well as these guys. 1236 01:17:12,810 --> 01:17:14,520 Look at them at the end of the experiment. 1237 01:17:14,520 --> 01:17:16,600 I mean, the real end of the experiment when the rat is now 1238 01:17:16,600 --> 01:17:20,530 dead and you discover these guys have brains that on all 1239 01:17:20,530 --> 01:17:23,050 sorts of measures look better than these brains. 1240 01:17:23,050 --> 01:17:26,670 Thicker cortex, more synapses, bigger brain. 1241 01:17:26,670 --> 01:17:29,810 Clearly, the enriched environment was having some 1242 01:17:29,810 --> 01:17:31,280 sort of affect. 1243 01:17:31,280 --> 01:17:36,110 These data, back in the 60s, were part of what motivated 1244 01:17:36,110 --> 01:17:37,420 Head Start. 1245 01:17:37,420 --> 01:17:44,700 The notion that kids in low socioeconomic environments had 1246 01:17:44,700 --> 01:17:48,800 lower IQs than kids in high socioeconomic environments, 1247 01:17:48,800 --> 01:17:52,020 they could be brought along to the same higher level by 1248 01:17:52,020 --> 01:17:54,570 putting them in school earlier. 1249 01:17:54,570 --> 01:17:58,430 Putting them in a preschool enrichment situation. 1250 01:17:58,430 --> 01:17:59,770 And it worked. 1251 01:17:59,770 --> 01:18:01,950 That if you went-- 1252 01:18:01,950 --> 01:18:02,240 [UNINTELLIGIBLE] 1253 01:18:02,240 --> 01:18:04,730 I need a spot-- 1254 01:18:04,730 --> 01:18:18,760 if you took low SES kids who plateaued out at this level 1255 01:18:18,760 --> 01:18:24,590 and you took them and put them in Head Start they went up to 1256 01:18:24,590 --> 01:18:29,840 this higher level that was the same level as 1257 01:18:29,840 --> 01:18:33,220 the high SES kids. 1258 01:18:33,220 --> 01:18:35,740 Well, what are Herrnstein and Murray talking about then when 1259 01:18:35,740 --> 01:18:36,920 they say you can't change it? 1260 01:18:36,920 --> 01:18:39,510 Well, Herrnstein and Murray said, yeah, 1261 01:18:39,510 --> 01:18:41,170 that was very nice. 1262 01:18:41,170 --> 01:18:44,040 Look what happened when these kids hit middle school and 1263 01:18:44,040 --> 01:18:45,270 high school. 1264 01:18:45,270 --> 01:18:48,740 And the answer is [UNINTELLIGIBLE], they went 1265 01:18:48,740 --> 01:18:52,560 right back down while the high SES kids stayed up. 1266 01:18:52,560 --> 01:18:55,890 Herrnstein and Murray concluded that what this is, 1267 01:18:55,890 --> 01:18:57,690 it's like a rubber band. 1268 01:18:57,690 --> 01:19:02,800 Yeah, you can stretch it a bit, but when you let go it 1269 01:19:02,800 --> 01:19:05,900 just snaps back to where it was. 1270 01:19:05,900 --> 01:19:09,340 The alternative view is yeah, it's exactly like a rubber 1271 01:19:09,340 --> 01:19:12,220 band, but why did you let go? 1272 01:19:12,220 --> 01:19:17,690 When you let go you drop these kids back into a lousy inner 1273 01:19:17,690 --> 01:19:21,900 city school rather than this enriched state and they fell 1274 01:19:21,900 --> 01:19:24,290 back to where they were. 1275 01:19:24,290 --> 01:19:26,060 The effects were transient. 1276 01:19:26,060 --> 01:19:26,910 You want to-- 1277 01:19:26,910 --> 01:19:31,230 sort of a silly example-- think about diabetes. 1278 01:19:31,230 --> 01:19:34,590 So Diabetes has a similar sort of graph except it's a little 1279 01:19:34,590 --> 01:19:39,200 more stark, no insulin you're dead by your 20s. 1280 01:19:39,200 --> 01:19:41,000 With insulin you're not dead. 1281 01:19:41,000 --> 01:19:43,460 OK, so here we're in the not dead state. 1282 01:19:45,960 --> 01:19:48,690 Let's say, OK, well you're done with that insulin stuff 1283 01:19:48,690 --> 01:19:51,940 now, we stopped giving you insulin. 1284 01:19:51,940 --> 01:19:55,070 Oh, look, they're dead now. 1285 01:19:55,070 --> 01:19:57,220 There wasn't any point to giving insulin. 1286 01:19:57,220 --> 01:20:01,500 Well, that's a stupid conclusion. 1287 01:20:01,500 --> 01:20:04,520 In Diabetes obviously, isn't that the conclusion is you 1288 01:20:04,520 --> 01:20:07,670 better keep giving insulin. 1289 01:20:07,670 --> 01:20:11,030 And you could argue that the same thing is going on here. 1290 01:20:11,030 --> 01:20:15,460 That we know that if you want more IQ points, putting people 1291 01:20:15,460 --> 01:20:18,450 in better environments works, but you gotta keep them there. 1292 01:20:22,960 --> 01:20:28,180 I will tell you one more factoid and leave it at that. 1293 01:20:31,290 --> 01:20:38,460 In adoption studies, if you adopt kids into typically, 1294 01:20:38,460 --> 01:20:41,290 when adoption moves you from a lower to a higher 1295 01:20:41,290 --> 01:20:45,800 socioeconomic status because that's just the typical reason 1296 01:20:45,800 --> 01:20:50,650 why kids would be put up for adoption, it doesn't go the 1297 01:20:50,650 --> 01:20:51,720 other direction, typically. 1298 01:20:51,720 --> 01:20:54,150 So most kids are moving from low to high 1299 01:20:54,150 --> 01:20:57,060 when they're adopted. 1300 01:20:57,060 --> 01:21:00,290 Who does their IQ correlate with? 1301 01:21:00,290 --> 01:21:03,630 The answer is it correlates with their biological 1302 01:21:03,630 --> 01:21:08,870 relatives because there is a genetic component to IQ. 1303 01:21:08,870 --> 01:21:12,580 What is their average IQ, however? 1304 01:21:12,580 --> 01:21:17,420 Their average IQ is the IQ of the environment that 1305 01:21:17,420 --> 01:21:18,600 they are now in. 1306 01:21:18,600 --> 01:21:22,090 So their IQ is, on average, indistinguishable from the 1307 01:21:22,090 --> 01:21:25,400 biological kids in the same family. 1308 01:21:25,400 --> 01:21:26,730 How can that be? 1309 01:21:26,730 --> 01:21:30,740 That doesn't sound like it ought to work. 1310 01:21:30,740 --> 01:21:33,120 But let me just draw you a quick picture and then you can 1311 01:21:33,120 --> 01:21:34,680 go off and think about it. 1312 01:21:34,680 --> 01:21:40,720 So here are these pairs of siblings. 1313 01:21:40,720 --> 01:21:44,580 And their IQs-- let's take pairs of siblings-- their IQs 1314 01:21:44,580 --> 01:21:47,010 are correlated with each other. 1315 01:21:47,010 --> 01:21:51,390 Now we're going to take 1 of each of those pairs and that 1316 01:21:51,390 --> 01:21:53,420 kid's going to get adopted out. 1317 01:21:53,420 --> 01:21:55,740 And that kids going to move, as a result, to a higher 1318 01:21:55,740 --> 01:21:57,480 socioeconomic status. 1319 01:21:57,480 --> 01:22:00,340 That doesn't change the kid's genetics any. 1320 01:22:00,340 --> 01:22:06,100 And so, the kid still has a IQ that's related to his brother 1321 01:22:06,100 --> 01:22:14,080 or sister, but it shifts all of-- so this is the kid who's 1322 01:22:14,080 --> 01:22:17,420 in a low socioeconomic state and stays there. 1323 01:22:17,420 --> 01:22:20,480 This is a kid going for low to high. 1324 01:22:20,480 --> 01:22:23,350 What happens is that the low to high move 1325 01:22:23,350 --> 01:22:25,330 pushes everybody up. 1326 01:22:25,330 --> 01:22:29,070 So the cloud of spots just moves up. 1327 01:22:29,070 --> 01:22:33,060 The correlation is the same. 1328 01:22:33,060 --> 01:22:38,100 The average IQ here is higher because going from a low to a 1329 01:22:38,100 --> 01:22:41,600 high socioeconomic status gives you IQ points. 1330 01:22:41,600 --> 01:22:45,750 If you really thought that what you wanted to do as a 1331 01:22:45,750 --> 01:22:49,050 society, if you really believed more IQ points mean 1332 01:22:49,050 --> 01:22:54,800 more good stuff, there's a clear enough way to do it. 1333 01:22:54,800 --> 01:22:57,510 It becomes a social policy issue about how you end up 1334 01:22:57,510 --> 01:23:00,540 doing this, but it seems perfectly clear that it is 1335 01:23:00,540 --> 01:23:03,180 possible to do that. 1336 01:23:03,180 --> 01:23:05,270 I'm covered in chalk.