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This novel approach could potentially be applied to investigate non-verbal aspects of social bonding in other dynamic interpersonal interactions such as between infants and parents and in non-verbal populations including those with communication disorders. Romantic interest is one of the most essential forms of social bonding and is fundamental to human society. Many previous studies have investigated social and personality factors related to the formation, maintenance, happiness and outcome of romantic relationships e.

Initial romantic interest in real-world settings can be examined using speed dating, a matchmaking process in which people have a series of short dates with potential romantic partners, because it simultaneously allows experimental manipulation and high external validity see Finkel and Eastwick, for a review.

The speed dating context has been widely used to investigate social and personality factors related to romantic interest e. However, the role of interpersonal interaction and non-verbal behavior in initial romantic interest has rarely been studied scientifically, despite being viewed as a crucial factor by the general public e. Reiman, Here, we measure how the interactive body sways of dyad members engaged in speed dating relate to romantic interest.

Body sway is a non-verbal behavioral index reflecting real-time interpersonal interactions across many settings. People rarely sit or stand perfectly still, but rather engage in subtle sway of their torso and head back and forth as a unit, typically without awareness.

When people coordinate joint actions, have a conversation, have a psychotherapy session or play music in an ensemble, their body sways tend to be coupled e. We examined both directional and similarity couplings between their body sways.


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Directional coupling was indexed by Granger causality GC. It determines to what extent the movements of one partner at each point in time can be predicted, or even influenced, by how the other partner just moved. It is a directional measure in that the coupling strength i. Such directional coupling dynamics have been shown to relate to social outcomes. At the same time, a parallel body of work has shown that interpersonally synchronized or similar movements, which can be indexed by cross-correlational CC coupling, are associated with interpersonal affiliation and cooperation e.

It is important to distinguish these two measures of coordination as they relate to interpersonal movement. CC quantifies the similarity between two time series, and GC quantifies the directional predictive power of each time series on the other. On the other hand, even if two people sway very differently and therefore have a low CC value, they can still have a high GC value in one or both directions, as long as there is a robust predictive relationship between their body sways.

Dating environments typically include background music; however, the effect of music on romantic interest has rarely been studied. Indeed, many settings where people experience romantic interactions, such as restaurants, bars, parties and dances, have music. Empirical studies have shown that high groove music promotes entrainment of body sway to the beat of the music e. Therefore, we further hypothesized that groove might promote romantic interest by enhancing interpersonal body sway coupling.

To investigate these hypotheses, we set up a real speed dating event. Body sway during each date was recorded while high- or low-groove music was played in the background. At the end of the event, contact information was given to those who matched. Fifty-five participants attended one of two speed dating sessions. Fourteen men age Thirteen men age Participants were recruited within the city of Hamilton, Ontario and surrounding areas.

No financial compensation was offered to ensure that the recruited participants found the prospect of dating highly motivating in itself, but the cost of transportation was reimbursed. Participants were blind to the hypotheses of the study, and signed informed consent was obtained from each participant in accordance with the Declaration of Helsinki For each date of each session, a different music excerpt was selected as background music.

Fifteen excerpts were selected for the experiment out of 43 possible songs.


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    6. However, because these songs had highly correlated groove, enjoyment and familiarity ratings, and our goal was to obtain representation from high- and low-groove songs while maintaining approximately similar levels of familiarity and enjoyment, we selected a number of additional songs to be rated. Nineteen raters aged These participants, who did not participate in the speed dating experiment, were students at McMaster University and were blind to the purpose of this study.

    Higher ratings indicated higher groove, familiarity or enjoyment, respectively.

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    Among the selected 15 excerpts Table S1 , 8 were classified as high-groove range 6. Two speed dating sessions were hosted across two evenings, and each participant was allowed to participate in one session only. Face-to-face contact between potential dates prior to the speed dating was minimized by asking men and women to arrive at separate entrances to the lab.

    After completing the questionnaire, the men moved to the next table for the next date. There were 15 dates including rest per session. Every participant experienced one date with each member of the opposite gender in the same session.

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    The experimenter explained the procedure and the questionnaire prior to the speed dating, indicated when each date started and ended, as well as when participants should fill out their questionnaires. A 4 min music excerpt was played during each date. The excerpts alternated between high- and low-groove, and the order was the same for both sessions Table S1.

    The loudness level see Stimuli section for details did not interfere with the conversations between dating partners but the music was still audible. Participants were not instructed to pay attention to or to respond to the background music. Markers on the headband were placed on the central left and right of the head.

    One additional marker was placed in the middle of each table as a reference. The answers to questions 2—5 used nine-point Likert item, with higher values indicating greater interest or attractiveness. Note that question 2 was the nine-point Likert item version of question 1, and the goal was to obtain a more fine-grained response than in question 1.

    However, 30 out of 54 participants had inconsistent responses across these two questions i. Given that the only difference between questions 1 and 2 was the number of available response points, we suspected that this discrepancy was simply because participants were inconsistent in using the scales across the experiment. Therefore, we used question 1 in the primary analyses, but also reported the results of question 2 in Table S5 , Supplementary Materials.

    Motion capture data were exported from Qualisys Track Manager 2. On average, gaps were only 0. GC is a statistical estimation, based on vector autoregressive models, of the magnitude of how much one time series is predicted by the history of another time series, taking into account how much it is predicted by its own previous history, in the form of a log-likelihood ratio. The larger the value of GC, the better the prediction and the more information is said to be flowing from one time series to another. Second, an optimal model order the number of preceding samples included was estimated by the Akaike information criterion for each date.

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    The optimal model order is a balance between maximizing goodness of fit and minimizing the number of coefficients length of the time series being estimated. Finally, in order to compare GC across dates, we fixed the model order at 18 2. Cross-correlation quantifies similarity between two time series as a function of a shifting time step between the time series. To empirically compare GC and cross-correlation, we performed cross-correlation analyses on the same preprocessed data to which we had applied GC.

    These two measures provide critically different insights. First, similarity is not the same as predictability.

    For example, if two time series were identical sinusoidal oscillators, their CC coupling would be perfect, but there would be no predictive coupling by GC, as the second time series cannot add any predictive information that is not already contained in the first time series. Second, CC does not imply directionality, just correlation, whereas GC quantifies directional predictive coupling strengths, such that it is possible for one time series to predict a second time series but at the same time that the reverse is not true see Dean and Dunsmuir, , for more details.

    Event information

    LMEM is an extension of a linear regression model. Our models assessed the predictors of interest i. Attractiveness was included in each model as a control variable. The glmer function was used to perform a logistic generalized LMEM when the predicted variable was binary MeetingAgain , and the odds ratios were reported as the estimates. If a participant failed to respond to a question for a dating partner, all the other responses for the same dating partner were excluded from analyses for that date.

    In total, for each LMEM, there were 54 clusters usable participants and observations. We did not include the interaction term between gender and GC or CC in the LMEMs, because we did not have any specific predictions about how gender would interact with body sway, and adding this factor and its interactions with GC and CC into the model would greatly increase the number of parameters that would need to be estimated and thus decrease the precision of the model.

    Nevertheless, gender is known to have important effects on romantic interest e. We treated our Likert data as an ordinal approximation of an interval variable and analyzed it with linear models. Every statistical test was performed two-tailed. The raw time series can be provided on a reasonable request and agreement. Given the nested nature of the current experiment, participants intercept and slope and dyads intercept were included as random effects.

    The logistic generalized LMEM showed a strong effect of attractiveness, such that participants were more interested in having a second date with dating partners who were perceived to be more physically attractive.