1
00:00:00,160 --> 00:00:02,320
This is Evolutionary Psychology of the Podcast.

2
00:00:02,400 --> 00:00:03,920
I'm David Petrozowski.

3
00:00:04,240 --> 00:00:05,520
And I'm David Pinsov.

4
00:00:05,760 --> 00:00:10,560
This is actual evolutionary psychology by actual Evolutionary Psychologist.

5
00:00:23,200 --> 00:00:25,120
David, did you hear that?

6
00:00:25,520 --> 00:00:26,320
What what?

7
00:00:26,400 --> 00:00:29,130
I I Oh well uh the nothing in particular.

8
00:00:29,290 --> 00:00:34,010
I was just wondering because what who we're talking to today is Greg Bryant, and Greg

9
00:00:34,010 --> 00:00:35,770
Bryant does all things acoustic.

10
00:00:36,170 --> 00:00:36,730
That's right.

11
00:00:36,810 --> 00:00:42,410
Uh we get into vocal communication, music, uh why

12
00:00:42,730 --> 00:00:48,490
different sounds evoke different emotions in us, whether those connections between sounds

13
00:00:48,490 --> 00:00:52,810
and emotions are arbitrary, or whether they have sort of interesting evolutionary

14
00:00:52,900 --> 00:00:53,860
explanations.

15
00:00:54,580 --> 00:00:59,300
Yeah, I think I think for me the mystery of why music makes us feel anything is was

16
00:00:59,300 --> 00:01:00,740
partially resolved by this.

17
00:01:00,820 --> 00:01:04,740
And I feel like Greg did an amazing job um organizing my thinking.

18
00:01:04,820 --> 00:01:06,340
And I knew about Greg's work.

19
00:01:06,420 --> 00:01:10,260
Um, but even this conversation for me really helped organize my thinking.

20
00:01:10,340 --> 00:01:14,340
Um, for example, understanding, yeah, like what's up with music, why do we feel emotions

21
00:01:14,340 --> 00:01:15,060
around that?

22
00:01:15,220 --> 00:01:18,020
Um, why is rock music the way it is?

23
00:01:18,100 --> 00:01:23,990
Um, and how is it related to yeah, um you will learn why electric guitars are

24
00:01:23,990 --> 00:01:27,510
distorted and what role that distortion is playing.

25
00:01:27,750 --> 00:01:33,110
Yeah the evolutionary psychology of uh overdrive is should be the title of

26
00:01:33,670 --> 00:01:38,310
of this episode maybe um uh yeah and then we also talk about Greg's other work.

27
00:01:38,710 --> 00:01:41,190
We spent a lot of time on music which is fantastic.

28
00:01:41,350 --> 00:01:45,910
Um but we also do talk about um some of his other work about and we get toward the end we

29
00:01:45,910 --> 00:01:46,950
talk about humor and laughter.

30
00:01:47,220 --> 00:01:52,020
Oh yeah, I talked a lot about laughter and um uh how it ties in with encryption theory and

31
00:01:52,100 --> 00:01:54,260
with my own work on uh mix-ups.

32
00:01:54,420 --> 00:01:58,020
Uh it was a really fun conversation and I learned a lot and had a great time.

33
00:01:58,260 --> 00:02:01,620
Yeah, so uh this is in acoustic format.

34
00:02:01,700 --> 00:02:03,460
So sit back, enjoy and listen.

35
00:02:03,540 --> 00:02:08,980
And yeah, I've I you'll learn a lot and uh maybe think about what you're doing with your

36
00:02:08,980 --> 00:02:13,620
ears slightly differently after you do the following with your ears, which is a weird way

37
00:02:13,620 --> 00:02:19,510
to say that, but I guess I'll enjoy listening to a podcast about sound with your ears.

38
00:02:21,670 --> 00:02:25,190
We usually do just you know like an introduction before your own, so we'll just get right

39
00:02:25,190 --> 00:02:25,590
into it.

40
00:02:25,750 --> 00:02:31,670
Um if evolutionary behavioral sciences were a heist movie, um, you would be the acoustic

41
00:02:31,670 --> 00:02:34,790
expert, is sort of my representation of you.

42
00:02:35,350 --> 00:02:38,950
Um uh have you always been an audiophile?

43
00:02:40,040 --> 00:02:44,200
Yeah, well, I mean it's funny you use the term audiophile because that is has a specific

44
00:02:44,200 --> 00:02:49,000
meaning, um, meaning that it's people that are into super high-end gear, and there's

45
00:02:49,000 --> 00:02:54,440
actually can be almost a religious element where you think there are differences in sound

46
00:02:54,440 --> 00:02:59,640
between the tube amp with the shielded cables, the whatever.

47
00:02:59,960 --> 00:03:03,850
Yeah, Versus a solid state modeling app that sounds exactly the same.

48
00:03:04,010 --> 00:03:04,490
And yeah.

49
00:03:04,890 --> 00:03:05,370
That's the thing.

50
00:03:05,450 --> 00:03:09,210
So I usually land on that side of the debate because I'm a perceptual psychologist, yeah.

51
00:03:09,370 --> 00:03:13,850
Um, but I am I mean I am an audiophile and that's I was raised by audiophiles.

52
00:03:13,930 --> 00:03:19,530
My dad was was into stereos and he was a um actually in the sound um

53
00:03:20,010 --> 00:03:25,500
in the army corps with uh the uh the uh radio corps, sorry.

54
00:03:25,900 --> 00:03:26,940
Nice army.

55
00:03:27,100 --> 00:03:32,460
So he was interested in that kind of stuff, and so we always had nice stereos, and my dad

56
00:03:32,460 --> 00:03:37,420
was really into music, and my brother got into that that way, and then he does is he's a

57
00:03:37,420 --> 00:03:42,300
software engineer who works on audio compression, so it's a sound situation in my family,

58
00:03:42,380 --> 00:03:43,900
so I guess that's where I get it.

59
00:03:44,060 --> 00:03:49,890
And then I was obviously very into music from a young age, And so yeah, I've always kind of

60
00:03:49,890 --> 00:03:50,450
been into sound.

61
00:03:50,530 --> 00:03:55,410
And that was sort of my attraction to um evolution and behavior was the connection between

62
00:03:55,410 --> 00:04:00,610
language and music and trying to think about, you know, speech communication and vocal

63
00:04:00,610 --> 00:04:01,570
communication in general.

64
00:04:01,810 --> 00:04:02,530
Yeah, that's awesome.

65
00:04:02,610 --> 00:04:07,410
I mean, uh, learn versus innate is a dumb distinction, but I mean it captures something in

66
00:04:07,410 --> 00:04:11,860
w looking at your family and being like, wow, we're really all into music, have you thought

67
00:04:11,860 --> 00:04:14,740
about, you know, like is it is it that because it was all around?

68
00:04:14,820 --> 00:04:16,420
Or or what's your take on those?

69
00:04:16,580 --> 00:04:16,740
Yeah.

70
00:04:17,140 --> 00:04:21,380
Yeah, that I mean that probably that must have played some role, right?

71
00:04:21,540 --> 00:04:26,980
I mean, it's hard to say, though it's funny though, the music thing is they're into music

72
00:04:26,980 --> 00:04:29,460
listening, but not into music performance.

73
00:04:29,780 --> 00:04:33,540
So I had to beg my parents to give me any kind of musical instrument.

74
00:04:33,700 --> 00:04:36,100
And I didn't really get one until I was about fifteen years old.

75
00:04:36,260 --> 00:04:39,890
I got an electric guitar, which I'm and at the time they were probably like, this is why we

76
00:04:39,890 --> 00:04:40,930
didn't want to do this.

77
00:04:42,530 --> 00:04:43,730
Because you were like distortion.

78
00:04:43,970 --> 00:04:44,610
What was it?

79
00:04:45,570 --> 00:04:51,490
It was a Hondo strat copy, and then a Sun 20-watt

80
00:04:51,650 --> 00:04:55,810
amp, which that I wish I still had that thing because Sun is a really cool make and they

81
00:04:55,810 --> 00:04:57,650
had a really distortion built in.

82
00:04:57,810 --> 00:05:01,160
But I was into like speed metal and punk rock.

83
00:05:01,240 --> 00:05:03,320
I mean, it was probably a nightmare for them.

84
00:05:03,560 --> 00:05:08,280
Um, but I mean, I would have played piano if I had one and I wanted a guitar when I was

85
00:05:08,280 --> 00:05:08,760
eight years old.

86
00:05:08,840 --> 00:05:12,280
I actually asked first for a guitar and they just wouldn't give me an instrument because I

87
00:05:12,360 --> 00:05:15,960
think they you know they had the classic, you know, you're we're gonna buy it for you and

88
00:05:15,960 --> 00:05:17,400
then it's just gonna sit around.

89
00:05:18,520 --> 00:05:21,720
And they didn't believe that I was really into it, and I was.

90
00:05:21,880 --> 00:05:22,200
Right.

91
00:05:22,520 --> 00:05:26,510
So in that sense, they've they hindered my music development.

92
00:05:27,070 --> 00:05:27,630
I'm sorry.

93
00:05:27,710 --> 00:05:28,110
Yeah.

94
00:05:28,430 --> 00:05:33,550
I probably would, you know, I would have different skills if I had learned m instruments

95
00:05:33,550 --> 00:05:34,750
when I was eight, you know.

96
00:05:34,990 --> 00:05:35,150
Yeah.

97
00:05:35,390 --> 00:05:38,510
Like I mean, in in some ways, maybe it would be better.

98
00:05:38,830 --> 00:05:40,750
I mean, I don't read music.

99
00:05:41,070 --> 00:05:46,000
Um, I understand music theory uh you know conceptually, but I can't sight read.

100
00:05:46,160 --> 00:05:49,280
And so if I look at some sheet music, I can't play it on the guitar.

101
00:05:49,360 --> 00:05:52,240
Without meaning it would be a lot of work for me to figure that out.

102
00:05:52,800 --> 00:05:55,840
Um and so that gives you different skills.

103
00:05:55,920 --> 00:06:01,600
Like, for example, I might have developed perfect pitch if I had learned the notes and the

104
00:06:01,600 --> 00:06:03,680
theory and the instrument when I was young.

105
00:06:03,840 --> 00:06:06,240
And now I'll never learn I'll never have perfect pitch.

106
00:06:06,320 --> 00:06:08,080
Once you're an adult, the evidence is pretty good.

107
00:06:08,480 --> 00:06:11,810
Once you pass that threshold, you never get perfect pitch.

108
00:06:11,890 --> 00:06:13,650
So I have good relative pitch.

109
00:06:14,290 --> 00:06:19,330
Um, but yeah, so there's these certain little skills that you get when you learn early that

110
00:06:19,330 --> 00:06:21,570
are just obtainable later.

111
00:06:21,810 --> 00:06:23,970
So yeah, it was kind of an interesting combination.

112
00:06:24,050 --> 00:06:25,970
They were really into the listening, but not the playing.

113
00:06:26,290 --> 00:06:26,610
Interesting.

114
00:06:26,770 --> 00:06:26,930
Yeah.

115
00:06:27,010 --> 00:06:27,810
Well, this is a PSA.

116
00:06:28,130 --> 00:06:28,450
Sorry, David.

117
00:06:28,690 --> 00:06:29,090
PSA.

118
00:06:29,330 --> 00:06:34,500
If your kid wants to play music, uh my editorial here is let them.

119
00:06:34,580 --> 00:06:38,100
And if they don't use it, you can always put the thing on reverb and get a good reset.

120
00:06:38,420 --> 00:06:40,820
Yeah, let them if they want to, but don't push it on them either.

121
00:06:40,980 --> 00:06:41,540
No, yes, exactly.

122
00:06:44,420 --> 00:06:47,380
I know so many people that were forced to take piano lessons that now have like an

123
00:06:47,380 --> 00:06:48,900
aversion, they don't even want to do anything.

124
00:06:49,300 --> 00:06:50,020
Totally, David.

125
00:06:50,100 --> 00:06:50,820
Yeah, really good.

126
00:06:51,060 --> 00:06:51,380
Exactly.

127
00:06:51,460 --> 00:06:52,100
Just like horror.

128
00:06:52,180 --> 00:06:57,270
It's it's it's you let them be autonomous and let the and let them let them do overdrive.

129
00:06:57,350 --> 00:06:59,430
You can give them uh headphones and stuff.

130
00:06:59,670 --> 00:07:01,750
Uh Greg, can you talk about perfect pitch?

131
00:07:01,990 --> 00:07:05,110
Cause that's like maybe a nice little hook into content.

132
00:07:05,350 --> 00:07:08,550
Um because uh you know I've been reading into this too.

133
00:07:08,630 --> 00:07:12,870
I came across this because I had always had this toy idea that perfect pitch was just

134
00:07:12,870 --> 00:07:14,710
something people are born with or not.

135
00:07:14,950 --> 00:07:18,550
Um for people who don't know what it is, can you explain what it is and then can you tell

136
00:07:18,550 --> 00:07:22,470
us if to extent you know I mean I'm I know this is not your primary research, but like what

137
00:07:22,470 --> 00:07:24,380
is the state of the art that we know about it?

138
00:07:24,700 --> 00:07:25,100
Yeah.

139
00:07:25,340 --> 00:07:30,700
Well, uh the idea of perfect pitch, my understanding, is that when um a person can map a

140
00:07:30,940 --> 00:07:32,620
tone onto a note.

141
00:07:33,180 --> 00:07:38,140
So if you hear, you know, a tone of whatever it might be, you can just say that's an A.

142
00:07:38,460 --> 00:07:38,860
That's right.

143
00:07:38,940 --> 00:07:44,300
And you if you say like do an A, they can hum it or uh Exactly.

144
00:07:44,590 --> 00:07:46,990
So they um and they nail it right exactly.

145
00:07:47,150 --> 00:07:47,790
Yeah, yeah.

146
00:07:48,030 --> 00:07:52,590
And so that's something that you learn that mapping between the sound and the category

147
00:07:52,590 --> 00:07:53,310
early.

148
00:07:53,550 --> 00:07:56,830
And um because obviously the category is culturally constrained.

149
00:07:56,910 --> 00:08:01,150
There's no way it could be an A to know an A because A is a cultural invention.

150
00:08:01,390 --> 00:08:02,190
Yeah, right, exactly.

151
00:08:02,430 --> 00:08:04,510
In German it's like there's another different note.

152
00:08:04,590 --> 00:08:06,160
I know Annie always talks about that.

153
00:08:06,480 --> 00:08:09,840
It's like it's a it's it's like a different there's like an H or something.

154
00:08:10,640 --> 00:08:11,680
I I forget the DS.

155
00:08:11,840 --> 00:08:16,080
I'm I'm completely a musical, so I'm is it the fun note, the huh?

156
00:08:18,640 --> 00:08:19,840
Yeah, that's exactly it's right.

157
00:08:20,080 --> 00:08:22,400
Yeah, good and talk to all of our German uh friends.

158
00:08:22,560 --> 00:08:23,040
So yeah.

159
00:08:25,670 --> 00:08:25,990
Yeah.

160
00:08:26,230 --> 00:08:31,830
So obviously, you know, you have to learn some system, and there's a lot of them out there,

161
00:08:32,070 --> 00:08:32,950
potentially.

162
00:08:33,190 --> 00:08:38,630
Um, so if it's learned within a critical period, which I think is it's right in the

163
00:08:38,630 --> 00:08:39,750
language critical period.

164
00:08:39,830 --> 00:08:41,750
So it's kind of like before your five.

165
00:08:41,910 --> 00:08:45,430
And people a lot of people say, I had perfect pitch and I never had music lessons.

166
00:08:45,510 --> 00:08:50,150
It's like, except that one time, you know, that it always, if you push them, it always, oh,

167
00:08:50,230 --> 00:08:51,270
there was one little thing.

168
00:08:51,430 --> 00:08:52,790
It doesn't take much, I think.

169
00:08:52,870 --> 00:08:54,860
You know, kids soak it up pretty fast.

170
00:08:55,100 --> 00:09:00,940
Um, once you pass that threshold, I think the evidence is that that you can't learn

171
00:09:00,940 --> 00:09:01,180
it.

172
00:09:01,420 --> 00:09:07,100
So you have to have some sort of relative pitch to the know you know the

173
00:09:07,100 --> 00:09:11,580
difference between it and so you have a reference pitch, then you can say what the note is

174
00:09:11,660 --> 00:09:12,780
if you're really good at it.

175
00:09:12,940 --> 00:09:13,660
You can learn that.

176
00:09:13,900 --> 00:09:15,020
And and can I just interrupt?

177
00:09:15,180 --> 00:09:19,270
Is it that because I mean, so you know, I I know somebody with perfect pitch, like it's

178
00:09:19,350 --> 00:09:23,750
when you when they play a note or you do a note, it's it's it would be like asking the most

179
00:09:23,750 --> 00:09:25,110
people like what color is this?

180
00:09:25,190 --> 00:09:26,950
It's like, oh my gosh, of course it's red, right?

181
00:09:27,030 --> 00:09:29,510
It's like it's boring, and I'm just like, I don't I don't know how you do that.

182
00:09:29,750 --> 00:09:35,350
Um, is it that the lessons create the discrete

183
00:09:35,350 --> 00:09:37,190
mappings between the notes?

184
00:09:37,270 --> 00:09:42,870
Or is it just that you need a linguistic um label, but the shelf, the

185
00:09:42,870 --> 00:09:47,360
cognitive shelf for that um note is already there, if that makes sense.

186
00:09:47,440 --> 00:09:52,080
Or if that I don't know if I'm making the uh well, I mean the instruments produce there's

187
00:09:52,080 --> 00:09:52,960
not a lot of variation.

188
00:09:53,040 --> 00:09:58,080
So I mean if they learned with a bad piano, for example, then that would probably screw the

189
00:09:58,080 --> 00:09:58,800
mechanism up.

190
00:09:59,040 --> 00:09:59,360
Okay.

191
00:09:59,600 --> 00:10:04,080
Um, because then people later would say they'd play A440 and then they learned it like

192
00:10:04,080 --> 00:10:05,840
A452.

193
00:10:06,000 --> 00:10:07,920
Oh, I think that's then they'd they would that might.

194
00:10:08,000 --> 00:10:12,800
I mean, I'd that would be kind of a bad experiment to do to do that.

195
00:10:13,280 --> 00:10:15,850
But um, but I think that could mess it up.

196
00:10:16,010 --> 00:10:19,450
So the thing is that people are learning with instruments that are tuned properly, you

197
00:10:19,450 --> 00:10:22,490
know, to like a regular piano that's tuned reasonably.

198
00:10:22,570 --> 00:10:24,090
But I mean there's probably some error in there.

199
00:10:24,330 --> 00:10:24,570
I see.

200
00:10:24,730 --> 00:10:25,530
So it's the intervals.

201
00:10:25,690 --> 00:10:31,290
They're actually learning the the the fundamental frequency intervals, or like what counts

202
00:10:31,290 --> 00:10:35,050
as a step in in a continuous range of frequencies.

203
00:10:35,450 --> 00:10:38,890
They do, except actually you learn it without reference pitch.

204
00:10:38,970 --> 00:10:42,490
So you add a context, just one note, you can do it.

205
00:10:42,620 --> 00:10:46,220
So that means you it's not necessarily about an interval because the interval is two or

206
00:10:46,220 --> 00:10:46,780
more notes.

207
00:10:47,020 --> 00:10:47,420
Oh, I see.

208
00:10:47,500 --> 00:10:48,220
I was using the wrong term.

209
00:10:48,380 --> 00:10:49,740
I just meant a space.

210
00:10:49,820 --> 00:10:50,060
Sorry.

211
00:10:50,140 --> 00:10:51,100
I I should just share it.

212
00:10:51,180 --> 00:10:52,060
I know nothing about music.

213
00:10:52,380 --> 00:10:55,260
Yeah, the interval is this a real relationship between two notes.

214
00:10:55,500 --> 00:10:55,980
Right, yeah.

215
00:10:56,060 --> 00:10:56,220
Right.

216
00:10:56,460 --> 00:10:58,780
And um, and so they learn those two.

217
00:10:59,180 --> 00:11:03,100
Um, and you learn the scales and all that stuff, you learn it in a different way when

218
00:11:03,100 --> 00:11:03,740
you're young.

219
00:11:04,140 --> 00:11:08,510
But yeah, so if you but just out of context, they're doing a mapping to some category,

220
00:11:08,670 --> 00:11:09,470
whatever it might be.

221
00:11:09,630 --> 00:11:13,630
If it's you know, if you're Western music, then you're learning the the letters.

222
00:11:13,790 --> 00:11:17,470
Um, but then in another system you're learning it in some other different way, but you're

223
00:11:17,470 --> 00:11:20,830
mapping the the frequency to the category.

224
00:11:21,390 --> 00:11:26,830
So I'm I'm curious, so um what the relationship is between our

225
00:11:26,830 --> 00:11:31,700
representation of sound and the objective reality of vibrational frequencies.

226
00:11:31,860 --> 00:11:34,980
Because I know we were talking with Nick Graziano about the relationship between our

227
00:11:34,980 --> 00:11:39,220
representation of color and the objective facts about uh color.

228
00:11:39,380 --> 00:11:44,260
And you know, we we're going into this this cool idea that like we we view white light as

229
00:11:44,260 --> 00:11:48,260
like a kind of purity or um absence of contamination, right?

230
00:11:48,420 --> 00:11:50,260
It's just like there's nothing there, it's pure and bright.

231
00:11:50,340 --> 00:11:53,300
But it's actually if you look into the physics of it, it's the opposite.

232
00:11:53,460 --> 00:11:56,420
White light is all the different wavelengths just sort of smooshed together.

233
00:11:56,580 --> 00:12:01,730
And it's weird that our representation of white doesn't quite fit the objective reality of

234
00:12:01,730 --> 00:12:01,810
it.

235
00:12:01,970 --> 00:12:05,570
There are plenty of other ways in which our representation of color doesn't quite fit the

236
00:12:05,570 --> 00:12:06,850
objective facts of color.

237
00:12:07,010 --> 00:12:11,890
Like, you know, if I see a red object in different lighting conditions, the frequencies of

238
00:12:11,890 --> 00:12:14,610
light that are hitting my retina are going to be very different, but I'm still going to see

239
00:12:14,610 --> 00:12:15,490
it as the same color.

240
00:12:15,650 --> 00:12:16,850
This is color constancy.

241
00:12:17,010 --> 00:12:20,930
We see an object that looks the same, even crazy.

242
00:12:21,250 --> 00:12:25,360
That like the objective frequencies of light are totally different, and yet it seems

243
00:12:25,360 --> 00:12:29,520
exactly the same from our perspective because color is designed to identify and

244
00:12:29,520 --> 00:12:33,600
differentiate objects and not to necessarily accurately represent reality.

245
00:12:33,680 --> 00:12:38,480
So are there similar ways in which our representation of sound is different from the

246
00:12:38,480 --> 00:12:43,440
objective physics of sound, or is it more accurate than it is with color or or how do how

247
00:12:43,440 --> 00:12:44,400
do we think about this?

248
00:12:44,990 --> 00:12:45,950
That's a great question.

249
00:12:46,030 --> 00:12:49,950
And I don't think I have a great answer, other than I would suspect, I think it's more

250
00:12:49,950 --> 00:12:55,470
accurate that those properties in the world are there to a more substantial degree.

251
00:12:55,630 --> 00:12:58,110
Whereas color is reflected off it.

252
00:12:58,190 --> 00:13:01,390
I mean, some would argue there is no color in the world in a sense, right?

253
00:13:01,470 --> 00:13:04,030
That we're it's like we're constructing it.

254
00:13:04,190 --> 00:13:07,070
Um and it does change in context considerably.

255
00:13:07,230 --> 00:13:12,720
I mean, you could there are contextual effects, I think, in in sound where um you know the

256
00:13:12,720 --> 00:13:17,680
same sound presented in two contexts would be perceived differently and then represented

257
00:13:17,680 --> 00:13:17,920
differently.

258
00:13:18,320 --> 00:13:18,800
Oh, interesting.

259
00:13:18,880 --> 00:13:21,280
So it's like sort of an analog to color constancy.

260
00:13:21,680 --> 00:13:23,520
I think there's some similarities there, though.

261
00:13:23,600 --> 00:13:26,560
I can't really come up with a really great um example exactly.

262
00:13:26,720 --> 00:13:32,400
But um in like auditory scene analysis, which is the study of how people perceive the

263
00:13:32,400 --> 00:13:37,280
auditory environment, how you localize a sound, and how you decide that these frequencies

264
00:13:37,280 --> 00:13:42,590
could should belong in this or belong to this source, and these frequencies belong in this

265
00:13:42,590 --> 00:13:45,310
source, even though it's coming in as a wash of frequencies.

266
00:13:45,390 --> 00:13:50,190
We have this coherent separation of sounds that seems like that that sounds coming from

267
00:13:50,190 --> 00:13:53,550
there and that's coming from there, even though all these frequencies are all around you

268
00:13:53,630 --> 00:13:54,750
and you have to parse it.

269
00:13:54,910 --> 00:14:00,350
So, in that sense, you're constructing these representations of things that are probably um

270
00:14:00,670 --> 00:14:06,110
not completely accurate as a function of what they really are and what those things are

271
00:14:06,110 --> 00:14:06,750
emitting.

272
00:14:07,000 --> 00:14:09,400
Um, but I think it's more accurate than color.

273
00:14:09,560 --> 00:14:11,480
Color seems to me more constructed.

274
00:14:11,640 --> 00:14:17,080
But that's I'm I'm not gonna bet much on that conjecture.

275
00:14:17,240 --> 00:14:21,080
But um I mean a fundamental frequency is a fundamental frequency, and I think we're

276
00:14:21,080 --> 00:14:27,000
detecting that in sources that have it um in a way that is pretty the the mapping is pretty

277
00:14:27,000 --> 00:14:28,280
isomorphic.

278
00:14:29,150 --> 00:14:29,470
Yeah.

279
00:14:30,030 --> 00:14:35,310
It I mean it seems like the the selection pressure for auditory scene analysis or knowing

280
00:14:35,310 --> 00:14:39,390
when certain sounds are coming from a particular source would be you know there'd be huge

281
00:14:39,390 --> 00:14:40,430
selection pressure on that.

282
00:14:40,590 --> 00:14:44,910
Like to know, you know, to differentiate the birds from the rustling in the leaves, you

283
00:14:44,910 --> 00:14:49,390
know, from the potential sound of a predator or someone else sneaking up on you, like being

284
00:14:49,390 --> 00:14:52,510
able to make those fine grained distinctions probably would have had huge fitness

285
00:14:52,510 --> 00:14:53,070
implications.

286
00:14:53,310 --> 00:14:53,470
Yeah.

287
00:14:53,550 --> 00:14:54,670
So like how big is it?

288
00:14:54,920 --> 00:14:55,160
Yeah.

289
00:14:55,240 --> 00:14:55,960
How big is it?

290
00:14:56,040 --> 00:14:56,440
Yeah.

291
00:14:56,600 --> 00:14:57,640
Um is it approaching?

292
00:14:57,720 --> 00:14:59,800
Is it is it uh receding?

293
00:14:59,960 --> 00:15:02,600
You know, those are really important distinctions, you know, survival relevant

294
00:15:02,600 --> 00:15:03,240
distinctions.

295
00:15:03,480 --> 00:15:04,280
Uh yeah.

296
00:15:04,520 --> 00:15:08,680
Um But there are, I mean, that's you just brought up a good one, like John Newhoff stuff

297
00:15:08,760 --> 00:15:10,120
with auditory looming, right?

298
00:15:10,280 --> 00:15:15,240
We overestimate how quickly something will arrive and we don't have that same thing for a

299
00:15:15,240 --> 00:15:17,080
thing that's moving away from us.

300
00:15:17,320 --> 00:15:22,600
So there are gonna be various kinds of you know, air management issues and and uh

301
00:15:22,760 --> 00:15:24,280
distortions and yeah.

302
00:15:24,590 --> 00:15:29,390
Craig, I wanted to uh I wanted to ask um w either you can pick which one you want to do.

303
00:15:29,470 --> 00:15:33,950
One is I have this feeling that like vision science has its own conference.

304
00:15:34,030 --> 00:15:36,990
Do do uh do acous does acoustics have its own science?

305
00:15:37,150 --> 00:15:38,190
And is it is large?

306
00:15:38,350 --> 00:15:38,430
Yeah.

307
00:15:38,830 --> 00:15:42,030
Like I feel like as an intro psych professor, I teach vision.

308
00:15:42,110 --> 00:15:43,630
I don't teach teach acoustics.

309
00:15:43,790 --> 00:15:48,270
I feel like acoustics gets the shaft a bit compared to like the other perceptions.

310
00:15:49,080 --> 00:15:49,480
Totally.

311
00:15:49,720 --> 00:15:53,320
The the main journal, the one of them, in my opinion, the best journal in the field for

312
00:15:53,320 --> 00:15:58,680
acoustics, Jazza, Journal of Acoustical Society of America, its impact factor is like 1.4.

313
00:15:59,000 --> 00:16:02,040
I mean, it's it's not um there's not a lot of work.

314
00:16:02,200 --> 00:16:05,480
I mean, there they there is a lot of different kinds of stuff, like underwater acoustics,

315
00:16:05,720 --> 00:16:07,880
and I mean, you know, it's a crazy field.

316
00:16:08,040 --> 00:16:12,920
And a lot of it is um not psychophysics, a lot of it's just pure, it's just you know,

317
00:16:13,080 --> 00:16:17,910
engineering and yeah, it's other not psychology or even behavioral science at all.

318
00:16:18,070 --> 00:16:21,510
But um yeah, no, there is definitely an acoustics field.

319
00:16:21,590 --> 00:16:23,110
It's way smaller than vision.

320
00:16:23,270 --> 00:16:27,990
I mean, when you have like acoustics, acoustic perception, sound perception, that's

321
00:16:27,990 --> 00:16:32,710
definitely uh a lot, you know, there's a lot less work out there, a lot less people doing

322
00:16:32,710 --> 00:16:32,870
it.

323
00:16:33,110 --> 00:16:36,070
I I I realize this even because, you know, we've done work together.

324
00:16:36,150 --> 00:16:40,710
And even I did social categorization work where I started with visual cues like race or or

325
00:16:40,710 --> 00:16:43,560
team membership visually marked, but I wanted to get into accent.

326
00:16:43,640 --> 00:16:46,200
And then like starting to make the acoustic stimuli.

327
00:16:46,360 --> 00:16:50,520
And you know, I consulted with you about how to do it, but I realized like there is very

328
00:16:50,520 --> 00:16:56,360
much a modality bias in the behavioral sciences, I feel like, at least from what I've seen.

329
00:16:56,520 --> 00:16:59,240
Um, and it sounds like that might be true.

330
00:16:59,400 --> 00:17:02,280
And is that because it's or why is that?

331
00:17:02,600 --> 00:17:04,600
Like I mean, it's a good question.

332
00:17:04,840 --> 00:17:10,430
Um what I do think is auditory perception and vision perception share this

333
00:17:10,430 --> 00:17:14,430
feature that they've made a lot of progress in understanding how the brain does it because

334
00:17:14,430 --> 00:17:17,470
of an inherent functionalist perspective from the beginning.

335
00:17:17,630 --> 00:17:17,870
Right.

336
00:17:17,950 --> 00:17:21,870
So people have been studying auditory perception since MLTs.

337
00:17:22,190 --> 00:17:27,790
And um, so I think we like vision perception, we have actually a better understanding

338
00:17:27,790 --> 00:17:29,550
relative to other areas of our psychology.

339
00:17:29,710 --> 00:17:33,390
We understand vision better because I think of the functionalist perspective and being able

340
00:17:33,390 --> 00:17:37,600
to come up with theories that are testable about how, and then you can look at how it

341
00:17:37,600 --> 00:17:38,720
happens in the brain.

342
00:17:38,960 --> 00:17:42,400
Um, so there are auditory perception people out there.

343
00:17:43,040 --> 00:17:48,000
But um I think one reason, I mean you can say this for vision too, but I think what one

344
00:17:48,000 --> 00:17:51,920
reason is the technical, there's a technical constraint that you have to really get into

345
00:17:52,240 --> 00:17:56,880
the um the acoustic theory and you and you also have to um be able to learn how to do

346
00:17:56,880 --> 00:18:00,480
acoustic analysis, which until recently was actually rather hard.

347
00:18:00,720 --> 00:18:06,170
And this is a funny thing with Lita, one of her first papers, um she was doing

348
00:18:06,570 --> 00:18:07,210
analysis.

349
00:18:07,370 --> 00:18:11,610
And it's really I I love this paper, and it really was a huge, it had a huge influence on

350
00:18:11,610 --> 00:18:11,770
me.

351
00:18:11,850 --> 00:18:15,610
It's called Um Acoustic Invariances and the Expression of Emotion and Speech.

352
00:18:15,690 --> 00:18:21,290
And she it was her honors thesis that she did in as an undergrad, posted JP, of course.

353
00:18:21,450 --> 00:18:27,440
Um, like all honors thesis is Um and I really love this paper.

354
00:18:27,520 --> 00:18:32,160
And then when I approached her early on and I was interested in working with them, and I

355
00:18:32,240 --> 00:18:34,080
she said that that gave her PTSD.

356
00:18:34,160 --> 00:18:38,960
I mean, it was so hard to do that analysis that she was like, I'm never doing acoustics

357
00:18:38,960 --> 00:18:39,200
again.

358
00:18:39,360 --> 00:18:43,040
She hated it because it was really difficult in those days.

359
00:18:43,280 --> 00:18:45,040
You didn't have software that did it.

360
00:18:45,120 --> 00:18:48,410
And so you had to actually fire up the oscilloscope.

361
00:18:48,490 --> 00:18:50,410
And I mean, it was kind of a nightmare.

362
00:18:50,490 --> 00:18:55,930
And so I think that probably had an impact on how people choosing that as an area of study

363
00:18:56,010 --> 00:18:59,130
because there was sort of a high learning curve, especially back in the day.

364
00:18:59,450 --> 00:19:02,810
Now you have software packages that are relatively easy to use.

365
00:19:02,970 --> 00:19:06,490
If you know how to use R, you can figure out how to do a lot of these things.

366
00:19:06,570 --> 00:19:10,410
And Pratt is a standalone program that allows you to do acoustic analysis.

367
00:19:10,810 --> 00:19:14,490
And so more people are doing it, which has the downside of people doing it and they don't

368
00:19:14,490 --> 00:19:15,530
really know what they're doing.

369
00:19:16,220 --> 00:19:19,820
So, but more people are doing it because it is accessible.

370
00:19:19,900 --> 00:19:25,420
And so I I think that um it's probably going to get better, but for some reason a lot of

371
00:19:25,420 --> 00:19:30,220
people just are not drawn to doing that kind of work where you're doing psychophysics

372
00:19:30,300 --> 00:19:30,860
essentially.

373
00:19:31,020 --> 00:19:31,260
Yeah.

374
00:19:31,420 --> 00:19:32,140
That's what I like.

375
00:19:32,380 --> 00:19:32,620
Yeah.

376
00:19:32,700 --> 00:19:36,380
My I always talk about my favorite movie is Jurassic Park and watching I've watched it so

377
00:19:36,380 --> 00:19:39,740
many times and like what I realized after watching it multiple times is the uh sound

378
00:19:39,740 --> 00:19:43,580
engineering is so important for highlighting and steel uh Spielberg does this with all of

379
00:19:43,580 --> 00:19:48,370
his movies is the sound tells you what to look at in the scene and you don't even realize

380
00:19:48,370 --> 00:19:48,530
it.

381
00:19:48,610 --> 00:19:52,530
But like, yeah, it's it's just such an interesting thing of like sound is so important.

382
00:19:53,010 --> 00:19:58,610
And but it f I it's just I I find it funny that it is so um there's so much instinct

383
00:19:58,610 --> 00:20:03,490
blindness generally, but then even like even among like behavioral scientists who take an

384
00:20:03,490 --> 00:20:06,770
evolutionary approach, there's like I feel like there's just not enough acoustic work, like

385
00:20:06,850 --> 00:20:08,050
it's like extra instinct blindness.

386
00:20:08,130 --> 00:20:08,930
Yeah, no, I agree.

387
00:20:09,080 --> 00:20:13,080
I mean, vision too, really, there's not that many people that are doing um vision

388
00:20:13,080 --> 00:20:14,600
perception in the ebb psych.

389
00:20:15,160 --> 00:20:21,080
I mean, you have people in in doing cognitive psychology and psychophysics that

390
00:20:21,080 --> 00:20:21,800
do vision.

391
00:20:21,960 --> 00:20:25,800
Um, you know, like we had one guy in my department where I went to grad school, Bruce

392
00:20:25,800 --> 00:20:28,520
Bridgman, who was into evolutionary psychology too.

393
00:20:28,760 --> 00:20:34,250
But um, you know, they're almost nobody in EbSight does that, right?

394
00:20:34,410 --> 00:20:40,250
I mean, there was um Josh New Yeah did that kind of stuff and then he left

395
00:20:40,250 --> 00:20:40,650
academia.

396
00:20:41,050 --> 00:20:44,250
Russell Jackson sort of doing his Russell left academia too.

397
00:20:45,530 --> 00:20:46,890
Oh yeah, okay, yeah.

398
00:20:47,530 --> 00:20:50,970
And so and of two, if you go into vision, you're gonna leave.

399
00:20:51,690 --> 00:20:52,010
Yeah.

400
00:20:53,130 --> 00:20:53,930
Don't say that.

401
00:20:54,330 --> 00:20:59,960
Um yeah, so there's there's for some reason uh psychophysics is not really taking

402
00:20:59,960 --> 00:21:02,360
hold in evolutionary psychology, unfortunately.

403
00:21:02,440 --> 00:21:04,680
I thought it would have been now, but yeah.

404
00:21:05,080 --> 00:21:08,200
Um, that that was, and I was gonna give you a choice, but that was the question.

405
00:21:08,360 --> 00:21:12,200
The other question, like, because this is a big hard question, and maybe we can just pause

406
00:21:12,200 --> 00:21:12,440
it.

407
00:21:12,600 --> 00:21:18,570
But my great mystery, and the thing I wanted to ask you more, is uh number

408
00:21:18,570 --> 00:21:20,170
one, what's up with music?

409
00:21:20,410 --> 00:21:24,570
And number two, I asked a bunch of people, like I told people you're coming on, and

410
00:21:24,570 --> 00:21:26,410
everyone wanted to know what your take on music was.

411
00:21:26,650 --> 00:21:32,010
And number two, what I find a mystery, and David has alluded to this, is number one, what's

412
00:21:32,010 --> 00:21:32,490
up with music?

413
00:21:32,570 --> 00:21:37,290
But number two, and equally, uh, why do why are emotions

414
00:21:37,850 --> 00:21:43,760
hooked up to these weird vibrations in the air, such that a vibration in the air

415
00:21:43,760 --> 00:21:48,400
can make me feel a certain way and really feel that way in a way that so many other things

416
00:21:48,400 --> 00:21:48,880
cannot.

417
00:21:49,040 --> 00:21:50,320
Like that is weird.

418
00:21:50,560 --> 00:21:55,040
I would never have predicted that like from first principles, at least for me.

419
00:21:55,200 --> 00:22:01,200
Um, and it's in and it's just um inevit so obvious and it's such a weird thing, I I feel

420
00:22:01,200 --> 00:22:01,520
like.

421
00:22:01,910 --> 00:22:02,230
Yeah.

422
00:22:02,390 --> 00:22:06,070
So the you know, you're saying like vocal emotion communication or something like that.

423
00:22:06,470 --> 00:22:11,110
No, I mean like if I'm at a concert and there's a symphony in front of me, that symphony

424
00:22:11,110 --> 00:22:11,590
can connect to the colour.

425
00:22:11,990 --> 00:22:13,350
It's a powerful experience to hear this loud.

426
00:22:13,590 --> 00:22:16,710
Yeah, and it's just like that is such a while how how is that?

427
00:22:16,790 --> 00:22:17,910
Why why isn't evolving?

428
00:22:24,550 --> 00:22:28,620
Listen, all these different emotions based on the the type of you know the difference

429
00:22:28,620 --> 00:22:33,340
between minor and major minor being more sad and gloomy like this is like it's so hooked up

430
00:22:33,340 --> 00:22:39,260
with the emotions like what what yeah yeah so well my take on it is that it's

431
00:22:39,260 --> 00:22:45,020
it's basically that vocal communication of emotion is underlying all of that

432
00:22:45,180 --> 00:22:49,260
and then cultural evolution has built I mean this is the simple answer we can get into it

433
00:22:49,340 --> 00:22:55,170
more detail but that cultural evolution has like built this whole um set of

434
00:22:55,170 --> 00:23:01,010
systems, technology and practices that have allowed us to make these huge sounds, like huge

435
00:23:01,010 --> 00:23:04,690
orchestras and giant sounds and movies and everything.

436
00:23:04,850 --> 00:23:10,770
But that the the underlying sensitivity to it is because we're sensitive to emotions

437
00:23:10,770 --> 00:23:11,650
and voices.

438
00:23:11,890 --> 00:23:17,720
And the reason that we are adapted to paying attention to emotion and voices because it's a

439
00:23:17,720 --> 00:23:20,920
really good mode of communication at a distance in particular, right?

440
00:23:21,000 --> 00:23:22,360
You don't need to see my face.

441
00:23:22,520 --> 00:23:28,040
And so I can give you information at a distance and allow you to make judgments about

442
00:23:28,040 --> 00:23:31,240
whatever situation is going on based on my emotional signal.

443
00:23:31,640 --> 00:23:36,990
But I really think cultural evolution has then built on top of that this um super

444
00:23:37,070 --> 00:23:38,270
stimulus, right?

445
00:23:38,590 --> 00:23:42,990
So we have a sensitivity to the sound frequencies because we have to pay attention to other

446
00:23:42,990 --> 00:23:44,350
people communicating with us.

447
00:23:44,510 --> 00:23:46,670
And then it just has been elaborated.

448
00:23:46,750 --> 00:23:50,030
I mean, if you have a super stimulus, then you're gonna have a super response.

449
00:23:50,590 --> 00:23:55,310
I mean, that might not be the most satisfying answer, but I think that it's probably some

450
00:23:55,310 --> 00:23:59,710
version of that that we have the pre ad we have an adaptation for detecting emotions and

451
00:23:59,710 --> 00:24:00,030
voices.

452
00:24:00,270 --> 00:24:02,580
Yeah, does that mean voices?

453
00:24:03,060 --> 00:24:08,980
This might be a dumb question, but like we also have uh words and linguistic labels for

454
00:24:08,980 --> 00:24:09,460
emotions.

455
00:24:09,620 --> 00:24:11,940
Like if I'm mad, I could say I'm pissed off.

456
00:24:12,020 --> 00:24:13,700
If I'm sad, I could say I'm sad.

457
00:24:14,020 --> 00:24:18,580
But if we just hear the words I'm pissed off or I'm sad, it doesn't have the same emotional

458
00:24:18,580 --> 00:24:24,190
effect than if I were to hear a symphony playing sad sounding music

459
00:24:24,430 --> 00:24:30,030
or like a punk rock band like playing angry sounding music with like a distorted guitar.

460
00:24:30,190 --> 00:24:34,670
Like there's something different about the sound than about the label, even though so like

461
00:24:34,750 --> 00:24:36,270
so so what's what's different there?

462
00:24:36,350 --> 00:24:40,270
Like what shouldn't the label be having the same effect if it has the same function to

463
00:24:40,270 --> 00:24:41,470
inform me about the emotion?

464
00:24:41,710 --> 00:24:42,190
Right.

465
00:24:42,430 --> 00:24:44,740
I mean the label is conceptual, right?

466
00:24:44,820 --> 00:24:49,940
And so you're informing somebody of a concept that then they can figure out might have

467
00:24:49,940 --> 00:24:52,020
something to do with your internal feeling.

468
00:24:52,260 --> 00:24:57,780
But the sound is actually directly reflecting your feeling and it's inducing the

469
00:24:57,780 --> 00:24:58,420
listener.

470
00:24:58,660 --> 00:25:04,420
And so that's the ancestral um evolved capacity that's as old as

471
00:25:04,420 --> 00:25:10,260
mammals, where you by producing a fear scream, you induce fear in

472
00:25:10,260 --> 00:25:12,230
your audience, Right.

473
00:25:12,550 --> 00:25:17,430
Or even like a s a high pitched string that kind of sounds reminiscent of a s of a scream

474
00:25:17,590 --> 00:25:18,470
could have the same effect.

475
00:25:18,630 --> 00:25:22,950
Like the, you know, the the shrieking thing from Psycho that like the violin sounds a lot

476
00:25:22,950 --> 00:25:23,350
like a scream.

477
00:25:23,750 --> 00:25:24,150
Exactly.

478
00:25:24,230 --> 00:25:24,390
Yeah.

479
00:25:24,470 --> 00:25:24,710
Yeah.

480
00:25:24,950 --> 00:25:25,350
That's right.

481
00:25:25,430 --> 00:25:27,670
It's got high frequencies, they're dissonant.

482
00:25:27,750 --> 00:25:33,190
Um, and so that dissonance is reflective of a highly aroused vocal

483
00:25:33,190 --> 00:25:36,470
communication system that's blowing it at blowing out the system a little bit, right?

484
00:25:36,550 --> 00:25:42,500
If I scream loud enough the amplitude is such that the waveforms can't resolve

485
00:25:42,500 --> 00:25:43,300
in my system.

486
00:25:43,460 --> 00:25:47,060
And so it's just like a speaker that can't handle the amplitude.

487
00:25:47,220 --> 00:25:52,340
And so you get this distortion that it that indicates you're overblowing the system.

488
00:25:52,500 --> 00:25:55,940
And that is going to be associated with high emotion.

489
00:25:56,180 --> 00:26:02,010
And then when you perceive that as an animal with um, you the ability to

490
00:26:02,010 --> 00:26:05,850
map that sound onto some sort of physiological state of the sender.

491
00:26:06,010 --> 00:26:09,850
And then that arouses you to actually have that state to some extent as well.

492
00:26:10,250 --> 00:26:14,970
So that's a very reductive stance to rock and roll and overdrive in rock and roll.

493
00:26:15,290 --> 00:26:15,530
Yeah.

494
00:26:15,690 --> 00:26:19,850
So Paul, Paul and I have a paper on that exact topic that just came out recently.

495
00:26:20,170 --> 00:26:20,570
Awesome.

496
00:26:21,380 --> 00:26:22,740
cultural evolution of distortion.

497
00:26:22,820 --> 00:26:28,180
And yeah, it's I mean, maybe that's a sad for punk rock or whatever, but I mean that's so

498
00:26:28,340 --> 00:26:29,780
the so the hmm, interesting.

499
00:26:29,940 --> 00:26:35,140
So when we f hear a scream, we're responding not only to the pitch but also to the

500
00:26:35,140 --> 00:26:35,540
distortion.

501
00:26:36,180 --> 00:26:37,140
David, you're getting an exile.

502
00:26:37,460 --> 00:26:38,900
I think your mic is still on.

503
00:26:38,980 --> 00:26:39,620
Yeah.

504
00:26:40,260 --> 00:26:40,820
For some reason.

505
00:26:41,140 --> 00:26:42,580
Speaking of speaking of audio, yeah.

506
00:26:42,900 --> 00:26:44,340
We've never had audio issues before.

507
00:26:46,580 --> 00:26:47,140
Did it go away?

508
00:26:47,380 --> 00:26:49,060
Oh, I bring audio issues with me, man.

509
00:26:49,300 --> 00:26:49,460
Okay.

510
00:26:49,540 --> 00:26:50,100
That's weird.

511
00:26:51,070 --> 00:26:53,710
Anyway, um, so are we with audio issues?

512
00:26:53,950 --> 00:26:54,110
Yeah.

513
00:26:54,350 --> 00:26:58,830
So when we feel afraid at the sound of a scream, are w what is triggering it is

514
00:26:58,830 --> 00:27:01,630
simultaneously the pitch and the distortion?

515
00:27:02,030 --> 00:27:02,270
Right.

516
00:27:02,350 --> 00:27:04,350
And which is a function of the loudness.

517
00:27:04,590 --> 00:27:04,750
Right.

518
00:27:04,990 --> 00:27:08,670
Um, but yeah, so there are not what we call nonlinearities.

519
00:27:08,830 --> 00:27:13,630
Um, this is kind of what in bioacoustics with the the category of different properties of

520
00:27:13,790 --> 00:27:16,670
sound that are have that uh association with harshness.

521
00:27:16,830 --> 00:27:22,440
So there are um subharmonics, there's chaos that's basically broadband

522
00:27:22,600 --> 00:27:23,560
kind of noise.

523
00:27:23,960 --> 00:27:26,760
That's the one that I think is most used in music.

524
00:27:26,920 --> 00:27:32,840
But then you also have um other kinds of um um sidebands and other

525
00:27:32,840 --> 00:27:38,760
little weird phenomena that are oftentimes just um bands of energy that

526
00:27:38,760 --> 00:27:44,600
are not in the harmonic series, and so they create they can create these sort of dissonant

527
00:27:44,600 --> 00:27:45,720
kind of sounds.

528
00:27:45,880 --> 00:27:51,290
Um but the the deterministic chaos is the one that I think is the closest

529
00:27:51,610 --> 00:27:54,650
to distortion, which is just broadband noise.

530
00:27:55,050 --> 00:27:56,810
That's a great band name if nobody's done that.

531
00:27:57,370 --> 00:27:58,010
Broadband noise.

532
00:27:58,250 --> 00:27:59,370
No deterministic chaos.

533
00:27:59,530 --> 00:28:00,250
Deterministic chaos.

534
00:28:01,610 --> 00:28:02,250
So it's interesting.

535
00:28:02,330 --> 00:28:06,410
So in terms of you know the the evolution of signaling, what we're talking about is an

536
00:28:06,410 --> 00:28:10,880
indice where it's not necessarily a costly signal, It's just that there's something about

537
00:28:11,040 --> 00:28:14,880
intrinsic to the production of the signal that makes it a reliable cue of, say,

538
00:28:14,960 --> 00:28:19,600
physiological arousal, that just as a byproduct of the body amping itself up and pumping

539
00:28:19,600 --> 00:28:24,160
itself full of energy and tensing muscles and tensing vocal cords, it will, as a byproduct

540
00:28:24,160 --> 00:28:28,720
of that, uh raise the pitch and the distortion of what's coming out.

541
00:28:29,920 --> 00:28:31,330
Basically, Yeah.

542
00:28:31,490 --> 00:28:35,890
And I mean, I think these things um started as cues and then they get ritualized into

543
00:28:35,890 --> 00:28:36,370
signals.

544
00:28:36,530 --> 00:28:42,450
And so then those nonlinearity, nonlinearity uh features, nonlinear features, um,

545
00:28:42,770 --> 00:28:46,050
would um be exaggerated, right?

546
00:28:46,210 --> 00:28:46,610
Essentially.

547
00:28:47,090 --> 00:28:51,730
Yeah, and so Greg, this byproduct idea or this elaboration, cultural evolution, uh,

548
00:28:51,890 --> 00:28:54,380
evolutionary byproduct idea, does it no?

549
00:28:54,460 --> 00:28:55,420
I I have two questions.

550
00:28:55,580 --> 00:28:59,980
One is is it is the idea that there are design features that it's really about con specific

551
00:28:59,980 --> 00:29:05,580
vocalization, or is it a broader thing where it could be other animals or or definitely

552
00:29:05,660 --> 00:29:09,900
could be other animals or even the acoustics of of non-living things?

553
00:29:10,060 --> 00:29:14,780
Or is it really seem like it's about um living vocalizations?

554
00:29:15,820 --> 00:29:20,930
Yeah, I mean I I think there probably are there's some general principles of the sound

555
00:29:20,930 --> 00:29:25,890
features of stuff in the world, you know, that you know if you hear a big sound like of

556
00:29:25,890 --> 00:29:30,450
trees falling down, exactly that's scary and stop because it's derived from your perception

557
00:29:30,450 --> 00:29:31,730
of animal vocalizations.

558
00:29:31,970 --> 00:29:32,130
Right.

559
00:29:32,290 --> 00:29:37,740
So we get all kinds of kinds of auditory C analysis mechanisms that are um

560
00:29:38,140 --> 00:29:43,900
but the um but the emotionality but the emotionality of the claim is that it

561
00:29:43,900 --> 00:29:49,660
seems to have design features that are for the living world vocalizations.

562
00:29:50,300 --> 00:29:52,540
And and for the other animals as well as people, right?

563
00:29:52,780 --> 00:29:55,900
So if you interact with other animals, you want to know if you're gonna get bit.

564
00:29:56,140 --> 00:29:56,380
Yes.

565
00:29:56,690 --> 00:29:58,210
Order Or whatever.

566
00:29:58,930 --> 00:30:01,890
If other animals are screaming, that's important information for you as well.

567
00:30:02,210 --> 00:30:03,650
Yeah, seriously no, not my species.

568
00:30:03,890 --> 00:30:04,290
I don't care.

569
00:30:04,450 --> 00:30:04,930
I don't care.

570
00:30:05,650 --> 00:30:05,810
Yeah.

571
00:30:06,130 --> 00:30:10,450
I mean, um a big dog that is growling at me is going to scare me as much as almost any

572
00:30:10,450 --> 00:30:11,010
person would.

573
00:30:11,090 --> 00:30:11,330
Yeah.

574
00:30:11,490 --> 00:30:11,890
Yeah.

575
00:30:12,290 --> 00:30:13,490
In the right context.

576
00:30:13,650 --> 00:30:13,970
Okay.

577
00:30:14,130 --> 00:30:18,450
Um though I might I I like dogs so much I might be like, oh come on, it's okay.

578
00:30:18,530 --> 00:30:24,120
You know but um so we definitely there there's uh actually an interesting uh paper

579
00:30:24,280 --> 00:30:30,040
um showing that people can detect arousal in vocalizations across the all animal

580
00:30:30,040 --> 00:30:30,360
kingdom.

581
00:30:30,600 --> 00:30:36,280
So even like insects make you know, and frogs and um

582
00:30:36,520 --> 00:30:40,920
birds and I mean all all kinds of animals have the same kinds of properties in their in

583
00:30:40,920 --> 00:30:45,800
their threat displays, which is th there's noise and it's loud, and people can

584
00:30:46,310 --> 00:30:49,110
arousal even in very distant animals.

585
00:30:49,190 --> 00:30:51,830
And so probably all animals have that basic ability.

586
00:30:52,230 --> 00:30:53,030
Is is interesting.

587
00:30:53,110 --> 00:30:56,790
So are is the thing that we're picking up on loudness and distortion that the louder and

588
00:30:56,790 --> 00:31:00,230
more distorted it is, the more we infer physiological arousal?

589
00:31:00,550 --> 00:31:00,950
Yeah.

590
00:31:01,190 --> 00:31:04,870
I mean, to that's what we that's what has been shown now.

591
00:31:04,950 --> 00:31:09,910
I mean, I feel like there's another element that I've been struggling to measure, and other

592
00:31:09,910 --> 00:31:13,510
people uh nobody's really got a good handle on it, but it's vocal effort.

593
00:31:13,750 --> 00:31:19,640
And so you can kind of tell where in the range of effort

594
00:31:19,720 --> 00:31:21,720
are is a person at any given moment.

595
00:31:21,880 --> 00:31:26,200
You know, so if I'm really even if I'm in a distance and the sound is quiet, you can tell

596
00:31:26,200 --> 00:31:27,560
I'm screaming my brains out.

597
00:31:27,720 --> 00:31:28,040
Yeah.

598
00:31:28,680 --> 00:31:31,720
And it's not the absolute loudness that's giving you the information.

599
00:31:31,800 --> 00:31:35,240
And so there's something about telling where people are in their range.

600
00:31:35,320 --> 00:31:36,840
And I think it's true for pitch as well.

601
00:31:37,080 --> 00:31:41,090
So you can tell where people are landing in their their ordinary pitch range.

602
00:31:41,170 --> 00:31:43,810
And we're usually more towards the low end, right?

603
00:31:43,890 --> 00:31:47,090
So I can go much more high than I can go down.

604
00:31:47,570 --> 00:31:47,810
Right.

605
00:31:47,970 --> 00:31:52,690
So I mean I could go down a little bit, but I mean um I can go I can get up there.

606
00:31:54,210 --> 00:31:59,010
And um so we can tell where people are are in their range.

607
00:31:59,090 --> 00:32:02,850
And so you know people like Dave Feimer and others have done research showing that when

608
00:32:02,940 --> 00:32:07,580
guys try to be lower pitched than they really are, that people can tell, even though

609
00:32:07,580 --> 00:32:09,340
they're not that much lower than their baseline.

610
00:32:09,420 --> 00:32:13,820
So if I'm just watching this all the time, it sounds like I'm trying to be low.

611
00:32:14,460 --> 00:32:20,060
And so I that w with vocal effort and also pitch effort or whatever you might call it, I

612
00:32:20,060 --> 00:32:22,140
think is an interesting thing that's really not been explored.

613
00:32:22,220 --> 00:32:26,700
And I think it's hugely important because how people manipulate that in a given context

614
00:32:26,700 --> 00:32:32,650
could be important for, you know, figuring out what they're intending and Interesting.

615
00:32:32,890 --> 00:32:38,650
Can we um so it's it the I I really like the example of hearing a scream from far away

616
00:32:38,730 --> 00:32:42,250
that we still know it was a scream, even though it sounds quiet to the ear.

617
00:32:42,330 --> 00:32:42,810
It's sort of like a lot of things.

618
00:32:45,690 --> 00:32:47,370
Yeah, which is really interesting.

619
00:32:47,450 --> 00:32:49,450
I mean, it's sort of analogous to distance perception.

620
00:32:49,530 --> 00:32:53,210
So, like something that's far away, like a big bear that's far away, we can still tell it's

621
00:32:53,210 --> 00:32:58,100
big, it's a big bear, but but the the actual stimulus hitting our retina is quite small,

622
00:32:58,260 --> 00:33:02,740
which suggests that we have algorithms for telling us that um yeah, this may seem like it's

623
00:33:02,740 --> 00:33:04,500
small, but it's actually big and it's far away.

624
00:33:04,660 --> 00:33:08,020
Or s similarly, this might seem like it's a quiet sound, but it's actually a really loud

625
00:33:08,020 --> 00:33:09,140
sound coming from far away.

626
00:33:09,220 --> 00:33:11,060
Like it seems like that would be pretty complicated.

627
00:33:11,540 --> 00:33:11,780
That's cool.

628
00:33:11,940 --> 00:33:12,260
Yeah, yeah.

629
00:33:12,340 --> 00:33:16,100
Like a train, you're like, oh, that sounds huge, but it's like not as loud as like the the

630
00:33:16,100 --> 00:33:19,460
baby that's a little bird talking or whatever, crying.

631
00:33:19,540 --> 00:33:19,940
Yeah, yeah.

632
00:33:20,340 --> 00:33:20,580
Yeah.

633
00:33:20,740 --> 00:33:24,740
I mean, with vision, you often have context that the sound doesn't provide.

634
00:33:25,220 --> 00:33:30,990
So if you see a bear, I mean, even if you don't know bears are big, you see a

635
00:33:30,990 --> 00:33:36,030
thing and you go, I can tell how far it is, I can see the trees, and that thing must be

636
00:33:36,030 --> 00:33:37,630
pretty big if I can see it from here.

637
00:33:37,790 --> 00:33:39,470
So you the context helps a lot.

638
00:33:39,710 --> 00:33:44,830
Whereas if you just saw a dot on a screen, a bear in a distance without any context, you

639
00:33:44,830 --> 00:33:45,550
wouldn't know.

640
00:33:45,710 --> 00:33:49,550
But if you heard a sound, no matter what, you'd be able to hear something without context.

641
00:33:49,710 --> 00:33:50,110
Yeah.

642
00:33:50,440 --> 00:33:52,520
This is this is anecdotal.

643
00:33:52,680 --> 00:33:58,360
Um, but people talk about how cellos are very close to the uh frequencies of human

644
00:33:58,360 --> 00:34:01,640
voices, and that those are that is also very emotionally evocative.

645
00:34:01,880 --> 00:34:02,920
Number one, is that true?

646
00:34:03,080 --> 00:34:08,760
And number two, if so, is that evidence for that there is a privileging of like con

647
00:34:09,080 --> 00:34:11,320
specific uh acoustics in the emotional?

648
00:34:11,480 --> 00:34:15,690
I would argue almost all instruments are in the range of okay vocal production.

649
00:34:15,770 --> 00:34:21,770
But DiCello is probably um if you rank them, that would probably be up there pretty

650
00:34:21,770 --> 00:34:22,090
high.

651
00:34:22,250 --> 00:34:22,570
Okay.

652
00:34:22,810 --> 00:34:28,170
Um and so yeah, but most instruments are in are in the voice range.

653
00:34:28,410 --> 00:34:28,650
Okay.

654
00:34:28,730 --> 00:34:33,610
So if we if we were aliens, if there's aliens and they do music, it's probably gonna be

655
00:34:33,690 --> 00:34:37,690
their instruments are going to be in their con specific vocalization range.

656
00:34:38,120 --> 00:34:39,800
Or that's not even sound.

657
00:34:40,120 --> 00:34:40,360
Sure.

658
00:34:42,920 --> 00:34:44,360
Non-sound music, yeah.

659
00:34:44,840 --> 00:34:48,440
That's interesting that there are a lot of logically possible instruments that we could

660
00:34:48,440 --> 00:34:51,000
have created, but but we didn't because they're not in the range.

661
00:34:51,240 --> 00:34:55,080
They don't emit sounds that are in the range of human vocal communication, so we don't find

662
00:34:55,080 --> 00:34:56,680
them interesting or engaging.

663
00:34:57,240 --> 00:34:57,800
Exactly.

664
00:34:57,960 --> 00:35:03,250
I mean we can have um instruments that play, you know, where the normal range is ten K or

665
00:35:03,250 --> 00:35:04,690
something, which is a super high pitch.

666
00:35:04,850 --> 00:35:05,410
We can hear it.

667
00:35:05,490 --> 00:35:07,490
I actually I might not be able to hear that anymore.

668
00:35:07,650 --> 00:35:11,330
But um, you know, normal people people under forty, whatever.

669
00:35:12,050 --> 00:35:14,130
Normal people under who are under forty.

670
00:35:14,690 --> 00:35:15,170
Yeah.

671
00:35:15,650 --> 00:35:18,050
Um there's a lot of people can hear ten K.

672
00:35:18,290 --> 00:35:21,170
And so you don't have an instrument that plays at ten K.

673
00:35:22,210 --> 00:35:22,530
Yeah.

674
00:35:22,740 --> 00:35:23,380
Oh, that makes sense.

675
00:35:23,620 --> 00:35:24,420
That's that's super powerful.

676
00:35:24,740 --> 00:35:26,340
We want it, we want it in that sweet spot.

677
00:35:26,500 --> 00:35:32,420
And so what would be interesting would be just to see um, you know, show I mean

678
00:35:32,660 --> 00:35:38,100
all instruments have to play within this the scale too, the normal scale, which is A440 for

679
00:35:38,100 --> 00:35:41,700
Western music, meaning that the note of A is 440 hertz.

680
00:35:41,940 --> 00:35:44,020
So that's just right there in the voice range.

681
00:35:44,100 --> 00:35:48,020
But it'd be interesting to see the distributions of different instruments and then compare

682
00:35:48,020 --> 00:35:48,660
that to voices.

683
00:35:48,740 --> 00:35:52,590
And I betcha it would be the voice distribution would look like this, and all the other

684
00:35:52,590 --> 00:35:54,670
instruments would be all like this clustering around it.

685
00:35:54,990 --> 00:35:55,390
That's okay.

686
00:35:55,790 --> 00:35:55,950
Okay.

687
00:35:56,030 --> 00:36:01,470
So so fear is like you got the distortion, the vocal effort, and the high pitch.

688
00:36:01,710 --> 00:36:06,750
It seems like anger would be similar components because when you're pissed off, you're also

689
00:36:06,750 --> 00:36:09,790
physiologically aroused and potentially ready to fight.

690
00:36:09,950 --> 00:36:14,060
But instead of a high pitch, it would be a low pitch because you want to convey

691
00:36:14,060 --> 00:36:14,780
formidability.

692
00:36:14,940 --> 00:36:18,060
Presumably, lower pitches uh convey larger size.

693
00:36:18,300 --> 00:36:23,180
And so anger would be something like distortion, vocal effort, and low pitch.

694
00:36:23,260 --> 00:36:24,220
Is that the idea there?

695
00:36:24,300 --> 00:36:24,620
Yeah.

696
00:36:25,100 --> 00:36:25,340
Yeah.

697
00:36:25,420 --> 00:36:29,100
Well, so loud and low actually correlates with physical strength.

698
00:36:29,340 --> 00:36:34,300
So if you can keep your voice loud and low at the same time, just loudness actually is the

699
00:36:34,300 --> 00:36:35,820
best predictor of physical strength.

700
00:36:36,370 --> 00:36:40,770
The louder you can produce a sound is actually correlated with your upper body strength.

701
00:36:40,930 --> 00:36:44,210
So how much can you bench press or how your hip strength or something like that?

702
00:36:44,370 --> 00:36:47,650
Whereas pitch doesn't correlate with strength, which is an interesting thing that you would

703
00:36:47,650 --> 00:36:49,330
think is true, but it's not.

704
00:36:49,570 --> 00:36:52,530
Um David Putz and I differ on this a little bit.

705
00:36:52,690 --> 00:36:56,530
We debate about that because he's the only one that actually can show is ever shown that

706
00:36:56,530 --> 00:36:58,450
there's a relationship between pitch and strength.

707
00:36:59,020 --> 00:37:03,100
I've tried to do it a bunch, and I can't show that, and other people can't show it.

708
00:37:03,260 --> 00:37:04,140
So we debate that.

709
00:37:04,300 --> 00:37:09,340
He had, and now we have a paper actually together that I guess will come out someday that

710
00:37:09,900 --> 00:37:14,140
is a meta-analysis that is showing at least a small effect of pitch.

711
00:37:14,220 --> 00:37:17,260
I'm not really a big believer in the pitch strength connection.

712
00:37:17,740 --> 00:37:19,020
But could it be size?

713
00:37:19,820 --> 00:37:21,100
Could it be size rather than strength?

714
00:37:21,180 --> 00:37:25,020
Just like if you have bigger vocal cords, there's a bigger body, it's going to be more

715
00:37:25,020 --> 00:37:26,140
resounding and lower pitch.

716
00:37:26,380 --> 00:37:30,470
And so it's, and you know, it's size isn't you know a kind of a cue of formidability, but

717
00:37:30,470 --> 00:37:31,590
it's not strength per se.

718
00:37:31,830 --> 00:37:32,630
Would you buy that?

719
00:37:33,030 --> 00:37:36,630
Um the problem is that vocal folds don't really scale with body size.

720
00:37:36,870 --> 00:37:37,430
Oh, interesting.

721
00:37:37,510 --> 00:37:37,670
Okay.

722
00:37:37,830 --> 00:37:43,030
Is it so you can have somebody who is a little person who has just the same size head and

723
00:37:43,030 --> 00:37:45,430
vocal folds as a person who's six feet tall.

724
00:37:45,750 --> 00:37:46,310
Oh, really?

725
00:37:46,710 --> 00:37:47,670
And so where is this?

726
00:37:47,830 --> 00:37:50,710
I have this idea like from Honey I Shrunk the Kids, when you shrink something, they go,

727
00:37:50,870 --> 00:37:52,260
Yeah, that that little tight.

728
00:37:52,500 --> 00:37:52,660
Yeah.

729
00:37:53,220 --> 00:37:53,940
Is that a myth?

730
00:37:54,100 --> 00:37:56,820
Like if you if you shrink something, it doesn't necessarily have to be a lot of if you

731
00:37:56,820 --> 00:38:00,340
shrink the kids with a magic machine, I think you might get a slightly different sound in

732
00:38:00,340 --> 00:38:00,900
their voices.

733
00:38:00,980 --> 00:38:01,140
Yeah.

734
00:38:01,780 --> 00:38:01,860
Okay.

735
00:38:02,180 --> 00:38:04,980
But because everything is smaller, including the vocal folds.

736
00:38:05,380 --> 00:38:08,580
Okay, but the but the vocal folds don't necessarily scale with the rest of the body.

737
00:38:08,660 --> 00:38:08,820
Okay.

738
00:38:09,140 --> 00:38:09,540
They don't.

739
00:38:09,620 --> 00:38:14,470
And um actually the vocal folds are unconstrained by other um morphological features in the

740
00:38:14,470 --> 00:38:15,110
larynx.

741
00:38:15,190 --> 00:38:21,190
So you can get huge vocal folds that um are differentiated at at puberty from

742
00:38:21,190 --> 00:38:27,190
testosterone in in small people who are skinny, short, weak people can have like these

743
00:38:27,190 --> 00:38:31,830
ridiculous vocal folds potentially because of the actions of testosterone.

744
00:38:31,990 --> 00:38:35,510
Um so but testosterone doesn't really correlate with strength either.

745
00:38:35,590 --> 00:38:37,670
So higher testosterone doesn't mean you're stronger.

746
00:38:37,990 --> 00:38:42,940
Is it is it maybe picking up uh uh on just like adult maleness?

747
00:38:44,060 --> 00:38:44,460
Yes.

748
00:38:44,620 --> 00:38:45,580
I think that's right.

749
00:38:45,820 --> 00:38:46,060
Okay.

750
00:38:46,220 --> 00:38:49,100
Yep. 'Cause that 'cause that would also be a cue of formidability potential.

751
00:38:51,020 --> 00:38:53,740
In the grand scheme of b the big picture of humans that yes.

752
00:38:53,900 --> 00:38:54,140
Yeah.

753
00:38:54,300 --> 00:38:54,620
Exactly.

754
00:38:54,780 --> 00:38:57,020
And so that's why you would see sex differences.

755
00:38:57,180 --> 00:39:01,420
Originally people showed that that low pitch was stronger, but then when you control for

756
00:39:01,420 --> 00:39:02,940
sex, that effect goes away.

757
00:39:03,390 --> 00:39:08,990
Um and so but the one the one thing I like about the I it occurred to me that you know

758
00:39:09,230 --> 00:39:12,830
what happens when people get stronger by training?

759
00:39:13,150 --> 00:39:14,990
What how does their voice change?

760
00:39:15,230 --> 00:39:18,990
And and it that that I think thought about that for a minute and I thought, okay, that's

761
00:39:18,990 --> 00:39:20,510
why a pitch is not a thing.

762
00:39:20,670 --> 00:39:25,240
When when you lift weights for a year and you get a lot stronger, your voice doesn't go

763
00:39:25,240 --> 00:39:25,960
down, right?

764
00:39:26,360 --> 00:39:32,200
But you do get um better ability to push air with the muscles that control the airflow in

765
00:39:32,200 --> 00:39:32,680
your lungs.

766
00:39:32,840 --> 00:39:36,760
Those are stronger, and so you can push air out more forcefully, so then you could produce

767
00:39:36,760 --> 00:39:38,120
a louder vocalization.

768
00:39:38,440 --> 00:39:42,680
But then when you are produce a louder vocalization, it makes the voice pitch go up because

769
00:39:42,680 --> 00:39:44,600
that's one of the mechanisms of pitch change.

770
00:39:44,760 --> 00:39:45,320
There are two.

771
00:39:45,480 --> 00:39:51,010
One is um tightening the vocal core the vocal folds with these um muscles in the

772
00:39:51,010 --> 00:39:51,490
larynx.

773
00:39:51,570 --> 00:39:54,050
And the other one is just pushing air through the system harder.

774
00:39:54,130 --> 00:39:56,450
And then that makes the vocal folds vibrate faster.

775
00:39:56,770 --> 00:39:59,970
So when you go up in volume, you go up in pitch.

776
00:40:00,290 --> 00:40:06,210
And so it seems to be your ability to mediate that increase in pitch when you

777
00:40:06,450 --> 00:40:11,410
increase the subglottal air pressure, it seems to be associated with actual strength.

778
00:40:11,570 --> 00:40:14,130
So keeping it loud and low at the same time.

779
00:40:14,290 --> 00:40:16,530
And then that relates to physical strength.

780
00:40:16,980 --> 00:40:19,060
Greg, what is format dispersion?

781
00:40:19,140 --> 00:40:21,140
You've told me for years, I still don't know what it is.

782
00:40:21,220 --> 00:40:23,140
And does that relate to physical size?

783
00:40:23,220 --> 00:40:26,500
And is that part of what we're talking about without using the technical language?

784
00:40:26,660 --> 00:40:30,980
Or is is format dispersion something different than size and strength and loudness?

785
00:40:31,540 --> 00:40:36,980
Um it it it's not I think there's a there's evidence that it's correlated with strength to

786
00:40:36,980 --> 00:40:40,020
the extent that body size is correlated with strength.

787
00:40:40,180 --> 00:40:42,740
Um so it's not giving you any other information.

788
00:40:44,030 --> 00:40:49,870
Formant dispersion essentially is the average distance between formants in the

789
00:40:49,870 --> 00:40:50,190
signal.

790
00:40:50,270 --> 00:40:56,110
And the formant is a resonating frequency that is a function of the shape of the vocal

791
00:40:56,110 --> 00:40:56,510
tract.

792
00:40:56,590 --> 00:41:01,230
So you have the source, which is the air coming up from your lungs, it goes through your

793
00:41:01,230 --> 00:41:06,510
vocal folds, the vocal folds vibrate at a certain rate, and then that determines the source

794
00:41:06,590 --> 00:41:07,950
fundamental frequency.

795
00:41:08,110 --> 00:41:13,760
But then that sound goes through the vocal tract, through up your throat and out of your

796
00:41:13,760 --> 00:41:18,240
mouth, and there's these resonating um cavities in your mouth, in your nose, and in the

797
00:41:18,240 --> 00:41:19,440
back of your throat.

798
00:41:19,600 --> 00:41:24,800
And so those resonating chambers cause the signal to have certain

799
00:41:24,800 --> 00:41:27,360
frequencies be um amplified.

800
00:41:27,600 --> 00:41:28,160
I see.

801
00:41:28,400 --> 00:41:30,000
So those are the formants.

802
00:41:30,320 --> 00:41:36,320
And so the because it's just a physical um calculation of cow of seeing how

803
00:41:36,430 --> 00:41:41,230
distant are those acoustically, and it's inversely related to the length of the tube.

804
00:41:41,390 --> 00:41:44,750
So formants that are closer together come from a longer tube.

805
00:41:44,990 --> 00:41:45,150
Okay.

806
00:41:45,310 --> 00:41:45,550
Yeah.

807
00:41:45,630 --> 00:41:50,670
And so just to analogize it in a guitar, like an acoustic guitar, you've got all the

808
00:41:50,670 --> 00:41:55,630
different uh frequencies or pitches being done by the strings, but then the body itself of

809
00:41:55,630 --> 00:41:57,550
the guitar is a resonating chamber.

810
00:41:57,790 --> 00:42:02,750
And that's going to in the and analogously, like basically our vocal cords are like the

811
00:42:02,800 --> 00:42:07,040
strings of a guitar and then our heads in a weird way are like a guitar body.

812
00:42:07,200 --> 00:42:12,160
Um I mean the guitar is a l is different enough where it's a little weird, but I think a

813
00:42:12,160 --> 00:42:13,760
better analogy is like a sax.

814
00:42:14,400 --> 00:42:14,560
Okay.

815
00:42:14,880 --> 00:42:18,480
Or an you know a wind instrument where this you actually have a source coming from the

816
00:42:18,480 --> 00:42:18,800
mouth.

817
00:42:19,040 --> 00:42:19,360
I see, yeah.

818
00:42:19,600 --> 00:42:22,480
That's the thing you produce, and then it goes through the body of the instrument.

819
00:42:22,800 --> 00:42:23,120
Yeah, yeah.

820
00:42:24,080 --> 00:42:24,240
Cool.

821
00:42:24,320 --> 00:42:24,560
Yeah.

822
00:42:24,640 --> 00:42:24,880
Yeah.

823
00:42:25,090 --> 00:42:25,410
Cool.

824
00:42:25,730 --> 00:42:25,970
Yeah.

825
00:42:26,370 --> 00:42:29,250
So if you have like a bigger head, are you gonna make difference?

826
00:42:29,490 --> 00:42:31,410
Is it gonna that like a lower pitch sound?

827
00:42:31,490 --> 00:42:34,530
Like a more head, yeah.

828
00:42:35,250 --> 00:42:35,810
I don't know.

829
00:42:36,210 --> 00:42:37,250
Let's get some data.

830
00:42:38,130 --> 00:42:39,090
Get some calipers out.

831
00:42:40,530 --> 00:42:44,610
I mean that it I don't that's a that's I've never really thought about the big head issue,

832
00:42:44,690 --> 00:42:47,410
but yeah, probably that would have some consequences for sure.

833
00:42:47,760 --> 00:42:51,120
Um I mean a big head is gonna probably have a longer vocal track for one.

834
00:42:51,280 --> 00:42:53,360
Um but that's not quite what you're getting at.

835
00:42:53,440 --> 00:42:58,880
And uh um it's just like a more hollow head, like more space in there to resound, you know.

836
00:43:01,120 --> 00:43:03,600
Yeah, the format dispersion intelligence hypothesis.

837
00:43:04,000 --> 00:43:07,440
Oh my god, it's yeah, a small brain, big skull.

838
00:43:08,720 --> 00:43:11,310
Um Yeah.

839
00:43:11,630 --> 00:43:14,670
Well, we're we're we're 25 episodes in or something like that.

840
00:43:14,830 --> 00:43:16,670
And now we're getting into bad episode.

841
00:43:16,830 --> 00:43:16,990
Okay.

842
00:43:18,270 --> 00:43:19,230
This is the line.

843
00:43:19,310 --> 00:43:20,670
All right, let's do it.

844
00:43:22,270 --> 00:43:23,470
Yeah, nice.

845
00:43:23,710 --> 00:43:27,230
Um, well, so you can see how format dispersion is related to body size, right?

846
00:43:27,310 --> 00:43:30,670
It gets it gives you a pretty much an index in the length of the vocal tract, which is

847
00:43:30,670 --> 00:43:31,790
correlated to body size.

848
00:43:31,870 --> 00:43:33,630
And this is true across mammals.

849
00:43:33,710 --> 00:43:38,140
Um, I mean it's not a huge effect, but it's much bigger effect than pitch for in terms of

850
00:43:38,140 --> 00:43:39,820
relating to strength and body size.

851
00:43:39,980 --> 00:43:42,700
Well body size even so low pitch isn't really related to body.

852
00:43:43,020 --> 00:43:46,940
So what what is it sound what is format dispersion sound like if not pitch?

853
00:43:48,140 --> 00:43:54,140
Um so if if you heard the pitch of a sound, it would you know I mean I could if

854
00:43:54,140 --> 00:43:58,530
I was prepared I'd be like the timbre of the sound It's like the difference between baby

855
00:43:58,530 --> 00:44:01,890
cooing because we we did the Greg and I did the stimulant and I had the same question.

856
00:44:02,050 --> 00:44:07,170
And for me, at least, the intuition is you can imagine a baby like making a low pitch

857
00:44:07,250 --> 00:44:10,930
sound, but it kind of sounds like cooing, you know, like kind of like if you just imagine

858
00:44:11,010 --> 00:44:16,530
like a baby kind of making a noise vocalizing, but it's kind of a deep for a baby

859
00:44:16,530 --> 00:44:22,530
vocalizing, and then imagine like a whale producing, and you kind of get that.

860
00:44:22,760 --> 00:44:26,600
I don't even know how to articulate it verbally, but it at least it kind of captures like

861
00:44:26,680 --> 00:44:30,280
imagine what a baby sounds like and what a whale sounds like, but they're both making low

862
00:44:30,360 --> 00:44:31,480
pitched noises.

863
00:44:33,080 --> 00:44:35,160
Yeah, though I mean, yeah, no, that's interesting.

864
00:44:35,240 --> 00:44:38,760
Uh is it like a kind of like um reverbiness?

865
00:44:39,160 --> 00:44:44,280
I think the the formant I mean, if you're asking what the formants are what they sound like

866
00:44:44,280 --> 00:44:50,210
relative to the source, think of just the source being something like I mean it's just a

867
00:44:50,210 --> 00:44:52,050
noise like that, maybe a little cleaner than that.

868
00:44:52,850 --> 00:44:55,330
It's it's formats being valves.

869
00:44:55,570 --> 00:45:00,210
So it's like AIO is I'm adjusting the the format.

870
00:45:00,530 --> 00:45:05,570
Um the I'm adjusting the formats because I'm adjusting the resonating chambers in my mouth

871
00:45:05,570 --> 00:45:07,730
by moving my tongue around and stuff like that.

872
00:45:07,890 --> 00:45:12,530
So the formats are like the vowel sounds that think about vowels changing across the

873
00:45:12,530 --> 00:45:13,410
different vowels.

874
00:45:13,570 --> 00:45:18,740
That's the action of formats shifting, especially the first Right.

875
00:45:18,900 --> 00:45:24,180
And then the underlying pitch of the voice, which is just the source sound of, you know,

876
00:45:24,260 --> 00:45:26,740
think of it as it's not really any vowel.

877
00:45:26,900 --> 00:45:31,380
I mean, it's probably closest to ah, which is open, like uh the open.

878
00:45:32,100 --> 00:45:32,260
Right.

879
00:45:32,340 --> 00:45:37,620
So if I'm singing the same note and I go ah ooh e, like the the change in the vowel

880
00:45:37,620 --> 00:45:40,980
represents the ch the shift in the in the f format format.

881
00:45:41,610 --> 00:45:41,930
Right.

882
00:45:42,090 --> 00:45:42,490
Yep.

883
00:45:42,730 --> 00:45:43,210
Exactly.

884
00:45:43,290 --> 00:45:47,770
And so and then, you know, you could you probably inadvertently raise the pitch a little

885
00:45:47,770 --> 00:45:51,850
bit when you are like there's some sometimes you could there's a perceptual interaction,

886
00:45:51,930 --> 00:45:55,850
but I mean in theory these things are independent dimensions, the source and the filter.

887
00:45:55,930 --> 00:46:00,010
Um though E would sound higher pitched, maybe people might make that judgment.

888
00:46:00,330 --> 00:46:00,650
Okay.

889
00:46:01,460 --> 00:46:01,860
Cool.

890
00:46:02,020 --> 00:46:02,340
All right.

891
00:46:02,420 --> 00:46:04,180
So we got we got fear, we got anger.

892
00:46:04,340 --> 00:46:07,220
What's going on with sad, lugubrious strings?

893
00:46:07,540 --> 00:46:07,700
Yeah.

894
00:46:07,860 --> 00:46:08,500
Or reflection.

895
00:46:08,580 --> 00:46:11,140
Or why does it make ref uh must reflect too?

896
00:46:11,300 --> 00:46:14,980
Because I think there's also like it's not just like seeing a snake, it also feels like it

897
00:46:14,980 --> 00:46:18,260
controls our thoughts to put it too s crudely.

898
00:46:18,580 --> 00:46:19,460
Um right?

899
00:46:19,540 --> 00:46:21,300
Like why do I feel reflective?

900
00:46:21,460 --> 00:46:23,220
Um that's interesting.

901
00:46:23,390 --> 00:46:29,070
Um, I mean, when you're sadness, you're trying to solicit um assistance at some level.

902
00:46:29,150 --> 00:46:33,710
If you take like the Ed Hagen-like approach to communicating sadness, which I like, um,

903
00:46:33,870 --> 00:46:39,870
there's something about you're expressing a a state that is not an immediate

904
00:46:39,870 --> 00:46:42,830
state about a moment where you need to act quick.

905
00:46:42,990 --> 00:46:48,180
It's more of a long it's a something about a long-term situation, and you're soliciting in

906
00:46:48,180 --> 00:46:54,180
some way, or you need whatever you you um need at least um to check on who's your

907
00:46:54,180 --> 00:46:58,180
ally and and I mean there it depends on the context.

908
00:46:58,260 --> 00:47:03,780
But so I think that it I like the idea of it in invoking inflection or a reflection because

909
00:47:03,780 --> 00:47:07,460
you have to think about like what is happening, what is being communicated.

910
00:47:07,540 --> 00:47:12,340
It's not as simple as something like I'm going to attack you or I'm scared of a thing.

911
00:47:12,500 --> 00:47:16,790
It's it seems like it's about other more complex long-term phenomena.

912
00:47:17,110 --> 00:47:17,270
Yeah.

913
00:47:17,910 --> 00:47:21,590
And I don't want to be um like I we're we're all on this journey together.

914
00:47:21,670 --> 00:47:23,670
Like I don't want to do a gotcha thing here.

915
00:47:23,750 --> 00:47:25,350
Um and I'm not doing it in this spirit.

916
00:47:25,510 --> 00:47:31,350
But like one thing that I I I I'm puzzled by is why there's fluidity with first and third

917
00:47:31,350 --> 00:47:31,670
person.

918
00:47:32,310 --> 00:47:38,070
Like, um, like why is it that if I hear sad music, I don't just react as if I'm hearing

919
00:47:38,070 --> 00:47:40,260
some something else that's sad.

920
00:47:40,980 --> 00:47:43,220
Why is it that it feels like it's in my head?

921
00:47:43,300 --> 00:47:44,980
You know, like I think that's the other part of it.

922
00:47:45,060 --> 00:47:50,660
Is like, why is it almost equally likely that it's almost as if

923
00:47:50,820 --> 00:47:54,980
this it's coming from me, which which feels right?

924
00:47:55,060 --> 00:47:58,500
Like, and I don't know if that's again or at the very least, like someone you care about.

925
00:47:58,580 --> 00:48:02,310
Like you could imagine like your enemy being sad and you not feeling sad, right?

926
00:48:02,470 --> 00:48:05,110
In fact, you might even feel happy because your enemy is sad, right?

927
00:48:05,350 --> 00:48:06,950
Yeah, but I'm like a whimpering animal.

928
00:48:07,030 --> 00:48:09,110
It's not like I'm like, oh, where's the whimpering animal?

929
00:48:09,270 --> 00:48:10,630
It's like who's sad around me?

930
00:48:10,710 --> 00:48:15,590
It's like I hear sad music and all of a sudden I'm like starting to reflect on the things

931
00:48:15,590 --> 00:48:18,150
that either were sad for me or might be sad.

932
00:48:18,390 --> 00:48:19,510
Why is it in me?

933
00:48:19,670 --> 00:48:20,230
Yeah.

934
00:48:20,550 --> 00:48:21,910
I mean that that's a good question.

935
00:48:21,990 --> 00:48:27,030
I mean uh the first past answer is that it's it's also because of affect induction which is

936
00:48:27,030 --> 00:48:30,920
what I had mentioned before like the fear scream kind of makes scared, right?

937
00:48:31,000 --> 00:48:33,480
And because that causes an adaptive reaction.

938
00:48:33,720 --> 00:48:39,080
And so I think all of emotions are communicated, could have that kind of inducing effect,

939
00:48:39,240 --> 00:48:39,560
right?

940
00:48:39,640 --> 00:48:41,480
I mean, it's contagious.

941
00:48:41,720 --> 00:48:45,960
Um, and so sadness can be contagious, um, as can happiness.

942
00:48:46,200 --> 00:48:52,120
Laughter is very contagious, um, and um obviously fear and anger are

943
00:48:52,120 --> 00:48:53,080
contagious as well.

944
00:48:53,160 --> 00:48:59,110
And so I think what you're describing could be some kind of conceptualization uh or

945
00:48:59,270 --> 00:49:04,790
conceptual um you know translation of this inducing sadness

946
00:49:04,790 --> 00:49:08,790
effect that would be my first past answer i have to think about that for a little bit

947
00:49:08,870 --> 00:49:12,470
though yeah i would sadness is an interesting yeah yeah i would i would also bet that

948
00:49:12,470 --> 00:49:17,350
sadness would be lower in vocal effort lower in distortion lower in volume definitely

949
00:49:17,830 --> 00:49:21,670
because in that case you're you what you're signaling is that you're not physiologically or

950
00:49:21,830 --> 00:49:24,960
that you're depleted down regulation in terms of Yeah, down regulation.

951
00:49:25,040 --> 00:49:28,800
You're you're defeated, you're tired, you're you're immobilized, you know, you're

952
00:49:28,800 --> 00:49:30,160
incapacitated, whatever.

953
00:49:30,320 --> 00:49:34,640
Like when you're in a sad state, your energy reserves are much lower, and and that is what

954
00:49:34,640 --> 00:49:35,920
is being signaled, presumably.

955
00:49:36,080 --> 00:49:39,600
Like if you think if I think of like a lot of sad music, it's often very quiet.

956
00:49:39,840 --> 00:49:41,600
Uh uh Yeah.

957
00:49:42,080 --> 00:49:42,960
Yeah, exactly.

958
00:49:43,040 --> 00:49:48,530
And there's I mean, the the the melodic contours are very much like sad vocals, you know,

959
00:49:48,770 --> 00:49:49,810
sad speech.

960
00:49:49,970 --> 00:49:55,650
Um, and you were talking earlier about um, you know, w why certain

961
00:49:55,810 --> 00:49:58,370
um tonality or um how did you put it?

962
00:49:58,530 --> 00:50:04,530
I mean, there's tonality and scales and other kinds of musical features are associated with

963
00:50:04,530 --> 00:50:07,170
the emotions in the speech.

964
00:50:07,810 --> 00:50:13,640
So the when we're speaking in more sad tones relative to anger, for example,

965
00:50:13,800 --> 00:50:19,320
um, there are different kinds of frequencies that are highlighted that are associated with

966
00:50:19,320 --> 00:50:21,000
those kinds of melodies.

967
00:50:21,320 --> 00:50:25,640
And so like all these different kinds of musical properties are you can show have been

968
00:50:25,640 --> 00:50:28,920
derived directly from the vocal expression of them.

969
00:50:29,610 --> 00:50:35,210
And so that Dan regulation, the dis descending contours and the melodies.

970
00:50:35,290 --> 00:50:39,210
And yeah, you don't have evidence of high arousal, so there's not distortion, you know.

971
00:50:39,290 --> 00:50:41,210
I mean a sad song with distortion ends up.

972
00:50:41,450 --> 00:50:45,210
I mean, I guess you could have sad punk rock or whatever, and it's that has distortion in

973
00:50:45,210 --> 00:50:45,610
it some level.

974
00:50:46,010 --> 00:50:47,770
Kind of sounds like laxymoron, yeah.

975
00:50:48,490 --> 00:50:49,050
Yeah.

976
00:50:49,450 --> 00:50:54,560
Like I can't I it's hard I'm having a hard time imagining like a sad electric guitar, you

977
00:50:54,560 --> 00:50:54,880
know?

978
00:50:56,000 --> 00:51:00,240
Or a a sad loud like distorted, yeah.

979
00:51:00,320 --> 00:51:00,400
Yeah.

980
00:51:00,640 --> 00:51:01,280
Then it's gonna sad.

981
00:51:01,520 --> 00:51:05,280
Yeah, there's it's definitely I mean you'd have to be pretty good to like get that across

982
00:51:05,600 --> 00:51:07,840
having th those kinds of characteristics.

983
00:51:08,160 --> 00:51:12,570
But it's also gonna be relative to other music that the person is producing, and then you

984
00:51:12,570 --> 00:51:18,570
hear the relative emotion, even though out of context, maybe it wouldn't be taken that way,

985
00:51:18,810 --> 00:51:19,530
potentially.

986
00:51:20,570 --> 00:51:20,810
Yeah.

987
00:51:20,970 --> 00:51:25,450
There's there's another like really mysterious part of it where there's also like a sense

988
00:51:25,450 --> 00:51:27,850
of triumphant that you hear in music.

989
00:51:27,930 --> 00:51:32,570
Like when it shifts from minor to major, like it's like, ah, you've achieved something

990
00:51:32,570 --> 00:51:33,370
momentous.

991
00:51:33,610 --> 00:51:38,560
Like that is is really mysterious to me because that doesn't seem to relate to vocal effort

992
00:51:38,560 --> 00:51:42,400
or distortion or anything like that, which would be a valid cue of physiological arousal.

993
00:51:42,480 --> 00:51:46,960
That's something that's just purely in the tones and the change from uh you know minor to

994
00:51:46,960 --> 00:51:47,280
major.

995
00:51:47,360 --> 00:51:49,680
And that I have no idea what's going on.

996
00:51:50,400 --> 00:51:51,920
Do you have any idea what's going on there?

997
00:51:52,320 --> 00:51:58,320
I mean I I I think that um b positive negative um and minor major

998
00:51:58,560 --> 00:52:02,290
have to do with positive and negative emotions in the voice again.

999
00:52:02,370 --> 00:52:07,330
And so when you're ex extremely happy, then you don't have those nonlinear features to the

1000
00:52:07,330 --> 00:52:10,850
same extent than when you are, I mean, you do a little bit though.

1001
00:52:10,930 --> 00:52:15,730
So people have done things like look at when people win in sports and other things.

1002
00:52:15,810 --> 00:52:17,330
And they're definitely high arousal.

1003
00:52:17,490 --> 00:52:22,930
So some of those nonlinear features are there just by by as a byproduct of the production.

1004
00:52:23,250 --> 00:52:28,370
But you don't emphasize them in the same way as if you're growling at somebody or you are

1005
00:52:28,380 --> 00:52:30,460
threatening them or whatever.

1006
00:52:31,740 --> 00:52:36,220
So the triumphant positive notes are going to be cleaner, right?

1007
00:52:36,540 --> 00:52:41,740
And then more negative songs or

1008
00:52:42,380 --> 00:52:48,060
melodies are going to be more distorted and have different kinds of melodic

1009
00:52:48,060 --> 00:52:50,540
structure that's associated with that negative emotion.

1010
00:52:50,780 --> 00:52:54,010
But I guess I would want to see the evidence for the triumphant thing.

1011
00:52:54,090 --> 00:52:55,690
I I would assume it'd be a little more clean.

1012
00:52:55,770 --> 00:53:01,770
You know, like horns, whatever, and um it's you know, like growling.

1013
00:53:02,250 --> 00:53:02,490
Yeah.

1014
00:53:02,810 --> 00:53:08,730
Is is there I mean, um, you know, if you there's certain traditions that are positive where

1015
00:53:08,730 --> 00:53:13,290
it's a positive triumphant kind of thing and it's big sounds with like noisy features.

1016
00:53:13,610 --> 00:53:17,460
So here's a here's a different, perhaps weirder question.

1017
00:53:17,700 --> 00:53:23,540
If you rewound the tape of evolution um and you replayed it, could it

1018
00:53:23,540 --> 00:53:29,540
be possible that um minor actually would connote happiness and major would connote

1019
00:53:29,540 --> 00:53:29,940
sadness?

1020
00:53:30,020 --> 00:53:31,540
Like, is this just an evolutionary accident?

1021
00:53:31,700 --> 00:53:36,340
Is it arbitrary that these emotions are mapped to these melodies and sound structures?

1022
00:53:36,500 --> 00:53:41,140
Or is there something inherent to these sound structures that connects to a particular

1023
00:53:41,140 --> 00:53:41,860
emotion?

1024
00:53:42,260 --> 00:53:42,500
Right.

1025
00:53:42,710 --> 00:53:43,670
I mean, that's a good question.

1026
00:53:43,830 --> 00:53:48,870
And I think the evidence that I like, this is Dale Perves and Dan Bowling and others have

1027
00:53:48,870 --> 00:53:53,990
done a lot of work looking at how musical features are related to vocal communication.

1028
00:53:54,150 --> 00:53:58,230
And it's a very voice centric approach to music that I like.

1029
00:53:58,550 --> 00:54:04,230
And their argument is that when you are producing emotions

1030
00:54:04,230 --> 00:54:08,790
vocally like sadness or anger, that there um are

1031
00:54:10,280 --> 00:54:16,200
the the relative frequencies end up mapping better to something like

1032
00:54:16,200 --> 00:54:22,120
a dissonant musical key than when you have a positive uh vocal emotions,

1033
00:54:22,200 --> 00:54:26,920
then those energy, those frequencies map better to um a major key.

1034
00:54:27,080 --> 00:54:31,320
Um, so the idea there is that major and minor is universal to some extent.

1035
00:54:31,480 --> 00:54:34,040
And I think there's evidence that not everybody hears it that way.

1036
00:54:34,120 --> 00:54:39,410
And there could be cultural evolution of musical traditions that make that small effect not

1037
00:54:39,410 --> 00:54:41,250
so much an effect anymore.

1038
00:54:41,570 --> 00:54:47,410
But my thinking of it is that it is derived from how

1039
00:54:47,410 --> 00:54:49,890
these kinds of emotions manifest in the voice.

1040
00:54:50,130 --> 00:54:56,050
And so there's really interesting work showing that if you normalize little

1041
00:54:56,210 --> 00:55:01,650
tiny snippets of a voice, the acoustics, and then you convert the

1042
00:55:01,650 --> 00:55:07,620
frequencies to just um uh octaves, So you have um ratios of frequencies

1043
00:55:07,620 --> 00:55:12,500
that start at where the maximum amplitude is in the in the signal, which is used at pretty

1044
00:55:12,500 --> 00:55:13,380
low frequency.

1045
00:55:13,540 --> 00:55:18,340
And then instead of having the x-axis be frequency, you have it just be octaves.

1046
00:55:18,580 --> 00:55:20,820
And then you take an octave slice.

1047
00:55:21,060 --> 00:55:25,860
Um, and if you look at that octave slice, the spectral structure, there are bumps that

1048
00:55:25,860 --> 00:55:28,340
correspond really well to the chromatic scale.

1049
00:55:28,570 --> 00:55:33,370
And so you can, you know, you octave is highlighted, the unison pitch is highlighted, and

1050
00:55:33,370 --> 00:55:35,610
then a fifth is get there's a bump.

1051
00:55:35,690 --> 00:55:39,130
And then all the little notes, there's a really cool figure in this one paper where they

1052
00:55:39,130 --> 00:55:44,410
just show exactly how the little bumps in the spectral structure of a speech snippet is

1053
00:55:44,410 --> 00:55:47,370
related to the chromatic scale and the Western musical scale.

1054
00:55:47,450 --> 00:55:50,330
And then they can also do that same kind of thing for musical scales.

1055
00:55:50,650 --> 00:55:55,210
So it's not just the intervals but the scales themselves actually there's there's certain

1056
00:55:55,210 --> 00:55:59,940
scales um happen more often in cultural evolution than others, right?

1057
00:56:00,020 --> 00:56:05,620
They cluster, and the way they cluster is related to how the spectral features of speech

1058
00:56:05,620 --> 00:56:07,700
map onto the chromatic scale.

1059
00:56:07,860 --> 00:56:13,700
So to me, that's really that that we are really it's a cultural elaboration of this basic

1060
00:56:13,700 --> 00:56:18,500
perceptual sensitivity to voices, and that we can cultural evolution can build so much

1061
00:56:18,500 --> 00:56:24,340
richness and elaboration on just that basic um predisposition we have perceptually to

1062
00:56:24,660 --> 00:56:29,310
that we like to listen to voices, or we have voice selective adaptations, right?

1063
00:56:29,390 --> 00:56:30,590
I mean, that's that's super clear.

1064
00:56:30,830 --> 00:56:31,550
Yeah, that's interesting.

1065
00:56:31,790 --> 00:56:37,310
I mean especially the part where like sp speech corresponds more to it the chromatic scale

1066
00:56:37,310 --> 00:56:40,830
than you know, say a major scale or a pentatonic scale or anything like that.

1067
00:56:40,990 --> 00:56:46,830
Um like jazz to me comes to mind as like really making use of the chromatic

1068
00:56:46,830 --> 00:56:51,950
scale, making use of just like lots of tiny changes in notes and just ascending

1069
00:56:51,950 --> 00:56:54,430
chromatically, you know, half step by half step.

1070
00:56:54,590 --> 00:56:58,480
Um and to me, intuitively, jazz sounds more conversational.

1071
00:56:58,560 --> 00:56:59,920
It sounds like the instruments are talking.

1072
00:57:00,080 --> 00:57:03,600
It sounds like a person, just you know, like if you hear people jamming, it's just like the

1073
00:57:03,600 --> 00:57:07,360
instruments are talking to each other, and it's just sounds like you don't know, more

1074
00:57:09,760 --> 00:57:12,160
rich metaphor that jazz musicians like a lot.

1075
00:57:12,240 --> 00:57:14,560
And they and I've I teach it that way in this one.

1076
00:57:14,640 --> 00:57:16,400
I used to teach a class called group communication.

1077
00:57:16,560 --> 00:57:21,520
I would draw explicit connections between conversational behavior and musical interaction.

1078
00:57:21,680 --> 00:57:26,030
And yeah, they're you're taking turns and I mean there's all these elements they map really

1079
00:57:26,030 --> 00:57:26,350
well.

1080
00:57:26,990 --> 00:57:27,390
Yeah.

1081
00:57:27,550 --> 00:57:33,470
So um I think that we we're we're sensitive to voices and that that's the kinds of sounds

1082
00:57:33,470 --> 00:57:33,950
we want to hear.

1083
00:57:34,030 --> 00:57:38,110
We've we've culturally evolved instruments that are in the same range and that the way

1084
00:57:38,190 --> 00:57:42,830
emotions are expressed are gonna if you would analyze it correctly you're gonna see map

1085
00:57:42,830 --> 00:57:45,070
onto the way we express those things vocally.

1086
00:57:45,230 --> 00:57:50,660
And so that's why we major minor consonants dissonance um descending versus ascending

1087
00:57:50,740 --> 00:57:53,860
melodic contours, they just all seem to map.

1088
00:57:54,100 --> 00:57:58,660
But then you have all these other features that cultural evolution uses to build musical

1089
00:57:58,660 --> 00:58:04,260
traditions that then it's just it's crazy when you think about the music varia variability

1090
00:58:04,260 --> 00:58:09,860
out there and that what basic adaptive problems and it that originally music solved is not

1091
00:58:10,180 --> 00:58:13,940
going to predict all the elaboration we see that culture has produced.

1092
00:58:14,650 --> 00:58:20,090
And you just mentioned music being and solving an or was it a cultural adaptation?

1093
00:58:20,170 --> 00:58:20,970
I mean, what is your take?

1094
00:58:21,050 --> 00:58:26,890
So I mean there's one idea that you know cultural selection or biological selection

1095
00:58:27,210 --> 00:58:32,890
uh made music an adaptation, you know, the big A, the, the A-word, the onerous concept.

1096
00:58:33,050 --> 00:58:34,010
Um what is your take?

1097
00:58:34,090 --> 00:58:39,620
So I mean there's one idea that you know music evolved for social bonding, for example.

1098
00:58:39,860 --> 00:58:43,860
Um, I think what's your take on that?

1099
00:58:44,100 --> 00:58:44,980
Um yeah.

1100
00:58:45,380 --> 00:58:51,060
So there's there's a really good um BBS um thing that so um Max

1101
00:58:51,220 --> 00:58:57,060
Krasnow and Sam and Ed Hagen and I um wrote a BBS Target article about

1102
00:58:57,060 --> 00:58:57,300
this.

1103
00:58:57,460 --> 00:59:02,170
And then it was, I think, the only time this has happened in BBS where we submitted two

1104
00:59:02,170 --> 00:59:03,130
target articles.

1105
00:59:03,290 --> 00:59:08,890
So we did one and then another group of folks, um, Pat Savage and um

1106
00:59:09,210 --> 00:59:13,610
Tecumseh Fitch and a bunch of folks um produced one that was about social bonding.

1107
00:59:13,770 --> 00:59:18,650
Cause we did this conference in in Paris or outside of Paris many years ago where we were

1108
00:59:18,650 --> 00:59:23,050
talking about the origins of music, and then two camps kind of happened us four, and then

1109
00:59:23,050 --> 00:59:25,290
everybody else that likes social bonding.

1110
00:59:25,520 --> 00:59:27,280
So we decided to do this BBS thing.

1111
00:59:27,360 --> 00:59:29,920
They took both papers and then they solicited commentaries for both.

1112
00:59:30,000 --> 00:59:31,760
And it's this huge, it's like a book.

1113
00:59:32,240 --> 00:59:35,920
And um there we go after the social bonding idea.

1114
00:59:36,080 --> 00:59:40,560
Um, and I don't want to pick on that here too much, but that the the idea is that um

1115
00:59:40,880 --> 00:59:46,160
because they you can show effects of people playing music together, cooperate more and like

1116
00:59:46,160 --> 00:59:50,880
each other more, and there's all these sort of proximate effects, um, they argue that it's

1117
00:59:50,880 --> 00:59:51,520
for that.

1118
00:59:51,910 --> 00:59:56,150
And we make the case it's actually not well suited for that, particularly.

1119
00:59:56,230 --> 00:59:59,670
Just because it has that effect doesn't mean that the adaptation is to cause social

1120
00:59:59,990 --> 01:00:00,470
bonding.

1121
01:00:00,550 --> 01:00:05,670
And we make the case rather that it's better to think of it as credible signaling.

1122
01:00:05,910 --> 01:00:11,430
So when you have a practiced um effect of a

1123
01:00:11,430 --> 01:00:17,420
coalition producing a coherent, obviously choreographed um show that

1124
01:00:17,420 --> 01:00:18,140
it takes practice.

1125
01:00:18,220 --> 01:00:20,540
It's an honest signal of time spent with one another.

1126
01:00:20,620 --> 01:00:24,940
Um it gives an honest indicator of how well you coordinate your actions.

1127
01:00:25,260 --> 01:00:31,020
And and so then you and the the idea is that that could have

1128
01:00:31,020 --> 01:00:37,020
caused selection for the ability to entrain physically with another person, which is a

1129
01:00:37,020 --> 01:00:39,340
human specific um adaptation.

1130
01:00:39,500 --> 01:00:45,350
No other, almost no other mammal can really do it well, um, where they can actually

1131
01:00:45,510 --> 01:00:51,110
readily learn how to entrain to another person with their motor abilities.

1132
01:00:51,350 --> 01:00:53,350
Um that's something that only people can do.

1133
01:00:53,510 --> 01:00:57,110
And so if you're gonna look for a musical adaptation, I think you gotta look for why would

1134
01:00:57,110 --> 01:01:00,070
people be coordinating their muscle movement like that.

1135
01:01:00,640 --> 01:01:02,960
um in a super um fine-grained way.

1136
01:01:03,040 --> 01:01:07,360
So that's the argument that Ed and I had developed a long time ago.

1137
01:01:07,680 --> 01:01:13,600
It's my first paper ever published, arguing that it's you're giving

1138
01:01:13,600 --> 01:01:18,960
an honest signal of coalition quality by coordinating your and music is a vehicle to do

1139
01:01:18,960 --> 01:01:19,280
that.

1140
01:01:20,160 --> 01:01:25,920
And then Sam and Max it's sort of developed this idea about how

1141
01:01:26,160 --> 01:01:32,050
infant caregiver communication caregivers signaling to infants that they um are

1142
01:01:32,050 --> 01:01:37,490
actually honestly have their attention and that that promoted this uh motivation to produce

1143
01:01:37,490 --> 01:01:42,210
melodies that that uh got the infant's attention and that it's hard to do that while doing

1144
01:01:42,210 --> 01:01:42,610
other things.

1145
01:01:42,690 --> 01:01:46,130
If you're singing to your kid you could be doing some other things but you definitely have

1146
01:01:46,210 --> 01:01:48,690
you there you have they have your attention.

1147
01:01:48,930 --> 01:01:54,560
And so it it that's an emotional communication mechanism that is sort of

1148
01:01:54,560 --> 01:01:58,800
exploiting the predisposition for detecting emotions and voices, right?

1149
01:01:58,960 --> 01:02:04,800
Some lullabies sound like friendly, affiliative kinds of vocalizations, but they're just

1150
01:02:04,960 --> 01:02:08,160
um, you know, ritualized, highly ritualized.

1151
01:02:08,320 --> 01:02:13,600
And so their argument was that that could be the origins of melody, is that the initial

1152
01:02:13,600 --> 01:02:17,680
singing between um kit for k caregivers singing to their offspring.

1153
01:02:18,000 --> 01:02:23,050
So we just make the case that these are two examples of credible signaling that could form

1154
01:02:23,050 --> 01:02:26,810
the basis for some adaptations that are actually for music.

1155
01:02:26,970 --> 01:02:32,250
But then most other things that we incorporate into the music faculty, as they say, are

1156
01:02:32,490 --> 01:02:35,050
byproducts of other things that we have.

1157
01:02:35,210 --> 01:02:40,250
So that's my view view: that there are a couple adaptations plus cheesecake, right?

1158
01:02:40,570 --> 01:02:45,620
And Pinker's idea of violatory cheesecake is sort of the hypothesis to beat, and that it's

1159
01:02:45,620 --> 01:02:49,940
culturally cobbled together all these mechanisms that really have solved all these other

1160
01:02:49,940 --> 01:02:50,500
problems.

1161
01:02:50,660 --> 01:02:54,260
Um, and language is also possibly a lot like that.

1162
01:02:54,420 --> 01:02:58,100
Like, what is what specific adaptation is there for language?

1163
01:02:58,180 --> 01:02:59,540
That's a big question.

1164
01:02:59,700 --> 01:03:03,540
Uh, I mean, now it's getting reduced down to recursion, is the only thing.

1165
01:03:03,700 --> 01:03:05,620
Um, even that's questionable.

1166
01:03:05,700 --> 01:03:11,190
Um, language is a whole nother can't affect so I think there's a similar there's an

1167
01:03:11,190 --> 01:03:16,310
analogous sort of argument argument there that you we're cobbling together all these

1168
01:03:16,310 --> 01:03:21,990
abilities and culturally, but then there could be these core adaptive problems that

1169
01:03:22,230 --> 01:03:26,470
that uh should be considered proper in the domain of music and not actual.

1170
01:03:26,870 --> 01:03:32,070
And those sound like they're getting into uh time because you know, music is because the

1171
01:03:32,070 --> 01:03:36,220
emotionality thing, there was no I mean, there was a little bit of a time commo component.

1172
01:03:36,380 --> 01:03:40,380
But there is, of course, this whole thing of like rhythm or prosody.

1173
01:03:40,940 --> 01:03:45,980
Um, and just in in the, you know, somebody can't be a good musician if they don't have good

1174
01:03:45,980 --> 01:03:49,740
rhythm or they don't have a good sense of timing, even if they're if they can if they're

1175
01:03:49,740 --> 01:03:53,980
like, yeah, this pitch is gonna make you feel like this, if they can't do that over time,

1176
01:03:54,140 --> 01:03:55,900
they're not gonna be a good musician.

1177
01:03:56,140 --> 01:04:01,580
Um so, but but it sounds like dancing or like coordinating movement is an example.

1178
01:04:01,900 --> 01:04:07,490
So is that correct that maybe these other uh proper domain-y things of

1179
01:04:08,130 --> 01:04:13,650
coordination and possibly this byproduct of melody um

1180
01:04:13,890 --> 01:04:16,130
is part of where timing comes in.

1181
01:04:16,610 --> 01:04:22,450
Yeah, well, I mean the the timing is we humans are really even the people with the worst

1182
01:04:22,450 --> 01:04:27,410
timing are um a pretty good relatively species, okay.

1183
01:04:27,490 --> 01:04:27,890
Yeah, yeah.

1184
01:04:28,220 --> 01:04:28,700
Definitely.

1185
01:04:28,940 --> 01:04:31,980
You cannot train a dog to play a drum, no matter how many treats.

1186
01:04:32,300 --> 01:04:33,580
Believe me, I've tried cases.

1187
01:04:33,900 --> 01:04:34,540
Yeah.

1188
01:04:35,420 --> 01:04:35,740
Right.

1189
01:04:35,820 --> 01:04:41,420
I mean, people have evidence for bonobos, um, which I mean, you don't see a bonobo in the

1190
01:04:41,420 --> 01:04:46,700
wild doing anything remotely like that, but you can get them to kind of, you know, keep a

1191
01:04:46,700 --> 01:04:48,540
beat along with the thing that they hear.

1192
01:04:48,620 --> 01:04:52,460
That I don't think they've ever shown a bonobo being able to keep a beat without something

1193
01:04:52,460 --> 01:04:53,340
to listen to.

1194
01:04:53,740 --> 01:04:59,590
Um, there's evidence that sea lions can bob their head to a beat you know but it takes a

1195
01:04:59,590 --> 01:05:05,350
lot of training and um and then birds can do it so there's the famous snowball the

1196
01:05:05,350 --> 01:05:09,990
cockatoo who dances and cockatoos are well known to dance and so all these animals are

1197
01:05:09,990 --> 01:05:14,390
vocal sea lions are the exception which is an interesting one but um all these animals are

1198
01:05:14,390 --> 01:05:20,390
vocal learners so um any animal that needs to pay attention to the periodic

1199
01:05:20,580 --> 01:05:26,420
features of some other vocalizer, and then being able to make the same sound,

1200
01:05:26,580 --> 01:05:28,020
and you have to learn how to do it.

1201
01:05:28,100 --> 01:05:30,260
It's not something that's just genetically endowed.

1202
01:05:30,500 --> 01:05:33,140
You actually have to experience the thing to produce it.

1203
01:05:33,300 --> 01:05:39,060
Animals that do that can then keep a beat to some exogenous stimulus

1204
01:05:40,260 --> 01:05:46,130
and then have the the the perception action coupling ability to then move their m muscles

1205
01:05:46,130 --> 01:05:47,490
to the thing that they hear.

1206
01:05:47,730 --> 01:05:50,210
But there's very few animals that actually have that ability.

1207
01:05:50,450 --> 01:05:55,650
Sea lions are interesting because they are not vocal learners, but they are in their clad,

1208
01:05:55,890 --> 01:05:57,090
they're our vocal learners.

1209
01:05:57,170 --> 01:06:01,970
So there's a latent ability in there that's not expressed in that species, but the the

1210
01:06:01,970 --> 01:06:03,170
ability is clearly there.

1211
01:06:03,250 --> 01:06:07,090
And then other related animals can do it, our vocal learners.

1212
01:06:07,410 --> 01:06:13,120
So um I think the evidence is pretty good that vocal learning is important for entrainment.

1213
01:06:13,200 --> 01:06:19,040
And so anybody, any human is at least is is kind of an expert in that sense, but then to be

1214
01:06:19,040 --> 01:06:23,360
really good at rhythm is then you're tapping into like the best people.

1215
01:06:23,840 --> 01:06:24,080
Right.

1216
01:06:24,160 --> 01:06:29,280
Or maybe the emotional expression of like of like the timing of uh David, I know you're

1217
01:06:29,280 --> 01:06:32,560
gonna say, is there any evidence of um emotion in music linkage?

1218
01:06:32,640 --> 01:06:38,450
So like are really good musicians better at linking emotions and sound

1219
01:06:38,610 --> 01:06:40,130
or emotions in general?

1220
01:06:40,450 --> 01:06:40,850
Yes.

1221
01:06:41,010 --> 01:06:41,330
Yes.

1222
01:06:41,490 --> 01:06:46,770
So trained musicians are better at identifying emotion categories in music and in

1223
01:06:46,770 --> 01:06:47,650
voices.

1224
01:06:48,850 --> 01:06:51,730
Not much, but there is an effect for sure.

1225
01:06:52,130 --> 01:06:52,450
Cool.

1226
01:06:52,610 --> 01:06:55,170
But then trained musicians are better than a lot of things.

1227
01:06:55,330 --> 01:06:59,940
You know, it's like there's there's so many variables like when you're a trained musician,

1228
01:07:00,020 --> 01:07:01,300
that's a pretty privileged situation.

1229
01:07:01,460 --> 01:07:01,780
Sure.

1230
01:07:01,940 --> 01:07:04,100
So you probably have a lot of advantages.

1231
01:07:04,260 --> 01:07:04,420
Yeah.

1232
01:07:04,500 --> 01:07:10,260
Um, and also um people you learn music, mu just learning music enhances other cognitive

1233
01:07:10,260 --> 01:07:10,980
abilities.

1234
01:07:11,140 --> 01:07:13,540
Um, it's sort of like exercises for the brain.

1235
01:07:13,620 --> 01:07:18,820
And so um, if you're doing music, that's good for your brain and it activates all all areas

1236
01:07:18,820 --> 01:07:22,020
of the brain, music triggers, unlike most other things.

1237
01:07:22,970 --> 01:07:23,770
Interesting.

1238
01:07:24,010 --> 01:07:29,930
Uh why do I remember so vividly and connect so deeply with the music I heard

1239
01:07:29,930 --> 01:07:32,410
during my teen years, maybe early twenties?

1240
01:07:32,570 --> 01:07:35,690
Like I feel like everyone has that experience where they really connect with music at that

1241
01:07:35,690 --> 01:07:35,930
age.

1242
01:07:36,330 --> 01:07:38,970
Uh and like all their favorite stuff is comes from that era.

1243
01:07:39,130 --> 01:07:42,890
Like, you know, my dad still listens to the the Beatles, like he's listened to that growing

1244
01:07:42,890 --> 01:07:46,250
up growing up, and uh like all those old bands from the sixties.

1245
01:07:46,570 --> 01:07:50,010
Um like what there's a strong age effect there.

1246
01:07:50,330 --> 01:07:50,890
Huge.

1247
01:07:51,340 --> 01:07:53,980
How do you have any sense of why that is?

1248
01:07:54,460 --> 01:07:55,020
Yeah.

1249
01:07:55,500 --> 01:08:00,300
I mean, I think this is it fits really well into the coalition signaling argument because

1250
01:08:01,820 --> 01:08:03,500
think of music as cultural capital.

1251
01:08:03,580 --> 01:08:06,140
You're learning the music of your culture.

1252
01:08:06,620 --> 01:08:10,700
You have to learn the songs, you have to learn the scale, the musical system, right?

1253
01:08:11,020 --> 01:08:12,380
And you learn that like language.

1254
01:08:12,540 --> 01:08:18,540
So as soon as you learn the musical system of your music, then you start being

1255
01:08:18,610 --> 01:08:20,930
unable to make distinctions in other systems.

1256
01:08:21,010 --> 01:08:26,130
And so you can't like Western music listeners can't hear that the sitar is out of tune, for

1257
01:08:26,130 --> 01:08:26,690
example.

1258
01:08:26,850 --> 01:08:31,970
Um there's kind of the famous story of Robbie Shankar tuning up his sitar for an audience

1259
01:08:32,050 --> 01:08:38,050
and he finished tuning, but it clapped and then and George mortified and and uh and then

1260
01:08:38,050 --> 01:08:42,370
he said something like Well if you like that I hope you really like the songs.

1261
01:08:43,970 --> 01:08:49,840
So um we we are like language, we close ourselves off to other languages once

1262
01:08:49,840 --> 01:08:52,080
we learn the distinctions that are important in our language.

1263
01:08:52,160 --> 01:08:55,280
So think of that as like the musical capital that you've learned.

1264
01:08:55,360 --> 01:08:56,880
I like the capital idea.

1265
01:08:57,040 --> 01:09:02,720
And so um during puberty, you're sort of there's I think like an imprinting process

1266
01:09:02,880 --> 01:09:08,030
where the culture a lot of cultural phenomena, including t whatever it is happens to be in

1267
01:09:08,030 --> 01:09:08,430
your culture.

1268
01:09:08,510 --> 01:09:14,110
In our case, it's like TV shows and music and movies and um the th sayings

1269
01:09:14,350 --> 01:09:16,190
and and accents or whatever.

1270
01:09:16,510 --> 01:09:17,470
And clothes, yeah.

1271
01:09:21,390 --> 01:09:27,150
Um y or memory systems are um there I think there's probably could be adaptations

1272
01:09:27,150 --> 01:09:30,940
there that um are sort of imprinting those memories.

1273
01:09:31,020 --> 01:09:34,460
So you remember the lyrics, you remember the songs in ways that you don't remember things

1274
01:09:34,460 --> 01:09:34,700
later.

1275
01:09:34,780 --> 01:09:36,940
And it's not just because you're getting old and you can't remember.

1276
01:09:37,020 --> 01:09:40,300
It's there's something specific about that puberty old time.

1277
01:09:41,100 --> 01:09:46,140
And so I think it's in an ancestral environment, that's just the way that you adopt the

1278
01:09:46,140 --> 01:09:49,100
culture that you're from and then you can honestly signal you're from it.

1279
01:09:49,260 --> 01:09:53,580
Because if you come in as an outsider, as an adult, you won't have that effect.

1280
01:09:53,740 --> 01:09:57,350
And so it would be easy to detect that you're an outsider.

1281
01:09:57,430 --> 01:10:00,550
And similar to like um David Dave's work on accents, right?

1282
01:10:00,710 --> 01:10:05,910
There's a certain thing about you learn accents, and it's hard to actually go into a group

1283
01:10:05,910 --> 01:10:09,750
and pretend you're from there and have the accent.

1284
01:10:09,830 --> 01:10:14,390
Like there's I don't think I could ever go to uh some village in England and pretend that

1285
01:10:14,390 --> 01:10:18,870
I'm no matter how long I live there, they would always know I was not one of them.

1286
01:10:19,110 --> 01:10:20,760
And so I think music's the same way.

1287
01:10:20,840 --> 01:10:25,400
And that's that's the effect you're describing is basically that process of encoding the

1288
01:10:25,400 --> 01:10:27,320
musical culture that you grew up in.

1289
01:10:27,560 --> 01:10:31,080
And so most people are like, music stopped being good after that, right?

1290
01:10:31,480 --> 01:10:36,280
It's just like I predict like what when people think music started sucking is like when

1291
01:10:36,280 --> 01:10:37,480
they were 30 or whatever.

1292
01:10:37,560 --> 01:10:37,880
Yeah.

1293
01:10:38,680 --> 01:10:39,880
Has there been work on them?

1294
01:10:40,040 --> 01:10:41,400
Has there been work on exception?

1295
01:10:41,720 --> 01:10:43,640
I'm always looking for new music, but the what?

1296
01:10:43,960 --> 01:10:45,880
Has there been empirical work on that?

1297
01:10:46,770 --> 01:10:50,610
very little actually there was a there was um um

1298
01:10:52,770 --> 01:10:57,410
i i shouldn't say that there i was gonna talk about an ehb paper it didn't make it but um

1299
01:10:57,810 --> 01:11:03,570
yeah not enough work on that i've always been fascinated with that idea and it's in

1300
01:11:03,730 --> 01:11:07,490
some ways some aspects of that are quite tractable i mean you just ask people about what

1301
01:11:07,490 --> 01:11:12,580
music they like yeah how old they are Showing showing the tendency would be easy.

1302
01:11:12,980 --> 01:11:14,980
If you if you didn't show it, you fucked up.

1303
01:11:15,060 --> 01:11:16,500
I mean it's like Yeah.

1304
01:11:16,660 --> 01:11:17,780
It's in the Simpsons.

1305
01:11:17,940 --> 01:11:21,780
Uh so Homer Simpson, there's a classic quote from the 90s Simpsons that rock achieved

1306
01:11:21,860 --> 01:11:23,380
perfection in 1974, right?

1307
01:11:23,460 --> 01:11:25,220
Like when they're listening to music in the car.

1308
01:11:25,300 --> 01:11:25,860
Yeah.

1309
01:11:27,300 --> 01:11:32,890
So um, but then I think what is a more difficult thing is to try to measure um

1310
01:11:33,130 --> 01:11:38,890
how that might impact other aspects of your judgment about other individuals and other

1311
01:11:38,890 --> 01:11:40,410
social effects and I don't know.

1312
01:11:40,570 --> 01:11:44,010
But yeah, uh that's a really interesting and super robust effect in my opinion.

1313
01:11:44,170 --> 01:11:44,410
Yeah.

1314
01:11:45,370 --> 01:11:46,010
Yeah, totally.

1315
01:11:46,410 --> 01:11:49,290
I have a weird thing where I'm thinking I'm echoing again.

1316
01:11:49,450 --> 01:11:49,690
Jeez.

1317
01:11:49,850 --> 01:11:53,930
Um I have a weird thing where in addition to memor you know, remembering the songs of that

1318
01:11:53,930 --> 01:11:59,220
era very vividly, I have a really vivid memory of all the different film scores from that

1319
01:11:59,220 --> 01:11:59,940
time in my life.

1320
01:12:00,100 --> 01:12:04,260
If you just name one movie from like the late nineties, early two thousands, I'll be able

1321
01:12:04,260 --> 01:12:06,500
to like tell you the hum the film score to it.

1322
01:12:06,660 --> 01:12:11,300
Like even rent like speed, I remember the melody from speed, like from Keanu Reeves.

1323
01:12:11,460 --> 01:12:12,820
Like I so it's a weird thing.

1324
01:12:13,060 --> 01:12:16,500
I I don't know if that's a common thing, but like I have a really good memory for film

1325
01:12:16,500 --> 01:12:20,340
scores, particularly from that time of my life, even if I saw the movie like maybe once or

1326
01:12:20,340 --> 01:12:20,660
twice.

1327
01:12:20,740 --> 01:12:21,940
Like Interesting.

1328
01:12:22,100 --> 01:12:25,220
Yeah, I mean we and you you're obviously pretty into film, right?

1329
01:12:25,300 --> 01:12:27,610
So then that's an attractor for you, right?

1330
01:12:27,850 --> 01:12:29,610
Um, and then that example's interesting.

1331
01:12:29,690 --> 01:12:33,370
So I'm older than you, and so that is not a period that I know, right?

1332
01:12:33,530 --> 01:12:36,970
I'm I wouldn't watching as many movies back then, and I certainly could not.

1333
01:12:37,050 --> 01:12:38,090
I've seen speed maybe twice.

1334
01:12:38,250 --> 01:12:42,330
And that's not true, but there's no way I couldn't even identify in a forced choice,

1335
01:12:42,410 --> 01:12:42,730
probably.

1336
01:12:42,890 --> 01:12:43,370
Yeah.

1337
01:12:45,210 --> 01:12:45,850
Yeah.

1338
01:12:46,170 --> 01:12:47,130
It's funny.

1339
01:12:47,370 --> 01:12:53,370
Um, I I could talk about music the whole time, but uh, but I I know there's a lot

1340
01:12:53,370 --> 01:12:59,320
of things that you've studied um in the acoustic modality that people are either sensitive

1341
01:12:59,320 --> 01:13:03,000
to, um, that maybe may be surprising for people.

1342
01:13:03,560 --> 01:13:09,480
Um are there are there ones that uh you think are cool

1343
01:13:09,480 --> 01:13:10,200
or exciting?

1344
01:13:10,360 --> 01:13:14,360
I mean they're all cool and exciting, but are there particular ones that either you've done

1345
01:13:14,360 --> 01:13:17,160
in the past or that you're working on now?

1346
01:13:17,750 --> 01:13:19,510
You mean areas of research or topics?

1347
01:13:19,990 --> 01:13:20,710
Yeah, yeah, yeah, yeah.

1348
01:13:20,790 --> 01:13:22,310
I mean things you're doing or colleagues.

1349
01:13:22,470 --> 01:13:25,990
But yeah, I mean I mean because I don't want to I don't want to have people just leave with

1350
01:13:25,990 --> 01:13:29,030
the impression that you're doing just music.

1351
01:13:29,190 --> 01:13:32,630
Um that's probably the the thing I've done the least empirical work.

1352
01:13:32,710 --> 01:13:33,590
That's all I think.

1353
01:13:33,910 --> 01:13:38,070
So I've done so yeah I do have some I have a couple papers on music perception stuff.

1354
01:13:38,470 --> 01:13:42,310
Yeah and I think it's fascinating but I do want to like you know I I want to be able to

1355
01:13:42,310 --> 01:13:46,100
talk a bit about what you've what else you've done empirically.

1356
01:13:46,260 --> 01:13:51,220
Um so can you give us, I don't know what the best like, is there a story of like have you

1357
01:13:51,220 --> 01:13:54,180
come around to like this this is the thing I'm focusing on?

1358
01:13:54,340 --> 01:13:57,140
Or is it just that there's like this you're a kid in a candy shop.

1359
01:13:57,220 --> 01:13:58,340
There's so much to do.

1360
01:13:58,580 --> 01:14:02,500
You have the lucky position of being an acoustic adaptationist and you're like, we're gonna

1361
01:14:02,500 --> 01:14:03,700
do this and this and this and this.

1362
01:14:03,860 --> 01:14:04,020
Yeah.

1363
01:14:04,260 --> 01:14:04,500
Yeah.

1364
01:14:04,660 --> 01:14:10,650
I mean, I guess I came into the whole thing interested in how people

1365
01:14:10,650 --> 01:14:12,810
use their voice to convey emotions.

1366
01:14:13,290 --> 01:14:16,170
And then the and and through music.

1367
01:14:16,650 --> 01:14:20,570
So what what are the musical properties of speech is kind of a thing to think about.

1368
01:14:20,650 --> 01:14:22,490
And so I got interested in prosody.

1369
01:14:22,730 --> 01:14:24,650
That's sort of the interface between language and music.

1370
01:14:24,730 --> 01:14:30,490
So prosody is this pitch and loudness and rhythm and some spectral features that are

1371
01:14:31,130 --> 01:14:36,970
acting on top of utterances that give you information that's beyond what's in the language.

1372
01:14:37,360 --> 01:14:40,800
And um I think it's a super powerful mechanism.

1373
01:14:40,960 --> 01:14:45,360
I mean, you can interpret a lot with from prosody without hearing any of the words.

1374
01:14:45,440 --> 01:14:50,560
You can content mask speech and people are really good at identifying all sorts of rich

1375
01:14:50,560 --> 01:14:53,120
dimensions about the speech signal just based on the melody.

1376
01:14:53,280 --> 01:14:57,120
So I get that's what originally drove drew me into this whole area of research.

1377
01:14:57,200 --> 01:15:03,150
And then Lita's paper um that I mentioned before was um you know focusing on

1378
01:15:03,150 --> 01:15:07,870
that, looking at um how you know there's these certain invariances and acoustic structure

1379
01:15:07,870 --> 01:15:10,750
across different emotions that are not about the language.

1380
01:15:10,990 --> 01:15:16,670
And um in 1983, that was kind of a novel idea, kind of, and so um, yeah,

1381
01:15:16,830 --> 01:15:19,870
so I got into that and then I started studying spontaneous speech.

1382
01:15:20,030 --> 01:15:23,550
And when I got my PhD in it's in cognitive psychology and psycholinguistics was the

1383
01:15:23,550 --> 01:15:24,830
emphasis in the program.

1384
01:15:25,950 --> 01:15:27,870
One of them, one of the big things.

1385
01:15:28,030 --> 01:15:33,640
And so I started working with my advisor Jeannie Foxtree, who studies spontaneous speech,

1386
01:15:33,720 --> 01:15:35,080
not from an evolutionary perspective.

1387
01:15:35,160 --> 01:15:36,520
That was an issue, actually.

1388
01:15:37,480 --> 01:15:43,080
But so we kind of settled on this topic of verbal irony and

1389
01:15:43,320 --> 01:15:48,280
the idea of whether when people produce verbal irony spontaneously like sarcasm, are they

1390
01:15:48,280 --> 01:15:52,040
doing some particular thing with their voice to express that indirect meaning?

1391
01:15:52,410 --> 01:15:57,530
or is it an illusion, which she thought it might be an illusion because she studied what um

1392
01:15:57,610 --> 01:15:59,130
syntactic disambiguation?

1393
01:15:59,210 --> 01:16:04,650
So people think that when you have a syntactically ambiguous utterance, then um when you

1394
01:16:04,650 --> 01:16:08,890
say it one way versus another in context, people think they say it differently.

1395
01:16:09,050 --> 01:16:11,370
But if you look acoustically, they don't.

1396
01:16:11,530 --> 01:16:16,940
And so if I say a man eating fish, And people are like, and then you might think of a man

1397
01:16:16,940 --> 01:16:19,820
eating a fish, or you might think of a shark, right?

1398
01:16:19,980 --> 01:16:25,100
And people assume that you say it differently when it's one thing versus another.

1399
01:16:25,180 --> 01:16:26,460
But if you look, they don't really.

1400
01:16:26,540 --> 01:16:28,700
And so we kind of hear something that's not really there.

1401
01:16:28,860 --> 01:16:30,620
So she thought this was true for sarcasm.

1402
01:16:30,700 --> 01:16:34,780
And I didn't believe I thought sarcasm actually did have some special thing that was

1403
01:16:34,780 --> 01:16:37,260
helping people understand that it was sarcasm.

1404
01:16:37,500 --> 01:16:39,580
And it turned out we were kind of both right.

1405
01:16:39,660 --> 01:16:41,020
There, it's less than you think.

1406
01:16:41,100 --> 01:16:45,950
It's it's pretty ambiguous, but there are certain things that people do when they're

1407
01:16:45,950 --> 01:16:49,790
speaking ironically that are, I think, helping listeners understand it.

1408
01:16:49,870 --> 01:16:51,230
So people slow down.

1409
01:16:51,550 --> 01:16:55,470
For example, that's the one most robust effect is a speed change.

1410
01:16:55,790 --> 01:17:00,910
And so when I was studying irony, and that was sort of the way I could get into

1411
01:17:00,910 --> 01:17:04,830
experimentally looking at the effect of prosody on people's language processing.

1412
01:17:04,990 --> 01:17:07,150
But then I noticed people were laughing a lot.

1413
01:17:07,310 --> 01:17:08,750
And that was the signal.

1414
01:17:08,980 --> 01:17:12,820
And so then that that got me going into like, well, where's laughter coming from?

1415
01:17:12,980 --> 01:17:18,500
And then it there's there was uh some existing um literature on laughter as a play

1416
01:17:18,500 --> 01:17:20,820
vocalization, which I was like, yes, that is it right there.

1417
01:17:20,900 --> 01:17:25,860
And and that's now still I'm doing a lot of work like that, um, looking at laughter as a

1418
01:17:25,860 --> 01:17:28,580
play vocalization and non-human animals including.

1419
01:17:28,820 --> 01:17:32,660
But um that's how I got into laughter is from this irony stuff.

1420
01:17:32,820 --> 01:17:38,570
And then I realized laughter is this really understudied and super tractable vocalization

1421
01:17:38,570 --> 01:17:42,330
that gives you a window into the evolution of vocal communication that's really nice

1422
01:17:42,490 --> 01:17:45,290
because it's homologous across all mammals.

1423
01:17:45,610 --> 01:17:50,410
But it's incorporated into our linguistic, you know, into speech.

1424
01:17:51,050 --> 01:17:56,810
And then it also is a good example of a vocalization that is produced by the

1425
01:17:56,810 --> 01:18:02,580
vocal motion system, which is a separate evolutionarily conserved system that is

1426
01:18:02,580 --> 01:18:07,540
used to produce crying, laughs, pain shrieks, copulation calls.

1427
01:18:07,860 --> 01:18:13,460
And then there's the speech system, which is a different control system for

1428
01:18:13,460 --> 01:18:16,980
articulators in the larynx and the mouth for speech.

1429
01:18:17,140 --> 01:18:23,070
And it's a different system, and only one can actually control the vocal apparatus of the

1430
01:18:23,070 --> 01:18:23,230
time.

1431
01:18:23,390 --> 01:18:27,470
So that's why crying children and laughing people can't talk, right?

1432
01:18:27,630 --> 01:18:31,630
Because that system is hijacking the vocal production mechanism.

1433
01:18:31,710 --> 01:18:34,350
And so the speech system can't get in there.

1434
01:18:34,510 --> 01:18:37,870
And that's why when you're talking and you're laughing while you're talking, then you're

1435
01:18:37,870 --> 01:18:43,870
giving evidence that you're laughing in from the volitional system because it's actually

1436
01:18:43,870 --> 01:18:44,910
happening with speech.

1437
01:18:45,150 --> 01:18:47,950
Otherwise, if you're really laughing, you have to stop talking.

1438
01:18:48,260 --> 01:18:54,260
So laughter is this really beautiful vocalization to study by

1439
01:18:54,260 --> 01:18:56,340
looking at how these two systems interact.

1440
01:18:56,420 --> 01:19:02,260
When we're unique in um all animals for having two vocal production systems like that, um,

1441
01:19:02,420 --> 01:19:06,100
this volitional system is super important for language and speech.

1442
01:19:06,180 --> 01:19:10,980
And then turns out that I think one of the targets of selection for that volitional system

1443
01:19:10,980 --> 01:19:14,490
is emulating the production of the vocal emotion system.

1444
01:19:14,570 --> 01:19:19,450
So we do fake laughs, fake cries, fake orgasms, fake pain streaks, or whatever.

1445
01:19:19,690 --> 01:19:25,370
And then that creates this whole extra level of dynamics if you're thinking about

1446
01:19:25,530 --> 01:19:31,450
like game theoretically, and like how you're gonna understand the equilibria that

1447
01:19:31,450 --> 01:19:32,650
emerge in communication systems.

1448
01:19:32,730 --> 01:19:36,250
And when now we have this added level of volitional control that just makes it way more

1449
01:19:36,250 --> 01:19:36,810
complicated.

1450
01:19:36,970 --> 01:19:39,530
So you could potentially be faking almost anything.

1451
01:19:39,850 --> 01:19:40,170
Interesting.

1452
01:19:40,890 --> 01:19:44,400
So laughter is a totally cool vocalization to study for that reason.

1453
01:19:44,720 --> 01:19:48,400
And um now I'm studying, I've been getting more into bioacoustics.

1454
01:19:48,480 --> 01:19:54,320
So um my grad student um is the director of a Gibbon Conservation Center in Santa Clarita,

1455
01:19:54,400 --> 01:19:55,920
and so she's got 40 gibbons.

1456
01:19:56,160 --> 01:20:00,560
And so I've been getting into gibbon and gibbons are interesting because they do a lot of

1457
01:20:00,560 --> 01:20:06,530
dueting, so they do territorial um um displays,

1458
01:20:06,690 --> 01:20:11,010
you know, mating pair duets, and they give information that this is our territory, and you

1459
01:20:11,010 --> 01:20:15,810
can get inform you can glean like how long they've been together, and there's other aspects

1460
01:20:15,810 --> 01:20:19,170
of their duetting that give you important information about the ecology.

1461
01:20:19,490 --> 01:20:23,250
And they produce these other vocals, I mean the crazy vocalizations, these gibbons.

1462
01:20:23,490 --> 01:20:27,970
And then I have a postdoc who is studying dog play vocalizations.

1463
01:20:28,050 --> 01:20:33,500
And this is more connected to laughter, where looking at dogs, and we just went and did

1464
01:20:33,500 --> 01:20:35,180
some foxes last week.

1465
01:20:35,340 --> 01:20:37,740
There's some Russian domesticated foxes down in San Diego.

1466
01:20:37,820 --> 01:20:43,100
So I've been getting more into this non-human animal vocalizations and trying to connect

1467
01:20:43,100 --> 01:20:48,060
those to things that we think might be true for human laughter and showing that, for

1468
01:20:48,060 --> 01:20:53,980
example, domestication in play vocalizations and canids could be informative regarding the

1469
01:20:53,980 --> 01:20:56,750
domestication of the play vocalizations in humans.

1470
01:20:56,910 --> 01:21:02,270
And as a domesticated species, we've now um our a lot of our vocal behavior has changed, in

1471
01:21:02,270 --> 01:21:07,150
part because we have this new speech system that allows us all this flexibility.

1472
01:21:07,790 --> 01:21:13,390
So laughter is just just seems to always pop up and it's

1473
01:21:14,030 --> 01:21:15,550
it's fun too.

1474
01:21:16,270 --> 01:21:16,510
Yeah.

1475
01:21:19,620 --> 01:21:25,060
Um do you have any ideas about I mean I have ideas about why laughter is contagious and why

1476
01:21:25,060 --> 01:21:29,620
hearing laughter makes you reciprocate it and why we you know you laugh and I laugh and you

1477
01:21:29,620 --> 01:21:33,220
laugh and I laugh and we you know it's it it's this almost this kind of duet.

1478
01:21:33,380 --> 01:21:34,340
Um I have ideas on that.

1479
01:21:34,420 --> 01:21:36,340
I'm curious if you have any thoughts on that.

1480
01:21:36,790 --> 01:21:37,110
Yeah.

1481
01:21:37,270 --> 01:21:42,310
Well, I mean, I've heard, I mean, I I know your the theory, which I think is overlaps

1482
01:21:42,310 --> 01:21:43,590
hugely with encryption.

1483
01:21:43,750 --> 01:21:43,910
Yeah.

1484
01:21:44,070 --> 01:21:49,830
And that's always been my approach is that laughter is um a signal of of decryption

1485
01:21:49,910 --> 01:21:50,390
essentially.

1486
01:21:50,470 --> 01:21:56,470
And then so you're giving covert um a covert signal of I have the information that you're

1487
01:21:56,470 --> 01:21:58,790
referring to to explicitly state it.

1488
01:21:59,020 --> 01:22:04,620
I know your thing is about mix-ups in particular, which we we could I think we've talked

1489
01:22:04,620 --> 01:22:05,100
about this a bit.

1490
01:22:05,180 --> 01:22:06,460
We'll probably can talk about it more.

1491
01:22:06,620 --> 01:22:11,500
I mean, you might say mix-ups are the higher the superordinate category, and I'd say

1492
01:22:11,500 --> 01:22:12,060
encryption is.

1493
01:22:12,140 --> 01:22:13,660
And so I mean we go back.

1494
01:22:14,140 --> 01:22:16,460
But those are highly compatible ideas.

1495
01:22:16,620 --> 01:22:16,780
Yeah.

1496
01:22:16,940 --> 01:22:20,620
Um, so we're definitely on the same um wavelength on that one.

1497
01:22:20,780 --> 01:22:26,700
But yeah, so laughter, I think, often functions as a way to signal to others that you have

1498
01:22:26,700 --> 01:22:29,570
information without explicitly stating it.

1499
01:22:29,810 --> 01:22:34,370
Um I also think that there's good evidence that laughter, co-laughter between multiple

1500
01:22:34,370 --> 01:22:39,650
individuals can form a chorus signal that alert outsiders or give not alert necessarily,

1501
01:22:39,730 --> 01:22:44,850
maybe sometimes, but more of like inform outsiders about the coalition strength of the

1502
01:22:44,850 --> 01:22:45,490
people laughing.

1503
01:22:45,570 --> 01:22:50,500
Like you can detect how well for one, I have empirical evidence that cross-culturally you

1504
01:22:50,500 --> 01:22:54,900
can detect how if people are friends or strangers in one second of co-laughter.

1505
01:22:55,060 --> 01:22:57,860
Um, and it doesn't work like that with speech exactly.

1506
01:22:58,020 --> 01:23:04,020
And so, but I think that um there could be some exaggerated features of laughter

1507
01:23:04,180 --> 01:23:09,860
can be explained as being a signal for wide broadcast um and giving information about

1508
01:23:09,860 --> 01:23:15,110
coalition strength that's beyond just friends or strangers, but you can actually get pretty

1509
01:23:15,110 --> 01:23:19,830
rich information about the constitution of a group by just paying attention for a minute on

1510
01:23:19,830 --> 01:23:22,390
who's laughing and and how loud.

1511
01:23:22,550 --> 01:23:28,150
And so I think laughter is doing stuff that's outside of the immediate group that the

1512
01:23:28,150 --> 01:23:29,750
laughter is occurring within.

1513
01:23:30,790 --> 01:23:31,750
Yeah, no, that's interesting.

1514
01:23:31,830 --> 01:23:37,760
I mean, my my thinking on the topic is that um i if we mix up our meanings of a

1515
01:23:37,760 --> 01:23:41,680
phrase, like for instance, you're confused about what whether hook up with means meet up

1516
01:23:41,680 --> 01:23:44,560
with or have sex with, and you say you hooked up with my mother, right?

1517
01:23:44,720 --> 01:23:50,000
Um if uh so that would be a pretty costly mix-up, you know, if you mix up those two

1518
01:23:50,000 --> 01:23:51,680
meanings of of hookup with.

1519
01:23:51,760 --> 01:23:56,240
Um and we uh we need some way of diffusing the costs of that mix-up so there's no grudge,

1520
01:23:56,320 --> 01:23:58,010
it doesn't damage our relationship.

1521
01:23:58,090 --> 01:24:01,770
And we not only need to diffuse those costs, we need to create common knowledge of the fact

1522
01:24:01,770 --> 01:24:03,130
that those costs have been diffused.

1523
01:24:03,370 --> 01:24:06,810
Like I need to know that you know, that I know, that there was a mix-up, and we're not

1524
01:24:06,810 --> 01:24:09,130
taking it seriously as actual communication, right?

1525
01:24:09,210 --> 01:24:13,050
And I think that is is part of the function of laughter and why laughter is contagious.

1526
01:24:13,210 --> 01:24:17,770
You want to reciprocate the laughter to to create common knowledge such that I send you the

1527
01:24:17,770 --> 01:24:21,740
message and you send me a receipt of the message, and I send you a receipt of your receipt

1528
01:24:21,740 --> 01:24:23,020
of the message, and so on.

1529
01:24:23,100 --> 01:24:26,060
It's sort of like you know, Stephen Pinker talks about this in his new book, sort of like

1530
01:24:26,060 --> 01:24:30,540
uh linking two electronic devices, or like you pairing two advice devices with the

1531
01:24:30,540 --> 01:24:30,940
Bluetooth.

1532
01:24:31,180 --> 01:24:35,420
Often they will send a message, and then the the the recipient system will send a receipt

1533
01:24:35,420 --> 01:24:41,260
of the message, and then the other system will send a receipt of the of the receipt, and so

1534
01:24:41,260 --> 01:24:44,510
on, on and on until they establish a connection.

1535
01:24:44,590 --> 01:24:48,590
And I think laughter is pretty similar and it's a way of creating uh common knowledge and

1536
01:24:48,590 --> 01:24:53,070
sort of ascending the almost infinite recursion of n thoughts about thoughts about

1537
01:24:53,070 --> 01:24:53,630
thoughts, right?

1538
01:24:53,790 --> 01:24:58,990
Um so I think that's that's what's going on with with contagious uh laughter and why we we

1539
01:24:58,990 --> 01:25:00,590
feel the need to reciprocate it.

1540
01:25:00,830 --> 01:25:06,830
Um and uh yeah I'm curious number one whether cool um yeah I'll I should I just put

1541
01:25:07,230 --> 01:25:07,790
out the preprint.

1542
01:25:07,870 --> 01:25:09,550
Maybe I'll send it to you uh when when we're done.

1543
01:25:09,630 --> 01:25:12,400
Um yeah but Yeah, you've been sitting on that for a while, huh?

1544
01:25:12,640 --> 01:25:13,840
Yeah, I've been sitting on that for a while.

1545
01:25:14,000 --> 01:25:16,000
Finally finished the damn preprint.

1546
01:25:16,240 --> 01:25:17,280
Uh yeah.

1547
01:25:17,440 --> 01:25:21,200
Uh I had to work through all the the math is was really a pain in the ass.

1548
01:25:21,280 --> 01:25:24,240
Uh um so that that took me a while to really finalize that.

1549
01:25:24,400 --> 01:25:29,600
But um yeah, uh I'm curious number one if you you find that idea plausible and and number

1550
01:25:29,600 --> 01:25:35,210
two, uh I I'm really curious about this uh distinction between fake and real

1551
01:25:35,210 --> 01:25:40,330
laughter, and whether you think there's some kind of evolutionary arms race between fake

1552
01:25:40,330 --> 01:25:43,290
laughters and fake laughter f detectors.

1553
01:25:43,450 --> 01:25:47,610
Um if that's behind this and whether that applies to other kinds of vocal signals as well.

1554
01:25:48,410 --> 01:25:54,330
Yeah, I think uh that it applies to all vocal signals in a sense that are um

1555
01:25:54,680 --> 01:25:56,360
produced by both systems, right?

1556
01:25:56,440 --> 01:26:00,760
Which is constrained by what the vocal emotion system produces, which is only nonverbal

1557
01:26:00,760 --> 01:26:01,480
vocalizations.

1558
01:26:01,640 --> 01:26:07,320
So yeah, I think it's the Athena activists and Naya have a paper that kind of started this

1559
01:26:07,480 --> 01:26:13,080
the spontaneous volitional laughter idea, and we talk about the arms race and that um

1560
01:26:13,240 --> 01:26:17,400
clearly there is going to be selection to detect whether people are producing something

1561
01:26:17,400 --> 01:26:22,410
volitionally that gives you information about what their potential intentions are.

1562
01:26:22,570 --> 01:26:27,610
Now, how I think a lot of times volitional laughter is also just polite laughter that

1563
01:26:27,610 --> 01:26:31,370
functions in a conversation and um is like a conversational marker.

1564
01:26:31,450 --> 01:26:32,730
So it's not deceptive.

1565
01:26:32,890 --> 01:26:35,690
It's not always going to be deceptive when I volitionally laugh.

1566
01:26:35,850 --> 01:26:40,090
It's I can I can give like a little signal that I understand you, sort of like what the

1567
01:26:40,090 --> 01:26:45,530
function would be in a for a real laugh, but I'm not emotionally triggered to do it.

1568
01:26:45,740 --> 01:26:51,500
Instead, I just do it as a signal that is not honest in the same way, but I

1569
01:26:51,500 --> 01:26:56,860
mean then you could talk about the honesty there is that I think a complicated issue.

1570
01:26:57,020 --> 01:27:03,020
But so it's not always a manipulative thing when we produce a volitional version

1571
01:27:03,020 --> 01:27:05,580
of something that the vocal motion system does.

1572
01:27:05,950 --> 01:27:10,830
Um I'm trying what was the first thing that you um whether you buy the idea that we're

1573
01:27:10,830 --> 01:27:15,310
diffusing the costs of like uh uh either a miscommunication or or a faux pas or something

1574
01:27:15,310 --> 01:27:19,550
like that and signaling that I like basically co-opting the play signal to to signal that

1575
01:27:19,630 --> 01:27:23,390
you know we're not taking this seriously, just as the play signal often communicates, you

1576
01:27:23,390 --> 01:27:26,990
know, this is not real aggression, I'm not actually chasing you or trying to hurt you.

1577
01:27:27,150 --> 01:27:28,270
This is just pretend.

1578
01:27:28,430 --> 01:27:32,990
I think that w uh evolution co-opted the play signal uh when we are mixed up with one

1579
01:27:32,990 --> 01:27:37,660
another or when we have a miscommunication to say to signal that we're not mad at each

1580
01:27:37,660 --> 01:27:41,580
other, this isn't yes, uh we're not ta treating this as real communication, we're sort of

1581
01:27:41,580 --> 01:27:43,660
treating it as a play uh interaction instead.

1582
01:27:43,900 --> 01:27:44,300
Yeah, yeah.

1583
01:27:44,460 --> 01:27:45,420
No, I think that's right.

1584
01:27:45,500 --> 01:27:48,220
And so the mix-up thing I think is totally consistent with that.

1585
01:27:48,460 --> 01:27:52,620
But I I also think there could be there are other reasons we laugh that have to do with

1586
01:27:52,620 --> 01:27:53,660
social cohesion.

1587
01:27:53,820 --> 01:27:58,550
So if you're with an old friend, you're not really signaling about mix-ups anymore, right?

1588
01:27:58,710 --> 01:28:03,830
Um, if somebody I've known for 40 years, um, I think that even though it might be derived

1589
01:28:03,830 --> 01:28:07,270
from that at some level, and you might be able to make an argument that it's sort of like

1590
01:28:07,350 --> 01:28:09,590
why do people laugh when they're by themselves?

1591
01:28:09,750 --> 01:28:13,350
It's not obviously a different function, it's that there, it's as if there's another person

1592
01:28:13,350 --> 01:28:13,590
there.

1593
01:28:13,750 --> 01:28:19,430
So laughing with an old friend could be as if there's a mis a mix-up that we don't have.

1594
01:28:19,670 --> 01:28:24,020
But I think it could also be that you are there, there's sort of endogenous reward when

1595
01:28:24,020 --> 01:28:28,340
you're laughing together so there is a social bonding effect that can happen and so you

1596
01:28:28,340 --> 01:28:34,100
might I I I think about like jokes between familiar people there the the mix-up angle I I'm

1597
01:28:34,100 --> 01:28:37,940
gonna think of some good examples for you though and see how you answer them.

1598
01:28:38,100 --> 01:28:42,660
I don't have them offhand but um I think there are examples of times where people are

1599
01:28:42,660 --> 01:28:47,780
laughing a lot very motivated to laugh and there's not a mix up but um I have to think of

1600
01:28:47,780 --> 01:28:52,190
some good ones because I I know what you'll you'll be able to figure out the mix up if you

1601
01:28:52,190 --> 01:28:52,910
dig deep enough.

1602
01:28:52,990 --> 01:28:55,070
I which is a good thing about the theory.

1603
01:28:55,230 --> 01:28:55,470
Yeah.

1604
01:28:55,630 --> 01:29:01,150
Um, but I think there's other things about just mutually recognizing preferences, um,

1605
01:29:01,390 --> 01:29:06,750
you know, or in some in some cases, like you might be critical of something that you both

1606
01:29:06,750 --> 01:29:06,990
know.

1607
01:29:07,150 --> 01:29:11,150
I know your view, and you know my view, and we each know that we know that.

1608
01:29:11,310 --> 01:29:14,590
Um, and so there's no need to signal that exactly anymore.

1609
01:29:14,670 --> 01:29:20,120
But there's still like has it that has a pleasurable um element to it that causes us to

1610
01:29:20,120 --> 01:29:21,960
still engage in the signaling system.

1611
01:29:22,120 --> 01:29:26,600
And so it could be that it came from a mix-up and now it's taken on this other kind of

1612
01:29:26,600 --> 01:29:31,960
function, which also could be a motivating proximate mechanism for generating a signal that

1613
01:29:31,960 --> 01:29:35,320
other people can hear that gives information about our coalition status.

1614
01:29:35,480 --> 01:29:40,520
So I guess that and that's consistent with your idea that it's not just about those people

1615
01:29:40,520 --> 01:29:44,870
figuring out a mix-up, but and like I mentioned before, it's it could be about signaling

1616
01:29:44,870 --> 01:29:48,150
something to other people outside of the group, which is why it's so loud.

1617
01:29:48,310 --> 01:29:48,550
Right?

1618
01:29:48,710 --> 01:29:52,070
Laughter is really ridiculously loud for what it should be doing.

1619
01:29:52,310 --> 01:29:57,030
A better covert signal for the mix-up would be much quieter, like a play vocalization in

1620
01:29:57,030 --> 01:29:58,230
most non-human animals.

1621
01:29:58,310 --> 01:29:59,110
They're very quiet.

1622
01:29:59,270 --> 01:30:01,750
They're not broadcasting that they're playing to everybody.

1623
01:30:01,910 --> 01:30:05,270
They're only, and this is why they're hard to measure because you got to really get in

1624
01:30:05,270 --> 01:30:05,910
there to record it.

1625
01:30:05,990 --> 01:30:10,390
And it's hard to tell who produces it because they're barely, you know, and they're barely

1626
01:30:10,390 --> 01:30:11,350
moving your mouth.

1627
01:30:11,510 --> 01:30:16,080
And so these are very dis they're um conspiratorial whispers, if you want to put it in the

1628
01:30:16,080 --> 01:30:19,680
Krebs and Dawkins sort of language, um play vocalizations are.

1629
01:30:19,760 --> 01:30:24,160
But but human laughter and some other species, only a few, are loud.

1630
01:30:24,480 --> 01:30:30,160
So that suggests another function that's beyond the dyadic interaction.

1631
01:30:30,400 --> 01:30:30,640
Yeah.

1632
01:30:31,360 --> 01:30:35,520
So I would just say there's other reasons we laugh, but I I like the mix-up idea explains a

1633
01:30:35,520 --> 01:30:36,080
lot, I think.

1634
01:30:36,400 --> 01:30:37,200
Yeah, that makes sense.

1635
01:30:37,280 --> 01:30:40,800
And yeah, uh if you come up with any stumpers, uh I'm I'm happy to hear them.

1636
01:30:40,960 --> 01:30:43,450
Yeah, I'm gonna please please do.

1637
01:30:44,250 --> 01:30:44,810
Science and action.

1638
01:30:45,050 --> 01:30:45,610
Science and action.

1639
01:30:46,250 --> 01:30:52,250
Yeah, no, my thinking with the the loudness is that um we coordinate and cooperate in much

1640
01:30:52,250 --> 01:30:53,210
larger groups.

1641
01:30:53,290 --> 01:30:58,490
And if we're suffering a mix-up, uh the the people who are mixed up not only need to know

1642
01:30:58,490 --> 01:31:02,330
it, but you know, all of their friends and associates and anyone who's nearby would benefit

1643
01:31:02,330 --> 01:31:06,410
from knowing it as well because we coordinate as a group our our social conventions are

1644
01:31:06,410 --> 01:31:09,920
community wide and not uh a function of you know two or three people.

1645
01:31:10,080 --> 01:31:14,000
You know, we we share the same language as a community, and if there's a miscommunication,

1646
01:31:14,080 --> 01:31:18,400
like we're all part playing that coordination game together of which sounds correspond to

1647
01:31:18,400 --> 01:31:19,360
which things in the world.

1648
01:31:19,520 --> 01:31:24,560
And insofar as there's a mix up between you know two people about which sounds correspond

1649
01:31:24,560 --> 01:31:28,240
to which things, then the entire linguistic community has an interest in becoming aware of

1650
01:31:28,240 --> 01:31:31,200
that and avoiding that kind of miscommunication as well.

1651
01:31:31,440 --> 01:31:36,950
So that would be my speculation for why um No, you should check out

1652
01:31:37,590 --> 01:31:43,270
Tom Flampson has the empirical part of well, there's the first encryption paper, which was

1653
01:31:43,270 --> 01:31:48,630
Tom and Clark did um that had some experiments about how much prior knowledge do you have

1654
01:31:48,710 --> 01:31:52,710
and how that affects humor or whatever, um, supporting the encryption idea.

1655
01:31:52,790 --> 01:31:57,030
But then he Tom has um other cross-cultural data from the people he was working with in

1656
01:31:57,030 --> 01:31:58,870
Brazil, uh, who's the name of the group?

1657
01:31:58,950 --> 01:31:59,750
I can't remember.

1658
01:31:59,990 --> 01:32:05,620
And um, and he has these examples of in communities, large the the whole

1659
01:32:05,620 --> 01:32:10,580
village is meeting and then people make these certain kinds of jokes that then cause people

1660
01:32:10,580 --> 01:32:10,980
to laugh.

1661
01:32:11,060 --> 01:32:15,380
And he was analyzing like who was laughing at what and how that related to their social

1662
01:32:15,380 --> 01:32:15,860
network.

1663
01:32:16,020 --> 01:32:20,740
And um I don't know if you if you've seen that paper, but I think that would be useful for

1664
01:32:20,740 --> 01:32:21,780
you to talk.

1665
01:32:21,860 --> 01:32:24,580
I mean, I think your theory would fit well with that.

1666
01:32:24,820 --> 01:32:25,450
Yeah, totally.

1667
01:32:25,610 --> 01:32:30,170
I mean the other the other idea I had for why laughter varies in volume and why it might be

1668
01:32:30,170 --> 01:32:34,970
louder in some contexts is if there's a larger cost to be diffused, you want to be much

1669
01:32:34,970 --> 01:32:36,810
more certain that that cost has been diffused.

1670
01:32:37,050 --> 01:32:41,290
And so you want to laugh louder and longer to really make sure that we both know that the

1671
01:32:41,290 --> 01:32:44,650
other one knows that the other one knows that this that you actually didn't sleep with my

1672
01:32:44,650 --> 01:32:45,450
mother, for example.

1673
01:32:45,610 --> 01:32:50,250
You know, like the so the costlier the mix up is, the louder and longer we should laugh to

1674
01:32:50,250 --> 01:32:54,180
really make sure that those costs have been so hooking up with the guy's a taco bell is not

1675
01:32:54,180 --> 01:32:55,300
as bad as hooking up with your mother.

1676
01:32:56,020 --> 01:32:59,140
Yeah, but I think it's evolved from example that you like.

1677
01:32:59,540 --> 01:32:59,860
Exactly.

1678
01:33:00,180 --> 01:33:04,420
His workshopping the the his workshopping I've been developing my material yeah through

1679
01:33:04,420 --> 01:33:04,820
traveling.

1680
01:33:06,820 --> 01:33:10,180
I mean that's that seems like a pretty testable prediction right there.

1681
01:33:10,420 --> 01:33:10,660
Yeah.

1682
01:33:10,740 --> 01:33:15,530
Yeah absolutely that that would be a cool way to work out you know what you know, have a

1683
01:33:15,530 --> 01:33:18,730
good convincing argument about what the costs are, which seems it seems like a hard

1684
01:33:18,730 --> 01:33:19,050
problem.

1685
01:33:19,130 --> 01:33:19,610
Uh yeah.

1686
01:33:19,770 --> 01:33:24,490
And it seems like intuitive it fits my personal experience and and a lot of like anecdotes

1687
01:33:24,490 --> 01:33:28,810
of like people when people really give a big belly laugh, it's usually at like kind of like

1688
01:33:28,810 --> 01:33:34,730
a like a dark joke or like a like a really like uh spicy, like costly mix up

1689
01:33:34,810 --> 01:33:39,420
that that, you know, um we I it seems intuitively right to me.

1690
01:33:39,660 --> 01:33:40,140
I don't know.

1691
01:33:40,300 --> 01:33:40,540
Yeah.

1692
01:33:40,940 --> 01:33:46,300
I mean, the the idea is similar to encryption in multiple ways, but one way is that people

1693
01:33:46,460 --> 01:33:51,260
will have examples that at first pass seems to violate the thing, and then you can come up

1694
01:33:51,260 --> 01:33:52,860
with actually that's encryption.

1695
01:33:53,020 --> 01:33:54,460
And then you can show how it's encryption.

1696
01:33:54,540 --> 01:33:58,140
It's not obvious immediately how it's encryption or how it's a mix-up.

1697
01:33:58,540 --> 01:34:01,660
And so I think something to work out there is like what is the difference?

1698
01:34:01,820 --> 01:34:07,100
Like are there encryptions that aren't mix-ups and are that but there's no mix-ups that

1699
01:34:07,100 --> 01:34:08,300
aren't encryption.

1700
01:34:09,930 --> 01:34:11,690
Um yeah.

1701
01:34:11,850 --> 01:34:13,850
Uh yeah, I guess that's true.

1702
01:34:13,930 --> 01:34:14,090
Yeah.

1703
01:34:14,170 --> 01:34:19,690
There there are no mix-ups that aren't encryption in some sense, and that you need to be uh

1704
01:34:19,930 --> 01:34:23,930
privy to the coordination game being played in order to understand that there has been a

1705
01:34:23,930 --> 01:34:24,250
mix up.

1706
01:34:24,570 --> 01:34:24,730
Right?

1707
01:34:24,810 --> 01:34:28,410
You need to speak that language, you need to be in that culture to know what was mixed up

1708
01:34:28,410 --> 01:34:28,810
with what.

1709
01:34:28,890 --> 01:34:31,050
And if you're not a member of that culture, you won't know.

1710
01:34:31,210 --> 01:34:37,060
Um so yeah, I guess in that sense, all mix-ups are in encryption are encrypted uh, you

1711
01:34:37,060 --> 01:34:39,940
know, among the people who are insiders to that culture.

1712
01:34:40,100 --> 01:34:40,420
Yeah.

1713
01:34:40,580 --> 01:34:40,740
Right.

1714
01:34:40,980 --> 01:34:46,100
Would would would a would one let me just push on that though, and I'm the bystander here.

1715
01:34:46,260 --> 01:34:52,180
Um, if somebody is like imagining like a new culture and they don't know, and the I'm just

1716
01:34:52,180 --> 01:34:57,860
imagining like this trope of like some westerner comes to like this very like belligerent

1717
01:34:58,100 --> 01:35:03,380
culture where like they might kill the person and they're like the per the the westerner is

1718
01:35:03,380 --> 01:35:08,850
sort of like says something which is inappropriate to the context, like oh nice weather

1719
01:35:08,930 --> 01:35:10,370
we're having, a kind of laugh.

1720
01:35:10,530 --> 01:35:15,090
You know, it's sort of a signal of like, I hope we're on the same page.

1721
01:35:15,250 --> 01:35:17,890
Um, but they are laughing as sort of a gauge.

1722
01:35:17,970 --> 01:35:23,890
Um, I mean, maybe that's a claim of like they're trying to probe for in um encryption, or

1723
01:35:23,890 --> 01:35:28,610
that that's a affiliative, don't we have some common understanding?

1724
01:35:28,690 --> 01:35:34,640
You know, um, some I don't know, maybe that's a a bad uh No, I mean I think that that fits

1725
01:35:34,640 --> 01:35:36,400
within David's idea for sure.

1726
01:35:38,080 --> 01:35:43,600
That you're you're you're trying to um exploit their their understanding of the signal,

1727
01:35:43,680 --> 01:35:48,080
even if the con they they we have mutual knowledge that the con we don't have the common

1728
01:35:48,080 --> 01:35:53,520
ground to for the language to handle it, but then I'm signaling as if we do as a good

1729
01:35:53,520 --> 01:35:53,920
thing.

1730
01:35:54,050 --> 01:35:58,050
Like I'm trying now, it sounded like you're being insincere in your laugh.

1731
01:35:58,210 --> 01:36:00,610
If you had a bad fake laugh, that might be costly, right?

1732
01:36:00,690 --> 01:36:05,090
You're if you say something inappropriate and you're like or whatever, then you know that

1733
01:36:05,090 --> 01:36:07,570
might be worse than not producing a laugh.

1734
01:36:07,730 --> 01:36:09,490
So you better make it a good one.

1735
01:36:10,210 --> 01:36:10,450
Yeah.

1736
01:36:10,690 --> 01:36:11,010
Yeah.

1737
01:36:11,170 --> 01:36:11,650
Uh yeah.

1738
01:36:12,050 --> 01:36:16,500
The more I think about humor and laughter, the more I realize just like how unfathomably

1739
01:36:16,500 --> 01:36:17,220
complicated it is.

1740
01:36:17,380 --> 01:36:21,380
Like every joke is just like you could write a whole book dissecting why it's funny.

1741
01:36:22,500 --> 01:36:23,140
And they do.

1742
01:36:23,380 --> 01:36:23,620
Yeah.

1743
01:36:24,100 --> 01:36:27,620
And and just like the dove into the humor literature, I'm sure you have.

1744
01:36:28,180 --> 01:36:28,660
Oh, I'm sure.

1745
01:36:28,740 --> 01:36:28,980
Yeah.

1746
01:36:29,060 --> 01:36:33,460
But even even beyond the funniness, there are like really complicated game theoretic

1747
01:36:33,460 --> 01:36:37,650
constraints about like the incentives of different uh actors in the social network and

1748
01:36:37,650 --> 01:36:41,410
whether they should laugh or whether they shouldn't or whether how loudly they should laugh

1749
01:36:41,570 --> 01:36:45,970
or like what their interests are in in terms of whether they want those costs to be

1750
01:36:45,970 --> 01:36:50,530
diffused or not, uh whether they should try to fake laughing if they don't get the joke,

1751
01:36:50,690 --> 01:36:56,610
like really complicated considerations that I feel like with cost for whatever decision you

1752
01:36:56,610 --> 01:37:02,050
make and then there um it it opens up a whole window of of costs and benefits that um hard

1753
01:37:02,050 --> 01:37:03,250
to go back on.

1754
01:37:03,840 --> 01:37:04,160
Yeah.

1755
01:37:04,320 --> 01:37:04,640
Yeah.

1756
01:37:04,720 --> 01:37:10,000
I I think the um the volitional laughter adds so I mean volitional production of anything

1757
01:37:10,160 --> 01:37:12,640
really adds so much complication there for sure.

1758
01:37:12,880 --> 01:37:17,120
And there was something else I was gonna say about the laughter signaling, and then it I

1759
01:37:17,280 --> 01:37:20,880
forgot, but I'll I'll give you a second because I just can't imagine I'm the humor

1760
01:37:20,880 --> 01:37:24,000
literature I imagine has no jokes in it whatsoever.

1761
01:37:24,720 --> 01:37:26,720
Like this I imagine like jokes are so bad.

1762
01:37:26,800 --> 01:37:31,040
I I've generally yeah, I'm sure like I imagine like there's an entire book on humor, like

1763
01:37:31,120 --> 01:37:33,830
the book, the blurbs in the back, like I didn't laugh once, right?

1764
01:37:33,910 --> 01:37:34,550
Like something like that.

1765
01:37:34,790 --> 01:37:35,430
Yeah, totally.

1766
01:37:35,830 --> 01:37:36,230
Yeah, yeah.

1767
01:37:37,750 --> 01:37:41,270
My my page, the preprint, uh has some genuinely funny funny jokes.

1768
01:37:41,750 --> 01:37:45,830
At least according to me, well, it has like, you know, all the movies that I grew up with,

1769
01:37:45,910 --> 01:37:49,110
all the comedies, like, you know, Dumb and Dumber and stuff like that, uh, Seinfeld

1770
01:37:49,350 --> 01:37:50,470
Seinfeld jokes.

1771
01:37:50,550 --> 01:37:54,630
Um so I I like to think that the humor in there is is is funnier than the average.

1772
01:37:54,950 --> 01:37:58,470
And then David, what you can do is you can be a testimonial, you can say this is

1773
01:37:58,470 --> 01:37:59,590
scientifically funny.

1774
01:37:59,670 --> 01:38:05,040
You could become that you're the you're the official if you don't recognize this, you are

1775
01:38:05,120 --> 01:38:07,440
yeah, you know, technically have the bad sense of humor.

1776
01:38:07,680 --> 01:38:07,920
Exactly.

1777
01:38:08,000 --> 01:38:09,600
Yes, this is empirically funny.

1778
01:38:09,680 --> 01:38:12,960
Yes, wonderful way to abuse my scientific credit credibility.

1779
01:38:15,520 --> 01:38:16,880
Actually, that dad joke was funny.

1780
01:38:16,960 --> 01:38:18,720
Yeah, you're you're actually mistaken.

1781
01:38:21,200 --> 01:38:24,000
Well, and that's the thing about the both of these ideas.

1782
01:38:24,080 --> 01:38:29,040
Um, the the mix-up and the encryption is that it explains why jokes are not universally

1783
01:38:29,040 --> 01:38:29,790
funny, right?

1784
01:38:29,870 --> 01:38:34,110
I mean, it's all context dependent because it, you know, it depends what we're talking

1785
01:38:34,110 --> 01:38:35,390
about and what the rules are.

1786
01:38:35,550 --> 01:38:41,310
And um, and so yeah, no other theory of humor really has been able to

1787
01:38:41,550 --> 01:38:42,670
point at that quite a bit.

1788
01:38:42,750 --> 01:38:47,150
There are a lot of theories of humor out there that are approximate and capture some

1789
01:38:47,470 --> 01:38:48,990
subgroup of jokes.

1790
01:38:49,070 --> 01:38:54,190
And it's also the emphasis on jokes is is I think is big they've gone too far.

1791
01:38:54,350 --> 01:39:00,260
The people that study humor um are thinking about the joke as sort of a proto humor.

1792
01:39:00,580 --> 01:39:04,500
I that just that's not the way I mean, I think most people that study humor would realize

1793
01:39:04,500 --> 01:39:09,460
it emerged first in conversational interactions that are context specific and that the joke

1794
01:39:09,460 --> 01:39:10,420
is a trope.

1795
01:39:10,580 --> 01:39:14,340
Um but it seems like there's way more emphas like punchlines and all that kind of stuff.

1796
01:39:14,420 --> 01:39:19,060
I mean yeah comic timing too is a super rich Yeah.

1797
01:39:19,220 --> 01:39:24,890
Um that fascinating I'm I'm sure there's tons of like things you could do with that.

1798
01:39:24,970 --> 01:39:28,890
I doubt you know people have done a fraction of of the experiments and and and studies that

1799
01:39:28,890 --> 01:39:30,410
you could do studying comic timing.

1800
01:39:30,490 --> 01:39:33,850
But like I mean my I I think it were oh let's go ahead.

1801
01:39:34,250 --> 01:39:37,130
Yeah no most thing about it is it's just like micro mix ups.

1802
01:39:37,210 --> 01:39:41,210
Like we have these conventions with like you know the timing of our speech, the speed, you

1803
01:39:41,210 --> 01:39:43,450
know how much we pause, you know, vocal intonation.

1804
01:39:43,530 --> 01:39:47,290
We just talked about, you know, how, you know, that your tone of voice uh communicates so

1805
01:39:47,290 --> 01:39:47,450
much.

1806
01:39:47,530 --> 01:39:48,890
I mean you could have mix-ups there.

1807
01:39:48,970 --> 01:39:54,800
Like you if I you know exaggerate um my my voice into kind of a s overly sing-songgy way,

1808
01:39:54,960 --> 01:39:55,840
like that can be kind of funny.

1809
01:39:55,920 --> 01:39:59,840
I'm like kind of mixed up about what the conventions are about how much I should change my

1810
01:39:59,840 --> 01:40:04,560
tone of voice, you know, um, or if I you speak too loudly or too slowly, or if I pause in

1811
01:40:04,560 --> 01:40:05,360
an awkward way.

1812
01:40:05,520 --> 01:40:10,960
Like I think these these kinds of considerations factor into what we call comic timing, as

1813
01:40:10,960 --> 01:40:16,570
it's layering in uh micro mix-ups about the uh the pacing and body language and

1814
01:40:16,890 --> 01:40:21,050
intonation of the delivery of the joke uh on top of the content of the joke itself, which

1815
01:40:21,050 --> 01:40:23,290
accentuates the the the comedy of it.

1816
01:40:24,090 --> 01:40:27,690
And it's really uh the resolution of the timing is is incredible.

1817
01:40:27,850 --> 01:40:32,170
I mean, if you just wait an extra hundred milliseconds, it matters.

1818
01:40:32,410 --> 01:40:37,960
And um so I've done some work looking at how um timing can affect pragmatic

1819
01:40:37,960 --> 01:40:41,480
inferences in quick interactions of just answering questions.

1820
01:40:41,720 --> 01:40:47,640
Um and you know, if you wait just a little extra in how you answer a question, it

1821
01:40:47,640 --> 01:40:52,200
has all these implications for what kinds of implicatures you're communicating, um, what

1822
01:40:52,200 --> 01:40:54,440
kinds of things the listener might draw from it.

1823
01:40:54,760 --> 01:41:00,680
I use an example in my class of um if you say um just in a normal greeting,

1824
01:41:00,760 --> 01:41:01,880
like, hey, how are you doing?

1825
01:41:01,960 --> 01:41:05,160
And so if um like ask me how I'm doing.

1826
01:41:05,480 --> 01:41:06,440
How are you doing?

1827
01:41:08,250 --> 01:41:08,890
Good.

1828
01:41:09,370 --> 01:41:11,130
Now ask again.

1829
01:41:11,450 --> 01:41:12,490
How you doing?

1830
01:41:12,810 --> 01:41:13,450
Good.

1831
01:41:14,410 --> 01:41:15,770
And then ask again.

1832
01:41:16,010 --> 01:41:17,210
How are you doing?

1833
01:41:17,610 --> 01:41:18,250
Good.

1834
01:41:18,810 --> 01:41:19,210
Right?

1835
01:41:19,290 --> 01:41:21,370
So that seemed to be kind of a normal.

1836
01:41:21,690 --> 01:41:27,450
If you go too fast, it's there's all there's much different inferences you draw than and if

1837
01:41:27,450 --> 01:41:29,850
I wait too long, then there's other complete.

1838
01:41:30,010 --> 01:41:34,730
And the difference between those is like maybe was a 200 milliseconds, you know, a fifth of

1839
01:41:34,730 --> 01:41:35,210
a second.

1840
01:41:35,370 --> 01:41:35,530
Yeah.

1841
01:41:35,770 --> 01:41:41,740
And so I think that basic principle of timing and pragmatics is then a particular

1842
01:41:41,740 --> 01:41:43,820
feature in comic timing that's important.

1843
01:41:44,140 --> 01:41:44,540
Totally.

1844
01:41:44,620 --> 01:41:48,940
And if you're just a little bit off of what the the social convention is, it can be very

1845
01:41:48,940 --> 01:41:49,100
funny.

1846
01:41:49,180 --> 01:41:50,700
Like ask me how I'm doing.

1847
01:41:51,020 --> 01:41:52,300
How you doing?

1848
01:41:53,980 --> 01:41:54,540
See, that was funny.

1849
01:41:56,060 --> 01:41:57,660
Like I didn't even wait for you to finish.

1850
01:41:57,900 --> 01:41:58,620
So yeah.

1851
01:41:59,820 --> 01:42:01,020
Yeah, there's so much there.

1852
01:42:01,260 --> 01:42:07,230
Um I also have another pitch for you to work on differentiating um a hostile or e evil

1853
01:42:07,230 --> 01:42:10,510
laugh from a friendly or convivial laugh.

1854
01:42:10,830 --> 01:42:13,310
I feel like that could be really interesting to look at.

1855
01:42:13,390 --> 01:42:18,750
Um I mean my my my thinking about why laughter can sometimes be hostile or creepy is that

1856
01:42:19,150 --> 01:42:23,150
uh mirth or amusement evolved to you know diffuse the cost of mix-ups.

1857
01:42:23,230 --> 01:42:27,460
And in order to do that, it needs to deactivate uh regions of the brain that process costs.

1858
01:42:27,540 --> 01:42:29,060
So deactivate negative emotions.

1859
01:42:29,140 --> 01:42:31,460
So i uh the same thing goes with if you're playing.

1860
01:42:31,540 --> 01:42:34,180
If you're having rough and tumble play, you want to deactivate your fears so you're not

1861
01:42:34,180 --> 01:42:35,940
actually afraid for your life.

1862
01:42:36,020 --> 01:42:39,380
You know, if you want to deactivate your anger so you're not like you know vengeful and

1863
01:42:39,380 --> 01:42:41,380
retaliating against the person who's play fighting with you.

1864
01:42:41,540 --> 01:42:44,660
But in in the same way, if if we're trying to diffuse the costs of mix-ups, we want to

1865
01:42:44,660 --> 01:42:49,540
deactivate those negative emotions, deactivate fear, anger, sadness, um, you know, uh w

1866
01:42:49,700 --> 01:42:50,500
whatever.

1867
01:42:50,740 --> 01:42:55,890
And uh the problem is though, uh, as soon as you have that machinery in place, um, that

1868
01:42:55,890 --> 01:42:58,930
makes you very threatening to people who are not sharing your mirth.

1869
01:42:59,250 --> 01:43:03,810
So if I'm laughing and mirthful, that means that my fear has been deactivated and uh and I

1870
01:43:03,810 --> 01:43:07,010
could just attack you without fear of uh you know, retaliation.

1871
01:43:08,210 --> 01:43:10,530
Um and my empathy might even be deactivated.

1872
01:43:10,610 --> 01:43:14,450
If you're crying in pain or sad about something, like I'm not gonna feel empathy for you

1873
01:43:14,530 --> 01:43:15,010
because I'm laughing.

1874
01:43:15,090 --> 01:43:15,650
I'm mirthful.

1875
01:43:15,730 --> 01:43:18,050
It's my those parts of my brain have been deactivated.

1876
01:43:18,220 --> 01:43:22,220
So for that reason, I think mirth can often signal that I have a lack of fear and that I

1877
01:43:22,220 --> 01:43:25,420
have a lack of empathy for your suffering, and that makes me really dangerous.

1878
01:43:25,580 --> 01:43:31,420
Um and so uh I I I would be I would be uh interested in in in maybe

1879
01:43:31,660 --> 01:43:35,900
uh you know evil laughter having a different signature, or maybe it maybe it doesn't.

1880
01:43:35,980 --> 01:43:36,300
I don't know.

1881
01:43:36,380 --> 01:43:38,940
Maybe we just prefer evil laughter.

1882
01:43:39,020 --> 01:43:43,210
Yeah, I think my most recent paper on laughter, it looks at that this issue, um, among

1883
01:43:43,210 --> 01:43:47,690
other things, but looking at um whether people can distinguish between different laugh

1884
01:43:47,690 --> 01:43:49,770
types that we've had people produce.

1885
01:43:49,930 --> 01:43:55,690
So we create contexts where you know malicious laughter and um um co amusement

1886
01:43:55,850 --> 01:43:58,250
and sarcastic and all these different things.

1887
01:43:58,410 --> 01:44:04,330
And it turns out that um it's really the dimensional view of emotion is the best way to

1888
01:44:04,410 --> 01:44:05,380
to predict it.

1889
01:44:05,540 --> 01:44:07,060
And in particular, valence.

1890
01:44:07,220 --> 01:44:10,580
So it seems that LAF does work on the dimension of valence.

1891
01:44:10,660 --> 01:44:15,060
So you can tell positive and negative, but we mix up positive categories at a high rate and

1892
01:44:15,060 --> 01:44:17,940
we mix up negative categories at a high rate.

1893
01:44:18,020 --> 01:44:20,980
And also with the machine learning algorithm, does the same kind of mix ups.

1894
01:44:21,060 --> 01:44:24,500
And so we can see what the acoustic features are that are associated with these different

1895
01:44:24,500 --> 01:44:30,290
laugh types, and then how those acoustic features predict it its valence almost completely,

1896
01:44:30,450 --> 01:44:31,890
a little bit of arousal too.

1897
01:44:32,050 --> 01:44:37,170
So the idea of a malicious laugh, like the whatever, is really a kind of a cultural

1898
01:44:37,170 --> 01:44:41,090
invention that doesn't exist in ordinary discourse, usually.

1899
01:44:41,410 --> 01:44:47,330
But I think what you're saying is right in that a context where you're laughing and you

1900
01:44:47,330 --> 01:44:51,410
know you know the other person's not laughing, and they know that you know they're not

1901
01:44:51,410 --> 01:44:57,140
laughing, and then you might hike up a little more negative um sort of energy in there to

1902
01:44:57,140 --> 01:45:00,740
make it sound a little darker because laughter could be very intimidating in certain

1903
01:45:00,740 --> 01:45:01,540
contexts.

1904
01:45:01,700 --> 01:45:04,100
Um it's not always positive, obviously.

1905
01:45:04,260 --> 01:45:07,060
And so in fact, it could be linked to any emotion.

1906
01:45:07,300 --> 01:45:12,740
But um, yeah, so I agree that that there could be situations like what you described where

1907
01:45:12,740 --> 01:45:17,780
the laughter is really communicating something about your lack of empathy or your lack of

1908
01:45:17,780 --> 01:45:20,660
concern for the other, and it's not about correcting a mistake.

1909
01:45:20,820 --> 01:45:22,340
Um same way.

1910
01:45:22,550 --> 01:45:26,230
And but there's no categorical evil laugh.

1911
01:45:26,630 --> 01:45:32,550
Um I think it's, I mean, other than in in movies that are you have to

1912
01:45:32,550 --> 01:45:33,830
practice your evil cackles.

1913
01:45:34,150 --> 01:45:34,310
Yeah.

1914
01:45:34,470 --> 01:45:35,910
Nobody does that seriously, right?

1915
01:45:35,990 --> 01:45:38,710
If you hear that, they're making they're doing the trope.

1916
01:45:39,190 --> 01:45:39,510
Yeah.

1917
01:45:40,070 --> 01:45:40,630
Interesting.

1918
01:45:40,870 --> 01:45:44,630
But I'm sure it plays on, you know, like this, like I think it's what you're both saying,

1919
01:45:44,710 --> 01:45:49,270
is like, you know, I mean, the tr a trope would be like you're a kid getting picked on, and

1920
01:45:49,350 --> 01:45:52,060
and the kids who are picking on you are laughing, right?

1921
01:45:52,140 --> 01:45:53,420
It's exactly what David's saying.

1922
01:45:53,500 --> 01:45:56,220
I mean, that's the proper domain of this trope, right?

1923
01:45:56,380 --> 01:45:56,540
Yeah.

1924
01:45:56,700 --> 01:46:00,380
It's not that the that laughter is the cackle, but it feels like it.

1925
01:46:00,460 --> 01:46:05,020
And then the trope, the the cackle sort of plays on that, yeah, elaborately.

1926
01:46:05,260 --> 01:46:05,420
Yeah.

1927
01:46:05,500 --> 01:46:07,340
I mean, I wonder how universal it is.

1928
01:46:07,420 --> 01:46:09,340
It probably would be recognized pretty widely.

1929
01:46:09,500 --> 01:46:13,580
I mean, I imagine I always do the Schwarz test because I've been there and I can imagine

1930
01:46:13,740 --> 01:46:14,300
how they'd react.

1931
01:46:14,380 --> 01:46:18,050
And it's like, what would a Schwarr person, how would they react to like a prototypical

1932
01:46:18,130 --> 01:46:19,650
malicious laugh like from the movies?

1933
01:46:19,730 --> 01:46:24,610
And would they understand in a forced choice, is this happy or is this is this e bad well,

1934
01:46:24,690 --> 01:46:25,810
it is happy in a way.

1935
01:46:25,890 --> 01:46:31,250
That's part of the meaning that's part of the talking about they're happy that it's bad for

1936
01:46:31,250 --> 01:46:31,410
you.

1937
01:46:31,570 --> 01:46:31,730
Yeah.

1938
01:46:32,130 --> 01:46:35,570
Like why why does everybody in your culture monologue when they're and tell the

1939
01:46:35,570 --> 01:46:38,610
protagonists what they're about to do so that they can foil the phone.

1940
01:46:39,650 --> 01:46:40,290
Yeah.

1941
01:46:44,200 --> 01:46:45,320
Yeah, it might.

1942
01:46:45,400 --> 01:46:50,840
I mean you could ask, like, you know, what is the moral character of the person laughing?

1943
01:46:50,920 --> 01:46:53,640
And like they might be able to say, like, that's that's a bad dude.

1944
01:46:54,040 --> 01:46:56,920
He's gonna do some, he's he's gonna, you know, hurt some people.

1945
01:46:57,160 --> 01:46:58,680
Yeah, he's happy doing bad.

1946
01:46:59,320 --> 01:47:02,760
Although these days they might be like, Oh, isn't that the third uh Spider Man movie or

1947
01:47:02,760 --> 01:47:03,160
something, right?

1948
01:47:05,400 --> 01:47:09,960
Well, right, yeah, that those kinds of experiments are becoming harder and harder to do,

1949
01:47:10,040 --> 01:47:12,440
yeah, to really get an isolated group.

1950
01:47:12,680 --> 01:47:13,320
Yeah.

1951
01:47:13,690 --> 01:47:14,010
Yeah.

1952
01:47:14,250 --> 01:47:18,410
Even then, I mean, when I was down there 20 years ago, I mean, you know, I think somebody

1953
01:47:18,410 --> 01:47:20,410
had a Madonna poster inside their hut.

1954
01:47:20,490 --> 01:47:21,530
I mean, you know.

1955
01:47:21,770 --> 01:47:22,010
Yeah.

1956
01:47:22,170 --> 01:47:22,890
Subjectively.

1957
01:47:23,050 --> 01:47:26,570
That's just the Chris von Rune thing where somebody who was like two days upriver and he

1958
01:47:26,570 --> 01:47:29,610
was like, Did you hear did you hear Michael Jackson just died?

1959
01:47:30,570 --> 01:47:32,410
And Chris von Rune was like, What?

1960
01:47:32,570 --> 01:47:35,850
And we actually checked because we had him on and I verified because he told me this when

1961
01:47:35,850 --> 01:47:36,730
we were in grad school.

1962
01:47:36,890 --> 01:47:40,720
It turns out he Michael Jackson had died and it just turned it just They knew before him.

1963
01:47:40,880 --> 01:47:45,680
He they knew before him he had he actually left the um he had been on the boat and during

1964
01:47:45,680 --> 01:47:49,120
that time Michael Jackson had died and it just transferred by word of mouth faster than

1965
01:47:49,120 --> 01:47:50,000
Chris got there.

1966
01:47:50,160 --> 01:47:55,200
Um so he got told some pop culture from oh two days upriver or something like that.

1967
01:47:55,520 --> 01:47:56,640
That's the state of the world now.

1968
01:47:56,800 --> 01:47:57,040
Yeah.

1969
01:47:57,200 --> 01:47:58,080
And that was a while ago.

1970
01:47:58,160 --> 01:47:58,320
Yeah.

1971
01:47:58,560 --> 01:47:59,760
Yeah, yeah, totally.

1972
01:48:00,240 --> 01:48:02,960
Uh Greg, we are getting toward the end.

1973
01:48:03,040 --> 01:48:06,640
We always have the question of should we have asked you anything?

1974
01:48:09,530 --> 01:48:13,530
Uh I can't really think of anything offhand.

1975
01:48:13,610 --> 01:48:16,170
It seems like you guys have covered it a lot.

1976
01:48:16,490 --> 01:48:22,090
Um you know, I guess um you I guess you didn't really ask me about the

1977
01:48:22,090 --> 01:48:23,610
non-human animal stuff.

1978
01:48:23,690 --> 01:48:28,410
Um, but I mean I think there's an in uh sort of implied thing about the evolutionary

1979
01:48:28,410 --> 01:48:34,300
history of these things is gonna um it's it's gonna be the case that a lot of the

1980
01:48:34,300 --> 01:48:38,460
phenomena we see in human vocalizations are gonna be in non-human animal vocalizations.

1981
01:48:38,540 --> 01:48:42,300
But yeah, is there something important or like that you think people it would be

1982
01:48:42,300 --> 01:48:44,940
interesting to know about the non-human stuff?

1983
01:48:45,660 --> 01:48:49,820
I mean, I guess uh when I think about it in terms of play vocalizations, I think the

1984
01:48:49,980 --> 01:48:55,750
interesting phenomena there is that the adaptive problem of signaling

1985
01:48:55,750 --> 01:49:00,470
something about play is present, and then how it manifests itself in any given species is

1986
01:49:00,470 --> 01:49:03,430
constrained by the communicative systems of that species.

1987
01:49:03,590 --> 01:49:08,150
So play vocalizations have very different sounds across animals because they're

1988
01:49:08,150 --> 01:49:10,470
incorporated into the vocal repertoire of those animals.

1989
01:49:10,630 --> 01:49:14,390
And so the level of analysis there is more appropriate to think about the adaptive problem

1990
01:49:14,390 --> 01:49:15,510
that's being solved.

1991
01:49:15,750 --> 01:49:21,300
And I think that know, that's where the adaptationist program is key.

1992
01:49:21,460 --> 01:49:26,020
And maybe a lot of people that study that stuff in non-human animals aren't always

1993
01:49:26,020 --> 01:49:26,980
adaptationists.

1994
01:49:27,940 --> 01:49:33,620
So you might miss um homologous features because you're not thinking about the adaptive

1995
01:49:33,620 --> 01:49:34,260
problem enough.

1996
01:49:34,580 --> 01:49:39,940
Um I used to say laughter was just a homologous across apes, and that was sort of what

1997
01:49:39,940 --> 01:49:42,830
people had suggested before I started thinking about this.

1998
01:49:42,990 --> 01:49:47,630
The other people that had like Van Hoof and Provine and others, they would all really talk

1999
01:49:47,630 --> 01:49:49,790
about um primates.

2000
01:49:50,110 --> 01:49:51,950
And now I've become convinced.

2001
01:49:52,030 --> 01:49:56,910
I have this recent paper I did with um anthropologist grad student Sasha Winkler looking at

2002
01:49:56,910 --> 01:50:01,550
play vo like every single instance of play vocalizations noted in the literature that we

2003
01:50:01,550 --> 01:50:06,800
could find, 65 species and all over the place, mammals mostly, but birds as well.

2004
01:50:06,960 --> 01:50:11,840
And so the birds, I would say, are that's convergent evolution, but with all the mammals, I

2005
01:50:11,840 --> 01:50:14,080
think those play vocalizations are homologous.

2006
01:50:14,240 --> 01:50:19,280
And I used to think that maybe like um play vocalization in rats saying rats laugh, which

2007
01:50:19,280 --> 01:50:20,560
they do quite a bit.

2008
01:50:20,720 --> 01:50:25,440
Um, I used to think that was an analogy that that was convergent evolution and it wasn't

2009
01:50:25,440 --> 01:50:29,920
homologous, but now I've been convinced that it's homologous, but it's just different, so

2010
01:50:29,920 --> 01:50:35,230
different, because rats are vocalizing in these high frequencies, and we people didn't even

2011
01:50:35,230 --> 01:50:39,550
know they were doing it until they just by accident, like 25 years ago.

2012
01:50:39,950 --> 01:50:45,470
Um, but the rat laugh is at 50 kilohertz, which is well beyond what we can hear.

2013
01:50:45,870 --> 01:50:50,350
Um, and so but I think it's a homologous behavior, even though it's radically different in

2014
01:50:50,350 --> 01:50:52,590
terms of the acoustic properties.

2015
01:50:52,910 --> 01:50:57,580
So I guess laughter might be like a hundred million years old.

2016
01:50:57,900 --> 01:50:58,620
Exactly.

2017
01:50:58,860 --> 01:50:59,180
Definitely.

2018
01:50:59,900 --> 01:51:03,580
Now, whether we call it laughter or not, I mean, do we want to call what humans do

2019
01:51:03,660 --> 01:51:05,580
something unique, which I think it is unique?

2020
01:51:05,820 --> 01:51:10,220
Um, you know, some people say chimps laugh, and then that's fair because they're close.

2021
01:51:10,380 --> 01:51:12,860
Um, but then do dogs laugh?

2022
01:51:13,020 --> 01:51:17,580
And you know, I think you know, you get the risk of anthropomorphizing the behavior.

2023
01:51:17,900 --> 01:51:22,530
Uh I prefer to certainly play vocalizations are you know probably a hundred billion years

2024
01:51:22,530 --> 01:51:23,250
old at least.

2025
01:51:23,490 --> 01:51:23,810
Yes.

2026
01:51:23,890 --> 01:51:24,130
Yeah.

2027
01:51:24,370 --> 01:51:25,010
Definitely.

2028
01:51:25,250 --> 01:51:25,490
Yeah.

2029
01:51:25,650 --> 01:51:26,290
Yeah.

2030
01:51:26,610 --> 01:51:30,530
So that's a nice little behavior to study in humans when it's something that's that

2031
01:51:30,530 --> 01:51:35,090
ancient, you know, and then it gets incorporated into even things like the conversational,

2032
01:51:35,170 --> 01:51:40,370
like we're playing when in this in this conversation we're having right now, we're playing.

2033
01:51:40,530 --> 01:51:40,770
Right.

2034
01:51:40,850 --> 01:51:42,530
There's all kinds of elements of play in there.

2035
01:51:42,610 --> 01:51:43,330
And yep.

2036
01:51:43,490 --> 01:51:48,200
And so it it's it's occurring at the highest level of communication, you know, it's in and

2037
01:51:48,200 --> 01:51:51,800
getting incorporated into their repertoire.

2038
01:51:52,040 --> 01:51:52,360
Yeah.

2039
01:51:52,520 --> 01:51:53,160
Yeah.

2040
01:51:54,040 --> 01:51:57,000
Uh do you you have music instruments behind you?

2041
01:51:57,080 --> 01:52:03,000
Do you want to either plug any of your uh existing music or are you gonna after we start

2042
01:52:03,080 --> 01:52:03,160
with it?

2043
01:52:03,320 --> 01:52:06,280
So we mean plug it as in like promote it or plug it as in like plug.

2044
01:52:06,680 --> 01:52:08,280
Well, actually, I meant they were both questions.

2045
01:52:08,360 --> 01:52:10,600
I was gonna say promote, but then I there's a five year old.

2046
01:52:11,800 --> 01:52:12,200
Yeah, exactly.

2047
01:52:12,280 --> 01:52:13,530
It was a it was a mix up, Yeah.

2048
01:52:13,850 --> 01:52:14,810
We will plug your work.

2049
01:52:14,970 --> 01:52:18,330
Uh Greg has some cool music uh by the name of G.

2050
01:52:18,490 --> 01:52:19,210
Bryant.

2051
01:52:19,290 --> 01:52:22,650
Uh he's got an album called Soft Assembly of a Die Hard.

2052
01:52:22,970 --> 01:52:25,050
Um I was just checking it out before we start talking.

2053
01:52:25,130 --> 01:52:25,770
It's good stuff.

2054
01:52:25,930 --> 01:52:31,850
So and if you're listening, if you're listening, do check out the YouTube um uh for

2055
01:52:31,850 --> 01:52:37,770
at least a moment to check out Greg's bass and banjo and um uh hollow body electric

2056
01:52:37,850 --> 01:52:43,420
and there's a strat on the floor and or on the floor and then there's a a lap stop guitar

2057
01:52:43,660 --> 01:52:44,860
right in the corner.

2058
01:52:45,020 --> 01:52:45,660
Yeah.

2059
01:52:46,220 --> 01:52:46,700
Yeah.

2060
01:52:46,940 --> 01:52:47,260
Awesome.

2061
01:52:47,740 --> 01:52:49,100
All kinds of goodies.

2062
01:52:49,900 --> 01:52:54,140
It's uh I'm not too crazy with the guitars.

2063
01:52:54,300 --> 01:52:58,700
I only have really one of everything that uh of its kind, mostly.

2064
01:52:58,860 --> 01:52:59,260
Yeah.

2065
01:52:59,500 --> 01:53:02,010
Um I have a couple basses, I guess.

2066
01:53:02,250 --> 01:53:06,010
But yeah, I'm not like one of these people that has like a whole row of electric guitars

2067
01:53:06,090 --> 01:53:07,210
that are all functionally the same.

2068
01:53:07,290 --> 01:53:09,290
You know, it's just like this one's uh 1970.

2069
01:53:09,610 --> 01:53:10,010
Yeah, yeah.

2070
01:53:10,330 --> 01:53:10,570
Yeah.

2071
01:53:11,370 --> 01:53:13,290
But that's because you still play it, right?

2072
01:53:13,370 --> 01:53:14,730
You you bought it and you play it.

2073
01:53:14,810 --> 01:53:16,330
So you play them all, yes.

2074
01:53:16,730 --> 01:53:17,770
Your parents were wrong.

2075
01:53:18,090 --> 01:53:19,610
And I can I sell stuff too.

2076
01:53:19,690 --> 01:53:21,370
I I unload equipment.

2077
01:53:21,760 --> 01:53:22,240
Very good.

2078
01:53:22,320 --> 01:53:22,640
Very good.

2079
01:53:23,200 --> 01:53:24,960
Well, Greg, uh super cool.

2080
01:53:25,040 --> 01:53:28,080
Um, really encourage everybody to check out uh Greg's work.

2081
01:53:28,160 --> 01:53:33,280
Um, if especially if you're interested in anything acoustic modality, he is the that that

2082
01:53:33,360 --> 01:53:37,040
that is his um he's the like the getaway driver of a heist movie.

2083
01:53:37,120 --> 01:53:42,000
Greg is the um the audio modality um for for this neck of science.

2084
01:53:42,080 --> 01:53:43,520
So uh it's super cool, Greg.

2085
01:53:43,600 --> 01:53:48,770
I always love coming across your work and it's um yeah, it's I always like whenever I see

2086
01:53:48,770 --> 01:53:52,450
your name on a paper, I'm always excited because I know I'm gonna learn something new and

2087
01:53:52,450 --> 01:53:55,810
crazy about nice compliment.

2088
01:53:56,050 --> 01:53:56,930
Well, it's been fun.

2089
01:53:57,010 --> 01:53:57,890
Thanks for inviting me.

2090
01:53:58,050 --> 01:54:03,410
And um yeah, I'm enjoying um the other I've been watching some of these and um I'm enjoying

2091
01:54:03,410 --> 01:54:03,490
them.

2092
01:54:03,650 --> 01:54:04,530
They're good conversations.

2093
01:54:04,610 --> 01:54:05,650
So keep it up.

2094
01:54:05,890 --> 01:54:06,210
Awesome.

2095
01:54:06,290 --> 01:54:06,450
Thanks.

2096
01:54:06,690 --> 01:54:07,170
Thank you.

2097
01:54:10,610 --> 01:54:16,540
David, it's funny to me that uh well, I guess if you listen to loud music, your your

2098
01:54:16,540 --> 01:54:21,580
ears get tired, but just listening to something, you don't feel like your ears get tired.

2099
01:54:21,980 --> 01:54:26,140
Yeah, if you're just listening to speech or to an interview, like city noise, like if

2100
01:54:26,140 --> 01:54:31,020
you're overwhelmed, like you can have audio overstimulation if there's like too much music.

2101
01:54:31,900 --> 01:54:32,300
That is true.

2102
01:54:33,180 --> 01:54:34,620
You need a break, you need a bit of silence.

2103
01:54:34,860 --> 01:54:37,100
Yeah, but it doesn't feel like it's in your ears.

2104
01:54:38,030 --> 01:54:42,190
No, yeah, I wonder it's kind of an interesting psychological question, actually.

2105
01:54:42,270 --> 01:54:45,950
Like, w what is the function of sonic tiredness?

2106
01:54:46,270 --> 01:54:48,750
Yeah, or perceived effort seems to be unique.

2107
01:54:48,990 --> 01:54:54,030
There's certain aspects of perceived effort across different sensation or perception

2108
01:54:54,030 --> 01:54:54,350
systems.

2109
01:54:54,670 --> 01:54:57,390
I mean it might be it might be the opportunity cost thing.

2110
01:54:57,640 --> 01:55:01,720
That like you know, you can only listen or focus on certain sounds to the exclusion of

2111
01:55:01,720 --> 01:55:01,960
others.

2112
01:55:02,200 --> 01:55:07,720
And if you're just like listening to this particular type of sound, you're eventually like

2113
01:55:07,800 --> 01:55:11,320
the opportunity costs get pretty big if like you're not getting any more information from

2114
01:55:11,320 --> 01:55:12,280
that sound stream.

2115
01:55:12,440 --> 01:55:12,920
I don't know.

2116
01:55:13,240 --> 01:55:19,150
Uh but so but it is the idea that like if you're just hearing one thing, Uh then

2117
01:55:19,550 --> 01:55:23,310
there's not competition for your attention, it's less fatiguing.

2118
01:55:23,630 --> 01:55:24,190
Yeah.

2119
01:55:25,870 --> 01:55:26,270
Maybe.

2120
01:55:26,350 --> 01:55:26,670
I don't know.

2121
01:55:26,910 --> 01:55:27,950
Maybe I'm just bullshitting.

2122
01:55:28,030 --> 01:55:28,350
I don't know.

2123
01:55:28,510 --> 01:55:28,670
Yeah.

2124
01:55:28,750 --> 01:55:34,750
Well, a PSA is um if you have um after moving from Germany to

2125
01:55:34,750 --> 01:55:40,110
the US, ever all this loud music and TVs in restaurants and third places.

2126
01:55:40,750 --> 01:55:42,590
I hate loud restaurants, loud bars.

2127
01:55:42,830 --> 01:55:43,310
Oh my god.

2128
01:55:43,720 --> 01:55:46,360
There's a as David says, there's a loneliness epidemic.

2129
01:55:46,520 --> 01:55:47,960
Please just turn it all off.

2130
01:55:48,200 --> 01:55:50,520
Like let people just have conversations, please.

2131
01:55:50,760 --> 01:55:51,560
Don't get me started.

2132
01:55:51,640 --> 01:55:51,880
Ugh.

2133
01:55:51,960 --> 01:55:56,040
And people have parties at these loud bars and you can't even hear anyone talking.

2134
01:55:56,120 --> 01:55:59,080
Like what uh it drives me uh insane.

2135
01:55:59,400 --> 01:55:59,560
Yeah.

2136
01:55:59,960 --> 01:56:04,360
So just just turn everything off and let people interact.

2137
01:56:04,760 --> 01:56:05,320
Yeah.

2138
01:56:06,040 --> 01:56:06,840
Could not agree more.

2139
01:56:07,400 --> 01:56:08,120
Yeah, great.

2140
01:56:08,280 --> 01:56:13,000
Um, I should end here, but but yeah, uh, please do Patreon.

2141
01:56:14,310 --> 01:56:18,470
I mean that was very reductive, but yeah, we we have a Patreon account if you would like to

2142
01:56:18,470 --> 01:56:20,390
um support the podcast.

2143
01:56:20,550 --> 01:56:22,390
Um please visit that.

2144
01:56:22,630 --> 01:56:24,070
What else can people do, David?

2145
01:56:24,390 --> 01:56:26,150
Uh they can leave us a kind review.

2146
01:56:26,230 --> 01:56:29,030
Uh they can say nice things about us on social media.

2147
01:56:29,190 --> 01:56:32,550
They can send us an email if they have any ideas for topics or guests.

2148
01:56:32,790 --> 01:56:33,670
That's pretty much it.

2149
01:56:33,910 --> 01:56:34,790
Yeah that's great.

2150
01:56:34,870 --> 01:56:38,790
And um yeah I just it was really nice to talk to Greg for two hours.

2151
01:56:38,950 --> 01:56:44,660
I wish we could um Oh, I don't know, play out his his

2152
01:56:44,660 --> 01:56:45,620
music, but you know what?

2153
01:56:45,700 --> 01:56:50,260
I I I I don't know if you know this, David, but I've been playing the full um theme song at

2154
01:56:50,260 --> 01:56:52,500
the end of the episodes because it's just so lovely.

2155
01:56:52,820 --> 01:56:53,860
Oh, thank you.

2156
01:56:54,020 --> 01:56:54,500
That's nice.

2157
01:56:54,980 --> 01:56:57,460
There's not enough Glockenspiel in life.

2158
01:56:57,860 --> 01:56:59,060
I could not agree more.

2159
01:56:59,140 --> 01:57:00,820
I'm a big fan of the Glockenspiel.

2160
01:57:01,060 --> 01:57:05,150
Yeah, so sit back, relax, and enjoy the next 30 thirty some sorry, I cut you off about

2161
01:57:05,150 --> 01:57:05,870
Glockenspiels.

2162
01:57:05,950 --> 01:57:06,510
I don't want to cut it down.

2163
01:57:06,670 --> 01:57:10,350
No, I was just gonna say it had a big moment in like the early two thousands, I feel like

2164
01:57:10,350 --> 01:57:11,790
with the Harry Potter theme, you know.

2165
01:57:12,030 --> 01:57:13,390
And then it just kinda disappeared.

2166
01:57:13,470 --> 01:57:14,510
Like what the what's the deal?

2167
01:57:14,750 --> 01:57:15,390
Bring it back.

2168
01:57:15,790 --> 01:57:16,590
It's a beautiful instrument.

2169
01:57:16,670 --> 01:57:22,510
So so that's our second rant here is uh turn everything off in these

2170
01:57:22,510 --> 01:57:26,110
third places and bring back the Glockenspiel.

2171
01:57:26,340 --> 01:57:26,660
Yep.

2172
01:57:26,900 --> 01:57:27,940
Bring it back.

2173
01:57:28,180 --> 01:57:28,340
Yeah.

2174
01:57:28,580 --> 01:57:29,460
See you next week.