1 00:00:01,440 --> 00:00:04,720 Speaker 1: Hey, welcome to Sign Stuff, a production of iHeartRadio. I'm 2 00:00:04,720 --> 00:00:08,200 Speaker 1: Hoorheit Champ, and today we're tackling the question can animals 3 00:00:08,480 --> 00:00:10,000 Speaker 1: appreciate music? 4 00:00:10,840 --> 00:00:11,039 Speaker 2: Now? 5 00:00:11,119 --> 00:00:13,400 Speaker 1: This is a special episode because we're going to talk 6 00:00:13,440 --> 00:00:16,400 Speaker 1: to three people who play music for animals, but to 7 00:00:16,520 --> 00:00:19,599 Speaker 1: each do it for a different reason. We're going to 8 00:00:19,680 --> 00:00:22,239 Speaker 1: talk to an artist who sings to exotic animals to 9 00:00:22,360 --> 00:00:25,920 Speaker 1: make online videos, an animal well for a specialist who 10 00:00:25,920 --> 00:00:29,720 Speaker 1: works in the meat industry, and a psychologist who's interested 11 00:00:29,840 --> 00:00:33,640 Speaker 1: in the musicality of animals. To bring your pets along 12 00:00:33,800 --> 00:00:36,240 Speaker 1: and home tune with us as we answer the question 13 00:00:36,720 --> 00:00:39,280 Speaker 1: can animals appreciate music? 14 00:00:43,240 --> 00:00:43,440 Speaker 3: Well? 15 00:00:45,520 --> 00:00:47,879 Speaker 1: Hey, everyone, all right. The first person we're talking to 16 00:00:47,880 --> 00:00:51,159 Speaker 1: today is a musician who's known for posting videos where 17 00:00:51,200 --> 00:00:55,640 Speaker 1: he sings to animals. If you spend time on social media, 18 00:00:56,000 --> 00:00:59,360 Speaker 1: chances are you've seen Lauris, Aesadian who goes by the 19 00:00:59,440 --> 00:01:03,200 Speaker 1: artist's name Plumes, playing a guitar outside in a farm 20 00:01:03,360 --> 00:01:08,720 Speaker 1: or a field, singing to cows, parrots, lamas, mirkats, pandas, 21 00:01:08,760 --> 00:01:10,240 Speaker 1: and even to raffs. 22 00:01:12,319 --> 00:01:20,399 Speaker 4: Las Plumes his. 23 00:01:20,480 --> 00:01:24,720 Speaker 1: Videos get millions of views, and his Instagram account, Plumes Official, 24 00:01:25,000 --> 00:01:28,720 Speaker 1: has over a million followers. My biggest question for Plumes 25 00:01:28,920 --> 00:01:34,160 Speaker 1: was do the animals actually react to his music? Well, 26 00:01:34,160 --> 00:01:36,160 Speaker 1: thank you so much, mister Assadian for joining us. 27 00:01:36,400 --> 00:01:37,560 Speaker 5: Of course, thanks for having me. 28 00:01:37,760 --> 00:01:39,320 Speaker 1: Can you please tell us who you are and what 29 00:01:39,400 --> 00:01:39,760 Speaker 1: you do. 30 00:01:40,000 --> 00:01:42,240 Speaker 5: Yeah, so I'm a singer. I'm a French singer and 31 00:01:42,280 --> 00:01:45,160 Speaker 5: my artist's name is prim and I sing for a 32 00:01:45,200 --> 00:01:49,480 Speaker 5: pretty unusual crowd, I would say, because it's animals, and yeah, 33 00:01:49,480 --> 00:01:50,600 Speaker 5: I've been doing this for awhile. 34 00:01:50,680 --> 00:01:50,920 Speaker 2: Now. 35 00:01:51,120 --> 00:01:53,840 Speaker 1: Yeah, you push your videos on Instagram. 36 00:01:53,440 --> 00:01:56,720 Speaker 5: Exactly, Yeah, on pretty much all social media platforms. 37 00:01:56,880 --> 00:01:59,240 Speaker 1: Why did you start playing music for animals? 38 00:01:59,320 --> 00:02:02,840 Speaker 5: Well, I've found out that carols like music over three 39 00:02:02,880 --> 00:02:05,680 Speaker 5: years ago now, so naturally I want to try it 40 00:02:05,680 --> 00:02:08,480 Speaker 5: out for myself, being a musician and at the time 41 00:02:08,480 --> 00:02:10,840 Speaker 5: I was living in the countryside with my grandma, so 42 00:02:11,160 --> 00:02:13,400 Speaker 5: there's lots of cals around, so it was the perfect 43 00:02:13,400 --> 00:02:16,120 Speaker 5: opportunity for me, and so I went in there. I 44 00:02:16,160 --> 00:02:17,320 Speaker 5: was pretty scared at first. 45 00:02:17,560 --> 00:02:19,600 Speaker 1: Were you afraid that they would do harm to you 46 00:02:19,680 --> 00:02:21,040 Speaker 1: or that they wouldn't like your music? 47 00:02:21,320 --> 00:02:23,800 Speaker 5: I guess both that they wouldn't like my music, so 48 00:02:24,080 --> 00:02:26,880 Speaker 5: they would do harm to me as a replication of 49 00:02:26,960 --> 00:02:29,880 Speaker 5: the thing. But there's giants, so I guess it can 50 00:02:29,919 --> 00:02:32,880 Speaker 5: be a bit overwhelming. And they're also very curious, so 51 00:02:33,040 --> 00:02:35,400 Speaker 5: they will come running towards you and you don't know 52 00:02:35,600 --> 00:02:37,560 Speaker 5: if they're going to stop or not. So I was 53 00:02:37,560 --> 00:02:40,480 Speaker 5: a bit scared. Now I'm not scared at all. Even 54 00:02:40,520 --> 00:02:42,840 Speaker 5: though you have to be careful with animals sometimes you 55 00:02:42,919 --> 00:02:45,880 Speaker 5: have to respect the boundaries and stuff. But yeah, now 56 00:02:46,000 --> 00:02:46,680 Speaker 5: I'm not scared. 57 00:02:46,880 --> 00:02:47,200 Speaker 3: Wow. 58 00:02:47,360 --> 00:02:50,560 Speaker 1: How do animals typically react to you playing music for them? 59 00:02:50,800 --> 00:02:53,440 Speaker 5: Well, they pretty much all want to get closer to 60 00:02:53,480 --> 00:02:56,320 Speaker 5: the music and investigate what the music is all about. 61 00:02:56,600 --> 00:02:59,320 Speaker 5: Some of them even like rub their heads against me 62 00:02:59,639 --> 00:03:03,240 Speaker 5: or try to have a contact, and others they just 63 00:03:03,280 --> 00:03:06,320 Speaker 5: stay around to listen to music and it can last 64 00:03:06,440 --> 00:03:08,040 Speaker 5: sometimes up to an hour. 65 00:03:08,320 --> 00:03:11,400 Speaker 1: Do you see them react to the specific songs, because 66 00:03:11,480 --> 00:03:13,280 Speaker 1: I know sometimes in the video it seems like they're 67 00:03:13,320 --> 00:03:14,600 Speaker 1: dancing to the music. 68 00:03:14,919 --> 00:03:15,119 Speaker 2: Yeah. 69 00:03:15,200 --> 00:03:18,440 Speaker 5: Sometimes, and some animals like parrots, have a sense of 70 00:03:18,560 --> 00:03:21,320 Speaker 5: rhythm and they will start dancing in rhythm to the 71 00:03:21,400 --> 00:03:24,200 Speaker 5: music and sometimes singing along and stuff like this. 72 00:03:24,680 --> 00:03:27,520 Speaker 1: Any other interesting reactions that you've noticed. 73 00:03:27,400 --> 00:03:30,320 Speaker 5: Mostly surprises, I would say, because there are some animals 74 00:03:30,320 --> 00:03:32,919 Speaker 5: that you didn't expect to come close because they're wild, 75 00:03:33,280 --> 00:03:35,920 Speaker 5: like the okps. I sang for an okp and I 76 00:03:36,040 --> 00:03:39,480 Speaker 5: was warned before that nothing is going to happen, basically 77 00:03:39,640 --> 00:03:42,240 Speaker 5: like you can try anyways, but it's not worth it 78 00:03:42,440 --> 00:03:44,800 Speaker 5: in a way. And I went in there anyways, and 79 00:03:45,040 --> 00:03:47,680 Speaker 5: he came right against me to listen to music for 80 00:03:47,720 --> 00:03:49,920 Speaker 5: like an hour, all right. 81 00:03:49,960 --> 00:03:52,600 Speaker 1: If you look at Plumes's account Plumes of VCL, you 82 00:03:52,680 --> 00:03:57,480 Speaker 1: see videos of them playing music to cows, goats, puppies, cats, pigs, 83 00:03:57,600 --> 00:04:15,640 Speaker 1: mini pigs, horses, parrots, capybaras, meerkats, copies, elephants, giraffes, donkeys, deer, sheep, lemurs, flamingos, tapiers, owls, pandas, penguins, camels, rhinoceroses, gibbons, seals, raccoons, tigers, 84 00:04:15,920 --> 00:04:18,960 Speaker 1: oh my. And in the videos you'll see the animals 85 00:04:19,000 --> 00:04:22,400 Speaker 1: appear to react to his music playing. The animals will 86 00:04:22,400 --> 00:04:25,799 Speaker 1: often come up to Plumes and we're curious or interested 87 00:04:26,040 --> 00:04:28,520 Speaker 1: in the music. There's one video where he plays a 88 00:04:28,640 --> 00:04:32,040 Speaker 1: Lady Gaga song to a white tiger and the tiger 89 00:04:32,200 --> 00:04:35,479 Speaker 1: seems to come over and sit down to listen. Or 90 00:04:35,480 --> 00:04:38,440 Speaker 1: in another video, he plays an Oasis song to some 91 00:04:38,520 --> 00:04:42,080 Speaker 1: orangutans and the orangutans not only come over to listen, 92 00:04:42,240 --> 00:04:45,320 Speaker 1: but one of them starts to clap, which made me 93 00:04:45,360 --> 00:04:49,039 Speaker 1: wonder what kind of music does each animal like to hear? 94 00:04:51,600 --> 00:04:54,960 Speaker 1: So in your videos you've sung Green Day to puppies, 95 00:04:55,240 --> 00:04:59,240 Speaker 1: Bruno Mars to horses, the Beatles to donkeys, Katie Perry 96 00:04:59,279 --> 00:05:01,839 Speaker 1: to flamingos. How do you pick the music to sing 97 00:05:01,839 --> 00:05:02,560 Speaker 1: to each animal? 98 00:05:02,760 --> 00:05:04,960 Speaker 5: Well, if there's a little nod or a little wink 99 00:05:05,200 --> 00:05:07,800 Speaker 5: to like a species, I like to choose that song. 100 00:05:08,000 --> 00:05:10,760 Speaker 5: I will play three Little Birds for parrots or things 101 00:05:10,760 --> 00:05:13,320 Speaker 5: like this, you know, But usually it's mostly like love 102 00:05:13,400 --> 00:05:15,919 Speaker 5: songs because I feel like the intent is very important 103 00:05:16,000 --> 00:05:18,760 Speaker 5: and somehow they can feel the intent that you're putting 104 00:05:18,760 --> 00:05:19,239 Speaker 5: out there. 105 00:05:19,600 --> 00:05:22,760 Speaker 1: So what has been your favorite animal to play for 106 00:05:22,960 --> 00:05:23,320 Speaker 1: so far? 107 00:05:23,760 --> 00:05:26,440 Speaker 5: I would say the rhinos because it was like the 108 00:05:26,480 --> 00:05:29,840 Speaker 5: most powerful expanse in a way, because once again we 109 00:05:29,839 --> 00:05:33,680 Speaker 5: were told that they might not approach and they ended 110 00:05:33,720 --> 00:05:35,839 Speaker 5: up being like right against me. I was on top 111 00:05:35,880 --> 00:05:38,560 Speaker 5: of a rock for safety issues, and we thought they 112 00:05:38,600 --> 00:05:41,720 Speaker 5: couldn't reach that rock. Turns out they could, and yeah, 113 00:05:41,760 --> 00:05:44,279 Speaker 5: they kind of touch me with their arms. It was 114 00:05:44,279 --> 00:05:46,160 Speaker 5: a bit dangerous this time. I'm not gonna lie, but 115 00:05:46,320 --> 00:05:49,080 Speaker 5: it was so powerful and we all like to de 116 00:05:49,120 --> 00:05:51,520 Speaker 5: season once in a lifetime moment for us. 117 00:05:51,600 --> 00:05:53,200 Speaker 1: So they did react to the music. 118 00:05:53,440 --> 00:05:55,960 Speaker 5: Yeah, yeah, they were very curious. And we even came 119 00:05:56,000 --> 00:05:58,640 Speaker 5: back the next day and the same thing happened. 120 00:05:58,400 --> 00:06:00,760 Speaker 1: And so they seemed to generally react to the music. 121 00:06:00,960 --> 00:06:01,159 Speaker 3: Yeah. 122 00:06:01,240 --> 00:06:03,200 Speaker 5: Yeah, a lot of people don't want to see that 123 00:06:03,240 --> 00:06:06,080 Speaker 5: animals are sensitive so that they would come anyways no 124 00:06:06,080 --> 00:06:08,599 Speaker 5: matter what. But turns out when well, there are twenty 125 00:06:08,640 --> 00:06:11,880 Speaker 5: minutes beforehand to set up the cameras and the mics 126 00:06:11,880 --> 00:06:14,160 Speaker 5: and stuff, and the animals don't come, and it's only 127 00:06:14,160 --> 00:06:16,640 Speaker 5: when the music stells that they end upcoming her. So 128 00:06:16,839 --> 00:06:18,159 Speaker 5: it's interesting to reat us. 129 00:06:18,200 --> 00:06:22,320 Speaker 1: Oh wow, Now I know what you're thinking. This doesn't 130 00:06:22,360 --> 00:06:26,840 Speaker 1: sound very scientific, and it's not. We're going to talk 131 00:06:26,880 --> 00:06:29,840 Speaker 1: to two scientists later in the program who do academic 132 00:06:29,880 --> 00:06:33,520 Speaker 1: research on the connections between animals and music. But what's 133 00:06:33,560 --> 00:06:36,960 Speaker 1: interesting about these videos is not how the animals react 134 00:06:36,960 --> 00:06:41,000 Speaker 1: to the music, but how one specific animal reacts to 135 00:06:41,080 --> 00:06:47,239 Speaker 1: the videos themselves. Well, I think the most interesting animal 136 00:06:47,279 --> 00:06:51,279 Speaker 1: reaction you have to your videos is from people. Yeah, 137 00:06:51,440 --> 00:06:53,360 Speaker 1: let me read you a couple of comments. I saw 138 00:06:53,680 --> 00:06:57,120 Speaker 1: somebody said, your videos make me look and feel animals 139 00:06:57,360 --> 00:06:59,680 Speaker 1: in a way that is totally new for me. And 140 00:06:59,760 --> 00:07:01,719 Speaker 1: that's if you sort of echo what you said the 141 00:07:01,760 --> 00:07:03,839 Speaker 1: first time you did this. What do you think is 142 00:07:03,880 --> 00:07:06,839 Speaker 1: happening to people who see these videos of you playing 143 00:07:06,839 --> 00:07:07,640 Speaker 1: to animals? 144 00:07:07,839 --> 00:07:10,440 Speaker 5: Yeah, I guess if I can make people click the 145 00:07:10,520 --> 00:07:12,960 Speaker 5: same way clicked for me when I first met animals, 146 00:07:13,160 --> 00:07:15,080 Speaker 5: it's great, you know, because I don't want to be 147 00:07:15,200 --> 00:07:18,040 Speaker 5: like telling people what to do, so I guess I 148 00:07:18,200 --> 00:07:20,960 Speaker 5: just put my videos online and people take what they 149 00:07:20,960 --> 00:07:23,800 Speaker 5: want from it. Like a lot of times, people tell me, yeah, 150 00:07:23,960 --> 00:07:26,760 Speaker 5: I stopped teaching meat since I first started watching your wids, 151 00:07:27,040 --> 00:07:28,520 Speaker 5: and that means a lot to me if I can 152 00:07:28,600 --> 00:07:31,200 Speaker 5: have a positive impact on the animal world. 153 00:07:31,480 --> 00:07:33,200 Speaker 1: When you said something click, what do you mean by that? 154 00:07:33,520 --> 00:07:37,000 Speaker 5: Well, for me, I stopped teaching meat today I met animals, 155 00:07:37,280 --> 00:07:40,720 Speaker 5: so there's definitely something that clicks for me. I was like, 156 00:07:40,920 --> 00:07:43,600 Speaker 5: how can I think for a cow in the afternoon 157 00:07:43,600 --> 00:07:44,760 Speaker 5: and then at night itter s. 158 00:07:44,800 --> 00:07:45,640 Speaker 3: Tech or whatever. 159 00:07:46,320 --> 00:07:49,320 Speaker 1: Somebody also said you are such a bright, beautiful spot 160 00:07:49,400 --> 00:07:52,000 Speaker 1: in a troubled world. Thank you for creating a bridge 161 00:07:52,040 --> 00:07:53,760 Speaker 1: between humans and animals. 162 00:07:54,120 --> 00:07:57,080 Speaker 5: That's very kay. I feel like It might sound a 163 00:07:57,120 --> 00:07:59,400 Speaker 5: little bit boomer, but I feel like maybe we lost 164 00:07:59,480 --> 00:08:02,679 Speaker 5: touch on generation with nature, and so maybe it feels 165 00:08:02,760 --> 00:08:05,960 Speaker 5: good for people to get that back, to see people 166 00:08:06,120 --> 00:08:09,400 Speaker 5: hanging out with animals. I think it can definitely feel 167 00:08:09,520 --> 00:08:12,280 Speaker 5: even powerful and soothing for people to watch. 168 00:08:12,560 --> 00:08:14,520 Speaker 1: Yeah, a lot of people mention that we're living in 169 00:08:14,600 --> 00:08:15,680 Speaker 1: very troubled times. 170 00:08:15,840 --> 00:08:18,240 Speaker 5: I know I have a lot of American people following me, 171 00:08:18,600 --> 00:08:20,840 Speaker 5: and I don't know much about politics and stuff, but 172 00:08:20,880 --> 00:08:23,200 Speaker 5: I guess it's not the best right now. I don't 173 00:08:23,200 --> 00:08:25,440 Speaker 5: really know, So if I can help to make them 174 00:08:25,440 --> 00:08:27,240 Speaker 5: feel a little bit better, that's great. 175 00:08:28,280 --> 00:08:31,160 Speaker 1: All right. So we have a first send account someone 176 00:08:31,200 --> 00:08:35,480 Speaker 1: who's played music to penguins, seals, drafts, and all kinds 177 00:08:35,480 --> 00:08:39,120 Speaker 1: of animals, and he reports that animals in general do 178 00:08:39,280 --> 00:08:43,480 Speaker 1: reactive with music and seem genuinely curious about it. Next, 179 00:08:43,559 --> 00:08:46,000 Speaker 1: we're going to talk to scientists who's done something similar 180 00:08:46,040 --> 00:08:49,560 Speaker 1: to plumes, but in a scientific setting. She's played music 181 00:08:49,600 --> 00:08:52,320 Speaker 1: to pigs to figure out what kind of music they 182 00:08:52,480 --> 00:08:55,160 Speaker 1: like to listen to and whether or not music can 183 00:08:55,200 --> 00:08:59,400 Speaker 1: make pigs feel emotions. We'll dig into that, but first 184 00:08:59,520 --> 00:09:02,800 Speaker 1: I should tell tell you about a terrible idea I had. 185 00:09:04,679 --> 00:09:06,880 Speaker 1: Is there anything that you would like to tell folks 186 00:09:07,000 --> 00:09:07,400 Speaker 1: out there? 187 00:09:07,520 --> 00:09:07,720 Speaker 3: Yeah? 188 00:09:07,760 --> 00:09:11,000 Speaker 5: Yes, So a lot of people are afraid to copy 189 00:09:11,320 --> 00:09:13,640 Speaker 5: my VIDs when they go and sing for animals. And 190 00:09:13,640 --> 00:09:15,480 Speaker 5: I would say, if you've seen make it, honestly, it's 191 00:09:15,480 --> 00:09:17,840 Speaker 5: so nice. If I can inspire people to do the 192 00:09:17,880 --> 00:09:20,120 Speaker 5: same thing, I won't take it badly at all. So 193 00:09:20,200 --> 00:09:22,480 Speaker 5: if you're a musician and you want to try it out, 194 00:09:22,840 --> 00:09:23,280 Speaker 5: please do. 195 00:09:23,720 --> 00:09:26,320 Speaker 1: Oh great, I might try it out for this episode. 196 00:09:26,400 --> 00:09:26,840 Speaker 3: All nice? 197 00:09:26,880 --> 00:09:27,800 Speaker 5: Did you play music? 198 00:09:27,920 --> 00:09:30,240 Speaker 1: I play a little piano and a little bit of guitar, 199 00:09:30,360 --> 00:09:32,240 Speaker 1: but I'm a terrible singer. I think that might be 200 00:09:32,320 --> 00:09:33,080 Speaker 1: the problem, but. 201 00:09:33,160 --> 00:09:35,520 Speaker 5: I don't think they care, honestly, can't the least judge 202 00:09:35,520 --> 00:09:36,680 Speaker 5: the audience that I've had. 203 00:09:38,160 --> 00:09:49,160 Speaker 1: Stay with us, We'll be right back, Welcome back. All right. 204 00:09:49,280 --> 00:09:53,400 Speaker 1: We're answering the question can animals appreciate music? And we 205 00:09:53,559 --> 00:09:56,800 Speaker 1: just heard from a musician play music for animals ranging 206 00:09:56,840 --> 00:10:01,160 Speaker 1: from giraffes to pandas, and according to him, the animals 207 00:10:01,200 --> 00:10:04,400 Speaker 1: do react, or at least they seem treious. So another 208 00:10:04,480 --> 00:10:08,240 Speaker 1: question is is this really true? Do animals have an actual 209 00:10:08,400 --> 00:10:11,920 Speaker 1: emotional reaction when you play human music for them? To 210 00:10:11,960 --> 00:10:14,439 Speaker 1: answer this question, I talk to someone who does research 211 00:10:14,559 --> 00:10:18,440 Speaker 1: in animal welfare, Professor Maria Camilla Sevaios. 212 00:10:20,040 --> 00:10:23,560 Speaker 2: I am Maria Camilla Sevadius, and I am Associate Professor 213 00:10:23,600 --> 00:10:26,160 Speaker 2: of Animal Welfare and Behavior at the University of Calgary. 214 00:10:26,520 --> 00:10:30,360 Speaker 2: I am a researcher and specifically my research focus in 215 00:10:30,559 --> 00:10:33,960 Speaker 2: finding strategies to improve the quality of life of animals, 216 00:10:34,040 --> 00:10:37,560 Speaker 2: especially captive animals. One of the research lines which I 217 00:10:37,760 --> 00:10:42,319 Speaker 2: have started is in a strong collaboration with doctor Erardo Rodriguez, 218 00:10:42,440 --> 00:10:47,080 Speaker 2: is identifying music as a possible environmental enrichment. 219 00:10:47,559 --> 00:10:51,119 Speaker 1: What is this idea of using music for animal welfare? 220 00:10:51,280 --> 00:10:54,360 Speaker 2: When we have captive animals, there are different strategies to 221 00:10:54,520 --> 00:10:58,000 Speaker 2: improve their welfare. Right, One way to improve their welfare 222 00:10:58,160 --> 00:11:02,319 Speaker 2: is giving them some environmental such as give them control 223 00:11:02,840 --> 00:11:06,720 Speaker 2: of the environment where they are or make that environment better. 224 00:11:07,120 --> 00:11:11,440 Speaker 2: Music is and now it is environment talentca so we 225 00:11:11,520 --> 00:11:15,280 Speaker 2: can use different kinds of sounds to improve the environment. 226 00:11:14,960 --> 00:11:15,480 Speaker 3: Where they are. 227 00:11:15,720 --> 00:11:18,600 Speaker 1: I guess what do we know about how animals respond 228 00:11:18,800 --> 00:11:19,360 Speaker 1: to music? 229 00:11:19,679 --> 00:11:23,400 Speaker 2: In animals, there is a lot of studies demonstrating that 230 00:11:23,600 --> 00:11:27,760 Speaker 2: different species of animals they react to music. They change 231 00:11:27,840 --> 00:11:32,280 Speaker 2: behaviors depending on the quality of the music or the 232 00:11:32,360 --> 00:11:34,920 Speaker 2: type of music that you put they will react right. 233 00:11:36,360 --> 00:11:39,200 Speaker 1: According to doctors Valius, there have been many studies that 234 00:11:39,280 --> 00:11:42,560 Speaker 1: have looked at how animals react to music. Scientists have 235 00:11:42,640 --> 00:11:48,120 Speaker 1: cleared music to gorillas, chimpanzees, sparrows, elephants, cows, and dogs, 236 00:11:48,480 --> 00:11:53,000 Speaker 1: to somewhat mixed results. Some studies find that the animals 237 00:11:53,040 --> 00:11:57,880 Speaker 1: do react while others don't, and usually the studies use 238 00:11:58,000 --> 00:12:02,400 Speaker 1: classical music. For example, a scientists have tested whether classical 239 00:12:02,480 --> 00:12:06,840 Speaker 1: music makes guerrillas less anxious. It does, or whether it 240 00:12:06,880 --> 00:12:11,280 Speaker 1: helps dogs sleep better. It does. In one study, scientists 241 00:12:11,320 --> 00:12:15,160 Speaker 1: from the University of twelf tested whether country music made 242 00:12:15,200 --> 00:12:19,000 Speaker 1: cows want to be milk more it does. It all 243 00:12:19,040 --> 00:12:22,120 Speaker 1: makes doctor Sivaias and her colleagues wonder if the type 244 00:12:22,120 --> 00:12:25,080 Speaker 1: of music played to pigs made a difference. 245 00:12:27,240 --> 00:12:31,040 Speaker 2: For example, we did a study in collaboration with doctor 246 00:12:31,040 --> 00:12:34,920 Speaker 2: Bernardo from the University of Antiochia and Juliana who was 247 00:12:34,960 --> 00:12:37,400 Speaker 2: the first author of this was her PhD. 248 00:12:37,559 --> 00:12:37,920 Speaker 3: Study. 249 00:12:38,240 --> 00:12:42,160 Speaker 2: We wanted to see first if pigs will react to 250 00:12:42,280 --> 00:12:46,160 Speaker 2: music right, and second, after we identified if they react 251 00:12:46,240 --> 00:12:50,040 Speaker 2: or not, what kind of music they like. More So Bernardo, 252 00:12:50,080 --> 00:12:52,400 Speaker 2: he is a musician so he created different kinds of 253 00:12:52,480 --> 00:12:56,599 Speaker 2: music where he knew all this spectro temporal characteristic of 254 00:12:56,640 --> 00:12:59,679 Speaker 2: the music. So, for example, number of instruments the high 255 00:12:59,720 --> 00:13:03,599 Speaker 2: frank and see contained the amplitude, the same troid desonance 256 00:13:03,800 --> 00:13:05,480 Speaker 2: right the spectral devia. 257 00:13:05,559 --> 00:13:06,640 Speaker 3: So there are a lot. 258 00:13:06,480 --> 00:13:10,120 Speaker 2: Of different things that you know as a musician that 259 00:13:10,200 --> 00:13:11,360 Speaker 2: you have the information. 260 00:13:12,240 --> 00:13:15,120 Speaker 1: Doctor Sibaias and her colleagues wanted to know what kind 261 00:13:15,160 --> 00:13:18,920 Speaker 1: of music pigs react to. So they created music pieces 262 00:13:19,040 --> 00:13:23,120 Speaker 1: that had different properties, how complex it is, what frequencies 263 00:13:23,160 --> 00:13:27,920 Speaker 1: it had, and specifically they varied something called consonants. Now 264 00:13:27,960 --> 00:13:30,000 Speaker 1: I'm not a musician, but the idea is that a 265 00:13:30,040 --> 00:13:34,319 Speaker 1: certain combination of notes seem more pleasant to us than others. 266 00:13:34,640 --> 00:13:37,479 Speaker 1: There's no exact definition, and it's all a bit subjective. 267 00:13:37,640 --> 00:13:41,439 Speaker 1: But for example, in Western tradition, this combination of notes 268 00:13:41,600 --> 00:13:57,800 Speaker 1: is considered consonant, whereas this combination is considered dissonant. Now 269 00:13:57,840 --> 00:14:08,120 Speaker 1: here's an example of consonant music. The scientists played the pigs. 270 00:14:10,200 --> 00:14:22,200 Speaker 1: Here's an example of the dissonant music they played. And 271 00:14:22,280 --> 00:14:24,560 Speaker 1: at the same time, while of the music was playing, 272 00:14:24,800 --> 00:14:28,400 Speaker 1: doctor Sebaias and her colleagues measured the pig's emotions. 273 00:14:29,960 --> 00:14:34,720 Speaker 2: So we use a specific indicator called qualitative behavior assessment, 274 00:14:34,920 --> 00:14:37,600 Speaker 2: which allows us to assess emotions. 275 00:14:37,960 --> 00:14:38,760 Speaker 1: How does this work. 276 00:14:39,120 --> 00:14:43,320 Speaker 2: This is a broad indicator where you have different terms, right, 277 00:14:43,680 --> 00:14:49,120 Speaker 2: such as for example, comfortable, stress, fearful, and there are 278 00:14:49,280 --> 00:14:53,560 Speaker 2: around twenty terms. Then you have a visual and analogical scale. 279 00:14:53,680 --> 00:14:57,520 Speaker 2: You observe the animal independent on how the animal interacts 280 00:14:57,520 --> 00:15:01,800 Speaker 2: with the environment, and you will give in that analogical scale. Okay, 281 00:15:01,840 --> 00:15:06,440 Speaker 2: I think this animal is happy, it's playful, it's comfortable, 282 00:15:06,560 --> 00:15:09,000 Speaker 2: or noise is stress, is fearful or things like that. 283 00:15:09,200 --> 00:15:12,000 Speaker 1: I see. This is a person with a paper observing 284 00:15:12,040 --> 00:15:16,480 Speaker 1: the animal and then rating all of these different adjectives correct. 285 00:15:16,080 --> 00:15:19,880 Speaker 2: And that allows us to identify how is the emotion 286 00:15:20,000 --> 00:15:22,400 Speaker 2: of the animal in terms of that specific situation. 287 00:15:23,120 --> 00:15:25,960 Speaker 1: All right, here's the experiment. There's two parts. In the first, 288 00:15:26,000 --> 00:15:29,440 Speaker 1: the scientists played music with different characteristics to a group 289 00:15:29,480 --> 00:15:32,680 Speaker 1: of pigs and observed how the pigs reacted based on 290 00:15:32,720 --> 00:15:34,240 Speaker 1: a scale of emotions. 291 00:15:35,400 --> 00:15:41,200 Speaker 2: From there, we identified, for example, that pigs they negatively react. 292 00:15:40,840 --> 00:15:42,240 Speaker 1: To this music. 293 00:15:42,440 --> 00:15:44,760 Speaker 2: If you put this on a music that will generate fear, 294 00:15:45,120 --> 00:15:48,760 Speaker 2: they will generate the stress, but consonant music will generate 295 00:15:48,960 --> 00:15:52,480 Speaker 2: like positive emotions such as play such as happy. 296 00:15:53,720 --> 00:15:56,920 Speaker 1: So, yeah, if you found the consonant notes in music 297 00:15:56,960 --> 00:16:01,240 Speaker 1: I played for you pleasant and the dissonant music notes unpleasant, 298 00:16:01,680 --> 00:16:04,240 Speaker 1: so the pigs they have the same reaction to the 299 00:16:04,320 --> 00:16:07,560 Speaker 1: music you and I have, okay. And then the second 300 00:16:07,560 --> 00:16:10,040 Speaker 1: part of the experiment they use the music pigs like 301 00:16:10,240 --> 00:16:13,360 Speaker 1: the consonant music as a form of therapy. 302 00:16:14,560 --> 00:16:17,600 Speaker 2: And then the next step was using that music that 303 00:16:17,680 --> 00:16:21,400 Speaker 2: we identified like that is spectro te characteristic music that 304 00:16:21,440 --> 00:16:25,280 Speaker 2: we identified that actually they were reacting better. It was 305 00:16:25,360 --> 00:16:28,440 Speaker 2: compared with pigs that did not receive any stimulus. 306 00:16:28,960 --> 00:16:31,680 Speaker 1: Here, the pigs were split into two groups. For one 307 00:16:31,680 --> 00:16:34,840 Speaker 1: group of pigs, the scientists would play the consonant music 308 00:16:35,160 --> 00:16:38,400 Speaker 1: at different times during the day, and for the other group, 309 00:16:38,520 --> 00:16:41,160 Speaker 1: they wouldn't play them any music at all. And then 310 00:16:41,160 --> 00:16:44,760 Speaker 1: they measured two things, how the pigs behaved and also 311 00:16:45,000 --> 00:16:48,640 Speaker 1: their levels of cortsol, which is a stress hormone. 312 00:16:50,280 --> 00:16:54,440 Speaker 2: Wholly unidentified that for example, pigs that received the music, 313 00:16:54,680 --> 00:16:57,480 Speaker 2: they would cope better with the environment in terms of 314 00:16:57,560 --> 00:17:02,280 Speaker 2: she evaliated behavioral characteristics and also physiological characteristics. Right, so 315 00:17:02,400 --> 00:17:07,080 Speaker 2: those big lids that received the environmental enrichment. Physiologically, they 316 00:17:07,240 --> 00:17:10,280 Speaker 2: had a better pattern. They are less. 317 00:17:10,119 --> 00:17:14,880 Speaker 1: Stress okay, somehow Okay, So to recap the found, picks 318 00:17:14,920 --> 00:17:18,600 Speaker 1: seem to have an emotional reaction to different kinds of music, 319 00:17:18,880 --> 00:17:20,920 Speaker 1: and that plain for them, the music that made them 320 00:17:21,040 --> 00:17:28,399 Speaker 1: exhibit more positive emotions lowered their stress level. Okay, So 321 00:17:28,440 --> 00:17:31,800 Speaker 1: then your study and other studies have shown that animals 322 00:17:31,880 --> 00:17:34,880 Speaker 1: have an emotional response to music. Is that true? 323 00:17:35,200 --> 00:17:39,800 Speaker 2: Well, so we speak about emotions, right. Other studies have 324 00:17:40,160 --> 00:17:45,800 Speaker 2: mostly focused on other indicators such as behavior or they 325 00:17:45,880 --> 00:17:50,520 Speaker 2: have physiological indicators that demonstrates that they are coping better. 326 00:17:50,720 --> 00:17:55,240 Speaker 2: But specific studies speaking about these generates emotions. We still 327 00:17:55,320 --> 00:17:58,840 Speaker 2: need to work and continue and trying to find emotions 328 00:17:58,920 --> 00:18:03,080 Speaker 2: to identify to evaluid emotions. It's difficult, right, Animals won't 329 00:18:03,119 --> 00:18:04,640 Speaker 2: tell you, oh, I feel good. 330 00:18:04,680 --> 00:18:07,480 Speaker 1: Right right? Well, what do you think was happening then 331 00:18:07,760 --> 00:18:11,160 Speaker 1: in the pig's brain when you played the music that 332 00:18:11,200 --> 00:18:13,199 Speaker 1: they seem to respond better to. 333 00:18:13,680 --> 00:18:17,120 Speaker 2: So, pigs they are extremely similar to humans, right, They 334 00:18:17,240 --> 00:18:20,800 Speaker 2: work physiologically, very very similar to us. So they have 335 00:18:20,960 --> 00:18:24,960 Speaker 2: all these structures in the brain that allows them to 336 00:18:25,200 --> 00:18:29,840 Speaker 2: process that sense, all the stimus so they will hear it, 337 00:18:30,000 --> 00:18:32,760 Speaker 2: it will enter, it will be processed in the year, 338 00:18:33,040 --> 00:18:35,840 Speaker 2: and then it will go through the ipotala mus and 339 00:18:36,000 --> 00:18:39,760 Speaker 2: it will go through the tiling cephalo. I would assume 340 00:18:39,840 --> 00:18:43,000 Speaker 2: that the process of the music would be pretty similar 341 00:18:43,080 --> 00:18:45,280 Speaker 2: to the process of music we have in humans, but 342 00:18:45,560 --> 00:18:46,800 Speaker 2: we don't know right there. 343 00:18:46,760 --> 00:18:50,040 Speaker 1: Is a lack of studies on that. This princess to 344 00:18:50,119 --> 00:18:53,200 Speaker 1: the main question of the episode. What's going on in 345 00:18:53,320 --> 00:18:57,400 Speaker 1: the animals' brain when it hears music? Can they actually 346 00:18:57,760 --> 00:19:00,560 Speaker 1: appreciate it? To find out, we're going to talk to 347 00:19:00,600 --> 00:19:04,240 Speaker 1: a neuroscientists studies the brains of animals to figure out 348 00:19:04,320 --> 00:19:08,400 Speaker 1: if they have something called musicality. Stay with us, you're 349 00:19:08,440 --> 00:19:22,080 Speaker 1: listening to science stuff. Welcome back. We're answering the question 350 00:19:22,440 --> 00:19:25,520 Speaker 1: can animals appreciate music? And here we get to the 351 00:19:25,600 --> 00:19:28,119 Speaker 1: heart of the matter, or should I say the brain 352 00:19:28,440 --> 00:19:31,080 Speaker 1: of the matter. In the last two segments, we confirm 353 00:19:31,119 --> 00:19:33,680 Speaker 1: that animals react to music and that they seem to 354 00:19:33,720 --> 00:19:37,479 Speaker 1: have an emotional response to different kinds of music. Now 355 00:19:37,520 --> 00:19:40,680 Speaker 1: we'll get to the question do they really appreciate music? 356 00:19:41,160 --> 00:19:44,280 Speaker 1: To answer that, I talk to a cognitive neuroscientist who's 357 00:19:44,320 --> 00:19:50,359 Speaker 1: been studying the musicality of animals. Well, thank you doctor 358 00:19:50,400 --> 00:19:51,800 Speaker 1: Raviani for joining us. 359 00:19:52,200 --> 00:19:54,840 Speaker 3: Thank you very much for having me. Can you please 360 00:19:54,880 --> 00:19:56,680 Speaker 3: tell us who you are and what do you do. Yes, 361 00:19:56,800 --> 00:20:00,159 Speaker 3: my name is Andre Ravignani. I am a researcher and 362 00:20:00,440 --> 00:20:04,040 Speaker 3: a professor at the Department of Human Neurosciences at Sapienza 363 00:20:04,160 --> 00:20:07,280 Speaker 3: University of Roman and also honorary professor at the Center 364 00:20:07,359 --> 00:20:10,160 Speaker 3: for Music in the Brain in Als in Denmark, which 365 00:20:10,240 --> 00:20:12,000 Speaker 3: is probably one of the few places in the world 366 00:20:12,040 --> 00:20:14,720 Speaker 3: where the study of neuroscience and music are combined. 367 00:20:14,880 --> 00:20:17,560 Speaker 1: So can you explain to us what is biomusicology. 368 00:20:17,760 --> 00:20:21,200 Speaker 3: It's a term that has been used a lot in 369 00:20:21,480 --> 00:20:25,600 Speaker 3: recent years to denote a biological approach to music, or 370 00:20:25,760 --> 00:20:28,960 Speaker 3: better a biological approach to musicality. And this is a 371 00:20:29,080 --> 00:20:32,159 Speaker 3: very important distinction. To make music is the you know, 372 00:20:32,280 --> 00:20:36,320 Speaker 3: the cultural artifact, the object of study of many fields 373 00:20:36,359 --> 00:20:39,920 Speaker 3: of humanities and arts. While musicality is defined as the 374 00:20:40,160 --> 00:20:44,679 Speaker 3: set of skills that allows us to produce, perceive music, 375 00:20:44,760 --> 00:20:46,560 Speaker 3: to move in time to music, and so on and 376 00:20:46,720 --> 00:20:51,000 Speaker 3: so forth. Musicality is more the set of psychological, cognitive, 377 00:20:51,320 --> 00:20:54,960 Speaker 3: biological building blocks that makes us musical animals. 378 00:20:55,119 --> 00:20:59,160 Speaker 1: And so you do this by comparing humans and animals exactly. 379 00:20:59,440 --> 00:21:03,760 Speaker 3: Our general approaches not to play Mozart to teenagers and 380 00:21:03,880 --> 00:21:06,440 Speaker 3: to cows and to see what effect it has on 381 00:21:06,560 --> 00:21:10,119 Speaker 3: their behavior. Our approach is to distill the building blocks 382 00:21:10,119 --> 00:21:10,920 Speaker 3: of musicality. 383 00:21:12,400 --> 00:21:16,760 Speaker 1: Doctor Ravinani studies the musicality of animals, which is basically 384 00:21:16,920 --> 00:21:20,440 Speaker 1: the ability to understand music, and these published papers about 385 00:21:20,480 --> 00:21:27,040 Speaker 1: disability and seals, chimpanzees, squirrel, monkeys, penguins, whales, dogs, dolphins, 386 00:21:27,280 --> 00:21:32,720 Speaker 1: rangutans and other animals. Now, according to doctor Ravinani, musicality 387 00:21:32,920 --> 00:21:35,600 Speaker 1: can be broken down into a set of skills that 388 00:21:35,840 --> 00:21:39,240 Speaker 1: some animals seem to have and others don't. 389 00:21:41,160 --> 00:21:43,960 Speaker 3: So, for instance, one of those is bit perceptions. For instance, 390 00:21:44,000 --> 00:21:45,760 Speaker 3: when we go to a club and we dance and 391 00:21:45,840 --> 00:21:48,800 Speaker 3: we're moving time to music, then we recruit these fairly 392 00:21:48,920 --> 00:21:52,320 Speaker 3: complex and neural capacity to moving time and to predict 393 00:21:52,320 --> 00:21:55,760 Speaker 3: the next bit. Other traits are vocal learning, so learning 394 00:21:56,040 --> 00:21:58,920 Speaker 3: sounds that do not belong to your natural repertoire learning 395 00:21:59,000 --> 00:22:01,840 Speaker 3: sounds that are not innate. You can think about absolute 396 00:22:01,880 --> 00:22:05,119 Speaker 3: and relative pitch. So imagine I play happy Birthday to you, 397 00:22:05,359 --> 00:22:08,200 Speaker 3: or imagine yeah, all the human beings singing happy birthday 398 00:22:08,240 --> 00:22:10,680 Speaker 3: to you, They're not always starting from the same note. 399 00:22:11,000 --> 00:22:13,600 Speaker 3: So I start from a sea and you start from 400 00:22:13,640 --> 00:22:16,639 Speaker 3: a C sharp. It's just the same melody, but presposed 401 00:22:16,680 --> 00:22:19,840 Speaker 3: of a semi tone, and to us it sounds exactly 402 00:22:19,920 --> 00:22:23,600 Speaker 3: the same unless you have absolute pitch. One related to 403 00:22:23,680 --> 00:22:27,480 Speaker 3: rhythm is meter perception. Another one is percussive behavior, and 404 00:22:27,520 --> 00:22:28,320 Speaker 3: there are many more. 405 00:22:28,480 --> 00:22:32,879 Speaker 1: Right according to doctor Ravinyanni, there's a list of skills that, 406 00:22:33,160 --> 00:22:37,240 Speaker 1: put together, add up to an ability to perceive, understand, 407 00:22:37,720 --> 00:22:41,560 Speaker 1: and make music, in other words, to appreciate music. So 408 00:22:41,680 --> 00:22:45,480 Speaker 1: now the question is do animals have these skills. We'll 409 00:22:45,520 --> 00:22:47,320 Speaker 1: start with deep perception. 410 00:22:49,040 --> 00:22:52,880 Speaker 3: Big perception is a very complex ability underlying our musicality, 411 00:22:52,960 --> 00:22:55,280 Speaker 3: and if we think about it for seconds, to us, 412 00:22:55,359 --> 00:22:58,920 Speaker 3: it seems natural to moving time to music. However, neurally 413 00:22:59,119 --> 00:23:03,240 Speaker 3: and psychologically, the process of bait perception is extremely complex 414 00:23:03,280 --> 00:23:06,840 Speaker 3: because music is not a metrino. It's a complex stream 415 00:23:06,920 --> 00:23:09,760 Speaker 3: of sounds. So the first thing that our brain needs 416 00:23:09,800 --> 00:23:12,879 Speaker 3: to do is to extract a recurring beat. So basically 417 00:23:13,000 --> 00:23:16,720 Speaker 3: we impose our expectations when is the next beat gonna come? 418 00:23:18,440 --> 00:23:22,120 Speaker 1: So recognizing a beat is not that simple, but from 419 00:23:22,200 --> 00:23:25,920 Speaker 1: studies of human brains. Scientists knew that beat perception was 420 00:23:26,040 --> 00:23:30,560 Speaker 1: related to talking or vocalizing because they share some of 421 00:23:30,600 --> 00:23:33,879 Speaker 1: the same brain areas, and sure enough, one of the 422 00:23:33,960 --> 00:23:37,720 Speaker 1: first animals beat perception was found in where parrots. 423 00:23:40,160 --> 00:23:43,680 Speaker 3: A study on a dancing parrot snowballed the cockatool showed 424 00:23:43,720 --> 00:23:45,760 Speaker 3: that actually, yeah, we have a second data point. So 425 00:23:46,119 --> 00:23:49,199 Speaker 3: the parrot was dancing in timed music and then speeding 426 00:23:49,280 --> 00:23:51,880 Speaker 3: up or slowing down depending on the bpm of the song, 427 00:23:51,960 --> 00:23:52,760 Speaker 3: and so on and so forth. 428 00:23:54,119 --> 00:23:57,639 Speaker 1: So parrots can keep a beat, and apparently sil can 429 00:23:57,760 --> 00:24:02,480 Speaker 1: sea lions and seals. Twenty thirteen, Ronan, the sea lion, 430 00:24:02,760 --> 00:24:06,680 Speaker 1: became famous as the only non human mammal known that 431 00:24:06,800 --> 00:24:10,000 Speaker 1: could keep a beat. Scientists started training Ronan when the 432 00:24:10,160 --> 00:24:13,240 Speaker 1: animal was only three years old to bob its head 433 00:24:13,440 --> 00:24:16,800 Speaker 1: to a beat, and just this year, the scientists showed 434 00:24:16,840 --> 00:24:20,040 Speaker 1: that Ronan could keep a beat as well or better 435 00:24:20,560 --> 00:24:24,199 Speaker 1: than humans. This is also something that doctor Gravignani has 436 00:24:24,200 --> 00:24:25,680 Speaker 1: studied with seals. 437 00:24:28,240 --> 00:24:32,160 Speaker 3: We did so called playback experiments where we broadcasted rhythmic 438 00:24:32,280 --> 00:24:36,639 Speaker 3: sounds of other seals to specific individual seals, and we 439 00:24:36,800 --> 00:24:39,480 Speaker 3: saw when they responded to the coal. So did they 440 00:24:39,560 --> 00:24:42,680 Speaker 3: respond to the sound in time with it, in synchrony 441 00:24:42,760 --> 00:24:45,320 Speaker 3: with it, and we found that in the harbor seals 442 00:24:45,359 --> 00:24:48,320 Speaker 3: they synchronized with the delay but very regularly, and they 443 00:24:48,400 --> 00:24:51,399 Speaker 3: adapt the bpm depending on the bpm of. 444 00:24:51,480 --> 00:24:53,560 Speaker 1: The sound, meaning they can tense the beat. 445 00:24:53,920 --> 00:24:56,040 Speaker 3: We need to do more research before we can say 446 00:24:56,080 --> 00:24:58,399 Speaker 3: that harbor seals can sense the beat, but definitely they 447 00:24:58,480 --> 00:25:00,240 Speaker 3: have some capacities to synchroniz eyes. 448 00:25:01,080 --> 00:25:05,640 Speaker 1: Another musicality skill that's been found in animals is perfect pitch. 449 00:25:07,040 --> 00:25:09,920 Speaker 3: That has been tested in quite a few bird species. 450 00:25:10,280 --> 00:25:13,400 Speaker 3: Birds so that they're very good at picking individual sounds, 451 00:25:13,400 --> 00:25:15,879 Speaker 3: so they have absolute pitch, something that is very rare 452 00:25:15,920 --> 00:25:18,439 Speaker 3: in humans, so they can recognize, okay, this is four 453 00:25:18,520 --> 00:25:21,720 Speaker 3: hundred herds, this is for fifty, for twenty, even without 454 00:25:21,760 --> 00:25:23,000 Speaker 3: being given a reference pitch. 455 00:25:23,320 --> 00:25:26,800 Speaker 1: And another skill is the ability to tell half notes 456 00:25:27,119 --> 00:25:28,399 Speaker 1: from quarter notes. 457 00:25:30,040 --> 00:25:33,600 Speaker 3: There is this integer ratio, a feature where you know, 458 00:25:33,680 --> 00:25:36,879 Speaker 3: if you think about we will rock you stompstonm clup, 459 00:25:37,160 --> 00:25:39,800 Speaker 3: stomp storm clup, so you have one unit of time 460 00:25:40,000 --> 00:25:42,280 Speaker 3: going from the first step to the second stone, and 461 00:25:42,400 --> 00:25:44,840 Speaker 3: then from the club to the next stomp. It's exactly 462 00:25:44,920 --> 00:25:48,480 Speaker 3: two units of time. And this produces some so called integeration. 463 00:25:48,680 --> 00:25:52,720 Speaker 3: This integer ratio we found in the injury lemurs of Madagascar. 464 00:25:52,800 --> 00:25:54,760 Speaker 3: So my colleagues at the University of Turin have been 465 00:25:55,000 --> 00:25:59,680 Speaker 3: recording their spontaneous vocalizations of these limours for about twenty years, 466 00:26:00,080 --> 00:26:03,440 Speaker 3: and based on this very large chorpus of limour songs, 467 00:26:03,680 --> 00:26:05,920 Speaker 3: we have seen that that's the first case of a 468 00:26:06,000 --> 00:26:09,640 Speaker 3: mammal that they are not humans that can produce this, meaning. 469 00:26:09,520 --> 00:26:12,600 Speaker 1: That they use the quarter note and the half note. 470 00:26:12,920 --> 00:26:15,399 Speaker 1: Is that kind of what you mean? Yeah, exactly, So 471 00:26:15,680 --> 00:26:19,640 Speaker 1: lemurs can keep track of half and quarter notes. Finally, 472 00:26:19,840 --> 00:26:24,760 Speaker 1: another skilled musicality, singing together, has also been found in animals. 473 00:26:26,160 --> 00:26:29,040 Speaker 3: Another interesting building blocks of music is the capacity for 474 00:26:29,240 --> 00:26:33,040 Speaker 3: vocal joint action or for vocal coordination. So gibbondes are apes. 475 00:26:33,400 --> 00:26:36,080 Speaker 3: They're the one apes the farthest away from us, but 476 00:26:36,160 --> 00:26:38,440 Speaker 3: they are still apes. They're not monkeys. And they sing 477 00:26:38,520 --> 00:26:40,880 Speaker 3: in duets. Right, So a male and a female will 478 00:26:40,920 --> 00:26:42,960 Speaker 3: pay a bond for a long while and they sing 479 00:26:43,080 --> 00:26:45,919 Speaker 3: to each other in a duet where some notes overlap 480 00:26:46,200 --> 00:26:48,399 Speaker 3: and some don't. And what we have seen is they 481 00:26:48,520 --> 00:26:52,399 Speaker 3: coordinate their song. Sometimes males do solos, sometimes males singing 482 00:26:52,520 --> 00:26:55,639 Speaker 3: a duet and if we compare the rhythm of their 483 00:26:55,720 --> 00:26:57,960 Speaker 3: song in a solo or in a duet, we see 484 00:26:57,960 --> 00:27:00,760 Speaker 3: a star difference between the two. So we see that 485 00:27:00,960 --> 00:27:04,239 Speaker 3: when the male sings in a duet, his notes are 486 00:27:04,520 --> 00:27:08,000 Speaker 3: much more adjusted and predictable to coordinate with the female. 487 00:27:08,160 --> 00:27:10,639 Speaker 3: This kind of vocal coordination that we deploy when we 488 00:27:10,720 --> 00:27:12,840 Speaker 3: sing in a choir or in many other contexts. 489 00:27:12,920 --> 00:27:16,560 Speaker 1: It's the ability to listen and then adapt your own 490 00:27:16,920 --> 00:27:17,600 Speaker 1: music production. 491 00:27:17,960 --> 00:27:19,479 Speaker 3: Yeah, your own vocal production. 492 00:27:19,600 --> 00:27:22,080 Speaker 1: Interesting. So we've seen that in the animal kingdom. 493 00:27:22,000 --> 00:27:25,119 Speaker 3: Indeed very often in singing privates and also in birds. 494 00:27:26,880 --> 00:27:30,920 Speaker 1: Now there are musicality skills that haven't been found in animals. 495 00:27:31,080 --> 00:27:34,080 Speaker 1: For example, the ability to recognize a melody even if 496 00:27:34,080 --> 00:27:36,720 Speaker 1: you play it on a different scale. That's called led 497 00:27:36,880 --> 00:27:41,520 Speaker 1: transposition or meter, which is recognizing patterns or groupings and 498 00:27:41,680 --> 00:27:46,080 Speaker 1: beats haven't been seen in any animal. But doctor Ramniani 499 00:27:46,160 --> 00:27:49,000 Speaker 1: argies that doesn't mean animals can't do it. It just 500 00:27:49,119 --> 00:27:51,119 Speaker 1: means we need to keep looking. 501 00:27:52,840 --> 00:27:56,040 Speaker 3: They take on messages that for every musicality trade. Every 502 00:27:56,119 --> 00:27:59,520 Speaker 3: time someone says, I reckon that this musicality building block 503 00:27:59,600 --> 00:28:02,880 Speaker 3: is unique human, then a few years later someone finds 504 00:28:02,960 --> 00:28:05,399 Speaker 3: a species that also has that traite, right, So it 505 00:28:05,640 --> 00:28:07,920 Speaker 3: particularly goes like that. So I think the take on 506 00:28:08,080 --> 00:28:11,080 Speaker 3: message of all these research until now is that even 507 00:28:11,160 --> 00:28:14,320 Speaker 3: though the full package of these musicality traits might be 508 00:28:14,440 --> 00:28:17,320 Speaker 3: uniquely human, for each trait we can find at least 509 00:28:17,320 --> 00:28:19,600 Speaker 3: another animal species that has it, so we are not 510 00:28:19,720 --> 00:28:20,720 Speaker 3: so unique after all. 511 00:28:22,800 --> 00:28:26,440 Speaker 1: All right, So the components of understanding and appreciating music 512 00:28:26,760 --> 00:28:30,480 Speaker 1: have been found in different animals, but to date humans 513 00:28:30,480 --> 00:28:33,400 Speaker 1: seem to be the only species with all the skills. 514 00:28:33,880 --> 00:28:38,280 Speaker 1: Now what does that mean for our main question? So 515 00:28:38,440 --> 00:28:41,280 Speaker 1: if someone were to ask you, do you think animals 516 00:28:41,360 --> 00:28:44,200 Speaker 1: can appreciate music? How would you answer that question? 517 00:28:44,600 --> 00:28:46,800 Speaker 3: I think the short answer to that that we really 518 00:28:46,880 --> 00:28:49,120 Speaker 3: do not know. I think that the first question to 519 00:28:49,200 --> 00:28:52,280 Speaker 3: ask is do they even care about it? So carrying 520 00:28:52,360 --> 00:28:56,560 Speaker 3: and appreciating music is a bit of an anthropocentric perspective 521 00:28:56,600 --> 00:28:59,400 Speaker 3: in a way, because for us it's such an important thing. 522 00:28:59,800 --> 00:29:02,240 Speaker 3: And then on top of that, music is the human 523 00:29:02,320 --> 00:29:06,480 Speaker 3: cultural artifact. Right would you enjoy listening for hours of 524 00:29:06,960 --> 00:29:12,120 Speaker 3: dolphin whistles or chimpanzee drumming? There are animal signals, animal sounds, 525 00:29:12,280 --> 00:29:13,720 Speaker 3: communicative sounds. 526 00:29:13,520 --> 00:29:15,880 Speaker 1: But it sounds like we have found some musicality in 527 00:29:15,960 --> 00:29:18,520 Speaker 1: some species. It is the question, then, can you connect 528 00:29:18,560 --> 00:29:22,360 Speaker 1: that musicality to feelings for some sort of reaction. 529 00:29:22,920 --> 00:29:26,040 Speaker 3: Yes, potentially some music sounds and no music sounds can 530 00:29:26,120 --> 00:29:29,560 Speaker 3: have some emotional value for different species. But this has 531 00:29:29,640 --> 00:29:32,720 Speaker 3: to do with more basic sound perception rather than music 532 00:29:32,800 --> 00:29:33,160 Speaker 3: per se. 533 00:29:33,480 --> 00:29:35,840 Speaker 1: I see. So the answer might be that they might 534 00:29:35,920 --> 00:29:39,240 Speaker 1: be able to appreciate music, but probably not human music. 535 00:29:39,360 --> 00:29:42,680 Speaker 3: Yeah, potentially species specific music. Actually, some colleagues in the 536 00:29:42,800 --> 00:29:45,960 Speaker 3: US have been working towards trying to understand whether we 537 00:29:46,000 --> 00:29:49,360 Speaker 3: can make species specific music. So there are even albums 538 00:29:49,400 --> 00:29:52,760 Speaker 3: out there on iTunes of cat music, and the peer 539 00:29:52,760 --> 00:29:55,600 Speaker 3: reviewed studies where they showed that you know, this cat 540 00:29:55,680 --> 00:29:59,720 Speaker 3: specific music played by a cello in used relaxation in cats, 541 00:29:59,840 --> 00:30:00,720 Speaker 3: and so on and so forth. 542 00:30:00,880 --> 00:30:03,800 Speaker 1: Okay, last question. The first part of this episode, I 543 00:30:04,160 --> 00:30:06,600 Speaker 1: told the musician who plays music for animals, I was 544 00:30:06,640 --> 00:30:08,360 Speaker 1: going to try it, and I'm terrible and I don't 545 00:30:08,360 --> 00:30:10,040 Speaker 1: think very well, but I'm gonna try to sing to 546 00:30:10,200 --> 00:30:12,600 Speaker 1: some animals, maybe a dog or a cat. How do 547 00:30:12,640 --> 00:30:14,280 Speaker 1: you think that dog or cat is going to react? 548 00:30:14,520 --> 00:30:16,840 Speaker 3: So, first of all, it depends on the species. So 549 00:30:17,200 --> 00:30:19,959 Speaker 3: a cat and a dog have already a very different 550 00:30:20,160 --> 00:30:23,360 Speaker 3: perceptual and cognitive world one from one another. Right, How 551 00:30:23,400 --> 00:30:25,040 Speaker 3: they see the world and how they feel the world 552 00:30:25,080 --> 00:30:28,280 Speaker 3: as we know is very different, right, even between breeds 553 00:30:28,320 --> 00:30:30,720 Speaker 3: of dogs. And then it also depends a lot on 554 00:30:30,840 --> 00:30:33,800 Speaker 3: the experiences that that cat or dog has done during 555 00:30:33,880 --> 00:30:37,360 Speaker 3: their life. Right, so you know, imagine that the dog 556 00:30:37,520 --> 00:30:39,120 Speaker 3: was raised by an owner. 557 00:30:39,200 --> 00:30:41,680 Speaker 4: That we always think we don't really high pitch to 558 00:30:41,840 --> 00:30:45,720 Speaker 4: convey that, come on here, cuties, Or imagine that the 559 00:30:45,800 --> 00:30:50,080 Speaker 4: dog was mistreated by someone with a very deep noise 560 00:30:50,280 --> 00:30:52,160 Speaker 4: like that, And then if you hit some. 561 00:30:52,280 --> 00:30:54,080 Speaker 3: Low notes, the dog is not going to have a 562 00:30:54,200 --> 00:30:57,480 Speaker 3: very good reaction, right, And then who knows? Again, I 563 00:30:57,560 --> 00:31:01,480 Speaker 3: don't explve the fact that many animals might be enjoying music, 564 00:31:01,720 --> 00:31:04,040 Speaker 3: our own music, their own music, but we still don't 565 00:31:04,080 --> 00:31:06,960 Speaker 3: know enough I see, Right, So if you pay music 566 00:31:07,000 --> 00:31:09,760 Speaker 3: to a catera dog, they either won't care or they 567 00:31:09,800 --> 00:31:13,080 Speaker 3: will react based on what those sounds have been associated 568 00:31:13,200 --> 00:31:15,360 Speaker 3: with in the past. And don't get me wrong, and 569 00:31:15,480 --> 00:31:18,320 Speaker 3: not belittling animals, I love animals. What I'm saying is 570 00:31:18,400 --> 00:31:21,960 Speaker 3: that their cognitive and perceptual world is very complex and 571 00:31:22,080 --> 00:31:25,280 Speaker 3: nuance like our own, and so the music that you're 572 00:31:25,320 --> 00:31:27,320 Speaker 3: going to play or sing is the result of a 573 00:31:27,400 --> 00:31:31,080 Speaker 3: bunch of cultural accumulation, and that probably not all the 574 00:31:31,200 --> 00:31:33,840 Speaker 3: nuances we see in them are gonna speak to them, 575 00:31:33,880 --> 00:31:34,600 Speaker 3: and vice versa. 576 00:31:35,560 --> 00:31:38,600 Speaker 1: All right, So to recap the whole episode, animals seem 577 00:31:38,680 --> 00:31:41,200 Speaker 1: to react to music. They can have what seems like 578 00:31:41,280 --> 00:31:44,520 Speaker 1: an emotional response to it. For example, you can play 579 00:31:44,640 --> 00:31:48,280 Speaker 1: music that lowers their stress level. And many species seem 580 00:31:48,320 --> 00:31:51,920 Speaker 1: to have the brain circuits to do single musical tasks 581 00:31:52,320 --> 00:31:56,120 Speaker 1: like keep a beat or tell notes apart or sing together. 582 00:31:56,840 --> 00:32:00,520 Speaker 1: But whether they can appreciate music might depend whether they 583 00:32:00,560 --> 00:32:02,760 Speaker 1: can even hear the sounds in that music and what 584 00:32:02,880 --> 00:32:06,320 Speaker 1: their life experience has been with those sounds. It's just 585 00:32:06,440 --> 00:32:10,040 Speaker 1: like how we don't necessarily appreciate a bird song, or 586 00:32:10,080 --> 00:32:15,320 Speaker 1: how some of us don't like heavy metal or country 587 00:32:15,400 --> 00:32:26,040 Speaker 1: music or music from another culture. All right, I'll leave 588 00:32:26,040 --> 00:32:28,760 Speaker 1: you with the image a man with a guitar in 589 00:32:28,840 --> 00:32:32,800 Speaker 1: a field playing music to an eight foot tall bird. 590 00:32:33,360 --> 00:32:37,040 Speaker 1: Here's Plumes singing a song. Hey road to an Ostrich. 591 00:32:38,280 --> 00:32:58,239 Speaker 1: Thanks for joining us, See you next time you've been 592 00:32:58,280 --> 00:33:03,080 Speaker 1: listening to science Stuffduction of iHeartRadio Bringing the produced by 593 00:33:03,120 --> 00:33:07,280 Speaker 1: me or hitch Ham prendedate by Rose Seguda, executive producer 594 00:33:07,400 --> 00:33:10,920 Speaker 1: Jerry Rowland, and audio engineer and mixer Kasey Pecrom. You 595 00:33:10,960 --> 00:33:13,720 Speaker 1: can follow me on social media. Just search for PhD 596 00:33:13,920 --> 00:33:16,600 Speaker 1: Comics and the name of your favorite platform. Be sure 597 00:33:16,680 --> 00:33:20,040 Speaker 1: to subscribe to sign stuff on the iHeartRadio app, Apple Podcasts, 598 00:33:20,160 --> 00:33:23,400 Speaker 1: or wherever you get your podcasts, and please tell your friends. 599 00:33:23,760 --> 00:33:29,240 Speaker 1: We'll be back next Wednesday with another episode. All right, today, 600 00:33:29,240 --> 00:33:34,480 Speaker 1: I'm singing for Chloe the Dog and Mango the Cat. Chloe, 601 00:33:34,960 --> 00:33:41,680 Speaker 1: you Mango, Here we go. It had to be. It 602 00:33:42,320 --> 00:33:54,720 Speaker 1: had to be. I wandered averund fine, elly fan soundbody 603 00:33:54,840 --> 00:34:00,960 Speaker 1: who's yeah? I don't think they care. You don't seem 604 00:34:00,960 --> 00:34:04,920 Speaker 1: that impressed. What are the animals doing? 605 00:34:04,960 --> 00:34:06,200 Speaker 2: What was the Mango? 606 00:34:06,280 --> 00:34:08,440 Speaker 1: The cat went behind you and we started just kind 607 00:34:08,440 --> 00:34:12,320 Speaker 1: of like laying out and stretching out, and so I 608 00:34:12,400 --> 00:34:13,680 Speaker 1: think he was definitely listening. 609 00:34:13,760 --> 00:34:16,800 Speaker 3: For sure, he comes and listens when I played the guitar. 610 00:34:16,920 --> 00:34:18,360 Speaker 3: She won't really hang out with me in here, but 611 00:34:18,440 --> 00:34:19,239 Speaker 3: he will all come and. 612 00:34:19,239 --> 00:34:21,320 Speaker 2: Play the guitar and he'll sit on the couch or 613 00:34:21,400 --> 00:34:22,800 Speaker 2: he'll just lay on the carpet next to me 614 00:34:22,840 --> 00:34:25,440 Speaker 1: And he'll just hang out so he does that her 615 00:34:25,560 --> 00:34:27,200 Speaker 1: not so much, but he definitely does