Are we on the same wavelength? Interpersonal complementarity as shared cyclical patterns during interactions.

Sadler, Pamela; Ethier, Nicole; Gunn, Gregory R.; Duong, David; Woody, Erik Z.

doi:10.1037/a0016232

Cited by 197 publications

(352 citation statements)

References 58 publications

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“…For example, researchers interested in questions of linguistic alignment (e.g., priming; Brennan & Clark, 1996;Cleland & Pickering, 2003;Kousidis & Dorran, 2009;Niederhoffer & Pennebaker, 2002;Reitter, Moore, & Keller, 2010) will find the two-channel recording method amenable to their research (e.g., transcription; Kreuz & Riordan, 2011). Additionally, by combining the FDM with pre-or postinteraction questionnaires, researchers interested in affective synchrony (e.g., Chartrand & Bargh, 1999;Lakin & Chartrand, 2003;Miles et al, 2011;Sadler et al, 2009;Valdesolo & Desteno, 2011) may begin to investigate questions of affective alignment in conjunction with other channels of communication. By combining research into these and other channels, the field can better understand the functions of interpersonal synchrony.…”

Section: Future Directionsmentioning

confidence: 99%

Frame-differencing methods for measuring bodily synchrony in conversation

2012

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The study of interpersonal synchrony examines how interacting individuals grow to have similar behavior, cognition, and emotion in time. Many of the established methods of analyzing interpersonal synchrony are costly and timeconsuming; the study of bodily synchrony has been especially laborious, traditionally requiring researchers to hand-code movement frame by frame. Because of this, researchers have been searching for more efficient alternatives for decades. Recently, some researchers (e.g., Nagaoka & Komori (IEICE Transactions on Information and Systems, 91(6), [1634][1635][1636][1637][1638][1639][1640] 2008); Ramseyer & Tschacher, 2008) have applied computer science and computer vision techniques to create framedifferencing methods (FDMs) to simplify analyses. In this article, we provide a detailed presentation of one such FDM, created by modifying and adding to existing FDMs. The FDM that we present requires little programming experience or specialized equipment: Only a few lines of MATLAB code are required to execute an automated analysis of interpersonal synchrony. We provide sample code and demonstrate its use with an analysis of brief, friendly conversations; using linear mixed-effects models, the measure of interpersonal synchrony was found to be significantly predicted by time lag (p < .001) and by the interaction between time lag and measures of interpersonal liking (p < .001). This pattern of results fits with existing literature on synchrony. We discuss the current limitations and future directions for FDMs, including their use as part of a larger methodology for capturing and analyzing multimodal interaction.Keywords Synchrony . Movement . Liking . Alignment . Frame-differencing methods . Image processing . MATLAB Conversation is arguably one of the most common-and important-modes of social interaction. Combining a variety of intrapersonal and interpersonal mechanisms, conversation presents a rich source of data for researchers in numerous areas, from linguistics and affect to posture and gesture. Interpersonal synchrony research lies at the intersection of many of these areas, seeking to characterize the way that interlocutors (individuals involved in conversation) grow to have similar behavior, cognition, and emotion over time. Many areas of research in the behavioral sciences have approached the issue of synchrony, resulting in a scattered terminology: accommodation

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Section: Future Directionsmentioning

confidence: 99%

Frame-differencing methods for measuring bodily synchrony in conversation

2012

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“…The same idea, however, can also be applied to the description of the moment-to-moment changes in the participants' behaviors occurring during dyadic interaction. To this aim, Pamela Sadler and her co-workers [49,74] have recently developed a method that relies on naïve observers' ratings of interpersonal behavior, based on the assumption that the circumplex structure of interpersonal behaviors is an arrangement intuitively accessible to raters [99]. The method uses a computer interface with which raters assess the participants' dominant and affiliative interpersonal behaviors (see Method for further details), and it has been shown to be highly reliable and to have strong convergent and discriminant validity with more traditional interpersonal measures [74], such as the Social Behavior Inventory [57].…”

Section: Interpersonal Dynamics During Dyadic Interactionsmentioning

confidence: 99%

“…By using the joystick method developed by [74] we compared the patterns characteristic for neurotypical (NT) male dyads (NT dyads) with the ones characteristic for male dyads (AS dyads) where one participant is a neurotypical and the other has the autistic spectrum disorder that is generally known as Asperger's syndrome (AS). Given that AS is characterized by deficits in social interaction [28,85], we hypothesized that there would be identifiable differences in the extent to and the specific ways in which AS participants and their NT co-participants would match each other's dominant and affiliative interpersonal behaviors.…”

Section: Doi: 107243/2054-992x-4-2mentioning

confidence: 99%

On the Asperger experience of interaction: Interpersonal dynamics in dyadic conversations

Stevanovic¹,

Henttonen²,

Koski³

et al. 2017

J Autism

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We compared the patterns of affiliative and dominant behavior displayed in male dyads where one participant has Asperger's syndrome (AS) with those displayed in male dyads with two neurotypical (NT) participants. Drawing on interpersonal theory, according to which affiliation and dominance constitute two orthogonal axes of the "interpersonal circle," we used a computer-joystick apparatus to assess the participants' moment-to-moment affiliative and dominant behaviors throughout conversation. The patterns of affiliation and dominance were subsequently studied in relation to post-conversation questionnaires that targeted the interactional experiences of the participants in the two different types of dyads (AS dyads, NT dyads). We found the overall interpersonal notion of complementarity to hold for AS and NT dyads alike: greater affiliation in one participant invoked greater affiliation in the co-participant, and greater dominance invoked greater submissiveness in the co-participant. The AS and NT dyads, however, differed with regard to how affiliative and dominant behaviors related to each other during the time course of a single conversation. Furthermore, we found important differences between the AS and NT dyads in how the different patterns of affiliation and dominance were experienced by the participants. For example, a high level of affiliation synchrony was experienced in more negative terms by the participants in the AS dyads than by those in the NT dyads, while a high level of dominance coordination was experienced in more positive terms by the participants in the AS dyads than by those in the NT dyads. The paper increases understanding of the details of the interactional deficits associated with AS and of the conditions in which AS participants may get maximally positive interactional experiences. More generally, our study highlights the necessity to take the study of individual differences in the experiences of patterns of affiliation and dominance into the official agenda of empirical interaction research.

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“…Interpersonal attitudes and personality characteristics have also been widely studied in relation to nonverbal behaviour (Argyle and Dean 1965;Burgoon and Poire 1999;Sadler et al 2009;Scherer 1978;Tusing and Dillard 2000). In this context, several authors have argued that nonverbal behaviour expresses two major dimensions that regulate interpersonal relationships: affiliation and dominance (Kiesler 1983;Mehrabian and Ksionzky 1972;Wiggins et al 1988).…”

Section: The Production and Perception Of Social Signals And Cuesmentioning

confidence: 99%

A psycho-ethological approach to social signal processing

Méhu

Scherer

2012

Cogn Process

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The emerging field of social signal processing can benefit from a theoretical framework to guide future research activities. The present article aims at drawing attention to two areas of research that devoted considerable efforts to the understanding of social behaviour: ethology and social psychology. With a long tradition in the study of animal signals, ethology and evolutionary biology have developed theoretical concepts to account for the functional significance of signalling. For example, the consideration of divergent selective pressures responsible for the evolution of signalling and social cognition emphasized the importance of two classes of indicators: informative cues and communicative signals. Social psychology, on the other hand, investigates emotional expression and interpersonal relationships, with a focus on the mechanisms underlying the production and interpretation of social signals and cues. Based on the theoretical considerations developed in these two fields, we propose a model that integrates the processing of perceivable individual features (social signals and cues) with contextual information, and we suggest that output of computer-based processing systems should be derived in terms of functional significance rather than in terms of absolute conceptual meaning.

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Are we on the same wavelength? Interpersonal complementarity as shared cyclical patterns during interactions.

Cited by 197 publications

References 58 publications

Frame-differencing methods for measuring bodily synchrony in conversation

Frame-differencing methods for measuring bodily synchrony in conversation

On the Asperger experience of interaction: Interpersonal dynamics in dyadic conversations

A psycho-ethological approach to social signal processing

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