Exploring the Application of AI-generated Artworks for the Study of Aesthetic Processing

Utz, Vanessa; DiPaola, Steve

doi:10.1109/mipr51284.2021.00073

Cited by 1 publication

(1 citation statement)

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“…In addition to prompt engineering, parameter tuning gives researchers even more control over the output of the algorithms. The combination of prompt engineering and parameter tuning gives has implications for experimental design, more generally, as algorithms are able to create novel stimuli with high degrees of experimenter control (see Utz & DiPaola, 2021).…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

Artificial intelligence and art: Identifying the aesthetic judgment factors that distinguish human- and machine-generated artwork.

Samo,

Highhouse

2023

Psychology of Aesthetics, Creativity, and the Arts

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Artistic creation has traditionally been thought to be a uniquely human ability. Recent advancements in artificial intelligence (AI), however, have enabled algorithms to create art that is nearly indistinguishable from human artwork. Existing research suggests that people have a bias against AI artwork but cannot accurately identify it in blind comparisons. The current study extends this investigation to examine the aesthetic judgment factors differentiating human and machine art. Results indicate that people are unable to accurately identify artwork source but prefer human art and experience more positive emotions in response to human artwork. The aesthetic judgment factors differentiating human-and machine-generated art were all related to positive emotionality. This finding has several implications for this research area and limitation and avenues for future research are discussed.

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Section: Limitations and Future Directionsmentioning

confidence: 99%