Interacting in Flow

684173 research-article2016 SGOXXX10.1177/2158244016684173SAGE OpenNelson et al. Article SAGE Open Interacting in Flow: An Analysis of October-December 2016: 1­–11 © The Author(s) 2016 DOI: 10.1177/2158244016684173 Rapport-Based Behavior as Optimal journals.sagepub.com/home/sgo Experience Andrew A. Nelson1, Jon E. Grahe2, and Fabian Ramseyer3 Abstract Theorists have long noted the nebulousness of dyadic rapport and its nonverbal correlates. In response to Tickle-Degnen’s call for a more theoretically complete analysis of the rapport construct, we empirically evaluated her adaptation of Csíkszentmihályi’s optimal experience model with the hope of better conceptualizing rapport’s behavioral manifestations. Dyads (Ndyad = 50) engaged in two interdependent tasks and completed a battery of post-task measures gauging their mood and experiences of rapport. To complement self-report measures, we coded rapport-based behavior using both subjective (thin-slice judgments) and objective (Motion Energy Analysis) methodologies. According to Tickle-Degnen’s model, dyadic exchanges with moderate levels of expressivity and interpersonal coordination should yield the highest levels of rapport. Although data from our objective coding did not trend in this manner, subjective measurements of coordinated expressivity matched the paradigm’s prediction. To our knowledge, this is the first study to empirically support Tickle-Degnen’s hypothesis and a novel step toward clarifying the rapport construct. Keywords rapport, dyad, optimal experience, synchrony, Motion Energy Analysis After decades of investigation, the construct of rapport correlates could similarly fit into an optimal experience remains a nebulous one, still evoking the image of “cornmeal model, where moderate (i.e., “optimal”) levels of behavior mush” (DePaulo & Bell, 1990, p. 305). In particular, the non- would be indicative of high dyadic rapport. verbal behavior indicative of rapport remains ill-defined and The current study furthers Tickle-Degnen’s work by inconsistent (Tickle-Degnen, 2006). Although researchers empirically evaluating her published model. Specifically, we have already isolated rapport into three, interrelated compo- examined the relationship between rapport and two of its nents—mutual attentiveness, positivity, and behavioral coor- behavioral correlates—namely, nonverbal expressivity and dination (Tickle-Degnen & Rosenthal, 1990)—those coordination—during dyadic interactions. Ultimately, empir- components do not always generate or signal rapport ically evaluating Tickle-Degnen’s (2006) optimal experience (DePaulo & Bell, 1990; Patterson, 1990). Not only are these model may be a crucial first step in distinguishing the “some- components observed in distinctly low-rapport circum- thing more” (DePaulo & Bell, 1990, p. 305) that character- stances (Cappella, 1996; DePaulo & Bell, 1990), but the izes the rapport construct. ways in which they correlate with rapport seem to vary across contexts (Tickle-Degnen, 2006). Continued work is thus needed to clarify the relationship Dyadic Rapport as Affect and Action between rapport and its behavioral correlates. Recognizing To begin, a two-component conceptualization of dyadic rap- this need, Tickle-Degnen (2006) cited the optimal experi- port—one that couples affect with behavior—is most relevant ence model of flow theory (Csíkszentmihályi, 1990; to our analysis. Tickle-Degnen and Rosenthal’s (1990) three- Csíkszentmihályi & LeFevre, 1989) as relevant to a more component model of rapport classically recognized its affective theoretically cogent examination of rapport. In this model, Csíkszentmihályi (1990) originally proposed that a person is 1 University of Kentucky, Lexington, KY, USA most likely to experience “flow” when a task is moderately 2 Pacific Lutheran University, Tacoma, WA, USA difficult. If a person finds the task too easy or too challeng- 3 University of Bern, Bern, Switzerland ing, suboptimal experiences result. Tickle-Degnen (2006) Corresponding Author: conceptualized rapport, like flow, as a kind of optimal expe- Andrew A. Nelson, University of Kentucky, Lexington, KY 40508, USA. rience. As such, she theorized that rapport and its behavioral Email: [email protected] Creative Commons CC-BY: This article is distributed under the terms of the Creative Commons Attribution 3.0 License (http://www.creativecommons.org/licenses/by/3.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). 2 SAGE Open and behavioral nature: Feelings of positivity are characterized Tickle-Degnen (2006) asserts that expressivity is the “raw by displays of affectionate touch, forward leaning, smiling, and action material” required for coordination (p. 387). Said dif- other affectionate gestures (Anderson, Guerrero, & Jones, ferently, when there is no expressivity during an interaction, 2006; Hendrick, 1990); mutual attention is conveyed through there is no behavior available for coordination. Coordination eye contact and posture sharing (Norton & Pettegrew, 1979); can refer to both behavior matching and interactional syn- and sensations of “balance” or “harmony” (Tickle-Degnen & chrony (Burgoon, Stern, & Dillman, 1995; Grammer, Kruck, Rosenthal, 1990) manifest through coordinated movement & Magnusson, 1998). Behavior-matching is the nearly simul- between partners. Indeed, to understand rapport as affect alone taneous mirroring of gestures, facial expressions, or other is to overlook how it is established, built, and maintained. behaviors between dyad members (Tickle-Degnen, 2006). In fact, various dual-system models—including Patterson’s Interactional synchrony occurs on a global level, as when (1995) parallel process model, Chartrand and Bargh’s (1999) dyads interact with a certain rhythm or smoothness (Bernieri perception-action system, and Tickle-Degnen’s (2006) signal– & Rosenthal, 1991; Tickle-Degnen, 2006). perception–action–signal loop—suggest a link between Evidence at both the behavioral and affective levels sup- dyadic action and social perception. Behavioral coordination ports coordination’s relevance to positive social outcomes, during a dyadic interaction elicits shared perceptions between including rapport (Cappella & Schreiber, 2006). Emotional interactants (Chartrand & Bargh, 1999), and more generally, contagion or “yoking” represents a good example of affec- nonverbal signaling works to convey information between an tive coordination (Cappella & Schreiber, 2006; Hatfield, actor and a perceiver (Tickle-Degnen, 2006). Thus, dyadic Cacioppo, & Rapson, 1993), as proximate individuals some- action is intrinsic to the formation and maintenance of rapport, times synchronize their own moods and emotions to reflect as it creates a bidirectional expressway for information sharing another’s emotional state. At the behavioral level, some sug- and rapport development (Dijksterhuis & Bargh, 2001; Grahe gest that synchrony and posture mimicry might contribute to & Bernieri, 1999; Tickle-Degnen, 2006). this kind of emotional convergence (Hatfield et al., 1993). Such an explanation makes sense when noting that interac- tional synchrony is recognized by both observers (Bernieri Nonverbal Expressivity as the et al., 1996; Cappella, 1997; Lakens & Stel, 2011) and inter- Groundwork of Interpersonal Behavior actants (Bernieri et al., 1996; Grahe & Bernieri, 2002; Grahe This dyadic action can be fundamentally understood as non- & Sherman, 2007; Vacharkulksemsuk & Fredrickson, 2012) verbal expressivity, which refers to the clarity by which an as a characteristic of high-rapport interactions. Humans may individual nonverbally communicates his or her emotions even have an innate preference for synchronized interaction (Boone & Buck, 2003). Expressivity requires “expressive- (Argyle, 1990; Harrist & Waugh, 2002; Tronick, 1989, ness,” which more generally refers to the behavioral activity 1990), suggesting that behavioral coordination is also inte- used to convey an affective state. Tickle-Degnen (2006) gral to establishing and maintaining rapport. asserts that such activity is integral to dyadic rapport devel- opment. Expressiveness allows for the communication of Dyadic Rapport Within the Optimal emotions and attitudes from an actor to a perceiver, and in Experience Model turn, the perceiver possesses more information from which to reciprocate appropriately and build rapport. Tickle-Degnen (2006) argued that dyadic rapport—in both Much research concludes that the nonverbal expression of its affective and behavioral nature—represents an optimal emotion remains a crucial ingredient in dyadic interactions. experience similar to a kind of “flow state” (Csíkszentmihályi, Expressivity is positively associated with ratings of relational 1990). An individual enters “flow” when she or he becomes quality and rapport (Fridlund & Russell, 2006; Grahe & unconditionally absorbed by a task at hand. While in flow, a Bernieri, 2002; Riggio & Riggio, 2002). Furthermore, related person’s anxieties often diminish and a feeling of ease results. studies have even experimentally suppressed participants’ A body of research suggests that individuals are most likely capacity for expressivity. Butler and colleagues (2003; Butler, to enter flow when they engage in an activity with a diffi- Lee, & Gross, 2007) discovered that directing dyad members culty that matches their skill level within that domain. If they to restrain their nonverbal behavior led to negative perceptions participate in a task that is either too difficult or too easy for of the interaction. Although the social importance of expres- their perceived skill level, suboptimal consequences such as sivity may vary across cultures (e.g., Butler et al., 2007), it anxiety or boredom can emerge (Csíkszentmihályi, 1990; does seem to be a critical variable in the rapport equation. Csíkszentmihályi & LeFevre, 1989; Mesurado & Richaud de Minzi, 2013; Nakamura, 1988). This difficulty/skill balance Interpersonal Coordination and was first identified by Nakamura (1988), and Csíkszentmihályi (1990) later integrated this phenomenon into his optimal Rapport Development experience theory. Theory and research further suggest that the coordination of Tickle-Degnen (2006) believed that a similar kind of opti- nonverbal action is important for rapport development. In fact, mal balance is also pertinent to the rapport ecosystem. She Nelson et al. 3 reworked optimal experience theory into a model that data, and we further anticipated that it would be supported extended its relevance to interpersonal behavior patterns dur- across both subjective and objective coding. ing rapport development. According to Tickle-Degnen’s (2006) model, optimal experiences are those where dyads feel and act in calm, yet attentive ways; suboptimal experi- Method ences foster overactive or underactive levels of action and Protocol/Data History affect (Tickle-Degnen, 2006). More specifically, when an actor’s expressivity is overactive, information is lost between The data in question were first collected in response to a pro- an actor and a perceiver. When expressivity is underactive, posal submitted to an undergraduate research initiative there is a shortage of nonverbal information passed between (Collective Undergraduate Research Project; Grahe, 2010). partners. Frantic behavior also makes coordination between In the fall of 2011, Dr. Fabian Ramseyer (e.g., Ramseyer & dyad members more challenging, and conversely, a lack of Horowitz, 2010) shared a collection of materials and proce- behavior means that there is less behavior to coordinate. dural information with this research initiative. His proposal Hence, moderate levels of expressivity should promote the outlined a methodological replication of Ramseyer and most rapport development. Horowitz (2010) that would further investigate the nature of Some research supports this postulation. Boone and Buck behavioral synchrony during cooperative interactions. Since (2003) discovered that especially high levels of expressivity his submission, several student teams have conducted con- hindered the formation of trust in unacquainted dyads. They ceptual replications of this design while also including novel also concluded that expressivity necessitates a moderate manipulations of their own. A copy of this base protocol is degree of expressiveness (Boone & Buck, 2003). In addition, shared on our Open Science Framework (OSF) project page a longitudinal examination of practitioner–client interactions (https://osf.io/bn3th/wiki/home/), as are PDF copies of both found that an intermediate—as opposed to high—frequency pre-task (https://osf.io/76rt9/) and post-task (https://osf.io/ of attentive or positive behavior was linked to the most favor- b8agm/) questionnaires. The resulting data have also been able rapport ratings (Tickle-Degnen & Gavett, 2003). The made publicly available via the Journal of Open Psychology relationship between expressivity and rapport levels could Data (Nelson, Grahe, & Ramseyer, 2014). thus be a nonlinear one, where chaotic (overactive) or sub- The current study is one such extension from this base dued (underactive) action yields the lowest rapport because it protocol. As a result, not all employed measures are pertinent disrupts the patterned responsiveness of interactions. to the present research question, so we only detail those rel- Interpersonal coordination may also behave in a similar way. evant to our optimal experience hypothesis. Those interested When coordination is gauged on a looseness-to-tightness in the full battery of measures may visit our OSF project spectrum (Bernieri & Rosenthal, 1991; Burgoon et al., 1995), page. moderate levels should be most conducive to rapport-building. Indeed, research validates this relationship between moderate Participants coordination and more positive interactional outcomes (Cappella, 1996; Jaffe, Beatrice, Stanley, Crown, & Jasnow, Undergraduate students (Ndyad = 50) from a small, liberal arts 2001; Tickle-Degnen & Gavett, 2003). university in the Pacific Northwest region of the United States enrolled in this study. Dropout tendencies and univer- sity demographics contributed to an imbalanced number of The Current Study participants across the different dyad makeup conditions; The current study extends this prior work by empirically specifically, we tested more female–female pairings (n = 27) evaluating Tickle-Degnen’s optimal experience model. than male–male (n = 10) and mixed-sex dyads (n = 13). Due Using a subset of data (Study 2; Nelson, Grahe, Ramseyer, & to the relatively small student population of the university, Serier, 2014) published in a public data repository, we exam- researchers accounted for possible familiarity between par- ined associations between dyads’ sensations of rapport and ticipants by using two self-report questions. We treated all their displays of expressivity/coordination across two inter- participants according to APA ethical guidelines. dependent tasks. In addition, research assistants coded all the nonverbal behavior present in these dyadic interaction using Procedure both subjective (thin-slice judgments; Ambady & Rosenthal, 1992) and objective (Motion Energy Analysis [MEA]; To assign a task order to each dyad, we generated a list of Ramseyer & Tschacher, 2006, 2011) methodologies. Thus, randomized numbers where each value corresponded to a the resulting data span multiple modes of analysis. Tickle- given task sequence. According to this randomization proce- Degnen’s model suggests that dyadic exchanges with moder- dure, 28 dyads were assigned a menu task first, whereas 22 ate or “optimal” levels of expressivity and coordination dyads were assigned a close-calls task first. Participants should be associated with the highest sensations of rapport. entered the lab upon arrival and chose which side of the table As such, we hypothesized that this pattern would arise in our to sit on; thereafter, participants could not switch their 4 SAGE Open seating arrangements. After reviewing informed consent and that they had either experienced themselves or heard about video consent forms, participants turned to the nearby com- from peers. Dyads had 6 min to complete each task, and we puters and completed pre-task assessments while the did not predict outcomes to differ across the tasks. For com- researcher remained in the room. Upon completion, we again plete copies of these prompts (Interactions 1 and 2), see our asked participants to sit at the central table. An experimenter OSF page (https://osf.io/a6kis/). then read one of the two dyadic task prompts aloud, answer- ing any questions accordingly. Afterward, the experimenter Interactant familiarity.  We did assess the extent to which each turned on a video camera and began a stopwatch before leav- dyad member might know his or her partner. Specifically, ing the room. Following the 6-min task, participants returned participants reported how long they had known their partner to their separate computers and answered the posttest mea- on a Likert-type scale from 1 (we’ve not known each other sures. Researchers repeated these procedures (excluding the before) to 6 (for more than 3 years). They also reported how initial pretest) for the second of the two dyadic tasks. well they knew their partner on a Likert-type scale from 1 (first time I’ve seen him or her) to 6 (we are close friends). Materials Post-task measures. After each task, both participants com- Pre-task measures.  Prior to their first dyadic interaction, par- pleted a battery of measures related to the preceding ticipants completed a battery of personality measures. interaction. Interpersonal difficulties.  We used the short-form Inventory Interpersonal closeness.  We used the Inclusion of Others in of Interpersonal Problems (IIP-32; Horowitz, Alden, Wig- Self (Aron, Aron, & Smollan, 1992) pictorial-based measure gins, & Pincus, 2000), a 32-item measure of interpersonal to gauge perceptions of interpersonal closeness between inter- behavior in which participants rate how much certain social actants. By using a series of inter-lapping circles to represent problems affect them (e.g., “It is hard for me to socialize with different levels of “closeness,” this measure instructed partici- other people”). Ratings were made on a 5-point Likert-type pants to choose the set of circles that “best describes their rela- scale ranging from 1 (not at all) to 5 (extremely). Because tionship.” Participants responded on a scale from 1 (no overlap we did not have any hypotheses specific to the eight IIP sub- between the circles) to 9 (nearly complete overlap of the cir- scales (i.e., Domineering, Vindictive, Cold, Socially Inhib- cles), with higher levels of overlap designating more closeness. ited, Nonassertive, Overly Accommodating, Self-sacrificing, Intrusive), we computed a mean score of interpersonal dif- Mood.  The Positive and Negative Affect Scale (PANAS; ficulties across the full 32 items (α = .84). Watson, Clark, & Tellegen, 1988) asked participants to recall the degree to which they encountered certain moods (for Empathy.  Next, we used a modified version of the Inter- instance, feeling “interested” or “hostile”) during the preced- personal Reactivity Index (IRI; Davis, 1980) to assess par- ing interaction (α = .89). Answers ranged on a Likert-type ticipants’ levels of empathy. Specifically, we retained three scale from 1 (not at all) to 5 (extremely), with higher scores of the original four IRI subscales, which allowed us to mea- indicating more positive moods. sure empathetic concern (e.g., “I am often quite touched by things I see happen”), perspective taking (e.g., “I try to look Rapport.  We used the Post-Interaction/Rapport Question- at everyone’s side of a disagreement before I make a deci- naire (IRQ; Bernieri, Davis, Rosenthal, & Knee, 1994) as sion”), and fantasy (“After seeing a play or a movie, I have our primary gauge of dyadic rapport during each interac- felt as though I were one of the characters”). Answers ranged tion. This measure asked participants to rate the presence of on a 5-point Likert-type scale from 1 (does not describe me certain rapport-based characteristics during the preceding well) to 5 (describes me very well). Again, we compiled all task (α = .95). Participants rated their experience based on items into a single empathy construct, yielding a mean empa- 18 different qualifiers (e.g., “coordination” or “intensity”), thy score for each participant (α = .86). answering on a Likert-type scale ranging from 1 (not at all) to 9 (extremely). Dyadic tasks. In accordance with Ramseyer’s proposal, researchers used both a dyadic “menu task” and a “close Behavioral Coding calls” experience (adapted from Chovil, 1991) as opportuni- ties for rapport-building. Beginning with the former, the We assessed displays of interpersonal behavior during each menu task asked participants to create an imaginary five- dyadic interaction using both subjective (i.e., thin-slice cod- course dinner menu comprised of foods they both disliked. ing) and objective (i.e., MEA) methodologies. We provide an The task was entirely verbal, so participants did not create overview of both methodologies below. hard copies of their agreed-upon menu. In the “close calls” prompt, researchers instructed each member of the dyad to Thin-slice coding.  The behavioral cues present during interper- disclose past “near-miss” or potentially dangerous situations sonal exchanges exist on objective and subjective levels (Grahe Nelson et al. 5 & Bernieri, 2002). Objective cues consist of quantifiable We also borrowed Tickle-Degnen’s (2006) conceptualiza- actions determined by time (for example, the duration of eye tion of suboptimal and optimal coordination for our coding contact between interactants) or quantity (the number of smiles scheme. Coordination/Synchrony referred to how harmoni- made by each of them). In contrast, subjective cues require the ous or in sync the dyad appeared while interacting. observer to speculate about an actor’s or dyad’s psychological Suboptimal coordination was characterized by an “empti- state. Even so, such speculation is often derived from objective ness” of behavior at one pole and as “disordered” behavior at behavior, as when a friend makes inferences about a couple’s the other pole; smooth, synchronous displays denoted opti- rapport based on concrete instances of smiling and touch. mal experience (Tickle-Degnen, 2006). Because group judg- Based on these cues, humans intuitively make judgments ments are often more reliable than single-coder assessments about interactions and their outcomes (Ambady, 2010; (Ambady et al., 2000), each coder separately evaluated all of DePaulo & Friedman, 1998). Research consistently finds the interactions for Activity/Expressivity as well as observer judgments to be accurate and in line with interac- Coordination/Synchrony. Inter-rater reliability scores sug- tants’ own perceptions of an interaction (e.g., Ambady & gested consistent coding for both Activity/Expressivity (α = Rosenthal, 1992). Moreover, quick judgments of interper- .93) and Coordination/Synchrony (α = .72) variables. sonal behavior—even when made at random times during an interaction—are no less accurate than longer judgments Motion Energy Analysis.  Subjective evaluations of movement, (Ambady, Bernieri, & Richeson, 2000; Ambady & Rosenthal, however, may be confounded by the observer’s inability to 1992). These so-called “thin-slice” judgments, operationally separate judgments of synchrony alone from gestalt percep- understood as impressions formed after 5 min or less, are reli- tions of rapport-building. In consequence, technological able across contexts (Ambady & Rosenthal, 1992). In some advancements now allow for more objective techniques of cases, these thin-slice judgments are even more accurate than synchrony measurement that can gauge interpersonal motion those made after a longer period of time (Patterson & exclusively. Grammer, Honda, Juette, and Schmitt (1999) Stockbridge, 1998). Accordingly, we isolated 30-s slices from first began using automatic movie analysis (AMA) to exam- each videotaped interaction for our coding purposes. To ine nonverbal courtship behavior as a way to clarify the reduce the risk of sampling artifact and potential bias, experi- “fuzziness” of interactional movement. AMA reads digitized menters selected the first 30 s from the second minute of each video footage of a given interaction. This video footage is interaction for coding purposes. recorded with a completely static camera in front of a stable Three research assistants unaware of study hypotheses background with stable light conditions. Motion energy is then coded each selection. Before coding began, these coders then detected by subtracting the image of one video frame attended multiple training sessions held by the second author, from the previous frame. The amount of change (i.e., the and during each session, they practiced using a coding frame-difference) serves as a quantifiable indicator of move- scheme specifically created for the purposes of this study ment. These differences paint a singular, overall picture of (see the appendix). Notably, our coding scheme focused on motion across any given amount of time. Not only is the pro- the primary behavioral correlates of rapport as noted by cess less subjective than manual video-coding, but it is both Tickle-Degnen (2006), and the scheme used language efficient and highly reliable (Grammer et al., 1999). directly taken from her optimal experience model. Overall, We used a similar program—MEA (Ramseyer, 2016; ratings were made on a 7-point Likert-type scale. A centered Ramseyer & Tschacher, 2011)—to objectively evaluate syn- “0” value represented optimal experience, and each pole of chronous behavior during each dyadic task. Like AMA, MEA the scale (“−3” on one side and “+3” on the other) repre- is based on the frame-differencing method. In frame differ- sented the extremes of suboptimal experience. encing, pixels from digital videos are converted into their In particular, coders used this scale to assess behavioral grayscale format, ranging from 0 (true black) to 255 (true displays of “Activity/Expressivity” and “Coordination/ white). Pixel hue changes between two subsequent video Synchrony.” Activity/Expressivity is the “raw behavioral frames, which are caused by a participant’s movement, are material” necessary for the development of interpersonal then calculated and conceptualized as motion energy (ME; coordination (Tickle-Degnen, 2006, p. 385). During subopti- Kupper, Ramseyer, Hoffmann, Kalbermatten, & Tschacher, mal interactions, interactants can feel “bored” or “anxious,” 2010). These calculations of ME are bounded by pre-deter- and their expressivity reflects these feelings; when interac- mined “maps” or drawn-out regions of a participant’s body. In tants are engaged in an optimal experience, however, they other words, if near-simultaneous motion is detected in adja- demonstrate “calm and energized” action (Tickle-Degnen, cent regions (for example, an upper body region) for both 2006). We used these same characterizations of suboptimal dyad members, it is conceptualized as synchronous move- and optimal expressivity in our own coding scheme (see the ment. Because ME calculations are bounded by these regions, appendix). Based on this scheme, coders rated the Activity/ shared movement does not need to occur between the same Expressivity of each dyad member separately, and these body parts across interactants (e.g., each person’s right arm) scores were averaged to yield a single Activity/Expressivity to register as synchrony. Furthermore, because MEA algo- score for each dyad. rithm reacts to pixel alterations, only the dynamic aspects of 6 SAGE Open movement are assessed. Thus, dyadic posture sharing or ratings in our coding scheme, researchers squared all scores “static” synchrony is not evaluated using this technique. For a and thus translated the scale into a linear format; scores now comprehensive overview of the MEA technique, we direct ranged from 0 to 9. Although we could have also linearized readers to Ramseyer and Tschacher (2011). scores by taking their absolute value, doing so would have In the current study, we isolated our analyses to the upper undervalued ratings of suboptimal experiences. Instead, we body region of each interactant, which extended from their squared these values to increase variability and to emphasize waist to the top of their head. Although it is theoretically pos- these suboptimal encounters. To also prevent future misinter- sible to do more acute analyses, smaller “maps” are prone to pretation of correlational analyses, we reversed the scale’s conflation from stray movements from other body parts direction so that higher values represented more optimal (Ramseyer & Tschacher, 2014). In addition, because MEA action. We then averaged evaluations of both behavioral mea- maps are static and drawn over the video footage after it is sures across the three coders, resulting in a single “Activity” recorded, body parts (e.g., a head) can easily cross the bound- and “Coordination” score for each interaction. In addition, aries of smaller maps due to participant movement. Although because “Activity” and “Coordination” ratings were highly we did use specific lower body and head maps, this upper related, r(50) = .91, p < .001, researchers decided to combine body region constituted the largest MEA map and the most both variables and create an aggregate measure of coordi- reliable area for analysis. nated expressivity. Further commentary on the lack of dis- Time series of raw movement quantification from MEA criminate validity between “Activity” and “Coordination” was then used to compute lagged cross-correlations of upper ratings is available in the “Discussion” section. body ME between partners. These cross-correlations repre- Researchers also conducted a series of control analyses to sent the degree of covariation of ME across partners. help identify potential confounds. A series of t tests revealed Because of the non-stationarity of movement behavior, no differences in our dependent variables between the menu cross-correlations are calculated in separate windows of task and close-calls task; similarly, task sequence had no 30 s. Each window provides a variety of cross-correlations effect on these same dependent variables (e.g., all tests had a between the upper body regions of both partners for different p > .05). As such, we averaged all dependent variables across time lags. Instantaneous (or “zero-lagged”) synchrony is rep- both tasks to yield a single set of scores for each dyad. After resented by ME changes that occur across the same subse- collapsing across tasks, we predictably discovered that indi- quent frames for both partners. However, because synchrony vidual scores on all dependent variables were positively cor- can also be directional (with one partner leading the move- related between dyad members (e.g., all tests had a p < .05). ment and the other partner following), the cross-correlations are shifted in time to also detect synchrony that occurs with Tests of the Optimal Experience Model a short time delay (or “lag”). We allowed for lags of up to 5 s. Accordingly, if one partner’s ME is matched by the other Researchers used a Pearson correlation matrix to evaluate the partner within this 5-s allowance, it also registers as syn- association between our dependent measures and behavioral chrony. Because analyses were completed at a frame rate of data (see Table 1, which also includes means and standard 10 frames/per second, this allows for the calculation of 50 deviations for these variables). First, with regard to objective negative-lagged correlations (0 to −5 s), 50 positive-lagged synchrony, MEA-generated scores did not share any signifi- correlations (0 to +5 s), and one zero-lagged correlation for cant association with self-reported rapport levels. This finding each 30-s window. These multiple cross-correlations were runs counter to our hypothesis. The only significant relation- then standardized using Fisher’s Z, and their absolute values ship between objective synchrony displays and all posttest were averaged to yield a single synchrony score for each measures occurred with feelings of interpersonal closeness, dyad. This global synchrony score served as our objective r(50) = .28, p = .048. Specifically, as dyads synchronized, they measure of nonverbal synchrony across each interaction. reported higher levels of closeness. Data resulting from our coders’ thin-slice judgments, however, did support Tickle- Degnen’s predictions. We discovered a strong positive rela- Results tionship between dyadic rapport and our composite measure of coordinated expressivity, r(50) = .46, p = .001. As displays Data Preparation of coordinated expressivity approached optimal levels, dyads’ Although our protocol yielded a robust assortment of data, the self-reported experience of rapport also increased. scope of this article only allowed for analyses pertinent to In addition, we conducted a hierarchical linear regression Tickle-Degnen’s model. In accordance with Kenny and col- to see whether this relationship retained significance after leagues’ (2006) principle of dyad nonindependence, we aver- accounting for the variance explained by other study vari- aged interactant rapport ratings (as measured by the IRQ) into ables. Our model consisted of four steps, with dyadic rap- mean dyad scores; likewise, evaluations of interpersonal port (as measured by the IRQ) entered as our dependent behavior were collapsed into dyadic averages. Because sub- variable. In Step 1, we included our pre-task measures of optimal behavior represented both positive and negative interpersonal difficulties (as measured by the IIP) and Nelson et al. 7 Table 1.  Correlation Matrix of Outcome Measures. M SD 1 2 3 4 5 1.   Dyadic rapport 6.69 0.83 .366** .628*** .186 .459** 2.   Interpersonal closeness 3.19 1.73 .585** .281* .169 3.   Mood 3.57 0.35 .095 .246 4.   Synchrony 0.11 0.02 .055 5.   Coordinated expressivity 7.71 1.83   Note. Variables represent scores averaged across both dyadic tasks. *p < .05. **p < .01. ***p < .001. Table 2.  Hierarchical Linear Regression Model Assessing Predictors of Dyadic Rapport. Step 1 Step 2 Step 3 Step 4 Predictor B(SE) β B(SE) β B(SE) β B(SE) β Pre-task measures   Interpersonal difficulties −0.37(0.33) −.15 −0.31(0.32) −.12 −0.24(0.28) −.10 −0.04(0.26) −.02  Empathy 0.75(0.26) .39** 0.75(0.25) .38** 0.58(0.23) .30* 0.63(0.20) .32** Dyad familiarity   Length of relationship −0.53(0.54) −.30 −0.24(0.47) −.13 −0.17(0.43) −.09   Quality of relationship 0.38(0.20) .57 0.12(0.20) .19 0.14(0.18) .21 Post-task measures   Interpersonal closeness −0.03(0.08) −.06 −0.06(0.07) −.12  Mood 1.33(0.33) .56** 1.13(0.31) .48** Interpersonal movement  Synchrony 5.35(4.59) .13   Coordinated expressivity 0.16(0.05) .35**  R2 .18 .29 .49 .61  F 5.16** 4.59** 6.89*** 7.89***  ΔR2 .18* .11* .20** .12** Note. Variables represent scores averaged across both dyadic tasks. *p < .05. **p < .01. ***p < 001. empathy (as measured by the IRI). In Step 2, we added both extends Csíkszentmihályi’s (1990) original framework to the measures of partner familiarity. In Step 3, we included post- rapport ecosystem. Her model acknowledges that rapport task measures of interpersonal closeness (as measured by the development depends on the nature—and not simply the IOS) and mood (as measured by the PANAS). In our final presence—of particular behavioral displays. Moreover, it step, we included MEA-gnereated synchrony and subjective recognizes that the association between coordinated expres- ratings of coordinated expressivity. Please see Table 2 for a sivity and dyadic rapport might be a nonlinear one. Analyses depiction of this model and associated results. involving the subjective judgments of interpersonal behavior The final model reached significance and explained a large suggest that as an interaction becomes plagued by lethargic portion of variance in rapport levels, R2 = .61, F(8, 49) = 7.89, or disorderly (i.e., suboptimal) behavior, a decrement in rap- p < .001. After accounting for the other variables, coordinated port levels is also observed. Conversely, interactions marked expressivity still emerged as a significant predictor of dyadic by coordinated, well-balanced (i.e., optimal) behavior are rapport, b = .16, p < .001. Moreover, this final step explained associated with higher rapport ratings. Objective measure- an additional 12% of the variance in rapport levels primarily ments of behavioral coordination did not, however, demon- due to the coordinated expressivity variable, ΔR2 = .12, ΔF(2, strate this pattern. 41) = 6.06, p = .005. Taken together, results from this analysis Indeed, only the subjectively coded behavior of our dyads further supported our hypothesis. supported our hypotheses, and this qualification warrants fur- ther discussion. One possible reason for this discrepancy between objective and subjective measures is already known: Discussion MEA-calculated synchrony and subjectively coded coordina- This investigation empirically evaluates Tickle-Degnen’s tion are theoretically distinct measurements. Not only did our (2006) adapted optimal experience model, which uniquely data support their independence, r(50) = .055, p > .05, but 8 SAGE Open past literature has operationalized these constructs differently. Finally, we acknowledge that our composite measure of Because MEA relies on frame differencing to detect synchro- coordinated expressivity may appear to approximate Tickle- nous behavior, Ramseyer and Tschacher (2011) concede that Degnen and Rosenthal’s (1990) component of dyadic posi- its output is limited to evaluations of dynamic synchrony; tivity. We cite three pieces of evidence to differentiate our static coordination, as in the mirroring of specific postures or measure of coordinated expressivity from positivity. First, gestures, is thus overlooked. In contrast, coders’ ratings of our task prompts were not designed to foster positive affect interpersonal coordination provided a more holistic measure between dyad members; in fact, these prompts were deliber- of synchrony, as they plainly coded how coordinated the ately meant to elicit challenges for the dyad. For example, dyads appeared. Whereas MEA is microanalytical in scope the menu task forced dyads to brainstorm foods that they and thereby generates synchrony scores piecemeal (Bernieri both disliked, which presumably led to some disagreements. & Rosenthal, 1991), thin-slice judgments encompass both Furthermore, the close-calls task asked participant to recall posture mimicry and displays of coordinated movement. anxiety-inducing events from their lives. Second, we did not A second but related explanation for conflicting results find a significant correlation between PANAS scores and rat- could be due to the gestalt nature of coordination itself. Most ings of coordinated expressivity. If coordinated expressivity basically, MEA is a measure of movement dynamics. It has was simply a proxy for feelings of positivity, we would the capacity to measure the amount, duration, speed, and expect a strong positive correlation between these variables. complexity of movements between people. Microanalytical And third, although we acknowledge that coders may have measures such as MEA do not, however, capture all channels been unable to separate their assessments of behavior from of communication in the behavior stream (Delaherche et al., affect, they were instructed to focus on behavior exclusively 2012), and thus, its scope is limited. MEA cannot account for when viewing the interactions. any psychological content beyond what is communicated by gross body movements, and our current setup yielded low- resolution videos where more psychologically informative Conclusion motion (i.e., facial expressions) could not be assessed. To our knowledge, this is the first data-based validation of Alternatively, accurate observer judgments appear to be intui- Tickle-Degnen’s model. Although these conclusions may fall tive (Ambady, 2010) and a product of human development short of clearing the murkiness surrounding rapport and its (DePaulo & Friedman, 1998), suggesting that computerized nonverbal indicators, they are still in conversation with measures might not yet be capable of reliably reproducing the Tickle-Degnen’s call for the construct’s reconceptualization. human evaluation process. It could also explain why the thin- Identifying rapport as optimal experience, however, demands slice judgment method is considered the “gestalt approach” in that future work explores the association between rapport’s relevant literature (Bernieri & Rosenthal, 1991). MEA’s affective nature and behavioral elements within different objective nature means it has the ability to record pure motion contexts (e.g., task demands, environment of interaction, untainted by accompanying psychological information. and/or presence of distractors). Nevertheless, with this objectivity comes a rigidity that— Reclassifying rapport as optimal experience also unlike human judgments—might fail to capture both behav- alludes to the potential overlap between rapport and flow ior and affect. Furthermore, it is possible that our measure of states. Although both constructs remain theoretically dis- rapport, which was based on the averages of dyads’ self- tinct, this overlap is significant in that it urges develop- reports, may reduce the association between affect and syn- ments associated with one state to inform the other. For chrony previously found in a larger sample based on example, Csíkszentmihályi’s (1990) original understand- comparable dyads (Tschacher, Rees, & Ramseyer, 2014). ing of optimal experience maintained that people most Also, the apparent lack of discriminant validity between often reach flow state when managing perceived chal- “Activity/Expressivity” and “Coordination/Synchrony” rat- lenges in a domain that they are sufficiently skilled in. ings deserves further mention. A distinction between both Future research could then explore whether active interde- variables is evident at the positive pole of the coding scheme pendent tasks of an optimal difficulty prime rapport devel- (i.e., the positive extreme of the Activity/Expressivity spec- opment. Investigations into the behavioral indicators of trum represents frenzied movement, and in turn, disharmoni- flow could also build upon work exploring rapport’s non- ous displays of frenzied movement on the Coordination/ verbal correlates. In sum, borrowing Csíkszentmihályi’s Synchrony spectrum) but not at the negative one. Coders (1990) model not only strengthens our theoretical grasp may have interpreted “bored” interpersonal activity as an on rapport, but in return, it may lend itself to advance- emptiness of activity; however, this emptiness should have ments in flow theory. been marked as suboptimal coordination. In this way, coders Last, we contend that these recommendations are well still gauged coordination on a looseness-to-tightness scale suited for data-sharing initiatives, and likewise, that open (Bernieri & Rosenthal, 1991); however, their judgments per- science platforms provide an ample opportunity for col- tain more to the organization and intensity of the movement laborations committed to refining the rapport construct. than to its synchronicity. Many open science projects, such as the Open Science Nelson et al. 9 Collaboration’s (2015) Reproducibility Project and the These data, as well as a summary of each study, is available Collaborative Replications and Education Project (Grahe here: http://openpsychologydata.metajnl.com/article/view/ et al., 2016), focus on close replications alone. In contrast, jopd.ae/13. this study utilized methodological replication as a means to We again encourage potential collaborators to visit our additionally tackle novel theoretical questions. We invite project profile via the OSF (https://osf.io/dyntp/) for further interested theorists to do the same, using four conceptually study information and access to materials. Ultimately, it is related studies and data sets recently published by Nelson our hope that future investigations make use of similar ave- et al. (2014) in The Journal of Open Psychology Data. nues to advance studies of the dyadic experience. Appendix Coding Scheme Used for Subjective Behavioral Judgments BEHAVIOR Coordination/Synchrony High scores represent much behavior, but not “in sync”. Low scores represent no interaction, but this interaction can have low levels of interpersonal activity. -3. -2. -1. 0. 1. 2. 3. Emptiness (-3) Harmony, flow, Synchronous (0) Disorder (3) Activity/Expressivity Rate individuals as described above. High scores represent frenzied behavior and low scores represent “bored” behavior. -3. -2. -1. 0. 1. 2. 3. Negative: bored (-3) Calm and Energized (0) Anxious (3) Authors’ Note associated with the grant helped compensate travel and presenta- tion expenses. This research was conducted as part of the S. Erving Severtson/ Forest Foundation Undergraduate Research Fellowship offered through Pacific Lutheran University. References Aron, A., Aron, E. N., & Smollan, D. (1992). Inclusion of Other Acknowledgments in the Self Scale and the structure of interpersonal closeness. A special thanks to Jae Kim, Eric Ottenbacher, and Matthew Smith Journal of Personality and Social Psychology, 63(4), 596-612. for help with data collection and video processing, as well as to Ambady, N. (2010). The perils of pondering: Intuition and thin slice Samantha Henderson, Branden Tipton, and Aylynn Bartlebaugh for judgments. Psychological Inquiry, 21, 271-278. their efforts in coding interpersonal behavior. We would like to also Ambady, N., Bernieri, F. J., & Richeson, J. A. (2000). 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A., & Tellegen, A. (1988). Development and Patterson, M. L. (1995). Invited article: A parallel process model of validation of brief measures of positive and negative affect: The nonverbal communication. Journal of Nonverbal Behavior, 19, PANAS scales. Journal of Personality and Social Psychology, 3-29. doi:10.1007/BF02173410 54, 1063-1070. doi:10.1037/0022-3514.5 Patterson, M. L., & Stockbridge, E. (1998). Effects of cogni- tive demand and judgment strategy on person perception Author Biographies accuracy. Journal of Nonverbal Behavior, 22, 253-263. doi:10.1023/A:1022996522793 Andrew A. Nelson is pursuing a PhD in Experimental Psychology Ramseyer, F. (2016). Motion Energy Analysis, MEA [Computer at The University of Kentucky. His research broadly examines the Software]. Retrieved from http://www.psync.ch developmental implications of stereotyping and prejudice, as well Ramseyer, F., & Horowitz, L. M. (2010). Interpersonal adapta- as how psychology-grounded interventions can address social tion—Determinants of nonverbal synchrony, relationship inequalities in schools. quality, and problem solving. Bern, Switzerland: Department Jon E. Grahe is a professor of psychology at Pacific Lutheran of Psychotherapy, University of Bern. University. He serves as the Managing Executive Editor of The Ramseyer, F., & Tschacher, W. (2006). Synchrony: A core concept Journal of Social Psychology and he is the president of Psi Chi. He for a constructivist approach to psychotherapy. Constructivism is an avid supporter of open science initiatives and the primary con- in the Human Sciences, 11, 150-171. tributor for the Collaborative Replications and Education Project. Ramseyer, F., & Tschacher, W. (2011). Nonverbal synchrony in psychotherapy: Coordinated body movement reflects relation- Fabian Ramseyer is a researcher and psychotherapist at a univer- ship quality and outcome. Journal of Consulting and Clinical sity psychotherapy clinic. His research focuses on the temporal Psychology, 79, 284-295. doi:10.1037/a0023419 dynamics of human interaction, with a special interest in nonverbal Ramseyer, F., & Tschacher, W. (2014). Nonverbal synchrony synchrony and in computerized tools for the assessment of nonver- of head- and body-movement in psychotherapy: Different bal behavior.