September 2017
Volume 17, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2017
Categorizing features of coordination from joint actions
Author Affiliations
  • Joseph Burling
    University of California, Los Angeles
  • Hongjing Lu
    University of California, Los Angeles
Journal of Vision August 2017, Vol.17, 63. doi:
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      Joseph Burling, Hongjing Lu; Categorizing features of coordination from joint actions. Journal of Vision 2017;17(10):63.

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      © ARVO (1962-2015); The Authors (2016-present)

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Our ability to perceive others' actions and coordinate our own body movements accordingly is essential for interacting with the social world. Interacting with others often requires precise control of our own limbs and body to adapt to sudden changes in movement. However, during passive observation of joint action between two persons, are observers sensitive to specific features of coordinated movement, and do groups of features emerge for different types social actions? Participants viewed short video sequences showing two actors performing ten different interpersonal interactions, such as shake hands, high-five, etc. In some trials, temporal misalignments were introduced that temporally shift one actor's movements forward or backward in time relative to the partner actor. The temporal offsets varied in magnitude for each lead/lag condition (exact timing depended on the total action duration). Participants rated degree of interactiveness on a scale of 1–7. First, we compared human interactiveness ratings across joint actions and found a significant interaction between offset magnitude and joint action type, p(F9,454 = 10.7) < .001. We found that temporal misalignment did not alter participant ratings for some joint actions, e.g, shake hands, tug-of-war, arguing and threaten. However, ratings varied depending on the temporal direction of misalignments for other joint actions, such as catch, high-five, chicken dance, skipping and threaten. Second, based on rating distributions across joint actions, we fit a generative probabilistic cluster model to group the distributions into latent classes, revealing shared characteristics among sets of joint actions. The resulting clusters organized joint actions by the dimensions of average rating score and sensitivity to offset directionality. Further analysis on the clustered structure of joint actions revealed that global motion synchrony, spatial proximity between actors, and local, brief, but highly salient moments of interpersonal coordination are critical features that impact judgments of interactiveness.

Meeting abstract presented at VSS 2017


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