Alexandra Paxton

Frame-differencing methods for measuring bodily synchrony in conversation

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 time-consuming; 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), 1634–1640, 2008); Ramseyer & Tschacher, 2008) have applied computer science and computer vision techniques to create frame-differencing 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.

Complexity matching in dyadic conversation

Recent studies of dyadic interaction have examined phenomena of synchronization, entrainment, alignment, and convergence. All these forms of behavioral matching have been hypothesized to play a supportive role in establishing coordination and common ground between interlocutors. In the present study, evidence is found for a new kind of coordination termed complexity matching. Temporal dynamics in conversational speech signals were analyzed through time series of acoustic onset events. Timing in periods of acoustic energy was found to exhibit behavioral matching that reflects complementary timing in turn-taking. In addition, acoustic onset times were found to exhibit power law clustering across a range of timescales, and these power law functions were found to exhibit complexity matching that is distinct from behavioral matching. Complexity matching is discussed in terms of interactive alignment and other theoretical principles that lead to new hypotheses about information exchange in dyadic conversation and interaction in general.

Argument disrupts interpersonal synchrony.

Research on interpersonal convergence and synchrony characterizes the way in which interacting individuals come to have more similar affect, behaviour, and cognition over time. Although its dynamics have been explored in many settings, convergence during conflict has been almost entirely overlooked. We present a simple but ecologically valid study comparing how different situational contexts that highlight affiliation and argument impact interpersonal convergence of body movement and to what degree emotional states affect convergence in both conversational settings. Using linear mixed-effect models, we found that in-phase bodily synchrony decreases significantly during argument. However, affective changes did not significantly predict changes in levels of interpersonal synchrony, suggesting that differences in affect valences between affiliation and argument cannot solely explain our results.

Movement dynamics reflect a functional role for weak coupling and role structure in dyadic problem solving.

Successful interaction requires complex coordination of body movements. Previous research has suggested a functional role for coordination and especially synchronization (i.e., time-locked movement across individuals) in different types of human interaction contexts. Although such coordination has been shown to be nearly ubiquitous in human interaction, less is known about its function. One proposal is that synchrony supports and facilitates communication (Topics Cogn Sci 1:305–319, 2009). However, questions still remain about what the properties of coordination for optimizing communication might look like. In the present study, dyads worked together to construct towers from uncooked spaghetti and marshmallows. Using cross-recurrence quantification analysis, we found that dyads with loosely coupled gross body movements performed better, supporting recent work suggesting that simple synchrony may not be the key to effective performance (Riley et al. 2011). We also found evidence that leader–follower dynamics—when sensitive to the specific role structure of the interaction—impact task performance. We discuss our results with respect to the functional role of coordination in human interaction.

PsyGlass: Capitalizing on Google Glass for naturalistic data collection

As commercial technology moves further into wearable technologies, cognitive and psychological scientists can capitalize on these devices to facilitate naturalistic research designs while still maintaining strong experimental control. One such wearable technology is Google Glass (Google, Inc.: www.google.com/glass), which can present wearers with audio and visual stimuli while tracking a host of multimodal data. In this article, we introduce PsyGlass, a framework for incorporating Google Glass into experimental work that is freely available for download and community improvement over time (www.github.com/a-paxton/PsyGlass). As a proof of concept, we use this framework to investigate dual-task pressures on naturalistic interaction. The preliminary study demonstrates how designs from classic experimental psychology may be integrated in naturalistic interactive designs with emerging technologies. We close with a series of recommendations for using PsyGlass and a discussion of how wearable technology more broadly may contribute to new or adapted naturalistic research designs.

Timescales of massive human entrainment.

The past two decades have seen an upsurge of interest in the collective behaviors of complex systems composed of many agents entrained to each other and to external events. In this paper, we extend the concept of entrainment to the dynamics of human collective attention. We conducted a detailed investigation of the unfolding of human entrainment—as expressed by the content and patterns of hundreds of thousands of messages on Twitter—during the 2012 US presidential debates. By time-locking these data sources, we quantify the impact of the unfolding debate on human attention at three time scales. We show that collective social behavior covaries second-by-second to the interactional dynamics of the debates: A candidate speaking induces rapid increases in mentions of his name on social media and decreases in mentions of the other candidate. Moreover, interruptions by an interlocutor increase the attention received. We also highlight a distinct time scale for the impact of salient content during the debates: Across well-known remarks in each debate, mentions in social media start within 5–10 seconds after it occurs; peak at approximately one minute; and slowly decay in a consistent fashion across well-known events during the debates. Finally, we show that public attention after an initial burst slowly decays through the course of the debates. Thus we demonstrate that large-scale human entrainment may hold across a number of distinct scales, in an exquisitely time-locked fashion. The methods and results pave the way for careful study of the dynamics and mechanisms of large-scale human entrainment.