Aesthetic evaluation of body movements shaped by embodied and arts experience: Insights from behaviour and fNIRS.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
28 10 2024
28 10 2024
Historique:
received:
27
09
2023
accepted:
04
10
2024
medline:
29
10
2024
pubmed:
29
10
2024
entrez:
29
10
2024
Statut:
epublish
Résumé
Aesthetic appreciation of full-body movements is likely shaped by our cumulative bodily experiences, yet most of the extant literature in this domain has focused on expertise and familiarity. We ran two experiments exploring individual differences in embodied experience and experience with the arts: In Study 1, we explored how participants' (n = 41) abilities to learn a choreography shaped their aesthetic perceptions while viewing learned vs. unknown movements, using functional near-infrared spectroscopy (fNIRS) to measure cortical activation over the Action Observation Network (i.e., inferior frontal gyrus [IFG], inferior parietal lobule, middle temporal gyrus [MTG]). Study 1 demonstrated that embodied experience enhanced ratings of enjoyment, familiarity, and reproducibility of movements, and that individual differences in participants' performance of the learned choreography were not associated with aesthetic ratings, but rather cortical activation in IFG and right MTG while viewing learned choreography. In Study 2, we combined the behavioural data from Study 1 with data from additional participants (total n = 141) to examine the relationship between arts experience and aesthetic perceptions of movements robustly. Study 2 revealed that previous arts and sports experience correlated with aesthetic judgements of familiarity and reproducibility of movements. Our findings highlight the relevance of examining individual experiences to fill theoretical gaps in our understanding of action aesthetics.
Identifiants
pubmed: 39468228
doi: 10.1038/s41598-024-75427-9
pii: 10.1038/s41598-024-75427-9
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
25841Informations de copyright
© 2024. The Author(s).
Références
Blacking, J. Movement and meaning: Dance in social anthropological perspective. Dance Res. 1, 89–99 (1983).
doi: 10.2307/1290805
Kirsch, L. P., Dawson, K. & Cross, E. S. Dance experience sculpts aesthetic perception and related brain circuits. Ann. N. Y. Acad. Sci. 1337, 130–139 (2015).
pubmed: 25773627
pmcid: 4402020
doi: 10.1111/nyas.12634
Orgs, G., Caspersen, D. & Haggard, P. You move, I watch, it matters: Aesthetic communication in dance. In Shared Representations 1st edn (eds Obhi, S. S. et al.) 627–653 (Cambridge University Press, 2016).
Vartanian, O. & Chatterjee, A. The aesthetic triad. In Brain, Beauty, and Art: Essays Bringing Neuroaesthetics into Focus 1st edn (eds Chatterjee, A. & Cardilo, E.) 27–30 (Oxford University Press, 2021).
Darda, K. M. & Cross, E. S. The role of expertise and culture in visual art appreciation. Sci. Rep. 12, 10666 (2022).
pubmed: 35739137
pmcid: 9219380
doi: 10.1038/s41598-022-14128-7
Cross, E. S. & Orlandi, A. The aesthetics of action and movement. In The Oxford Handbook of Empirical Aesthetics 1st edn (eds Nadal, M. & Vartanian, O.) 605–622 (Oxford University Press, 2020).
Kirsch, L. P., Urgesi, C. & Cross, E. S. Shaping and reshaping the aesthetic brain: Emerging perspectives on the neurobiology of embodied aesthetics. Neurosci. Biobehav. Rev. 62, 56–68 (2016).
pubmed: 26698020
doi: 10.1016/j.neubiorev.2015.12.005
Kirsch, L. P. & Cross, E. S. The influence of sensorimotor experience on the aesthetic evaluation of dance across the life span. In Progress in Brain Research, Vol. 237 (eds Christensen J. F. & Gomila, A.) 291–316 (Elsevier, 2018).
Wang, Z. Evaluation of Creativity in Contemporary Dance in Terms of Audience Perception. Creat. Res. J. 36, 234 (2022).
doi: 10.1080/10400419.2022.2107849
Cross, E. S., Kirsch, L., Ticini, L. F. & Schütz-Bosbach, S. The impact of aesthetic evaluation and physical ability on dance perception. Front. Hum. Neurosci (2011).
doi: 10.3389/fnhum.2011.00102
pubmed: 21960969
pmcid: 3177045
Orlandi, A., Cross, E. S. & Orgs, G. Timing is everything: Dance aesthetics depend on the complexity of movement kinematics. Cognition 205, 104446 (2020).
pubmed: 32932073
doi: 10.1016/j.cognition.2020.104446
Reber, R., Schwarz, N. & Winkielman, P. Processing fluency and aesthetic pleasure: Is beauty in the perceiver’s processing experience?. Personal. Soc. Psychol. Rev. 8, 364–382 (2004).
doi: 10.1207/s15327957pspr0804_3
Zeki, S. Inner Vision: An Exploration of Art and the Brain (Oxford University Press, 1999).
Orgs, G., Hagura, N. & Haggard, P. Learning to like it: aesthetic perception of choreographic patterns. Cogn. Process. 13, S28–S29 (2013).
Berg, S. C. Le Sacre Du Printemps: Seven Productions from Nijinsky to Martha Graham. https://dokumen.pub/le-sacre-du-printemps-seven-productions-from-nijinsky-to-martha-graham-0835718425-9780835718424.html (1988).
Carbon, C. C. The cycle of preference: Long-term dynamics of aesthetic appreciation. Acta Psychol. 134, 233–244 (2010).
doi: 10.1016/j.actpsy.2010.02.004
Vinken, P. M. & Heinen, T. How does the amount of movement and observer expertise shape the perception of motion aesthetics in dance?. Hum. Mov. 23, 46–55 (2022).
doi: 10.5114/hm.2021.106170
Department for Culture, Media and Sport. Taking Part focus on: Cross-sector participation [Statistical Release] (2016). https://www.gov.uk/government/statistics/taking-part-april-2016-focus-on-reports .
Belk, R. W., Semenik, R. J. & Andreasen, A. R. Co-patronage patterns in arts-related leisure activities. In SV-Consumer Behavior, Vol. SV-04 (eds Hirschman, E. C. & Holbrook, M. B.) 95–100 (Association for Consumer Research, 1981). https://www.acrwebsite.org/volumes/12236/volumes/sv04/SV-04/full .
Monroy, E., Imada, T., Sagiv, N. & Orgs, G. Dance Across Cultures: Joint Action Aesthetics in Japan and the UK. Empir. Stud. Arts 40, 209–227 (2022).
doi: 10.1177/02762374211001800
Rose, D., Müllensiefen, D., Lovatt, P. & Orgs, G. The Goldsmiths Dance Sophistication Index (Gold-DSI): A psychometric tool to assess individual differences in dance experience. Psychol. Aesthet. Creat. Arts (2020).
doi: 10.1037/aca0000340
Waugh, M. So you think you can dance? Investigating perceived dance efficacy and dance program participation in older adults (Western Sydney University, 2022).
Sevdalis, V. & Raab, M. Individual differences in athletes’ perception of expressive body movements. Psychol. Sport Exerc. 24, 111–117 (2016).
doi: 10.1016/j.psychsport.2016.02.001
Cross, E. S., Kraemer, D. J. M., Hamilton, A. F. d. C., Kelley, W. M. & Grafton, S. T. Sensitivity of the Action Observation Network to Physical and Observational Learning. Cereb. Cortex 19, 315–326 (2009).
Ono, Y. et al. Motor learning and modulation of prefrontal cortex: an fNIRS assessment. J. Neural Eng. 12, 066004 (2015).
pubmed: 26401727
doi: 10.1088/1741-2560/12/6/066004
Sumanapala, D. K., Fish, L. A., Jones, A. L. & Cross, E. S. Have I grooved to this before? Discriminating practised and observed actions in a novel context. Acta Psychol. (Amst.) 175, 42–49 (2017).
pubmed: 28284106
doi: 10.1016/j.actpsy.2017.02.008
Freedberg, D. & Gallese, V. Motion, emotion and empathy in esthetic experience. Trends Cogn. Sci. 11, 197–203 (2007).
pubmed: 17347026
doi: 10.1016/j.tics.2007.02.003
Pinti, P. et al. The present and future use of functional near-infrared spectroscopy (fNIRS) for cognitive neuroscience. Ann. N. Y. Acad. Sci. 1464, 5–29 (2020).
pubmed: 30085354
doi: 10.1111/nyas.13948
Yokota, H., Kamijo, K., Mizuguchi, N., Kubo, H. & Nakata, H. Motor imagery and action observation of whole-body movements for experienced motor repertoire: an fNIRS study. J. Sports Med. Phys. Fitness 12, 107–117 (2023).
doi: 10.7600/jpfsm.12.107
Calvo-Merino, B., Grèzes, J., Glaser, D. E., Passingham, R. E. & Haggard, P. Seeing or doing? Influence of visual and motor familiarity in action observation. Curr. Biol. 16, 1905–1910 (2006).
pubmed: 17027486
doi: 10.1016/j.cub.2006.07.065
Moffat, R., Caruana, N. & Cross, E. S. Inhibiting responses under the watch of a recently synchronized peer increases self-monitoring: evidence from functional near-infrared spectroscopy. Open Biol. (2024).
doi: 10.1098/rsob.230382
pubmed: 38378138
pmcid: 10878812
Broadwell, P. & Tangherlini, T. R. Comparative K-Pop Choreography Analysis through Deep-Learning Pose Estimation across a Large Video Corpus. Digit. Hum. Q. 15, 1–25 (2021).
Chatterjee, A., Thomas, A., Smith, S. E. & Aguirre, G. K. The neural response to facial attractiveness. Neuropsychology 23, 135–143 (2009).
pubmed: 19254086
doi: 10.1037/a0014430
Cela-Conde, C. J. et al. Dynamics of brain networks in the aesthetic appreciation. Proc. Natl. Acad. Sci. 110, 10454–10461 (2013).
pubmed: 23754437
pmcid: 3690613
doi: 10.1073/pnas.1302855110
Siqi-Liu, A., Harris, A. M., Atkinson, A. P. & Reed, C. L. Dissociable processing of emotional and neutral body movements revealed by μ-alpha and beta rhythms. Soc. Cogn. Affect. Neurosci. 13, 1269–1279 (2018).
pubmed: 30351422
pmcid: 6277737
Hobson, H. M. & Bishop, D. V. M. Mu suppression – A good measure of the human mirror neuron system?. Cortex 82, 290–310 (2016).
pubmed: 27180217
pmcid: 4981432
doi: 10.1016/j.cortex.2016.03.019
Michels, L. et al. Simultaneous EEG-fMRI during a Working Memory Task: Modulations in Low and High Frequency Bands. PLoS one (2010).
doi: 10.1371/journal.pone.0010298
pubmed: 21151902
pmcid: 2997783
Babiloni, C. et al. Human Cortical Electroencephalography (EEG) Rhythms during the Observation of Simple Aimless Movements: A High-Resolution EEG Study. NeuroImage 17, 559–572 (2002).
pubmed: 12377134
doi: 10.1006/nimg.2002.1192
McFarland, D. J., Miner, L. A., Vaughan, T. M. & Wolpaw, J. R. Mu and Beta Rhythm Topographies During Motor Imagery and Actual Movements. Brain Topogr. 12, 177–186 (2000).
pubmed: 10791681
doi: 10.1023/A:1023437823106
Zimeo Morais, G. A., Balardin, J. B. & Sato, J. R. fNIRS Optodes’ Location Decider (fOLD): a toolbox for probe arrangement guided by brain regions-of-interest. Sci. Rep. 8, 3341 (2018).
pubmed: 29463928
pmcid: 5820343
doi: 10.1038/s41598-018-21716-z
Di Nota, P. M., Chartrand, J. M., Levkov, G. R., Montefusco-Siegmund, R. & DeSouza, J. F. X. Experience-dependent modulation of alpha and beta during action observation and motor imagery. BMC Neurosci. 18, 28 (2017).
pubmed: 28264664
pmcid: 5340035
doi: 10.1186/s12868-017-0349-0
Cao, Z., Hidalgo, G., Simon, T., Wei, S.-E. & Sheikh, Y. OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields. Preprint at arXiv:1812.08008 (2019).
Moffat, R. & Cross, E. S. Evaluations of dyadic synchrony: observers’ traits influence estimation and enjoyment of synchrony in mirror-game movements. Sci. Rep. 14, 2904 (2024).
pubmed: 38316911
pmcid: 10844651
doi: 10.1038/s41598-024-53191-0
Zhou, J. et al. Skeleton-based Human Keypoints Detection and Action Similarity Assessment for Fitness Assistance. 2021 IEEE 6th Int. Conf. Signal Image Process. (2021).
Gray, J. T., Neisser, U., Shapiro, B. A. & Kouns, S. Observational Learning of Ballet Sequences: The Role of Kinematic Information. Ecol. Psychol. 3, 121–134 (1991).
doi: 10.1207/s15326969eco0302_4
Prousali, E. A Neuroaesthetic approach to Performance Perception. 17/2, (2022).
Cross, E. S., Hamilton, A. F. & Grafton, S. T. Building a motor simulation de novo: Observation of dance by dancers. NeuroImage 31, 1257–1267 (2006).
pubmed: 16530429
doi: 10.1016/j.neuroimage.2006.01.033
Sevdalis, V. & Keller, P. E. Captured by motion: dance, action understanding, and social cognition. Brain Cogn. 77, 231–236 (2011).
pubmed: 21880410
doi: 10.1016/j.bandc.2011.08.005
Trost, W. J., Labbé, C. & Grandjean, D. Rhythmic entrainment as a musical affect induction mechanism. Neuropsychologia 96, 96–110 (2017).
doi: 10.1016/j.neuropsychologia.2017.01.004
Sánchez, C. V. Rhythm. Int. Lex. Aesthet. (2022).
doi: 10.7413/18258630132
Ross, J. M., Iversen, J. R. & Balasubramaniam, R. Motor simulation theories of musical beat perception. Neurocase 22, 558–565 (2016).
pubmed: 27726485
doi: 10.1080/13554794.2016.1242756
Karpati, F. J., Giacosa, C., Foster, N. E. V., Penhune, V. B. & Hyde, K. L. Dance and music share gray matter structural correlates. Brain Res. 1657, 62–73 (2017).
pubmed: 27923638
doi: 10.1016/j.brainres.2016.11.029
Hu, S., Gu, J., Liu, H. & Huang, Q. The moderating role of social media usage in the relationship among multicultural experiences, cultural intelligence, and individual creativity. Inf. Technol. People 30, 265–281 (2017).
doi: 10.1108/ITP-04-2016-0099
Pruccoli, J., De Rosa, M., Chiasso, L., Perrone, A. & Parmeggiani, A. The use of TikTok among children and adolescents with Eating Disorders: experience in a third-level public Italian center during the SARS-CoV-2 pandemic. Ital. J. Pediatr. 48, 138 (2022).
pubmed: 35907912
pmcid: 9338669
doi: 10.1186/s13052-022-01308-4
Kwasa, J. et al. Demographic reporting and phenotypic exclusion in fNIRS. Front. Neurosci. (2023).
doi: 10.3389/fnins.2023.1086208
pubmed: 38105926
pmcid: 10722402
Pollonini, L. et al. Auditory cortex activation to natural speech and simulated cochlear implant speech measured with functional near-infrared spectroscopy. Hear. Res. 309, 84–93 (2014).
pubmed: 24342740
doi: 10.1016/j.heares.2013.11.007
Peirce, J. W., Hirst, R. J. & MacAskill, M. R. Building Experiments in PsychoPy. SAGE Publications Ltd. https://uk.sagepub.com/en-gb/eur/building-experiments-in-psychopy/book273700 (2023).
Strangman, G. E., Li, Z. & Zhang, Q. Depth Sensitivity and Source-Detector Separations for Near Infrared Spectroscopy Based on the Colin27 Brain Template. PLoS One 8, e66319 (2013).
pubmed: 23936292
pmcid: 3731322
doi: 10.1371/journal.pone.0066319
Rolls, E. T., Joliot, M. & Tzourio-Mazoyer, N. Implementation of a new parcellation of the orbitofrontal cortex in the automated anatomical labeling atlas. NeuroImage 122, 1–5 (2015).
pubmed: 26241684
doi: 10.1016/j.neuroimage.2015.07.075
Tzourio-Mazoyer, N. et al. Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain. NeuroImage 15, 273–289 (2002).
pubmed: 11771995
doi: 10.1006/nimg.2001.0978
Brigadoi, S. & Cooper, R. J. How short is short? Optimum source–detector distance for short-separation channels in functional near-infrared spectroscopy. Neurophotonics 2, 025005 (2015).
pubmed: 26158009
pmcid: 4478880
doi: 10.1117/1.NPh.2.2.025005
Huppert, T. J. Commentary on the statistical properties of noise and its implication on general linear models in functional near-infrared spectroscopy. Neurophotonics 3, 010401 (2016).
pubmed: 26989756
pmcid: 4773699
doi: 10.1117/1.NPh.3.1.010401
Gramfort, A. et al. MEG and EEG data analysis with MNE-Python. Front. Neurosci. 7, 1–13 (2013).
doi: 10.3389/fnins.2013.00267
Luke, R. et al. Analysis methods for measuring passive auditory fNIRS responses generated by a block-design paradigm. Neurophotonics 8, 025008 (2021).
pubmed: 34036117
pmcid: 8140612
doi: 10.1117/1.NPh.8.2.025008
Abraham, A. et al. Machine learning for neuroimaging with scikit-learn. Front. Neuoinformatics 8, 14 (2014).
Delpy, D. T. et al. Estimation of optical pathlength through tissue from direct time of flight measurement. Phys. Med. Biol. 33, 1433–1442 (1988).
pubmed: 3237772
doi: 10.1088/0031-9155/33/12/008
Kocsis, L., Herman, P. & Eke, A. The modified Beer-Lambert law revisited. Phys. Med. Biol. 51, N91–N98 (2006).
pubmed: 16481677
doi: 10.1088/0031-9155/51/5/N02
Santosa, H., Zhai, X., Fishburn, F. & Huppert, T. J. The NIRS Brain AnalyzIR Toolbox. Algorithms 11, 73 (2018).
pubmed: 38957522
pmcid: 11218834
doi: 10.3390/a11050073
Strangman, G. E., Franceschini, M. A. & Boas, D. A. Factors affecting the accuracy of near-infrared spectroscopy concentration calculations for focal changes in oxygenation parameters. NeuroImage 18, 865–879 (2003).
pubmed: 12725763
doi: 10.1016/S1053-8119(03)00021-1
Glover, G. H. Deconvolution of impulse response in event-related BOLD fMRI. NeuroImage 9, 416 (1999).
pubmed: 10191170
doi: 10.1006/nimg.1998.0419
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing (2020).
RStudio Team. RStudio: Integrated Development for R. RStudio, PBC (2020).
Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting Linear Mixed-Effects Models Using lme4. J. Stat. Softw. (2015).
doi: 10.18637/jss.v067.i01
Barr, D. J. Random effects structure for testing interactions in linear mixed-effects models. Front. Psychol. 4, 328 (2013).
pubmed: 23761778
pmcid: 3672519
doi: 10.3389/fpsyg.2013.00328
Bates, D., Kliegl, R., Vasishth, S. & Baayen, H. Parsimonious Mixed Models. Preprint at arXiv.1506.04967 (2015).
Lenth, R. V. emmeans: Estimated marginal means, aka least-squares means [Computer software] (2021).
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Methodol. 57, 289–300 (1995).
doi: 10.1111/j.2517-6161.1995.tb02031.x