#Savethedate: Polifonia seminar #3 on 3 May with a presentation by Mila Bertolo
We are happy to announce the third episode of the Polifonia series of seminars with experts in AI, musicology, and related fields. The Polifonia seminar #3 will take place on May 3 at 6 pm CET and will include a presentation by Mila Bertolo from McGill University (Montreal). In this third Polifonia seminar we will explore the way infants perceive lullabies and we will take a closer look at the cross-culturally consistent features in childhood melodies.
We are happy to announce the third episode of the Polifonia series of seminars with experts in AI, musicology, and related fields. The Polifonia seminar #3 will take place on May 3 at 6 pm CET and will include a presentation by Mila Bertolo from McGill University (Montreal). In this third Polifonia seminar we will explore the way infants perceive lullabies and we will take a closer look at the cross-culturally consistent features in childhood melodies.
Save the date and join via Zoom at the link.
Download the programme here.
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Date: 3 May 2022
Time: 18:00 – 19.00 CET
Programme: 18:00-18.30: Mila Bertolo (McGill University, Canada), Q&A starting at 18.30
Title: Infants relax in response to unfamiliar foreign lullabies
Abstract: Mila Bertolo will present a recent paper (Bainbridge & Bertolo et al., 2020, Nature Human Behaviour) that finds that infants are sensitive to the cross-culturally consistent features of lullabies, in that they relax to them even when the songs are drawn from unfamiliar cultures and sung in unfamiliar languages by unfamiliar voices. Such findings highlight the importance of previous and continuing work done on building corpora of culturally diverse music. These findings speak to broader theories concerning the possible evolutionary basis of music.
Bio: Mila Bertolo is a PhD student of Neuroscience at McGill University. She works on questions of how and why we understand music, drawing on developmental psychology, cross-cultural corpus work, and cross-species studies.
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This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement N. 101004746