What Makes An Art Piece More Enjoyable?
One could argue that how enjoyable an artwork appears to be is entirely subjective to individuals’ personal aesthetic preferences. However, scientific research has identified several features of artworks that can make them more appealing. For instance, Redies et al. (2020) found image properties such as brightness and choice of colour which reflect global image structure and image composition impacts the rating of affective pictures. Similarly, Kotabe et al. (2017) found that natural portraits exhibit significant aesthetic appeal.
Summary of Vessel et al. (2023)
Vessel et al. ’s (2023) research focused on the impact of personal relevance of an artwork’s subject matter on viewer responses. The findings suggest that whether or not someone finds a work of art beautiful and moving has more to do with how well the subject matter resonates with their sense of who they are and their worldview than any particular features of the image itself.
Experiment 1A (relationship between self-relevance and perceived aesthetic value):
In the beginning phase of the study, 33 individuals were presented with 148 artwork images. These images were curated to showcase an array of eras, artistic styles, and genres originating from the Americas, Europe, and Asia. An added criterion for selection was that these paintings had limited reproductions, ensuring a lower likelihood of prior exposure to the participants. An independent group evaluated each painting's naturalness and degree of disorder.
Attendees were asked to assign two scores to every piece of art: one indicating the depth of emotional response it evoked (serving as the aesthetic value in this study) and another for its 'self-relevance'. This term 'self-relevance' was described to participants as the degree to which elements in the artwork resonated with their personal experiences, memories, or the facets of their identity.
The feedback demonstrated diverse perceptions among participants concerning the self-relevance and aesthetic value of the artworks. Interestingly, self-relevance was responsible for approximately 28% of the variation in aesthetic scores, while attributes like naturalness and disorder contributed a mere 8% to this variance. This means, artworks that scored high in self-relevance often correlated with a higher aesthetic appreciation.
Experiment 1B (replication of 1A with a larger sample):
In order to extend their findings, the team then replicated the procedures with a larger sample of 208 participants to view and rate a subset of 42 artworks for beauty, as well as how moving they were, and self-relevance. Consistent with the preliminary study, artworks with stronger personal relevance elicited deeper emotional responses. Additionally, artworks that resonated more personally with participants were often deemed more beautiful. This implies that the deeper a painting's personal resonance, the more it was perceived as aesthetically pleasing.
Experiment 2 (aesthetic appeal of AI-generated arts compared to genuine arts):
In the final experiment, researchers investigated if higher self-relevance directly influenced aesthetic ratings. Using a questionnaire on 45 participants' experiences and identities, the team generated personalized artworks for each individual using a neural network called ‘Style Transfer’ (an AI technique used to generate novel artworks by applying the style of one artwork to a photograph). Participants were allocated into the following experimental groups:
Real Artworks Condition: This condition consisted of 20 real paintings selected from a set used in Experiment 1A, covering various time periods, styles, genres, and cultural origins.
Generated-Control Condition: This condition involved novel artworks generated using the style-transfer algorithm. The style sources for these artworks were selected from online collections of art museums such as the Metropolitan Museum of Art, the National Gallery of Art, and the Rijksmuseum.
Self-Relevant Condition: In this condition, synthetic artworks were created using the style-transfer algorithm, specifically tailored to reflect participant-specific attributes such as autobiographical memories. The style transfer was applied to self-relevant photographs selected for each participant.
Other-Relevant Condition: This condition also involved synthetic artworks generated using the style-transfer algorithm. However, the style transfer was applied to other-relevant photographs, which were not participant-specific.
Results revealed that participants favoured their self-relevant, artificially-generated artworks over others and even slightly preferred them to real paintings. However, genuine artworks (created by human), despite their low self-relevance ratings, were still found to be more aesthetically pleasing overall, suggesting the AI-generated style transfer method missed capturing certain elements intrinsic to real art.
Limitations & Future directions:
The study presents several limitations and future directions worth noting. For instance, The study primarily used visual artworks, it would be valuable to investigate the role of self-relevance in other art forms, such as music or literature. In addition, the study did not explore the neural mechanisms underlying the processing of self-relevant artworks. Hence, future research could delve into this aspect to gain a deeper understanding of the cognitive processes involved in aesthetic perception.
In spite of these areas of limitations, the study remains a pioneering effort in independently examining the impact of psychological self-relevance on aesthetic appreciation, serving as a notable advancement in the interdisciplinary discourse of psychology and art. Its findings open the door for additional research, possibly sparking further explorations into the complex link between individual connection and the way we perceive art.
Kotabe, H. P., Kardan, O., & Berman, M. G. (2017). The nature-disorder paradox: A perceptual study on how nature is disorderly yet aesthetically preferred. Journal of Experimental Psychology: General, 146(8), 1126-1142.
Redies, C., Grebenkina, M., Mohseni, M., Kaduhm, A., & Dobel, C. (2020). Global image properties predict ratings of affective pictures. Frontiers in psychology, 11, 953.
Vessel, E. A., Pasqualette, L., Uran, C., Koldehoff, S., Bignardi, G., & Vinck, M. (2023). Self-Relevance Predicts the Aesthetic Appeal of Real and Synthetic Artworks Generated via Neural Style Transfer. Psychological Science, 34(9), 1007-1023. https://doi.org/10.1177/09567976231188107
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