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Much work on communication and joint action conceptualizes interaction as a dynamical system. Under this view, dynamic properties of interaction should be shaped by the context in which the interaction is taking place. Here we explore interpersonal movement coordination or synchrony—the degree to which individuals move in similar ways over time—as one such context-sensitive property. Studies of coordination have typically investigated how these dynamics are influenced by either high-level constraints (i.e., slow-changing factors) or low-level constraints (i.e., fast-changing factors like movement). Focusing on nonverbal communication behaviors during naturalistic conversation, we analyzed how interacting participants' head movement dynamics were shaped simultaneously by high-level constraints (i.e., conversation type; friendly conversations vs. arguments) and low-level constraints (i.e., perceptual stimuli; non-informative visual stimuli vs. informative visual stimuli). We found that high- and low-level constraints interacted non-additively to affect interpersonal movement dynamics, highlighting the context sensitivity of interaction and supporting the view of joint action as a complex adaptive system.
Human interaction is a complex and dynamic process. From the subtle modulation of speech to the dynamic displacement of the body in posture or gesture, humans must fluidly organize behavior in time across multiple modalities to interact effectively with one another. Contributing to the ongoing debate about the underlying mechanisms of interpersonal processes (for reviews, see Brennan et al., 2010; Dale et al., 2013; Barr, 2014; Paxton et al., 2016), we here build on previous work (Paxton et al., 2016) to propose that context is critical for understanding how interaction unfolds. By using advances in wearable technology (Paxton et al., 2015) to manipulate task parameters during an interactive experiment, we explore the influence of context on dynamics of body movement during conversation and turn to a particular theoretical framework to help understand it: dynamical systems theory (DST).
From biomes to hurricanes, many physical and biological systems are recognized as complex dynamical systems. These systems exhibit what are called emergent properties—that is, characteristic behaviors that emerge not by instructions from some top-down controller but as a function of local interactions among the component parts within given contextual pressures. A famous example of this is the so-called “butterfly effect.” This principle suggests that subtle factors in a present context may cascade into larger effects, which themselves serve as a context that constrains ongoing behavior (e.g., Lorenz, 1963). While it began in the physical and mathematical sciences, DST has become a powerful lens for understanding human behavior and cognition as well (Barton, 1994; Mathews et al., 1999).
DST—along with other complexity sciences (cf. Mathews et al., 1999)—provides a conceptual and analytic framework to capture the context-sensitive, soft-assembled, emergent properties of cognitive, behavioral, and affective phenomena. Though its influence is still growing in psychology more broadly, DST principles and analyses have led to novel insights into such phenomena as reading (e.g., Van Orden and Goldinger, 1994), gaze (e.g., Engbert et al., 2005), cultural evolution (e.g., Kenrick et al., 2003), general cognitive function (e.g., Van Orden et al., 2003), and more. DST—and, more specifically, a branch called synergetics (Haken, 1977)—has significantly influenced the understanding of self-organizing principles in cognition (e.g., Haken, 1990; Stadler and Kruse, 1990; Haken and Portugali, 1996).
Increasingly, cognitive scientists interested in social phenomena are recognizing the value of DST to understanding human interaction (e.g., Vallacher et al., 2002; Coleman et al., 2007). Within this area, DST may be uniquely equipped to explore interpersonal coordination—the idea that individuals influence one another's behavior, cognition, and emotion as a result of their interaction. By shifting analysis away from the individual and conceptualizing the dyad as the focus of analysis, we can begin to explore the behavioral, cognitive, and emotional dynamics that emerge from the contextual pressures constraining the dyadic system—like the specific task or type of conversation in which the dyad is engaging.
Interpersonal coordination has been an increasingly influential way to capture interpersonal dynamics over the last few decades (Condon and Sander, 1974). This phenomenon has been studied under a variety of names—like accommodation, alignment, the “chameleon effect,” contagion, coordination, coupling, mimicry, synchrony, and synergy1. Interestingly, the idea that coordination and other behaviors are adaptive in the DST sense extends to even some of the earliest works in this domain (Sander, 1975).
Within interpersonal coordination research, the interpersonal synergies perspective has perhaps the strongest connection to DST ideas (Riley et al., 2011). Historically, most work on interpersonal coordination has tended to be characterized by what we have called a “more is better” perspective (see Abney et al., 2015). This perspective holds that individuals tend to become more similar over time as a result of their interaction and that this increased similarity tends to be better for a variety of interaction outcomes (e.g., Pickering and Garrod, 2004).
However, the interpersonal synergies perspective posits that interpersonal dynamics are fundamentally shaped by a variety of factors that exert pressure on the interpersonal system. Under this view, interacting participants will not necessarily become uniformly more similar over time. Instead, different contextual factors—like interactants' relationship, goals, physical or perceptual environment, affordances (in the Gibsonian sense; e.g., Gibson et al., 1999) and conversation type—will lead to different configurations of behavioral channels (e.g., Fusaroli and Tylén, 2016).
Inspired by research on DST, we have elsewhere proposed a classification system for different components of an interaction (Paxton et al., 2016), dividing the influences on communication dynamics into top-level and bottom-level systems. Top-level systems function at a lower frequency, change over longer timescales, and tend to have fewer degrees of freedom; bottom-level systems, by contrast, function at a higher frequency, can change over very short timescales, and tend to have more degrees of freedom2. Examples of top-level systems would include conversational contexts and interpersonal relationships; bottom-level systems would include body movement or phonetics.
Studies of coordination often focus on only one of these systems at a time—like how coordination influences rapport (e.g., Hove and Risen, 2009) or how perceptual information influences coordination (e.g., Richardson et al., 2007a). In this paper, we explore how simultaneous constraints on both systems influence coordination: high-level contextual constraints (i.e., those affecting the overarching top-level systems) and low-level contextual constraints (i.e., those affecting the rapidly changing bottom-level systems).
Approaching nonverbal social behavior during conversation from the synergies perspective, the present study focuses on how high- and low-level contextual constraints can change interpersonal coordination over time in naturalistic interaction. Specifically, we explored how conversation type—whether argument or a friendly conversation—and perceptual information—either informative or noisy perceptual signals3—altered coordination of interacting participants' head movements. We proposed four hypotheses, guided by previous findings.
Keeping with our earlier work (Paxton and Dale, 2013a,b), we use “coordination” as a general term for the idea that individuals affect one another's behavior over time as a result of their interaction. We use “synchrony” as a specific case of coordination: Interacting individuals are synchronized to the extent that they tend to exhibit the same behavior at the same time. Although we do not explore time-locked phase synchrony here (cf. Richardson et al., 2007a), we use time series analyses to quantify whether interacting individuals generally tend to behave similarly in time.
Previous work suggests that interacting individuals' gross body movements (Nagaoka and Komori, 2008; Paxton and Dale, 2013a) and head movements specifically (Ramseyer and Tschacher, 2014; Paxton et al., 2015) become more similar over time as a result of their interaction. Therefore, we expect that we will find that participants' head movement will be synchronized. That is, we anticipate that participants will be more likely to move (or not move) their heads at the same time than not.
Mounting evidence suggests that coordination dynamics are sensitive to high-level contextual perturbations (Miles et al., 2011), including conversation type (Paxton and Dale, 2013a; Abney et al., 2014; Main et al., 2016). Despite some exceptions—for example, when analyzing gaze patterns (Paxton et al., under review) and when discussing assigned (rather than personally held) beliefs (Tschacher et al., 2014)—conflict has been found to decrease interpersonal synchrony (Paxton and Dale, 2013a; Abney et al., 2014). We therefore expect to find some difference in movement synchrony between the two conversation types (H2); directionally, we expect that argument will decrease synchrony (H2A).
Low-level contextual constraints—like perceptual information—have been relatively less studied in coordination research. This may have been due to limitations in previous experiment tools: Any perturbations to the dyadic system have had to expose both participants in a naturalistic, face-to-face interaction to the same environmental stimulus. A previous study found that holding a conversation over loud ambient noise—as compared with an otherwise silent room—led to an increase in head movement synchrony (Boker et al., 2002). This supports the idea that interpersonal coordination may serve to boost the “signal” in communication within the “noise” of the environment (Richardson and Dale, 2005; Shockley et al., 2009).
Although the concept of “information” has a variety of meanings within cognitive science, we here simply mean that the signal is imbued by the participant as having relevance to some task. For the present study, this is not a signal that is relevant to the conversation itself but to another memory task. It is contrasted with signals in the environment that are not directly relevant to any task at hand—signals that we may call “noise.” Crucially, in the current study, both sets of stimuli are otherwise identical, allowing us to disentangle the effects of the stimulus itself and the information imbued in the signal by the interlocutor.
The current study extends previous work to see whether visual “noise” can serve the same function as auditory noise—boosting synchrony and, possibly, comprehension. We hypothesize that nonverbal communication signals will respond to low-level contextual constraints or perturbations (H3). Directionally, we expect that noise will increase synchrony (H3A).
While previous studies have focused on the effects of either high-level constraints (e.g., Miles et al., 2011; Paxton and Dale, 2013a; Abney et al., 2014; Main et al., 2016) or low-level constraints (e.g., Boker et al., 2002; Richardson et al., 2007a), we are unaware of any studies to date that have combined the two. We see our work as providing a vital step in the exploration of interaction and coordination under the DST perspective: If communication is a dynamical system, we would expect to see that behavior is context sensitive and does not uniformly react to all constraints (cf. Riley et al., 2011; Paxton et al., 2016). Therefore, we hypothesize that head movement synchrony will be non-additively sensitive to both high- and low-level constraints; however, as the first such study of these simultaneous dynamics (of which we are aware), we do not have a directional hypothesis.
Forty-two undergraduate students from the University of California, Merced participated as 21 dyads. Dyads were created as participants individually signed up for experiment appointments per their own availability through the online subject pool system. Each participant received course credit in return for participation. By chance, dyads included some pairs of women (n = 9; 43%), some pairs of men (n = 3; 14%), and mixed-gender pairs (n = 9; 43%) according to participants' self-reported gender identities. Participants in 2 dyads reported being acquainted with one another prior to the experiment (10%).
Additional dyads—not included in the counts above—participated but were not analyzed here. Two (2) additional dyads were excluded due to lack of conflict in the argumentative conversation, as we have done in previous work using a similar paradigm (Paxton and Dale, 2013a).
We also experienced technical difficulties with the servers running our data collection program for a number of additional dyads. In order to be included in the present analysis, each participant in the dyad must have had recorded movement data for at least 4.5 min (including the calibration period; see Section 2.3) of each of the two conversations described in Section 2.2. In an additional 21 dyads (not included in the counts above), the server failed to record the minimum 4.5 min of movement data for at least 1 of the 2 participants in at least 1 of the 2 conversations. This occurred because the program used to run the data collection software prioritizes fidelity of the connection to the data collection server above all (see Paxton et al., 2015); any perturbation of that connection causes the program to be terminated. However, until the point of termination, data were continuously and regularly sampled.
For example, assume that participants A and B are participating in the experiment. For the first 3 min, the movements of participants A and B are sampled regularly (according to Section 2.2). At minute 4 of the first conversation, participant A's connection to the server is perturbed, causing the server to disconnect participant A's movement tracker. Participant A's regularly sampled data for the first 3 min are saved, but no further data for participant A are recorded, although the conversation continues as usual. Participant B's tracker, however, remains connected to the server, and after being regularly sampled for the rest of the 8-min conversation, participant B's data are saved. Even if all 8 min of movement data were successfully saved for both participants in the second conversation, this dyad would be excluded from our analysis. Although participant A has an unbroken 3-min movement time series from the first conversation and an unbroken 8-min movement time series from the second conversation, this dyad would not have the minimum 4.5 min of movement data for both participants in both conversations.
Although this prioritization led us to discard a number of dyads due to insufficient data, it also allowed us to ensure that the behavior of the included dyads were continuously and regularly sampled during the experiment—leading to very few missing samples in the included dyads. We chose this cutoff prior to analysis and did not explore other thresholds for inclusion.
This study was carried out in accordance with the recommendations of Institutional Review Board of the University of California, Merced, with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the Institutional Review Board of the University of California, Merced.
As noted below (see Section 2.2.2), the informed consent process did not give any foreknowledge of the specific phenomenon (i.e., similarity of movement), conversation prompts or topics, nor the hypotheses of the study. Data were collected by research assistants blind to study hypotheses for 20 of the 21 dyads; data for the remaining (1) dyad was collected by the first author. The first author also assisted in data collection for 4 dyads.
The experiment had one within-dyads and one between-dyads element. Conversation type was a within-dyads condition and was adapted from Paxton and Dale (2013a): Each dyad had one argumentative conversation and one affiliative conversation. Conversation order was counterbalanced (randomly assigned) to prevent order effects. For the between-dyads condition, each dyad was randomly assigned to a “noise” (n = 9; 43%) or a “dual-task” (n = 12; 57%) condition4. Both conditions are described in greater detail below.
Upon arriving, participants were separated and led to private (semi-enclosed) areas with desks within the lab. Each was then given a series of questionnaires, including a sociopolitical opinion questionnaire. The opinion questionnaire neutrally inquired about the participant's opinion on a variety of issues (e.g., abortion, death penalty, marriage equality5, whether Spanish should be an official U.S. language, whether student loans should be partially forgiven by the U.S. government). The participant responded to each question in a brief, open-ended response area and by indicating opinion strength on a 1 (feel very weakly) to 4 (feel very strongly) scale.
After both participants completed the questionnaires, they were brought together in a small, private space. Participants were seated facing one another in stationary chairs approximately 0.97 m (3.17 feet) apart (measured at the front legs). Participants were told that they would be having “two conversations for about 8 min each” for a study “about how people hold conversations,” but no information about the nature or emotional valence of the prompts was given. (If asked, participants were told that they would be given the conversation topic immediately before beginning each conversation.) The experimenter then told the participants to take a few minutes to introduce themselves to one another while the experimenter stepped outside of the room to complete some last-minute paperwork before beginning the experiment.
The experimenter then left the room for approximately 3 min. Unknown to the participants, the experimenter spent this time comparing the two participants' opinion surveys to identify up to 3 topics for which participants (a) wrote the most differing opinions and (b) indicated the strongest opinions. We refer to these as “candidate argumentative topics” below.
After 3 min, the experimenter re-entered the room and gave each participant a Google Glass (Alphabet, Inc.), a piece of wearable technology worn like glasses that features a small quartz screen over the wearer's right eye and an on-board processor on the wearer's right temple. The experimenter then explained the device to the participants, adjusted the Glass (as necessary) to fit each participant, and tested to ensure that each participant could fully see the screen. (For complete fitting procedure, see Paxton et al., 2015.) Participants were reminded that they would be having “a couple of conversations about different topics” and that they would “[be given] the topic for each conversation right before [they] start.” They were told that the Google Glass would be “recording information about the conversation,” but the nature of the recorded information was not described in detail to avoid drawing participants' attention to their head movements6.
Each Google Glass ran the PsyGlass program (Paxton et al., 2015). Once initialized, each participant's PsyGlass program randomly generated a screen color at 1Hz (i.e., 1 color per s), with a 0.9 probability of generating a blue screen and a 0.1 probability of generating a red screen. (If colort were the same as colort+1, the screen did not flicker or otherwise indicate that it was refreshing). PsyGlass also recorded participants' head movements by measuring the three-dimensional accelerometer data (i.e., x, y, z axes, with the origin point set as the position at the time of initialization) at 250 Hz (i.e., 1 sample per 4 ms), which were transmitted to and stored on the experiment server at 4 Hz (i.e., 1 transmission per 250 ms). All code for the program can be downloaded from the GitHub (GitHub, Inc.; http://www.github.com) repository for PsyGlass (http://www.github.com/a-paxton/PsyGlass).
Before initializing PsyGlass on each participant's device, both participants were given instructions about their between-dyads condition. All dyads—across both task conditions—were exposed to the same stimuli through PsyGlass, the same red-and-blue screens. The two conditions differed only in the interpretation or significance of the colored screens. In the noise condition, the dyad was told that the flashing screens were a result of a bug in the program and that participants should have conversations as normal. In the dual-task condition, the dyad was told to remember the number of times that the screen turned red while having their conversation and that they would be asked to write that number down after the conversation was finished (similar to the oddball paradigm; Squires et al., 1975). After answering any participant questions, the PsyGlass program was initialized.
Again, each dyad held 2, 8-min conversations—one affiliative conversation and one argumentative conversation. In both cases, participants were instructed to stay on the assigned topics or on topics very similar to the assigned topic. After assigning each prompt, the experimenter remained seated behind a computer outside of the participants' immediate peripheral vision, surreptitiously monitoring the conversation.
The affiliative prompt was identical across all dyads, asking them to discuss media that they both enjoyed, find something that they both enjoyed, and talk about why they liked it. The goal of the affiliative prompt was to emphasize similarity and engender rapport between participants.
The argumentative prompt relied on the candidate argumentative topics identified from the opinion surveys. The prompt asked participants to discuss their views on the top-rated candidate topic (again using neutral phrasing) and asked participants to “try to convince one another of [their] opinions.” If the conversation stopped altogether or shifted away from being argumentative in nature (e.g., if both participants came to a consensus), the next highest-rated candidate topic was assigned. If the second candidate topic again failed to produce sustained argumentative conversation, the third candidate topic was assigned.
After initializing PsyGlass for the first time, the prompt for the first (randomly assigned) conversation type was given. After 8 min of conversation, the experimenter informed the participants that their conversation was over, and PsyGlass was terminated. Participants then removed their Google Glass and were led to their private desks to complete two brief questionnaires about the conversation (not analyzed here), including—for dual-task condition dyads—the number of times they had seen a red screen. Once both participants had completed the questionnaires, participants were brought back to the joint space and re-fitted with the Google Glass. After ensuring that both participants could again see the entire screen, PsyGlass was initialized, and the remaining prompt was given.
Participants were not given any foreknowledge about the topics or type of conversation before being assigned the relevant prompt. That is, if participants were assigned to have the affiliative conversation first, they had no knowledge that their second conversation would have an argumentative prompt; the same applied if the participants had had the argumentative conversation first.
Each participant produced one movement time series for each conversation. The time series captured timestamped accelerometer values along x, y, z axes. After applying an anti-aliasing zero-phase fourth-order Butterworth filter, we downsampled the data to 10 Hz, a sampling rate similar to those utilized in our previous movement coordination work (Paxton and Dale, 2013a; Abney et al., 2015; Paxton et al., 2015). We transformed these three-dimensional values into a single value for acceleration at each time point by taking the three-dimensional Euclidean distance of the time series. We then applied a smoothing zero-phase second-order Butterworth filter to the acceleration signal for each participant.
We then trimmed the movement data to remove the time between the PsyGlass initialization and the beginning of the conversation data. Immediately before beginning each conversation (i.e., after having been given the appropriate prompt), participants were asked to produce a brief bout of high-velocity head movement (nodding and shaking their heads rapidly). This was done under the guise of “initializing the program” but was used as a marker for the beginning of the conversation data.
Because each dyad took 60–120 s to test PsyGlass and hear the conversation prompt, we used derivatives of acceleration to identify the latest moment of intense movement by both participants during that window. We explored both acceleration's first-order (jerk) and second-order (jounce) derivatives to identify possible markers. The cutoff points identified by jerk and jounce were significantly correlated, r = 0.62, t(40) = 4.94, p < 0.0001. However, because jounce produced more conservative (i.e., later) estimates of cutoff times, we used jounce.
Cutoff times for each conversation were created at the dyad level. For each participant in each dyad, we identified the time of largest jounce in the first 60–120 s of the conversation. We then chose the more conservative (i.e., later) cutoff point of the two participants, which we applied as both participants' cutoff points.
Finally, we truncated both participants' time series in each conversation to the shorter of the two lengths, if they were not already identical (e.g., due to server failure). Because PsyGlass initializes data collection simultaneously for both participants (Paxton et al., 2015), we did not need to time-align the beginning of the conversation.
After applying these cutoffs, conversations had an average of 6.54 min (range = 2.62–9.26 min) of recorded movement data. It is important to emphasize that dyads completed the full experimental conditions even when they did not have complete movement records: Connectivity issues with the experimental server only resulted in a failure to record the movement time series after the perturbation to the data collection server occurred. We find it important to note because if participants had experienced different experimental conditions (e.g., if some had held only a 5-min first conversation while others had held an 8-min first conversation), we could not infer that the intended manipulations (i.e., conversation type and task condition) were the cause of any effects, rather than any of the unintended conditions (e.g., shorter conversations).
We measured coordination by combining cross-recurrence quantification analysis (CRQA) and growth curve analysis (GCA). This combination allows us to quantify the amount of moment-to-moment coordination occurring between interacting participants, along with longer-scale trends. We describe these techniques briefly below, but a more detailed explanation of the benefits of using CRQA and GCA together can be found in Main et al. (2016)7. We then used a linear mixed-effects model to analyze the resulting data.
CRQA is an outgrowth of recurrence quantification analysis (RQA), a nonlinear time series analysis that captures the structure and patterns of states visited by a single dynamical system over time (Eckmann et al., 1987). CRQA extends RQA by capturing the amount to which two different systems co-visit similar states in time and has become a staple for analyzing human data from a dynamical systems perspective (e.g., Shockley et al., 2003; Dale and Spivey, 2006; Richardson et al., 2007b; Gorman et al., 2012; Anderson et al., 2013; Fusaroli et al., 2014; Vallacher et al., 2015). Detailed explanations of CRQA and its applications in a variety of settings are available in Marwan et al. (2007), Coco and Dale (2014), and Main et al. (2016).
In our case, CRQA allows us to quantify when two participants moved in similar ways during conversation. Unlike studies of more rhythmic movements (e.g., tapping to a metronome), head movement dynamics during conversation comprise both periodic (e.g., underlying postural sway) and non-periodic (e.g., nodding intermittently during conversation) components—leaving phase-coupling analyses (e.g., Richardson et al., 2007a) less suitable for our current purposes. We chose CRQA as a method that does not assume or require periodicities and that can be more resilient to the noise inherent in a new method (i.e., measuring interpersonal dynamics with head-mounted accelerometers in Google Glass).
Current best practices for continuous CRQA include reconstructing the phase space for each pair of signals using time-delay embedding (Shockley et al., 2003; Riley and Van Orden, 2005) and then calculating recurrent points by identifying the radius size at which overall recurrence rate (RR) of the plot is equal to 5% (cf. Marwan et al., 2007; Konvalinka et al., 2011). More detailed information on phase space reconstruction and embedding are available from March et al. (2005) and Iwanski and Bradley (1998). We follow these best practices to calculate CRQA for each conversation of each dyad8. The parameters for each dyad are available in the OSF and GitHub repositories for the project (see Section 3).
CRQA was implemented in R (R Core Team, 2016) using the crqa library (Coco and Dale, 2014). We obtained the diagonal recurrence profile (DRP) for each conversation of each dyad. The DRP captures how much coordination occurs within a “window” of relative time between participants. Here, we target a window of ±5 s, consistent with previous work on body movement coordination generally (Paxton and Dale, 2013a) and head movement specifically (Ramseyer and Tschacher, 2014). With a sampling rate of 10 Hz, this creates a window of interest of ±50 samples. Intuitively, the DRP can be read much like a cross-correlation profile (Paxton and Dale, 2013a), with some differences. (For more on the differences between DRPs and cross-correlation profiles, see Main et al., 2016.)
Essentially, the DRP allows us to explore similarities in patterns of movement that are independent of absolute time while revealing patterns of relative time. The DRP captures leading and following patterns along with simultaneous movement. In other words, we are able to use DRPs to see, at any given time in the conversation, whether participants are more likely to be moving in similar ways (i.e., higher rate of recurrence or RR) or in dissimilar ways (i.e., lower rate of recurrence or RR).
Because both participants will have the same length time series (because of identical sampling rates within the experiment), Participant A and Participant B will both have samples for all time points, t. The DRP compares Participant A's head movement at t with Participant B's head movement at t − 50, …, t, …, t + 50. When Participant B's t < 0, the DRP captures the degree to which Participant B leads the movement state for Participant A at t; when Participant B's t > 0, the DRP captures the degree to which Participant A at t leads the movement state for Participant B. When we compare Participant A's movement at t with Participant B's movement at t, the DRP captures the amount to which both participants engaged in movement at the same time. The DRP also captures the reverse—comparing Participant B's head movement at t with Participant A's head movement at t − 50, …, t, …, t + 50.
GCA is a time series analysis used to quantify the degree to which changes over time can be best described by various orthogonal polynomials (Mirman et al., 2008). Rather than assuming that data are described by a linear relationship, GCA determines how well the data are fit by polynomial relationships (e.g., linear, quadratic, cubic) and disentangles the contribution of each polynomial independently. In the current analysis, we focus only on the first- and second-order orthogonal polynomials.
In other words, GCA allows us to distinguish how much the linear and quadratic forms separately contribute to the overall shape of the data. As a result, GCA is a powerful technique for quantitatively comparing DRPs, allowing us to explore leading/following patterns (with the linear lag term) and coordination patterns (with the quadratic lag term).
All data analysis was performed in R (R Core Team, 2016). Using the lme4 library (Bates et al., 2015), we created a linear mixed-effects model to quantify the effects of linear lag (LL; leading/following) and quadratic lag (QL; coordination) with conversation type (within-dyads; dummy-coded: affiliative  or argumentative ) and task (between-dyads; dummy-coded: dual-task  or noise ) on head movement recurrence rate (RR). Dyad and conversation number were included as random intercepts; for both random intercepts, we included the maximal random slope structure that permitted model convergence using backwards selection per current best practices for linear mixed-effects models (Barr et al., 2013). Compared against the random-intercepts-only model, the maximal model justified by the data better fits the data; these results are provided in the supplemental repositories for the project (see Section 3).
As discussed below (see Section 3), our data and analysis materials—including code with the precise specifications for all models—are freely available in public repositories for the project. For interested readers, we here provide the single-equation mathematical form of our linear mixed-effects model using Barr et al.'s (2013) conventions:
Equation (1) estimates the recurrence rate RR for any dyad d at lag t. It does so by estimating the global coefficients—notated as β1,…,15—for each fixed effect: conversation type c, task condition k, linear (i.e., orthogonal first-order polynomial) lag l, quadratic (i.e., orthogonal second-order polynomial) lag q, and all interaction terms. Random intercepts for dyad identity D0 and conversation number N0 are included. We also include the maximal slope structure that permit model convergence using backwards selection from the fully maximal model in accordance with current best practices (Barr et al., 2013). The fixed effects included in the maximal slope structure for random intercepts are noted above (βn + Dnd + Nnd).
Although we report effects of LL in the model (noted l above), we are cautious in interpreting them. Participants were paired by a fairly random process (i.e., by individual sign-ups for open experimental timeslots that did not allow participants to see their partner's identity) and were randomly assigned to their seat in the interaction space (i.e., by arrival time; each chair was closer to one or the other of the private questionnaire spaces). Unlike previous studies (Main et al., 2016), we had no a priori expectations about or reasons to expect leading/following behaviors; therefore, we refrain from deeply interpreting any LL results.
In keeping with recommended baselines for nonlinear analyses, we also create a baseline using a Fourier phase-randomization analysis (Theiler et al., 1992; Kantz and Schreiber, 2004). Phase randomization creates a surrogate dataset that contains the same power spectrum as the real data but differs in phases, retaining the autocorrelations of the original time series. Here, we use the nonlinearTseries package (Garcia, 2015) in R (R Core Team, 2016) to create 10 phase-randomized surrogate time series for each conversation of each dyad to provide a more robust baseline analysis. We then perform CRQA over these new time series using the same parameters as the real data. Essentially, the resulting recurrence dynamics capture the amount of similarity that emerges by chance between the two time series (in this case, interacting individuals)9.
In our Supplementary Materials on GitHub and the OSF (see Section 3), we also perform a baseline analysis using a sample-wise shuffled baseline, a more common baseline technique in interpersonal coordination research that breaks temporal correspondence between two time series by separately randomizing (or shuffling) the order of each sample from the real behavior time series (Dale et al., 2011; Louwerse et al., 2012). Although this destroys more inter-sample dependencies, the sample-wise shuffled baseline also destroys the autocorrelation of the time series. This creates a somewhat less conservative baseline, as shuffled baselines cannot strongly account for the hysteresis of the system. Because the samples are shuffled independently, the temporal dynamics of shuffled baselines through their reconstructed phase-spaces are not influenced by their previous time-steps. By retaining the autocorrelation of the individual time series in the phase-randomization surrogate analysis, we are able to account for the chance that two individual time series might “live” in similar regions for some amount of time simply due to their own dynamics, rather than the influence of the other time series.
We provide the results from analyses using the sample-wise shuffled baseline in our Supplementary Materials (see Section 3). The results are highly similar to those performed against the phase-randomization baseline, although our results suggest that the phase-randomization baseline provides a more conservative metric for the amount of synchrony that might occur by chance.
We have made data and code (including code for data preparation and analysis) for the project freely available according to current best practices for data stewardship. Due to the nature of self-disclosure in the conversation data (especially in the argumentative context), we were permitted to release only limited information about each dyad: de-identified movement time series for each participant in each conversation, the dyad's assigned experimental condition, and the dyad's gender makeup.
Current best practices for open science include the sharing of data and code in public repositories (see Nosek et al., 2015; Blohowiak et al., 2016; Gewin, 2016; Kidwell et al., 2016). Two prominent venues for storing and sharing materials are the Open Science Framework (OSF; http://osf.io) and GitHub (GitHub, Inc.; https://www.github.com/). Both OSF and GitHub serve as platforms to share materials, promote community contribution, and facilitate open re-use (and re-analysis) of materials by others through appropriate attribution. Furthermore, the OSF allows researchers to “freeze” specific versions of the project—for example, at the point of publication (as we have done here)—providing a crystallized, unmodifiable snapshot of all files at that time.
All code can also be freely accessed through our project's GitHub repository: https://www.github.com/a-paxton/dual-conversation-constraints
All analyses were performed in accordance with the model specifications described in Section 2.4.3. We here present only the standardized model, as it allows us to interpret estimates as effect sizes (see Keith, 2005). (The unstandardized model is available in the project's OSF and GitHub repositories; see Section 3.) Full standardized model results are presented in Table Table1.1. For clarity within the text, we reference main and interaction terms in parentheses within the text so that readers can easily find the relevant values in Table Table11.
Results indeed suggested that high- and low-level constraints influence coordination dynamics—even in some unexpected ways. Contrary to our hypothesis H1, we did not find evidence of overall time-locked synchrony. Participants' head movements were, in fact, better described by a turn-taking pattern with slight leading-following dynamics (LL × QL).
Consistent with our hypotheses H2 and H2A—and replicating our previous findings (Paxton and Dale, 2013a)—we found that argument significantly decreased RR compared to affiliative conversations (conversation; see Figure Figure1).1). Conversation also affected moment-to-moment coupling dynamics: Recurrence during the affiliative conversations was higher but more diffuse, while recurrence in the argumentative conversation was lower and showed a distinct turn-taking pattern (conversation × QL).
Interestingly, although we hypothesized that the noise condition would increase RR compared to the dual-task condition, we did not find a significant main effect of task condition (task). Instead, we found that task affected the dynamics of coordination only in conjunction with other pressures (task × conversation × QL). We explored these patterns in greater depth by analyzing each of the conversation types (i.e., affiliative and argumentative conversations) separately.
Results for the standardized models exploring the complex interaction term are presented in Table Table2.2. For clarity, we again refer in the text only to the model variables so that readers can find the relevant statistics in the model. As with the first model, we ran both standardized and unstandardized versions of these models, but we present only the standardized models in the text. Additional information—including the unstandardized models—can be found in the OSF and GitHub repositories for the project (see Section 3).
As in the main model, both follow-up models showed that head movement showed turn-taking patterns with some leader-follower dynamics (LL × QL). No other effects reached significance in the post-hoc analyses of the affiliative conversations.
The results of the post-hoc analyses of argumentative conversations, however, showed context-sensitive responses to low-level constraints. Overall, participants demonstrated a much stronger turn-taking pattern of head movement during argumentative conversations (QL). These effects were much more pronounced during the dual-task condition than in the noise condition (task × QL), with recurrence exhibiting the characteristic U-shaped DRP of turn-taking behavior.
The patterns outlined above hold even compared to baseline measures of synchrony. For brevity, tables of results comparing real data to phase-randomized surrogate baseline data are included in Appendix. Table TableA1A1 is the companion to Table Table1;1; Table TableA2A2 is the companion to Table Table2.2. In these tables, the “data” variable refers to either the baseline surrogate data (data = −0.5) or the real experimental data (data = 0.5).
Again, only the standardized models are presented in the text of the current paper. Unstandardized models—along with standardized and unstandardized models performed with the sample-wise shuffled baseline—are available on the project's GitHub and OSF repositories (see Section 3). Due to the complexity of the overall model (Table (TableA1),A1), we use the post-hoc models (Table (TableA2)A2) as a framework for discussing the results.
Strikingly, these results suggested that the level of recurrence observed in the affiliative conversations was not overall significantly different from baseline (data), although the two datasets did differ in their dynamics (data × LL × QL). The surrogate data showed significantly lower leading-following patterns (data × LL) and exhibited no turn-taking nor synchrony patterns (data × QL significant, but not QL).
The affiliative conversations also differed from baseline with the task data. The results suggested a trend toward significantly higher overall recurrence in the noise condition than we would expect to see by chance, although it did not reach significance (data × task). We did, however, find a significant difference in the coordination dynamics between the two task conditions (data × task × QL): Compared to the flat recurrence profile of the baseline in both conditions, the dual-task condition demonstrated more of the inverted-U-shape of synchrony, while the noise condition demonstrated more of the U-shape of turn-taking.
Unlike the affiliative conversations, we found that levels of recurrence were—overall—significantly lower than baseline (data). In other words, participants coordinated with one another even less than what would be expected by chance, and that decreased recurrence was more likely to appear in a turn-taking pattern (data × QL) with some leader-follower effects (data × LL).
Task constraints also exerted significant effects on the dynamics of recurrence. In addition to showing different leader-follower behaviors across the two tasks (data × task × LL), the data revealed differences in the temporal patterning of movement across the two tasks (data × task × QL). Essentially, participants' head movements showed much stronger turn-taking patterns in the argumentative conversations in the dual-task condition than in the noise condition, which had a relatively flat recurrence profile.
Communication is a rich, complex phenomenon that plays a central role in daily human life. We use conversation flexibly, allowing us to engage in mundane transactions, bond over shared interests, collaborate to complete joint tasks, and argue about our political opinions. Although these different communicative contexts are part of our everyday experiences, the scientific study of these dynamics have largely centered on friendly or collaborative contexts. The current study aimed to contribute to a fuller picture of communicative dynamics by investigating how conflict (a high-level contextual perturbation) and rapidly changing visual information (a low-level contextual perturbation) interact to affect the dyadic system.
Here, we specifically targeted interpersonal synchrony of head movement—that is, the similarity of participants' head movement over time during their interaction. We used PsyGlass (Paxton et al., 2015), a stimulus-presenting and movement-recording application on Google Glass, to capture the acceleration time series of participants' head motion during naturalistic conversations shaped by high-level (i.e., argumentative or affiliative conversational context) and low-level (i.e., noise or dual-task visual information condition) constraints. From the theoretical position that human interaction is a complex adaptive system, we hypothesized that interaction dynamics should be sensitive to each of these constraints.
Our analyses found support for some—but not all—of our hypotheses. Taken together, our results support the idea of interaction as a complex adaptive system while highlighting inconsistencies within previous literature and suggesting avenues for future research.
Perhaps most unexpectedly, we did not find support for our hypothesis that participants would be synchronized in their head movement patterns (H1). Instead, participants' head movement tended to exhibit time-lagged synchrony or turn-taking dynamics (cf. Butler, 2011). These results stand in contrast with previous work on head movement synchrony, which has shown that individuals tend to synchronize their head movements during conversation.
Interestingly, these patterns resemble those observed in speech signals during friendly and argumentative conversations (Paxton and Dale, 2013c). Of course, this suggests that the current measure of head movement may be influenced by speaking. Future work should disentangle the ways that intrapersonal coupling of head movement and speaking may influence interpersonal head movement coordination.
However, relatively little research has targeted head movement synchrony, and the existing work in this area has used very different methods and analyses. For example, Boker et al. (2002) (a) tracked head movements with passive three-dimensional motion-tracking sensors at 80 Hz, (b) analyzed Euclidean velocity, (c) did not mention whether a filter was used on the movement time series, (d) calculated synchrony through windowed cross-correlation (i.e., a linear time series analysis) and (e) did not use a baseline. On the other hand, Ramseyer and Tschacher (2014) (a) tracked head movements through video (i.e., by quantifying displaced pixels from frame to frame in a region of interest around the head) at an unspecified sampling rate, (b) analyzed a “flattened” velocity (i.e., 2D projection of 3D movement), (c) filtered movement signals with an unspecified filter, (d) calculated synchrony as the absolute value of the windowed cross-correlation coefficients between participants, and (e) used a “window-wise” shuffled baseline (i.e., preserving local structure within the data by shuffling 1-min chunks rather than shuffling all samples independently). By contrast, we (a) tracked head movements with active head-mounted sensors at 10 Hz (after downsampling), (b) analyzed Euclidean acceleration, (c) filtered movement signals with a low-pass Butterworth filter, (d) calculated synchrony with cross-recurrence quantification analysis (i.e., a nonlinear time series analysis without windowing), and (e) used a phase-randomized baseline (and, in our Supplementary Materials, a sample-wise shuffled baseline). Future work should explore the degree to which these and other factors may influence findings of head movement synchrony. Our task also differed by integrating high- and low-level constraints. We turn to these next.
Conversational context modulated these patterns of coordination (supporting H2). Consistent with previous research (Paxton and Dale, 2013a), we also found support for our directional hypothesis. Argument decreased synchrony (supporting H2A): Participants moved in more dissimilar ways during argumentative conversations relative to affiliative ones.
The way in which the two high-level contexts influenced synchrony was particularly interesting. Synchrony during friendly conversations was indistinguishable from chance, while synchrony during argumentative arguments was significantly lower than what would be expected by chance. This contrasted with our previous work (Paxton and Dale, 2013a), which found that overall body movement synchrony during friendly conversations was higher than expected by chance and that synchrony during arguments was not significantly different than chance. However, this again would be consistent with the patterns observed in speech rather than movement (Paxton and Dale, 2013c), as mentioned earlier.
We also found that low-level contextual constraints influenced coordination dynamics (supporting H3), but the results surrounding our directional hypothesis were more nuanced (H3A). We found no significant differences in the overall levels of synchrony in the presence of informative or uninformative visual input, instead finding differences in the moment-to-moment dynamics of coordination across high-level contextual constraints. The effects of task condition emerged only during arguments, again supporting the idea that emergent behaviors—like synchrony—are context-dependent: Head movements exhibited a marked turn-taking pattern during argumentative conversations in the dual-task condition but had relatively flat temporal correspondence in the noise condition.
In finding an interaction effect for the low-level contextual constraint, the current study may highlight the importance of the cognitive interpretation of the perceptual information in the environment. Previous work on auditory perceptual information simply introduced a noisy background stimulus (Boker et al., 2002); no additional interpretation was needed. The current work, by contrast, presented the same perceptual stimulus to participants (i.e., changing blue and red screens), and the two conditions differed by the significance (or lack thereof) of that stimulus.
Although we found no main effect between task conditions (see Section 5.5), the differences of these two conditions relative to one another can meaningfully inform some of our understanding of these phenomena. The turn-taking coordination dynamics during arguments in the dual-task condition (compared with the flat profile in the noise condition; see Figure Figure1)1) may suggest a slight reworking of the influences seen in previous work. The auditory noise of Boker et al. (2002) would have presented task-relevant difficulties, since hearing and speaking are directly affected by ambient noise. By contrast, our “noise” condition—a flashing screen—may not have directly impacted conversation, compared with the increased cognitive load of performing a working memory task while having a complex conversation. This suggests a slight change in what may boost coordination: Like the auditory noise of Boker et al. (2002) and the dual-task condition of the present study, perhaps constraints must be task-relevant in order to influence movement coordination.
The partial support for H3A provided the strongest evidence for context sensitivity of conversation to high- and low-level constraints. Our results both supported and failed to support our directional hypothesis, depending on the context. The effects of high- and low-level contextual constraints were neither uniform nor additive; instead, high- and low-level contextual effects interacted to produce unique patterns. We interpret these results as fitting with the idea that conversation can be fruitfully conceptualized through dynamical systems theory (DST), supporting our final hypothesis (H4).
While previous work explored only perceptual noise within “free conversation” (p. 350; Boker et al., 2002), the present study asked participants to engage in two distinct discourse activities—argument or friendly conversation. This allowed us not only to explore the effects of low-level contextual constraints in a new modality (i.e., vision) but provided us with an opportunity to combine it with a growing emphasis on exploring coordination dynamics in different conversational contexts.
Our results add nuance to previous findings about perceptual noise: Rather than uniformly increasing coordination (cf. Boker et al., 2002), low-level contextual pressures alter coordination dynamics only in some conversational contexts. Our results also add nuance to previous findings about conversational context: Rather than uniformly decreasing synchrony (Paxton and Dale, 2013a; Abney et al., 2014), argument's effects can be modulated by low-level perturbations. Moreover, these low-level perturbations affect behavior differently depending on the overarching high-level context—exerting a stronger influence on coordination dynamics during argument compared to affiliative conversations. Most strikingly, this particular combination of high- and low-level context has led to unique behavioral dynamics, leading both synchrony in both friendly and argumentative conversations to decrease (relative to chance) and to reorganize their temporal dynamics.
Of the contributions of the current study, we believe that our results most compellingly speak to the importance of recognizing conversation as a complex dynamical system. Consistent with the interpersonal synergies perspective on coordination (e.g., Riley et al., 2011), we find that coordination is sensitive to contextual constraints. Put simply, coordination—as one property of interaction, which we view as a complex dynamical system—is simultaneously sensitive to low-level perceptual information, cognitive interpretation of this low-level information, and high-level interpersonal goals.
The current paper provides one of the first simultaneous explorations of high- and low-level contextual constraints in naturalistic conversation. As a result, the study has several limitations that are opportune areas for future directions.
First, we found that the difference in recurrence between affiliative and argumentative conversations was modulated by task: Argumentative conversations were more strongly affected by task condition than affiliative conversations (see Table Table2).2). However, this pattern could have emerged in a variety of ways: For example, compared to non-visually-disrupted conversation, noise could have decreased coordination; the dual-task condition could have increased coordination; both could have decreased, with noise simply leading to a greater decrease; both could have increased, with dual-task simply leading to a greater increase; or some other pattern may be at work. Simply put, although we can address relative differences between the two conditions, we cannot make strong claims as to the precise mechanism behind the differences in absolute coordination from the current study. Future work should include a baseline condition without any visual noise (holding all other experimental pressures equal) in order to target these possibilities. (A baseline condition would also help choose among similar causes behind the difference in peakedness between noise and dual-task conditions in argument.)
Second, we here only investigated linear (i.e., leading/following) and quadratic (i.e., synchrony or turn-taking) patterns across all dyads. As we have observed in our previous work, these data appear to exhibit interesting dyad-specific effects (see Figure Figure2),2), and future work should investigate them as dyad-level analogs to individual differences. It may be of interest to include higher-order polynomial patterns (e.g., cubic, quartic) in future analyses, both in describing the observed data and in understanding what they might mean psychologically or interpersonally.
Third, research should continue across additional modalities and contexts. Not all constraints should affect conversation equally; therefore, there should be no expectation that the same dynamics will emerge across all modalities. The effect of low-level constraints in a joint task-performance environment may be quite different than naturalistic conversation. Similarly, introducing perturbations of varying severity to different perceptual modalities may unequally affect interpersonal dynamics. Future work should continue to map out these effects to better understand interaction.
Finally, we present only a first exploration of these dynamics; our findings should be replicated, especially in larger samples. The sample included here is fairly normative for conversational coordination research (for discussion of sample sizes, see Paxton and Dale, 2013a); the only other study exploring the effects of perceptual perturbations on conversation dynamics (to the authors' knowledge) included only 4 dyads (Boker et al., 2002). Issues of open science and reproducibility are particularly salient at this time to psychology and cognitive science (cf. Open Science Collaboration, 2015), so we provide (1) open-source code for our data collection techniques (on the PsyGlass GitHub repository: http://www.github.com/a-paxton/PsyGlass), (2) a high level of methodological detail about our procedure (in Section 2.2), (3) our data (on OSF: https://osf.io/4yqz8/), and (4) open-source code for our data preparation and analysis techniques (on OSF, https://osf.io/4yqz8/, and GitHub, https://www.github.com/a-paxton/dual-conversation-constraints). These tools will help us and other researchers interested in interpersonal coordination and communication dynamics to integrate our practices, resources, and findings so that we can—together—better refine our understanding of human social behavior.
In this paper, we explore the dynamics of human interaction in an experiment and analyses inspired by ideas from complex adaptive systems. Patterns of nonverbal behavior during conversation change based on both high-level contextual constraints—like what kind of conversation people are having—and low-level contextual constraints—like the significance of visual information in the environment. Replicating previous work, we find that argument decreases movement synchrony. Interestingly, we find that high-level constraints interact with low-level ones, mitigating or exacerbating the effects of argument depending on the cognitive interpretation of the perceptual stimuli. We see our results as contributing to the growing view that patterns of communication—even subtle signatures of body movement—are shaped by the host of contextual factors that surround the conversation.
Experiment design: AP and RD. Data collection: AP. Data analysis: AP and RD. Manuscript writing and editing: AP and RD.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Our thanks go to the undergraduate research assistants from the University of California, Merced who helped with data collection (in alphabetical order): Neekole Acorda, Kyle Carey, Nicole Hvid, Krina Patel, and Keith Willson. We also thank Aaron Culich for his assistance with computation resources on Jetstream, which was made possible through the XSEDE Campus Champion program and the Berkeley Research Computing (BRC) program of Research IT at the University of California, Berkeley. This project was funded in part by a Moore-Sloan Data Science Environments Fellowship to AP, thanks to the Gordon and Betty Moore Foundation through Grant GBMF3834 and the Alfred P. Sloan Foundation through Grant 2013-10-27 to the University of California, Berkeley.
This appendix provides the results of analyses comparing the real experimental data to the phase-randomized surrogate baseline data. Table TableA1A1 compares the real and baseline data using the analysis scheme provided in Section 2.4. Table TableA2A2 provides post-hoc analyses diving into the differences between the affiliative and argumentative conversations.
|Data × LL||−0.006||0.008||−0.769||0.4400|
|Data × QL||0.025||0.009||2.800||0.0050||**|
|Conversation × Task||0.090||0.096||0.940||0.3500|
|Data × Conversation||−0.320||0.047||−6.875||<0.001||***|
|Data × Task||−0.114||0.006||−20.019||<0.001||***|
|Data × Conversation × Task||0.044||0.047||0.950||0.3400|
|Task × LL||−0.006||0.008||−0.782||0.4300|
|Data × Task × LL||−0.012||0.008||−1.417||0.1570|
|Conversation × LL||0.001||0.008||0.142||0.8900|
|Data × Conversation × LL||−0.029||0.008||−3.495||<0.001||***|
|Task × Conversation × LL||−0.019||0.008||−2.270||0.0230||*|
|Data × Task × Conversation × LL||−0.013||0.008||−1.580||0.1140|
|Task × QL||0.006||0.006||1.010||0.3100|
|Data × Task × QL||0.012||0.009||1.338||0.1810|
|Conversation × QL||0.022||0.006||3.772||0.0002||***|
|Data × Conversation × QL||0.017||0.006||3.061||0.0020||**|
|Task × Conversation × QL||0.040||0.006||7.054||<0.001||***|
|Data × Task × Conversation × QL||0.025||0.006||4.454||<0.001||***|
|LL × QL||−0.026||0.008||−3.129||0.0020||**|
|Data × LL × QL||−0.011||0.008||−1.328||0.1840|
|Task × LL × QL||−0.005||0.008||−0.600||0.5500|
|Data × Task × LL × QL||0.002||0.008||0.197||0.8400|
|Conversation × LL × QL||0.013||0.008||−1.541||0.1230|
|Data × Task × LL × QL||0.015||0.008||1.882||0.0600||.|
|Conversation × Task × LL × QL||0.004||0.008||0.465||0.6400|
|Data × Conversation × Task × LL × QL||−0.004||0.008||−0.429||0.6700|
The model's fixed effects alone accounted for 8% of the variance (marginal R2 = 0.08), while the fixed and random effects accounted for 51% of the variance (conditional R2 = 0.51). .p < 0.10;
|Data × LL||0.027||0.013||2.145||0.032||*|
|Data × QL||0.009||0.016||0.583||0.560|
|Data × Task||−0.191||0.105||−1.820||0.069||.|
|Task × LL||0.015||0.030||0.486||0.630|
|Task × QL||−0.042||0.009||−4.755||<0.001||***|
|Data × Task × LL||0.002||0.013||0.128||0.900|
|Data × Task × QL||−0.016||0.016||−1.044||0.300|
|LL × QL||−0.016||0.013||−1.249||0.212|
|Data × LL × QL||−0.032||0.013||−2.526||0.012||*|
|Task × LL × QL||−0.011||0.013||−0.838||0.400|
|Data × Task × LL × QL||0.006||0.013||0.493||0.620|
|Data × LL||−0.030||0.010||−3.015||0.003||**|
|Data × QL||0.037||0.007||5.262||<0.001||***|
|Data × Task||−0.061||0.076||−0.800||0.420|
|Task × LL||−0.022||0.019||−1.145||0.250|
|Task × QL||0.040||0.012||3.259||0.001||**|
|Data × Task × LL||−0.021||0.010||−2.119||0.034||*|
|Data × Task × QL||0.033||0.007||4.630||<0.001||***|
|LL × QL||−0.033||0.010||−3.303||0.001||**|
|Data × LL × QL||0.004||0.010||0.392||0.700|
|Task × LL × QL||−0.001||0.010||−0.095||0.920|
|Data × Task × LL × QL||−0.002||0.010||−0.164||0.870|
To follow up on the interaction terms in the main model (see Table TableA1),A1), we targeted each conversation type in separate models, using their own standardized datasets. The affiliative model's fixed effects alone accounted for 2% of the variance (marginal R2 = 0.02), while the fixed and random effects accounted for 42% of the variance (conditional R2 = 0.42). The argumentative model's fixed effects alone accounted for 10% of the variance (marginal R2 = 0.10), while the fixed and random effects accounted for 64% of the variance (conditional R2 = 0.64)..p < 0.10;
2We recognize that this “top” vs. “bottom” categorization is a simplification, as it likely approximates a spectrum of spatial or temporal scales; we nevertheless feel this organizing scheme is useful for emphasizing the differing role of either end of this spectrum.
3By “informative” we mean to say simply that participants must attend to the stimulus for a secondary task. We do not mean that the stimulus will be informative for the conversation.
4A subset of the affiliative conversations across both between-dyads conditions served as a brief proof-of-concept study in an earlier methodological paper (Paxton et al., 2015). Only the affiliative conversation of dyads who were assigned to the affiliative-first conversation order were included in that analysis.
5Described to the participants as “gay and lesbian marriage.”
6Participants were also video- and audio-recorded with separate equipment, but this information is not under consideration in the current paper.
7Although Main et al. (2016) present the categorical case, the same principles apply to the continuous case, which we employ here.
8We do recognize that there are open questions about how to best handle phase space reconstruction for RQA that can extend to CRQA, particularly with regard to the choice of embedding dimension (e.g., Marwan et al., 2007). While some previous work suggests that an embedding dimension of 1 (m = 1) is sufficient, we follow current recommendations to determine embedding dimension with false nearest neighbors for each participant in the dyad (Riley and Van Orden, 2005) and selecting the higher embedding dimension of the two (Marwan et al., 2007).
9We thank a reviewer for suggesting this more robust analysis.