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Dynamic Coordination Patterns in Tango Argentino: A Cross-Fertilization of Subjective Explication Methods and Motion Capture

Part of the Springer Tracts in Advanced Robotics book series (STAR,volume 111)

Abstract

This contribution surveys strategies for analyzing highly coordinated forms of collaborative dance improvisation, based on tango argentino. Specifically, we take interest (a) in micro-coordination at the <1 s timescale in dance elements such as steps or rotations, (b) in meso-scale patterns of 2–8 step “figures”, and (c) in general enabling macro-patterns maintained throughout, a kind of “grammar” of tango. Across these timescales, dancers supply individual action-readiness, dynamic stability, proper form and connectivity, while jointly “managing” structures of interpersonal coordination such as enabling configurations. Our study engages qualitative and quantitative methods in a dialogue. Starting with micro-genetic elicitation interviews, dancers reported ideomotor concepts, perceptual triggers, and didactic imagery. Besides general (e.g. postural) habits, task-specific forms or techniques, and attentional foci, this yields insights into the interlocking contributions and information flow between tango leaders and followers within units as small as half-steps. The subjective data was then “frontloaded” into a motion-capture study in which six expert couples, fitted with 2 × 21 light-point reflectors, executed various tango techniques. We developed kinematic indicators for individual and interpersonal coordination (degree of coupling, relative movement onset and action timing, role specifics); we also measured geometries underlying various tango tasks and style variations. The combined data suggests a micro-coordination model where dynamic interdependencies enable precise mutually adaptive action. The criss-crossing signals, e.g. when the increasing lability of the leader’s torso triggers the follower’s leg extension at the beginning of a forward step, suggests task-, phase- and body-part specific contingencies whereby leaders and followers micro-coordinate actions with respect to one another.

Keywords

  • Weight Shift
  • Informational Model
  • Interpersonal Coordination
  • Resonance Loop
  • Argentine Tango

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Notes

  1. 1.

    Qualitative studies include mother-child interaction [10, 33, 40, 53], collaborative creativity [35, 36], music ensembles [39], flamenco [25], theater improvisation [26], bodywork [22], and psychotherapy [16]. Dynamic systems researchers and scholars of interpersonal synergies have applied quantitative interpersonal coordination measures (cf. [7, 29, 34]). Examples include rugby, basketball, and futsal [4, 30, 48], horseback riding [23, 31], jazz [38, 49] and workplace teams [41].

  2. 2.

    Dynamic systems theorists might speak of order parameters that remain stable over time (as opposed to the more specific patterning of transient “movemes”). Order parameters designate collective variables that best represent the macroscopic patterns arising from non-linear interactions and synergies of lower-level components.

  3. 3.

    This is a figure in which the follower orbits around the leader, using side-, forward-, side- and backsteps in succession.

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Acknowledgments

This research was supported by FWF grants P-19436 and P-23067. We wish to thank Mario Heller and Arnold Baca for assistance with organizing and setting up the motion capture study. We also thank the six teacher couples who participated in the motion capture study and our numerous interviewees. Finally, we express our gratitude towards Germano Milite and Betka Fislova for the photographic stills and for their generous support throughout the study.

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Kimmel, M., Preuschl, E. (2016). Dynamic Coordination Patterns in Tango Argentino: A Cross-Fertilization of Subjective Explication Methods and Motion Capture. In: Laumond, JP., Abe, N. (eds) Dance Notations and Robot Motion. Springer Tracts in Advanced Robotics, vol 111. Springer, Cham. https://doi.org/10.1007/978-3-319-25739-6_10

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