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

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          Abstract

          Introduction

          Artificial 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?.

          Methods

          Across 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).

          Results

          Results 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.

          Discussion

          These 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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          SIMR: an R package for power analysis of generalized linear mixed models by simulation

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            Too real for comfort? Uncanny responses to computer generated faces

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              The effort heuristic

              Justin Kruger, Derrick Wirtz, Leaf Van Boven … (2004)
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                Author and article information

                Contributors
                Kohinoor M. Darda: URI : https://loop.frontiersin.org/people/1722606/overviewRole: Role: Role: Role: Role: Role: Role: Role: Role: Role: Role: Role: Role:
                Aaron Maiwald: URI : https://loop.frontiersin.org/people/2773542/overviewRole: Role: Role: Role: Role: Role: Role: Role: Role:
                Tanvi Raghuram: Role: Role: Role: Role:
                Emily S. Cross: Role: Role: Role: Role: Role: Role:
                Journal
                Journal ID (nlm-ta): Front Hum Neurosci
                Journal ID (iso-abbrev): Front Hum Neurosci
                Journal ID (publisher-id): Front. Hum. Neurosci.
                Title: Frontiers in Human Neuroscience
                Publisher: Frontiers Media S.A.
                ISSN (Electronic): 1662-5161
                Publication date (Electronic): 25 November 2024
                Publication date Collection: 2024
                Volume: 18
                Electronic Location Identifier: 1413066
                Affiliations
                [1] 1Advancement and Research in the Sciences and Arts (ARISA) Foundation , Pune, India
                [2] 2Institute of Cognitive Science, Universität Osnabrück , Osnabrück, Germany
                [3] 3Professorship for Social Brain Sciences, ETH Zurich , Zurich, Switzerland
                Author notes

                Edited by: Antonella Maselli, National Research Council (CNR), Italy

                Reviewed by: Isabella Poggi, Roma Tre University, Italy

                Tapio Takala, Aalto University, Finland

                *Correspondence: Kohinoor M. Darda, kohinoor@ 123456arisafoundation.org ; Emily S. Cross, ecross@ 123456ethz.ch

                †These authors share first authorship

                †ORCID: Kohinoor M. Darda, orcid.org/0000-0002-2692-7360

                Aaron Maiwald, orcid.org/0009-0000-2897-2862

                Tanvi Raghuram, orcid.org/0000-0001-5475-9109

                Emily S. Cross, orcid.org/0000-0002-1671-5698

                Article
                DOI: 10.3389/fnhum.2024.1413066
                PMC ID: 11625543
                PubMed ID: 39655063
                SO-VID: 98411dc7-7cb1-40a0-a634-7fa4d44f3aad
                Copyright © Copyright © 2024 Darda, Maiwald, Raghuram and Cross.
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                Date received : 06 April 2024
                Date accepted : 10 September 2024
                Page count
                Figures: 6, Tables: 2, Equations: 0, References: 43, Pages: 16, Words: 12227
                Funding
                The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This project was supported by a Leverhulme grant to EC (PLP-2018-152) and the Professorship for Social Brain Sciences at ETH Zurich. Open access funding by ETH Zurich.
                Categories
                Subject: Human Neuroscience
                Subject: Original Research
                Custom metadata
                section-at-acceptance Cognitive Neuroscience

                ScienceOpen disciplines: Neurosciences
                Keywords: robots,dance,aesthetics,social robotics,choreography,artificial intelligence
                Data availability:
                ScienceOpen disciplines: Neurosciences
                Keywords: robots, dance, aesthetics, social robotics, choreography, artificial intelligence

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