Guest Artist: Uriel Tsachor

Center for the Performing ArtsOctober 22, 2015Thursday Evening8:00 p.m

Bodily expressed emotion understanding through integrating Laban movement analysis

Body movements carry important information about a person's emotions or mental state and are essential in daily communication. Enhancing the ability of machines to understand emotions expressed through body language can improve the communication of assistive robots with children and elderly users, provide psychiatric professionals with quantitative diagnostic and prognostic assistance, and aid law enforcement in identifying deception. This study develops a high-quality human motor element dataset based on the Laban Movement Analysis movement coding system and utilizes that to jointly learn about motor elements and emotions. Our long-term ambition is to integrate knowledge from computing, psychology, and performing arts to enable automated understanding and analysis of emotion and mental state through body language. This work serves as a launchpad for further research into recognizing emotions through analysis of human movement

2014-2015 Master Class - Uriel Tsachor (Piano)

https://spiral.lynn.edu/conservatory_masterclasses/1027/thumbnail.jp

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

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

A Somatic Movement Approach to Fostering Emotional Resiliency through Laban Movement Analysis

Although movement has long been recognized as expressing emotion and as an agent of change for emotional state, there was a dearth of scientific evidence specifying which aspects of movement influence specific emotions. The recent identification of clusters of Laban movement components which elicit and enhance the basic emotions of anger, fear, sadness and happiness indicates which types of movements can affect these emotions (Shafir et al., 2016), but not how best to apply this knowledge. This perspective paper lays out a conceptual groundwork for how to effectively use these new findings to support emotional resiliency through voluntary choice of one’s posture and movements. We suggest that three theoretical principles from Laban Movement Analysis (LMA) can guide the gradual change in movement components in one’s daily movements to somatically support shift in affective state: (A) Introduce new movement components in developmental order; (B) Use LMA affinities-among-components to guide the expansion of expressive movement range and (C) Sequence change among components based on Laban’s Space Harmony theory to support the gradual integration of that new range. The methods postulated in this article have potential to foster resiliency and provide resources for self-efficacy by expanding our capacity to adapt emotionally to challenges through modulating our movement responses

A Somatic Movement Approach to Fostering Emotional Resiliency through Laban Movement Analysis

Although movement has long been recognized as expressing emotion and as an agent of change for emotional state, there was a dearth of scientific evidence specifying which aspects of movement influence specific emotions. The recent identification of clusters of Laban movement components which elicit and enhance the basic emotions of anger, fear, sadness and happiness indicates which types of movements can affect these emotions (Shafir et al., 2016), but not how best to apply this knowledge. This perspective paper lays out a conceptual groundwork for how to effectively use these new findings to support emotional resiliency through voluntary choice of one's posture and movements. We suggest that three theoretical principles from Laban Movement Analysis (LMA) can guide the gradual change in movement components in one's daily movements to somatically support shift in affective state: (A) Introduce new movement components in developmental order; (B) Use LMA affinities-among-components to guide the expansion of expressive movement range and (C) Sequence change among components based on Laban's Space Harmony theory to support the gradual integration of that new range. The methods postulated in this article have potential to foster resiliency and provide resources for self-efficacy by expanding our capacity to adapt emotionally to challenges through modulating our movement responses

How shall i count the ways? A method for quantifying the qualitative aspects of unscripted movement with Laban Movement Analysis

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

Corrigendum: How Do We Recognize Emotion From Movement? Specific Motor Components Contribute to the Recognition of Each Emotion (Frontiers in Psychology, (2019), 10, (1389), 10.3389/fpsyg.2019.01389)

In the original article, there was a mistake in Figure 3 and Figure 4 as published. Figure 3 was mistakenly published attached to the figure legend of Figure 4, while Figure 4 was mistakenly put together with the figure legend for Figure 3. The corrected Figure 3 and Figure 4 appear below. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated

How do we recognize emotion from movement? Specific motor components contribute to the recognition of each emotion

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

Emotion regulation through movement: Unique sets of movement characteristics are associated with and enhance basic emotions

We have recently demonstrated that motor execution, observation and imagery of movements expressing certain emotions can enhance corresponding affective states and therefore could be used for emotion regulation. But which specific movement(s) should one use in order to enhance each emotion? This study aimed to identify, using Laban Movement Analysis (LMA), the Laban motor elements (motor characteristics) that characterize movements whose execution enhances each of the basic emotions: anger, fear happiness, and sadness. LMA provides a system of symbols describing its motor elements, which gives a written instruction (motif) for the execution of a movement or movement-sequence over time. Six senior LMA experts analyzed a validated set of video clips showing whole body dynamic expressions of anger, fear, happiness and sadness, and identified the motor elements that were common to (appeared in) all clips expressing the same emotion. For each emotion, we created motifs of different combinations of the motor elements common to all clips of the same emotion. Eighty subjects from around the world read and moved those motifs, to identify the emotion evoked when moving each motif and to rate the intensity of the evoked emotion. All subjects together moved and rated 1241 motifs, which were produced from 29 different motor elements. Using logistic regression, we found a set of motor elements associated with each emotion which, when moved, predicted the feeling of that emotion. Each emotion was predicted by a unique set of motor elements and each motor element predicted only one emotion. Knowledge of which specific motor elements enhance specific emotions can enable emotional self-regulation through adding some desired motor qualities to one’s personal everyday movements (rather than mimicking others’ specific movements) and through decreasing motor behaviors which include elements that enhance negative emotions