Participants

One hundred participants were recruited through databases at Aarhus University, Denmark. The study was approved by the Central Denmark Region Committees on Health Research Ethics, and informed consent was obtained from all participants, who received a remuneration of 200 DKK. Methods were carried out in accordance with approved guidelines by the Helsinki Declaration. Only males were included since hormonal fluctuations in females affect endogenous OT levels48. All participants were non-musicians, defined as having no more than four years of formal or informal (self-taught) music training, and not having practiced music actively in the last four years. Participants were randomly assigned to either the OT or placebo group. One dyad from the placebo group had to be excluded since the two participants knew each other in advance, giving a total of 50 subjects (25 dyads) in the OT group and 48 subjects (24 dyads) in the placebo group (Table 2).

Table 2 Participants’ descriptive statistics and characteristics. Full size table

Materials and Apparatus

Before arriving at the lab, participants completed three personality questionnaires online: The Empathizing Quotient (EQ)49, The Systemizing Quotient (SQ)50, and the Toronto Alexithymia Scale (TAS)41,51. EQ and SQ measure autistic traits, particularly related to mentalizing, attention to detail, and strong interests in patterns and systems. The TAS-20 measures the ability to identify and describe feelings, and externally oriented thinking. At the lab, current mood ratings were provided on a pre-test questionnaire, using a 7-point Likert scale. Participants then performed the rhythm sub-test of the Musical Ear Test52, assessing baseline rhythmic skill via a discrimination task. A post-test questionnaire asked participants to provide further demographics and ratings of current mood, tapping partner and task liking, and guess whether they had been given OT or placebo.

Upon careful instructions, either the synthetic OT analogue Syntocinon® or a non-active placebo nasal spray (isotonic saline 0.9%) was self-administered intranasally to both dyad members. Each participant received a total dose of 24 IU for the OT dyads10,53. Following guidelines by Guastella et al.53, they took six bursts – three in each nostril in an alternating fashion – leaving approximately 10 sec between each burst.

Tapping data were recorded and stimuli presented following the procedure of Konvalinka et al.31. Tapping was recorded on Yamaha keyboards, and a mixer was used to control auditory feedback; specifically, participants heard via headphones either 1) the computer-generated metronome, 2) feedback from their own key presses, or 3) feedback from their partner’s key presses. Before each trial, participants always heard an 8-beat long metronome indicating the intended tapping tempo, 120 bpm. The elaborate set-up ensured an auditory delay time between pressing the key and hearing the sound of no more than 6 ms.

Task and Procedure

The first part of the study took place in a room with both dyad members present. They were introduced and told that they would be ‘tapping partners’ during the main experiment. They then stayed in the same room, but performed tasks and completed questionnaires individually. Pre-test questionnaire and the MET test (~8 mins) were completed. Participants were given instructions to self-administer the nasal spray and then wait for 30 min while watching a documentary about glaciers (i.e. with no social relevance), separately on two laptops with headphones, to allow OT to be taken up by the CNS9.

During the second part of the experiment, dyad members were placed in two separate rooms, eliminating visual and external auditory feedback. A different experimenter to the one that had provided the nasal spray was responsible for administering the tapping tasks. Tapping was recorded for the following three conditions, which varied in degree of social interaction (Fig. 1):

a Computer metronome/non-social tapping. Both only hear completely regular (with no variability) computer-generated beats at 120 bpm. b Bidirectional coupling/tapping to responsive other responsive other. Both participants hear beats generated by the other participant, but not their own tapping. c Unidirectional coupling/tapping to unresponsive other. Both participants hear only tapping of either member one or member two, creating a leader-follower relationship. In half of the trials, member one taps to self while member two taps to member one. In the other half, this relationship is inversed.

Approximately 45 min after nasal spray administration, participants began the tapping experiment. Both dyad members were asked to tap with the index finger of their dominant hand for 17 bars (i.e. 68 beats) by pressing the keys corresponding to notes C3 and E3, respectively, following the initial 8-beat metronome. This comprised one trial of the experiment. Two different notes were chosen to allow participants to identify the sounds as coming from oneself or the other. Participants were told to start tapping as soon as they heard the metronome. Before each trial, the experimenter notified participants what they would be tapping to: 1) computer; 2) self, or 3) other. Participants were not told whether the other participant would be tapping to them or not (i.e. whether the coupling would be bidirectional or unidirectional). This was to ensure that their responses were unaffected by their beliefs about the directionality of the interaction and were instead fully a product of the interaction itself. The following instructions were given: “when hearing computer or other, continue tapping to the beat established by the initial metronome, while at the same time synchronising with the computer or the other member, respectively; when hearing self, continue tapping to the beat established by the initial metronome”. Each condition was carried out four times in semi-random order. The tapping part of the experiment lasted approximately 20 min. The study was double-blinded: neither participants nor the experimenter leading the tapping task knew whether dyads had been given OT or placebo.

Pre-processing

Data were pre-processed for the dyadic analyses of interpersonal interaction (i.e. synchronisation indices, windowed cross-correlations, asynchronies), by ensuring that taps were aligned between pairs54. Thus, if one pair member skipped a beat, the tap of the other was removed, to ensure the shifts in the lagged cross-correlations were not artefactual. Similarly, if they drifted away from the given tempo (i.e. hear-self condition), we kept them aligned to each other’s closest beats in time, and removed a beat as soon as the other advanced in period. The maximum difference allowed in aligned taps between the two members was half of the average period (i.e. since the tempo was 120 bpm they could not be misaligned by more than 250 ms).

Analysis

Five types of measures were computed from the data; synchronisation indices (SI), standard deviation (SD) of inter-tap intervals (ITIs), mean ITIs, asynchronies and windowed cross-correlations.

One way of measuring the strength of synchronization is to calculate synchronisation indices (SIs), which are quantified statistically using the distribution of phase difference between two signals55. SIs are based on variance of relative phase56, calculated with the formula in equation (1):

where N is the number of taps in each trial. θ1 and θ2 are the respective phases of each member in the dyad (or the participant and the computer in the computer condition). t n corresponds to each discrete tapping instance. In other words, θ1(t n ) is the phase for tap 1, and θ2(t n ) is the estimated phase for tap 2, etc. The index is a unitless number, which ranges from 0 to 1, representing the absence of synchronisation and perfect synchrony, respectively.

SIs were calculated for dyads of participants in the unidirectional and bidirectional conditions, i.e. an SI value represents the synchronisation between the two members of a given dyad. For the bidirectional condition, dyad SI represented the synchronisation between the two dyad members averaged across trials. For unidirectional tapping, dyad SI was obtained by averaging both across trials and across leading and following conditions (i.e. the measure represented synchronisation regardless of whether it was member 1 or member 2 who was following or leading). SIs were calculated individually for the computer condition, between the individual tapper and the regular 120 bpm computer metronome. Here, individual SIs were averaged first across trials and then across the two dyad members to obtain the dyad synchronisation.

ITIs were computed for each trial and averaged for each participant (OT N = 50, Placebo N = 48). SD of ITIs was used as a measure of individuals’ tapping variability. For the unidirectional condition, data were split into ‘leading’ (tap to self) and ‘following’ (tap to unresponsive other). This ‘interacter-role’ was a within-subjects variable, since both dyad members were given the opportunity to act as both leader and follower. This allowed us to test the extent to which, when controlling for self-paced tapping variability, OT improves following variability. The effects of oxytocin and condition on mean ITIs indicated tapping speed. Here, the analysis unit was individuals’ mean ITI (OT N = 50, Placebo N = 48). For unidirectional mean ITIs, the values were averaged across leading and following.

Negative and positive asynchronies (negative and positive error in seconds from tapping target) were computed to investigate the extent to which tappers anticipated or reacted to tapping targets, respectively. We focused on the unidirectional condition, since only this condition had shown differences in synchronisation and variability. Furthermore, we did not analyse asynchronies in the bidirectional condition where tappers were constantly switching between leading and following (as could be seen in the windowed cross correlations). In other words, it would be near impossible to determine whether it was member 1 or member 2 who was leading or following and thus impossible to determine whether the asynchrony was positive or negative for the given tapper. In the unidirectional condition, there was a more stable pattern of leading and following, and thus asynchronies could be calculated for this condition. Positive and negative asynchronies were calculated independently, so the measures are representative even though participants shifted between premature/anticipatory and reactive modes of tapping. For each trial, the average negative asynchrony was calculated by summing the negative asynchronies and dividing by the number of negative asynchronies. The same was done for positive asynchronies. Finally, the data were averaged across trials, representing the average negative and positive asynchrony for each participant (OT N = 50, Placebo N = 48) when acting as follower (the leader would have the inverse of the follower’s asynchrony). Also the number of taps associated with positive and negative tapping errors were compared between the two groups, and variability (SD) of absolute asynchronies was computed, to investigate the stability of tapping performance. We performed correlations with the asynchrony data and SIs, and compared correlation coefficients between the two groups using Fisher’s r-to-z transformations.

In order to quantify how much each participant adapted to their partner based on their partner’s previous ITI, lag −1, 0 and +1 correlation coefficients were computed from a cross-correlation between the ITIs of the two members in each dyad57. Following the procedures reported in Konvalinka et al.31, we used a moving window size of 6 taps, a maximum lag of 3, and a window and lag increment of 1. The correlation coefficients for lag −1, 0, and +1 were computed between the participants’ ITIs across these short intervals of time and were averaged per condition for each pair. This analysis gave an indication of the directionality of the interaction – namely, whether the participants were not interacting with each other (i.e., uncorrelated), whether there was a clear leader–follower dynamic where one participant led the other towards their own tempo, or whether the adjustment of ITIs was mutual. For example, a positive lag −1 correlation alone would indicate a leader–follower dynamic such that one member is adjusting to the previous tap of the other. This means that if the ‘leader’ went faster on the previous movement tap, the ‘follower’ would speed up on the next one. Similarly, a positive lag +1 correlation would indicate the other member is adapting (and thus is the follower). A positive lag 0 correlation would mean that the participants are correlated in real time, indicating that when one speeds up, the other speeds up as well; finally, a negative lag 0 correlation would mean that the two participants were anti-correlated, such that when one speeds up, the other slows down54,58. The coefficients were compared across groups using 2 (OT vs Placebo) × 4 (hearing self; member 1 acting as leader, member 2 acting as follower; member 1 acting as follower, member 2 acting as leader; bidirectional coupling) multivariate analysis of variance (MANOVA) and lag −1, 0, and +1 correlation coefficients as the dependent variables.

Some participants failed to complete one, both or all the questionnaires. Where participants had incorrectly left blank responses or ticked more than one response, we replaced the data with their average score.

Statistical Analysis

Statistical analyses were performed in R (except for windowed cross-correlations, which were analysed in MATLAB). To examine the effects of oxytocin, conditions and personality on our various measures, separate linear mixed models were used (LMM). For synchronisation indices (SI) pairs were entered as a random factor, since data points represented average dyad synchronisation (OT = 25, Placebo = 24). The dyadic nature of these data meant that we could not include SQ and TAS scores in the analysis for SI, since the scores represented individual’s personality and not dyad’s. The following fixed factors were specified for SI: group, condition, group × condition.

For variability (SD ITIs), we first investigated the computer and bidirectional conditions. Individual subjects were specified as a random factor, and group, condition, group × condition, group × SQ and group × TAS were set as fixed factors. For the unidirectional condition, data were split into ‘leading’ (tap to self) and ‘following’ (tap to unresponsive other). This ‘interacter-role’ was a within-subjects variable, since both dyad members were given the opportunity to act as both leader and follower. Individual subjects were the random factor, and the following were specified as fixed factors: group, role, group × role, group × SQ and group × TAS.

For tapping speed, we specified the LMM with individual subjects as the random factor and the following as fixed factors: group, condition, group × condition, group × SQ and group × TAS. Finally, for asynchronies, we investigated positive and negative asynchronies separately, with subjects as the random factor and group, group × SQ and group × TAS were the fixed factors.

We first tested the full models, and depending on the significance of the results, reduced the models to include only factors that had a significant effect on the outcome variables. The full models were compared to the reduced models with an ANOVA and with Akaike’s Corrected Information Criterion (ACI). If the ANOVA showed no significant improvement of the more complex model, and the ACI was lower for the reduced model, we reported the F-ratios and performed subsequent post-hoc Bonferroni corrected comparisons on the reduced model. In cases where the models were reduced to no longer include the SQ and TAS scores, the model was rerun with the full number of subjects N = 98 (i.e. for tapping speed and both asynchrony measures).