2023
DOI: 10.1111/nyas.14972
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Confronting ethical and social issues related to the genetics of musicality

Abstract: New interdisciplinary research into genetic influences on musicality raises a number of ethical and social issues for future avenues of research and public engagement. The historical intersection of music cognition and eugenics heightens the need to vigilantly weigh potential risks and benefits of these studies and use of their outcomes. Here we bring together diverse perspectives (complex trait genetics, music cognition, musicology, bioethics, and neuroscience) to guide ethical use of this new knowledge. We d… Show more

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Cited by 8 publications
(3 citation statements)
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“…Furthermore, two separate linear regression analyses were performed to test hypothesis 2, with ERI ratio as an independent variable and perceived stress and work engagement as the outcome variables. The adjusted R-squared was used to assess the goodness-of-fit measure ( Gordon, 2023 ).…”
Section: Methodsmentioning
confidence: 99%
“…Furthermore, two separate linear regression analyses were performed to test hypothesis 2, with ERI ratio as an independent variable and perceived stress and work engagement as the outcome variables. The adjusted R-squared was used to assess the goodness-of-fit measure ( Gordon, 2023 ).…”
Section: Methodsmentioning
confidence: 99%
“…Endocranial imprints can provide direct empirical evidence of brain evolution revealing evolutionary changes of brain size, shape and sulcal morphology in palaeoanthropology 2,6,[92][93][94][95][96] . However, given the historical burden of craniometry, using endocranial metrics as phenotypes and correlating them with anatomical or behavioural traits requires vigilant and thorough ethical considerations to avoid misappropriation of presented results 97,98 . Craniometry, the study of skull shape, size, and proportions, was prevalent in anthropology during the 18th and early 19th centuries.…”
Section: Historical Context Of Craniology and Phrenology Warrants A C...mentioning
confidence: 99%
“…For example, some studies have improved the accuracy of rhythm detection by analyzing videos of musical instruments being played and the corresponding audio. In addition, deep neural networks, especially convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been widely used for feature learning and pattern recognition of music rhythms [13][14][15].…”
Section: Introductionmentioning
confidence: 99%