1 00:00:00,080 --> 00:00:02,960 Speaker 1: Guess what, mango, what's that? Will? Actually wait before we 2 00:00:03,000 --> 00:00:07,280 Speaker 1: get started. Let's let's high five? Okay five? Oh and 3 00:00:07,280 --> 00:00:11,920 Speaker 1: and fist bump and oh yeah and chess bump. I 4 00:00:11,960 --> 00:00:14,760 Speaker 1: feel like you're just stalling. What's this all about? Well, 5 00:00:14,840 --> 00:00:17,960 Speaker 1: there's there. This is actually very relevant. There there's research 6 00:00:18,040 --> 00:00:20,360 Speaker 1: from the NBA that shows that a good predictor of 7 00:00:20,400 --> 00:00:23,279 Speaker 1: team performance is how often the team high fives or 8 00:00:23,400 --> 00:00:27,360 Speaker 1: fist bumps, chess bumps, and head slaps. So I feel 9 00:00:27,400 --> 00:00:29,440 Speaker 1: like it might help us have a good show. I'm 10 00:00:29,480 --> 00:00:31,920 Speaker 1: gonna draw the line of head slapping, but that is 11 00:00:31,920 --> 00:00:34,760 Speaker 1: pretty interesting. I'm not a dent sure what the head 12 00:00:34,760 --> 00:00:37,120 Speaker 1: slapping thing is, even though I watched a lot of basketball. 13 00:00:37,159 --> 00:00:39,720 Speaker 1: But there's actually another basketball fact that I think is 14 00:00:39,760 --> 00:00:42,960 Speaker 1: even more interesting. After looking at the results of tens 15 00:00:42,960 --> 00:00:46,480 Speaker 1: of thousands of NBA and college games, these researchers found 16 00:00:46,479 --> 00:00:49,319 Speaker 1: that teams trailing by a single point at halftime are 17 00:00:49,360 --> 00:00:52,040 Speaker 1: actually more likely to win than the team's winning by 18 00:00:52,040 --> 00:00:54,920 Speaker 1: a point. In fact, they found it was statistically equivalent 19 00:00:55,000 --> 00:00:58,040 Speaker 1: to a two point halftime lead. That's crazy. So why 20 00:00:58,120 --> 00:01:01,080 Speaker 1: is that? Well, it's all about amy. But the science 21 00:01:01,080 --> 00:01:04,080 Speaker 1: of perfect timing is pretty fascinating and help us make 22 00:01:04,120 --> 00:01:06,360 Speaker 1: sense of it. We've got Daniel Pink, the brilliant author 23 00:01:06,400 --> 00:01:09,360 Speaker 1: of a brand new book called Win, The Scientific Secrets 24 00:01:09,360 --> 00:01:11,600 Speaker 1: of Perfect Time, and he's here to explain it all. 25 00:01:11,720 --> 00:01:35,560 Speaker 1: So let's dive in. Hey, their podcast listeners, welcome to 26 00:01:35,600 --> 00:01:37,960 Speaker 1: part time genius Samuel Pearson, And as always, I'm joined 27 00:01:37,959 --> 00:01:40,080 Speaker 1: by my good friend Man Guesh Ticketer and the man 28 00:01:40,160 --> 00:01:43,160 Speaker 1: on the other side of the soundproof glass playing Cindylopper's 29 00:01:43,240 --> 00:01:46,800 Speaker 1: Time after Time very softly in our headphones. That's our 30 00:01:46,840 --> 00:01:50,160 Speaker 1: friend and producer, Tristan McNeil. I didn't realize what it 31 00:01:50,200 --> 00:01:53,520 Speaker 1: was until he slowly turned it off. Rascal. Anyway, Mango, 32 00:01:53,600 --> 00:01:55,960 Speaker 1: are you ready to talk about perfect timing? I am, 33 00:01:56,000 --> 00:01:58,320 Speaker 1: you know, I think what's so striking and reading Daniel's 34 00:01:58,320 --> 00:02:00,600 Speaker 1: book was just how little science we've put behind our 35 00:02:00,640 --> 00:02:03,800 Speaker 1: decisions on when to do certain things. Like we constantly 36 00:02:03,840 --> 00:02:06,440 Speaker 1: analyze how to do things, how to take a test, 37 00:02:06,520 --> 00:02:09,120 Speaker 1: how to organize a productive meeting, how to be healthier, 38 00:02:09,280 --> 00:02:12,080 Speaker 1: But when it comes to the when decisions, we really 39 00:02:12,160 --> 00:02:15,160 Speaker 1: leave that to a gut feeling. So I'm super excited 40 00:02:15,200 --> 00:02:17,280 Speaker 1: to talk to Daniel Pink about his research because this 41 00:02:17,320 --> 00:02:19,960 Speaker 1: book is really fascinating. I'm with you on that, all right, Well, 42 00:02:20,040 --> 00:02:22,480 Speaker 1: let's not hold off any longer. Today we're joined by 43 00:02:22,520 --> 00:02:25,600 Speaker 1: the author of several books, including the New York Times bestsellers, 44 00:02:25,720 --> 00:02:28,919 Speaker 1: Drive to Sell as Human and A Whole New Mind. 45 00:02:29,240 --> 00:02:31,680 Speaker 1: But his newest book that just came out yesterday actually 46 00:02:32,080 --> 00:02:35,840 Speaker 1: is called When the Scientific Secrets of Perfect Timing. Daniel Pink. 47 00:02:35,880 --> 00:02:39,840 Speaker 1: Welcome to Part Time Genius. Thanks great to be here. Now, Daniel, 48 00:02:39,880 --> 00:02:42,200 Speaker 1: you open the book with a story about a study 49 00:02:42,200 --> 00:02:45,080 Speaker 1: out of Cornell. There these two sociologists. They're doing a 50 00:02:45,080 --> 00:02:49,480 Speaker 1: big data analysis of five hundred million tweets, you know, 51 00:02:49,520 --> 00:02:51,720 Speaker 1: along with the other research that have helped find these 52 00:02:51,760 --> 00:02:54,480 Speaker 1: patterns of the day. So can you talk a little 53 00:02:54,480 --> 00:02:56,320 Speaker 1: bit about that and how the heck do you do 54 00:02:56,360 --> 00:03:00,920 Speaker 1: an analysis of five hundred million tweets? Well, fortunately I 55 00:03:00,960 --> 00:03:04,920 Speaker 1: didn't do this analysis. Um, these guys that Cornell did it. 56 00:03:05,080 --> 00:03:07,520 Speaker 1: And it's actually one of the interesting things about how 57 00:03:07,600 --> 00:03:09,919 Speaker 1: research has done these days and how we can use 58 00:03:10,240 --> 00:03:14,680 Speaker 1: giant amounts of data to find hidden insights. So essentially 59 00:03:14,760 --> 00:03:18,440 Speaker 1: what they did was this, so tweets remember our actual text, right, 60 00:03:18,800 --> 00:03:22,760 Speaker 1: so their words or letters, characters, and um, there is 61 00:03:23,080 --> 00:03:26,480 Speaker 1: a piece of software called LUKE. That's the acronym l 62 00:03:26,520 --> 00:03:29,600 Speaker 1: I w C for the Linguistic Inventory Word count. This 63 00:03:29,680 --> 00:03:33,600 Speaker 1: software allows us to measure what essentially the emotional content 64 00:03:33,639 --> 00:03:35,280 Speaker 1: of the work. So if you if I look at 65 00:03:35,320 --> 00:03:38,240 Speaker 1: a word like like bummed out and say, okay, that's 66 00:03:38,560 --> 00:03:41,920 Speaker 1: someone who is a little bit low emotion, excited high emotion. 67 00:03:42,520 --> 00:03:44,320 Speaker 1: And so what you can do is that instead of 68 00:03:44,320 --> 00:03:46,480 Speaker 1: going through these things one by one. This is the 69 00:03:46,480 --> 00:03:48,960 Speaker 1: great thing about computers is ability to crunch these numbers. 70 00:03:49,040 --> 00:03:52,400 Speaker 1: They throw all of these five million tweets into this 71 00:03:52,640 --> 00:03:57,600 Speaker 1: program and their question is does people's mood as reflected 72 00:03:57,600 --> 00:04:00,680 Speaker 1: by these tweets change over the course of a day. 73 00:04:01,040 --> 00:04:05,760 Speaker 1: And the answer was heck yeah, and how so. Well, 74 00:04:05,840 --> 00:04:09,200 Speaker 1: what they found was this really intriguing pattern. There was 75 00:04:09,240 --> 00:04:12,040 Speaker 1: a peak, a trough, and a recovery. And again it's 76 00:04:12,080 --> 00:04:15,920 Speaker 1: obviously controlled for time zone. Uh. Early in the day 77 00:04:16,160 --> 00:04:19,719 Speaker 1: where people had a more positive mood. That positive mood 78 00:04:19,880 --> 00:04:22,960 Speaker 1: was fairly steady until about noon. Then in the early 79 00:04:23,000 --> 00:04:26,200 Speaker 1: to mid afternoon it began to dip. It dipped considerably 80 00:04:26,640 --> 00:04:29,760 Speaker 1: over the afternoon and then rose again in the late 81 00:04:29,800 --> 00:04:33,679 Speaker 1: afternoon early evening. And what they found were essentially three stages, 82 00:04:33,720 --> 00:04:37,120 Speaker 1: a peak, a trough, and a recovery that as we 83 00:04:37,160 --> 00:04:41,800 Speaker 1: had positive mood in the morning, pretty strong dive in 84 00:04:41,880 --> 00:04:44,480 Speaker 1: mood in the early to mid afternoon, and then recovery 85 00:04:44,560 --> 00:04:48,480 Speaker 1: later in the day. It's very interesting. Yeah. So so 86 00:04:48,520 --> 00:04:51,640 Speaker 1: obviously you know we're not all the same. And we've 87 00:04:51,640 --> 00:04:54,279 Speaker 1: talked a little bit about like circadian rhythms and in 88 00:04:54,400 --> 00:04:57,040 Speaker 1: our episode on sleep awhile back, but you talk about 89 00:04:57,160 --> 00:05:00,480 Speaker 1: chronotypes and how do you use them to perform Can 90 00:05:00,520 --> 00:05:02,440 Speaker 1: you tell us exactly what a chronotype is and how 91 00:05:02,440 --> 00:05:05,479 Speaker 1: people identify themselves as whatever. So there's a whole field 92 00:05:05,480 --> 00:05:09,640 Speaker 1: is study called chronobiology and chrono for time biology for 93 00:05:09,800 --> 00:05:13,080 Speaker 1: study of life, and it looks at exactly as you say, 94 00:05:13,120 --> 00:05:16,560 Speaker 1: are daily rhythms. What it finds is that people have 95 00:05:16,760 --> 00:05:22,039 Speaker 1: certain type, certain propensities. Some of us rise early, fall 96 00:05:22,080 --> 00:05:25,360 Speaker 1: asleep earlier, some of us rise late, fall asleep late. 97 00:05:25,480 --> 00:05:28,719 Speaker 1: Some people are larks morning people, some people are owls 98 00:05:28,800 --> 00:05:31,440 Speaker 1: evening people. But the truth is that most of us 99 00:05:31,440 --> 00:05:32,800 Speaker 1: are kind of in between what I like to call 100 00:05:32,880 --> 00:05:35,920 Speaker 1: third birds. And if you're a lark or a third bird, 101 00:05:36,000 --> 00:05:39,080 Speaker 1: you generally go through the day in that in the 102 00:05:39,160 --> 00:05:41,040 Speaker 1: order that I just mentioned, a peak, a trough, a 103 00:05:41,120 --> 00:05:43,880 Speaker 1: recovery that you have. In the mornings, you're generally at 104 00:05:43,920 --> 00:05:45,760 Speaker 1: your best, both in terms of mood and in terms 105 00:05:45,760 --> 00:05:48,600 Speaker 1: of vigilance. In the early afternoons there's a pretty significant 106 00:05:48,640 --> 00:05:51,800 Speaker 1: deterioration and then some kind of recovery later in the 107 00:05:51,880 --> 00:05:53,760 Speaker 1: day for the but the one and five of us 108 00:05:53,800 --> 00:05:56,240 Speaker 1: were very strong owls nighttime types. These are people who 109 00:05:56,560 --> 00:06:00,320 Speaker 1: go to sleep just naturally very late and wake up late, 110 00:06:00,680 --> 00:06:03,880 Speaker 1: people for whom eight o'clock staff meetings are just a 111 00:06:03,960 --> 00:06:07,039 Speaker 1: form of torture um those folks. And to go in 112 00:06:07,279 --> 00:06:10,560 Speaker 1: more or less the reverse order recovery truck peak, and 113 00:06:10,600 --> 00:06:14,400 Speaker 1: that would probably be you, mango, Yeah, yeah, what time 114 00:06:14,440 --> 00:06:15,880 Speaker 1: do you Well, here's a here's the test. Let's we 115 00:06:15,880 --> 00:06:17,720 Speaker 1: can test you right now. All right, we'll do we'll 116 00:06:17,720 --> 00:06:20,920 Speaker 1: do the basically back in the envelope chronotype test here. So, 117 00:06:21,480 --> 00:06:22,920 Speaker 1: so let's say it's a day where you don't have 118 00:06:22,960 --> 00:06:24,800 Speaker 1: to wake up to an alarm clock, which for many 119 00:06:24,800 --> 00:06:27,680 Speaker 1: people as a weekend. Okay, so what time would you 120 00:06:27,720 --> 00:06:30,520 Speaker 1: usually go to sleep? Yeah, I mean, I think kids 121 00:06:30,560 --> 00:06:33,839 Speaker 1: have thrown everything off, but I think probably between three 122 00:06:33,880 --> 00:06:39,120 Speaker 1: and four. Oh, my lord, four am. Yeah, oh, I 123 00:06:39,160 --> 00:06:40,720 Speaker 1: don't even have to do the rest of this time. 124 00:06:42,360 --> 00:06:47,160 Speaker 1: And what time would you usually wake up? I don't know, ten, 125 00:06:47,320 --> 00:06:50,480 Speaker 1: I'd say ten. Okay. So so what we would do 126 00:06:50,520 --> 00:06:53,040 Speaker 1: in this case was we would find your your midpoint 127 00:06:53,040 --> 00:06:58,400 Speaker 1: of sleep. So if you if so your midpoint of sleep, 128 00:06:58,440 --> 00:06:59,720 Speaker 1: if you can go to sleep at three and wake 129 00:06:59,800 --> 00:07:01,880 Speaker 1: up at and your midpoint of sleep, would you, guys 130 00:07:01,960 --> 00:07:06,400 Speaker 1: be six thirty am, which would make you a pretty 131 00:07:06,400 --> 00:07:11,720 Speaker 1: strong owl. Strong about one out of five people are 132 00:07:12,120 --> 00:07:16,480 Speaker 1: pretty strong owls um and I I like the designation 133 00:07:16,480 --> 00:07:19,880 Speaker 1: of a strong owl. Yea's a very strong owl. Yeah 134 00:07:20,000 --> 00:07:22,800 Speaker 1: or uh so, yeah, that's that's that's that's pretty alley. 135 00:07:23,080 --> 00:07:27,680 Speaker 1: How are you You don't be asking, I'm oh man, 136 00:07:27,720 --> 00:07:32,640 Speaker 1: oh man, you're a serious owl then, yeah, yeah, well congratulations. 137 00:07:32,280 --> 00:07:34,880 Speaker 1: He's been that way for a long time. In college, 138 00:07:34,880 --> 00:07:37,440 Speaker 1: he was paired up with another owl, and so we 139 00:07:37,480 --> 00:07:39,920 Speaker 1: all had to be aware that they would just wake 140 00:07:40,000 --> 00:07:42,280 Speaker 1: up at about what about two in the afternoon and 141 00:07:42,640 --> 00:07:45,000 Speaker 1: talk about what they were going to get for breakfast. Well, 142 00:07:45,320 --> 00:07:47,360 Speaker 1: what's interesting is that is that that that's one reason 143 00:07:47,400 --> 00:07:50,160 Speaker 1: I asked to your age is that most of us 144 00:07:50,160 --> 00:07:52,280 Speaker 1: go through a period between about age fourteen and twenty 145 00:07:52,320 --> 00:07:57,120 Speaker 1: four when we become the owliest in our life. Um. 146 00:07:57,200 --> 00:07:59,560 Speaker 1: And that has to do with hormones and whatever. So 147 00:07:59,600 --> 00:08:03,360 Speaker 1: there's a pretty soon that they can shift, beginning basically 148 00:08:03,440 --> 00:08:07,280 Speaker 1: post puberty, where people shift literally in some cases to 149 00:08:07,560 --> 00:08:12,080 Speaker 1: three hours later into the day. Um. But then as 150 00:08:12,160 --> 00:08:15,120 Speaker 1: time goes on, they go back to their earlier So 151 00:08:15,240 --> 00:08:16,840 Speaker 1: to what you have is you have like little kids 152 00:08:16,920 --> 00:08:23,920 Speaker 1: is I think magnis is discovered are pretty large, pretty long, um. 153 00:08:23,960 --> 00:08:26,600 Speaker 1: And we basically are are strong larks earlier in our 154 00:08:26,640 --> 00:08:28,560 Speaker 1: life and then later in life. The older you get, 155 00:08:28,560 --> 00:08:31,720 Speaker 1: the larker you become in general. Well, you talk about 156 00:08:31,720 --> 00:08:33,319 Speaker 1: this a little bit in the book when you're talking 157 00:08:33,320 --> 00:08:36,319 Speaker 1: about those teenage years, and we've heard this before, the 158 00:08:36,360 --> 00:08:39,520 Speaker 1: advocacy for a later start time, you know, in the 159 00:08:39,520 --> 00:08:43,920 Speaker 1: American Academy of Pediatrics actually issuing a policy statement urging 160 00:08:43,960 --> 00:08:46,440 Speaker 1: schools to start I think it was no no earlier 161 00:08:46,480 --> 00:08:49,960 Speaker 1: than eight thirty, and yet fewer than one in five 162 00:08:50,040 --> 00:08:52,959 Speaker 1: schools actually follow this, as you indicated the book. Well, 163 00:08:52,960 --> 00:08:55,720 Speaker 1: why do you think that is? Because they don't take 164 00:08:55,760 --> 00:08:58,120 Speaker 1: these time and questions seriously. They don't take questions of 165 00:08:58,120 --> 00:09:01,160 Speaker 1: when seriously these schools. And I don't mean to pick 166 00:09:01,160 --> 00:09:03,719 Speaker 1: on schools because I think it's true for all our institutions. 167 00:09:04,040 --> 00:09:06,600 Speaker 1: We take very seriously, Okay, what are we gonna do? Alright, 168 00:09:06,600 --> 00:09:08,960 Speaker 1: So they take curriculum very seriously. What are we gonna do, 169 00:09:09,120 --> 00:09:10,320 Speaker 1: How are we gonna do it, How are we going 170 00:09:10,360 --> 00:09:13,400 Speaker 1: to teach it? We have professional development days to improve 171 00:09:13,400 --> 00:09:15,360 Speaker 1: our pedagogy. How are we going to do it? Who 172 00:09:15,400 --> 00:09:18,440 Speaker 1: are we gonna do it with? They take hiring pretty seriously. Um, 173 00:09:18,760 --> 00:09:21,800 Speaker 1: But then we take these questions of when, and we say, ah, 174 00:09:21,840 --> 00:09:24,160 Speaker 1: that doesn't really matter. That's like we we we take 175 00:09:24,200 --> 00:09:25,840 Speaker 1: these questions of when and when we sit them at 176 00:09:25,840 --> 00:09:27,920 Speaker 1: the kids table rather than at the groun ups table. 177 00:09:28,240 --> 00:09:30,600 Speaker 1: And that's a huge mistake. These questions of when, these 178 00:09:30,679 --> 00:09:33,559 Speaker 1: questions of timing, as you're saying, with school star times, 179 00:09:33,720 --> 00:09:38,240 Speaker 1: have a material effect on people's well being. School star 180 00:09:38,320 --> 00:09:42,240 Speaker 1: times alone, Um, there is evidence that these early star times, 181 00:09:42,280 --> 00:09:47,120 Speaker 1: again for people who are very ali who which teenagers 182 00:09:47,200 --> 00:09:49,800 Speaker 1: tend to be starting school at seven thirty in the 183 00:09:49,800 --> 00:09:54,120 Speaker 1: morning is ridiculous. They're barely even awake. And consequence of 184 00:09:54,160 --> 00:09:58,800 Speaker 1: that is dire the You have higher rates of depression, 185 00:09:59,000 --> 00:10:02,840 Speaker 1: higher rates of OBEs, the increased incidents of auto accidents, 186 00:10:03,160 --> 00:10:07,120 Speaker 1: higher dropout rates, reduced performance on santidized tests, and schools 187 00:10:07,120 --> 00:10:09,760 Speaker 1: that have done something about this, and again we're not 188 00:10:10,000 --> 00:10:12,880 Speaker 1: we're not. We're not talking about like a Magnat schedule 189 00:10:12,920 --> 00:10:16,160 Speaker 1: where you start school at three in the afternoon. We're 190 00:10:16,200 --> 00:10:18,200 Speaker 1: talking about like starting it at like nine in the 191 00:10:18,200 --> 00:10:20,520 Speaker 1: morning rather than seven thirty in the morning. Schools that 192 00:10:20,559 --> 00:10:23,320 Speaker 1: have made those that that modest step that basically followed 193 00:10:23,320 --> 00:10:27,040 Speaker 1: the recommendations of the American Accountmy of Pediatrics have seen 194 00:10:27,240 --> 00:10:30,200 Speaker 1: low and behold higher test scores, lower dropout rate. If 195 00:10:30,240 --> 00:10:34,160 Speaker 1: you just think about the workplace, time of day explains 196 00:10:34,200 --> 00:10:40,560 Speaker 1: about twenty of the variance in our performance on cognitive tasks. 197 00:10:40,600 --> 00:10:43,520 Speaker 1: So you know, so it doesn't mean the timing is everything, 198 00:10:43,880 --> 00:10:46,640 Speaker 1: but it means it's a freaking big thing, right right right, Well, 199 00:10:46,640 --> 00:10:49,560 Speaker 1: we'll speaking of that material difference as you mentioned, you know, 200 00:10:49,600 --> 00:10:53,400 Speaker 1: not just affecting teenagers but adults as well. You you 201 00:10:53,480 --> 00:10:56,679 Speaker 1: talk about this Bermuda triangle of our days in the afternoons, 202 00:10:56,720 --> 00:10:58,800 Speaker 1: and and we we all know that there tends to 203 00:10:58,800 --> 00:11:02,559 Speaker 1: be that afternoon. We've all experienced that, especially after man 204 00:11:02,559 --> 00:11:05,280 Speaker 1: guess and I have been downstairs eating ramen for lunch 205 00:11:05,280 --> 00:11:10,680 Speaker 1: and afternoon slump. But actually hearing from you and hearing 206 00:11:10,800 --> 00:11:14,520 Speaker 1: the hard evidence of this, whether it's with standardized test 207 00:11:14,679 --> 00:11:17,240 Speaker 1: or even results from juries, can you talk a little 208 00:11:17,240 --> 00:11:20,440 Speaker 1: bit about that impact. It's huge and it's terrifying. There's 209 00:11:20,480 --> 00:11:23,959 Speaker 1: research from Denmark showing the kids score systematically lower if 210 00:11:23,960 --> 00:11:26,280 Speaker 1: they take standardized tests in the afternoon versus they take 211 00:11:26,320 --> 00:11:28,840 Speaker 1: them in the morning. Okay, so just think about that 212 00:11:28,880 --> 00:11:31,679 Speaker 1: in terms of the extent to which standardized tests affect 213 00:11:31,720 --> 00:11:35,320 Speaker 1: the kid's fate, where they affect education policy. Again, time 214 00:11:35,320 --> 00:11:37,960 Speaker 1: of day is having this massive effect, but it's invisible 215 00:11:38,000 --> 00:11:40,880 Speaker 1: to us. If you look at something like you make 216 00:11:40,920 --> 00:11:43,720 Speaker 1: an interesting point about about juries or criminal justice system, 217 00:11:43,760 --> 00:11:47,559 Speaker 1: there's some really good experimental evidence showing that if you 218 00:11:47,760 --> 00:11:52,079 Speaker 1: have two defendants. That's a famous experiment, one one named uh. 219 00:11:52,440 --> 00:11:56,320 Speaker 1: Some participants have a defendant name Robert Garner, some have 220 00:11:56,400 --> 00:11:59,960 Speaker 1: one name ROBERTA. Garcia. And if you have the same 221 00:12:00,000 --> 00:12:03,760 Speaker 1: set of facts juries that deliberate in the morning, treat 222 00:12:03,800 --> 00:12:06,320 Speaker 1: those defendants the same juries that have the same set 223 00:12:06,360 --> 00:12:09,560 Speaker 1: of facts and deliberate in the afternoon during this trough period. 224 00:12:09,960 --> 00:12:13,599 Speaker 1: Guess what they are more likely to convict Garcia and 225 00:12:13,679 --> 00:12:18,640 Speaker 1: exonerate Garner on the same set of facts. Right, but 226 00:12:18,640 --> 00:12:20,760 Speaker 1: but but wait, there's more, Because now we can go 227 00:12:20,800 --> 00:12:24,040 Speaker 1: into healthcare. Doctors and nurses are far less likely to 228 00:12:24,040 --> 00:12:26,040 Speaker 1: wash their hands in the afternoons and in the mornings 229 00:12:26,440 --> 00:12:29,959 Speaker 1: if you look at if you look at things like anesthesia, 230 00:12:30,240 --> 00:12:33,000 Speaker 1: you're three times more likely to have an anesthesia error 231 00:12:33,040 --> 00:12:37,320 Speaker 1: in an afternoon procedure than in the morning procedure. Uh. Yeah, 232 00:12:37,440 --> 00:12:40,520 Speaker 1: the whole thing is terrifying and so over. Even even 233 00:12:40,520 --> 00:12:42,839 Speaker 1: if we look at things like auto accidents, there's some 234 00:12:42,880 --> 00:12:45,520 Speaker 1: good research out of the UK. You know, when do 235 00:12:45,679 --> 00:12:48,560 Speaker 1: auto accidents peak? If you're just for how many cars 236 00:12:48,600 --> 00:12:51,240 Speaker 1: are on the road? Big surprise, they peeked between like 237 00:12:51,320 --> 00:12:54,439 Speaker 1: four and six am. Okay, because it's the middle of 238 00:12:54,480 --> 00:12:56,400 Speaker 1: the night and it's really dark. But the second most 239 00:12:56,400 --> 00:12:59,040 Speaker 1: common time is between two and four pm. It's perfectly 240 00:12:59,120 --> 00:13:02,680 Speaker 1: light outside. Yeah. We don't take these kinds of time 241 00:13:02,679 --> 00:13:06,720 Speaker 1: and day effects nearly seriously enough, and they have a big, 242 00:13:06,760 --> 00:13:09,480 Speaker 1: big effect on literally in some cases of life and death. 243 00:13:09,679 --> 00:13:12,080 Speaker 1: So these are so scary statistics. Now, we have several 244 00:13:12,120 --> 00:13:14,079 Speaker 1: more questions for you. But before we get to those, 245 00:13:14,120 --> 00:13:29,559 Speaker 1: let's take a quick break. Welcome back to Part Time Genius. 246 00:13:29,559 --> 00:13:32,040 Speaker 1: We're talking to Daniel Pink, the author of When The 247 00:13:32,080 --> 00:13:35,640 Speaker 1: Scientific Secrets of Perfect Finding. Daniel, I, I know we 248 00:13:35,720 --> 00:13:38,280 Speaker 1: talked about the fact that I'm not a morning person, 249 00:13:38,559 --> 00:13:42,079 Speaker 1: but I was curious about morning exercise because that's something 250 00:13:42,080 --> 00:13:43,720 Speaker 1: you talk about in your book. Can you tell about 251 00:13:43,720 --> 00:13:46,199 Speaker 1: the benefits of doing it then versus afternoon and what 252 00:13:46,240 --> 00:13:48,120 Speaker 1: it means for your body. Yeah, this is something that 253 00:13:48,160 --> 00:13:49,920 Speaker 1: I was really interested in because I was trying to 254 00:13:49,920 --> 00:13:53,360 Speaker 1: figure it out for myself. Um and so um. It's 255 00:13:53,360 --> 00:13:55,319 Speaker 1: pretty easy to figure out whether you should exercise in 256 00:13:55,360 --> 00:13:58,360 Speaker 1: the morning or the afternoon. It all depends on what 257 00:13:58,480 --> 00:14:00,840 Speaker 1: your goals are. So if you're also to lose weight, 258 00:14:01,040 --> 00:14:03,200 Speaker 1: exercise in the morning, you're gonna burn up more calories 259 00:14:03,240 --> 00:14:05,600 Speaker 1: than typically. If you actually in the morning, you're gonna 260 00:14:05,640 --> 00:14:07,520 Speaker 1: get a mood boost for a bigger period of the day. 261 00:14:07,840 --> 00:14:10,040 Speaker 1: Exercise later in the day you might sleep through some 262 00:14:10,080 --> 00:14:12,760 Speaker 1: of that mood boost. There's some good evidence showing that 263 00:14:13,040 --> 00:14:15,360 Speaker 1: morning exercise makes us slightly more likely to stick to 264 00:14:15,400 --> 00:14:18,440 Speaker 1: the routine and to stopster and levels actually peak in 265 00:14:18,440 --> 00:14:21,360 Speaker 1: the morning. If you're doing some strength based training, mornings 266 00:14:21,400 --> 00:14:23,680 Speaker 1: can be good for that. On the other hand, afternoon 267 00:14:23,760 --> 00:14:27,040 Speaker 1: late afternoon is good for other types of things. So 268 00:14:27,400 --> 00:14:30,920 Speaker 1: one of the big things that affects our physiology over 269 00:14:30,960 --> 00:14:33,040 Speaker 1: the course of the day is a change in body temperature, 270 00:14:33,040 --> 00:14:36,040 Speaker 1: believe it or not. And when our body temperature tends 271 00:14:36,040 --> 00:14:38,120 Speaker 1: to rise to its highest point in the late afternoon 272 00:14:38,120 --> 00:14:42,240 Speaker 1: in early evening UM, that makes our muscles warmer and 273 00:14:42,280 --> 00:14:44,640 Speaker 1: so we're more likely to avoid injury. So if you're 274 00:14:44,760 --> 00:14:47,880 Speaker 1: prone to injury, you're concerned about injury, afternoon exercise is better. 275 00:14:48,520 --> 00:14:51,480 Speaker 1: There's some really intriguing evidence showing that you might actually 276 00:14:52,080 --> 00:14:55,680 Speaker 1: perform at a higher level during afternoon and early evening 277 00:14:55,680 --> 00:14:59,960 Speaker 1: activity UM because mine function is the highest, your strength 278 00:15:00,080 --> 00:15:03,320 Speaker 1: is higher, your reaction time is higher. There's some very 279 00:15:03,320 --> 00:15:07,840 Speaker 1: intriguing evidence about athletic records, particularly in speed events, disproportionately 280 00:15:07,840 --> 00:15:12,240 Speaker 1: being broken at between about four and seven local time. 281 00:15:12,760 --> 00:15:16,560 Speaker 1: Really fascinating, and it's all about our you know, basically 282 00:15:16,560 --> 00:15:19,480 Speaker 1: about a bye temperature is also again this is related 283 00:15:19,640 --> 00:15:21,840 Speaker 1: up and this has actually ended up sealing the deal 284 00:15:21,880 --> 00:15:25,320 Speaker 1: for me is that because we're doing a little bit better, 285 00:15:25,360 --> 00:15:27,280 Speaker 1: we're you know, our our lung function is a little 286 00:15:27,280 --> 00:15:30,800 Speaker 1: bit higher muscles are warmer, we're not risking exercise, we're 287 00:15:30,840 --> 00:15:35,040 Speaker 1: not risking injury as much. That people tend to um 288 00:15:35,160 --> 00:15:37,640 Speaker 1: enjoy the workouts a little bit more in the afternoon, 289 00:15:37,680 --> 00:15:39,800 Speaker 1: even if they're doing the exact same thing in the morning, 290 00:15:40,360 --> 00:15:45,000 Speaker 1: they feel a little less taxing UM. And so for me, 291 00:15:45,200 --> 00:15:49,120 Speaker 1: I am an uh an afternoon early evening exerciser, and 292 00:15:49,160 --> 00:15:51,000 Speaker 1: I think it's for that last reason. When I when 293 00:15:51,000 --> 00:15:53,760 Speaker 1: I go do exercise in the morning, I hate it. 294 00:15:53,760 --> 00:15:56,040 Speaker 1: It feels like it feels like torture. When I do 295 00:15:56,080 --> 00:15:58,760 Speaker 1: it in the afternoon, I actually enjoyed a little bit more. 296 00:15:58,880 --> 00:16:01,120 Speaker 1: And so if I were trying to lose a lot 297 00:16:01,120 --> 00:16:03,400 Speaker 1: of weight, I might change I might change things. But 298 00:16:03,760 --> 00:16:07,360 Speaker 1: I exercise just so I don't go crazy. And so afternoon, um, 299 00:16:07,520 --> 00:16:10,200 Speaker 1: afternoon works for me. And I think it's because of 300 00:16:10,600 --> 00:16:13,280 Speaker 1: just that my body has warmed up. I'm performing a 301 00:16:13,320 --> 00:16:15,880 Speaker 1: little bit of higher level and it's just it's less 302 00:16:15,920 --> 00:16:19,080 Speaker 1: unpleasant than it is in the mornings. Yeah. Well, back 303 00:16:19,120 --> 00:16:21,800 Speaker 1: to the idea of productivity and things that we can 304 00:16:21,800 --> 00:16:24,800 Speaker 1: do to to be more productive. You talk about, you know, 305 00:16:24,880 --> 00:16:29,720 Speaker 1: the importance of or the value of of napping, can you, 306 00:16:29,920 --> 00:16:31,800 Speaker 1: And I think we we've we've heard that before, but 307 00:16:31,880 --> 00:16:33,720 Speaker 1: can you talk a little bit about, you know, what 308 00:16:33,760 --> 00:16:37,560 Speaker 1: the ideal nap is and what we're looking for in 309 00:16:37,600 --> 00:16:40,640 Speaker 1: that and and trying to be more productive. Yeah, the 310 00:16:40,680 --> 00:16:42,880 Speaker 1: ideal nap is a less shorter than I ever realized 311 00:16:43,640 --> 00:16:45,800 Speaker 1: I was. You know, I'm not. I haven't been a 312 00:16:45,800 --> 00:16:47,880 Speaker 1: big napper. And the reason is that when I woke 313 00:16:47,920 --> 00:16:49,960 Speaker 1: up from a nap, I felt, I felt terrible. I 314 00:16:50,000 --> 00:16:54,360 Speaker 1: felt you know, groggy and cobwebs in my head. Um. 315 00:16:54,480 --> 00:16:57,320 Speaker 1: And what I discovered essentially is that I was doing 316 00:16:57,360 --> 00:17:00,600 Speaker 1: it wrong. That the ideal nap is is very very 317 00:17:00,640 --> 00:17:03,000 Speaker 1: short maybe you know, usually no more than twenty minutes 318 00:17:03,120 --> 00:17:06,840 Speaker 1: or so. Um. And what happens if we knap longer 319 00:17:06,880 --> 00:17:08,680 Speaker 1: than that is that we begin to accumulate what's called 320 00:17:08,720 --> 00:17:12,280 Speaker 1: sleep inertia. That's that boggy, groggy feeling that we have, 321 00:17:12,960 --> 00:17:14,800 Speaker 1: and it takes us some time to dig out of 322 00:17:14,840 --> 00:17:16,840 Speaker 1: that to get the benefits of the nap. So you 323 00:17:16,880 --> 00:17:19,919 Speaker 1: basically start with a deficit and then have to climb 324 00:17:19,920 --> 00:17:22,159 Speaker 1: out of the deficit. If you have a nap of 325 00:17:22,160 --> 00:17:23,600 Speaker 1: twenty minutes or so, you get a lot of the 326 00:17:23,600 --> 00:17:26,760 Speaker 1: benefit without any of the deficit. And so the super 327 00:17:26,760 --> 00:17:30,320 Speaker 1: short naps literally between ten and twenty minutes, seemed to 328 00:17:30,320 --> 00:17:33,680 Speaker 1: be the maximum bank for the buck when it comes 329 00:17:33,680 --> 00:17:37,439 Speaker 1: to napping, which I I love because I'm from a 330 00:17:37,480 --> 00:17:44,080 Speaker 1: sistic culture. Yeah, but you know, there's something to be 331 00:17:44,119 --> 00:17:46,320 Speaker 1: said for you know. What's what's interesting is that in 332 00:17:46,359 --> 00:17:52,360 Speaker 1: the the blaze of westernization and American style capitalism, we've 333 00:17:52,400 --> 00:17:55,520 Speaker 1: obliterated cs IS, when in fact, there's actually some pretty 334 00:17:55,520 --> 00:18:00,199 Speaker 1: good scientific evidence for restoring some kind of modern the Yes, 335 00:18:00,359 --> 00:18:03,520 Speaker 1: I mean, I'm not talking about taking three hours um 336 00:18:03,640 --> 00:18:07,960 Speaker 1: for lunch every single day, but basically taking breaks and 337 00:18:08,400 --> 00:18:13,280 Speaker 1: pauses much more seriously than we take them. Right now. Well, uh, 338 00:18:13,359 --> 00:18:15,720 Speaker 1: one of the things, because I'm so focused on mornings now, 339 00:18:16,280 --> 00:18:19,360 Speaker 1: I want to ask about is breakfast and how important 340 00:18:19,359 --> 00:18:22,359 Speaker 1: it really is and where meals play a role in 341 00:18:22,359 --> 00:18:25,800 Speaker 1: in uh, you know, having the most productive days. Um, 342 00:18:25,960 --> 00:18:27,720 Speaker 1: is breakfast is important? I think the answer from the 343 00:18:27,760 --> 00:18:33,320 Speaker 1: research is a clear and conclusive maybe, um, some of 344 00:18:33,359 --> 00:18:35,919 Speaker 1: the some of the and this has to do with 345 00:18:35,920 --> 00:18:39,040 Speaker 1: some of the methodologies, which are observational studies rather than 346 00:18:39,119 --> 00:18:42,760 Speaker 1: randomized control experiences. So you know, these observational studies found 347 00:18:42,760 --> 00:18:44,760 Speaker 1: that people who breakfast are healthy but we don't know 348 00:18:44,800 --> 00:18:47,000 Speaker 1: whether breakfast is causing their healthiness. It could just be 349 00:18:47,040 --> 00:18:50,040 Speaker 1: the healthy people like the breakfast, um, people who are 350 00:18:50,040 --> 00:18:53,600 Speaker 1: already healthy, or eating breakfast and has no causal effect, etcetera, etcetera. 351 00:18:53,760 --> 00:18:56,439 Speaker 1: Some of these pro breakfast studies are actually funded by 352 00:18:56,440 --> 00:18:58,840 Speaker 1: cereal companies, so that should make us raise our eyebrows 353 00:18:58,840 --> 00:19:02,200 Speaker 1: a little bit. In terms of time of day and eating, 354 00:19:02,520 --> 00:19:05,320 Speaker 1: there's some very interesting research, pretty new stuff right now 355 00:19:05,400 --> 00:19:08,879 Speaker 1: on what's called time restricted feeding. The showing that you 356 00:19:08,880 --> 00:19:11,879 Speaker 1: could you might be able to get a certain greater 357 00:19:11,960 --> 00:19:15,440 Speaker 1: weight loss if you restrict you're eating to a certain 358 00:19:15,800 --> 00:19:19,880 Speaker 1: ten or twelve hour period um, and that weight gain 359 00:19:19,960 --> 00:19:23,399 Speaker 1: could be a factor in weight gain, could be eating 360 00:19:23,480 --> 00:19:27,119 Speaker 1: too late in the day. Um, that's it's it's as 361 00:19:27,160 --> 00:19:29,560 Speaker 1: early stages now. It's pretty intriguing. I actually looked at 362 00:19:29,600 --> 00:19:32,600 Speaker 1: some of the research on lunch and it turns out 363 00:19:32,600 --> 00:19:35,119 Speaker 1: that lunch is a pretty powerful Again here I'm not 364 00:19:35,119 --> 00:19:38,840 Speaker 1: talking about physiology, I'm talking about psychology. The lunch ends 365 00:19:38,880 --> 00:19:41,639 Speaker 1: up being a pretty powerful restorative for us, much more 366 00:19:41,680 --> 00:19:44,679 Speaker 1: than I would have expected. There's a very strong argument 367 00:19:44,680 --> 00:19:47,359 Speaker 1: in the research for taking a lunch break. Uh, you know, 368 00:19:47,400 --> 00:19:50,760 Speaker 1: not just having a tune of salad sandwich dripping onto 369 00:19:50,800 --> 00:19:55,280 Speaker 1: your computer while you're trying to answer you know, but 370 00:19:55,280 --> 00:19:57,600 Speaker 1: but but but you know, being intentional and taking a 371 00:19:57,680 --> 00:19:59,560 Speaker 1: lunch break. It doesn't have to be massive, you know, 372 00:20:00,160 --> 00:20:04,480 Speaker 1: taking half an hour and going outside and you know, 373 00:20:04,480 --> 00:20:06,360 Speaker 1: if the weather is right, you know, eating a sandwich 374 00:20:06,400 --> 00:20:09,760 Speaker 1: on a bench and not working. Um, the evidence is 375 00:20:09,800 --> 00:20:12,960 Speaker 1: showing that that is where stories are. Energy can boost 376 00:20:13,000 --> 00:20:18,080 Speaker 1: our mood, can actually improve our productivity and creativity. And 377 00:20:18,200 --> 00:20:21,600 Speaker 1: the larger point here is that this is something where 378 00:20:21,600 --> 00:20:25,160 Speaker 1: I've changed my own behavior, is that just in general, 379 00:20:25,640 --> 00:20:30,640 Speaker 1: we haven't taken breaks seriously enough. Um. We have thought 380 00:20:30,720 --> 00:20:37,080 Speaker 1: of breaks as you know, soft or deviations from performance. 381 00:20:37,600 --> 00:20:41,240 Speaker 1: I am as guilty as anybody about this. I I've 382 00:20:41,280 --> 00:20:43,359 Speaker 1: never been a big break taker. I've always thought it 383 00:20:43,400 --> 00:20:46,360 Speaker 1: was better just to power through. And it's actually not, 384 00:20:46,960 --> 00:20:49,520 Speaker 1: um that that breaks all. We have to start thinking 385 00:20:49,560 --> 00:20:53,120 Speaker 1: of breaks as not a deviation from performance, but actually 386 00:20:53,160 --> 00:20:56,919 Speaker 1: part of performance and recognize that part of being a 387 00:20:56,960 --> 00:20:59,320 Speaker 1: professional means taking a break every once in a while. 388 00:20:59,640 --> 00:21:01,439 Speaker 1: So when I was reading the book, one thing I 389 00:21:01,480 --> 00:21:03,399 Speaker 1: wasn't clear about, and I was just curious, from your 390 00:21:03,440 --> 00:21:06,679 Speaker 1: own perspective, where does social media plan to that, Like 391 00:21:06,680 --> 00:21:08,679 Speaker 1: when you're on a break and eating a sandwich on 392 00:21:08,760 --> 00:21:13,320 Speaker 1: a you know, in nature at a park. How how 393 00:21:13,359 --> 00:21:17,439 Speaker 1: does like looking at your phone influence that? The real answers. 394 00:21:17,440 --> 00:21:19,320 Speaker 1: It depends, So it depends on what you're looking at 395 00:21:19,320 --> 00:21:22,359 Speaker 1: in your phone. In general, though, what the research shows 396 00:21:22,560 --> 00:21:26,000 Speaker 1: is that the best breaks you're fully detached, particularly from work. 397 00:21:26,280 --> 00:21:28,119 Speaker 1: So social media is a big part of your work. 398 00:21:28,359 --> 00:21:30,320 Speaker 1: If you're looking at things, oh, what are they saying 399 00:21:30,320 --> 00:21:33,480 Speaker 1: about my product or um, you know what's in the news, 400 00:21:33,560 --> 00:21:35,879 Speaker 1: and that it's going to affect my business, then it's 401 00:21:35,880 --> 00:21:37,880 Speaker 1: actually not that great of an idea. There's a lot 402 00:21:37,960 --> 00:21:41,840 Speaker 1: to be said for full detachment as breaks rather than 403 00:21:41,880 --> 00:21:44,480 Speaker 1: semi detachment. So so for me, for instance, I've changed 404 00:21:44,480 --> 00:21:46,560 Speaker 1: my ways on this is that when I have lunch, 405 00:21:47,119 --> 00:21:52,080 Speaker 1: I will literally not bring my phone, um, you know, 406 00:21:52,200 --> 00:21:53,960 Speaker 1: just you know, I don't take a long lunch break 407 00:21:54,000 --> 00:21:57,240 Speaker 1: at all, maybe five minutes, but I will leave my 408 00:21:57,240 --> 00:22:00,280 Speaker 1: phone in my office and so so I don't risk 409 00:22:00,400 --> 00:22:04,480 Speaker 1: being semi detached. Um. On the other hand, believe it 410 00:22:04,560 --> 00:22:07,680 Speaker 1: or not, and it's gonna sound crazy, but um, if 411 00:22:07,680 --> 00:22:12,080 Speaker 1: you're using social media and as a form of detachment, 412 00:22:12,840 --> 00:22:15,680 Speaker 1: that is, you're looking at hilarious of videos or something 413 00:22:15,760 --> 00:22:17,600 Speaker 1: like that, I have nothing to do with your work, 414 00:22:18,200 --> 00:22:20,080 Speaker 1: then it's not the worst thing in the world to 415 00:22:20,680 --> 00:22:23,280 Speaker 1: have that kind of a break. There's actually some evidence, 416 00:22:23,320 --> 00:22:25,040 Speaker 1: I mean, believe it or not, there's some evidence that 417 00:22:25,280 --> 00:22:28,000 Speaker 1: people who take breaks watching it's gonna sound like it's 418 00:22:28,080 --> 00:22:30,560 Speaker 1: made up, It's totally not made up. People who take 419 00:22:30,600 --> 00:22:35,000 Speaker 1: breaks and watch dog videos during their breaks end up 420 00:22:35,080 --> 00:22:36,960 Speaker 1: coming back from the breaks a little bit more restored. 421 00:22:37,560 --> 00:22:40,080 Speaker 1: So that's probably not true for people who run kennels. 422 00:22:40,400 --> 00:22:42,720 Speaker 1: So it really depends on, you know, how you're using 423 00:22:42,760 --> 00:22:46,840 Speaker 1: social media. I find it, well, social media is very complicated, 424 00:22:46,800 --> 00:22:49,720 Speaker 1: and I'm squarely in the middle. I don't consider it, 425 00:22:50,119 --> 00:22:52,639 Speaker 1: you know, the devil's technology in order. I consider it 426 00:22:52,720 --> 00:22:55,040 Speaker 1: the you know, the panacea for all that ails the world. 427 00:22:55,359 --> 00:22:57,760 Speaker 1: It's obviously somewhere in the middle of that. What I 428 00:22:57,800 --> 00:23:00,560 Speaker 1: have found personally, and again this is just a complete 429 00:23:00,960 --> 00:23:04,240 Speaker 1: personal experience and observation, not based on any research or 430 00:23:04,280 --> 00:23:08,880 Speaker 1: anything like that. I find that Twitter raises my stress level, 431 00:23:09,520 --> 00:23:13,080 Speaker 1: uh increases my cortisol level because on Twitter it seems 432 00:23:13,119 --> 00:23:17,040 Speaker 1: like everybody's always complaining about something or becoming alarmed by 433 00:23:17,080 --> 00:23:22,040 Speaker 1: something or yelling at somebody. So for that I find 434 00:23:22,080 --> 00:23:24,760 Speaker 1: it's not that for me personally, it's not that useful. Yeah, 435 00:23:24,920 --> 00:23:27,080 Speaker 1: that makes a lot of sense. I know. We have 436 00:23:27,119 --> 00:23:29,480 Speaker 1: a few other big questions for you, Daniel. Before we 437 00:23:29,480 --> 00:23:44,360 Speaker 1: get to those, let's take a quick break. Welcome back 438 00:23:44,359 --> 00:23:46,639 Speaker 1: to Part Time Genius. We're talking to Daniel Pink, the 439 00:23:46,680 --> 00:23:50,760 Speaker 1: author of When the Scientific Secrets of Perfect Timing and Daniel, 440 00:23:50,920 --> 00:23:53,919 Speaker 1: before you came on, Mango and I were actually talking 441 00:23:53,960 --> 00:23:56,040 Speaker 1: about some of the studies that were done around the 442 00:23:56,040 --> 00:24:00,560 Speaker 1: game of basketball and that fact about being behind at 443 00:24:00,600 --> 00:24:04,920 Speaker 1: the half is actually more advantageous than being ahead by 444 00:24:04,920 --> 00:24:07,600 Speaker 1: a point. And so well, why is this and and 445 00:24:07,600 --> 00:24:09,480 Speaker 1: and and what does this mean for us in other 446 00:24:09,520 --> 00:24:12,840 Speaker 1: areas of our lives. Yeah, I love that piece of research. 447 00:24:12,880 --> 00:24:16,880 Speaker 1: And again it's another um one of those insights that 448 00:24:17,000 --> 00:24:20,920 Speaker 1: scholars have been able to uncover using big data. That 449 00:24:21,040 --> 00:24:23,560 Speaker 1: particular piece of research, I think it was somewhere around 450 00:24:23,600 --> 00:24:26,680 Speaker 1: eighteen thousand NBA games. Just to take a step back, 451 00:24:26,720 --> 00:24:30,440 Speaker 1: I mean, one of the things it's important to understand 452 00:24:30,680 --> 00:24:34,520 Speaker 1: is how bizarre this finding is, because in general, a 453 00:24:34,560 --> 00:24:38,840 Speaker 1: team that's ahead at halftime has a better chance of winning. Now, 454 00:24:40,119 --> 00:24:44,360 Speaker 1: that shouldn't be a shocker, okay, because they have more points, right, 455 00:24:45,440 --> 00:24:48,199 Speaker 1: you know, they already have more points, right, and the 456 00:24:48,280 --> 00:24:50,520 Speaker 1: game's half over all, right, and they have a lead, 457 00:24:51,119 --> 00:24:53,760 Speaker 1: so you know, mathematically it's not that complicated. The other 458 00:24:53,760 --> 00:24:56,040 Speaker 1: thing is is that their halftime lead could be a 459 00:24:56,080 --> 00:24:58,879 Speaker 1: proxy that they hate they have better players, or they 460 00:24:58,880 --> 00:25:01,600 Speaker 1: have a better coach or something like that. What's interesting 461 00:25:01,680 --> 00:25:03,639 Speaker 1: is is, you, guys, point out is the exception to that, 462 00:25:03,680 --> 00:25:06,120 Speaker 1: which is the teams that are down by one are 463 00:25:06,240 --> 00:25:08,760 Speaker 1: more likely to win? Why is that? And it goes 464 00:25:08,840 --> 00:25:12,879 Speaker 1: to some of the science of midpoints. Um. Midpoints have 465 00:25:13,000 --> 00:25:15,080 Speaker 1: two effects on our behavior. They either bring us down 466 00:25:15,119 --> 00:25:17,160 Speaker 1: or they fire us up. And one of the cases 467 00:25:17,160 --> 00:25:20,360 Speaker 1: where midpoints fire us up is that if we're slightly behind. 468 00:25:20,760 --> 00:25:23,639 Speaker 1: There's something about being slightly behind at the midpoint that 469 00:25:23,760 --> 00:25:26,920 Speaker 1: is galvanizing. Now because if you're if you're way ahead 470 00:25:26,920 --> 00:25:30,160 Speaker 1: at the midpoint, you can become complacent. If you're way behind, 471 00:25:30,920 --> 00:25:33,479 Speaker 1: you say okay, it's over, it's you know, you give up. 472 00:25:33,880 --> 00:25:37,159 Speaker 1: But both in terms of the research on basketball games 473 00:25:37,320 --> 00:25:40,080 Speaker 1: and also some experimental research, which is a better way 474 00:25:40,119 --> 00:25:44,120 Speaker 1: to get at causation is showing that when people feel 475 00:25:44,119 --> 00:25:46,920 Speaker 1: like they're slightly behind at the midpoint, they work a 476 00:25:47,000 --> 00:25:49,960 Speaker 1: little harder. And one of the things that you can do, 477 00:25:50,200 --> 00:25:52,000 Speaker 1: and you know you can at some level. You can 478 00:25:52,040 --> 00:25:53,840 Speaker 1: trick yourself, you can trick your team, or it can 479 00:25:53,920 --> 00:25:56,720 Speaker 1: be the actual honest account of what's going on. I 480 00:25:56,800 --> 00:25:58,800 Speaker 1: do this all the time, is like, Okay, I'm at 481 00:25:58,800 --> 00:26:00,640 Speaker 1: the midpoint of something, I'm slightly find I gotta get 482 00:26:00,680 --> 00:26:03,040 Speaker 1: my button gear. Um. And so, if you're managing a 483 00:26:03,119 --> 00:26:06,840 Speaker 1: project and you're hit the midpoint, team, hey we're doing 484 00:26:06,840 --> 00:26:10,240 Speaker 1: pretty well, but we're a little bit behind. That's really galvanizing. 485 00:26:11,320 --> 00:26:15,000 Speaker 1: That's that's pretty fascinating. So and so is that represented 486 00:26:15,000 --> 00:26:17,440 Speaker 1: in why people do so many marathons at like twenty 487 00:26:17,520 --> 00:26:19,639 Speaker 1: nine or thirty nine in those nine years. That's a 488 00:26:19,680 --> 00:26:22,080 Speaker 1: different phenomenon that has to do with endings. You know, 489 00:26:22,119 --> 00:26:25,399 Speaker 1: as you point out people who run merit first time marathons, 490 00:26:25,840 --> 00:26:30,639 Speaker 1: their age is disproportionately ends and nine, say, forty nine 491 00:26:30,680 --> 00:26:32,320 Speaker 1: year olds or three times more likely to run a 492 00:26:32,320 --> 00:26:35,879 Speaker 1: marath first time marathon than fifty year olds. That's crazy. 493 00:26:36,240 --> 00:26:38,040 Speaker 1: Twenty nine year olds or twice as likely to run 494 00:26:38,080 --> 00:26:39,720 Speaker 1: a first time marathon is twenty eight year olds or 495 00:26:39,760 --> 00:26:42,600 Speaker 1: thirty year olds a right, it makes no sense physiologically. 496 00:26:42,600 --> 00:26:43,879 Speaker 1: What what's happening is that when we get to the 497 00:26:44,000 --> 00:26:48,000 Speaker 1: end of something, endings also have a galonizing effect. So 498 00:26:48,040 --> 00:26:49,840 Speaker 1: when we can see the end, we sometimes kick a 499 00:26:49,840 --> 00:26:53,000 Speaker 1: little harder. And that's particularly true when it comes to 500 00:26:53,080 --> 00:26:57,400 Speaker 1: things that are sources of meaning. Um So people have 501 00:26:57,880 --> 00:27:01,000 Speaker 1: bucket lists, and people have things they want to accomplish 502 00:27:01,000 --> 00:27:04,240 Speaker 1: in their lives, and people have these these purely arbitrary 503 00:27:04,320 --> 00:27:07,679 Speaker 1: markers of decades. They say, Oh, my gosh, time is 504 00:27:07,680 --> 00:27:09,680 Speaker 1: moving fast. I gotta get going. I'm gonna to run 505 00:27:09,680 --> 00:27:13,480 Speaker 1: a marathon. That's pretty amazing. And that's why you're holding 506 00:27:13,480 --> 00:27:15,879 Speaker 1: off MAGO. You're thirty eight now and next year is 507 00:27:15,920 --> 00:27:19,600 Speaker 1: you're big here. So ten years from now, start training, 508 00:27:19,680 --> 00:27:23,359 Speaker 1: start training. So I know. One thing I was curious 509 00:27:23,359 --> 00:27:27,040 Speaker 1: about is, uh, what's the best time to deliver bad news? 510 00:27:27,040 --> 00:27:30,320 Speaker 1: Do you have any thoughts on that The best time 511 00:27:30,359 --> 00:27:35,040 Speaker 1: to deliver bad news? Well, I think the best the 512 00:27:35,280 --> 00:27:37,560 Speaker 1: best time, in the best way. The best time to 513 00:27:37,600 --> 00:27:41,520 Speaker 1: deliver bad news in general is when the recipient's mood 514 00:27:41,760 --> 00:27:45,159 Speaker 1: is higher that you know, more positive than negative. And 515 00:27:45,200 --> 00:27:47,119 Speaker 1: what we know in general for most people is that 516 00:27:47,600 --> 00:27:50,119 Speaker 1: their moods are slightly better in the mornings and in 517 00:27:50,119 --> 00:27:52,320 Speaker 1: the late afternoons in early evenings than in the afternoon. 518 00:27:52,840 --> 00:27:57,560 Speaker 1: So that is I think that's generally a good In general, 519 00:27:57,840 --> 00:27:59,480 Speaker 1: it's a good time to do it. In terms of 520 00:28:00,760 --> 00:28:04,000 Speaker 1: the classic formulation that everybody on the planet has used, 521 00:28:04,000 --> 00:28:05,719 Speaker 1: I've got some good news and some bad news, there 522 00:28:05,800 --> 00:28:08,120 Speaker 1: is a very clear answer. If you have good news 523 00:28:08,160 --> 00:28:11,560 Speaker 1: and bad news to deliver, always give the bad news first. Um. 524 00:28:11,600 --> 00:28:13,679 Speaker 1: And the reason for that is that people has to 525 00:28:13,680 --> 00:28:16,960 Speaker 1: do with the science of endings. People prefer endings that elevate. 526 00:28:17,040 --> 00:28:22,719 Speaker 1: They prefer rising sequences to declining sequences, and so um um. 527 00:28:22,720 --> 00:28:24,879 Speaker 1: And what's interesting about that. And this is again another 528 00:28:24,920 --> 00:28:27,600 Speaker 1: area where I've changed my own behavior. I used to 529 00:28:27,600 --> 00:28:30,000 Speaker 1: be Mr, Okay, got good news and bad news. Let 530 00:28:30,000 --> 00:28:31,760 Speaker 1: me give you the good news first, you know, try 531 00:28:31,760 --> 00:28:35,280 Speaker 1: to lay down that cushion. And but when you ask 532 00:28:35,359 --> 00:28:36,919 Speaker 1: people what do you want to hear first, the good 533 00:28:36,960 --> 00:28:38,760 Speaker 1: news or bad news? Four to five people say I 534 00:28:38,760 --> 00:28:41,040 Speaker 1: want the bad news first, and so you better off 535 00:28:41,040 --> 00:28:43,280 Speaker 1: to give you the bad news first, uh, and ending 536 00:28:43,280 --> 00:28:46,000 Speaker 1: with the good news. Um. Again, it has to do 537 00:28:46,040 --> 00:28:49,440 Speaker 1: with our preferences about endings that elevate rising sequences over 538 00:28:49,480 --> 00:28:52,040 Speaker 1: declining sequences. Well, and I like that you offer a 539 00:28:52,040 --> 00:28:54,360 Speaker 1: few tips on this, whether it's you know, how to 540 00:28:54,520 --> 00:28:57,920 Speaker 1: end vacations or how to end our work days. You know, 541 00:28:57,960 --> 00:29:01,240 Speaker 1: as you mentioned, people like an elevated ending. Can you 542 00:29:01,240 --> 00:29:02,760 Speaker 1: can you talk a little bit about some of these 543 00:29:02,800 --> 00:29:05,040 Speaker 1: suggestions that you've given in the book. I think the 544 00:29:05,040 --> 00:29:08,680 Speaker 1: most important thing is to be intentional about endings and 545 00:29:08,800 --> 00:29:12,720 Speaker 1: to recognize that they're the endings disproportionately affect how people 546 00:29:12,800 --> 00:29:16,640 Speaker 1: remember entire experiences. So if you look at something like 547 00:29:16,720 --> 00:29:20,160 Speaker 1: customer transactions, um, I think that businesses should be much 548 00:29:20,200 --> 00:29:25,760 Speaker 1: more uh, much more attention to how a purchase experience ends, 549 00:29:26,440 --> 00:29:30,440 Speaker 1: um um. So you can see this anecdotally and Yelp 550 00:29:30,480 --> 00:29:33,600 Speaker 1: reviews of restaurants. If you if you actually read the 551 00:29:33,640 --> 00:29:36,400 Speaker 1: help reviews of restaurants, you find that a disproportionate number 552 00:29:36,480 --> 00:29:39,920 Speaker 1: of these reviews talk about how the meal ended. You know, 553 00:29:40,240 --> 00:29:42,200 Speaker 1: they screw up the check and they were jerks about it. 554 00:29:42,280 --> 00:29:44,760 Speaker 1: They gave me a deserted and expect and that affects 555 00:29:44,760 --> 00:29:48,880 Speaker 1: their home. In terms of vacations, pick one of the 556 00:29:49,080 --> 00:29:50,200 Speaker 1: what do you think is gonna be one of the 557 00:29:50,240 --> 00:29:53,239 Speaker 1: best moments and put it towards the end. The end 558 00:29:53,280 --> 00:29:57,400 Speaker 1: of an experience disproportionately affects our memory of it. I've 559 00:29:57,400 --> 00:30:00,240 Speaker 1: got some great examples from teachers around the country about 560 00:30:00,280 --> 00:30:02,440 Speaker 1: how teachers have marked the end of a semester or 561 00:30:02,520 --> 00:30:05,440 Speaker 1: the end of a year. One one of my favorites 562 00:30:05,480 --> 00:30:08,000 Speaker 1: is this fellow who's an economics teacher at a high 563 00:30:08,040 --> 00:30:11,640 Speaker 1: school outside of Chicago, and what he does is that 564 00:30:11,760 --> 00:30:14,320 Speaker 1: at the end of people senior years, he has them 565 00:30:14,320 --> 00:30:16,720 Speaker 1: write a letter to themselves that he mails to them 566 00:30:16,760 --> 00:30:21,120 Speaker 1: five years later. It's an awesome thing. There's another college 567 00:30:21,160 --> 00:30:23,240 Speaker 1: teacher who at the end of a semester, she takes 568 00:30:23,240 --> 00:30:26,200 Speaker 1: her students out to a local pub and they make 569 00:30:26,240 --> 00:30:29,600 Speaker 1: toasts to each other. And so just being intentional about 570 00:30:29,720 --> 00:30:32,880 Speaker 1: endings and giving them a little bit of lift can 571 00:30:33,120 --> 00:30:37,400 Speaker 1: dramatically shape how people remember an entire year, long, semester, 572 00:30:37,480 --> 00:30:40,280 Speaker 1: long experience. That's really good, and you talk a little 573 00:30:40,280 --> 00:30:42,760 Speaker 1: bit about how we might end our work days. I 574 00:30:42,800 --> 00:30:45,960 Speaker 1: thought there were some great suggestions there as well. Sure, 575 00:30:46,000 --> 00:30:48,360 Speaker 1: I mean, you know, I again, I think it's a lot. 576 00:30:48,520 --> 00:30:50,760 Speaker 1: I think it's a lot about being intentional about how 577 00:30:50,760 --> 00:30:52,480 Speaker 1: we end our work day. So one of the things 578 00:30:52,480 --> 00:30:54,760 Speaker 1: that you can do at the one of the things 579 00:30:54,760 --> 00:30:56,360 Speaker 1: you can do at the very end of your work day, 580 00:30:56,400 --> 00:30:59,600 Speaker 1: and something that I do is that I actually mark 581 00:30:59,680 --> 00:31:03,560 Speaker 1: my progress. I actually use an app called I'd done 582 00:31:03,560 --> 00:31:05,600 Speaker 1: this that sends me an email at the end of 583 00:31:05,600 --> 00:31:07,160 Speaker 1: every day. It says what you get done today, and 584 00:31:07,200 --> 00:31:09,960 Speaker 1: I make sure the ritualize, Okay, here's what I got 585 00:31:10,000 --> 00:31:12,080 Speaker 1: done today, So I have a sense of progress, so 586 00:31:12,120 --> 00:31:15,200 Speaker 1: I mark that progress. Um. There's some great research from 587 00:31:15,360 --> 00:31:19,400 Speaker 1: Teresa Amabulae at Harvard Business School about how making progress 588 00:31:19,480 --> 00:31:22,240 Speaker 1: is the single largest day to day motivator on the job. 589 00:31:22,960 --> 00:31:24,920 Speaker 1: So that's one of the things that I myself do. 590 00:31:25,400 --> 00:31:27,520 Speaker 1: You can also do things like there's something about a 591 00:31:27,520 --> 00:31:30,240 Speaker 1: sense of completion. So one of the things I try 592 00:31:30,240 --> 00:31:31,440 Speaker 1: to do I don't always do a good job of 593 00:31:31,480 --> 00:31:35,640 Speaker 1: this is layout what I'm gonna do the following day, 594 00:31:35,720 --> 00:31:38,320 Speaker 1: so I have a sense of completion and I can 595 00:31:38,400 --> 00:31:40,360 Speaker 1: kind of close the door on the day, take a 596 00:31:40,400 --> 00:31:44,200 Speaker 1: break detached from work to the extent that's possible. Uh. 597 00:31:44,280 --> 00:31:45,640 Speaker 1: There are also things at the end of the day 598 00:31:45,640 --> 00:31:48,760 Speaker 1: as as mood boosters. One of the you know, it's 599 00:31:48,840 --> 00:31:51,040 Speaker 1: it's really remarkable the research I'm doing something good for 600 00:31:51,080 --> 00:31:54,280 Speaker 1: somebody else boosts our moods. It ends up being doing 601 00:31:54,280 --> 00:31:56,920 Speaker 1: something good for someone else can be a profoundly selfish 602 00:31:56,960 --> 00:32:00,600 Speaker 1: act in terms of its benefit to us. So you know, 603 00:32:00,640 --> 00:32:02,320 Speaker 1: maybe at the end of the day thank somebody you 604 00:32:02,360 --> 00:32:05,840 Speaker 1: hadn't thank before. But again, it's really about being aware 605 00:32:05,840 --> 00:32:08,920 Speaker 1: and being intentional, um and and these these small things 606 00:32:08,960 --> 00:32:11,719 Speaker 1: can make a big difference. Well, before we end here, 607 00:32:11,760 --> 00:32:13,440 Speaker 1: I did want to ask you a little bit about 608 00:32:13,920 --> 00:32:17,360 Speaker 1: the sinkers high and uh, the tips on sinking with 609 00:32:17,400 --> 00:32:19,720 Speaker 1: other people, because I really like that that in your book, 610 00:32:20,160 --> 00:32:22,440 Speaker 1: you can you talk a little bit about that, sure, 611 00:32:22,960 --> 00:32:25,520 Speaker 1: you know, I also have a chapter on how groups 612 00:32:25,520 --> 00:32:27,640 Speaker 1: synchronize in time, So whether they are people who are 613 00:32:27,680 --> 00:32:32,240 Speaker 1: delivering lunches, whether they're rowers, whether they're choral singers, and um, 614 00:32:32,280 --> 00:32:34,800 Speaker 1: there is something about synchronizing with other people in time 615 00:32:34,880 --> 00:32:37,840 Speaker 1: that makes us feel really, really good. There's some good 616 00:32:37,920 --> 00:32:41,040 Speaker 1: evidence on rowers High that that when we synchronize with 617 00:32:41,080 --> 00:32:45,160 Speaker 1: other people, actually are pain thresholds increase, our immune response 618 00:32:45,240 --> 00:32:49,400 Speaker 1: improves at a physiological level, we do better. There's some 619 00:32:49,720 --> 00:32:54,080 Speaker 1: incredible effects to our mood and even to our propensity 620 00:32:54,200 --> 00:32:57,640 Speaker 1: to do good. Deeds afterwards. So it's really quite fascinating. 621 00:32:57,640 --> 00:32:59,200 Speaker 1: I don't have a great explanation for it. I just 622 00:32:59,360 --> 00:33:01,680 Speaker 1: noted the phenomenon and some of the research behind it. 623 00:33:01,800 --> 00:33:05,240 Speaker 1: But there's something about synchronizing with other people in time, 624 00:33:05,320 --> 00:33:08,280 Speaker 1: like choral singing that makes us feel really, really good 625 00:33:08,320 --> 00:33:12,880 Speaker 1: that could be somehow evolutionarily programmed to feel good because 626 00:33:12,920 --> 00:33:16,600 Speaker 1: it has some kind of advantage to us. That's really interesting. Well, 627 00:33:16,640 --> 00:33:18,280 Speaker 1: as we said at the top of the show, this 628 00:33:18,400 --> 00:33:21,440 Speaker 1: is uh, it's interesting that's been We've gone this long 629 00:33:21,480 --> 00:33:25,080 Speaker 1: without really thinking about the win of all these questions. 630 00:33:25,080 --> 00:33:27,200 Speaker 1: As we've talked about before. You know, we've focused so 631 00:33:27,280 --> 00:33:30,000 Speaker 1: much on the how and the why. But this has 632 00:33:30,000 --> 00:33:32,800 Speaker 1: been so fascinating to learn these things and listeners, I 633 00:33:32,840 --> 00:33:35,640 Speaker 1: hope you'll check out this new book When The Scientific 634 00:33:35,680 --> 00:33:38,520 Speaker 1: Secrets of Perfect Timing. Daniel Pink thanks so much for 635 00:33:38,600 --> 00:33:40,880 Speaker 1: joining us today. Thanks to you guys both for a 636 00:33:40,880 --> 00:33:56,280 Speaker 1: great interview with a lot of fun. Thanks again for listening. 637 00:33:56,440 --> 00:33:58,640 Speaker 1: Part Time Genius is a production of how stuff works 638 00:33:58,640 --> 00:34:01,240 Speaker 1: and wouldn't be possible without everal brilliant people who do 639 00:34:01,280 --> 00:34:04,280 Speaker 1: the important things. We couldn't even begin to understand. Tristin 640 00:34:04,360 --> 00:34:06,960 Speaker 1: McNeil does the editing thing. Noel Brown made the theme 641 00:34:07,000 --> 00:34:09,840 Speaker 1: song and does the mixy mixy sound thing. Jerry Rowland 642 00:34:09,920 --> 00:34:13,200 Speaker 1: does the exact producer thing. Gave Loesier is our lead researcher, 643 00:34:13,239 --> 00:34:16,240 Speaker 1: with support from the Research Army including Austin Thompson, Nolan 644 00:34:16,280 --> 00:34:18,680 Speaker 1: Brown and Lucas Adams. Eve Jeff Cook gets the show 645 00:34:18,680 --> 00:34:20,920 Speaker 1: to your ears, Good job, Eaves. If you like what 646 00:34:20,960 --> 00:34:22,960 Speaker 1: you heard, we hope you'll subscribe, And if you really 647 00:34:23,000 --> 00:34:24,759 Speaker 1: really like what you've heard, maybe you could leave a 648 00:34:24,760 --> 00:34:27,920 Speaker 1: good review for us to get to Jason, who