The Influence of Creator Information on Preference for Artificial Intelligence- and Human-generated Artworks
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Purpose: Researchers have shown that aesthetic judgments of artworks depend on contexts, such as the authenticity of an artwork (Newman & Bloom, 2011) and an artwork’s location of display (Kirk et al., 2009; Silveira et al., 2015). The present study aims to examine whether contextual information related to the creator, such as whether an artwork was created by a human or artificial intelligence (AI), influences viewers’ preference judgments of an artwork. Methods: Images of Impressionist landscape paintings were selected as human-made artworks. AI-made artwork stimuli were created using Google’s Deep Dream Generator by mimicking the Impressionist style via deep learning algorithms. Participants performed a preference rating task on each of the 108 artwork stimuli accompanied by one of the two creator labels. After this task, an art experience questionnaire (AEQ) was given to participants to examine whether individual differences in art experience influence their preference judgments. Results: Setting AEQ scores as a covariate in a two-way ANCOVA analysis, the stimuli with the human-made context were preferred over the stimuli with the AI-made context. Regarding the types of stimuli, the viewers preferred AI-made stimuli to human-made stimuli. There was no interaction effect between the two factors. Conclusion: These results suggest that preferences for visual artworks are influenced by the contextual information of the creator when the individual differences in art experience are controlled.
Abstract
Purpose: Researchers have shown that aesthetic judgments of artworks depend on contexts, such as the authenticity of an artwork (Newman & Bloom, 2011) and an artwork’s location of display (Kirk et al., 2009; Silveira et al., 2015). The present study aims to examine whether contextual information related to the creator, such as whether an artwork was created by a human or artificial intelligence (AI), influences viewers’ preference judgments of an artwork. Methods: Images of Impressionist landscape paintings were selected as human-made artworks. AI-made artwork stimuli were created using Google’s Deep Dream Generator by mimicking the Impressionist style via deep learning algorithms. Participants performed a preference rating task on each of the 108 artwork stimuli accompanied by one of the two creator labels. After this task, an art experience questionnaire (AEQ) was given to participants to examine whether individual differences in art experience influence their preference judgments. Results: Setting AEQ scores as a covariate in a two-way ANCOVA analysis, the stimuli with the human-made context were preferred over the stimuli with the AI-made context. Regarding the types of stimuli, the viewers preferred AI-made stimuli to human-made stimuli. There was no interaction effect between the two factors. Conclusion: These results suggest that preferences for visual artworks are influenced by the contextual information of the creator when the individual differences in art experience are controlled.
참고문헌 (34)
- Brainard, D. H, "The psychophysics toolbox", Spatial Vision, 10(4), pp.433~436 (1997)
- Belke, B, "When challenging art gets liked: Evidences for a dual preference formation process for fluent and non-fluent portraits", PloS one, 10(8), pp.e0131796~ (2015)
- Berlyne, D. E, "Novelty, complexity, and hedonic value", Perception & Psychophysics, 8(5-A), pp.279~286 (1970)
- Bernberg, R. E, "Prestige suggestion in art as communication", The Journal of Social Psychology, 38(1), pp.23~30 (1953)
- Boselie, F, "Against prototypicality as a central concept in aesthetics", Empirical Studies of the Arts, 9(1), pp.65~73 (1991)
- Chatterjee A, "The assessment of art attributes", Empirical Studies of Arts, 28(2), pp.207~222 (2010)
- Dearden, P, "Factors influencing landscape preferences : An empirical investigation", Landscape Planning, 11(4), pp.293~306 (1984)
- Elgammal, A, "Can: Creative adversarial networks, generating “art” by learning about styles and deviating from style norms", arXiv Preprint arXiv:1706.07068, ~ (2017)
- Faul, F, "G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences", Behavior Research Methods, 39(2), pp.175~191 (2007)
- Feist, G. J, "Openness to experience, non-conformity, and the preference for abstract art", Empirical Studies of the Arts, 22(1), pp.77~89 (2004)
- Flexas, A, "Cognitive control and unusual decisions about beauty : An fMRI study", Frontiers in Human Neuroscience, 8(520), pp.1~9 (2014)
- Gangadharbatla, H, "The role of ai attribution knowledge in the evaluation of artwork", Empirical Studies of the Arts, 40(2), pp.125~142 (2021)
- Gergen, K. J, "Two forms of inference and problems in the assessment of creativity", Proceedings of the Annual Convention of the American Psychological Association, pp.215~216 (1965)
- Hekkert, P, "Complexity and prototypicality as determinants of the appraisal of cubist paintings", British Journal of Psychology, 81(4), pp.483~495 (1990)
- Hong, J. W, "Artificial intelligence, artists, and art: Attitudes toward artwork produced by humans vs. artificial intelligence", ACM Transactions on Multimedia Computing, Communications, and Applications, 15(2s), pp.1~16 (2019)
- Huang, M, "Human cortical activity evoked by the assignment of authenticity when viewing works of art", Frontiers in Human Neuroscience, 5(134), pp.1~9 (2011)
- Isham, E. A, "Effects of youth authorship on the appraisal of paintings", Psychology of Aesthetics, Creativity, and the Arts, 4(4), pp.235~ (2010)
- Jakesch, M, "Finding meaning in art:Preferred levels of ambiguity in art appreciation", Quarterly Journal of Experimental Psychology, 62(11), pp.2105~2112 (2009)
- Kirk, U, "Modulation of aesthetic value by semantic context : An fMRI study", NeuroImage, 44(3), pp.1125~1132 (2009)
- Leder, H, "A model of aesthetic appreciation and aesthetic judgments", British Journal of Psychology, 95(4), pp.489~508 (2004)
- Leder, H, "Entitling art : Influence of title information on understanding and appreciation of paintings", Acta Psychologica, 121(2), pp.176~198 (2006)
- Leder, H, "Ten years of a model of aesthetic appreciation and aesthetic judgments : The aesthetic episode-Developments and challenges in empirical aesthetics", British Journal of Psychology, 105(4), pp.443~446 (2014)
- Lee, K, "Psychometric properties of the HEXACO personality inventory", Multivariate Behavioral Research, 39(2), pp.329~358 (2004)
- Martindale, C, "Priming, prototypicality, and preference", Journal of Experimental Psychology:Human Perception and Performance, 14(4), pp.661~ (1988)
- Mastandrea, S, "Preferences for ancient and modern art museums:Visitor experiences and personality characteristics", Psychology of Aesthetics, Creativity, and the Arts, 3(3), pp.164~173 (2009)
- "Inceptionism: going deeper into neural networks" (2015)[URL]
- Muth, C, "The aesthetic aha:On the pleasure of having insights into Gestalt", Acta Psychologica, 144(1), pp.25~30 (2013)
- Newman, G. E, "Art and authenticity:The importance of originals in judgments of value", Journal of Experimental Psychology: General, 141(3), pp.558~569 (2012)
- Pelli, D. G, "The VideoToolbox software for visual psychophysics", Spatial Vision, 10, pp.437~442 (1997)
- Ragot, M, "Ai-generated vs. human artworks. a perception bias towards artificial intelligence?", Extended abstracts of the 2020 CHI conference on human factors in computing systems, pp.1~10 (2020)
- Silveira, S, "Is it the picture or is itthe frame? An fMRI study on the neurobiology of framing effects", Frontiers in Human Neuroscience, 9, pp.528~ (2015)
- Song, J, "Familiarity and novelty in aesthetic preference: The effects of the properties of the artwork and the beholder", Frontiers in Psychology, 12 (2021)
- Szegedy, C, "Going deeper with convolutions", Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.1~9 (2015)
- 윤요선, "정서적 미술작품에 대한 선호가 성격 유형에 의해 달라지는가?", 감성과학, 19(3), pp.15~26 (2016), 한국감성과학회[DOI]