What kind of impacts can artwork have on viewers? Establishing a taxonomy for aesthetic impacts.

Szczegóły
Abstrakt

Tytuł:: What kind of impacts can artwork have on viewers? Establishing a taxonomy for aesthetic impacts.
Autorzy:: Christensen AP; Penn Center for Neuroaesthetics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.; Psychology and Human Development, Peabody College, Vanderbilt University, Nashville, Tennessee, USA.
Cardillo ER; Penn Center for Neuroaesthetics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Chatterjee A; Penn Center for Neuroaesthetics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Źródło:: British journal of psychology (London, England : 1953) [Br J Psychol] 2023 May; Vol. 114 (2), pp. 335-351. Date of Electronic Publication: 2022 Dec 14.
Typ publikacji:: Journal Article
Język:: English
Imprint Name(s):: Publication: 2011- : West Sussex, England : Wiley-Blackwell
Original Publication: London ; New York : Cambridge University Press, [1953]-
MeSH Terms:: Art*
Neurosciences*
Humans ; Emotions/physiology ; Esthetics ; Psychometrics
References:: Asparouhov, T., & Muthén, B. (2009). Exploratory structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 16(3), 397-438. https://doi.org/10.1080/10705510903008204.
Baumberger, C. (2013). Art and understanding: In defence of aesthetic cognitivism. PhilPapers. https://philpapers.org/rec/BAUAAU-2.
Berlyne, D. E. (1960). The new experimental aesthetics. In D. E. Berlyne (Ed.), Studies in the new experimental aesthetics (pp. 1-25). Hemisphere Publication Services.
Berlyne, D. E. (1978). Curiosity and learning. Motivation and Emotion, 2, 97-175. https://doi.org/10.1007/BF00993037.
Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008.
Burke, E. (1958). In J. T. Boulton (Ed.), A philosophical enquiry into the origin of our ideas of the sublime and beautiful. Routledge & Kegan Paul. (Original work published 1759). https://doi.org/10.7312/burk90112.
Chatterjee, A. (2014). The aesthetic brain: How we evolved to desire beauty and enjoy art. Oxford University Press.
Chatterjee, A., Widick, P., Sternschein, R., Smith, W. B., II, & Bromberger, B. (2010). The assessment of art attributes. Empirical Studies of the Arts, 28(2), 207-222. https://doi.org/10.2190/EM.28.2.f.
Christensen, A. P. (2018). NetworkToolbox: Methods and measures for brain, cognitive, and psychometric network analysis in R. The R Journal, 10(2), 422-439. https://doi.org/10.32614/RJ-2018-065.
Christensen, A. P., Cardillo, E. R., & Chatterjee, A. (2022). Can art promote understanding? A review of the psychology and neuroscience of aesthetic cognitivism. PsyArXiv. https://doi.org/10.31234/osf.io/t8c47.
Christensen, A. P., Garrido, L. E., & Golino, H. (2021). Comparing community detection algorithms in psychological data: A Monte Carlo simulation. PsyArXiv. https://doi.org/10.31234/osf.io/hz89e.
Christensen, A. P., Garrido, L. E., & Golino, H. (2022). Unique variable analysis: A network psychometrics method to detect local dependence. PsyArXiv. https://doi.org/10.31234/osf.io/4kra2.
Christensen, A. P., & Golino, H. (2021). On the equivalency of factor and network loadings. Behavior Research Methods, 53(4), 1563-1580. https://doi.org/10.3758/s13428-020-01500-6.
Christensen, A. P., & Kenett, Y. N. (2021). Semantic network analysis (SemNA): A tutorial on preprocessing, estimating, and analyzing semantic networks. Psychological Methods. Advance online publication. https://doi.org/10.1037/met0000463.
Cotter, K. N., Chen, D. F., Christensen, A. P., Kim, K. Y., & Silvia, P. J. (2021). Measuring art knowledge: Item response theory and differential item functioning analysis of the aesthetic fluency scale. Psychology of Aesthetics, Creativity, and the Arts. Advance online publication. https://doi.org/10.1037/aca0000397.
Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695(5), 1-9.
Epskamp, S., & Fried, E. I. (2018). A tutorial on regularized partial correlation networks. Psychological Methods, 23, 617-634. https://doi.org/10.1037/met0000167.
Fayn, K., Silvia, P. J., Erbas, Y., Tiliopoulos, N., & Kuppens, P. (2018). Nuanced aesthetic emotions: Emotion differentiation is related to knowledge of the arts and curiosity. Cognition and Emotion, 32(3), 593-599. https://doi.org/10.1080/02699931.2017.1322554.
Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486(3-5), 75-174. https://doi.org/10.1016/j.physrep.2009.11.002.
Friedman, J., Hastie, T., & Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9, 432-441. https://doi.org/10.1093/biostatistics/kxm045.
Golino, H., & Christensen, A. P. (2022). EGAnet: Exploratory graph analysis - A framework for estimating the number of dimensions in multivariate data using network psychometrics. https://CRAN.R-project.org/package=EGAnet.
Golino, H., Christensen, A. P., Moulder, R., Kim, S., & Boker, S. M. (2021). Modeling Latent Topics in Social Media using Dynamic Exploratory Graph Analysis: The Case of the Right-wing and Left-wing Trolls in the 2016 US Elections. Psychometrika, 87(1), 156-187. https://doi.org/10.1007/s11336-021-09820-y.
Golino, H., & Epskamp, S. (2017). Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research. PLoS One, 12(6), e0174035. https://doi.org/10.1371/journal.pone.0174035.
Golino, H., Shi, D., Christensen, A. P., Garrido, L. E., Nieto, M. D., Sadana, R., Thiyagarajan, J. A., & Martinez-Molina, A. (2020). Investigating the performance of exploratory graph analysis and traditional techniques to identify the number of latent factors: A simulation and tutorial. Psychological Methods, 25(3), 292-320. https://doi.org/10.1037/met0000255.
Goodman, N. (1968). Languages of art: An approach to a theory of symbols. Bobbs-Merril.
Hayn-Leichsenring, G. U., Kenett, Y. N., Schulz, K., & Chatterjee, A. (2020). Abstract art paintings, global image properties, and verbal descriptions: An empirical and computational investigation. Acta Psychologica, 202, 102936. https://doi.org/10.1016/j.actpsy.2019.102936.
Hidi, S. E., & Renninger, K. A. (2020). On educating, curiosity, and interest development. Current Opinion in Behavioral Sciences, 35, 99-103. https://doi.org/10.1016/j.cobeha.2020.08.002.
Jiménez, M., Abad, F. J., Garcia-Garzon, E., Golino, H., Christensen, A. P., & Garrido, L. E. (2022). Dimensionality assessment in generalized bi-factor structures: A network psychometrics approach. PsyArXiv. https://doi.org/10.31234/osf.io/2ujdk.
Jones, P. J., Mair, P., & McNally, R. J. (2018). Visualizing psychological networks: A tutorial in R. Frontiers in Psychology, 9, 1742. https://doi.org/10.3389/fpsyg.2018.01742.
Kant, I. (1986). Critique of judgment (W. S. Pluhar Trans.). Hackett Publishing. (Original work published 1790).
Kenett, Y. N., Kenett, D. Y., Ben-Jacob, E., & Faust, M. (2011). Global and local features of semantic networks: Evidence from the Hebrew mental lexicon. PLoS One, 6(8), e23912. https://doi.org/10.1371/journal.pone.0023912.
Lancichinetti, A., & Fortunato, S. (2012). Consensus clustering in complex networks. Scientific Reports, 2(1), 1-7. https://doi.org/10.1038/srep00336.
Leder, H., Belke, B., Oeberst, A., & Augustin, D. (2004). A model of aesthetic appreciation and aesthetic judgments. British Journal of Psychology, 95(4), 489-508. https://doi.org/10.1348/0007126042369811.
Leising, D., Burger, J., Zimmermann, J., Bäckström, M., Oltmanns, J. R., & Connelly, B. S. (2020). Why do items correlate with one another? A conceptual analysis with relevance for general factors and network models. PsyArXiv. https://doi.org/10.31234/osf.io/7c895.
Lyssenko, N., Redies, C., & Hayn-Leichsenring, G. U. (2016). Evaluating abstract art: Relation between term usage, subjective ratings, image properties and personality traits. Frontiers in Psychology, 7, 973. https://doi.org/10.3389/fpsyg.2016.00973.
Massara, G. P., Di Matteo, T., & Aste, T. (2017). Network filtering for big data: Triangulated maximally filtered graph. Journal of Complex Networks, 5(2), 161-178. https://doi.org/10.1093/comnet/cnw015.
Menninghaus, W., Wagner, V., Wassiliwizky, E., Schindler, I., Hanich, J., Jacobsen, T., & Koelsch, S. (2019). What are aesthetic emotions? Psychological Review, 126(2), 171-195. https://doi.org/10.1037/rev0000135.
Muth, C., Hesslinger, V. M., & Carbon, C. C. (2015). The appeal of challenge in the perception of art: How ambiguity, and the opportunity for insight affect appreciation. Psychology of Aesthetics, Creativity, and the Arts, 9(3), 206-216. https://doi.org/10.1037/a0038814.
Muth, C., Raab, M. H., & Carbon, C.-C. (2017). Expecting the unexpected: How gallery visitors experience semantic instability in art. Art & Perception, 5(2), 121-142. https://doi.org/10.1163/22134913-00002062.
Newman, M. E. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 103(23), 8577-8582. https://doi.org/10.1073/pnas.0601602103.
Pelowski, M. (2015). Tears and transformation: Feeling like crying as an indicator of insightful or “aesthetic” experience with art. Frontiers in Psychology, 6, 1006. https://doi.org/10.3389/fpsyg.2015.01006.
Pelowski, M., Hur, Y.-J., Cotter, K. N., Ishizu, T., Christensen, A. P., Leder, H., & McManus, I. C. (2021). Quantifying the if, the when, and the what of the sublime: A survey and latent class analysis of incidence, emotions, and distinct varieties of personal sublime experiences. Psychology of Aesthetics, Creativity, and the Arts, 15(2), 216-240. https://doi.org/10.1037/aca0000273.
Pelowski, M., Markey, P. S., Forster, M., Gerger, G., & Leder, H. (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.
Pelowski, M., Specker, E., Gerger, G., Leder, H., & Weingarden, L. S. (2020). Do you feel like I do? A study of spontaneous and deliberate emotion sharing and understanding between artists and perceivers of installation art. Psychology of Aesthetics, Creativity, and the Arts, 14(3), 276-293. https://doi.org/10.1037/aca0000201.
R Core Team. (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Retrieved from https://www.R-project.org/.
Renninger, K. A., & Hidi, S. E. (2020). To level the playing field, develop interest. Policy Insights From the Behavioral and Brain Sciences, 7(1), 10-18. https://doi.org/10.1177/2372732219864705.
Rodriguez-Boerwinkle, R. M., Fekete, A., Silvia, P. J., & Cotter, K. N. (2021). The art of feeling different: Measuring and exploring the diversity of emotions experienced during a museum visit. PsyArXiv. https://doi.org/10.31234/osf.io/f7g5a.
Schindler, I., Hosoya, G., Menninghaus, W., Beermann, U., Wagner, V., Eid, M., & Scherer, K. R. (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.
Schloerke, B., Cook, D., Larmarange, J., Briatte, F., Marbach, M., Thoen, E., Elberg, A., & Crowley, J. (2021). GGally: Extension to ‘ggplot2’. https://CRAN.R-project.org/package=GGally.
Silvia, P. J. (2010). Confusion and interest: The role of knowledge emotions in aesthetic experience. Psychology of Aesthetics, Creativity, and the Arts, 4(2), 75-80. https://doi.org/10.1037/a0017081.
Skov, M., & Nadal, M. (2020). There are no aesthetic emotions: Comment on Menninghaus et al. (2019). Psychological Review, 127(4), 640-649. https://doi.org/10.1037/rev0000187.
Song, W. M., Di Matteo, T., & Aste, T. (2012). Hierarchical information clustering by means of topologically embedded graphs. PLoS One, 7(3), e31929. https://doi.org/10.1371/journal.pone.0031929.
Specker, E., Forster, M., Brinkmann, H., Boddy, J., Immelmann, B., Goller, J., Pelowski, M., Rosenberg, R., & Leder, H. (2020). Warm, lively, rough? Assessing agreement on aesthetic effects of artworks. PLoS One, 15, e0232083. https://doi.org/10.1371/journal.pone.0232083.
Specker, E., Forster, M., Brinkmann, H., Boddy, J., Pelowski, M., Rosenberg, R., & Leder, H. (2020). The Vienna art interest and art knowledge questionnaire (VAIAK): A unified and validated measure of art interest and art knowledge. Psychology of Aesthetics, Creativity, and the Arts, 14(2), 172-185. https://doi.org/10.1037/aca0000205.
Specker, E., Leder, H., Rosenberg, R., Hegelmaier, L. M., Brinkmann, H., Mikuni, J., & Kawabata, H. (2018). The universal and automatic association between brightness and positivity. Acta Psychologica, 186, 47-53. https://doi.org/10.1016/j.actpsy.2018.04.007.
Specker, E., Leder, H., & Zwaan, R. (2018). Looking on the bright side: Replicating the association between brightness and positivity. Collabra: Psychology, 4(1), 34. https://doi.org/10.1525/collabra.168.
Stolnitz, J. (1992). On the cognitive triviality of art. The British Journal of Aesthetics, 32(3), 191-200. https://doi.org/10.1093/bjaesthetics/32.3.191.
Trainer, L., Steele-Inama, M., & Christopher, A. (2012). Uncovering visitor identity: A citywide utilization of the Falk visitor-identity model. Journal of Museum Education, 37(1), 101-114. https://doi.org/10.1080/10598650.2012.11510722.
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.
Vogl, E., Pekrun, R., Murayama, K., & Loderer, K. (2020). Surprised-curious-confused: Epistemic emotions and knowledge exploration. Emotion, 20(4), 625-641. https://doi.org/10.1037/emo0000578.
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.
Wickham, H. (2016). ggplot2: Elegant graphics for data analysis. Springer-Verlag. https://ggplot2.tidyverse.org.
Zhang, B., & Horvath, S. (2005). A general framework for weighted gene co-expression network analysis. Statistical Applications in Genetics and Molecular Biology, 4(1), 17. https://doi.org/10.2202/1544-6115.1128.
Grant Information:: John Templeton Foundation; Templeton Religion Trust
Contributed Indexing:: Keywords: aesthetic cognitivism; affect; cognition; semantic network
Entry Date(s):: Date Created: 20221215 Date Completed: 20230411 Latest Revision: 20230411
Update Code:: 20240105
DOI:: 10.1111/bjop.12623
PMID:: 36519205
: Czasopismo naukowe

Full Text Finder

What kinds of impacts can visual art have on a viewer? To identify potential art impacts, we recruited five aesthetics experts from different academic disciplines: art history, neuroscience, philosophy, psychology and theology. Together, the group curated a set of terms that corresponded to descriptive features (124 terms) and cognitive-affective impacts (69 terms) of artworks. Using these terms as prompts, participants (n = 899) were given one minute to generate words for each term related to how an artwork looked (descriptive features) or made them think or feel (cognitive-affective impacts). Using network psychometric approaches, we identified terms that were semantically similar based on participants' responses and applied hierarchical exploratory graph analysis to map the relationships between the terms. Our analyses identified 17 descriptive dimensions, which could be further reduced to 5, and 11 impact dimensions, which could be further reduced to 4. The resulting taxonomy demonstrated overlap between the descriptive and impact networks as well as consistency with empirical evidence. This taxonomy could serve as the foundation to empirically evaluate art's impacts on viewers.
(© 2022 The British Psychological Society.)

Informacja