1
00:00:03,800 --> 00:00:06,680
Speaker 1: Welcome to Stuff to Blow your Mind from how Stuff

2
00:00:06,680 --> 00:00:13,800
Speaker 1: Works dot com. Hey, welcome to Stuff to Blow your Mind.

3
00:00:13,840 --> 00:00:15,960
Speaker 1: My name is Robert Lamb and I'm Julie Douglas. It

4
00:00:16,040 --> 00:00:18,599
Speaker 1: is September two thousand and twelve, which means we have

5
00:00:18,800 --> 00:00:23,160
Speaker 1: just um experienced the world has just experienced another Ignoble

6
00:00:23,360 --> 00:00:26,560
Speaker 1: Prize ceremony. This, of course, is the ceremony that takes

7
00:00:26,560 --> 00:00:29,040
Speaker 1: place every year in Harvard, put on by the publication

8
00:00:29,560 --> 00:00:34,240
Speaker 1: UH the Anals of Improbable Research, which is a fabulous publication.

9
00:00:34,320 --> 00:00:36,640
Speaker 1: Do check it out. Do a website. They have a

10
00:00:36,640 --> 00:00:42,360
Speaker 1: great website and they focus on the ridiculousness, the the unexpected,

11
00:00:42,479 --> 00:00:48,080
Speaker 1: and the often grotesque and hilarious aspects of legitimate scientific research.

12
00:00:48,280 --> 00:00:50,960
Speaker 1: So this isn't like fringe stuff where like some guy

13
00:00:50,960 --> 00:00:53,960
Speaker 1: in a in a shed in uh In in rural

14
00:00:54,000 --> 00:00:58,200
Speaker 1: Mississippi is trying to to to create an artificial gravity

15
00:00:58,200 --> 00:01:02,120
Speaker 1: device or something. We're talking about actual legitimate scientific research

16
00:01:02,600 --> 00:01:08,440
Speaker 1: that often winds its way into strange territory. Yes, it's true,

17
00:01:08,560 --> 00:01:10,520
Speaker 1: and one of the things that they are trying to

18
00:01:10,560 --> 00:01:15,360
Speaker 1: accomplish with the Ignoble UH ceremonies, they say, is to

19
00:01:15,560 --> 00:01:17,800
Speaker 1: make you laugh and then make you think, this is

20
00:01:17,840 --> 00:01:19,720
Speaker 1: a really important premise of this and I think that

21
00:01:19,760 --> 00:01:22,440
Speaker 1: you just highlighted that. UM. As you will note, the

22
00:01:22,560 --> 00:01:25,039
Speaker 1: name is ig Noble, which is sort of a spoof

23
00:01:25,120 --> 00:01:29,960
Speaker 1: on the Nobel Prizes. And each year the Ignoble Prizes

24
00:01:30,080 --> 00:01:32,399
Speaker 1: honor achievements that do make people laugh and then make

25
00:01:32,400 --> 00:01:35,320
Speaker 1: them think, and they are awarded to ten people and

26
00:01:35,400 --> 00:01:38,679
Speaker 1: winners do travel from around the world and on their

27
00:01:38,680 --> 00:01:42,240
Speaker 1: own dime, by the way, UM to a gallus ceremony

28
00:01:42,280 --> 00:01:46,760
Speaker 1: at Harvard where Nobel laureates then present them with their prize.

29
00:01:47,600 --> 00:01:50,280
Speaker 1: And the prize itself is made of really cheap material

30
00:01:50,880 --> 00:01:55,200
Speaker 1: that's prone to disintegrate. Yeah, it's a and the reason

31
00:01:55,360 --> 00:01:57,960
Speaker 1: that everyone travels here and everyone everyone's in on the joke.

32
00:01:58,000 --> 00:02:00,920
Speaker 1: Everyone loves it because with it with a few small

33
00:02:00,960 --> 00:02:03,760
Speaker 1: exceptions here and there, because it is a celebration of

34
00:02:03,800 --> 00:02:06,440
Speaker 1: the work. It's not a ridiculing of it. And it's

35
00:02:06,480 --> 00:02:09,600
Speaker 1: not using it to uh to push some sort of agenda.

36
00:02:09,880 --> 00:02:11,720
Speaker 1: Well like we saw with the whole shrimp on a

37
00:02:11,720 --> 00:02:15,080
Speaker 1: treadmill thing where uh, individuals that wish to cut the

38
00:02:15,160 --> 00:02:17,720
Speaker 1: U S Science budget, we're using it like look at

39
00:02:17,720 --> 00:02:20,320
Speaker 1: this with people are spending money and using time on

40
00:02:20,320 --> 00:02:22,359
Speaker 1: on this experiment where they just put a shrimp on

41
00:02:22,400 --> 00:02:25,800
Speaker 1: a treadmill, ignoring the fact that that particular experiment was

42
00:02:25,919 --> 00:02:30,440
Speaker 1: very interested in in in how bacteria affects these important

43
00:02:30,440 --> 00:02:34,440
Speaker 1: food creatures in a polluted environment. It was just the

44
00:02:34,480 --> 00:02:38,560
Speaker 1: catalyst that they were making, belittling it and saying this

45
00:02:38,600 --> 00:02:42,919
Speaker 1: is stupid science that accomplishes nothing. Ignobles Ist is the

46
00:02:43,080 --> 00:02:46,440
Speaker 1: entirely different direction, the positive direction, which is saying, here's

47
00:02:46,480 --> 00:02:51,240
Speaker 1: the study that illuminates something hilarious, but that also illuminates

48
00:02:51,400 --> 00:02:55,840
Speaker 1: something important about the scientific workings of the universe. Okay, well,

49
00:02:55,880 --> 00:02:58,639
Speaker 1: let's talk about about who this group is that selects

50
00:02:58,760 --> 00:03:03,280
Speaker 1: the the nominate or makes the nominations, and then also

51
00:03:03,320 --> 00:03:06,920
Speaker 1: what the process is. Basically, we have a group of

52
00:03:06,960 --> 00:03:10,880
Speaker 1: people and we did we mentioned Mark Abraham's yet, but

53
00:03:10,960 --> 00:03:13,840
Speaker 1: he is one of the principals. He is the editor

54
00:03:13,880 --> 00:03:17,360
Speaker 1: and co founder of the Annals of Improbable Research and

55
00:03:17,400 --> 00:03:21,320
Speaker 1: the founder and master of ceremonies for the prizes. And he,

56
00:03:21,520 --> 00:03:25,120
Speaker 1: along with a few other Nobel laureates, science writers, athletes,

57
00:03:25,200 --> 00:03:27,840
Speaker 1: politicians from time to time to you, right, they make

58
00:03:27,919 --> 00:03:30,920
Speaker 1: up the board that nominates the studies and the inventions.

59
00:03:31,360 --> 00:03:34,000
Speaker 1: So basically they are flooded every year with up to

60
00:03:34,120 --> 00:03:37,280
Speaker 1: nine thousand submissions. That's a lot they have to wade

61
00:03:37,280 --> 00:03:39,880
Speaker 1: through all of that. They also seek out nominations as well,

62
00:03:40,200 --> 00:03:43,360
Speaker 1: our nominees, I should say, and Abraham says that they

63
00:03:43,400 --> 00:03:46,560
Speaker 1: will quietly get in touch with them, offer them the prize,

64
00:03:46,560 --> 00:03:50,040
Speaker 1: and give them the opportunity to quietly decline the honor

65
00:03:50,240 --> 00:03:52,839
Speaker 1: if they want to, and if they say no, he says,

66
00:03:52,920 --> 00:03:55,200
Speaker 1: that's absolutely fine, and that's it. He said that they

67
00:03:55,240 --> 00:03:57,440
Speaker 1: don't mention it, and they don't even keep records at

68
00:03:57,480 --> 00:04:00,000
Speaker 1: that point because again, this is something that they want

69
00:04:00,000 --> 00:04:02,000
Speaker 1: to make sure the person is comfortable with and it

70
00:04:02,040 --> 00:04:04,560
Speaker 1: really is an honor. Well, the exception that comes to

71
00:04:04,600 --> 00:04:09,080
Speaker 1: mind that was the was when the gay bomb. Yeah yeah,

72
00:04:09,200 --> 00:04:10,760
Speaker 1: and we'll talk about that in a moment, but I

73
00:04:10,760 --> 00:04:13,080
Speaker 1: wanted to awesome give you guys a sense of the

74
00:04:13,120 --> 00:04:17,360
Speaker 1: ceremony itself. Paper airplanes are launched and then the winners

75
00:04:17,600 --> 00:04:21,360
Speaker 1: are confined to a sixty second speech. If if they

76
00:04:21,360 --> 00:04:25,960
Speaker 1: go beyond that sixty seconds, they at least in this

77
00:04:26,040 --> 00:04:28,760
Speaker 1: year's awards, they had two girls, not one, but two girls,

78
00:04:28,920 --> 00:04:32,000
Speaker 1: eight year old year olds come up and say please stop,

79
00:04:32,640 --> 00:04:35,520
Speaker 1: you're boring me, Please stop, you're boring me, over and

80
00:04:35,600 --> 00:04:39,920
Speaker 1: over again until the recipient stopped, because you got to

81
00:04:39,960 --> 00:04:44,520
Speaker 1: keep the point short, and it's a it's not about it.

82
00:04:44,640 --> 00:04:47,520
Speaker 1: It's about celebrating the awesomeness of any of these given products,

83
00:04:47,560 --> 00:04:50,880
Speaker 1: not boring usarily with all of the hardcore details. I

84
00:04:50,920 --> 00:04:52,920
Speaker 1: just wish they would do that for the oscars. It

85
00:04:52,920 --> 00:04:55,640
Speaker 1: would be awesome. Okay, so let's give an example of

86
00:04:55,680 --> 00:05:01,480
Speaker 1: a past recipient. Uh Dr Elena Bodnar invented that in

87
00:05:01,520 --> 00:05:04,919
Speaker 1: an emergency can be quickly converted into a pair of

88
00:05:05,000 --> 00:05:07,520
Speaker 1: protective face masks. So we have one for yourself and

89
00:05:07,560 --> 00:05:10,279
Speaker 1: one for a friend, which I think is brilliant. Of course,

90
00:05:10,360 --> 00:05:12,599
Speaker 1: you know again as the catalyst for is that the

91
00:05:12,600 --> 00:05:15,080
Speaker 1: part that is so very funny, but it actually has

92
00:05:15,160 --> 00:05:18,920
Speaker 1: practical implications here for us. Yeah, and in that one

93
00:05:18,960 --> 00:05:20,760
Speaker 1: they had a lot of fun with because I think

94
00:05:20,800 --> 00:05:23,200
Speaker 1: even the ceremony after that they were still having people

95
00:05:23,240 --> 00:05:26,360
Speaker 1: come on with. They brought the brawl back and they

96
00:05:26,360 --> 00:05:28,400
Speaker 1: put it on the Nobel Laureates faces, which I thought

97
00:05:28,440 --> 00:05:30,280
Speaker 1: was pretty anes because it also brings to mind the

98
00:05:30,560 --> 00:05:33,640
Speaker 1: old flick Weird Science, you know, where they had the

99
00:05:33,800 --> 00:05:36,120
Speaker 1: they were doing the crazy Man science experiment. They wore

100
00:05:36,120 --> 00:05:38,680
Speaker 1: the braws in their head. Yeah. Some of the some

101
00:05:38,760 --> 00:05:40,640
Speaker 1: of the other winners we've discussed in the past. There

102
00:05:40,680 --> 00:05:43,960
Speaker 1: was the the study that I believe this was a

103
00:05:44,560 --> 00:05:48,200
Speaker 1: New Mexico or no, it was Nevada study University of

104
00:05:48,320 --> 00:05:52,480
Speaker 1: Las Vegas, where they were looking at how ovulating strippers

105
00:05:52,520 --> 00:05:57,720
Speaker 1: received more tips than normal than the other strippers at

106
00:05:57,720 --> 00:06:00,240
Speaker 1: a strip club. That's been brought into question by the

107
00:06:00,240 --> 00:06:03,560
Speaker 1: way it has, But yes, I mean, if that doesn't

108
00:06:03,880 --> 00:06:05,600
Speaker 1: deserve an I know, well, I don't know what does,

109
00:06:06,320 --> 00:06:09,320
Speaker 1: because the story, like the the instant picture you get

110
00:06:09,480 --> 00:06:13,080
Speaker 1: is a bunch of researchers spending their time at a

111
00:06:13,120 --> 00:06:16,520
Speaker 1: strip club. Uh. Though the question that they're speaking to

112
00:06:16,560 --> 00:06:20,080
Speaker 1: answer all the cheeky stuff aside is legitimate because we're

113
00:06:20,160 --> 00:06:23,440
Speaker 1: very interested in human behavior. How does and how does uh?

114
00:06:23,560 --> 00:06:26,880
Speaker 1: How how did these various animal properties of ourselves influenced

115
00:06:26,960 --> 00:06:29,640
Speaker 1: the the the culture that we have built on top

116
00:06:29,680 --> 00:06:32,640
Speaker 1: of it. Uh. There was of course the gay bomb

117
00:06:33,080 --> 00:06:36,000
Speaker 1: and this was the one where uh, nobody from the

118
00:06:36,040 --> 00:06:39,480
Speaker 1: US military actually showed up to accept the prize, but

119
00:06:39,560 --> 00:06:41,600
Speaker 1: it was and I feel like we discussed this one

120
00:06:41,600 --> 00:06:43,440
Speaker 1: in more detail in the past. But the idea was,

121
00:06:43,600 --> 00:06:46,240
Speaker 1: all right, you get some enemies in the trench over there.

122
00:06:46,279 --> 00:06:47,760
Speaker 1: I mean they would not now obviously wouldn't be in

123
00:06:47,760 --> 00:06:51,480
Speaker 1: a trench, but I like the simple version of the

124
00:06:51,320 --> 00:06:53,120
Speaker 1: of it. You know, we're in this trench there, in

125
00:06:53,160 --> 00:06:56,640
Speaker 1: that trench. We wanna either kill them or make them

126
00:06:56,640 --> 00:07:00,520
Speaker 1: not kill us. Both would be great, preferably both because

127
00:07:00,960 --> 00:07:04,920
Speaker 1: you know, we're soldiers, right, So let's fire something into

128
00:07:04,960 --> 00:07:08,440
Speaker 1: their trench that makes them love each other in a

129
00:07:08,560 --> 00:07:11,360
Speaker 1: very biblical fashion. So in other words, let's distract them

130
00:07:11,400 --> 00:07:15,800
Speaker 1: from war and it releases chemical that induces them to then, uh,

131
00:07:15,920 --> 00:07:19,119
Speaker 1: cuddle with each other. Yes, at least it's pretty great. Yeah,

132
00:07:19,200 --> 00:07:21,360
Speaker 1: it's and it was. They did a fabulal spit on

133
00:07:21,400 --> 00:07:24,560
Speaker 1: Dirty Rock where they lampoon did as well. Um, I mean,

134
00:07:24,760 --> 00:07:26,960
Speaker 1: and and this is a fascinating because on one level,

135
00:07:27,800 --> 00:07:32,760
Speaker 1: it's hilarious the idea of forcing a makeout party on

136
00:07:32,760 --> 00:07:36,800
Speaker 1: on your your enemies, but also it's it's very telling,

137
00:07:36,800 --> 00:07:39,200
Speaker 1: like the idea that we are we are these creatures

138
00:07:39,240 --> 00:07:42,360
Speaker 1: that could potentially be manipulated in this fashion, that that

139
00:07:42,440 --> 00:07:46,360
Speaker 1: we ultimately don't have that much control over our our

140
00:07:46,480 --> 00:07:51,360
Speaker 1: violent or passive tendencies. Um. I wanted to point this out.

141
00:07:52,280 --> 00:07:56,560
Speaker 1: In an interview with Ira Plateau of Science Friday, Abraham's

142
00:07:56,640 --> 00:07:59,480
Speaker 1: talked about the worth of this sort of thing by

143
00:07:59,600 --> 00:08:03,880
Speaker 1: using an example of a shrew. Um. Now, these anthropologists

144
00:08:03,880 --> 00:08:07,320
Speaker 1: Brian Crandell and Peter Stall, who who did the study

145
00:08:07,360 --> 00:08:10,240
Speaker 1: on the shrew, they did not get an ignoble prize

146
00:08:10,280 --> 00:08:13,360
Speaker 1: and they perhaps could um for this, but right now

147
00:08:13,400 --> 00:08:16,320
Speaker 1: they were just covered in the Animals of Improbable research

148
00:08:16,360 --> 00:08:20,360
Speaker 1: for this. But they were featured because, uh, they worked

149
00:08:20,360 --> 00:08:22,520
Speaker 1: with the shrew, which is, you know, as a reminder,

150
00:08:22,600 --> 00:08:27,800
Speaker 1: a small, nearly blind animal. Yes, they boiled it and

151
00:08:27,840 --> 00:08:30,000
Speaker 1: then one of them it was never said in the study,

152
00:08:30,160 --> 00:08:35,840
Speaker 1: but either Crandell or Stall actually swallowed it whole. Okay, um,

153
00:08:35,880 --> 00:08:37,800
Speaker 1: And they wanted to make sure that there was you know,

154
00:08:37,960 --> 00:08:41,360
Speaker 1: no sort of masticating, no chewing going on here. They

155
00:08:41,360 --> 00:08:45,360
Speaker 1: wanted to deliver it to their gut whole. And then

156
00:08:45,640 --> 00:08:48,160
Speaker 1: this is it gets even better. For three days, the

157
00:08:48,200 --> 00:08:52,480
Speaker 1: anthropologists examined the resulting feces to discover how the human

158
00:08:52,480 --> 00:08:56,040
Speaker 1: digestive system might break the shrew down. Okay, all that

159
00:08:56,080 --> 00:09:01,440
Speaker 1: seems absolutely crazy and ridiculous, right, uh. But the reason

160
00:09:01,520 --> 00:09:03,600
Speaker 1: they wanted to do this is because they thought there

161
00:09:03,640 --> 00:09:06,160
Speaker 1: is a gap in the knowledge of how something gets

162
00:09:06,480 --> 00:09:11,040
Speaker 1: um digested and the remains, the resulting remains, and what

163
00:09:11,240 --> 00:09:13,920
Speaker 1: is actually there. In other words, if you're an anthropologist

164
00:09:14,000 --> 00:09:17,440
Speaker 1: or an archaeologist and you stumble upon remains that were digested,

165
00:09:18,000 --> 00:09:20,760
Speaker 1: you don't know how much of those remains are a

166
00:09:20,760 --> 00:09:23,520
Speaker 1: result of the process of digestion or the actual like

167
00:09:23,679 --> 00:09:26,800
Speaker 1: teeth marks in the tearing apart of a jaw, which

168
00:09:26,800 --> 00:09:28,760
Speaker 1: would give you a really good idea of how big

169
00:09:28,840 --> 00:09:32,120
Speaker 1: or small the animal was. Right, So, what they did

170
00:09:32,160 --> 00:09:35,280
Speaker 1: discover when they went through all the feces is that

171
00:09:35,320 --> 00:09:37,480
Speaker 1: a lot of the bones of the shrew, including a

172
00:09:37,480 --> 00:09:40,240
Speaker 1: lot of the big bones, were missing, and one of

173
00:09:40,280 --> 00:09:43,319
Speaker 1: the big jawbones was missing. Four of the teeth the

174
00:09:43,360 --> 00:09:45,520
Speaker 1: shrews teeth were missing, and a big chunk of its

175
00:09:45,520 --> 00:09:49,480
Speaker 1: school was missing, just from this digestive process. So this

176
00:09:49,520 --> 00:09:53,920
Speaker 1: tells us something now going forward as an anthropologist of

177
00:09:54,080 --> 00:09:56,040
Speaker 1: what you might be looking at, You're not probably going

178
00:09:56,080 --> 00:09:58,079
Speaker 1: to make the same assumptions if you know that the

179
00:09:58,640 --> 00:10:01,480
Speaker 1: digestive system can play a as much of a part

180
00:10:01,520 --> 00:10:05,800
Speaker 1: as let's say, the creatures chief or the humans teeth. Um.

181
00:10:06,600 --> 00:10:08,240
Speaker 1: So I thought that was a good example to share.

182
00:10:08,559 --> 00:10:11,200
Speaker 1: Of course, not everybody really gets in on the humor

183
00:10:11,320 --> 00:10:15,040
Speaker 1: of the thing. Robert May, who at the time was

184
00:10:15,080 --> 00:10:19,640
Speaker 1: the British government's Chief Scientific Advisor, UH, he wrote a

185
00:10:19,720 --> 00:10:23,280
Speaker 1: letter to both Nature UH, you know, the Science Journal,

186
00:10:23,440 --> 00:10:27,000
Speaker 1: as well as the Annals and Probable Research, and he

187
00:10:27,040 --> 00:10:30,160
Speaker 1: took the ignoble Prize agenas to task any UH for

188
00:10:30,360 --> 00:10:34,640
Speaker 1: quote ridiculing serious work um and UH, also arguing that

189
00:10:34,720 --> 00:10:38,400
Speaker 1: the awards should only target anti science and pseudoscience and

190
00:10:38,480 --> 00:10:40,960
Speaker 1: leave the real scientists to their labor. So his he

191
00:10:41,040 --> 00:10:42,760
Speaker 1: was like, this is horribly youre just making fun of

192
00:10:42,800 --> 00:10:44,880
Speaker 1: these I don't think it was a particularly informed opinion.

193
00:10:44,960 --> 00:10:46,360
Speaker 1: I think it was more of like a gut reaction

194
00:10:46,400 --> 00:10:48,200
Speaker 1: to him hearing about it. It's it's kind of my

195
00:10:48,240 --> 00:10:51,080
Speaker 1: take on it. But but he did. Here was somebody

196
00:10:51,440 --> 00:10:55,360
Speaker 1: who did not get the joke. Yeah, And again Abraham said,

197
00:10:55,600 --> 00:10:57,959
Speaker 1: we are not ridiculing, which is shining a light on science,

198
00:10:58,000 --> 00:10:59,720
Speaker 1: and we're doing it in a fun way in which

199
00:10:59,800 --> 00:11:02,640
Speaker 1: most people don't necessarily think of sciences is fun, you know,

200
00:11:02,800 --> 00:11:06,080
Speaker 1: lighthearted thing. But here we are talking about things in

201
00:11:06,120 --> 00:11:08,960
Speaker 1: a way that um that again, as he says, makes

202
00:11:08,960 --> 00:11:11,440
Speaker 1: you laugh and makes you think, well, I can't help

203
00:11:11,440 --> 00:11:14,880
Speaker 1: but be reminded of a little tidbit from Mary Roaches

204
00:11:15,040 --> 00:11:18,360
Speaker 1: packing from Mars, because she was talking about the space program,

205
00:11:18,480 --> 00:11:21,440
Speaker 1: especially in its early days. You kind of had two groups.

206
00:11:21,880 --> 00:11:24,920
Speaker 1: You had the scientists who were very much immersed in

207
00:11:24,960 --> 00:11:28,320
Speaker 1: this world of science and world of scientific research and

208
00:11:28,400 --> 00:11:33,120
Speaker 1: hardcore facts and trying to factor out these various equations

209
00:11:33,120 --> 00:11:35,960
Speaker 1: to to do amazing things in space. And then you

210
00:11:36,040 --> 00:11:38,720
Speaker 1: had the astronauts who were, hey, you had more of

211
00:11:38,720 --> 00:11:41,240
Speaker 1: a cowboy mentality. You know a lot of these guys

212
00:11:41,240 --> 00:11:44,920
Speaker 1: where they were former experimental jet pilots and all this,

213
00:11:45,000 --> 00:11:48,360
Speaker 1: so there was a big divide between their outlook on life,

214
00:11:48,360 --> 00:11:50,960
Speaker 1: and occasionally it would clash. And the example that Mary

215
00:11:51,040 --> 00:11:53,120
Speaker 1: Roach brought up was be you know, the scientists were

216
00:11:53,160 --> 00:11:55,880
Speaker 1: trying to figure out how to do long distant space

217
00:11:55,920 --> 00:11:58,360
Speaker 1: flights and they were like, well, uh, let's you know,

218
00:11:58,400 --> 00:12:00,679
Speaker 1: I'm thinking about ways to make food for there for

219
00:12:00,760 --> 00:12:03,400
Speaker 1: a long flight through space. Um, let's see, there's got it.

220
00:12:03,480 --> 00:12:05,680
Speaker 1: There's probably a really good way that we could make

221
00:12:05,920 --> 00:12:09,520
Speaker 1: portions of the spaceship edible and potentially come up with

222
00:12:09,520 --> 00:12:14,040
Speaker 1: a way to turn the astronauts poop into food and

223
00:12:14,040 --> 00:12:16,000
Speaker 1: and the astronauts Scott winted this and they're like, no,

224
00:12:16,080 --> 00:12:19,240
Speaker 1: we're not doing that. So you had this clash between

225
00:12:20,559 --> 00:12:24,000
Speaker 1: the scientific world in the outside world, and that is

226
00:12:24,000 --> 00:12:25,600
Speaker 1: where a lot of the humor tends to come from

227
00:12:25,600 --> 00:12:28,640
Speaker 1: in these experiments because you have you have scientists immersed

228
00:12:28,679 --> 00:12:32,160
Speaker 1: in their studies when then the closed confines of the

229
00:12:32,240 --> 00:12:35,280
Speaker 1: lab and then they bring out these results that that

230
00:12:35,400 --> 00:12:38,000
Speaker 1: may not necessarily even seem all that funny to them,

231
00:12:38,080 --> 00:12:40,440
Speaker 1: but when you expose it to the rest of the world,

232
00:12:41,440 --> 00:12:43,120
Speaker 1: that's that's where a lot of the humor comes. Yeah,

233
00:12:43,200 --> 00:12:45,920
Speaker 1: when you back up and take some perspective, I think

234
00:12:45,920 --> 00:12:48,400
Speaker 1: it's so interesting. Although some of the scientists know that

235
00:12:48,559 --> 00:12:51,720
Speaker 1: if they're having fun with it, but um for for them,

236
00:12:51,760 --> 00:12:54,600
Speaker 1: it's you know, sometimes it's even a thought experiment, which

237
00:12:54,640 --> 00:12:58,200
Speaker 1: we've talked about being so um valuable in trying to

238
00:12:58,240 --> 00:13:01,360
Speaker 1: have breakthroughs. All Right, there's a little background on what

239
00:13:01,520 --> 00:13:04,559
Speaker 1: the Ignable prizes are all about. We're gonna take a

240
00:13:04,640 --> 00:13:07,040
Speaker 1: quick break and when we come back, we're going to

241
00:13:07,120 --> 00:13:10,520
Speaker 1: dive into some of the two thousand and twelves more

242
00:13:10,600 --> 00:13:19,080
Speaker 1: notable in all right, we're back. So we're not going

243
00:13:19,160 --> 00:13:21,800
Speaker 1: to get through all of them are certainly not in

244
00:13:21,840 --> 00:13:24,600
Speaker 1: this podcast, but we're gonna we're gonna highlight some of

245
00:13:24,640 --> 00:13:26,559
Speaker 1: the ones that kind of resonated with us and we

246
00:13:26,600 --> 00:13:31,520
Speaker 1: thought were particularly um amusing. Yeah. One that I thought

247
00:13:31,600 --> 00:13:33,400
Speaker 1: was great both of us that were great is the

248
00:13:33,440 --> 00:13:36,280
Speaker 1: two thousand and twelve Neuroscience Prize. And this is largely

249
00:13:36,360 --> 00:13:41,520
Speaker 1: a story about a dead salmon's brain activity and about

250
00:13:41,920 --> 00:13:45,840
Speaker 1: statistical correction methods in F M R. I. Okay, so

251
00:13:45,840 --> 00:13:47,920
Speaker 1: first of all, why is this funny? First of all,

252
00:13:48,600 --> 00:13:51,440
Speaker 1: dead fish. There's just a dead fish in and of

253
00:13:51,520 --> 00:13:54,000
Speaker 1: itself is hilarious. I couldn't help but think of the

254
00:13:54,120 --> 00:13:57,200
Speaker 1: character Lose Zealand from the Muppets. You remember him with

255
00:13:57,280 --> 00:13:59,559
Speaker 1: the I had the big frilly collar and the mustache

256
00:13:59,600 --> 00:14:02,199
Speaker 1: and the eyes and just throw fish, boomerang fish. It's

257
00:14:02,200 --> 00:14:04,640
Speaker 1: part of his act, or any kind of a skip

258
00:14:04,679 --> 00:14:08,319
Speaker 1: where somebody is slapped with a fish. It's just it's true,

259
00:14:08,360 --> 00:14:11,439
Speaker 1: it's interesting, hilarious, it's it's on par with a rubber chicken. Right,

260
00:14:11,960 --> 00:14:14,520
Speaker 1: And the idea that you could study dead fish, I

261
00:14:14,520 --> 00:14:17,440
Speaker 1: mean it's not it's not hilarious. Factly, because the dead

262
00:14:17,480 --> 00:14:20,280
Speaker 1: fish is still that's it's an animal, it's a it's

263
00:14:20,320 --> 00:14:23,080
Speaker 1: an organism. We can perform a necroxy on that species

264
00:14:23,080 --> 00:14:25,280
Speaker 1: and learn all sorts of things. But there's something just

265
00:14:25,360 --> 00:14:28,280
Speaker 1: hilarious about the idea of a dead fish being anything

266
00:14:28,320 --> 00:14:32,520
Speaker 1: other than something rotting on the bank or or laid

267
00:14:32,520 --> 00:14:36,240
Speaker 1: out on ice at the grocery store. Well, um, in

268
00:14:36,280 --> 00:14:39,000
Speaker 1: the in the salmon comes to us actually kind of

269
00:14:39,040 --> 00:14:42,760
Speaker 1: by accident, or rather to the researchers Craig Bennett, Abego Baird,

270
00:14:42,960 --> 00:14:47,480
Speaker 1: Michael Miller, and George Wolford, we're all looking at a

271
00:14:47,640 --> 00:14:53,720
Speaker 1: rather preparing themselves for an upcoming experiment or study with humans,

272
00:14:54,440 --> 00:14:57,560
Speaker 1: and they were I think it was a social stimuli study.

273
00:14:57,960 --> 00:15:00,640
Speaker 1: But anyway, they had to test out fm R I

274
00:15:00,840 --> 00:15:04,440
Speaker 1: machine and get some baseline operations on it. So at

275
00:15:04,440 --> 00:15:07,560
Speaker 1: first they used a pumpkin to try to gather some

276
00:15:07,800 --> 00:15:11,920
Speaker 1: statistical data and do some scanning. Um. And then they thought, well,

277
00:15:11,960 --> 00:15:14,320
Speaker 1: that's not, this doesn't quite work out. We can't really

278
00:15:14,320 --> 00:15:17,000
Speaker 1: see that too. Well, that's not there's going on inside

279
00:15:17,000 --> 00:15:19,160
Speaker 1: of a pumpkin. Yeah, exactly, you could see the seeds.

280
00:15:19,160 --> 00:15:21,240
Speaker 1: But then they said, okay, a cornish game hen would

281
00:15:21,240 --> 00:15:24,160
Speaker 1: be great. Did that wasn't quite on par with what

282
00:15:24,240 --> 00:15:27,800
Speaker 1: they needed. Finally they settled on in an Atlantic salmon

283
00:15:28,000 --> 00:15:30,640
Speaker 1: because it had, you know, the sort of definition it needed,

284
00:15:30,720 --> 00:15:34,280
Speaker 1: had the guts um, it was pretty much ideal. So

285
00:15:34,440 --> 00:15:38,240
Speaker 1: they ran all the usual anatomical scans and then they

286
00:15:38,320 --> 00:15:41,560
Speaker 1: ran the experimental set of the study as well, and

287
00:15:41,600 --> 00:15:44,480
Speaker 1: then they actually showed the salmon images of people in

288
00:15:44,600 --> 00:15:49,920
Speaker 1: social situations, either socially inclusive situations or socially exclusive situations,

289
00:15:50,520 --> 00:15:53,720
Speaker 1: and the salmon was asked to respond saying how it felt.

290
00:15:54,640 --> 00:15:57,440
Speaker 1: So of course, they basically just did the baseline of

291
00:15:57,480 --> 00:16:00,600
Speaker 1: the machine testing and then they took the data and

292
00:16:00,600 --> 00:16:04,520
Speaker 1: they stored it away. Well. Two years later, one of

293
00:16:04,520 --> 00:16:06,840
Speaker 1: the authors of the study was running a seminar on

294
00:16:06,920 --> 00:16:10,760
Speaker 1: how to properly analyze FMR eyes, and they thought to themselves,

295
00:16:10,840 --> 00:16:12,600
Speaker 1: you know, what, what sort of data can we use

296
00:16:12,720 --> 00:16:16,240
Speaker 1: to try to illustrate what we're talking about? And they

297
00:16:16,280 --> 00:16:19,520
Speaker 1: came on their sam and data. And the reason why

298
00:16:19,560 --> 00:16:23,040
Speaker 1: they were using or looking for data is because what

299
00:16:23,120 --> 00:16:26,960
Speaker 1: they wanted to try to illustrate is that information can

300
00:16:27,000 --> 00:16:29,400
Speaker 1: get so bogged down from these fm R eyes. In

301
00:16:29,400 --> 00:16:32,760
Speaker 1: other words, they create so much data UM, and they're

302
00:16:32,800 --> 00:16:35,920
Speaker 1: broken down into sections called voxels, and these voxels can

303
00:16:35,960 --> 00:16:38,240
Speaker 1: have something like a hundred and thirty thousand pieces of

304
00:16:38,320 --> 00:16:42,760
Speaker 1: data and UM, and they're looking at this contrast selection

305
00:16:42,960 --> 00:16:47,800
Speaker 1: and they're trying to do these comparisons, and doing all

306
00:16:47,840 --> 00:16:50,600
Speaker 1: the statistics can be a problem because as soon as

307
00:16:50,640 --> 00:16:54,840
Speaker 1: you have that much data, you can actually produce false positives,

308
00:16:55,200 --> 00:16:57,800
Speaker 1: all right, So what they wanted to do is they

309
00:16:57,840 --> 00:17:01,600
Speaker 1: wanted to do something called multiple correct comparisons. In other words,

310
00:17:01,680 --> 00:17:06,160
Speaker 1: you correct for all these different points of data before

311
00:17:06,720 --> 00:17:11,560
Speaker 1: you really run your final fMRI data sets. And they

312
00:17:11,720 --> 00:17:15,520
Speaker 1: used the salmon. They took these scans and they then

313
00:17:15,720 --> 00:17:20,040
Speaker 1: ran it against uh what they call their normal multiple comparisons,

314
00:17:20,520 --> 00:17:24,520
Speaker 1: and they found that there was actually brain activity it

315
00:17:24,560 --> 00:17:27,480
Speaker 1: looked like in the dead salmon when they ran it

316
00:17:27,560 --> 00:17:31,199
Speaker 1: across these multiple comparisons. And then they ran it across

317
00:17:31,200 --> 00:17:35,080
Speaker 1: their multiple corrected comparisons and they saw no brain activity. Right,

318
00:17:35,119 --> 00:17:37,800
Speaker 1: and that's the correct thing. So all of it seems ridiculous,

319
00:17:38,160 --> 00:17:40,720
Speaker 1: but the point of it is that uh fm r

320
00:17:40,760 --> 00:17:44,800
Speaker 1: I s are widely used and they're widely cited in media,

321
00:17:45,240 --> 00:17:47,040
Speaker 1: and they have told us so much about our brain

322
00:17:47,119 --> 00:17:51,480
Speaker 1: or mental abilities UM and even certain diseases, but there

323
00:17:51,560 --> 00:17:54,480
Speaker 1: wasn't really a control or a control that was being

324
00:17:54,520 --> 00:17:57,480
Speaker 1: widely used. So Bennett says, Okay, fine, laugh at the

325
00:17:57,520 --> 00:18:00,920
Speaker 1: salmon thing. But in the year before work was released

326
00:18:01,600 --> 00:18:06,840
Speaker 1: around something like of fMRI I, papers didn't use the

327
00:18:06,920 --> 00:18:11,560
Speaker 1: proper statistical correction methods. And now that they ran their

328
00:18:11,560 --> 00:18:14,800
Speaker 1: paper and everybody's laughing about the salmon, that number has dropped.

329
00:18:16,320 --> 00:18:18,680
Speaker 1: So it's an important aspect of it. It It seems like

330
00:18:18,800 --> 00:18:21,800
Speaker 1: it's kind of minutia. But if all of us are

331
00:18:21,920 --> 00:18:24,760
Speaker 1: depending on the data that comes out of fMRI I,

332
00:18:24,880 --> 00:18:28,520
Speaker 1: and it is certainly a technology that is so widely used,

333
00:18:28,560 --> 00:18:31,360
Speaker 1: then you want it to be corrected for to get

334
00:18:31,359 --> 00:18:34,080
Speaker 1: the best results. Yeah. I mean the great thing about

335
00:18:34,080 --> 00:18:37,040
Speaker 1: studies like this is that at once they seem to

336
00:18:37,080 --> 00:18:42,600
Speaker 1: be both ridiculous and pointless and then also highly important

337
00:18:42,640 --> 00:18:46,439
Speaker 1: and illuminating about an important topic. So yeah, so I

338
00:18:46,440 --> 00:18:48,600
Speaker 1: mean this is a perfect example. I think, you know,

339
00:18:48,680 --> 00:18:50,320
Speaker 1: of course they were having fun with it as they

340
00:18:50,320 --> 00:18:52,920
Speaker 1: were asking the dead salm and questions about how they

341
00:18:52,960 --> 00:18:57,280
Speaker 1: felt about social stimuli. We could also mention the fluid

342
00:18:57,359 --> 00:19:02,240
Speaker 1: dynamics UM work is also pretty which on the surfaces

343
00:19:02,400 --> 00:19:05,280
Speaker 1: hilarious because the fluid Dynamics prize went to the paper

344
00:19:05,560 --> 00:19:09,640
Speaker 1: walking with coffee, Why does it spill? This study came

345
00:19:09,680 --> 00:19:12,680
Speaker 1: from a team of Russian, Canadian, and US scientists who

346
00:19:12,680 --> 00:19:15,840
Speaker 1: were studying the dynamics of liquid slashing to learn what

347
00:19:15,960 --> 00:19:19,760
Speaker 1: happens when a person walks while carrying a cup of coffee, which,

348
00:19:20,760 --> 00:19:23,399
Speaker 1: which which this is of course on the surfaces is

349
00:19:23,440 --> 00:19:25,760
Speaker 1: great because there's nothing more every day than a cup

350
00:19:25,840 --> 00:19:29,840
Speaker 1: of coffee, and that's certainly slashing coffee. So the idea

351
00:19:29,880 --> 00:19:33,800
Speaker 1: that you would study something so seemingly low steak is

352
00:19:33,840 --> 00:19:37,080
Speaker 1: that is hilarious. Well, and I love that these guys

353
00:19:37,520 --> 00:19:42,440
Speaker 1: physicists Ruslan Katchnikov and HC. Meyer, they were at a conference,

354
00:19:42,440 --> 00:19:45,920
Speaker 1: say that they were watching all of their colleagues slashing

355
00:19:45,920 --> 00:19:49,000
Speaker 1: their coffee around, and they sat down as we all,

356
00:19:49,080 --> 00:19:50,520
Speaker 1: you know, most of us wouldn't say, wow, what a

357
00:19:50,640 --> 00:19:53,200
Speaker 1: my coffee, were like really actually, like, what's the physics

358
00:19:53,200 --> 00:19:56,119
Speaker 1: behind coffee slashing around? Well, most of us would probably

359
00:19:56,200 --> 00:19:58,520
Speaker 1: leave it right there, because guys were like, let's actually

360
00:19:58,560 --> 00:20:01,480
Speaker 1: get to the bottom of this, which I thought was fascinating.

361
00:20:01,800 --> 00:20:05,320
Speaker 1: Piece of fluid dynamics papers and fluid dynamics publications and

362
00:20:05,760 --> 00:20:09,600
Speaker 1: conferences that they're always really amazing, Like there's we try

363
00:20:09,640 --> 00:20:11,400
Speaker 1: to keep an eye on the various studies that come out,

364
00:20:11,440 --> 00:20:13,359
Speaker 1: you know, because we're we're looking at Eureka alert in

365
00:20:13,359 --> 00:20:15,919
Speaker 1: all those places in an intent to keep up with

366
00:20:16,040 --> 00:20:19,359
Speaker 1: with current science and cover it. And the fluid dynamic

367
00:20:19,400 --> 00:20:21,440
Speaker 1: papers will come out and it's always crazy because it'll

368
00:20:21,480 --> 00:20:23,840
Speaker 1: they'll range from stuff like one paper in particular that

369
00:20:23,920 --> 00:20:26,920
Speaker 1: really blew my mind. It was dealing with how nuclear

370
00:20:27,000 --> 00:20:31,360
Speaker 1: fallout moves through an urban city. So there's an idea

371
00:20:31,400 --> 00:20:34,359
Speaker 1: of something potentially very high stakes because you use you

372
00:20:34,440 --> 00:20:37,000
Speaker 1: analyze how fluids flow and again flow, how fluids be

373
00:20:37,040 --> 00:20:39,919
Speaker 1: it air, you know, airs, gases, waters, The way they

374
00:20:39,960 --> 00:20:42,080
Speaker 1: move through an environment is complex. That's why, I mean,

375
00:20:42,119 --> 00:20:44,239
Speaker 1: it's one of the reasons. It's whether prediction is so

376
00:20:44,280 --> 00:20:48,119
Speaker 1: complicated and impossible beyond a certain point. Uh, it's a

377
00:20:48,160 --> 00:20:50,240
Speaker 1: it's it's so when we get down to the science

378
00:20:50,440 --> 00:20:52,960
Speaker 1: of how those movements work, it gets really rich and

379
00:20:53,080 --> 00:20:56,639
Speaker 1: complex and uh and if we're looking at how uh

380
00:20:57,000 --> 00:20:59,160
Speaker 1: fall outspreads through the city, that allows us to better

381
00:20:59,680 --> 00:21:02,720
Speaker 1: uh plan out our means of responding to a disaster.

382
00:21:02,960 --> 00:21:05,080
Speaker 1: But then On the other hand, these same papers and

383
00:21:05,359 --> 00:21:08,840
Speaker 1: publications will also analyze things such as this, like how

384
00:21:08,880 --> 00:21:12,360
Speaker 1: does how does coffee slash out of a saucer? And

385
00:21:12,520 --> 00:21:16,760
Speaker 1: ultimately they're illuminating that they're adding more data to the

386
00:21:16,800 --> 00:21:20,560
Speaker 1: same equation, how do these how do fluids move? Like?

387
00:21:20,640 --> 00:21:24,280
Speaker 1: What are the physics of these of these fluid interactions? Right?

388
00:21:24,320 --> 00:21:26,800
Speaker 1: Because it's a mental exercise, right, because you can apply

389
00:21:26,880 --> 00:21:30,600
Speaker 1: this to rocketry, like shifting weight that could destabilize a

390
00:21:30,640 --> 00:21:34,320
Speaker 1: missile or a rocket, so um, if you're interested. They

391
00:21:34,359 --> 00:21:37,560
Speaker 1: did come to the conclusion that the slashing is due

392
00:21:37,600 --> 00:21:41,359
Speaker 1: to a complex motion of a cup and also the

393
00:21:41,440 --> 00:21:48,320
Speaker 1: biomechanics of walking and the low viscocity liquid dynamics. And

394
00:21:49,040 --> 00:21:51,280
Speaker 1: this is great because all of those things, I mean,

395
00:21:51,280 --> 00:21:52,919
Speaker 1: it's a pretty much a no brainer. We pretty much

396
00:21:53,000 --> 00:21:56,600
Speaker 1: knew all of that already. But there's something hilarious but

397
00:21:56,680 --> 00:22:01,160
Speaker 1: also really fulfilling about somebody setting out to scientifically answer

398
00:22:01,200 --> 00:22:03,960
Speaker 1: a question that we already had an answer to. Like

399
00:22:04,040 --> 00:22:05,679
Speaker 1: it always it always kind of blows my mind. It's

400
00:22:05,720 --> 00:22:07,919
Speaker 1: like it's just something that we like, here's an example

401
00:22:07,920 --> 00:22:09,439
Speaker 1: of something that we just we just saw it, we

402
00:22:09,480 --> 00:22:12,439
Speaker 1: took it on faith. It turned out to be exactly

403
00:22:12,480 --> 00:22:13,960
Speaker 1: what we thought it would be. And when we have

404
00:22:14,000 --> 00:22:18,480
Speaker 1: the scientific reasons why, But what if the reasons had

405
00:22:18,480 --> 00:22:21,480
Speaker 1: been different? You know, never know until you actually prove it.

406
00:22:21,520 --> 00:22:24,600
Speaker 1: You know, this is basically a proof of common sense

407
00:22:24,760 --> 00:22:27,240
Speaker 1: with science. I like that proof of common sense. And

408
00:22:27,359 --> 00:22:29,240
Speaker 1: if you wanted to, or if the industry wanted to,

409
00:22:29,359 --> 00:22:33,160
Speaker 1: it could change the design if it's coffee cups. Right, Um,

410
00:22:33,200 --> 00:22:36,000
Speaker 1: all right, So I think that gives a good couple

411
00:22:36,040 --> 00:22:40,320
Speaker 1: of examples. We have some other ones that we will

412
00:22:40,440 --> 00:22:44,320
Speaker 1: leave for our part two, and just to wet your appetite,

413
00:22:44,760 --> 00:22:47,639
Speaker 1: what they have to do with monkey butts, thwarting along

414
00:22:47,680 --> 00:22:53,800
Speaker 1: winded orators, and preventing gastro intestinal explosions. Yes, so all

415
00:22:53,840 --> 00:22:55,679
Speaker 1: of those things are definitely worth tuning into on the

416
00:22:55,680 --> 00:23:00,200
Speaker 1: next episode, so join us for that. In the mean time,

417
00:23:00,280 --> 00:23:02,239
Speaker 1: if you would like to reach out to us and

418
00:23:02,560 --> 00:23:06,480
Speaker 1: share something about a current or past ignoble winner that

419
00:23:06,480 --> 00:23:09,280
Speaker 1: you thought was particularly fascinating, you can find this on

420
00:23:09,400 --> 00:23:12,240
Speaker 1: Tumblr and on Facebook. We are stuff to blow your

421
00:23:12,240 --> 00:23:14,800
Speaker 1: mind on both of those and on Twitter we go

422
00:23:14,840 --> 00:23:17,480
Speaker 1: by the handle blow the Mind, and you can always

423
00:23:17,560 --> 00:23:19,920
Speaker 1: drop us a line and blow the mind at Discovery

424
00:23:20,000 --> 00:23:29,360
Speaker 1: dot com for more on this and thousands of other topics.

425
00:23:29,600 --> 00:23:35,560
Speaker 1: Is it how Stuff Works dot com