The emergence of dynamic digital arts, such as dynamic generative art, has reshaped how aesthetic experiences can be studied, emphasizing their inherently dynamic and evolving nature. Within the framework of computational aesthetics, which seeks to model and quantify human perceptions of beauty, this study extends the focus from static to dynamic stimuli. We investigate the temporal relationship between evolving image features and aesthetic judgments, exploring how dynamic visual properties influence the progression of aesthetic judgments over time. By introducing temporal offsets in the analysis and employing linear and nonlinear statistical models, we examine continuous aesthetic ratings alongside an extensive set of image features. Our findings offer a novel perspective on the enduring impact of image features on aesthetic experience.
AchantaR.HemamiS.EstradaF.SusstrunkS. (2009). Frequency-tuned salient region detection. 2009 ieee conference on computer vision and pattern recognition (pp. 1597–1604).
2.
AleemH.Correa-HerranI.GrzywaczN. M. (2017). Inferring master painters’ esthetic biases from the statistics of portraits. Frontiers in Human Neuroscience, 11. https://doi.org/10.3389/fnhum.2017.00094
3.
AleemH.GrzywaczN. M. (2023). The temporal instability of aesthetic preferences. Psychology of Aesthetics, Creativity, and the Arts.
4.
al RifaieM. M.UrsynA.ZimmerR.JavidM. A. J. (2017). On symmetry, aesthetics and quantifying symmetrical complexity. In CorreiaJ.CiesielskiV.LiapisA. (Eds.), Computational intelligence in music, sound, art and design (pp. 17–32). Springer International Publishing.
5.
AugustinM.LederH.HutzlerF.CarbonC.-C. (2008). Style follows content: On the microgenesis of art perception. Acta Psychologica, 128(1), 127–138. https://doi.org/10.1016/j.actpsy.2007.11.006
6.
BelfiA. M.VesselE. A.BrielmannA.IsikA. I.ChatterjeeA.LederH.StarrG. G. (2019). Dynamics of aesthetic experience are reflected in the default-mode network. NeuroImage, 188, 584–597. https://doi.org/10.1016/j.neuroimage.2018.12.017
7.
BerlyneD. E. (1970). Novelty, complexity, and hedonic value. Perception & Psychophysics, 8(5), 279–286. https://doi.org/10.3758/BF03212593
8.
BertaminiM.MakinA.RamponeG. (2013). Implicit association of symmetry with positive valence, high arousal and simplicity. i-Perception, 4(5), 317–327. https://doi.org/10.1068/i0601jw
9.
BornsteinR. F.Craver-LemleyC. (2022). Mere exposure effect. In PohlR. F. (Ed.), Cognitive illusions: Intriguing phenomena in thinking, judgment, and memory (3rd ed., pp. 241–258). Routledge/Taylor & Francis Group.
10.
BrachmannA.RediesC. (2016). Using convolutional neural network filters to measure left-right mirror symmetry in images. Symmetry, 8(12), 144. https://doi.org/10.3390/sym8120144
11.
BrachmannA.RediesC. (2017). Computational and experimental approaches to visual aesthetics. Frontiers in Computational Neuroscience, 11. https://doi.org/10.3389/fncom.2017.00102
12.
BradyN.FieldD. J. (2000). Local contrast in natural images: Normalisation and coding efficiency. Perception, 29(9), 1041–1055. https://doi.org/10.1068/p2996
13.
BraunJ.AmirshahiS. A.DenzlerJ.RediesC. (2013). Statistical image properties of print advertisements, visual artworks and images of architecture. Frontiers in Psychology, 4, 808. https://doi.org/10.3389/fpsyg.2013.00808
14.
BrieberD.NadalM.LederH.RosenbergR. (2014). Art in time and space: Context modulates the relation between art experience and viewing time. PLOS ONE, 9(6), 1–8. https://doi.org/10.1371/journal.pone.0099019
BrielmannA. A.ValeL.PelliD. G. (2017). Beauty at a glance: The feeling of beauty and the amplitude of pleasure are independent of stimulus duration. Journal of Vision, 17(14), 9–9. https://doi.org/10.1167/17.14.9
17.
CetinicE.GrgicS. (2013). Automated painter recognition based on image feature extraction. In Proceedings ELMAR-2013 (pp. 19–22). IEEE.
18.
DattaR.JoshiD.LiJ.WangJ. Z. (2006). Studying aesthetics in photographic images using a computational approach. In LeonardisA.BischofH.PinzA. (Eds.), Computer vision – eccv 2006 (pp. 288–301). Springer Berlin Heidelberg.
19.
DavisG. (1998). A wavelet-based analysis of fractal image compression. IEEE Transactions on Image Processing, 7(2), 141–154. https://doi.org/10.1109/83.660992
20.
DingL.HuangH.ZangY. (2017). Image quality assessment using directional anisotropy structure measurement. IEEE Transactions on Image Processing, 26(4), 1799–1809. https://doi.org/10.1109/TIP.2017.2665972
21.
DonderiD. C. (2006). An information theory analysis of visual complexity and dissimilarity. Perception, 35(6), 823–835. https://doi.org/10.1068/p5249
22.
DosilR.PardoX. M.Fdez-VidalX. R.García-DíazA.LeboránV. (2013). A new radial symmetry measure applied to photogrammetry. Pattern Analysis and Applications, 16, 637–646. https://doi.org/10.1007/s10044-012-0281-y
23.
FangY.MaK.WangZ.LinW.FangZ.ZhaiG. (2015). No-reference quality assessment of contrast-distorted images based on natural scene statistics. IEEE Signal Processing Letters, 22(7), 838–842. https://doi.org/10.1109/LSP.2014.2372333
24.
FriedenbergJ.LibyB. (2016). Perceived beauty of random texture patterns: A preference for complexity. Acta Psychologica, 168, 41–49. https://doi.org/10.1016/j.actpsy.2016.04.007
25.
GabardaS.CristóbalG. (2007). Blind image quality assessment through anisotropy. Journal of the Optical Society of America A: Optics and Image Science, 24(12), B42–B51. https://doi.org/10.1364/JOSAA.24.000B42
26.
GalanterP. (2016). Generative art theory. In PaulC. (Ed.), A companion to digital art (pp. 146–180). John Wiley & Sons, Ltd.
27.
GartusA.LederH. (2017). Predicting perceived visual complexity of abstract patterns using computational measures: The influence of mirror symmetry on complexity perception. PLOS ONE, 12(11), 1–29. https://doi.org/10.1371/journal.pone.0185276
28.
GrafL. K. M.LandwehrJ. R. (2015). A dual-process perspective on fluency-based aesthetics. Personality and Social Psychology Review, 19(4), 395–410. https://doi.org/10.1177/1088868315574978
29.
GrahamD. J.FieldD. J. (2008). Statistical regularities of art images and natural scenes: Spectra, sparseness and nonlinearities. Spatial Vision, 21(1–2), 149–164. https://doi.org/10.1163/156856807782753877
30.
GunselB.SarielS.IcogluO. (2005). Content-based access to art paintings. IEEE international conference on image processing 2005 (Vol. 2, pp. II–558).
31.
Hayn-LeichsenringG. U.LehmannT.RediesC. (2017). Subjective ratings of beauty and aesthetics: Correlations with statistical image properties in western oil paintings. i-Perception, 8(3), 2041669517715474. https://doi.org/10.1177/2041669517715474
32.
Hironori TakimotoF. O.KanagawaA. (2021). Image aesthetics assessment based on multi-stream cnn architecture and saliency features. Applied Artificial Intelligence, 35(1), 25–40. https://doi.org/10.1080/08839514.2020.1839197
33.
HoenigF. (2005). Defining computational aesthetics. Proceedings of the first eurographics conference on computational aesthetics in graphics, visualization and imaging (pp. 13–18).
JacobsenT.HöfelL. (2002). Aesthetic judgments of novel graphic patterns: Analyses of individual judgments. Perceptual and Motor Skills, 95(3), 755–766. https://doi.org/10.2466/pms.2002.95.3.755
36.
JahanianA.VishwanathanS.AllebachJ. P. (2015). Learning visual balance from large-scale datasets of aesthetically highly rated images (Vol. 9394). SPIE.
KimuraA.YonetaniR.HirayamaT. (2013). Computational models of human visual attention and their implementations: A survey. IEICE Transactions on Information and Systems, E96.D(3), 562–578. https://doi.org/10.1587/transinf.E96.D.562
LederH.BelkeB.OeberstA.AugustinD. (2004). A model of aesthetic appreciation and aesthetic judgments. British Journal of Psychology, 95(4), 489–508. https://doi.org/10.1348/0007126042369811
41.
LederH.NadalM. (2014). 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), 443–464. https://doi.org/10.1111/bjop.12084
42.
LiQ.LinW.XuJ.FangY. (2016). Blind image quality assessment using statistical structural and luminance features. IEEE Transactions on Multimedia, 18(12), 2457–2469. https://doi.org/10.1109/TMM.2016.2601028
43.
LocherP.KrupinskiE.Mello-ThomsC.NodineC. (2008). Visual interest in pictorial art during an aesthetic experience. Spatial Vision, 21(1–2), 55–77. https://doi.org/10.1163/156856808782713762
44.
ManteV.FrazorR. A.BoninV.GeislerW. S.CarandiniM. (2005). Independence of luminance and contrast in natural scenes and in the early visual system. Nature Neuroscience, 8(12), 1690–1697. https://doi.org/10.1038/nn1556
45.
MayerS.LandwehrJ. R. (2018). Quantifying visual aesthetics based on processing fluency theory: Four algorithmic measures for antecedents of aesthetic preferences. Psychology of Aesthetics, Creativity, and the Arts, 12(4), 399–431. https://doi.org/10.1037/aca0000187
46.
McManusI. C.St ¨overK.KimD. (2011). Arnheim's gestalt theory of visual balance: Examining the compositional structure of art photographs and abstract images. i-Perception, 2(6), 615–647. https://doi.org/10.1068/i0445aap
47.
MengP.MengX.HuR.ZhangL. (2023). Predicting the aesthetics of dynamic generative artwork based on statistical image features: A time-dependent model. PLOS ONE, 18(9), e0291647. https://doi.org/10.1371/journal.pone.0291647
48.
MullinC.Hayn-LeichsenringG.RediesC.WagemansJ. (2017). The gist of beauty: An investigation of aesthetic perception in rapidly presented images. Electronic Imaging, 29(14), 248–248. https://doi.org/10.2352/ISSN.2470-1173.2017.14.HVEI-152
49.
MunarE.NadalM.RossellóJ.FlexasA.MorattiS.MaestúF.Cela- CondeC. J. (2012). Lateral orbitofrontal cortex involvement in initial negative aesthetic impression formation. PLOS ONE, 7(6), 1–7. https://doi.org/10.1371/journal.pone.0038152
50.
NakaK. I.RushtonW. A. H. (1966). S-potentials from colour units in the retina of fish (cyprinidae). The Journal of Physiology, 185(3), 536–555. https://doi.org/10.1113/jphysiol.1966.sp008001
51.
NoguchiY.MurotaM. (2013). Temporal dynamics of neural activity in an integration of visual and contextual information in an esthetic preference task. Neuropsychologia, 51 (6), 1077–1084. https://doi.org/10.1016/j.neuropsychologia.2013.03.003
52.
ObradorP.SaadM. A.SuryanarayanP.OliverN. (2012). Towards category-based aesthetic models of photographs. In SchoeffmannK.MerialdoB.HauptmannA. G.NgoC. W.AndreopoulosY.BreitenederC. (Eds.), Advances in multimedia modeling (pp. 63–76). Springer Berlin Heidelberg.
PelowskiM.MarkeyP. S.ForsterM.GergerG.LederH. (2017). Move me, astonish me… delight my eyes and brain: The Vienna integrated model of top-down and bottom-up processes in art perception (vimap) and corresponding affective, evaluative, and neurophysiological correlates. Physics of Life Reviews, 21, 80–125. https://doi.org/10.1016/j.plrev.2017.02.003
PesaranM. H.ShinY.SmithR. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326. https://doi.org/10.1002/jae.616
57.
R Core Team (2022). R: A language and environment for statistical computing [Computer software manual]. R Foundation for Statistical Computing.
58.
ReberR.SchwarzN.WinkielmanP. (2004). Processing fluency and aesthetic pleasure: Is beauty in the perceiver’s processing experience?Personality and Social Psychology Review, 8(4), 364–382. https://doi.org/10.1207/s15327957pspr0804_3
59.
ReberR.WinkielmanP.SchwarzN. (1998). Effects of perceptual fluency on affective judgments. Psychological Science, 9(1), 45–48. https://doi.org/10.1111/1467-9280.00008
60.
RediesC.AmirshahiS. A.KochM.DenzlerJ. (2012). PHOG-derived aesthetic measures applied to color photographs of artworks, natural scenes and objects. Springer.
61.
RediesC.BrachmannA.WagemansJ. (2017). High entropy of edge orientations characterizes visual artworks from diverse cultural backgrounds. Vision Research, 133, 130–144. https://doi.org/10.1016/j.visres.2017.02.004
62.
RediesC.H ¨anischJ.BlickhanM.DenzlerJ. (2007). Artists portray human faces with the Fourier statistics of complex natural scenes. Network: Computation in Neural Systems, 18(3), 235–248. https://doi.org/10.1080/09548980701574496
63.
RediesC.HasensteinJ.DenzlerJ. (2008). Fractal-like image statistics in visual art: Similarity to natural scenes. Spatial Vision, 21(1–2), 137–148. https://doi.org/10.1163/156856807782753921
ShinY.YuB.Greenwood-NimmoM. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ardl framework. In SicklesR. C.HorraceW. C. (Eds.), Festschrift in honor of peter schmidt: Econometric methods and applications (pp. 281–314). Springer New York.
68.
SilvermanB. W.VassilicosJ. C.FieldD. J. (1999). Wavelets, vision and the statistics of natural scenes. Philosophical transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 357(1760), 2527–2542. https://doi.org/10.1098/rsta.1999.0446
TaylorR.SpeharB.HagerhallC.Van DonkelaarP. (2011). Perceptual and physiological responses to jackson pollock’s fractals. Frontiers in Human Neuroscience, 5. https://doi.org/10.3389/fnhum.2011.00060
72.
TinioP. P.LederH.StrasserM. (2011). Image quality and the aesthetic judgment of photographs: Contrast, sharpness, and grain teased apart and put together. Psychology of Aesthetics, Creativity, and the Arts, 5(2), 165. https://doi.org/10.1037/a0019542
73.
VerhavertS.WagemansJ.AugustinM. D. (2018). Beauty in the blink of an eye: The time course of aesthetic experiences. British Journal of Psychology, 109(1), 63–84. https://doi.org/10.1111/bjop.12258
74.
WillemsS.Van der LindenM. (2006). Mere exposure effect: A consequence of direct and indirect fluency–preference links. Consciousness and Cognition, 15(2), 323–341. https://doi.org/10.1016/j.concog.2005.06.008
75.
YangC.ZhangL.LuH.RuanX.YangM.-H. (2013). Saliency detection via graph- based manifold ranking. 2013 ieee conference on computer vision and pattern recognition (pp. 3166–3173).
ZhouY.TanY.LiG. (2014). Computational aesthetic measurement of photographs based on multi-features with saliency. In Intelligent computing theory (pp. 357–366). Springer International Publishing.
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