Evidence map›Paper›PMID 39655063›Full record

ArticleFrontiers in human neuroscience2024

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

Kohinoor M Darda, Aaron Maiwald, Tanvi Raghuram, Emily S Cross

Abstract read
In one paragraph

Article in Frontiers in human neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Kohinoor M Darda *Advancement and Research in the Sciences and Arts (ARISA) Foundation, Pune, India.
Aaron Maiwald *Institute of Cognitive Science, Universität Osnabrück, Osnabrück, Germany.
Tanvi RaghuramAdvancement and Research in the Sciences and Arts (ARISA) Foundation, Pune, India.
Emily S CrossProfessorship for Social Brain Sciences, ETH Zurich, Zurich, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 Results: Results pointed toward agent congruence: In 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.

Indexed as

aestheticsartificial intelligencechoreographydancerobotssocial robotics

Identifiers

PMID39655063
PMCPMC11625543

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.