Dancing robots: aesthetic engagement is shaped by stimulus and knowledge cues to human animacy

IntroductionArtificial intelligence (AI) and robots are increasingly shaping the aesthetic preferences of art consumers, influencing how they perceive and engage with artistic works. This development raises various questions: do cues to the humanness of the origin of an artwork or artist influence o...

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Main Authors: Kohinoor M. Darda, Aaron Maiwald, Tanvi Raghuram, Emily S. Cross
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-11-01
Series:Frontiers in Human Neuroscience
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/fnhum.2024.1413066/full
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author Kohinoor M. Darda
Aaron Maiwald
Tanvi Raghuram
Emily S. Cross
author_facet Kohinoor M. Darda
Aaron Maiwald
Tanvi Raghuram
Emily S. Cross
author_sort Kohinoor M. Darda
collection DOAJ
description IntroductionArtificial intelligence (AI) and robots are increasingly shaping the aesthetic preferences of art consumers, influencing how they perceive and engage with artistic works. This development raises various questions: do cues to the humanness of the origin of an artwork or artist influence our aesthetic preferences?.MethodsAcross two experiments, we investigated how the perception and appreciation of dance is influenced by cues to human animacy. We manipulated Agent Form (human-like or robot-like dancer), Belief about Movement Source (human motion capture or computer animation), Source of Choreography (human- or computer-generated), and Belief about Choreography Source (believed to be human- or computer-generated).ResultsResults pointed toward agent congruence: In Experiment 1, robot agents were preferred when the movement source was believed to be computer animation. In Experiment 2, robot agents were preferred when the choreography was believed to be computer-generated, while choreographies believed to be human-generated were generally preferred. Participants could not accurately identify the actual source of choreography. These results persisted beyond the effects of age, dance expertise, technological expertise, attitudes toward AI, and perceived familiarity, complexity, evocativeness, technical competence, or reproducibility of the dance. Dance expertise, technological expertise, and attitudes toward AI independently impacted aesthetic judgments.DiscussionThese findings provide insights into the design of robotic dance, highlighting features of dance choreography and audience characteristics that influence aesthetic engagement. To enhance AI-driven creative productions, shaping perceptions will be crucial for better audience reception and engagement.
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spelling doaj-art-b9be37daa78d46f98481b96567beb23d2025-08-20T02:22:36ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612024-11-011810.3389/fnhum.2024.14130661413066Dancing robots: aesthetic engagement is shaped by stimulus and knowledge cues to human animacyKohinoor M. Darda0Aaron Maiwald1Tanvi Raghuram2Emily S. Cross3Advancement and Research in the Sciences and Arts (ARISA) Foundation, Pune, IndiaInstitute of Cognitive Science, Universität Osnabrück, Osnabrück, GermanyAdvancement and Research in the Sciences and Arts (ARISA) Foundation, Pune, IndiaProfessorship for Social Brain Sciences, ETH Zurich, Zurich, SwitzerlandIntroductionArtificial intelligence (AI) and robots are increasingly shaping the aesthetic preferences of art consumers, influencing how they perceive and engage with artistic works. This development raises various questions: do cues to the humanness of the origin of an artwork or artist influence our aesthetic preferences?.MethodsAcross two experiments, we investigated how the perception and appreciation of dance is influenced by cues to human animacy. We manipulated Agent Form (human-like or robot-like dancer), Belief about Movement Source (human motion capture or computer animation), Source of Choreography (human- or computer-generated), and Belief about Choreography Source (believed to be human- or computer-generated).ResultsResults pointed toward agent congruence: In Experiment 1, robot agents were preferred when the movement source was believed to be computer animation. In Experiment 2, robot agents were preferred when the choreography was believed to be computer-generated, while choreographies believed to be human-generated were generally preferred. Participants could not accurately identify the actual source of choreography. These results persisted beyond the effects of age, dance expertise, technological expertise, attitudes toward AI, and perceived familiarity, complexity, evocativeness, technical competence, or reproducibility of the dance. Dance expertise, technological expertise, and attitudes toward AI independently impacted aesthetic judgments.DiscussionThese findings provide insights into the design of robotic dance, highlighting features of dance choreography and audience characteristics that influence aesthetic engagement. To enhance AI-driven creative productions, shaping perceptions will be crucial for better audience reception and engagement.https://www.frontiersin.org/articles/10.3389/fnhum.2024.1413066/fullrobotsdanceaestheticssocial roboticschoreographyartificial intelligence
spellingShingle Kohinoor M. Darda
Aaron Maiwald
Tanvi Raghuram
Emily S. Cross
Dancing robots: aesthetic engagement is shaped by stimulus and knowledge cues to human animacy
Frontiers in Human Neuroscience
robots
dance
aesthetics
social robotics
choreography
artificial intelligence
title Dancing robots: aesthetic engagement is shaped by stimulus and knowledge cues to human animacy
title_full Dancing robots: aesthetic engagement is shaped by stimulus and knowledge cues to human animacy
title_fullStr Dancing robots: aesthetic engagement is shaped by stimulus and knowledge cues to human animacy
title_full_unstemmed Dancing robots: aesthetic engagement is shaped by stimulus and knowledge cues to human animacy
title_short Dancing robots: aesthetic engagement is shaped by stimulus and knowledge cues to human animacy
title_sort dancing robots aesthetic engagement is shaped by stimulus and knowledge cues to human animacy
topic robots
dance
aesthetics
social robotics
choreography
artificial intelligence
url https://www.frontiersin.org/articles/10.3389/fnhum.2024.1413066/full
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AT aaronmaiwald dancingrobotsaestheticengagementisshapedbystimulusandknowledgecuestohumananimacy
AT tanviraghuram dancingrobotsaestheticengagementisshapedbystimulusandknowledgecuestohumananimacy
AT emilyscross dancingrobotsaestheticengagementisshapedbystimulusandknowledgecuestohumananimacy