Poster No:
842
Submission Type:
Abstract Submission
Authors:
Frank Pollick1, Naree Kim2, Donald Glowinski3, Jukka-Pekka Kauppi4, Antonio Camurri5, Jussi Tohka4, Seon Hee Jang2
Institutions:
1University of Glasgow, Glasgow, UK, 2Sejong University, Seoul, Republic of Korea, 3University of Geneva, Geneva, Switzerland, 4University of Eastern Finland, Kuopio, Finland, 5University of Genoa, Genoa, Italy
First Author:
Co-Author(s):
Naree Kim
Sejong University
Seoul, Republic of Korea
Jussi Tohka
University of Eastern Finland
Kuopio, Finland
Introduction:
While much is known about the neural basis of action perception, less is known about the processing of more complex aspects of action interpretation. Actions can be performed in different styles and these stylistic differences can convey meaning (Pollick et al. 2001; Chen, Pollick, and Lu 2023). Ballet provides an example of a corpus of actions that can be performed according to different stylistic guidelines (Jang and Pollick 2011). In this research we used fMRI to investigate brain responses to viewing short segments of ballet performed in classical, romantic and modern ballet styles. Dance theory would suggest that these different styles would produce different subjective impressions. Specifically, classical ballet emphasises form while romantic ballet emphasises emotion and embodiment. We were interested in whether differences in brain processing would be obtained.
Methods:
Dance stimuli were black and white videos without audio of 3 ballet dances with the face blurred out, each 90 second in duration performed by the same skilled ballerina, starting and ending in the same posture. For each clip a measure of the Motion Index of the dancer (Noble et al. 2014; Lillywhite et al. 2022) was calculated. The three dance pieces were chosen on the basis that each should be representative of its genre and included Odette's solo in Act II of Swan Lake for Classical ballet, the female solo dance from Agon for Modern ballet, and Giselle's solo dance in Act II of Giselle for Romantic ballet. These three dances were termed Classical, Modern and Romantic. Eighteen dance-naïve individuals (8 males, 10 females) participated in the study and viewed all three ballet dance videos while being scanned. Presentation order of the clips was counterbalanced across participants. Brain data were acquired during stimulus presentation using a 3T Tim Trio Siemens scanner with a TR of two seconds, resulting in 45 volumes of each dance used in subsequent analyses. Preprocessing included 3D motion correction, high pass filtering set to one cycle, and normalization of the data into Talairach space. Data analysis was performed using BrainVoyager and the ISC Toolbox (Kauppi, Pajula, and Tohka 2014; Hasson et al. 2004). Analyses included calculation of ISC maps, and comparison of ISC maps as well as examination of the Motion Index as a predictor in the general linear model (Noble et al. 2014).
Results:
ISC analysis was performed on each dance clip individually, and all dances revealed ISC in occipital, temporal and parietal areas, while only Romantic and Classical dance clips revealed ISC in frontal cortex. The greatest volume of ISC was found for the Romantic dance clip, and this was due primarily to greater ISC in a large occipitotemporal cluster as well as several clusters in frontal cortex. Statistical comparison of ISC maps (Herbec et al. 2015) revealed several differences. Notably, Classical dance obtained greater ISC than Romantic in lingual gyrus while Romantic dance had greater ISC than Classical in cuneus, precuneus and inferior parietal cortex. Results from using the Motion Index as a predictor showed an overlapping region for all three dances in bilateral occipitotemporal cortex, as well as activity in fusiform gyrus for Classical dance and lingual gyrus for Modern dance.
Conclusions:
Results showed that for the prototypical ballet dance segments chosen, there were differences between styles found in both ISC maps and Motion Index results. Broadly speaking, and with a focus on Classical and Romantic dance, these results speak toward greater processing of the Classical ballet segment in ventral regions and greater processing of the Romantic ballet segment in dorsal regions. These results provide a first indication that the neural processing of different dance styles can be identified and could form a basis for aesthetic interpretation. Generalising these results past dance reveals the potential to design brain response to viewed movement that could be useful in rehabilitation.
Emotion, Motivation and Social Neuroscience:
Social Neuroscience Other 1
Motor Behavior:
Mirror System 2
Perception, Attention and Motor Behavior:
Perception: Visual
Keywords:
FUNCTIONAL MRI
Motor
NORMAL HUMAN
Perception
Social Interactions
1|2Indicates the priority used for review
Provide references using author date format
Chen, Yi-Chia, Frank Pollick, and Hongjing Lu. 2023. ‘Aesthetic Preferences for Prototypical Movements in Human Actions’. Cognitive Research: Principles and Implications 8 (1): 55. https://doi.org/10.1186/s41235-023-00510-0.
Hasson, Uri, Yuval Nir, Ifat Levy, Galit Fuhrmann, and Rafael Malach. 2004. ‘Intersubject Synchronization of Cortical Activity During Natural Vision’. Science 303 (5664): 1634–40. https://doi.org/10.1126/science.1089506.
Herbec, Aleksandra, Jukka-Pekka Kauppi, Corinne Jola, Jussi Tohka, and Frank E. Pollick. 2015. ‘Differences in fMRI Intersubject Correlation While Viewing Unedited and Edited Videos of Dance Performance’. Cortex 71 (October): 341–48. https://doi.org/10.1016/j.cortex.2015.06.026.
Jang, Seon Hee, and Frank E Pollick. 2011. ‘Experience Influences Brain Mechanisms of Watching Dance’. Dance Research 29 (supplement): 352–77. https://doi.org/10.3366/drs.2011.0024.
Kauppi, Jukka-Pekka, Juha Pajula, and Jussi Tohka. 2014. ‘A Versatile Software Package for Inter-Subject Correlation Based Analyses of fMRI’. Frontiers in Neuroinformatics 8. https://www.frontiersin.org/articles/10.3389/fninf.2014.00002.
Lillywhite, Amanda, Dewy Nijhof, Donald Glowinski, Bruno L. Giordano, Antonio Camurri, Ian Cross, and Frank E. Pollick. 2022. ‘A Functional Magnetic Resonance Imaging Examination of Audiovisual Observation of a Point-Light String Quartet Using Intersubject Correlation and Physical Feature Analysis’. Frontiers in Neuroscience 16 (September): 921489. https://doi.org/10.3389/fnins.2022.921489.
Noble, Katie, Donald Glowinski, Helen Murphy, Corinne Jola, Phil McAleer, Nikhil Darshane, Kedzie Penfield, Sandhiya Kalyanasundaram, Antonio Camurri, and Frank E. Pollick. 2014. ‘Event Segmentation and Biological Motion Perception in Watching Dance’. Art & Perception 2 (1–2): 59–74. https://doi.org/10.1163/22134913-00002011.
Pollick, Frank E, Helena M Paterson, Armin Bruderlin, and Anthony J Sanford. 2001. ‘Perceiving Affect from Arm Movement’. Cognition 82 (2): B51–61. https://doi.org/10.1016/S0010-0277(01)00147-0.