Editorial: Methods in cognitive neuroscience: dance movement 2023

Abstract

Over the past few decades, dance has emerged as a unique medium to explore the intricacies of the human brain, mind, and body. Dance provides a dynamic lens to examine neural representations of complex movement, and understand individual, cultural, and universal factors influencing our emotional and aesthetic evaluation of movement. Dance research has vast potential and uses a wide range of neuroscientific methods-including psychological and brain measures, behavioral training procedures, and kinematic analyses. Yet the field is still in its infancy, grappling with the lack of standardized methods and guidelines that can ensure accessibility and scientific rigor. This absence of methodological standardization and frameworks can threaten the growth of this promising domain, limiting its ability to address the complexity of studying human movement in an artistic context. Recent efforts aim to focus on scientific rigor, embrace cross-cultural perspectives beyond western dance and culture, and expand understanding of two-body and multiple-body aesthetics, paving the way for a more holistic understanding of human movement and cognition in dance.In response to these challenges, this Research Topic, part of the Methods in Frontiers in Human Neuroscience series, highlights novel methodological advancements applicable to the investigation of the cognitive neuroscience of dance and complex movement. It focuses on neuroaesthetics and the social, affective, and cognitive dimensions of dance. A key aim is to foster interdisciplinary dialogue among experts from various fields, drawing on strengths from research in empirical aesthetics, cognitive neuroscience, social cognition, and computer vision.The five studies showcased in this collection embody this interdisciplinary approach, incorporating diverse techniques such as neuroimaging and computational kinematic analysis, and implementing comprehensive research protocols.Baker and colleagues introduce a computational approach for classifying Hip Hop dance genres, using 17 full-body movement features derived from 3D joint positions. Their findings highlight the significance of features like body expandedness and sharp movements frequency in distinguishing between genres. This method outperformed simpler machine learning classifiers and revealed both convergences and divergences when compared with human participant performance, suggesting promising potential for future applications in other movement domains. Additionally, the authors employed Latent Semantic Analysis to reduce the feature dimensionality and characterize genres through key aspects like vertical movement (bounce), momentum, and rhythmic regularity, providing new insights into Hip Hop dance evolution.Moffat and colleagues use kinematic analysis together with questionnaires to investigate the relationship between dyadic-level embodiment (body competence and body perception scores) and movement features (synchrony and complexity) during a movement mirroring game. Participants move their arms spontaneously, while a confederate closely matched their arm movements in space and time, with the roles then reversed. Results revealed that when the more experienced member of the dyad (the confederate) followed the participant's movements, the dyad achieved greater synchrony. Notably, synchrony-but not complexity-was positively associated with dyadic body competence scores. This study highlights the importance of considering dyadic-level embodiment to gain insight into behaviors in interactive social contexts, which may extend to interactions with non-human agents as well.Directly addressing the theme of non-human agents and dance, Darda and colleagues, explore how stimulus and knowledge cues related to human animacy affect the aesthetic appreciation of dance. Participants watched Bharatanatyam dance videos performed by human and robotic avatars, with variations in whether the choreographies were human-or computergenerated (stimulus cues