@article{leh_dancing_2025,
	title = {Dancing through the uncanny valley: {On} the likeability of model-generated dance movements},
	issn = {1931-390X},
	shorttitle = {Dancing through the uncanny valley},
	doi = {10.1037/aca0000726},
	abstract = {The esthetic perception of model-generated dance movements was analyzed by varying the human likeness of the observed movements through the implementation of a model based on movement primitives. Likeability ratings and electrodermal activity in response to these model-generated movements varying in the number of underlying primitives were acquired for dancers and dance-novices. The results show that movements with a high degree of human likeness were generally associated with higher esthetic valuations. In dance experts, however, results show an uncanny valley effect in that likeability dropped for the most human-like model-generated kinematics. Additionally, the motion energy of the movement sequences also shows a remarkable association with esthetic responses, although to a lower degree for dancers than novices. Overall, these results not only extend the perceptual phenomenon known as the uncanny valley to the esthetic perception of model-generated movement kinematics, but also emphasize the relevance of sensorimotor experience for the esthetic perception of expressive movements. (PsycInfo Database Record (c) 2025 APA, all rights reserved)},
	journal = {Psychology of Aesthetics, Creativity, and the Arts},
	author = {Leh, Amalaswintha and Endres, Dominik and Hegele, Mathias},
	year = {2025},
	note = {Place: US
Publisher: Educational Publishing Foundation},
	keywords = {Aesthetics, Artificial Intelligence, Artists, Dance, Models, Motion Perception, Motor Processes},
	file = {Snapshot:C\:\\Users\\duoyi\\Zotero\\storage\\VURGHP6A\\doiLanding.html:text/html},
}