1 00:00:03,320 --> 00:00:06,200 Speaker 1: Welcome to Stuff to Blow your Mind from how Stuff 2 00:00:06,200 --> 00:00:13,480 Speaker 1: Works dot com. Hey, welcome to Stuff to Blow your Mind. 3 00:00:13,560 --> 00:00:16,360 Speaker 1: My name is Robert lamp and Julia Douglas. Julie, here's 4 00:00:16,480 --> 00:00:19,280 Speaker 1: here's a question for you, just to lead into today's episode. 5 00:00:19,520 --> 00:00:23,120 Speaker 1: Do we live in a user it's all world? Sure? Right? 6 00:00:23,160 --> 00:00:25,560 Speaker 1: I mean have you seen those universal symbols of men 7 00:00:25,600 --> 00:00:28,480 Speaker 1: and women on bathroom doors? And we all just kind 8 00:00:28,480 --> 00:00:30,760 Speaker 1: of look like that, Like I constantly have a skirt 9 00:00:30,760 --> 00:00:35,559 Speaker 1: on with my hands outstretched and yours are always by 10 00:00:35,560 --> 00:00:38,400 Speaker 1: your side, the pants on. Yeah, you go to a 11 00:00:38,560 --> 00:00:40,760 Speaker 1: you go to the restaurant, certainly in the States, do 12 00:00:40,760 --> 00:00:43,479 Speaker 1: you order your meal comes on a giant plate? Right, 13 00:00:44,120 --> 00:00:46,479 Speaker 1: that's the amount of food that feeds the the the 14 00:00:46,520 --> 00:00:49,239 Speaker 1: average person, and so you're supposed to eat it, all right, 15 00:00:49,760 --> 00:00:52,680 Speaker 1: everybody wants that giant plate of food. Actually, I was 16 00:00:52,920 --> 00:00:54,440 Speaker 1: at a restaurant not too long ago and there was 17 00:00:54,440 --> 00:00:56,279 Speaker 1: a British woman next to me, and she started just 18 00:00:56,480 --> 00:01:00,240 Speaker 1: talking about how terrible it is United Stay. It's like 19 00:01:00,280 --> 00:01:03,240 Speaker 1: the giant portions. And I had never really thought about 20 00:01:03,280 --> 00:01:05,520 Speaker 1: it because I'm so used to that restaurant dining, and 21 00:01:05,560 --> 00:01:08,280 Speaker 1: I thought she's right, Yeah, bring you out of a 22 00:01:08,319 --> 00:01:10,160 Speaker 1: bowl of something and it's it's it should be in 23 00:01:10,200 --> 00:01:13,000 Speaker 1: the middle of a table with a family of four 24 00:01:13,120 --> 00:01:16,039 Speaker 1: or five dining on it, but no, instead it's your 25 00:01:16,319 --> 00:01:20,319 Speaker 1: your personal trough of food. Yeah, it's United States one's 26 00:01:20,800 --> 00:01:24,440 Speaker 1: size fits all meal, just for you. And that's where 27 00:01:24,440 --> 00:01:27,880 Speaker 1: it gets into this idea of of um, this kind 28 00:01:27,880 --> 00:01:30,680 Speaker 1: of like what is average? This question is there really 29 00:01:30,720 --> 00:01:35,000 Speaker 1: an average? And this bell curve that we have all 30 00:01:35,000 --> 00:01:39,120 Speaker 1: been introduced to in primary school, elementary school and onward 31 00:01:39,840 --> 00:01:43,000 Speaker 1: tends to kind of rule our lives even after we've 32 00:01:43,080 --> 00:01:46,280 Speaker 1: left school, right, and we're gonna look at We're gonna 33 00:01:46,480 --> 00:01:50,480 Speaker 1: look into this idea today. This this myth of average. Yeah, 34 00:01:50,520 --> 00:01:53,680 Speaker 1: if you want to imagine the bell curve here, um, 35 00:01:53,720 --> 00:01:56,880 Speaker 1: And certainly we're gonna have varying degrees of familiarity with this, 36 00:01:57,840 --> 00:02:00,720 Speaker 1: but it's basically looks like a bell. It's it's a 37 00:02:00,840 --> 00:02:03,320 Speaker 1: it's a it's a line, and then the line is 38 00:02:03,360 --> 00:02:05,720 Speaker 1: going sort of flat, and then it curves up and 39 00:02:05,720 --> 00:02:08,240 Speaker 1: then it curves back down again. And the idea here 40 00:02:08,639 --> 00:02:12,040 Speaker 1: is that is that on a performance standpoint, as far 41 00:02:12,040 --> 00:02:15,520 Speaker 1: as the statistics of performance, the idea is that you 42 00:02:15,639 --> 00:02:20,080 Speaker 1: have a very small group that is underperforming, that's at 43 00:02:20,080 --> 00:02:23,200 Speaker 1: the very bottom. And then you have a small group 44 00:02:23,280 --> 00:02:27,239 Speaker 1: that is just really performing at a high level and 45 00:02:27,280 --> 00:02:29,600 Speaker 1: they're at the top. And then you have this larger 46 00:02:29,639 --> 00:02:32,919 Speaker 1: group in the middle and they that is the realm 47 00:02:32,960 --> 00:02:39,040 Speaker 1: of the average. Yes, bell curves are normal probability distributions, 48 00:02:39,280 --> 00:02:41,840 Speaker 1: and that's what I think it's interesting about this probability 49 00:02:41,960 --> 00:02:45,440 Speaker 1: because we take this kind of distribution and we use 50 00:02:45,520 --> 00:02:48,200 Speaker 1: them in real world scenarios, which we'll talk about in 51 00:02:48,200 --> 00:02:51,560 Speaker 1: a second. But what you just just described is this 52 00:02:51,680 --> 00:02:54,480 Speaker 1: idea that we have an equivalent number of people above 53 00:02:54,600 --> 00:02:57,519 Speaker 1: and below average, and that there's a very small number 54 00:02:57,560 --> 00:03:00,840 Speaker 1: of people who are two standard deviations above and below 55 00:03:00,880 --> 00:03:03,040 Speaker 1: the average. So if you're thinking about that plotted out 56 00:03:03,040 --> 00:03:06,560 Speaker 1: on that line, that Bell curve, then those those outliers 57 00:03:06,840 --> 00:03:08,800 Speaker 1: would be the people who are super high achievers and 58 00:03:08,840 --> 00:03:12,920 Speaker 1: people who are at the very low ends of achievement. Yeah. 59 00:03:13,040 --> 00:03:16,320 Speaker 1: So like from a from a corporate standpoint, most of 60 00:03:16,320 --> 00:03:18,160 Speaker 1: your company is going to be in the middle. That's 61 00:03:18,160 --> 00:03:20,280 Speaker 1: where most of your money and resources are going, just 62 00:03:20,320 --> 00:03:22,720 Speaker 1: because that's where the most people are. But that small 63 00:03:22,760 --> 00:03:24,960 Speaker 1: percentage of the top, those are the ones that are 64 00:03:25,000 --> 00:03:27,520 Speaker 1: that there, there's really a lot of potential for those 65 00:03:27,560 --> 00:03:29,960 Speaker 1: are the ones that are really bring innovative ideas and 66 00:03:30,040 --> 00:03:33,280 Speaker 1: high performance to the table. And then that the bottom, 67 00:03:33,320 --> 00:03:36,480 Speaker 1: the outliers, outliers at the very bottom. Uh, those are 68 00:03:36,480 --> 00:03:39,200 Speaker 1: the ones that you're going to want to cut uh 69 00:03:39,280 --> 00:03:42,280 Speaker 1: and and regularly cut those. That's the slack that you 70 00:03:42,280 --> 00:03:44,840 Speaker 1: want to get rid of to tighten up the rope. Yeah. Indeed, 71 00:03:45,160 --> 00:03:48,000 Speaker 1: and we use this again, this is just a probability 72 00:03:48,040 --> 00:03:53,240 Speaker 1: distribution in these real world scenarios to decide how well 73 00:03:53,440 --> 00:03:57,080 Speaker 1: children are learning, which dictates how and what they learned. 74 00:03:57,160 --> 00:04:00,520 Speaker 1: We use it to assess workplace performance and don't racist. 75 00:04:00,520 --> 00:04:03,080 Speaker 1: And that's where it becomes sort of like, let's let's 76 00:04:03,080 --> 00:04:05,280 Speaker 1: look at this model a little bit closer, because we 77 00:04:05,320 --> 00:04:09,320 Speaker 1: have now reverse engineered a budget based on the Bell curve, 78 00:04:10,320 --> 00:04:13,840 Speaker 1: and it could be that the Bell curve is quite off. 79 00:04:14,040 --> 00:04:17,200 Speaker 1: In fact, research conducted in two thousand and eleven and 80 00:04:17,279 --> 00:04:21,120 Speaker 1: two thousand and twelve by Ernest oh Boyle Jr. And 81 00:04:21,360 --> 00:04:25,080 Speaker 1: Herman Agwynas examined the performance of more than six hundred 82 00:04:25,160 --> 00:04:29,440 Speaker 1: and thirty thousand people involved in four areas of human performance. 83 00:04:29,520 --> 00:04:34,400 Speaker 1: Academics writing so writing papers athletes at the professional and 84 00:04:34,560 --> 00:04:40,480 Speaker 1: collegiate levels, politicians, and entertainers. And they found that performance 85 00:04:40,839 --> 00:04:45,800 Speaker 1: and of these groups did not follow a normal distribution, 86 00:04:45,960 --> 00:04:50,160 Speaker 1: did not follow the Bell curve. Rather, those groups fell 87 00:04:50,200 --> 00:04:55,240 Speaker 1: into what is called a power law distribution. And according 88 00:04:55,279 --> 00:04:58,159 Speaker 1: to a Forbes magazine article the Myth of the Bell Curve, 89 00:04:59,120 --> 00:05:01,479 Speaker 1: this power law to attribution is also known as a 90 00:05:01,560 --> 00:05:03,680 Speaker 1: long tale because we're looking at a picture of it 91 00:05:03,800 --> 00:05:08,880 Speaker 1: right now. If you think about um a rectangle and 92 00:05:09,480 --> 00:05:11,920 Speaker 1: one side of that rectangle being a sort of tale, 93 00:05:12,360 --> 00:05:15,840 Speaker 1: that's more of the distribution. They say that is in 94 00:05:16,040 --> 00:05:19,240 Speaker 1: keeping with what is really going on, that's reflecting reality. 95 00:05:19,360 --> 00:05:22,159 Speaker 1: And they say that most people fall below the mean, 96 00:05:22,720 --> 00:05:25,640 Speaker 1: and roughly ten to of the population are above the 97 00:05:25,680 --> 00:05:29,359 Speaker 1: average and often far above the average, and a large 98 00:05:29,400 --> 00:05:32,960 Speaker 1: population are slightly below average, in a small group are 99 00:05:33,000 --> 00:05:36,240 Speaker 1: far below average. So they say that this idea of 100 00:05:36,360 --> 00:05:40,080 Speaker 1: average is actually pretty meaningless when you think about what's 101 00:05:40,080 --> 00:05:42,520 Speaker 1: happening in real time. Yeah, and I mean it's even 102 00:05:42,640 --> 00:05:45,680 Speaker 1: it's even worse than meaningless when you start looking at 103 00:05:45,680 --> 00:05:50,560 Speaker 1: the idea that, rather than describing how we perform and 104 00:05:50,560 --> 00:05:54,520 Speaker 1: and really being a telling model of human behavior and 105 00:05:54,600 --> 00:05:59,280 Speaker 1: human potential, the Bell curve might actually be constraining our performance. 106 00:05:59,600 --> 00:06:02,599 Speaker 1: They work creating that we're taking the statistical model of 107 00:06:02,680 --> 00:06:07,640 Speaker 1: human behavior and trying to shoehorn our actual behavior into it. Yeah, 108 00:06:07,640 --> 00:06:10,280 Speaker 1: I mean, because think about a company or a classroom, 109 00:06:10,320 --> 00:06:13,719 Speaker 1: and let's say that the company classrooms, Um, they're full 110 00:06:13,880 --> 00:06:17,560 Speaker 1: of hyper performers. Okay, Let's say nineteen out of the 111 00:06:17,640 --> 00:06:22,960 Speaker 1: twenty kids like they're performing at crazy rates. Okay, they 112 00:06:23,000 --> 00:06:25,000 Speaker 1: are still going to be graded on the Bell curve. 113 00:06:25,839 --> 00:06:28,040 Speaker 1: Let's say that nineteen of the twenty employees at a 114 00:06:28,080 --> 00:06:32,000 Speaker 1: workplace are hyper performers. They're still gonna their raises. Their 115 00:06:32,000 --> 00:06:34,480 Speaker 1: performance are still gonna be doled out based on the 116 00:06:34,520 --> 00:06:37,560 Speaker 1: Bell curve because again, that budget has been reverse engineered, 117 00:06:37,600 --> 00:06:41,279 Speaker 1: so there's only a certain amount of money and percentages 118 00:06:41,640 --> 00:06:44,599 Speaker 1: that are going to be distributed across that performance. So 119 00:06:44,640 --> 00:06:46,520 Speaker 1: a lot of people lose out in the scenarios. And 120 00:06:46,760 --> 00:06:49,800 Speaker 1: basically it's saying, here is a model for what performance 121 00:06:49,800 --> 00:06:53,000 Speaker 1: should look like. If you don't recognize that model in 122 00:06:53,160 --> 00:06:55,320 Speaker 1: the group that you're judging, then you must be making 123 00:06:55,320 --> 00:06:57,920 Speaker 1: a mistake. So even in that group of high performers 124 00:06:58,560 --> 00:07:01,200 Speaker 1: at a company, you in end up having to rate 125 00:07:01,680 --> 00:07:05,080 Speaker 1: some high performers as average, and and some average of 126 00:07:05,200 --> 00:07:08,720 Speaker 1: performers as as low performers. And you're and that's just 127 00:07:08,760 --> 00:07:13,520 Speaker 1: gonna end up hurting morale and and driving away talented individuals. Yeah, 128 00:07:13,560 --> 00:07:16,400 Speaker 1: which is not to say that the idea of universal design, 129 00:07:16,600 --> 00:07:18,840 Speaker 1: which is basically we're talking about here when we talk 130 00:07:18,880 --> 00:07:22,800 Speaker 1: about Bell curve model, isn't helpful, because it is right. 131 00:07:22,840 --> 00:07:25,520 Speaker 1: We can talk about universal design in the ways that 132 00:07:25,520 --> 00:07:28,920 Speaker 1: our streets are laid out right or even um like 133 00:07:29,000 --> 00:07:32,480 Speaker 1: catching utensils that are made for any size hand, not 134 00:07:32,600 --> 00:07:35,120 Speaker 1: just a giant hand or a small hand. Um. But 135 00:07:35,240 --> 00:07:38,640 Speaker 1: it's not so great when you actually talk about the 136 00:07:38,640 --> 00:07:45,280 Speaker 1: individual him or herself, and you have companies, institutions, education, um, 137 00:07:45,320 --> 00:07:50,840 Speaker 1: trying to mandate a sort of universal paradigm to place 138 00:07:51,000 --> 00:07:54,280 Speaker 1: over it. And so this brings us to a new idea, 139 00:07:54,360 --> 00:07:58,160 Speaker 1: to a new movement kind of revolutionary approach, and that 140 00:07:58,320 --> 00:08:01,600 Speaker 1: is to to ban the to to throw the idea 141 00:08:01,600 --> 00:08:05,040 Speaker 1: of the average out, to say, hey, this institutional model 142 00:08:05,400 --> 00:08:09,320 Speaker 1: should not is false and should not dictate how we 143 00:08:09,480 --> 00:08:13,240 Speaker 1: organize our lives and our industries and our educational system. Yeah. 144 00:08:13,240 --> 00:08:15,400 Speaker 1: And the biggest proponent of this idea, of this man. 145 00:08:15,480 --> 00:08:17,960 Speaker 1: The average is Todd Rose, who's a faculty member at 146 00:08:18,000 --> 00:08:21,520 Speaker 1: Harvard Graduate School of Education. He talked about how in 147 00:08:21,680 --> 00:08:24,720 Speaker 1: nineteen fifty two the U. S. Air Force had a problem. 148 00:08:24,760 --> 00:08:27,760 Speaker 1: They had really good pilots flying better planes, all this 149 00:08:27,880 --> 00:08:30,080 Speaker 1: money that they had sunk into better planes, but they 150 00:08:30,080 --> 00:08:33,760 Speaker 1: were getting worse results and they didn't know why. And 151 00:08:33,840 --> 00:08:36,000 Speaker 1: finally they figured out that it had to deal with 152 00:08:36,040 --> 00:08:39,320 Speaker 1: the design of the cockpit, which was designed based on 153 00:08:39,559 --> 00:08:43,120 Speaker 1: the average man. And they had an Air Force researcher 154 00:08:43,120 --> 00:08:45,800 Speaker 1: by the name of Gilbert Daniels who conducted a study 155 00:08:45,800 --> 00:08:50,040 Speaker 1: and found that none as zero of the four thousand 156 00:08:50,120 --> 00:08:54,120 Speaker 1: pilots were average on all of the ten dimensions of 157 00:08:54,200 --> 00:08:58,959 Speaker 1: size that he measured on them. We're talking about height, shoulders, chest, 158 00:08:59,160 --> 00:09:05,400 Speaker 1: waste its legs, uh, their reach, right torso, neck, and thighs. 159 00:09:05,480 --> 00:09:08,319 Speaker 1: And he proved that there was no such thing as 160 00:09:08,360 --> 00:09:13,320 Speaker 1: an average pilot, but that they have a jagged size profile, 161 00:09:13,840 --> 00:09:17,560 Speaker 1: so no one is the same one every single dimension. 162 00:09:17,840 --> 00:09:20,360 Speaker 1: And just because let's say you might be the average height, 163 00:09:20,400 --> 00:09:22,520 Speaker 1: it doesn't mean that you're the average weight or you 164 00:09:22,559 --> 00:09:26,760 Speaker 1: have the average torso length. And so the Air Force 165 00:09:26,840 --> 00:09:30,679 Speaker 1: took that information and they decided to ban the average 166 00:09:31,360 --> 00:09:33,960 Speaker 1: and they refused to buy fighter jets where the cockpit 167 00:09:34,080 --> 00:09:37,800 Speaker 1: was made for the average pilot, and instead they wanted 168 00:09:37,840 --> 00:09:40,360 Speaker 1: them to design to what they called the edges of 169 00:09:40,440 --> 00:09:44,920 Speaker 1: dimensions of size, so saying basically, hey, we're gonna have 170 00:09:44,920 --> 00:09:47,720 Speaker 1: tall pilots, we're gonna have short pilots. We need you 171 00:09:47,800 --> 00:09:52,040 Speaker 1: to design with these extremes in mind, instead of just saying, hey, 172 00:09:52,080 --> 00:09:55,040 Speaker 1: this is the average person. One size fits all, which 173 00:09:55,320 --> 00:09:58,520 Speaker 1: is not the kind of mandate that that that anyone 174 00:09:58,640 --> 00:10:02,120 Speaker 1: wants to hear in the manu factoring industry, because one 175 00:10:02,160 --> 00:10:04,920 Speaker 1: size fits all is a good system if you are 176 00:10:05,000 --> 00:10:09,080 Speaker 1: making a screw driver, if you're making you know, to 177 00:10:09,120 --> 00:10:11,200 Speaker 1: your point, you know, just some sort of ikea part 178 00:10:11,360 --> 00:10:14,839 Speaker 1: or or or standard furniture product to go in your 179 00:10:14,840 --> 00:10:18,880 Speaker 1: house exactly. I mean, that's the whole manufacturing business is 180 00:10:18,920 --> 00:10:22,520 Speaker 1: based on that. But here you have like this really 181 00:10:22,600 --> 00:10:26,480 Speaker 1: expensive equipment. You want it to be interacted with in 182 00:10:26,520 --> 00:10:30,240 Speaker 1: the correct way, and then all has to do with dimensions. Yeah, 183 00:10:30,320 --> 00:10:32,880 Speaker 1: I mean, yeah, you have high performers who need to 184 00:10:32,960 --> 00:10:35,960 Speaker 1: use a high performance aircraft and you need to you 185 00:10:35,960 --> 00:10:37,960 Speaker 1: need these two need to meet. It reminds me a 186 00:10:37,960 --> 00:10:41,560 Speaker 1: lot of our relationship with computers, and not just computers, 187 00:10:41,559 --> 00:10:44,360 Speaker 1: like even just like desk equipment in general, but everything 188 00:10:44,400 --> 00:10:47,640 Speaker 1: everything that surrounds computing. The idea that that the computing 189 00:10:47,679 --> 00:10:50,720 Speaker 1: experience should be made as human as possible, so that 190 00:10:50,840 --> 00:10:53,920 Speaker 1: humans can use the machine, can use the software, can 191 00:10:54,040 --> 00:10:56,280 Speaker 1: use the chair and the table, everything involved in the 192 00:10:56,320 --> 00:10:59,040 Speaker 1: office environment. That they should be able to use it 193 00:10:59,040 --> 00:11:02,079 Speaker 1: without wearing themselves to the level of the machine. The 194 00:11:02,360 --> 00:11:04,520 Speaker 1: machine should meet that the human user, not the other 195 00:11:04,520 --> 00:11:06,840 Speaker 1: way around. And so here we see the same idea 196 00:11:07,120 --> 00:11:13,160 Speaker 1: with with with with institutions, with with with design in general. Yeah, 197 00:11:13,200 --> 00:11:15,559 Speaker 1: and that's what Todd Rose says. He says that just 198 00:11:15,760 --> 00:11:19,360 Speaker 1: like size, each student, every single one of them, has 199 00:11:19,400 --> 00:11:24,040 Speaker 1: a jagged learning profile, meaning they have strengths through average 200 00:11:24,040 --> 00:11:26,560 Speaker 1: at some things, and they have weaknesses. He says, we 201 00:11:26,600 --> 00:11:29,960 Speaker 1: all do, even geniuses have weaknesses. And he says, if 202 00:11:30,000 --> 00:11:33,360 Speaker 1: you design those learning environments on average, odds are you've 203 00:11:33,400 --> 00:11:36,440 Speaker 1: designed them for nobody. He says, so, no, wonder we 204 00:11:36,480 --> 00:11:39,240 Speaker 1: have a problem. We've created learning environments that, because they 205 00:11:39,240 --> 00:11:43,040 Speaker 1: are designed on average, cannot possibly do what we expected 206 00:11:43,080 --> 00:11:45,880 Speaker 1: them to do, which is nurture individual potential. And he 207 00:11:45,920 --> 00:11:49,440 Speaker 1: talks about how we are in a very unique situation 208 00:11:49,600 --> 00:11:53,960 Speaker 1: right now technologically because we can serve the individual We 209 00:11:53,960 --> 00:11:56,520 Speaker 1: can serve the individual student and the way that they 210 00:11:56,600 --> 00:12:01,920 Speaker 1: learn and follow those jagged profiles by giving them an iPad, 211 00:12:02,080 --> 00:12:05,160 Speaker 1: in giving them different programs to bolster learning in the 212 00:12:05,200 --> 00:12:07,360 Speaker 1: areas that they're a week or if they were really 213 00:12:07,360 --> 00:12:13,559 Speaker 1: really high performers been challenging them with supplementation also provided 214 00:12:13,600 --> 00:12:16,960 Speaker 1: by technology. And he's spot on about this, I think, 215 00:12:17,000 --> 00:12:21,439 Speaker 1: because what he's saying is that schools, they spent an 216 00:12:21,600 --> 00:12:24,040 Speaker 1: enormous amount of money on iPads. I think he said 217 00:12:24,040 --> 00:12:28,480 Speaker 1: that they're like the second largest customer of um or 218 00:12:28,720 --> 00:12:32,559 Speaker 1: consumer of iPads, and at least in the United States. 219 00:12:32,640 --> 00:12:35,319 Speaker 1: So if you have the technology at your disposal, if 220 00:12:35,360 --> 00:12:37,760 Speaker 1: you are spending the money, why not begin to work 221 00:12:37,800 --> 00:12:41,040 Speaker 1: with the possibilities of what those programs can offer on 222 00:12:41,080 --> 00:12:44,600 Speaker 1: an individual level. Because we had talked about in our 223 00:12:45,280 --> 00:12:49,000 Speaker 1: podcast about Finland and why they're turning out such incredibly 224 00:12:49,080 --> 00:12:53,719 Speaker 1: well rounded, smart kids who only have one test, one 225 00:12:53,760 --> 00:12:57,000 Speaker 1: mandatory test at age of sixteen. It's because they're serving 226 00:12:57,040 --> 00:13:02,200 Speaker 1: those kids at the individual level, and they're spending less 227 00:13:02,200 --> 00:13:05,840 Speaker 1: than the United States is on education per child to 228 00:13:05,920 --> 00:13:08,280 Speaker 1: do that. You know, I can't help but think back 229 00:13:08,320 --> 00:13:12,280 Speaker 1: to the wire when we're talking about this, mainly because 230 00:13:12,480 --> 00:13:16,160 Speaker 1: creator David Simon has often stated that that that in 231 00:13:16,240 --> 00:13:19,560 Speaker 1: that show, you essentially have a Greek tragedy, but instead 232 00:13:19,640 --> 00:13:23,200 Speaker 1: of gods, you have institutions, because institutions are the gods 233 00:13:23,200 --> 00:13:27,559 Speaker 1: of modern society. And so in in this topic, we 234 00:13:27,559 --> 00:13:29,679 Speaker 1: we kind of have to ask the question what kind 235 00:13:29,760 --> 00:13:33,839 Speaker 1: of god suits, uh, the denizen of the modern world better. 236 00:13:34,240 --> 00:13:38,000 Speaker 1: One is the personal god that is is involved in 237 00:13:38,040 --> 00:13:41,079 Speaker 1: your life and uh and and wants to mold you. 238 00:13:41,160 --> 00:13:45,000 Speaker 1: And the other is this abstract, distant god. And to 239 00:13:45,080 --> 00:13:47,520 Speaker 1: reach that god, you have to change yourself. You have 240 00:13:47,559 --> 00:13:51,240 Speaker 1: to jump through the hoops of of religious ritual to 241 00:13:51,640 --> 00:13:55,559 Speaker 1: possibly interact with it. Oh my gosh. And as we 242 00:13:55,600 --> 00:13:57,920 Speaker 1: always say, it goes back to the Platonic ideal and 243 00:13:58,040 --> 00:14:00,240 Speaker 1: Plato and this idea that we're all just, you know, 244 00:14:01,240 --> 00:14:05,080 Speaker 1: these cheap copies of perfection. But you know, we've decided 245 00:14:05,120 --> 00:14:07,120 Speaker 1: that we're cheap copies on the on the bell curve 246 00:14:07,400 --> 00:14:12,439 Speaker 1: instead of you know, on the jagged edge of dimensions. 247 00:14:12,559 --> 00:14:15,640 Speaker 1: And this even relates to healthcare. If you think about 248 00:14:15,800 --> 00:14:21,160 Speaker 1: health insurance, which sanctions, treatments, and the myth of average 249 00:14:21,200 --> 00:14:24,960 Speaker 1: can really put people at a disadvantage. Here Rose says 250 00:14:25,040 --> 00:14:27,160 Speaker 1: that if you look at the area of cancer, you 251 00:14:27,200 --> 00:14:31,080 Speaker 1: see an exponential increase in effective research and treatment when 252 00:14:31,160 --> 00:14:34,960 Speaker 1: the individual with all of his or her genetic predispositions, 253 00:14:35,000 --> 00:14:37,960 Speaker 1: diet and environment is considered as opposed to just hey, 254 00:14:38,040 --> 00:14:41,960 Speaker 1: here's this, here's how we approach cancer, this in this 255 00:14:42,120 --> 00:14:45,240 Speaker 1: very universal way. And he said that's it's really only 256 00:14:45,240 --> 00:14:47,440 Speaker 1: when you get down to the individual level that you're 257 00:14:47,480 --> 00:14:51,400 Speaker 1: making progress. And if you think about it, um even 258 00:14:51,400 --> 00:14:54,560 Speaker 1: a drug therapies, and this is from the case for 259 00:14:54,640 --> 00:14:57,680 Speaker 1: Impersonalized Medicine the third edition. It says many patients do 260 00:14:57,760 --> 00:15:00,440 Speaker 1: not benefit from the first drug they are offered in treatment. 261 00:15:00,480 --> 00:15:07,600 Speaker 1: For example, of depression patients, arthritis patients, asthma patients, and 262 00:15:08,440 --> 00:15:11,520 Speaker 1: of diabetic patients will not respond to initial treatment. And 263 00:15:11,520 --> 00:15:14,840 Speaker 1: we know initial treatment is something that is offered because 264 00:15:15,760 --> 00:15:19,560 Speaker 1: based on the average that they have, that is the 265 00:15:19,600 --> 00:15:21,680 Speaker 1: thing that they think will work the best. Right, and 266 00:15:21,680 --> 00:15:23,920 Speaker 1: it sounds good on paper, right, treat the average patient 267 00:15:24,040 --> 00:15:27,080 Speaker 1: and then adjust accordingly based on the feedback. Yeah, except 268 00:15:27,160 --> 00:15:30,040 Speaker 1: as as it seems as mounting evidence would seem to 269 00:15:30,200 --> 00:15:34,200 Speaker 1: show us this average is indeed a myth. All right, 270 00:15:34,240 --> 00:15:36,320 Speaker 1: we're gonna take a quick break, and when we come back, 271 00:15:36,560 --> 00:15:39,000 Speaker 1: we're going to talk a little more about this topic 272 00:15:39,200 --> 00:15:47,960 Speaker 1: and even read a few listener mails. Hey we're back, 273 00:15:48,160 --> 00:15:50,880 Speaker 1: and we're of course talking about the myth of average. 274 00:15:50,880 --> 00:15:53,680 Speaker 1: We're talking about what happens when you have these have 275 00:15:53,720 --> 00:15:58,480 Speaker 1: an institutional model of human performance, and then you start 276 00:15:58,680 --> 00:16:01,120 Speaker 1: trying to live your life and and have the whole 277 00:16:01,160 --> 00:16:04,640 Speaker 1: culture work around those models, and the and the the 278 00:16:04,760 --> 00:16:09,800 Speaker 1: growing revelation that this average person that everything is centered 279 00:16:09,800 --> 00:16:13,480 Speaker 1: around doesn't really exist. Yeah. And the thing too is 280 00:16:13,520 --> 00:16:18,640 Speaker 1: that this system is just completely permeated culture. Right, it's systemic. 281 00:16:18,880 --> 00:16:21,200 Speaker 1: There is there to stay it with. Seems so Todd 282 00:16:21,280 --> 00:16:23,040 Speaker 1: Rose one of one of the things that he really 283 00:16:23,040 --> 00:16:25,280 Speaker 1: wants to do is to try to take this apart 284 00:16:25,320 --> 00:16:29,120 Speaker 1: a bit and look more towards the individual talent method. 285 00:16:29,160 --> 00:16:31,640 Speaker 1: And he has something called the Variability Project, and his 286 00:16:31,760 --> 00:16:34,400 Speaker 1: idea is that you have this, you know, this system 287 00:16:34,520 --> 00:16:36,880 Speaker 1: in place for a hundred and fifty years based on 288 00:16:36,920 --> 00:16:41,280 Speaker 1: averages trying to understand individuals, and you have to now 289 00:16:41,360 --> 00:16:45,840 Speaker 1: take this information about the myth of average and try 290 00:16:45,920 --> 00:16:48,880 Speaker 1: to rework it. And so he says, there are three 291 00:16:48,960 --> 00:16:53,920 Speaker 1: broad challenges data, models, and the nature of science to 292 00:16:54,080 --> 00:16:57,200 Speaker 1: address the science of the individual reaching its full potential 293 00:16:57,200 --> 00:17:01,280 Speaker 1: in all different fields. So what he's doing may seem 294 00:17:01,360 --> 00:17:03,800 Speaker 1: a little bit pine in the sky right now because 295 00:17:03,840 --> 00:17:07,000 Speaker 1: it's it's UH. I say that only because again it's 296 00:17:07,080 --> 00:17:11,520 Speaker 1: very systemaic. This this UM, this average idea and Bell 297 00:17:11,600 --> 00:17:15,040 Speaker 1: curve that's in place. So that's there's so many different 298 00:17:15,119 --> 00:17:19,480 Speaker 1: fields that he has to try to get into and influence. 299 00:17:19,800 --> 00:17:23,399 Speaker 1: That being said, he and his organization are starting to 300 00:17:24,000 --> 00:17:28,360 Speaker 1: provide papers on the topic and really trying to educate people, UM, 301 00:17:28,480 --> 00:17:32,480 Speaker 1: why why this is sort of erroneous thinking, and how 302 00:17:32,520 --> 00:17:36,640 Speaker 1: you can get to students, to workers, UM, to health 303 00:17:36,640 --> 00:17:39,399 Speaker 1: care treatments in a much more effective way, you know. 304 00:17:39,440 --> 00:17:41,520 Speaker 1: And just just to go back to A. Gwynas for 305 00:17:41,560 --> 00:17:43,440 Speaker 1: a second. One of the authors on that two thousand 306 00:17:43,480 --> 00:17:48,280 Speaker 1: and twelve study UM he described the Bell curve as 307 00:17:48,440 --> 00:17:53,199 Speaker 1: as possibly being accurate in describing human performance in the 308 00:17:53,240 --> 00:17:58,800 Speaker 1: presence of an external constraint UH, such as an assembly line. 309 00:17:59,240 --> 00:18:02,040 Speaker 1: You have simply line their parts moving by, and you 310 00:18:02,119 --> 00:18:06,040 Speaker 1: have skilled workers doing their bit to UH to contribute 311 00:18:06,080 --> 00:18:09,520 Speaker 1: to the finished air conditioning unit at the end of 312 00:18:09,520 --> 00:18:13,600 Speaker 1: the line, right, but you're gonna have talented individuals on 313 00:18:13,640 --> 00:18:17,479 Speaker 1: there who are not who could work faster if not 314 00:18:17,600 --> 00:18:22,159 Speaker 1: held back by the pace of the line, by the 315 00:18:22,160 --> 00:18:26,000 Speaker 1: the the outside constraint that is applied to them by 316 00:18:26,000 --> 00:18:29,560 Speaker 1: the institution. Yeah, and it's really the institution is key here. 317 00:18:29,600 --> 00:18:32,119 Speaker 1: And it's interesting to think about this because you're thinking, Okay, 318 00:18:32,119 --> 00:18:36,919 Speaker 1: this is about manufacturing, right, could this possibly apply to 319 00:18:37,200 --> 00:18:41,240 Speaker 1: sort of like the Krendl crem of higher education? Could 320 00:18:41,320 --> 00:18:45,080 Speaker 1: IVY Leagues be a kind of assembly line. Well, there's 321 00:18:45,119 --> 00:18:48,400 Speaker 1: an excellent article on this by David Brooks that published 322 00:18:48,960 --> 00:18:51,639 Speaker 1: online in The Atlantic. Of course it's The Atlantic, so 323 00:18:51,680 --> 00:18:55,760 Speaker 1: it's it's really long but very thorough breakdown of the 324 00:18:55,840 --> 00:18:59,359 Speaker 1: state of higher education, especially as as it relates to 325 00:18:59,440 --> 00:19:02,560 Speaker 1: IVY Lee For instance. He argues that, uh, that that 326 00:19:02,680 --> 00:19:05,520 Speaker 1: right now, we kind of have the convergence of two models. 327 00:19:05,560 --> 00:19:07,520 Speaker 1: There's the older model where to get into an IVY 328 00:19:07,560 --> 00:19:09,840 Speaker 1: League school you had to be somebody a very you know, 329 00:19:10,320 --> 00:19:13,000 Speaker 1: class based model and to have the clout to get in. 330 00:19:13,240 --> 00:19:15,320 Speaker 1: And then you have the newer model to to get 331 00:19:15,320 --> 00:19:17,840 Speaker 1: into an IVY League school. To get into it, to 332 00:19:17,840 --> 00:19:20,280 Speaker 1: be a high achiever in society. You had to be 333 00:19:20,320 --> 00:19:22,359 Speaker 1: an overachiever. You had to just work and work and 334 00:19:22,400 --> 00:19:24,679 Speaker 1: work the right person, Yeah, you had to be the 335 00:19:24,800 --> 00:19:29,040 Speaker 1: right person as opposed to the being from the right class. 336 00:19:29,320 --> 00:19:31,560 Speaker 1: So they end up in this environment where they're just 337 00:19:31,880 --> 00:19:34,680 Speaker 1: they're just performing at a high level all the time. 338 00:19:34,720 --> 00:19:38,040 Speaker 1: They're expected and expecting themselves to just knock get out 339 00:19:38,040 --> 00:19:42,800 Speaker 1: of the park, assignment after assignment, project after project. Uh, 340 00:19:42,960 --> 00:19:46,760 Speaker 1: just domino after domino. Right. And as as as Brooks 341 00:19:46,800 --> 00:19:49,200 Speaker 1: says in the PC says quote, learning is supposed to 342 00:19:49,240 --> 00:19:51,720 Speaker 1: be about falling down and getting up again until you 343 00:19:51,800 --> 00:19:54,800 Speaker 1: do it right. But in an academic culture that demands 344 00:19:54,840 --> 00:19:59,119 Speaker 1: constant achievement, failures seem so perilous that the best and 345 00:19:59,160 --> 00:20:03,760 Speaker 1: brightest often spend their young years in terrariums of excellence. Uh. 346 00:20:03,840 --> 00:20:07,280 Speaker 1: And this is what author William Dershowitz, who's a former 347 00:20:07,480 --> 00:20:12,600 Speaker 1: professor of English at Yale, terms a violent aversion to risk. 348 00:20:13,160 --> 00:20:16,240 Speaker 1: So you can imagine where you were an institution like 349 00:20:16,320 --> 00:20:19,679 Speaker 1: this would produce an individual that could go on to 350 00:20:19,880 --> 00:20:23,480 Speaker 1: achieve great things within a similar institution, you know, the 351 00:20:23,600 --> 00:20:27,160 Speaker 1: right kind of uh, financial firm, etcetera, where there again, 352 00:20:27,480 --> 00:20:30,920 Speaker 1: are are these dominoes to knock down one after the other. 353 00:20:31,280 --> 00:20:34,600 Speaker 1: But that kind of individual, of that kind of thinking 354 00:20:34,600 --> 00:20:37,760 Speaker 1: that's been in a sense institutionalized by the the Ivy 355 00:20:37,840 --> 00:20:40,880 Speaker 1: League system is not going to perform well in other 356 00:20:40,920 --> 00:20:45,200 Speaker 1: areas of society. Yeah, Derschwitz, he has a book called 357 00:20:45,280 --> 00:20:48,800 Speaker 1: Excellent Sheep, and he says that the Ivy League is 358 00:20:48,880 --> 00:20:52,840 Speaker 1: churning out students who are super people, alien species. I 359 00:20:52,920 --> 00:20:57,000 Speaker 1: think that one's fair. Uh, and bionic hamsters. I mean this, 360 00:20:57,200 --> 00:21:00,520 Speaker 1: this is rough stuff here, But again I think bion 361 00:21:00,600 --> 00:21:03,119 Speaker 1: a canster matches up with some people I've met that 362 00:21:03,119 --> 00:21:06,000 Speaker 1: would fit that moment camps up. It's kind of awesome 363 00:21:06,000 --> 00:21:08,520 Speaker 1: in a way. Yeah, I'm gonna put that on my resume. 364 00:21:08,920 --> 00:21:13,520 Speaker 1: I'm not there. You go, and he says, as you said, 365 00:21:13,520 --> 00:21:16,040 Speaker 1: that system manufactured students who are smart and talented and driven, 366 00:21:16,040 --> 00:21:20,040 Speaker 1: but they're also anxious, timid, and lost, with little intellectual 367 00:21:20,160 --> 00:21:24,000 Speaker 1: curiosity and stunted sense of purpose, trapped in a bubble 368 00:21:24,040 --> 00:21:26,959 Speaker 1: of privilege, heading meekly in the same direction. Great at 369 00:21:26,960 --> 00:21:28,880 Speaker 1: what they're doing, but no idea why they're doing it. 370 00:21:29,320 --> 00:21:30,920 Speaker 1: And so I think it kind of goes back down 371 00:21:30,920 --> 00:21:34,840 Speaker 1: to that whole individual versus universal level, because at the 372 00:21:34,840 --> 00:21:38,479 Speaker 1: individual level, as Brooks has said, there is failure. You 373 00:21:38,600 --> 00:21:41,359 Speaker 1: must fail, you must fail and get up and do 374 00:21:41,400 --> 00:21:44,639 Speaker 1: it again in order to learn and find purpose. But 375 00:21:44,920 --> 00:21:49,480 Speaker 1: at the universal level and at the university level, there 376 00:21:49,640 --> 00:21:55,800 Speaker 1: is only success. That is what the big push is right, 377 00:21:55,920 --> 00:22:01,160 Speaker 1: just to succeed and not to individualize the content that 378 00:22:01,280 --> 00:22:04,600 Speaker 1: you are are taking in. So you could even say 379 00:22:04,640 --> 00:22:08,480 Speaker 1: that it's just all about regurgitation as opposed to percolating 380 00:22:08,480 --> 00:22:13,120 Speaker 1: on something, permeating your worldview and figuring it out for yourself. 381 00:22:13,200 --> 00:22:16,359 Speaker 1: What doesn't matter to you as a person. So again 382 00:22:16,400 --> 00:22:18,680 Speaker 1: I can't help but come back to that to David 383 00:22:18,720 --> 00:22:23,400 Speaker 1: Simon's about institutions as God's and this, uh, this idea 384 00:22:23,480 --> 00:22:26,439 Speaker 1: that we don't want that distant God that requires us 385 00:22:26,440 --> 00:22:29,119 Speaker 1: to jump through hoops and jump through ritual We we 386 00:22:29,200 --> 00:22:34,880 Speaker 1: want this institutional God that that sees us as an individual. Yeah, 387 00:22:34,920 --> 00:22:36,840 Speaker 1: And I find actually a lot of comfort in this 388 00:22:37,000 --> 00:22:40,159 Speaker 1: idea of the myth of the average, because you know, 389 00:22:40,400 --> 00:22:44,280 Speaker 1: too often I think we we hear the statistic of 390 00:22:44,440 --> 00:22:47,680 Speaker 1: you fall into this category in that category, and we're 391 00:22:47,720 --> 00:22:52,640 Speaker 1: so completely categorized and labeled that we don't necessarily follow 392 00:22:53,440 --> 00:22:56,359 Speaker 1: the individual path for ourselves. And I think this is 393 00:22:56,359 --> 00:22:58,840 Speaker 1: a very subconscious thing. In fact, I think all of us, 394 00:22:58,880 --> 00:23:00,879 Speaker 1: if you, if you thought yourself for a moment, do 395 00:23:00,920 --> 00:23:05,359 Speaker 1: I subconsciously seed myself to a kind of average out 396 00:23:05,400 --> 00:23:09,480 Speaker 1: there or an idea of what is average? Um? I 397 00:23:09,560 --> 00:23:11,720 Speaker 1: think all of us would probably say, yeah, there's a 398 00:23:11,840 --> 00:23:15,199 Speaker 1: certain sort of standard. But I hold myself to and 399 00:23:16,040 --> 00:23:17,919 Speaker 1: the you know, I guess the idea is that that 400 00:23:18,000 --> 00:23:22,680 Speaker 1: standard is built of myths, right, So it's very interesting 401 00:23:22,680 --> 00:23:24,200 Speaker 1: to look at it that way. And I even think 402 00:23:24,240 --> 00:23:27,080 Speaker 1: about some of the science reporting that we do sometimes, 403 00:23:27,119 --> 00:23:29,359 Speaker 1: because you know, we're creating these narratives and these stories 404 00:23:29,400 --> 00:23:32,240 Speaker 1: about what's happening and how we move through the world 405 00:23:32,280 --> 00:23:34,520 Speaker 1: and why we do what we do. But you can't 406 00:23:34,520 --> 00:23:39,280 Speaker 1: even just take one study or you know, one certain 407 00:23:39,320 --> 00:23:42,760 Speaker 1: aspect of it and say that this is a universal truth. 408 00:23:43,560 --> 00:23:47,320 Speaker 1: It's just sort of coloring the perception of of a 409 00:23:47,400 --> 00:23:50,400 Speaker 1: greater narrative of what's going on. And I think sometimes 410 00:23:50,560 --> 00:23:53,080 Speaker 1: it's just it's so easy for us to want to 411 00:23:53,080 --> 00:23:58,000 Speaker 1: take that easy, simple structure that Bell curve and apply 412 00:23:58,040 --> 00:24:00,639 Speaker 1: it to our life and get that answer. Now. Indeed, indeed, 413 00:24:00,920 --> 00:24:03,600 Speaker 1: there's a certain comfort in that I mean, whoot. Have 414 00:24:03,680 --> 00:24:05,639 Speaker 1: you ever met someone who said, I would like to 415 00:24:05,680 --> 00:24:09,000 Speaker 1: be a statistic, I would like to be representative of 416 00:24:09,040 --> 00:24:12,560 Speaker 1: a statistic. I feel like I have heard people make 417 00:24:12,640 --> 00:24:17,560 Speaker 1: that that plea, uh, when it's beneficial to be a statistic. 418 00:24:18,280 --> 00:24:21,040 Speaker 1: That is true, Yeah, that is true. But you know, 419 00:24:21,119 --> 00:24:22,600 Speaker 1: most of us don't want to be treated like a 420 00:24:22,640 --> 00:24:26,160 Speaker 1: statistic right now. Like I said, I think most people 421 00:24:26,200 --> 00:24:29,000 Speaker 1: want that that. They don't want the impersonal institutional God, 422 00:24:29,040 --> 00:24:32,159 Speaker 1: they want the personal one. And that ultimately is the 423 00:24:32,520 --> 00:24:36,320 Speaker 1: model that makes the most sense in terms of meeting 424 00:24:36,320 --> 00:24:38,439 Speaker 1: the individual, in terms of getting the most out of 425 00:24:38,440 --> 00:24:41,760 Speaker 1: the individual, you know, as far as performance goes, and 426 00:24:41,840 --> 00:24:44,679 Speaker 1: just how we work as human beings. Indeed, and especially 427 00:24:44,720 --> 00:24:46,600 Speaker 1: when you look at it these in larger constructs like 428 00:24:46,720 --> 00:24:50,199 Speaker 1: education or healthcare or corporations, it really does begin to 429 00:24:50,400 --> 00:24:54,640 Speaker 1: matter to again the individual. Alright. Well, on that note, 430 00:24:54,840 --> 00:24:56,760 Speaker 1: I'm going to call over the robot here and we're 431 00:24:56,800 --> 00:25:00,600 Speaker 1: gonna gonna do a couple of quick list their mails. 432 00:25:03,119 --> 00:25:05,920 Speaker 1: All right. This one comes to us from Peter Kron, 433 00:25:06,000 --> 00:25:08,560 Speaker 1: who is a long time listener to the show. UH 434 00:25:08,600 --> 00:25:12,000 Speaker 1: and UH runs the Elecord record label King de Luxe. UH. 435 00:25:12,200 --> 00:25:14,719 Speaker 1: So he has some stuff here to add in about happiness. Uh. 436 00:25:14,720 --> 00:25:16,560 Speaker 1: And I mentioned the record labl stuff because it kind 437 00:25:16,560 --> 00:25:18,680 Speaker 1: of plays into what he's talking about here. He says, 438 00:25:18,720 --> 00:25:21,760 Speaker 1: I just listened to the Happiness podcast, uh, the Mathematics 439 00:25:21,800 --> 00:25:25,199 Speaker 1: of happiness uh, and couldn't stop thinking about this dichotomy 440 00:25:25,240 --> 00:25:28,080 Speaker 1: between short term and long term happy. So now I 441 00:25:28,080 --> 00:25:30,040 Speaker 1: thought i'd come in a bit. What you were saying 442 00:25:30,040 --> 00:25:33,400 Speaker 1: about luring expectations and yet shooting for the moon both 443 00:25:33,480 --> 00:25:35,639 Speaker 1: makes sense, but they're at odds with each other. I 444 00:25:35,680 --> 00:25:37,920 Speaker 1: think you guys nailed it on the head with being 445 00:25:37,960 --> 00:25:41,840 Speaker 1: realistic about things, although maybe there's two layers, one super 446 00:25:42,080 --> 00:25:45,560 Speaker 1: super ambitious layer of expectations in another base level. I 447 00:25:45,600 --> 00:25:49,520 Speaker 1: think though they tie together. What long term satisfaction is 448 00:25:49,560 --> 00:25:52,760 Speaker 1: often based upon. With for example, big art projects is 449 00:25:52,800 --> 00:25:55,760 Speaker 1: peer review. You can try to create something truly grand 450 00:25:55,880 --> 00:25:58,600 Speaker 1: and in the back or front of your mind, expect 451 00:25:58,600 --> 00:26:01,520 Speaker 1: people the wow over it the second it's released to 452 00:26:01,520 --> 00:26:04,960 Speaker 1: the public, but then in execution it gets watered down 453 00:26:05,119 --> 00:26:08,000 Speaker 1: over and over until it barely resembles what one set 454 00:26:08,000 --> 00:26:11,040 Speaker 1: out to make, or it just evolved. You no longer 455 00:26:11,119 --> 00:26:13,919 Speaker 1: expect the same reaction. In fact, sometimes artists end up 456 00:26:13,960 --> 00:26:16,840 Speaker 1: hating it at the point of release, in part because 457 00:26:16,840 --> 00:26:20,080 Speaker 1: of overexposure, but also because they felt like they swung 458 00:26:20,160 --> 00:26:23,600 Speaker 1: and missed. But then the reaction far surpasses the new 459 00:26:23,680 --> 00:26:26,840 Speaker 1: expectations and the artist starts feeling great about their work 460 00:26:27,000 --> 00:26:30,920 Speaker 1: and build warm memories about the overall experience. In other words, 461 00:26:30,960 --> 00:26:34,280 Speaker 1: it's complicated. Well, and it just reminded me of of 462 00:26:34,320 --> 00:26:37,120 Speaker 1: when we've talked about memory and the role of memory 463 00:26:37,160 --> 00:26:40,879 Speaker 1: and taking that memory out and reframing that memory. And 464 00:26:40,960 --> 00:26:43,080 Speaker 1: so when you talk about the long term, you are 465 00:26:43,080 --> 00:26:46,720 Speaker 1: talking about long term memory and that sort of hindsight. 466 00:26:47,080 --> 00:26:51,040 Speaker 1: So happiness becomes even more complicated in that sense. Indeed, Yeah, 467 00:26:51,080 --> 00:26:53,000 Speaker 1: I mean as we as we we really you know, 468 00:26:53,040 --> 00:26:55,240 Speaker 1: try to drive home in that that episode and in 469 00:26:55,280 --> 00:26:58,840 Speaker 1: other episodes we've talked about happiness and finding, you know, 470 00:26:58,920 --> 00:27:02,000 Speaker 1: some level of nmity in your life. It's it's difficult 471 00:27:02,040 --> 00:27:04,719 Speaker 1: because it's our life is not one constant state. It's 472 00:27:04,720 --> 00:27:06,880 Speaker 1: one state after the other. It's this up and down. 473 00:27:07,320 --> 00:27:12,520 Speaker 1: That's our t shirt happiness period. It's difficulty, Alright. This 474 00:27:12,560 --> 00:27:15,119 Speaker 1: one comes to us from Brian Brian Writeson and says, hey, 475 00:27:15,200 --> 00:27:17,480 Speaker 1: I just listen to your podcast over breakfast as is 476 00:27:17,520 --> 00:27:20,840 Speaker 1: my custom. When I was thinking about adult lullabies and 477 00:27:20,840 --> 00:27:24,239 Speaker 1: how we seem to prefer ones that feature morbidity, I 478 00:27:24,320 --> 00:27:27,480 Speaker 1: was instantly reminded of the podcast Welcome to night Vale, 479 00:27:27,640 --> 00:27:30,880 Speaker 1: in which the silky voice Cecil Southey explains the bizarre 480 00:27:30,880 --> 00:27:33,400 Speaker 1: and often horrifying news that occurs in the fictional town 481 00:27:33,400 --> 00:27:35,720 Speaker 1: of night Vale. While I myself don't listen to it 482 00:27:35,760 --> 00:27:38,600 Speaker 1: while following asleep for fear of missing anything in the story, 483 00:27:38,880 --> 00:27:40,560 Speaker 1: I know that a great many of my friends do. 484 00:27:40,680 --> 00:27:43,000 Speaker 1: They claim it helps them greatly. Anyway, if you're not 485 00:27:43,040 --> 00:27:45,639 Speaker 1: familiar with Welcome to night Vale, I highly suggest you 486 00:27:45,720 --> 00:27:47,920 Speaker 1: check it out. I suspect Robert in particular it would 487 00:27:47,920 --> 00:27:50,679 Speaker 1: be fond of it. Keep up the great work. I 488 00:27:50,760 --> 00:27:53,720 Speaker 1: love that because that analogy is perfect to lullabies because 489 00:27:53,720 --> 00:27:55,639 Speaker 1: the night Vale they really, I mean he Cecil is 490 00:27:55,680 --> 00:27:58,480 Speaker 1: talking about these horrific events, which again are told in 491 00:27:58,520 --> 00:28:03,199 Speaker 1: this just lullaby hushing voice, and it really sort of 492 00:28:03,280 --> 00:28:05,960 Speaker 1: ramps up the creepiness. But also there you go. I mean, 493 00:28:05,960 --> 00:28:08,199 Speaker 1: that's the same thing that lullabies are doing when we 494 00:28:08,240 --> 00:28:11,840 Speaker 1: sing this into a little infant's ears right about, you know, 495 00:28:11,920 --> 00:28:15,880 Speaker 1: their their cradle rocking over in them spilling out. Yeah, 496 00:28:15,920 --> 00:28:18,399 Speaker 1: I have I have checked out night Vale before. It 497 00:28:18,480 --> 00:28:22,280 Speaker 1: is it isn't a very interesting and unique podcast. Um, 498 00:28:22,560 --> 00:28:25,119 Speaker 1: I haven't had the chance to really dive into it, 499 00:28:25,160 --> 00:28:28,239 Speaker 1: but I had a solo drive several months back, and 500 00:28:28,320 --> 00:28:31,600 Speaker 1: I loaded up on podcasts and I ended up spending 501 00:28:31,640 --> 00:28:35,359 Speaker 1: a long period driving through the dark, through the cold 502 00:28:35,440 --> 00:28:38,320 Speaker 1: rain and listening to like the first four episodes, and 503 00:28:38,360 --> 00:28:40,280 Speaker 1: I was, I was, I was really impressed. It's one 504 00:28:40,280 --> 00:28:41,880 Speaker 1: of those works that I feel like, Oh man, I 505 00:28:41,920 --> 00:28:43,680 Speaker 1: wish I had come up with that. I wish I could. 506 00:28:44,040 --> 00:28:48,040 Speaker 1: It's such a it's such a great concept and great execution. Yeah. 507 00:28:48,120 --> 00:28:50,320 Speaker 1: So indeed, you know, listen to us in the morning 508 00:28:50,320 --> 00:28:53,240 Speaker 1: over breakfast as Brian does, and then at the night 509 00:28:53,560 --> 00:28:56,560 Speaker 1: at night, maybe consider listening to night Vale. All right, 510 00:28:56,600 --> 00:28:58,480 Speaker 1: So there you have it. Um, Hey, you want to 511 00:28:58,560 --> 00:29:00,760 Speaker 1: check out more episodes, You want to check out that 512 00:29:01,040 --> 00:29:03,840 Speaker 1: the cergical wings thing we just mentioned here, Head on 513 00:29:03,880 --> 00:29:06,360 Speaker 1: over the Stuff to Blow your Mind dot com. Click 514 00:29:06,400 --> 00:29:08,840 Speaker 1: on that podcast tab and you'll find all the podcast 515 00:29:08,880 --> 00:29:11,440 Speaker 1: episodes we've ever done, going back to the very beginning, 516 00:29:11,480 --> 00:29:14,280 Speaker 1: all streaming there. Many of the more recent ones also 517 00:29:14,360 --> 00:29:18,360 Speaker 1: include art and links out to relate content on our 518 00:29:18,400 --> 00:29:21,600 Speaker 1: side and elsewhere. You can also find UM links out 519 00:29:21,600 --> 00:29:24,160 Speaker 1: to our social media accounts there as well as videos 520 00:29:24,360 --> 00:29:26,880 Speaker 1: as well as blog posts and hey, be sure to 521 00:29:26,960 --> 00:29:29,760 Speaker 1: check us out on YouTube where we are mind Stuff Show. 522 00:29:30,160 --> 00:29:32,400 Speaker 1: And on the topic of myth of averages, do you 523 00:29:32,440 --> 00:29:34,680 Speaker 1: feel like any of that rings true to you in 524 00:29:34,840 --> 00:29:39,080 Speaker 1: terms of the classroom or at work or any other 525 00:29:39,320 --> 00:29:42,720 Speaker 1: institution that you've been involved with? UM? Is that kind 526 00:29:42,720 --> 00:29:44,840 Speaker 1: of one of those things that, once you become aware of, 527 00:29:45,080 --> 00:29:48,200 Speaker 1: you begin to see your experience filtered through this kind 528 00:29:48,240 --> 00:29:51,200 Speaker 1: of mythical average. Let us know your thoughts on that, 529 00:29:51,240 --> 00:29:52,960 Speaker 1: and you can do that by sending us an email 530 00:29:53,320 --> 00:30:00,840 Speaker 1: at blow the Mind at how stuff works dot com. 531 00:30:01,000 --> 00:30:03,520 Speaker 1: For more on this and thousands of other topics, visit 532 00:30:03,560 --> 00:30:10,560 Speaker 1: hastaff works dot com.