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