Artificial Intelligence
Cognitive Science
Aesthetic preference
Art Appreciation
Abstract Art Artificial Intelligence
Cognitive Science
Aesthetic preference
Art Appreciation,
Abstract Art.

How to Cite



We conducted an experiment to explore the effect on aesthetic judgments influenced by the presence and awareness of the title of the abstract paintings produced by Artificial Intelligence. Fifty-two participants (52 students from the Faculty of Fine Arts) were randomly signed into control and experimental groups. Participants of the control group were asked to rate five abstract paintings created by various artists, while the experimental group also rated the same paintings only differing in the names of the author that they were made by Artificial Intelligence. Consequently, in our research, we adopted Berlyne's psychobiological theory, which focuses on the role of arousal as one of the primary determinants of aesthetic preference. The results suggest that the name of AI on title can function as a novelty and surprising reference to denote performance for our visual arts perception despite the fact that it is not created by AI. However, “complexity,” “interestingness,” and “ambiguity” variables didn’t show any statistic significant. These findings extend past research by demonstrating that title presentation affects the perception of abstract art by the participants.



ALVAREZ, S., WINNER, E., HAWLEY-DOLAN, A. and SNAPPER, L. (2015). What Gaze Fixation and Pupil Dilation Can Tell Us About Perceived Differences Between Abstract Art by Artists Versus by Children and Animals. Perception, 44(11), pp.1310-1331.

BELKE, B., LEDER, H., STROBACH, T. and CARBON, C.C., (2010). Cognitive fluency: High-level processing dynamics in art appreciation. Psychology of Aesthetics, Creativity, and the Arts, 4(4), p.214.

BERLYNE, D. (1960). Conflict, arousal, and curiosity. New York, NY: McGraw-Hill.

BERLYNE, D.E., (1967). Arousal and reinforcement. In: D. Levine, (ed.), Nebraska symposium on motivation. Lincoln, NE: University of Nebraska Press.

BERLYNE, D. (1971). Aesthetics and psychobiology. New York: Appleton-Century-Crofts.

BERLYNE, D.E. (ed.), (1974). Studies in the new experimental aesthetics. Washington: Hemisphere.

BERLYNE, D.E. and OGILVIE, J.C., (1974). Dimensions of perception of paintings. Studies in the new experimental aesthetics, pp.181-226.

BUBIĆ, A., SUŠAC, A. and PALMOVIĆ, M., (2016). Observing individuals viewing art: The effects of titles on viewers’ eye-movement profiles. Empirical Studies of the Arts, 35(2), pp.194-213.

CHAMBERLAIN, R., MULLIN, C., SCHEERLINCK, B. and WAGEMANS, J. (2018). Putting the art in artificial: Aesthetic responses to computer-generated art. Psychology of Aesthetics, Creativity, and the Arts, 12(2), pp.177-192.

CHMIEL, A. and SCHUBERT, E., (2017). Back to the inverted-U for music preference: A review of the literature. Psychology of Music, 45(6), pp.886-909.

CLEEREMANS, A., GINSBURGH, V., KLEIN, O. and NOURY, A., (2016). What’s in a name? The effect of an artist’s name on aesthetic judgments. Empirical Studies of the Arts, 34(1), pp.126-139.

COECKELBERGH, M., (2016). Can Machines Create Art?. Philosophy & Technology, 30(3), pp.285-303.

COHN, G., (2018). AI Art at Christie’s Sells for $432,500. [online] Nytimes.com. Available at: https://www.nytimes.com/2018/10/25/arts/design/ai-art-sold-christies.html [Accessed 2 Nov. 2018].

COLTON, S., (2012). The painting fool: Stories from building an automated painter. In Computers and creativity (pp. 3-38). Springer, Berlin, Heidelberg.

COLTON, S., HALSKOV, J., VENTURA, D., GOULDSTONE, I., COOK, M. and FERRER, B.P., (2015). The Painting Fool Sees! New Projects with the Automated Painter. ICCC (pp. 189-196).

CUPCHIK, G.C., (1974). An experimental investigation of perceptual and stylistic dimensions of paintings suggested by art history. Studies in the new experimental aesthetics, pp.235-257.

CUPCHIK, G.C., (1986). A decade after Berlyne: New directions in experimental aesthetics. Poetics, 15(4-6), pp.345-369.

EISNER, E., (2002). The arts and the creation of mind. New Haven: Yale University Press.

ELGAMMAL, A. and SALEH, B., (2015). Quantifying creativity in art networks. arXiv preprint arXiv:1506.00711.

ELGAMMAL, A., Liu, B., ELHOSEINY, M. and MAZZONE, M., (2017). CAN: Creative adversarial networks, generating" art" by learning about styles and deviating from style norms. arXiv preprint arXiv:1706.07068.

FINSTAD, K., (2010). Response interpolation and scale sensitivity: Evidence against 5-point scales. Journal of Usability Studies, 5(3), pp.104-110.

FISHER, J., (1984). Entitling. Critical Inquiry, 11(2), pp.286-298.

FOSSA, F., (2017). Creativity and the Machine. How Technology Reshapes Language. Odradek Studies in Philosophy of Literature, Aesthetics, and New Media, 3(1-2), pp. 177-213,

GARSON, G.D., (2012). Testing statistical assumptions. Asheboro, NC: Statistical Associates Publishing.

GATYS, L.A., ECKER, A.S. and BETHGE, M., (2016). Image style transfer using convolutional neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2414-2423).

GERGER, G. and LEDER, H., (2015). Titles change the esthetic appreciations of paintings. Frontiers in Human Neuroscience, 9, p.464.

GOODFELLOW, I., POUGET-ABADIE, J., MIRZA, M., Xu, B., WARDE-FARLEY, D., OZAIR, S., COURVILLE, A. and BENGIO, Y., (2014). Generative adversarial nets. In Advances in neural information processing systems (pp. 2672-2680).

GRAMPP, W.D., (1989). Pricing the priceless: art, artists, and economics. New York: Basic Books.

HAIR, J.F., BLACK, W.C., BABIN, B.J., ANDERSON, R.E. and TATHAM, R.L., (1998). Multivariate data analysis. NJ: New Jersey: Prentice hall.

HAWLEY-DOLAN, A. and WINNER, E., (2011). Seeing the mind behind the art: People can distinguish abstract expressionist paintings from highly similar paintings by children, chimps, monkeys, and elephants. Psychological Science, 22(4), pp.435-441.

HERTZMANN, A., (2018). Can Computers Create Art?. Arts, 7(2), p.18.

HUSSAIN, F., 1(965). Quelques problèmes d’esthétique expérimentale. Sciencias de l’Art, 2, pp.103-114.

ISRAFILZADE, K. and PILELIENĖ, L., (2018). Can machines paint?. 5th International Multidisciplinary Scientific Conference on Social Sciences and Arts SGEM 2018, 18(6.3), pp. 109–116.

JACOBSEN, T., (2006). Bridging the arts and sciences: A framework for the psychology of aesthetics. pp.155-162.

KAKOUDAKI, D., (2014). Anatomy of a robot: Literature, cinema, and the cultural work of artificial people. New Brunswick, NJ: Rutgers University Press.

KIRK, U., SKOV, M., HULME, O., CHRISTENSEN, M.S. and ZEKI, S., (2009). Modulation of aesthetic value by semantic context: An fMRI study. Neuroimage, 44(3), pp.1125-1132.

LEDER, H., BELKE, B., OEBERST, A. and AUGUSTIN, D., (2004). A model of aesthetic appreciation and aesthetic judgments. British journal of psychology, 95(4), pp.489-508.

LEDER, H., CARBON, C.C. and RIPSAS, A.L., (2006). Entitling art: Influence of title information on understanding and appreciation of paintings. Acta psychologica, 121(2), pp.176-198.

LEVINSON, J., (1985). Titles. The Journal of Aesthetics and Art Criticism, 44(1), pp.29-39.

LÉVY, C.M., MACRAE, A. and KÖSTER, E.P., (2006). Perceived stimulus complexity and food preference development. Acta psychologica, 123(3), pp.394-413.

MARTINDALE, C. and MOORE, K., (1989). Relationship of musical preference to collative, ecological, and psychophysical variables. Music Perception: An Interdisciplinary Journal, 6(4), pp.431-445.

MARTINDALE, C., MOORE, K. and BORKUM, J., (1990). Aesthetic preference: Anomalous findings for Berlyne's psychobiological theory. The American Journal of Psychology, pp.53-80.

MULLENNIX, J.W. and ROBINET, J., (2018). Art expertise and the processing of titled abstract art. Perception, 47(4), pp.359-378.

NISSEL, J., HAWLEY-DOLAN, A. and WINNER, E. (2015). Can Young Children Distinguish Abstract Expressionist Art From Superficially Similar Works by Preschoolers and Animals?. Journal of Cognition and Development, 17(1), pp.18-29.

NOLL, A.M., (1966). Human or machine: A subjective comparison of Piet Mondrian’s “Composition with Lines” (1917) and a computer-generated picture. The psychological record, 16(1), pp.1-10.

NUNEZ, G.A., (2019). Between Utopia and Dystopia: Contemporary Art and Its Conflicting Representations of Scientific Knowledge. Handbook of Popular Culture and Biomedicine (pp. 245-258). Springer, Cham.

OBVIOUS ART, (2018). Edmond de Belamy - Obvious Art. [online] Available at: https://obvious-art.com/edmond-de-belamy.html [Accessed 21 Feb. 2019].

PALMER, S.E., SCHLOSS, K.B. and SAMMARTINO, J., (2013). Visual aesthetics and human preference. Annual review of psychology, 64, pp.77-107.

PAN, Y. (2016). Heading toward Artificial Intelligence 2.0. Engineering, 2(4), pp.409-413.

PELOWSKI, M., MARKEY, P.S., LAURING, J.O. and LEDER, H., (2016). Visualizing the impact of art: An update and comparison of current psychological models of art experience. Frontiers in human neuroscience, 10, p.160.

RUSSELL, P.A. and MILNE, S., (1997). Meaningfulness and hedonic value of paintings: Effects of titles. Empirical Studies of the Arts, 15(1), pp.61-73.

SCHWAB, K. (2017). The fourth industrial revolution. New York: Crown Business.

SHAMIR, L., NISSEL, J. and WINNER, E. (2016). Distinguishing between Abstract Art by Artists vs. Children and Animals. ACM Transactions on Applied Perception, 13(3), pp.1-17.

SILVIA, P.J., (2005). Emotional responses to art: From collation and arousal to cognition and emotion. Review of general psychology, 9(4), pp.342-357.

STEENKAMP, J.B.E. and GEYSKENS, I., (2006). How country characteristics affect the perceived value of web sites. Journal of marketing, 70(3), pp.136-150.

STEINERT, S., (2016). Art: Brought to You by Creative Machines. Philosophy & Technology, 30(3), pp.267-284.

STOCK, M., (2019). Ai-Da, the humanoid robot artist, gears up for first solo exhibition. [online] U.S. Available at: https://www.reuters.com/article/us-tech-robot-artist/ai-da-the-humanoid-robot-artist-gears-up-for-first-solo-exhibition-idUSKCN1T6215 [Accessed 21 Jul. 2019].

VARNEDOE, K., (2006). Pictures of nothing: abstract art since Pollock. Princeton, NJ: Princeton University Press.

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