Spectators’ aesthetic experience of sound and movement in dance performance: A transdisciplinary investigation.

Abstract

We utilise qualitative audience research and functional brain imaging (fMRI) to examine the aesthetic experience of watching dance both with and without music. This transdisciplinary approach was motivated by the recognition that the aesthetic experience of dance revealed through conscious interpretation could have neural correlates in brain activity. When audiences were engaged in watching dance accompanied by music, the fMRI data revealed evidence of greater intersubject correlation in a left anterior region of the superior temporal gyrus known to be involved in complex audio processing. Moreover, the qualitative data revealed how spectators derived pleasure from finding convergences between two complex stimuli (dance and music). Without music, greater intersubject correlation was found bilaterally in a posterior region of the superior temporal gyrus, showing that bodily sounds such as breath provide a more salient auditory signal than music in primary auditory regions.Watching dance without music also resulted in increased intersubject correlation amongst spectators in the parietal and occipitotemporal cortices, suggesting a greater influence of the body than when interpreting the dance stimuli with music. Similarly, the audience research found evidence of corporeally focused experience, but suggests that while embodied responses were common across spectators, they were accompanied by different evaluative judgements. In this paper we discuss a study that was carried out as part of the Watching Dance: KeywordsKinesthetic Empathy project (www.watchingdance.org). The Watching Dance project. This was a transdisciplinary exploration of the extent to which spectators' experiences of dance were based upon kinesthetic empathy. The present paper focuses on spectators' aesthetic experience of sound and movement, investigated through a combination of qualitative audience research and functional brain imaging (fMRI).As a multi-modal form, dance invites research into how different sensory modalities interact with each other. Although the relationship between dance and music is central to Western theatre dance practice (see for example Jordan, 2000;2008), and has begun to be addressed by neurocognitive approaches , it has not hitherto been studied in combination with qualitative research on dance audiences in theatre settings. Our approach parallels the recent proposals of how aesthetic aspects of dance can (Christensen & CalvoMerino, 2013) and should (Jola & Christensen, 2015) be a subject for empirical research into the audience experience. Further, our research emphasis aligns with the current surge of interest in multisensory aspects of performance (Banes & Lepecki, 2007;Bläsing, 2015; Chapple & Kattenbelt, 2006;Di Benedetto, 2010;McKinney, 2012;Vass-Rhee 2010;Viaud-Delmon et al. 2012). Within this context of developing research into the multi-modal aspects of dance, our aim was to research audiences' responses to dance movement when accompanied by different combinations of movement and sound.The specific que...

Computational Feature Analysis of Body Movements Reveals Hierarchical Brain Organization

“…cSymmetry and cLightness correlated with brain activity patterns in OTC and IPL. These regions are known to have functional specificity for bodies and body parts (Engelen et al 2015), as well as biological motion (Decety and Grèzes 1999) and have previously been shown to elicit similar pattern of brain activity over participants passively viewing dance clips (Herbec et al 2015;Reason et al 2016). Interestingly, representational similarities were found for cSymmetry in both V3/V6, in OTC and IPL, indicating that this feature may be related to extensive posture/motion (and thus visual) differences as well as more abstract properties of body, limb, and biological motion.…”

Functional brain segmentation using inter‐subject correlation in fMRI

“…So far, one of the most promising approaches to analyze such complex functional magnetic resonance imaging (fMRI) data sets is inter-subject correlation (ISC) analysis [Hasson et al, 2004], applied to fMRI data sets collected using naturalistic stimuli, such as movies/video [Golland et al, 2007;Hasson et al, 2004;Nummenmaa et al, 2012;Reason et al, 2016] and music [Abrams et al, 2013;Trost et al, 2015]. So far, one of the most promising approaches to analyze such complex functional magnetic resonance imaging (fMRI) data sets is inter-subject correlation (ISC) analysis [Hasson et al, 2004], applied to fMRI data sets collected using naturalistic stimuli, such as movies/video [Golland et al, 2007;Hasson et al, 2004;Nummenmaa et al, 2012;Reason et al, 2016] and music [Abrams et al, 2013;Trost et al, 2015].…”

“…While the amount of complex neuroimaging data sets collected in naturalistic experiments is increasing, a major bottleneck remains to be the lack of proper analysis methods. So far, one of the most promising approaches to analyze such complex functional magnetic resonance imaging (fMRI) data sets is inter-subject correlation (ISC) analysis [Hasson et al, 2004], applied to fMRI data sets collected using naturalistic stimuli, such as movies/video [Golland et al, 2007;Hasson et al, 2004;Nummenmaa et al, 2012;Reason et al, 2016] and music [Abrams et al, 2013;Trost et al, 2015]. ISC-based analysis is conceptually simple, involving voxel-wise computations of correlation coefficients between time series of all subjects.…”

Comparing fMRI inter-subject correlations between groups using permutation tests

“…ISC maps have been shown to align well with the activation maps of the traditional block design stimuli (Pajula et al, 2012), and it has been applied in a number of studies, e.g., (Hasson et al, 2008;Abrams et al, 2013;Jääskeläinen et al, 2008;Englander et al, 2012;Kauppi et al, 2010;Nummenmaa et al, 2012;Wilson et al, 2007). In addition 10 to localizing shared processing within a group, the ISC methodology has been extended to compare ISCs between two similar, but not identical, stimuli within a single group of subjects (Herbec et al, 2015;Reason et al, 2016), as well as to compare between two groups of subjects experiencing the same stimuli (Hasson et al, 2009;Salmi et al, 2013;Byrge et al, 2015). In this paper, we are interested in the latter scenario.…”