How Shall I Count the Ways? A Method for Quantifying the Qualitative Aspects of Unscripted Movement With Laban Movement Analysis

Abstract

There is significant clinical evidence showing that creative and expressive movement processes involved in dance/movement therapy (DMT) enhance psycho-social well-being. Yet, because movement is a complex phenomenon, statistically validating which aspects of movement change during interventions or lead to significant positive therapeutic outcomes is challenging because movement has multiple, overlapping variables appearing in unique patterns in different individuals and situations. One factor contributing to the therapeutic effects of DMT is movement’s effect on clients’ emotional states. Our previous study identified sets of movement variables which, when executed, enhanced specific emotions. In this paper, we describe how we selected movement variables for statistical analysis in that study, using a multi-stage methodology to identify, reduce, code, and quantify the multitude of variables present in unscripted movement. We suggest a set of procedures for using Laban Movement Analysis (LMA)-described movement variables as research data. Our study used LMA, an internationally accepted comprehensive system for movement analysis, and a primary DMT clinical assessment tool for describing movement. We began with Davis’s (1970) three-stepped protocol for analyzing movement patterns and identifying the most important variables: (1) We repeatedly observed video samples of validated ( Atkinson et al., 2004 ) emotional expressions to identify prevalent movement variables, eliminating variables appearing minimally or absent. (2) We use the criteria repetition, frequency, duration and emphasis to eliminate additional variables. (3) For each emotion, we analyzed motor expression variations to discover how variables cluster: first, by observing ten movement samples of each emotion to identify variables common to all samples; second, by qualitative analysis of the two best-recognized samples to determine if phrasing, duration or relationship among variables was significant. We added three new steps to this protocol: (4) we created Motifs (LMA symbols) combining movement variables extracted in steps 1–3; (5) we asked participants in the pilot study to move these combinations and quantify their emotional experience. Based on the results of the pilot study, we eliminated more variables; (6) we quantified the remaining variables’ prevalence in each Motif for statistical analysis that examined which variables enhanced each emotion. We posit that our method successfully quantified unscripted movement data for statistical analysis.

How Do We Recognize Emotion From Movement? Specific Motor Components Contribute to the Recognition of Each Emotion

“…Using the knowledge that execution, imagination, and observation of emotional movements can enhance affect (Shafir et al, 2013, p. 219–227), Shafir et al (2016) took this one step further and used LMA to identify which aspects of movement might be responsible for enhancing the specific emotions of happiness, sadness, fear, and anger. They coded validated (Atkinson et al, 2004, p. 717–746) video-clips of whole body emotional expressions to examine which Laban motor components appeared in those clips, and then asked LMA experts to move different combinations of those motor components and to note which emotion was enhanced by moving each combination [for further explanation about the methods used in that study see also (Tsachor and Shafir, 2019, p. 572)]. Statistical analysis of these data yielded the following results: Happiness was enhanced by the Laban motor components of: Jumping, Rhythmic (reinitiating) movements, Spreading, Free-Flow, Lightness, moving Up, and Rising.…”

Dance on the Brain: Enhancing Intra- and Inter-Brain Synchrony

“…At present, limited research has been conducted to investigate the effectiveness of these innate patterns of movement to brain function and physiology (Chatfield and Barr, 1994 ). The primary work in this field has focused on the connection between movement patterns, quantified through LMA, and their connection to emotional expression or the expressive quality of movement (Bernardet et al, 2019 ; Melzer et al, 2019 ; Tsachor and Shafir, 2019 ). More recently, LMA in combination with EEG has been used to extract neural signatures linked to expressive human movement (Cruz-Garza et al, 2014 ).…”

Latinx students embodying justice‐centered science: Agency through imagining via the performing arts

“…LBMS was useful in analyzing embodied activities, in general, but also in analyzing moments in which movement was not the foregrounded mode of communication, such as during the fishbowl discussion. Consistent with our epistemological commitment to embodiment, LBMS helped us notice, for instance, attentional shifts and helped us tune into emotions through children's body movements (Tsachor & Shafir, 2017, 2019). Using LBMS helped bring specificity to movements as well as connected movements to ideas and feelings, illuminating children's science knowledge and identity construction.…”

“…To code children's body movements, we used a method common in performing arts research and practice, the Laban/Bartenieff Movement System (LBMS) (Fernandes, 2014; Studd & Cox, 2019; Tsachor & Shafir, 2017, 2019). This analytical system brings attention to embodiments along four different dimensions: Body, Effort, Space, and Shape (Table 3), which helped bring out the varied aspects of movement, shedding light on the connections between movements and ideas and identities expressed.…”