1 00:00:00,040 --> 00:00:03,480 Speaker 1: M hey, everybody, Chuck here and welcome to another Saturday 2 00:00:03,520 --> 00:00:07,080 Speaker 1: Select Stuff You Should Know episode where Josh and I 3 00:00:07,120 --> 00:00:10,799 Speaker 1: take turns picking from the classics of some of our 4 00:00:10,800 --> 00:00:13,960 Speaker 1: favorite episodes. Because some of you may not know that 5 00:00:14,040 --> 00:00:16,319 Speaker 1: there are far more than three episodes that you see 6 00:00:16,640 --> 00:00:19,320 Speaker 1: at Apple Podcasts. We have over a thousand, so you 7 00:00:19,400 --> 00:00:21,840 Speaker 1: dig into the archives. And my pick this week is 8 00:00:22,320 --> 00:00:27,360 Speaker 1: from July two thousand fourteen. Is brain size related to intelligence? 9 00:00:28,120 --> 00:00:31,400 Speaker 1: I just remember loving this one. Super fascinated about the brain, 10 00:00:31,440 --> 00:00:33,800 Speaker 1: as is Josh. It's one of our one of our 11 00:00:33,800 --> 00:00:36,879 Speaker 1: favorite organs, to be honest, and how it relates to 12 00:00:36,920 --> 00:00:39,559 Speaker 1: intelligence is pretty interesting. So we get into all that 13 00:00:39,600 --> 00:00:50,960 Speaker 1: in this episode. Please to enjoy right now, Welcome to 14 00:00:51,400 --> 00:01:00,200 Speaker 1: Stuff you Should Know from House Stuff Works dot com. 15 00:00:59,480 --> 00:01:02,600 Speaker 1: M hey, and welcome to the podcast. I'm Josh Clark 16 00:01:02,640 --> 00:01:06,160 Speaker 1: and there's Charles W Chuck Bryant, and this is Stuff 17 00:01:06,200 --> 00:01:10,160 Speaker 1: you Should Know. Yes, I think this might complete our brain. 18 00:01:11,120 --> 00:01:13,479 Speaker 1: I don't know about that, because we've done Einstein's brain, 19 00:01:13,760 --> 00:01:17,240 Speaker 1: done difference between men and women's brains, and now we're 20 00:01:17,280 --> 00:01:21,280 Speaker 1: tackling brain size, which is sort of puts on both 21 00:01:21,280 --> 00:01:24,199 Speaker 1: of us a little bit. It does, but there's always 22 00:01:24,200 --> 00:01:26,959 Speaker 1: new findings. So yeah, that's what I'm saying. Now, if 23 00:01:27,000 --> 00:01:29,840 Speaker 1: there's ever an ongoing sweet, it's got to be the 24 00:01:29,880 --> 00:01:33,560 Speaker 1: brain man there. It's growing all the time, it's changing shape, 25 00:01:33,800 --> 00:01:38,360 Speaker 1: changing size, changing connections. You can almost say that sweet 26 00:01:38,440 --> 00:01:43,560 Speaker 1: is plastic plasticity. Baby. Yeah, that's right. Um, you sent 27 00:01:43,640 --> 00:01:45,800 Speaker 1: me something that I think we should talk about first, Chuck. 28 00:01:46,480 --> 00:01:49,000 Speaker 1: This is kind of like, um, this is just like 29 00:01:49,040 --> 00:01:54,880 Speaker 1: a grab bag hodgepodge of loosely related studies that all 30 00:01:54,920 --> 00:01:57,160 Speaker 1: find that we don't really know the answer to the 31 00:01:57,280 --> 00:02:03,280 Speaker 1: question like does big brain mean a smarter person? Yeah, 32 00:02:03,360 --> 00:02:08,560 Speaker 1: because there's a lot of different conflicting findings. Well, a 33 00:02:08,600 --> 00:02:11,119 Speaker 1: few million years ago, our brains started growing a lot, 34 00:02:11,200 --> 00:02:14,480 Speaker 1: and that kind of timed out with becoming smarter and 35 00:02:14,560 --> 00:02:17,000 Speaker 1: using tools and things. So I mean there's a little 36 00:02:17,000 --> 00:02:20,240 Speaker 1: bit of you call it evidence. Well, yeah, there's definitely 37 00:02:20,320 --> 00:02:23,119 Speaker 1: some plenty of evidence. I think that's what the confounding 38 00:02:23,160 --> 00:02:26,040 Speaker 1: part is that there's plenty of evidence that yes, as 39 00:02:26,080 --> 00:02:31,079 Speaker 1: a brain grows, it is correlated to intelligence. But then 40 00:02:31,160 --> 00:02:34,360 Speaker 1: that's only holding true up to a point. I sounded 41 00:02:34,440 --> 00:02:38,480 Speaker 1: like a Soviet immigrant. Just then that's only holding true 42 00:02:38,560 --> 00:02:43,160 Speaker 1: up to a certain point. A. Right, he's in Branson, Missouri, though, 43 00:02:43,240 --> 00:02:45,040 Speaker 1: did you know that. I think he's got like his 44 00:02:45,080 --> 00:02:47,920 Speaker 1: own restaurant or something. Well, he's own theater, and I 45 00:02:47,960 --> 00:02:52,040 Speaker 1: think all the theaters are food buttery. We should go 46 00:02:52,120 --> 00:02:53,800 Speaker 1: to Branson, man that I want to see what that 47 00:02:53,800 --> 00:02:57,600 Speaker 1: place is all about. Okay, you know, yeah, I mean 48 00:02:57,600 --> 00:03:01,920 Speaker 1: it's a parade of stars. If it's nineteen seventy three, 49 00:03:02,080 --> 00:03:05,480 Speaker 1: you know. Uh yeah, I bet your boy Ronnie mills 50 00:03:05,520 --> 00:03:09,480 Speaker 1: Up is there. Yeah, if he doesn't have a place there, 51 00:03:09,480 --> 00:03:13,520 Speaker 1: I'll bet he plays Branson fairly regularly. He sublets he 52 00:03:13,560 --> 00:03:18,960 Speaker 1: would play well there. Um, so Branson, how do we 53 00:03:19,040 --> 00:03:24,120 Speaker 1: even get them? Oh? Yeah? So brain size does correlate 54 00:03:24,160 --> 00:03:27,280 Speaker 1: to intelligence to a certain extent. But you can point 55 00:03:27,280 --> 00:03:30,280 Speaker 1: out like, well, sperm whale has a seventeen pound brain. 56 00:03:30,840 --> 00:03:33,640 Speaker 1: Holy cow, it must be the smartest animal on the planet. 57 00:03:33,720 --> 00:03:36,760 Speaker 1: Well it's not. I'm sorry to tell you sperm whale 58 00:03:36,800 --> 00:03:39,840 Speaker 1: fans that it's not the smartest animal on the planet. 59 00:03:40,040 --> 00:03:42,600 Speaker 1: Humans are the smartest animal on the planet, and don't 60 00:03:42,600 --> 00:03:45,600 Speaker 1: you forget it. But we don't have the largest brains, 61 00:03:46,320 --> 00:03:49,800 Speaker 1: so you kind of take that idea of Okay, Well, 62 00:03:49,800 --> 00:03:52,440 Speaker 1: if it's not brain size. Maybe it has to do 63 00:03:52,520 --> 00:03:55,160 Speaker 1: with the size of your brain in relation to your body. 64 00:03:55,880 --> 00:03:58,240 Speaker 1: Then we start to get a little closer to jack pot. 65 00:03:58,600 --> 00:04:00,680 Speaker 1: But even still, I just want to I want to 66 00:04:00,720 --> 00:04:03,640 Speaker 1: spoil it for everybody. There's no definitive answer. Yeah, but 67 00:04:03,800 --> 00:04:06,360 Speaker 1: sometimes those are the best ones because we get to 68 00:04:06,400 --> 00:04:10,320 Speaker 1: explore all this stuff and we can't really get anything 69 00:04:10,320 --> 00:04:14,120 Speaker 1: wrong because nobody knows what's right. Oh I like this, 70 00:04:14,200 --> 00:04:17,840 Speaker 1: then yeah, I've turned you know these Yeah, Now now 71 00:04:17,839 --> 00:04:19,760 Speaker 1: I feel good about this because I can't be wrong 72 00:04:19,920 --> 00:04:23,120 Speaker 1: right exactly. Well, I guess we should start off with 73 00:04:23,279 --> 00:04:27,080 Speaker 1: um a little bit about what determines what size brain 74 00:04:27,160 --> 00:04:30,480 Speaker 1: you do have. And again they don't know everything about it, 75 00:04:30,520 --> 00:04:34,360 Speaker 1: but they think that genetics plays apart. They know this 76 00:04:34,560 --> 00:04:40,240 Speaker 1: in part by studying twins and identical twins. Um have, 77 00:04:41,520 --> 00:04:45,000 Speaker 1: of course the same genes. Fraternal twins have about half 78 00:04:45,000 --> 00:04:47,960 Speaker 1: the same genes, and there's a greater correlation in brain 79 00:04:48,000 --> 00:04:51,760 Speaker 1: size if you're identical then fraternal. Yeah, so genetics, you know, 80 00:04:51,839 --> 00:04:53,800 Speaker 1: looks like it probably plays a role. Yeah, And they've 81 00:04:53,839 --> 00:04:59,520 Speaker 1: done a lot of exploration into what genes in particular 82 00:05:00,040 --> 00:05:02,279 Speaker 1: have to do with brain size, and they've isolated a 83 00:05:02,320 --> 00:05:09,200 Speaker 1: few um one is called beta catenin. There's a hyphen 84 00:05:09,200 --> 00:05:13,000 Speaker 1: in there too. It's pretty flashy. Gene not beta carotene, 85 00:05:13,240 --> 00:05:16,640 Speaker 1: no caten in. And who's who's coming up with the 86 00:05:16,839 --> 00:05:19,760 Speaker 1: naming convention for genes? They are all over the place, 87 00:05:20,480 --> 00:05:22,159 Speaker 1: Like you can't look at the name of a gene 88 00:05:22,200 --> 00:05:25,080 Speaker 1: and be like, that's obviously a gene unless there's like 89 00:05:25,120 --> 00:05:27,360 Speaker 1: a couple of weird consonants and a number and then 90 00:05:27,400 --> 00:05:30,120 Speaker 1: another consonant. Then you're like, well, that's a gene. But 91 00:05:30,560 --> 00:05:34,760 Speaker 1: you're right, that's a gene. It is a gene, and 92 00:05:34,800 --> 00:05:37,280 Speaker 1: it's a gene that they know controls brain growth because 93 00:05:37,320 --> 00:05:40,279 Speaker 1: they've injected poor mice with this stuff until their heads exploded. 94 00:05:40,400 --> 00:05:43,800 Speaker 1: Exactly their heads they fell short of exploding, but a 95 00:05:43,800 --> 00:05:46,080 Speaker 1: lot of the mice died as a result of their 96 00:05:46,080 --> 00:05:50,159 Speaker 1: heads growing too big. But they're there. Their brains grew big, 97 00:05:50,480 --> 00:05:52,760 Speaker 1: and as a result of their brains growing big, they 98 00:05:52,839 --> 00:05:58,800 Speaker 1: exhibited um more intelligence, higher cognitive function. They injected these 99 00:05:58,839 --> 00:06:03,760 Speaker 1: things with a gene proteins your brain. That, yeah, that 100 00:06:03,839 --> 00:06:07,320 Speaker 1: made them smarter. Yeah, but that, like Molly points out 101 00:06:07,360 --> 00:06:10,120 Speaker 1: in this article, you can't. That doesn't mean we should 102 00:06:10,120 --> 00:06:12,680 Speaker 1: start doing that because these mice are dying, so you 103 00:06:12,680 --> 00:06:14,760 Speaker 1: can't just you can't play god as they say, no, 104 00:06:14,839 --> 00:06:17,080 Speaker 1: but it does make you wonder, like, Okay, you don't 105 00:06:17,080 --> 00:06:19,599 Speaker 1: want to shoot up beta cat and in like before 106 00:06:19,640 --> 00:06:23,000 Speaker 1: the S A T S or anything like that, but like, 107 00:06:23,160 --> 00:06:26,159 Speaker 1: is there a way to kind of tamper a little 108 00:06:27,000 --> 00:06:30,520 Speaker 1: less but still tamper with that gene? Something put under 109 00:06:30,520 --> 00:06:32,960 Speaker 1: your tongue and let it dissolve. Maybe sure, like you know, 110 00:06:33,200 --> 00:06:37,520 Speaker 1: just epigenetically, just a tad bit. Yeah not maybe you know, 111 00:06:37,680 --> 00:06:40,120 Speaker 1: amplify it, just a tad and see what happens. But 112 00:06:40,680 --> 00:06:43,440 Speaker 1: it would cause your brain to grow, because that gene 113 00:06:43,520 --> 00:06:46,880 Speaker 1: is partially responsible for the size that your brain gets too. 114 00:06:47,240 --> 00:06:51,160 Speaker 1: That's right as far as large brains go. Yes, like 115 00:06:51,240 --> 00:06:54,800 Speaker 1: it makes it larger. Yes, if you want to talk 116 00:06:54,839 --> 00:06:58,640 Speaker 1: smaller brains, there's another gene. And boy, you're right. The 117 00:06:58,720 --> 00:07:01,400 Speaker 1: names are all over the place, just convention wise. It's 118 00:07:01,400 --> 00:07:03,839 Speaker 1: not like they have different names that some are numbers 119 00:07:03,839 --> 00:07:10,200 Speaker 1: and dashes. And to get it together, people espm uh 120 00:07:10,240 --> 00:07:15,560 Speaker 1: it's an abbreviation for abnormal spindle like uh, micro cephally associated. 121 00:07:15,960 --> 00:07:19,360 Speaker 1: That's the name of the gene. Yeah, that's just shameful. 122 00:07:19,480 --> 00:07:22,440 Speaker 1: And they say, microcephaliely associated, because that is a condition 123 00:07:22,920 --> 00:07:25,320 Speaker 1: that you've probably seen before when you're born with a 124 00:07:25,320 --> 00:07:28,080 Speaker 1: small head and small brain, which probably means you're going 125 00:07:28,120 --> 00:07:32,480 Speaker 1: to have some cognitive impairment. So so right there, smaller 126 00:07:32,520 --> 00:07:38,200 Speaker 1: brain is is correlated to lesser or lower faculty your 127 00:07:38,280 --> 00:07:40,760 Speaker 1: cognitive function. Yeah, we should look into that more for 128 00:07:40,800 --> 00:07:44,960 Speaker 1: another podcast. So I think it's uh from the images 129 00:07:45,000 --> 00:07:49,880 Speaker 1: I saw, it looks like you remember, like the movie Freaks. Yes, 130 00:07:50,040 --> 00:07:52,960 Speaker 1: that that guy. I think that's so cute the deal. 131 00:07:53,120 --> 00:07:55,600 Speaker 1: I think you have like normal sized nose and ears 132 00:07:55,600 --> 00:07:59,520 Speaker 1: and eyes, but your actual head and skull and brain 133 00:07:59,560 --> 00:08:03,840 Speaker 1: are small. But you don't look like the the um, 134 00:08:04,000 --> 00:08:08,440 Speaker 1: the African safari guy from Beetlejuice not proportionately shrunk, right, 135 00:08:08,600 --> 00:08:12,960 Speaker 1: It's yeah, you get him down so small right, favorite, 136 00:08:13,040 --> 00:08:15,880 Speaker 1: What was that guy's name from Freaks? I've read about 137 00:08:15,960 --> 00:08:21,320 Speaker 1: him before. No, he had his stage name. It was 138 00:08:21,360 --> 00:08:24,680 Speaker 1: like Topsy or Flopsy or something like that. And he 139 00:08:24,720 --> 00:08:29,840 Speaker 1: apparently like the greatest personality of all times, just so 140 00:08:30,000 --> 00:08:34,360 Speaker 1: lovable and was just exploited basically his whole life until 141 00:08:34,480 --> 00:08:37,480 Speaker 1: that movie like kind of got vengeance for him because 142 00:08:37,520 --> 00:08:42,000 Speaker 1: he was a real life side show performer. Well that's good. Yeah, 143 00:08:43,080 --> 00:08:47,040 Speaker 1: Topsy It wasn't Topsy that was the elephant that Edison electrocuted, 144 00:08:47,880 --> 00:08:50,800 Speaker 1: but it was something along those lines. Just a fun, 145 00:08:51,360 --> 00:08:56,040 Speaker 1: fun name. Yeah, so check. There's another gene. This is 146 00:08:56,080 --> 00:08:59,800 Speaker 1: a little more genetically sounding e m x two. Yeah 147 00:08:59,840 --> 00:09:03,480 Speaker 1: that sequel. Sure it sounds like a dirt bike or 148 00:09:03,480 --> 00:09:39,199 Speaker 1: a gene Yeah, yea, yeah, it's UM. Again, we're not 149 00:09:39,400 --> 00:09:41,400 Speaker 1: We're not saying like, well, these are the genes that 150 00:09:41,559 --> 00:09:44,240 Speaker 1: provide you with your intellect. These are just genes that 151 00:09:44,360 --> 00:09:47,440 Speaker 1: they are saying. These things have to do with the 152 00:09:47,480 --> 00:09:51,320 Speaker 1: size of your brain, and they we also have figured 153 00:09:51,320 --> 00:09:54,880 Speaker 1: out that if you mess with these genes, you may 154 00:09:54,960 --> 00:10:00,520 Speaker 1: also be messing with cognitive function. So we're laying the groundwork. Everybody, 155 00:10:00,600 --> 00:10:03,160 Speaker 1: just bear with us. The e m x two gene 156 00:10:03,640 --> 00:10:08,520 Speaker 1: UM apparently has to do with the growth of your um, 157 00:10:08,760 --> 00:10:12,760 Speaker 1: the functional subdivisions of the cortex. You have different cortices, 158 00:10:13,040 --> 00:10:16,200 Speaker 1: they're responsible for different things. So like if in that 159 00:10:16,360 --> 00:10:18,360 Speaker 1: New York Times article you send me, the guy uses 160 00:10:18,360 --> 00:10:22,120 Speaker 1: the visual cortex as an example, UM, where like you 161 00:10:22,240 --> 00:10:25,480 Speaker 1: get all your visual sensory input and your brain puts 162 00:10:25,480 --> 00:10:28,120 Speaker 1: it all together. That cortex, that region of your brain 163 00:10:28,160 --> 00:10:31,960 Speaker 1: is responsible for a fairly specific but also very um 164 00:10:32,120 --> 00:10:37,960 Speaker 1: complicated task. Now, this one functional cortex that the e 165 00:10:38,200 --> 00:10:41,320 Speaker 1: m X two gene is responsible for, UM has to 166 00:10:41,320 --> 00:10:47,760 Speaker 1: do with basically input, sensory input and motor output. So 167 00:10:47,840 --> 00:10:51,520 Speaker 1: your behavior, Like, um, if I came across this desk 168 00:10:51,559 --> 00:10:54,600 Speaker 1: at you and like pinched your cheeks, you'd like jump back, 169 00:10:54,679 --> 00:10:57,640 Speaker 1: I'd say, what is it Wednesday? Right? Right? Yeah, you 170 00:10:57,679 --> 00:11:02,880 Speaker 1: would make the connection that's Wednesday. Your behavior would occur. So, UM, 171 00:11:02,920 --> 00:11:05,080 Speaker 1: this e m x two gene has to do with 172 00:11:05,240 --> 00:11:09,440 Speaker 1: that cortex and its size. What they found is that 173 00:11:09,559 --> 00:11:14,200 Speaker 1: bigger is not necessarily better. If you have a lower 174 00:11:14,240 --> 00:11:17,320 Speaker 1: expression of it and you have a smaller subdivision of 175 00:11:17,320 --> 00:11:22,719 Speaker 1: that cortex, you're not gonna do very well behaviorally functionally, 176 00:11:23,120 --> 00:11:26,199 Speaker 1: Like you won't be able to hit a baseball very well. Right, 177 00:11:27,000 --> 00:11:29,920 Speaker 1: But if it's too big, if that gene over expresses, 178 00:11:30,320 --> 00:11:33,000 Speaker 1: you don't get better at hitting a baseball. With this 179 00:11:33,080 --> 00:11:36,640 Speaker 1: in particular, it seems to be fine tuned. So if 180 00:11:36,679 --> 00:11:39,120 Speaker 1: you're not hitting that sweet spot, you're never going to 181 00:11:39,240 --> 00:11:42,800 Speaker 1: hit a baseball. And there may be myriad other problems, 182 00:11:42,920 --> 00:11:45,400 Speaker 1: but you're definitely not going to hit that baseball. So 183 00:11:45,440 --> 00:11:49,920 Speaker 1: that means you're born with baseball talent pretty much. Yeah, 184 00:11:49,920 --> 00:11:52,760 Speaker 1: because it's genetic, and there's like there's some people who 185 00:11:52,760 --> 00:11:55,200 Speaker 1: are so close to perfect that like baseball just comes 186 00:11:55,240 --> 00:12:00,160 Speaker 1: naturally to the right, right, like Robert Redford exactly, he 187 00:12:00,240 --> 00:12:03,040 Speaker 1: really gave those lights a wall. Yeah, he's the natural. 188 00:12:03,800 --> 00:12:09,280 Speaker 1: So that's the genetic basis of it, Chuck. Yeah, and uh, 189 00:12:09,360 --> 00:12:13,440 Speaker 1: I guess we should talk some about size if it's important, 190 00:12:13,800 --> 00:12:16,640 Speaker 1: because this is sort of the debate that keeps going 191 00:12:16,679 --> 00:12:21,640 Speaker 1: on and on. Is brain size corollary to intelligence levels? Uh? 192 00:12:21,679 --> 00:12:24,800 Speaker 1: They did find or they have found links between if 193 00:12:24,800 --> 00:12:27,440 Speaker 1: you have a lot of brain growth, um, if it's 194 00:12:27,480 --> 00:12:31,360 Speaker 1: disproportionate early on, Um, they've linked that and early on 195 00:12:31,440 --> 00:12:35,160 Speaker 1: meaning the first twelve months too, they've linked that to autism. 196 00:12:35,240 --> 00:12:38,880 Speaker 1: So super rapid growth may what it may do is 197 00:12:39,080 --> 00:12:45,439 Speaker 1: just prevent those neural connections from happening like they should right. Um. 198 00:12:45,480 --> 00:12:48,800 Speaker 1: And actually, well that's kind of links into that tethering 199 00:12:48,800 --> 00:12:50,760 Speaker 1: thing I sent you too, it does, which we will 200 00:12:50,760 --> 00:12:56,199 Speaker 1: talk about later. Um. In biology, though, is there's kind 201 00:12:56,240 --> 00:13:00,400 Speaker 1: of this consensus that it's that's not the only determinant. Yeah, 202 00:13:00,480 --> 00:13:03,520 Speaker 1: that has a lot to do with um environment as well, 203 00:13:04,440 --> 00:13:07,320 Speaker 1: I think kind of as a whole. People who investigate 204 00:13:07,920 --> 00:13:11,320 Speaker 1: correlations between brain size and intelligence have totally abandoned the 205 00:13:11,360 --> 00:13:17,880 Speaker 1: idea that UM, like, your brain is predetermined to grow 206 00:13:17,920 --> 00:13:21,680 Speaker 1: a certain way and then that's that. Yeah, well, size wise, 207 00:13:21,720 --> 00:13:23,640 Speaker 1: they've also found that eight if you have a d 208 00:13:23,800 --> 00:13:26,120 Speaker 1: h D, if you're an adolescent, your brain might be 209 00:13:26,200 --> 00:13:29,439 Speaker 1: three to four percent smaller than your classmate who does 210 00:13:29,480 --> 00:13:32,560 Speaker 1: not have a d h D. UM, and your brain 211 00:13:32,760 --> 00:13:36,480 Speaker 1: shrinks as you get older, but doesn't necessarily lose functionality 212 00:13:36,559 --> 00:13:40,000 Speaker 1: because of the shrinkage. It's not to say when you 213 00:13:40,000 --> 00:13:42,720 Speaker 1: get older, you don't, you know, lose functionality, but it's 214 00:13:42,720 --> 00:13:45,360 Speaker 1: not due to the size. No, they think that, um, 215 00:13:45,400 --> 00:13:50,400 Speaker 1: it's probably mostly due to build up. Really okay, that 216 00:13:50,520 --> 00:13:54,480 Speaker 1: you're you're synapses are just kind of built up with 217 00:13:54,679 --> 00:13:58,679 Speaker 1: gunk remnant proteins from years and decades of firings. Then 218 00:13:58,720 --> 00:14:02,480 Speaker 1: you shake that off by can tinuing to use it. Yes, 219 00:14:02,600 --> 00:14:06,160 Speaker 1: that's one thing. But also they've recently found that um, 220 00:14:06,360 --> 00:14:09,600 Speaker 1: while you sleep, they think now the function of sleep. 221 00:14:10,000 --> 00:14:12,640 Speaker 1: They didn't notice it before until I don't know what 222 00:14:12,760 --> 00:14:15,920 Speaker 1: kind of new imaging technology they used, but they found 223 00:14:15,920 --> 00:14:20,120 Speaker 1: out that there's this whole channel of like basically a 224 00:14:20,160 --> 00:14:23,800 Speaker 1: sewerage system that just clears out all the gunk from 225 00:14:23,800 --> 00:14:27,720 Speaker 1: your brain while you're sleeping at night, and it just 226 00:14:28,680 --> 00:14:32,320 Speaker 1: that's why you are cognitively refreshed. That makes sense from sleep. 227 00:14:32,480 --> 00:14:35,800 Speaker 1: And we've done we did one on why sleep so important? Right, Yes, 228 00:14:35,920 --> 00:14:37,560 Speaker 1: I think that was in there. We've done a bunch. 229 00:14:37,680 --> 00:14:40,680 Speaker 1: There's a whole sleep suite too, alright. So one of 230 00:14:40,680 --> 00:14:44,760 Speaker 1: the reasons why, uh, people are still debating whether or 231 00:14:44,760 --> 00:14:48,400 Speaker 1: not brain size equals more intelligence or less intelligence is 232 00:14:48,440 --> 00:14:51,200 Speaker 1: because when there are a lot of different ways to 233 00:14:51,200 --> 00:14:55,480 Speaker 1: measure the brain um, you know, like do you take 234 00:14:55,520 --> 00:14:58,040 Speaker 1: a tape measure and go around it or do you 235 00:14:58,080 --> 00:15:00,720 Speaker 1: go from the middle out or do you do it 236 00:15:00,840 --> 00:15:04,760 Speaker 1: proportion to your body size, which is a real measurement 237 00:15:04,800 --> 00:15:08,800 Speaker 1: called encephalation quotient. And if they're talking about your body size, 238 00:15:08,800 --> 00:15:12,880 Speaker 1: what if you're super fat? Like there are all these 239 00:15:12,880 --> 00:15:15,040 Speaker 1: different ways and no one, I don't think has ever 240 00:15:15,120 --> 00:15:17,080 Speaker 1: come to a consensus on the best way to actually 241 00:15:17,120 --> 00:15:19,080 Speaker 1: do the measurement in the first place. No. And there's 242 00:15:19,120 --> 00:15:23,720 Speaker 1: another really big outstanding question is how do you measure intelligence? 243 00:15:23,760 --> 00:15:28,720 Speaker 1: Like our i Q tests actually legitimate. Yeah, So when 244 00:15:28,800 --> 00:15:33,000 Speaker 1: you have two parts of your equation that are both hinky, 245 00:15:33,560 --> 00:15:36,680 Speaker 1: how can you come up with an answer? Well, it 246 00:15:36,760 --> 00:15:40,720 Speaker 1: depends like if you're comparing species to species that in 247 00:15:40,840 --> 00:15:44,720 Speaker 1: sephilization quotation and actually has been proven to be pretty effective. 248 00:15:45,360 --> 00:15:50,640 Speaker 1: So like the proportion of your brain to your body 249 00:15:50,720 --> 00:15:54,400 Speaker 1: size is a pretty decent predictor of your e Q 250 00:15:54,760 --> 00:15:58,000 Speaker 1: is what it's called as a mammal. Yes, as as 251 00:15:58,000 --> 00:16:00,200 Speaker 1: a mammal. When you go outside of mammals, that it's 252 00:16:00,280 --> 00:16:04,800 Speaker 1: less and less effective. Um. But with humans, for example, 253 00:16:05,360 --> 00:16:09,960 Speaker 1: we our our brain is like two point seven pounds 254 00:16:09,960 --> 00:16:13,000 Speaker 1: on average, it's something like around two to three. I've 255 00:16:13,040 --> 00:16:14,760 Speaker 1: seen as much as five, but I think it's about 256 00:16:14,840 --> 00:16:19,120 Speaker 1: three percent of our body weight. Um. But it uses 257 00:16:19,160 --> 00:16:23,360 Speaker 1: up about of the energy, which is another measure, Yes, 258 00:16:23,760 --> 00:16:26,600 Speaker 1: like how much energy is your brain require? The more 259 00:16:26,800 --> 00:16:30,880 Speaker 1: energy your brain requires. In addition to things like e Q, 260 00:16:31,880 --> 00:16:33,920 Speaker 1: you can get a pretty good idea of like how 261 00:16:33,960 --> 00:16:37,720 Speaker 1: intelligent that being is. Well, Einstein's brain remembers this was 262 00:16:37,760 --> 00:16:40,400 Speaker 1: the same size, but different parts were bigger than others. Right, 263 00:16:40,600 --> 00:16:44,320 Speaker 1: so supposedly let's come under fire lately. Yeah, somebody was 264 00:16:44,360 --> 00:16:47,960 Speaker 1: like you, these studies are terrible, Like, you can't make 265 00:16:48,000 --> 00:16:53,480 Speaker 1: these huge leaps and bounds and um in conclusions just 266 00:16:53,600 --> 00:16:58,760 Speaker 1: from a couple of strips of brain tissue, But apparently 267 00:16:58,800 --> 00:17:01,600 Speaker 1: most studies have. Well I think that's sort of like 268 00:17:01,640 --> 00:17:04,960 Speaker 1: a juicy thing that people like to talk about Einstein's brain. Yeah, 269 00:17:05,080 --> 00:17:07,040 Speaker 1: you know, I could see people making that leap, right, 270 00:17:07,040 --> 00:17:09,520 Speaker 1: and the idea that like, oh, well, Einstein's brain is 271 00:17:09,560 --> 00:17:12,520 Speaker 1: just like anybody else's, well, that means that anybody could 272 00:17:12,560 --> 00:17:15,399 Speaker 1: be a genius. You know, it really argues in favor 273 00:17:15,440 --> 00:17:17,960 Speaker 1: of the nurture side of things. But if his brain 274 00:17:18,119 --> 00:17:22,040 Speaker 1: is structurally different, well, the genius is an inborn natural thing. 275 00:17:22,640 --> 00:17:25,840 Speaker 1: It's just nature versus nurture played out on poure. Einstein 276 00:17:25,880 --> 00:17:29,119 Speaker 1: sliced that brain. Yeah, that lived in a garage for 277 00:17:29,200 --> 00:17:32,920 Speaker 1: many years, like everyone's brain. So here's the most controversial thing. 278 00:17:33,400 --> 00:17:36,480 Speaker 1: Like when you're comparing species to species, like you said, 279 00:17:36,600 --> 00:17:41,680 Speaker 1: especially among mammals, chuck, um, it's easier to say like, yes, 280 00:17:41,720 --> 00:17:46,040 Speaker 1: this eq thing works, But within a single species, that's 281 00:17:46,040 --> 00:17:51,120 Speaker 1: when things starting to fall apart. For instance, specifically among humans, 282 00:17:51,400 --> 00:17:55,080 Speaker 1: um men tend to have about a hundred grahams more 283 00:17:55,359 --> 00:17:59,680 Speaker 1: mass to their brain than women. But if a bigger 284 00:17:59,720 --> 00:18:04,399 Speaker 1: brain means that you are more intelligent, then does that 285 00:18:04,440 --> 00:18:06,720 Speaker 1: mean that men are more intelligent than women? I think 286 00:18:06,760 --> 00:18:09,240 Speaker 1: we all know that women are more intelligent than men. Okay, 287 00:18:09,240 --> 00:18:13,280 Speaker 1: so then brain size really has nothing to do with it, 288 00:18:13,320 --> 00:18:17,960 Speaker 1: at least intra species, That's what I'm saying. But um, 289 00:18:18,000 --> 00:18:19,880 Speaker 1: there have been plenty of studies that I'm sure got 290 00:18:19,880 --> 00:18:23,200 Speaker 1: a lot of people's hackles up. Um. One guy named 291 00:18:23,800 --> 00:18:28,800 Speaker 1: Michael McDaniel, who's a psychologist, basically entered the news cycle 292 00:18:29,640 --> 00:18:33,000 Speaker 1: bursting onto the scene in two thousand five, which is 293 00:18:33,000 --> 00:18:36,160 Speaker 1: always a little bit like, yeah, who's this guy? Right? Yeah, 294 00:18:36,240 --> 00:18:38,600 Speaker 1: but he came up with a study that was ready 295 00:18:38,640 --> 00:18:42,920 Speaker 1: made for CNN. Yeah. I mean he said flat out 296 00:18:42,960 --> 00:18:47,040 Speaker 1: that bigger brains means you're smarter, and that I E 297 00:18:47,600 --> 00:18:51,440 Speaker 1: or e G. Which one is it? Uh? I EVE 298 00:18:51,520 --> 00:18:54,600 Speaker 1: in this case, men are smarter than women. Yeah, yeah, 299 00:18:54,800 --> 00:18:58,640 Speaker 1: that's what he was saying. Yeah, because he basically put 300 00:18:58,840 --> 00:19:03,760 Speaker 1: brain imaging tests an i Q tests together and said, well, 301 00:19:03,920 --> 00:19:08,040 Speaker 1: there's a direct correlation between the two. And again with 302 00:19:08,080 --> 00:19:11,800 Speaker 1: these tests they did, uh, they compared um or they 303 00:19:11,840 --> 00:19:15,120 Speaker 1: converted s a T scores of a hundred thousand seventeen 304 00:19:15,160 --> 00:19:18,480 Speaker 1: and eighteen year old to an i Q score and 305 00:19:18,600 --> 00:19:20,120 Speaker 1: I don't see why they even had to do that. 306 00:19:20,400 --> 00:19:22,520 Speaker 1: And they found that males average three point six three 307 00:19:22,560 --> 00:19:26,399 Speaker 1: IQ points higher, but um, I don't know. It just 308 00:19:26,440 --> 00:19:28,920 Speaker 1: seems really hinky because first of all, they used ten 309 00:19:28,920 --> 00:19:32,000 Speaker 1: thousand more females and males, so that's gonna excue things. 310 00:19:32,560 --> 00:19:35,880 Speaker 1: And then it's an S A t R. What does 311 00:19:35,880 --> 00:19:38,320 Speaker 1: that even mean? Yeah, which has been proven time and 312 00:19:38,359 --> 00:19:40,560 Speaker 1: time again to be biased. Yeah, and then they converted 313 00:19:40,600 --> 00:19:42,600 Speaker 1: that to an i Q S cource with some like 314 00:19:42,720 --> 00:19:45,280 Speaker 1: I guess machine. Seems like some things would be lost 315 00:19:45,280 --> 00:19:47,359 Speaker 1: in translation. Yeah, I think it's a bunch of bunks. 316 00:19:47,560 --> 00:19:49,760 Speaker 1: So yeah, I think you're not alone in the idea 317 00:19:49,840 --> 00:19:51,520 Speaker 1: that it's a bunch of bunks. So a lot of 318 00:19:51,720 --> 00:19:55,879 Speaker 1: um scientists have said, okay, all right, this whole brain 319 00:19:56,760 --> 00:20:02,800 Speaker 1: size correlating to intelligence sting of phrenology. Yes, when you're 320 00:20:02,840 --> 00:20:05,960 Speaker 1: talking about looking at it just in the human species, right, 321 00:20:06,800 --> 00:20:09,280 Speaker 1: So what is it though? I mean, surely there's got 322 00:20:09,280 --> 00:20:14,520 Speaker 1: to be some biological part or aspect of the brain 323 00:20:15,119 --> 00:20:20,720 Speaker 1: that correlates to intelligence. If it's not size, then maybe 324 00:20:20,760 --> 00:20:24,320 Speaker 1: it's the number of neurons that you have, Yeah, neural connections. 325 00:20:24,359 --> 00:20:26,800 Speaker 1: A lot of people have thought that that was kind 326 00:20:26,840 --> 00:20:31,119 Speaker 1: of the second to most recent wave in thinking about 327 00:20:31,800 --> 00:20:36,480 Speaker 1: what brain structure correlates to intelligence. Yeah, this, um was 328 00:20:36,520 --> 00:20:38,720 Speaker 1: this the New York Times one about the tether hypothesis. 329 00:20:39,240 --> 00:20:42,240 Speaker 1: This is just December of last year, so it's pretty recent. 330 00:20:42,800 --> 00:20:46,280 Speaker 1: And a couple of neuroscientists from Havred so you know, 331 00:20:46,320 --> 00:20:51,880 Speaker 1: they're right. Um, they had a pretty simple explanation. Um, 332 00:20:51,880 --> 00:20:54,280 Speaker 1: when back in the day, when tooktok had a little 333 00:20:54,280 --> 00:20:57,960 Speaker 1: bitty tiny brain. Uh. Their argument is that the neurons 334 00:20:58,000 --> 00:21:03,239 Speaker 1: were tightly tethered in a pretty simple connection pattern, and 335 00:21:03,280 --> 00:21:05,960 Speaker 1: that when our brains started getting bigger, those tethers were 336 00:21:05,960 --> 00:21:09,359 Speaker 1: torn apart and it formed Uh, it enabled us. We 337 00:21:09,440 --> 00:21:12,239 Speaker 1: formed new neurons and new neural pathways and new circuits. Right, 338 00:21:12,600 --> 00:21:14,360 Speaker 1: That's that makes a lot of sense to me. Yeah, 339 00:21:14,440 --> 00:21:16,880 Speaker 1: It's like the brain size might have been about the same, 340 00:21:16,920 --> 00:21:20,160 Speaker 1: and it was, but the neuro connections were still following 341 00:21:20,280 --> 00:21:26,280 Speaker 1: the primal animal connectivity, where it's like they they connect 342 00:21:26,320 --> 00:21:31,400 Speaker 1: in a predictable way, whereas with this untethered idea, they 343 00:21:31,480 --> 00:21:35,800 Speaker 1: just blossomed out. And do you know the idea of um, 344 00:21:35,840 --> 00:21:38,560 Speaker 1: like like what neuro connections look like today. Rather than 345 00:21:38,600 --> 00:21:41,400 Speaker 1: following like straight predictable lines, they were all over the place, 346 00:21:41,640 --> 00:21:45,080 Speaker 1: and from these new connections, new associations arose, and that 347 00:21:45,840 --> 00:21:48,480 Speaker 1: gave rise to intellect. According to this, yeah, it's pretty 348 00:21:48,520 --> 00:21:52,560 Speaker 1: simple smart. Yeah, Like I think sometimes the simplest hypotheses 349 00:21:52,680 --> 00:21:55,359 Speaker 1: might be on target with acam's razor my friend, or 350 00:21:55,400 --> 00:21:57,000 Speaker 1: maybe they just speak to me because I'm a dummy, 351 00:21:57,800 --> 00:22:03,680 Speaker 1: rightether of got play the other ball? Um, So that's 352 00:22:04,359 --> 00:22:08,160 Speaker 1: that is a competing explanation. Another one that I've seen, 353 00:22:08,920 --> 00:22:12,639 Speaker 1: uh says that it's not not the neurons, not the 354 00:22:12,720 --> 00:22:15,719 Speaker 1: number of neurons, not even the number of neural connections, 355 00:22:16,240 --> 00:22:19,840 Speaker 1: it's the chemistry and the complexity of the neuro transmitters 356 00:22:19,880 --> 00:22:24,959 Speaker 1: that are being conducted between these neurons. And from this 357 00:22:25,000 --> 00:22:28,320 Speaker 1: has kind of come this new idea that it's folly 358 00:22:28,359 --> 00:22:32,240 Speaker 1: to even say, well, humans are obviously smarter than a 359 00:22:32,280 --> 00:22:36,719 Speaker 1: bottle knows dolphins, even though they're smart because the bottle 360 00:22:36,760 --> 00:22:45,080 Speaker 1: knows dolphins. Um experience understanding of life is so radically 361 00:22:45,080 --> 00:22:48,880 Speaker 1: different from humans. Yeah, you can't. You can't compare intellect intellect. Yeah, 362 00:22:49,119 --> 00:22:51,399 Speaker 1: you can't say, well, a dolphin can't you know, talk 363 00:22:51,400 --> 00:22:54,119 Speaker 1: and speak, but I can't do things a dolphin can do, right, 364 00:22:54,320 --> 00:22:56,840 Speaker 1: Or maybe dolphins are speaking, I mean to one another, 365 00:22:57,240 --> 00:22:59,680 Speaker 1: just not to us. That doesn't make them less intelligent 366 00:23:00,000 --> 00:23:02,000 Speaker 1: along and thanks for all the fish and we're not Yeah, 367 00:23:02,040 --> 00:23:05,360 Speaker 1: that's a good one. And we're not like anthropomorphizing here. Basically, 368 00:23:05,560 --> 00:23:12,200 Speaker 1: there's there is very little um point I guess. Yeah, 369 00:23:12,240 --> 00:23:15,520 Speaker 1: I don't get it to comparing the two. There's tremendous 370 00:23:15,560 --> 00:23:19,800 Speaker 1: point to getting to cracking this code and understanding dolphin 371 00:23:19,880 --> 00:23:24,720 Speaker 1: intellect for bird intellect, degreed octopus intellect and human intellect. 372 00:23:25,240 --> 00:23:29,080 Speaker 1: But to compare them is it's an exercise and futility. 373 00:23:29,119 --> 00:23:31,000 Speaker 1: There's no point to it. I don't get it. Yeah, 374 00:23:31,480 --> 00:23:34,639 Speaker 1: I mean, compared dolphin A to dolphin B one, maybe 375 00:23:34,680 --> 00:23:37,800 Speaker 1: smarter one might have a little patchy mustache and hang 376 00:23:37,840 --> 00:23:40,679 Speaker 1: out at the gas station a lot. That's not the 377 00:23:40,720 --> 00:23:43,640 Speaker 1: smart one. Yeah. I think people do this to either. 378 00:23:43,800 --> 00:23:46,679 Speaker 1: I think they're trying to claim some either superiority of 379 00:23:46,760 --> 00:23:49,240 Speaker 1: animals over humans or humans over animals, right, And that's 380 00:23:49,359 --> 00:23:51,520 Speaker 1: kind of that's a big issue these days, Like there 381 00:23:51,600 --> 00:23:54,480 Speaker 1: there are groups um animal rights groups that are trying 382 00:23:54,480 --> 00:23:59,840 Speaker 1: to further animal rights by getting them inalienable rights like 383 00:24:00,040 --> 00:24:05,080 Speaker 1: humans have right which would really screw up the zoos system. Yeah, 384 00:24:05,840 --> 00:24:08,280 Speaker 1: you can look for our podcast on that too. We did. Yeah, 385 00:24:08,280 --> 00:24:10,240 Speaker 1: that was a good one. Yeah. I think we landed 386 00:24:10,480 --> 00:24:16,040 Speaker 1: pretty heavily against zoos. Yeah we did. But to each 387 00:24:16,080 --> 00:24:19,160 Speaker 1: their own. But I haven't been to a zoos since then. 388 00:24:51,240 --> 00:24:53,560 Speaker 1: What is this Project Enigma? I thought that was pretty interesting. 389 00:24:54,080 --> 00:24:57,199 Speaker 1: It was another genetic thing, this UM. It was. It 390 00:24:57,280 --> 00:25:00,200 Speaker 1: was neat though in that like this Australian re which 391 00:25:00,240 --> 00:25:02,240 Speaker 1: just said, hey, we have something called the Internet and 392 00:25:02,440 --> 00:25:07,359 Speaker 1: m R I S and willing participants, so everybody starts 393 00:25:07,400 --> 00:25:09,879 Speaker 1: sending in your brain scans. Is that who proved that 394 00:25:09,960 --> 00:25:13,120 Speaker 1: the complexity of the neural activity was the most important thing? 395 00:25:13,760 --> 00:25:16,040 Speaker 1: Was that project? No, that was a different one. That 396 00:25:16,160 --> 00:25:21,280 Speaker 1: was there was a new scientist or no scientific American 397 00:25:21,400 --> 00:25:27,160 Speaker 1: article that UM that explored that idea as the the 398 00:25:27,160 --> 00:25:32,800 Speaker 1: synaptic proteins that create intellect or intelligence. But the Project 399 00:25:32,920 --> 00:25:37,080 Speaker 1: Enigma basically found that there is a single mutation on 400 00:25:37,440 --> 00:25:41,840 Speaker 1: a specific gene where if you have a C instead 401 00:25:41,880 --> 00:25:45,960 Speaker 1: of T, I think you um have a bigger brain. 402 00:25:46,000 --> 00:25:49,280 Speaker 1: And they correlated that to more intelligence. Yeah, but again 403 00:25:49,800 --> 00:25:52,919 Speaker 1: using the i Q test. Well, what I'm tired of 404 00:25:52,960 --> 00:25:57,040 Speaker 1: are the studies that throw out the results that don't 405 00:25:57,240 --> 00:26:01,159 Speaker 1: make a good fun headline. You know, right. There was 406 00:26:01,200 --> 00:26:04,400 Speaker 1: this one um from Smithsonian magazine from December of last 407 00:26:04,480 --> 00:26:07,320 Speaker 1: year that uh, well they it wasn't from them, was 408 00:26:07,359 --> 00:26:11,320 Speaker 1: from the Proceedings of the Royal Society b and Smithsonian 409 00:26:11,400 --> 00:26:15,080 Speaker 1: reported on it. But it was a study of country 410 00:26:15,119 --> 00:26:18,480 Speaker 1: mice and city mice. Well a bunch of animals. But 411 00:26:18,560 --> 00:26:22,040 Speaker 1: they found that city mice and vole. I don't even 412 00:26:22,040 --> 00:26:24,919 Speaker 1: know what that is vl e perrie voles, what is 413 00:26:24,920 --> 00:26:30,639 Speaker 1: that like? They're very sweet. They they are monogamous, like 414 00:26:30,680 --> 00:26:33,439 Speaker 1: a bowl weevil. No, that's a bug. A vole is 415 00:26:33,480 --> 00:26:36,480 Speaker 1: like a like a prairie dog. You should look up 416 00:26:36,520 --> 00:26:38,600 Speaker 1: prairie balls. Very cute and the idea that they're very 417 00:26:38,600 --> 00:26:41,960 Speaker 1: sweetest even better. You wouldn't like shoot one for being 418 00:26:41,960 --> 00:26:44,439 Speaker 1: on your property. No, I mean some people would, but 419 00:26:45,080 --> 00:26:48,840 Speaker 1: not good people. I know someone who does that kind 420 00:26:48,840 --> 00:26:51,840 Speaker 1: of thing shots at voles, not voles, but wood chucks 421 00:26:52,920 --> 00:26:56,800 Speaker 1: with with the air rifles. That's not nice. He knows 422 00:26:56,800 --> 00:27:01,800 Speaker 1: who he is. Boom. But the study basically said that 423 00:27:02,200 --> 00:27:07,560 Speaker 1: city mice and city bowls had larger brains than country mice, 424 00:27:08,040 --> 00:27:10,160 Speaker 1: And of course that makes a big headline because people 425 00:27:10,640 --> 00:27:12,399 Speaker 1: are going to try and make the point that, you know, 426 00:27:12,480 --> 00:27:14,639 Speaker 1: people that live in urban environments are smarter and the 427 00:27:14,880 --> 00:27:18,520 Speaker 1: hillbillies out in the country are dumber. They study ten animals, 428 00:27:18,520 --> 00:27:20,239 Speaker 1: only two of them showed that, and some of them 429 00:27:20,280 --> 00:27:25,080 Speaker 1: showed the opposite that the I think bats and shrew's 430 00:27:25,320 --> 00:27:28,760 Speaker 1: actually the country versions had larger brains. So they don't 431 00:27:28,760 --> 00:27:30,399 Speaker 1: say any of that in the study because they just 432 00:27:30,440 --> 00:27:32,840 Speaker 1: want a headline that says, you know, if you live 433 00:27:32,840 --> 00:27:35,080 Speaker 1: in a city, you're smarter. Yeah, and we I mean, 434 00:27:35,080 --> 00:27:39,080 Speaker 1: we've been addressing this lately, like there is like a 435 00:27:39,160 --> 00:27:45,000 Speaker 1: symbiotic collusion between bad science and bad science reporting, you know, 436 00:27:45,119 --> 00:27:47,840 Speaker 1: yeah that results and stuff like that, where it's just like, 437 00:27:48,920 --> 00:27:53,000 Speaker 1: you know, city city people are smarter than country people, right, 438 00:27:53,200 --> 00:27:57,120 Speaker 1: says this one study that where the data was massaged, right, Well, 439 00:27:57,119 --> 00:27:59,800 Speaker 1: can you city person, can you go make butter with 440 00:27:59,840 --> 00:28:03,600 Speaker 1: your hands? I have to say, can you farm land? No? 441 00:28:03,960 --> 00:28:09,320 Speaker 1: Of course not um, but there it is possible that 442 00:28:09,359 --> 00:28:14,200 Speaker 1: there's a basis to this or as city people are 443 00:28:14,720 --> 00:28:18,840 Speaker 1: their brains are more stimulated than country folk. Perhaps and 444 00:28:18,920 --> 00:28:22,680 Speaker 1: so more neural connections, more plasticity takes place. I don't 445 00:28:22,800 --> 00:28:24,879 Speaker 1: I don't think that's true. I think there's just as 446 00:28:24,960 --> 00:28:28,359 Speaker 1: much stimulation in nature as there is in a city 447 00:28:28,359 --> 00:28:31,879 Speaker 1: full of people spends once you're stimulated by Look at Darwin, 448 00:28:31,960 --> 00:28:34,120 Speaker 1: he spent his entire life living in the country. Yeah, 449 00:28:34,160 --> 00:28:36,920 Speaker 1: for sure, But I mean like we're also we also 450 00:28:36,960 --> 00:28:41,080 Speaker 1: have hundreds of thousands of years of latent inhibition built 451 00:28:41,160 --> 00:28:43,320 Speaker 1: up against a lot of the stuff in nature, whereas 452 00:28:43,360 --> 00:28:46,760 Speaker 1: the stuff in Times Square is relatively new, so our 453 00:28:46,800 --> 00:28:50,520 Speaker 1: brain isn't doesn't defend against it quite as easily. So 454 00:28:51,000 --> 00:28:53,800 Speaker 1: hence we're possibly more stimulated. I think it depends on 455 00:28:53,840 --> 00:28:56,320 Speaker 1: what you're doing with your time. Sure, if you're in 456 00:28:56,400 --> 00:29:00,200 Speaker 1: the country sitting around watching reality TV, if you're in 457 00:29:00,240 --> 00:29:04,320 Speaker 1: the city in your apartment watching reality TV. I found 458 00:29:04,320 --> 00:29:08,440 Speaker 1: this other study today to UM from Germany about pornography 459 00:29:08,600 --> 00:29:13,160 Speaker 1: may reduce your brain size another kind of sexy headline 460 00:29:13,880 --> 00:29:18,360 Speaker 1: and um sample size at three And it's always self 461 00:29:18,440 --> 00:29:21,160 Speaker 1: reported to and when you're dealing with porn and self reporting, 462 00:29:21,680 --> 00:29:24,440 Speaker 1: just do you look at porn? Check the box. Do 463 00:29:24,480 --> 00:29:27,920 Speaker 1: you think your brain is small? Check the box? Boom, Yeah, 464 00:29:28,240 --> 00:29:31,240 Speaker 1: how smart are you? Somebody called news Week. Uh, they 465 00:29:31,280 --> 00:29:34,560 Speaker 1: cannot say that watching porn caused a decrease in brain matter, 466 00:29:34,640 --> 00:29:38,280 Speaker 1: but they did say they found that the volume of striatom, 467 00:29:38,400 --> 00:29:41,600 Speaker 1: a brain region that's been associated with reward processing and 468 00:29:41,680 --> 00:29:48,040 Speaker 1: motivated behavior, was smaller the more pornography you consumed. And 469 00:29:48,080 --> 00:29:50,760 Speaker 1: basically where they're at is, we don't know whether it's 470 00:29:50,840 --> 00:29:54,840 Speaker 1: causing this or if people that oh yeah, they get 471 00:29:54,880 --> 00:29:57,959 Speaker 1: struck down and they're like, yeah, they're just end up 472 00:29:57,960 --> 00:30:03,240 Speaker 1: watching porn more so again Idiocracy Again, that movie. There's 473 00:30:03,320 --> 00:30:06,360 Speaker 1: probably been no other movie that's made more of a 474 00:30:06,440 --> 00:30:11,440 Speaker 1: legitimate appearance in our episodes than that movie than Indiocracy. Yeah, yeah, 475 00:30:11,480 --> 00:30:13,920 Speaker 1: I think you're right. Yeah, Mike Judge, he's onto something. 476 00:30:14,560 --> 00:30:18,560 Speaker 1: Do you watch Silicon Valley? I haven't, I know, Uh, 477 00:30:18,720 --> 00:30:21,160 Speaker 1: Kamal nan Gianni's in it. Yeah, he's funny. Does he 478 00:30:21,240 --> 00:30:24,240 Speaker 1: do well? Yeah, Martin Starr, it's uh yeah, it's a 479 00:30:24,240 --> 00:30:26,560 Speaker 1: good show. Nice, it's I think it was high time 480 00:30:26,600 --> 00:30:28,480 Speaker 1: that someone took on the tech industry and like a 481 00:30:28,480 --> 00:30:32,400 Speaker 1: comedy like that. Yeah, leave it to Mike Judge. I know. Yeah, 482 00:30:32,480 --> 00:30:37,040 Speaker 1: if there's any crusade or satiric crusader, people feeling good 483 00:30:37,040 --> 00:30:41,400 Speaker 1: about themselves. Right, Yeah, it's Mike Judge. Uh. If you 484 00:30:41,760 --> 00:30:46,920 Speaker 1: want to know more about brain size in relation to whatever, 485 00:30:47,480 --> 00:30:49,760 Speaker 1: just type in brain in the search bar. How stiff 486 00:30:49,800 --> 00:30:53,360 Speaker 1: works dot com and it's get lost. That's what I say. Yeah, 487 00:30:53,480 --> 00:30:55,960 Speaker 1: type in brain and just go on a trip. Man. 488 00:30:56,640 --> 00:31:00,800 Speaker 1: All right, that's right, journey. I think that the slogan 489 00:31:00,920 --> 00:31:04,440 Speaker 1: for how stuff works. That's right, um answers, I said, 490 00:31:04,440 --> 00:31:08,959 Speaker 1: search parts time for what listener mail? Yes, okay, I'm 491 00:31:09,000 --> 00:31:13,720 Speaker 1: gonna call this help for a fan in need. It's nice. Hey, guys, 492 00:31:13,720 --> 00:31:15,240 Speaker 1: I want some help please. My wife and I are 493 00:31:15,240 --> 00:31:17,880 Speaker 1: expecting our first kid this summer, and thirteen days ago 494 00:31:17,920 --> 00:31:20,080 Speaker 1: we also found out that my wife has staged four 495 00:31:20,400 --> 00:31:24,160 Speaker 1: breast cancer. So we are spending our third trimester getting chemo. 496 00:31:24,680 --> 00:31:27,480 Speaker 1: My goodness, I know. Uh, we're gonna kick cancer in 497 00:31:27,520 --> 00:31:30,560 Speaker 1: the butt. We have no doubt, but we're scared and overwhelmed. Obviously, 498 00:31:31,160 --> 00:31:33,320 Speaker 1: we're doing chemo now. Then we'll have the baby. Get 499 00:31:33,360 --> 00:31:37,720 Speaker 1: more chemo than bilateral mystechtomy than radiation. We have great 500 00:31:37,760 --> 00:31:40,080 Speaker 1: doctors and great fins and families, so even in the 501 00:31:40,080 --> 00:31:42,880 Speaker 1: face of this, we feel very lucky. Uh. And by 502 00:31:42,920 --> 00:31:47,840 Speaker 1: the way, I gotta follow up more recently, that says, uh, 503 00:31:47,960 --> 00:31:51,480 Speaker 1: there is no gestational diabetes and the cancer is already shrinking. 504 00:31:51,600 --> 00:31:53,600 Speaker 1: Oh it's great. So things are going great so far. 505 00:31:53,680 --> 00:31:55,840 Speaker 1: Thanks for keeping us in suspense. I know I was 506 00:31:55,840 --> 00:31:57,920 Speaker 1: gonna wait till the end. Uh. And he asked for 507 00:31:57,960 --> 00:31:59,760 Speaker 1: a couple of favors. He said, First of all, if 508 00:31:59,760 --> 00:32:02,280 Speaker 1: you want follow and promote my tumbler uh to keep 509 00:32:02,280 --> 00:32:06,000 Speaker 1: people updated, it is um H T T P colon 510 00:32:06,040 --> 00:32:12,000 Speaker 1: slash slash gala freakidiki g A l l i U 511 00:32:12,360 --> 00:32:14,640 Speaker 1: f R e k y d e e k y 512 00:32:14,800 --> 00:32:18,240 Speaker 1: dot tumbler dot com. He says, we're huge nerds and 513 00:32:18,280 --> 00:32:20,880 Speaker 1: doctor who fans. So that was lost on me some 514 00:32:21,080 --> 00:32:23,480 Speaker 1: doctor who reference. I guess apparently. So does that have 515 00:32:23,560 --> 00:32:26,080 Speaker 1: to do with the phone booth? Maybe that's the only 516 00:32:26,080 --> 00:32:30,080 Speaker 1: thing you know about same here. Um. Secondly, I'm biking 517 00:32:30,080 --> 00:32:33,440 Speaker 1: a hundred and fifty miles to raise money, and could 518 00:32:33,480 --> 00:32:35,320 Speaker 1: you plug that and you can go to g O 519 00:32:35,440 --> 00:32:40,920 Speaker 1: O dot g L slash two w j z x Q. 520 00:32:41,640 --> 00:32:44,120 Speaker 1: These people don't like normal words. Well that's one of 521 00:32:44,120 --> 00:32:46,520 Speaker 1: those shortened r L s. Oh. I see it's a 522 00:32:46,560 --> 00:32:49,760 Speaker 1: Google uh. And then third how about a shout out, 523 00:32:49,800 --> 00:32:51,800 Speaker 1: and I think that's what we're doing here. My wife 524 00:32:51,880 --> 00:32:54,120 Speaker 1: is a little shy, so just use her nickname, the Mayor. 525 00:32:56,480 --> 00:33:00,240 Speaker 1: That's hilarious. So where's a sash during chemo and child birth? 526 00:33:00,360 --> 00:33:02,240 Speaker 1: I guess so, I mean I call Emily of the boss, 527 00:33:02,280 --> 00:33:05,719 Speaker 1: So I guess it's the mayor. The mayor is like 528 00:33:05,760 --> 00:33:08,600 Speaker 1: the boss of several bosses, I would guess. Yeah. We 529 00:33:08,680 --> 00:33:10,240 Speaker 1: used to call my friend Justin who you know, the 530 00:33:10,280 --> 00:33:13,800 Speaker 1: mayor of Atlanta, because everywhere you went nobody knew him. 531 00:33:13,800 --> 00:33:15,880 Speaker 1: He's a sociable fellow. But now we just call him 532 00:33:15,880 --> 00:33:18,440 Speaker 1: the manager of Atlanta because everywhere you go he has 533 00:33:18,480 --> 00:33:20,960 Speaker 1: some improvement to that place, like the lighting is not 534 00:33:21,040 --> 00:33:23,120 Speaker 1: quite right, or the doors should be over there in 535 00:33:23,160 --> 00:33:28,040 Speaker 1: the kitchen is not located properly Brits. Yeah, uh, and 536 00:33:28,040 --> 00:33:30,800 Speaker 1: then forth. My wife works in public policy, specifically helping 537 00:33:30,800 --> 00:33:33,880 Speaker 1: women and families get themselves out of poverty and advocating 538 00:33:33,880 --> 00:33:36,760 Speaker 1: for low income workers. So there you have it. An 539 00:33:36,760 --> 00:33:40,360 Speaker 1: awesome and incredible woman who dedicates her considerable talents helping others, 540 00:33:40,760 --> 00:33:43,720 Speaker 1: is pregnant and his breast cancer kind of hard to say. No, right, 541 00:33:43,960 --> 00:33:48,400 Speaker 1: I'm not above guilt tripping so Bob from Swithmore, Pennsylvania. Um, 542 00:33:49,880 --> 00:33:51,600 Speaker 1: there you go. People should go and check out that 543 00:33:51,640 --> 00:33:54,200 Speaker 1: stuff and support your bike ride. And I hope things 544 00:33:54,200 --> 00:33:56,760 Speaker 1: have continued to progress well for your wife and child 545 00:33:56,800 --> 00:34:00,840 Speaker 1: and keep us updated. Yeah, and you keep me updated 546 00:34:01,400 --> 00:34:04,520 Speaker 1: at the very least. If not everybody listening, I will. Okay, 547 00:34:05,400 --> 00:34:07,960 Speaker 1: thanks a lot, Bob and the mayor. Good luck to 548 00:34:08,000 --> 00:34:11,320 Speaker 1: you both. And um, let's see if you want to 549 00:34:11,320 --> 00:34:14,680 Speaker 1: get in touch with us, whether you're a mayor a 550 00:34:14,719 --> 00:34:18,480 Speaker 1: provincial governor who knows. You can get in touch with 551 00:34:18,560 --> 00:34:22,080 Speaker 1: us on Twitter at s Y s K podcast. You 552 00:34:22,120 --> 00:34:24,480 Speaker 1: can join us on Facebook dot com slash stuff you 553 00:34:24,520 --> 00:34:27,000 Speaker 1: Should Know. You can send us an email to Stuff 554 00:34:27,080 --> 00:34:31,000 Speaker 1: Podcast at how stuff Works dot com and join us 555 00:34:31,000 --> 00:34:33,080 Speaker 1: at our home on the web, Stuff you Should Know 556 00:34:33,200 --> 00:34:40,920 Speaker 1: dot com For more on this and thousands of other topics. 557 00:34:41,160 --> 00:34:49,960 Speaker 1: Is it how stuff Works dot com