Progress in Aesthetic Models: From Formal Regularities to Situated Multilevel Valuation

Qi Li (李琪); Chaoyuan Zhang; Qi Li (李琦)

doi:10.62177/chst.v3i3.1701

Authors

Qi Li (李琪) Shanxi University
Chaoyuan Zhang Shanxi University
Qi Li (李琦) Shanxi University

DOI:

https://doi.org/10.62177/chst.v3i3.1701

Keywords:

Aesthetic Experience, Empirical Aesthetics, Neuroaesthetics, Processing Fluency, Predictive Processing, Computational Aesthetics, Aesthetic Valuation, Contextual Aesthetics

Abstract

Aesthetic models have expanded from accounts centered on formal order, prototypicality, complexity, and processing fluency to multilevel explanations involving predictive dynamics, affective valuation, self-related meaning, expertise, cultural context, and computational representation. This structured integrative review synthesizes foundational and recent evidence from empirical aesthetics, cognitive psychology, neuroaesthetics, and computational image assessment. The evidence shows that no single tradition supersedes the others: formal variables constrain perception; recurrent cognitive operations transform perception into appraisal; distributed neural systems support sensation, valuation, emotion, memory, and meaning; and context and individual differences determine which criteria are applied. Computational models have improved large-scale prediction, distributional scoring, and personalization, but predictive performance alone does not identify psychological mechanisms. To connect these levels, the review proposes a Situated Multilevel Aesthetic Model comprising object representation, predictive processing, affective valuation, self-meaning integration, and social-institutional context. The model treats aesthetic outcomes and their uncertainty as time-dependent functions of stimulus, viewer, and situation. Methodological priorities include construct-specific outcomes, factorial context manipulation, time-resolved measurement, multilevel and cross-cultural sampling, preservation of disagreement in datasets, and intervention-based validation of explanations. The resulting framework positions aesthetic modeling as an evidence-integrated enterprise in which psychological theory defines constructs, neuroscience constrains process, computation tests generalization, and humanistic inquiry identifies the institutional conditions of value.

Downloads

Download data is not yet available.

References

Smith, J. D. & Melara, R. J. (1990). Aesthetic preference and syntactic prototypicality in music: 'Tis the gift to be simple. Cognition, 34(3), 279-298. https://doi.org/10.1016/0010-0277(90)90007-7

Reber, R., Schwarz, N., & Winkielman, P. (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

Leder, H., Belke, B., Oeberst, A., et al. (2004). A model of aesthetic appreciation and aesthetic judgments. British Journal of Psychology, 95(4), 489-508. https://doi.org/10.1348/0007126042369811

Hekkert, P., Snelders, D., & Van Wieringen, P. C. W. (2003). ‘Most advanced, yet acceptable’: Typicality and novelty as joint predictors of aesthetic preference in industrial design. British Journal of Psychology, 94(1), 111-124. https://doi.org/10.1348/000712603762842147

Van de Cruys, S. & Wagemans, J. (2011). Putting Reward in Art: A Tentative Prediction Error Account of Visual Art. i-Perception, 2(9), 1035-1062. https://doi.org/10.1068/i0466aap

Chatterjee, A. & Vartanian, O. (2014). Neuroaesthetics. Trends in Cognitive Sciences, 18(7), 370-375. https://doi.org/10.1016/j.tics.2014.03.003

Leder, H. & Nadal, M. (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

Pearce, M. T., Zaidel, D. W., Vartanian, O., et al. (2016). Neuroaesthetics. Perspectives on Psychological Science, 11(2), 265-279. https://doi.org/10.1177/1745691615621274

Pelowski, M., Markey, P. S., Forster, M., et al. (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

Menninghaus, W., Wagner, V., Hanich, J., et al. (2017). The Distancing-Embracing model of the enjoyment of negative emotions in art reception. Behavioral and Brain Sciences, 40, e347. https://doi.org/10.1017/S0140525X17000309

Skov, M. (2019). Aesthetic Appreciation: The View From Neuroimaging. Empirical Studies of the Arts, 37(2), 220-248. https://doi.org/10.1177/0276237419839257

Nadal, M. & Skov, M. (2024). The sensory valuation account of aesthetic experience. Nature Reviews Psychology, 4(1), 49-63. https://doi.org/10.1038/s44159-024-00385-y

Li, R. & Zhang, J. (2020). Review of computational neuroaesthetics: bridging the gap between neuroaesthetics and computer science. Brain Informatics, 7(1), 16. https://doi.org/10.1186/s40708-020-00118-w

Graf, L. K. M. & Landwehr, J. 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

Silvia, P. J. (2005). What Is Interesting? Exploring the Appraisal Structure of Interest. Emotion, 5(1), 89-102. https://doi.org/10.1037/1528-3542.5.1.89

Frascaroli, J., Leder, H., Brattico, E., et al. (2024). Aesthetics and predictive processing: grounds and prospects of a fruitful encounter. Philosophical Transactions of the Royal Society B: Biological Sciences, 379(1895), 20220410. https://doi.org/10.1098/rstb.2022.0410

Muth, C. & Carbon, C. C. (2013). The Aesthetic Aha: On the pleasure of having insights into Gestalt. Acta Psychologica, 144(1), 25-30. https://doi.org/10.1016/j.actpsy.2013.05.001

Carbon, C. C. (2011). Cognitive Mechanisms for Explaining Dynamics of Aesthetic Appreciation. i-Perception, 2(7), 708-719. https://doi.org/10.1068/i0463aap

Sarasso, P., Neppi-Modona, M., Sacco, K., et al. (2020). “Stopping for knowledge”: The sense of beauty in the perception-action cycle. Neuroscience & Biobehavioral Reviews, 118, 723-738. https://doi.org/10.1016/j.neubiorev.2020.09.004

Wassiliwizky, E. & Menninghaus, W. (2021). Why and How Should Cognitive Science Care about Aesthetics? Trends in Cognitive Sciences, 25(6), 437-449. https://doi.org/10.1016/j.tics.2021.03.008

Pelowski, M. (2015). Tears and transformation: feeling like crying as an indicator of insightful or “aesthetic” experience with art. Frontiers in Psychology, 6. https://doi.org/10.3389/fpsyg.2015.01006

Pizzolante, M., Pelowski, M., Demmer, T. R., et al. (2024). Aesthetic experiences and their transformative power: a systematic review. Frontiers in Psychology, 15, 1328449. https://doi.org/10.3389/fpsyg.2024.1328449

Christensen, A. P., Cardillo, E. R., & Chatterjee, A. (2025). Can art promote understanding? A review of the psychology and neuroscience of aesthetic cognitivism. Psychology of Aesthetics, Creativity, and the Arts, 19(1), 1-13. https://doi.org/10.1037/aca0000541

Marković, S. (2012). Components of Aesthetic Experience: Aesthetic Fascination, Aesthetic Appraisal, and Aesthetic Emotion. i-Perception, 3(1), 1-17. https://doi.org/10.1068/i0450aap

Menninghaus, W., Wagner, V., Wassiliwizky, E., et al. (2019). What are aesthetic emotions? Psychological Review, 126(2), 171-195. https://doi.org/10.1037/rev0000135

Schindler, I., Hosoya, G., Menninghaus, W., et al. (2017). Measuring aesthetic emotions: A review of the literature and a new assessment tool. PLOS ONE, 12(6), e0178899. https://doi.org/10.1371/journal.pone.0178899

Kawabata, H. & Zeki, S. (2004). Neural Correlates of Beauty. Journal of Neurophysiology, 91(4), 1699-1705. https://doi.org/10.1152/jn.00696.2003

Cela-Conde, C. J., Marty, G., Maestú, F., et al. (2004). Activation of the prefrontal cortex in the human visual aesthetic perception. Proceedings of the National Academy of Sciences, 101(16), 6321-6325. https://doi.org/10.1073/pnas.0401427101

Jacobsen, T., Schubotz, R. I., Höfel, L., et al. (2006). Brain correlates of aesthetic judgment of beauty. NeuroImage, 29(1), 276-285. https://doi.org/10.1016/j.neuroimage.2005.07.010

Di Dio, C., Macaluso, E., & Rizzolatti, G. (2007). The Golden Beauty: Brain Response to Classical and Renaissance Sculptures. PLoS ONE, 2(11), e1201. https://doi.org/10.1371/journal.pone.0001201

Cupchik, G. C., Vartanian, O., Crawley, A., et al. (2009). Viewing artworks: Contributions of cognitive control and perceptual facilitation to aesthetic experience. Brain and Cognition, 70(1), 84-91. https://doi.org/10.1016/j.bandc.2009.01.003

Vartanian, O. & Skov, M. (2014). Neural correlates of viewing paintings: Evidence from a quantitative meta-analysis of functional magnetic resonance imaging data. Brain and Cognition, 87, 52-56. https://doi.org/10.1016/j.bandc.2014.03.004

Brown, S., Gao, X., Tisdelle, L., et al. (2011). Naturalizing aesthetics: Brain areas for aesthetic appraisal across sensory modalities. NeuroImage, 58(1), 250-258. https://doi.org/10.1016/j.neuroimage.2011.06.012

Vartanian, O., Navarrete, G., Chatterjee, A., et al. (2013). Impact of contour on aesthetic judgments and approach-avoidance decisions in architecture. Proceedings of the National Academy of Sciences, 110(supplement_2), 10446-10453. https://doi.org/10.1073/pnas.1301227110

Vessel, E. A., Starr, G. G., & Rubin, N. (2012). The brain on art: intense aesthetic experience activates the default mode network. Frontiers in Human Neuroscience, 6. https://doi.org/10.3389/fnhum.2012.00066

Vessel, E. A., Starr, G. G., & Rubin, N. (2013). Art reaches within: aesthetic experience, the self and the default mode network. Frontiers in Neuroscience, 7. https://doi.org/10.3389/fnins.2013.00258

Vessel, E. A., Isik, A. I., Belfi, A. M., et al. (2019). The default-mode network represents aesthetic appeal that generalizes across visual domains. Proceedings of the National Academy of Sciences, 116(38), 19155-19164. https://doi.org/10.1073/pnas.1902650116

Skov, M. & Nadal, M. (2020). A Farewell to Art: Aesthetics as a Topic in Psychology and Neuroscience. Perspectives on Psychological Science, 15(3), 630-642. https://doi.org/10.1177/1745691619897963

Mastandrea, S., Fagioli, S., & Biasi, V. (2019). Art and Psychological Well-Being: Linking the Brain to the Aesthetic Emotion. Frontiers in Psychology, 10, 739. https://doi.org/10.3389/fpsyg.2019.00739

Swami, V. (2013). Context matters: Investigating the impact of contextual information on aesthetic appreciation of paintings by Max Ernst and Pablo Picasso. Psychology of Aesthetics, Creativity, and the Arts, 7(3), 285-295. https://doi.org/10.1037/a0030965

Kirk, U. (2008). The Neural Basis of Object-Context Relationships on Aesthetic Judgment. PLoS ONE, 3(11), e3754. https://doi.org/10.1371/journal.pone.0003754

Li, Q. (2025). How Context and Painting Attributes Affect Aesthetic Judgment Across Expertise. Empirical Studies of the Arts, 43(1), 402-423. https://doi.org/10.1177/02762374241262606

Arai, S. & Kawabata, H. (2016). Appreciation Contexts Modulate Aesthetic Evaluation and Perceived Duration of Pictures. Art and Perception, 4(3), 225-239. https://doi.org/10.1163/22134913-00002052

Iosifyan, M. (2021). Theory of Mind Increases Aesthetic Appreciation in Visual Arts. Art & Perception, 9(2), 113-133. https://doi.org/10.1163/22134913-bja10011

Yeh, Y. C. & Peng, Y. Y. (2019). The Influences of Aesthetic Life Experience and Expertise on Aesthetic Judgement and Emotion in Mundane Arts. International Journal of Art & Design Education, 38(2), 492-507. https://doi.org/10.1111/jade.12213

Carbon, C. C. (2020). Ecological Art Experience: How We Can Gain Experimental Control While Preserving Ecologically Valid Settings and Contexts. Frontiers in Psychology, 11, 800. https://doi.org/10.3389/fpsyg.2020.00800

Specker, E., Tinio, P. P. L., & van Elk, M. (2017). Do you see what I see? An investigation of the aesthetic experience in the laboratory and museum. Psychology of Aesthetics, Creativity, and the Arts, 11(3), 265-275. https://doi.org/10.1037/aca0000107

Cox, R. F. A. & van Klaveren, L. M. (2024). The Embodied Experience of Abstract Art: An Exploratory Study. Ecological Psychology, 36(2), 111-122. https://doi.org/10.1080/10407413.2024.2355901

Świątek, A. H., Szcześniak, M. G., Stempień, M., et al. (2024). The mediating effect of the need for cognition between aesthetic experiences and aesthetic competence in art. Scientific Reports, 14(1), 3408. https://doi.org/10.1038/s41598-024-53957-6

Redies, C. (2015). Combining universal beauty and cultural context in a unifying model of visual aesthetic experience. Frontiers in Human Neuroscience, 09. https://doi.org/10.3389/fnhum.2015.00218

Larrain, A. & Haye, A. (2019). Self as an Aesthetic Effect. Frontiers in Psychology, 10, 1433. https://doi.org/10.3389/fpsyg.2019.01433

Murray, N., Marchesotti, L., & Perronnin, F. (2012). AVA: A large-scale database for aesthetic visual analysis. In 2012 IEEE Conference on Computer Vision and Pattern Recognition (pp. 2408-2415). IEEE. https://doi.org/10.1109/CVPR.2012.6247954

Kong, S., Shen, X., Lin, Z., et al. (2016). Photo Aesthetics Ranking Network with Attributes and Content Adaptation. In Computer Vision - ECCV 2016 (pp. 662-679). Springer International Publishing. https://doi.org/10.1007/978-3-319-46448-0_40

Talebi, H. & Milanfar, P. (2018). NIMA: Neural Image Assessment. IEEE Transactions on Image Processing, 27(8), 3998-4011. https://doi.org/10.1109/TIP.2018.2831899

Zeng, H., Cao, Z., Zhang, L., et al. (2020). A Unified Probabilistic Formulation of Image Aesthetic Assessment. IEEE Transactions on Image Processing, 29, 1548-1561. https://doi.org/10.1109/TIP.2019.2941778

He, S., Zhang, Y., Xie, R., et al. (2022). Rethinking Image Aesthetics Assessment: Models, Datasets and Benchmarks. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (pp. 942-948). International Joint Conferences on Artificial Intelligence Organization. https://doi.org/10.24963/ijcai.2022/132

Yang, Y., Xu, L., Li, L., et al. (2022). Personalized Image Aesthetics Assessment with Rich Attributes. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 19829-19837). IEEE. https://doi.org/10.1109/CVPR52688.2022.01924

Brielmann, A. A. & Pelli, D. G. (2018). Aesthetics. Current Biology, 28(16), R859-R863. https://doi.org/10.1016/j.cub.2018.06.004

Li, Z., Yan, X., Wei, X., et al. (2025). IAACLIP: Image Aesthetics Assessment via CLIP. Electronics, 14(7), 1425. https://doi.org/10.3390/electronics14071425