Caroline P. Whyatt
Elizabeth B. Torres
ABSTRACT
The 'social dance' is an implicit, yet vital, characteristic of dyadic interactions. Attempts to characterize this complex behavior have illustrated unconscious levels of content and temporal entrainment within artificial social contexts. Yet, when viewed in a naturalistic setting, this complex systems problem faces a number of methodological and theoretical challenges. Utilizing precise kinematic recordings while adopting the 'micro-movement' approach, cross coherence analysis and tenets of graph theory, this paper presents an analytical framework to characterize unfolding, nonlinear temporal exchange and entrainment across a social dyad. This framework is empirically demonstrated within a clinical domain of individuals with known social difficulties: Autism Spectrum Disorder (ASD). Results illustrate the ability for this objective methodology to quantify variability in social dynamics, and profile dyadic entrainment during naturalistic exchange -- with no a priori constraints or limitations. Viewed within the context of a clinical assessment tool for ASD, results facilitate consideration of clinician impact on dyadic exchange, and point to a potential refinement of core tasks associated with such clinical batteries.
- American Psychiatric Association et al. 2013. Diagnostic and statistical manual of mental disorders (DSM-5®). American Psychiatric Pub.Google Scholar
- Marie Avril, Chloë Leclère, Sylvie Viaux, Stéphane Michelet, Catherine Achard, Sylvain Missonnier, Miri Keren, David Cohen, and Mohamed Chetouani. 2014. Social signal processing for studying parent--infant interaction. Frontiers in psychology 5 (2014), 1437.Google Scholar
- Christopher A Buneo, Murray R Jarvis, Aaron P Batista, and Richard A Andersen. 2002. Direct visuomotor transformations for reaching. Nature 416, 6881 (2002), 632--636.Google Scholar
- Emilie Delaherche, Mohamed Chetouani, Ammar Mahdhaoui, Catherine Saint-Georges, Sylvie Viaux, and David Cohen. 2012. Interpersonal synchrony: A survey of evaluation methods across disciplines. IEEE Transactions on Affective Computing 3, 3 (2012), 349--365. Google ScholarDigital Library
- Christopher D Frith, Daniel M Wolpert, et al. 2003. The neuroscience of social interaction. (2003).Google Scholar
- Apostolos P Georgopoulos, Andrew B Schwartz, Ronald E Kettner, et al. 1986. Neuronal population coding of movement direction. Science 233, 4771 (1986), 1416--1419.Google Scholar
- Kenneth Kaye and Alan Fogel. 1980. The temporal structure of face-to-face communication between mothers and infants. Developmental Psychology 16, 5 (1980), 454.Google ScholarCross Ref
- Catherine Lord, Pamela C DiLavore, and Katherine Gotham. 2012. Autism diagnostic observation schedule. Western Psychological Services Torrance, CA.Google Scholar
- Livio Provenzi, Renato Borgatti, Giorgia Menozzi, and Rosario Montirosso. 2015. A dynamic system analysis of dyadic flexibility and stability across the Face-to-Face Still-Face procedure: application of the State Space Grid. Infant Behavior and Development 38 (2015), 1--10.Google ScholarCross Ref
- Michael J Richardson, Kerry L Marsh, Robert W Isenhower, Justin RL Goodman, and Richard C Schmidt. 2007. Rocking together: Dynamics of intentional and unintentional interpersonal coordination. Human movement science 26, 6 (2007), 867--891.Google Scholar
- Giacomo Rizzolatti, Rosolino Camarda, Leonardo Fogassi, Maurizio Gentilucci, Giuseppe Luppino, and Massimo Matelli. 1988. Functional organization of inferior area 6 in the macaque monkey. Experimental brain research 71, 3 (1988), 491--507.Google Scholar
- Mikail Rubinov and Olaf Sporns. 2010. Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 3 (2010), 1059--1069.Google ScholarCross Ref
- Catherine Saint-Georges, Ammar Mahdhaoui, Mohamed Chetouani, Raquel S Cassel, Marie-Christine Laznik, Fabio Apicella, Pietro Muratori, Sandra Maestro, Filippo Muratori, and David Cohen. 2011. Do parents recognize autistic deviant behavior long before diagnosis? Taking into account interaction using computational methods. PloS one 6, 7 (2011), e22393.Google ScholarCross Ref
- RC Schmidt, Nicole Christianson, Claudia Carello, and Reuben Baron. 1994. Effects of social and physical variables on between-person visual coordination. Ecological Psychology 6, 3 (1994), 159--183.Google ScholarCross Ref
- RC Schmidt, Paula Fitzpatrick, Robert Caron, and Joanna Mergeche. 2011. Understanding social motor coordination. Human movement science 30, 5 (2011), 834--845.Google Scholar
- Elizabeth B Torres, Maria Brincker, Robert W Isenhower, Polina Yanovich, Kimberly A Stigler, John I Nurnberger, Dimitris N Metaxas, and Jorge V José. 2013. Autism: the micro-movement perspective. Frontiers in integrative neuroscience 7 (2013).Google Scholar
- Elizabeth B Torres, Robert W Isenhower, Jillian Nguyen, Caroline Whyatt, John I Nurnberger, Jorge V Jose, Steven M Silverstein, Thomas V Papathomas, Jacob Sage, and Jonathan Cole. 2016. Toward precision psychiatry: statistical platform for the personalized characterization of natural behaviors. Frontiers in neurology 7 (2016).Google Scholar
- Elizabeth B Torres and Brian Lande. 2015. Objective and personalized longitudinal assessment of a pregnant patient with post severe brain trauma. Frontiers in human neuroscience 9 (2015).Google Scholar
- E von Holst. 1971. H. Mittelstaedt The principle of reafference: interactions between the central nervous system and the peripheral organs. PC Dodwell (ed. and trans.), Perceptual Processing: Stimulus Equivalence and Pattern Recognition.(Reprinted from Die Naturwissenschaften, 1950.) Appleton, New York (1971).Google Scholar
- Peter Welch. 1967. The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Transactions on audio and electroacoustics 15, 2 (1967), 70--73.Google ScholarCross Ref
- Caroline Whyatt and Cathy Craig. 2013. Sensory-motor problems in Autism. Frontiers in integrative neuroscience 7 (2013), 51.Google Scholar
- Justin HG Williams, Gordon D Waiter, Anne Gilchrist, David I Perrett, Alison D Murray, and Andrew Whiten. 2006. Neural mechanisms of imitation and fimirror neuronfifunctioning in autistic spectrum disorder. Neuropsychologia 44, 4 (2006), 610--621.Google ScholarCross Ref
- Daniel M Wolpert and Mitsuo Kawato. 1998. Multiple paired forward and inverse models for motor control. Neural networks 11, 7 (1998), 1317--1329. Google ScholarDigital Library
Index Terms
- The social-dance: decomposing naturalistic dyadic interaction dynamics to the 'micro-level'
Recommendations
Investigating the usability of social networking sites for teenagers with autism
CHINZ '10: Proceedings of the 11th International Conference of the NZ Chapter of the ACM Special Interest Group on Human-Computer InteractionTeenagers with Autism Spectrum Disorder (ASD) tend to be socially isolated due to the interpersonal challenges of autism, yet they enjoy using computers. This study investigated the hypothesis that social networking sites provide the appropriate ...
Social media use among adults with autism spectrum disorders
Adults with autism spectrum disorders (ASD) are at risk for significant social and emotional difficulties, yet show strengths and interests in screen-based technology. Previous studies have found that screen-based social media use can enhance social ...
Synchronized movement in social interaction
Inputs-Outputs '13: Proceedings of the 2013 Inputs-Outputs Conference: An Interdisciplinary Conference on Engagement in HCI and PerformanceSocial interaction is a core aspect of human life that affects individuals' physical and mental health. Social interaction usually leads to mutual engagement in diverse areas of cognitive, emotional, physiological and physical activity involving both ...
Comments