Abstract
We have developed a deep learning-based AI creativity system, which can be used to create computer-generated artworks, in the form of still images as well as time-based pieces (videos). Within the scope of this article, we will briefly describe our system and will then demonstrate its application in a psychological study on aesthetic experiences. We also propose a new hypothesis regarding a potential interaction between the neural architecture of the two visual pathways, and the effect of movement perception on the formation of aesthetic judgments. Specifically, we postulate that perceived movement within the visual scene engages reflexive attention, an attentional focus shift towards the processing of visual changes, and subsequently affects how information are relayed via the dorsal and ventral streams. We outline a recent pilot study in support of our proposed framework, which serves as the first study that investigates the relationship between the two visual streams and aesthetic experiences. Our study demonstrated evidence for our hypothesis, with time-based artworks showing higher aesthetic appeal at slower playback speeds.
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Belke B, Leder H, Carbon CC (2015) When challenging art gets liked: evidences for a dual preference formation process for fluent and non-fluent portraits. PLoS ONE 10(8):e0131796. https://doi.org/10.1371/journal.pone.0131796
Brown S, Gao X, Tisdelle L, Eickhoff SB, Liotti M (2011) Naturalizing aesthetics: brain areas for aesthetic appraisal across sensory modalities. NeuroImage 58:250–358
Desimone R (1998) Visual attention mediated by biased competition in extrastriate visual cortex. Philoso Trans R Soc B Biol Sci 353:1245–1255
DiPaola S, McCaig G, Gabora L (2018) Informing Artificial intelligence generative techniques using cognitive theories of human creativity. Procedia Comput Sci Special Issue: Bio Inspired Cognitive Architectures, 11 pages
DiPaola S (2017) Exploring the cognitive correlates of artistic practice using a parameterized non-photorealistic toolkit. Leonardo 50
DiPaola S, McCaig R (2016) Using artificial intelligence techniques to emulate the creativity of a portrait painter. In: Proceedings of Electronic Visualization and the Arts. British Computer Society, London. 8 pages
DiPaola S (2014) Using a contextual focus model for an automatic creativity algorithm to generate art work. Procedia Comput Sci Spec Issue: Bio Inspired Cogn Architectures 41:212–219
DiPaola S, Gabora L (2009) Incorporating characteristics of human creativity into an evolutionary art algorithm. Genet Program Evolvable Mach J 10(2):97–110
Goodale MA, Milner AD (1992) Separate visual pathways for perception and action. TINS 15:19–25
Haertel M, Carbon CC (2014) Is this a “Fettecke” or just a “greasy corner”? About the capability of laypersons to differentiate between art and non-art via object’s originality. I-Percept 5:602–610
Hopfinger JB, Mangun GR (1998) Reflexive attention modulates processing of visual stimuli in human extrastriate cortex. Psychol Sci 9:441–447
How to speed up/slow down a video (2019). FFMPEG Wiki. Accessed 30 mar 2019
Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R et al (2014) Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the ACM international conference on multimedia, pp 675–678. ACM
Kringelbach ML (2005) The human orbitofrontal cortex: linking reward to hedonic experience. Nature Rev Neurosci 6:691–702
Leder H, Belke B, Oeberst A, Augustin D (2004) A model of aesthetic appreciation and aesthetic judgments. Br J Psychol 95:489–508
Mordvintsev A, Olah C, Tyka M (2015) Online Blog. http://googleresearch.blogspot.ca/2015/06/inceptionism-going-deeper-into-neural.html
Pelowski M, Leder H, Mitschke V, Specker E, Gerger G, Tinio PPL, Vaporova E, Bieg T, Husslein-Arco A (2018) Capturing aesthetic experiences with installation art: an empirical assessment of emotion, evaluations, and mobile eye tracking in Olafur Eliasson’s “Baroque, Baroque!”. Front Psychol 9:1255. https://doi.org/10.3389/fpsyg.2018.01255
Rolls ET (2005) Taste, olfactory, and food texture processing in the processing in the brain, and the control of food intake. Physiol Behav 85:45–56
Rushworth MFS, Behrens TEJ, Rudebeck PH, Walton ME (2008) Contrasting roles for cingulate and orbitofrontal cortex in decisions and social behavior. Trends Cogn Sci 11:168–176
Wallis JD (2007) Orbitofrontal cortex and its contribution to decision-making. Annu Rev Neurosci 30:31–56
Acknowledgements
We acknowledge our colleague Graeme McCaig who was instrumental in creating our modified Deep Dream system and was invaluable in his mentorship of the work. This work was partially supported by SSHRC and NSERC grants respectively.
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Utz, V., DiPaola, S. (2020). Aesthetic Judgments, Movement Perception and the Neural Architecture of the Visual System. In: Samsonovich, A. (eds) Biologically Inspired Cognitive Architectures 2019. BICA 2019. Advances in Intelligent Systems and Computing, vol 948. Springer, Cham. https://doi.org/10.1007/978-3-030-25719-4_70
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