ABSTRACT
[This corrects the article DOI: 10.3389/fpsyg.2019.01389.].
ABSTRACT
Are there movement features that are recognized as expressing each basic emotion by most people, and what are they? In our previous study we identified sets of Laban movement components that, when moved, elicited the basic emotions of anger, sadness, fear, and happiness. Our current study aimed to investigate if movements composed from those sets would be recognized as expressing those emotions, regardless of any instruction to the mover to portray emotion. Our stimuli included 113 video-clips of five Certified Laban Movement Analysts (CMAs) moving combinations of two to four movement components from each set associated with only one emotion: happiness, sadness, fear, or anger. Each three second clip showed one CMA moving a single combination. The CMAs moved only the combination's required components. Sixty-two physically and mentally healthy men (n = 31) and women (n = 31), ages 19-48, watched the clips and rated the perceived emotion and its intensity. To confirm participants' ability to recognize emotions from movement and to compare our stimuli to existing validated emotional expression stimuli, participants rated 50 additional clips of bodily motor expressions of these same emotions validated by Atkinson et al. (2004). Results showed that for both stimuli types, all emotions were recognized far above chance level. Comparing recognition accuracy of the two clip types revealed better recognition of anger, fear, and neutral emotion from Atkinson's clips of actors expressing emotions, and similar levels of recognition accuracy for happiness and sadness. Further analysis was performed to determine the contribution of specific movement components to the recognition of the studied emotions. Our results indicated that these specific Laban motor components not only enhance feeling the associated emotions when moved, but also contribute to recognition of the associated emotions when being observed, even when the mover was not instructed to portray emotion, indicating that the presence of these movement components alone is sufficient for emotion recognition. This research-based knowledge regarding the relationship between Laban motor components and bodily emotional expressions can be used by dance-movement and drama therapists for better understanding of clients' emotional movements, for creating appropriate interventions, and for enhancing communication with other practitioners regarding bodily emotional expression.
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.
