This kind of skilled intentionality is founded on a history of engaging in the relevant practices in which many details of the sociomaterial environment have been encountered (Rietveld, 2008a; Rietveld and Kiverstein, 2014; Myin, 2016). Having the ability to act in accordance with both the point and the details of the sociomaterial practice is having skill – in this case an architect's skill. In the process of coordinating with an evolving field of relevant affordances offered by the sociomaterial environment, the architects tend toward grip on their design. Thus, although during this episode the mobile sculpture was still non-existent in a sense, it is perfectly concrete as the coordination with sociomaterial aspects of the environment is realizing the affordance of designing a mobile sculpture.

# CONCLUDING REMARKS

By making the human form of life the starting point for ecological psychology, and thus foregrounding sociomateriality in each situation, this paper showed how the Skilled Intentionality Framework integrates affordances with social coordination. In the SIF affordances are defined as relations between aspects of the sociomaterial environment in flux and abilities available in a form of life. By showing how the sociomaterial entanglement re-appears when taking three different perspectives on (i) the whole form of life – and the persistent landscape of affordances it implies, and (ii) the zoomed in perspective of an observing behavioral scientist or dynamicist observing an actor located at a particular place in the landscape of affordances and (iii) the lived perspective of person engaged in action – we showed what a situated, integrated take on engagement with affordances looks like. Thus we showed how different aspects of the notion of affordances and of coordination fit in: while theories and methods of (social) coordination tend to focus on the zoomed in observer's perspective on the (inter)actions within an evolving landscape of affordances, those studying affordance perception are mostly focusing on affordances as we encounter them from our lived perspective – as agent-scaled perceived resources for action (e.g., Warren, 1984; Oudejans et al., 1996).

However, as we have stressed throughout this paper, these different viewpoints offer complementary (and not necessarily exhaustive) perspectives on the sociomaterial entanglement of the form of life as a whole (see Klaassen et al., 2010). The perspectives suggest that both fields of ecological psychology could consider broadening their scope in two principled ways. First, they could broaden the range of phenomena within their own perspective. As we have detailed, one is never coordinating with other people in isolation. The sociomateriality of the landscape of affordances in flux urges the study of social coordination to include coordination with materiality, i.e., as sociomaterial synergies. Moreover, zooming out emphasizes a focus not just at the scale of immediate interpersonal (e.g., dyadic) interaction, but to also include nesting scales of coordinating (Wijnants et al., 2012; Schmidt et al., 2014) with more distant dealings and places (Heft, 1996; Bruineberg and Rietveld, 2014; Van Dijk and Withagen, 2016) and perhaps even entire practices (Rietveld and Brouwers, 2016) and language games (Rietveld and Kiverstein, 2014; Van Dijk, 2016). Furthermore, from a lived perspective, one never encounters an affordance in isolation. Studies on affordances should thus take note of the sociomaterial context by studying affordance perception in the context of a field of relevant affordances embedded in a behavior setting (Heft, 2007, 2012) and/or a sociomaterial practice (Rietveld and Brouwers, 2016; Rietveld et al., 2016). By taking a more situated approach, both fields can thus contribute to the same overall goal of extending the reach of ecological psychology toward dealing with case of so-called "higher" cognition.

Second, as each of the three perspectives discussed foregrounds different aspects of the form of life, but backgrounds or neglects others, we believe each field should aim to keep an eye on at least one other perspective on the form of life to be able to claim to see the whole picture. Ethnography highlights

the importance of this as it aims to link a zoomed in perspective on concrete situations, or interviews based on individual's lived perspective, to the regularities at the level of the sociomaterial practice as a whole; i.e., to what we have called a zoomed out view on the form of life. Ecological psychology could thus benefit from including ethnographical methods and social sciences that thematize the patterned practice of the form of life (e.g., Roepstorff, 2008). That way we can get a clear view of the richness of the landscape of available affordances offered by our evolving sociomaterial environment (e.g., Malafouris, 2014) as it persists and changes within cultures, communities, and behavior settings.

The view that we have presented in its multitude of perspectives on the whole does not need to rely on (ontological) priorities, in the sense that it does not need to presuppose a hierarchical and pre-structured world (see Van Dijk and Withagen, 2014; Van Dijk, 2016; see also Hodges and Baron, 1992; Ingold, 2011). For example, the notion of "higher" cognition that we discussed is indicative of a supposed hierarchy, but its use can be avoided once we realize that the phenomenon the notion aims to single out (e.g., architects designing a novel sculpture) amounts to adequately coordinating with multiple affordances simultaneously across increasing scales of sociomateriality. Conceptually this required the notion of a constitutive entanglement. One of the merits of our view on the constitutive entanglement is that it ties our concepts in with a process that constitutes the whole while forming its parts. In this way it opens up our theory to the scrutiny of dynamical methods, in which it is common to distinguish between macro-level patterns of activity and microlevel patterns of activity. For example, we can formalize the dynamics of the agent-environment system as a whole, or focus on a part and use the tools and concepts of dynamical systems theory to increase our understanding of the dynamics of multiple simultaneous affordance-related states of action readiness (see Bruineberg and Rietveld, 2014; Bruineberg et al., 2016).

# REFERENCES

Finally, in a fragment quoted by Costall (1997), Gibson tried to clarify how affordances are objective and subjective through their relational persistence: "Affordances are both objective and persisting and, at the same time, subjective, because they relate to the species or individual for whom something is afforded" (Gibson, 1982, p. 237). Our view makes sense of this idea by showing how the distinction between the individual and the "species" is not the most relevant one. By rather talking about a form of life by focusing more on "how an animal lives than [on] where it lives" (Gibson, 1979, p. 128), i.e., on its way of life, we showed that affordances are both persisting environmental resources which can solicit an individual and persisting relations in the ecological niche. Yet they are continuously forming in the multitude of our activities that make up our form of life.

# AUTHOR CONTRIBUTIONS

LvD and ER: conception of the work, drafting the work.

# FUNDING

We gratefully acknowledge the support obtained from the European Research Council in the form of an ERC Starting Grant (679190) awarded to ER.

# ACKNOWLEDGMENTS

We are indebted to Julian Kiverstein, Martin Stokhof, Jelle Bruineberg, and Rob Withagen for our discussions and for their insightful comments on earlier versions of this paper. We thank Azille Coetzee for proof reading our text. We are moreover grateful to Harry Heft and Cor Baerveldt for their helpful suggestions.

**Conflict of Interest Statement:** 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.

Copyright © 2017 van Dijk and Rietveld. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Dynamics of Social Interaction: Kinematic Analysis of a Joint Action

Quentin Moreau1,2,3 \*, Lucie Galvan2 , Tatjana A. Nazir2 and Yves Paulignan2

1 "Sapienza" Università di Roma, Dipartimento di Psicologia, Roma, Italy, 2 Laboratoire sur le Langage, le Cerveau et la Cognition, Institut des Sciences Cognitives, Centre de National de la Recherche Scientifique – Université Claude-Bernard Lyon 1, Lyon, France, 3 Istituto di Ricovero e Cura a Carattere Scientifico, Fondazione Santa Lucia, Laboratorio di Neuroscienze Sociali, Roma, Italy

Non-verbal social interaction between humans requires accurate understanding of the others' actions. The cognitivist approach suggests that successful interaction depends on the creation of a shared representation of the task, where the pairing of perceptive and motor systems of partners allows inclusion of the other's goal into the overarching representation. Activity of the Mirror Neurons System (MNS) is thought to be a crucial mechanism linking two individuals during a joint action through action observation. The construction of a shared representation of an interaction (i.e., joint action) depends upon sensorimotor cognitive processes that modulate the ability to adapt in time and space. We attempted to detect individuals' behavioral/kinematic change resulting in a global amelioration of performance for both subjects when a common representation of the action is built using a repetitive joint action. We asked pairs of subjects to carry out a simple task where one puts a base in the middle of a table and the other places a parallelepiped fitting into the base, the crucial manipulation being that participants switched roles during the experiment. We aimed to show that a full comprehension of a joint action is not an automatic process. We found that, before switching the interactional role, the participant initially placing the base orientated it in a way that led to an uncomfortable action for participants placing the parallelepiped. However, after switching roles, the action's kinematics by the participant who places the base changed in order to facilitate the action of the other. More precisely, our data shows significant modulation of the base angle in order to ease the completion of the joint action, highlighting the fact that a shared knowledge of the complete action facilitates the generation of a common representation. This evidence suggests the ability to establish an efficient shared representation of a joint action benefits from physically taking our partner's perspective because simply observing the actions of others may not be enough.

Keywords: joint action, kinematics and dynamics, social interactions, reach-to-grasp, motor system

# INTRODUCTION

Humans are constantly communicating with their fellows. Of all the great apes, it was only humans who developed a complex verbal language allowing us to communicate our wishes, our intentions and our feelings. Leibniz (1765) described language as the mirror of understanding, a powerful instrument used by an individual to express their own internal processes and to

#### Edited by:

Michael J. Richardson, University of Cincinnati, USA

#### Reviewed by:

Lynden K. Miles, University of Aberdeen, UK Jessica Lindblom, University of Skövde, Sweden

\*Correspondence: Quentin Moreau<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Cognitive Science, a section of the journal Frontiers in Psychology

Received: 31 May 2016 Accepted: 12 December 2016 Published: 27 December 2016

#### Citation:

Moreau Q, Galvan L, Nazir TA and Paulignan Y (2016) Dynamics of Social Interaction: Kinematic Analysis of a Joint Action. Front. Psychol. 7:2016. doi: 10.3389/fpsyg.2016.02016

Moreau et al. Dynamics of Social Interaction

describe external objects for others and consequently socially interact with them. However, social communication is not limited to just verbal communication. Through their behavior, humans change their environment, satisfy internal needs, and achieve personal goals (Lemon, 2008). From our early days as infants, a large number of our actions are performed in social contexts, allowing us to develop social skills and the ability to coordinate with interacting agents. One way to study these behaviors is to focus on joint actions (Sebanz et al., 2006). Classical orchestras, collective sports, and ballets are just a few examples of how people can coordinate their movements to achieve a common goal. But such actions are not only accomplished by musicians or those competing in team sports. Simple joint actions are ubiquitous in our everyday life, such as lifting a heavy table with another person or shaking hands with a colleague. Social interactions require dynamic and efficient encoding of others' gesture and a spatiotemporal synchronization of the individuals involved (Sebanz et al., 2006). On the question of the mechanisms of interaction, differing views have been proposed over the years. For example, Coordination Dynamics have explored the social influence of one person on another, highlighting a spontaneous and immediate coordination of their actions while engaging in interpersonal sensorimotor interactions (see Coey et al., 2012 for a review). This theory places social cognition in an embodied-embedded constraint, where social behavior is defined as self-organized. The brain is dynamically in interaction with the environment and other natural sytems (Coey et al., 2012). On the other hand, the most traditional approach of social cognition is set in a cognitivist framework. Evidence indicating that action production and action perception rely on common mechanisms led to the Theory of Event Coding (Hommel et al., 2001). If perception and action rely on common codes, it makes the integration of one's own and co-actor's action for joint actions relatively straightforward (bottom-up processes). On a more top-down perceptive, co-representation of conspecifics during joint action is thought to be a key feature to understanding others' goals and actions (Sebanz et al., 2006).

One cerebral network suggested to play a role in matching observed and executed actions is the Mirror Neurons System (MNS). Since mirror neurons (MN) were discovered more than 20 years ago, we have been able to apply a neuroscientific approach and a new understanding of social interaction. Initially discovered in non-human primates (di Pellegrino et al., 1992) and in humans (Buccino et al., 2001; Rizzolatti and Craighero, 2004; Mukamel et al., 2010), MN are visuo-motor neurons located in the premotor cortex and the inferior parietal lobule, automatically firing during the execution of an action and the observation of the same action performed by another person. MNS has been proposed as the fundamental neural mechanism at the basis of the understanding of others' actions and successful non-verbal social interactions (Gallese et al., 2004). The discovery of MN has given rise to a large number of interpretations of their potential roles in human cognition: understanding of action (Rizzolatti et al., 2001), imitation (Gallese, 2003), empathy (Gallese, 2001, 2003), mind-reading (Gallese and Goldman, 1998), and emergence of language (Rizzolatti and Arbib, 1998).

In recent years, great progress has been made by investigating neural processes during interpersonal motor interactions (Newman-Norlund et al., 2007; Newman-Norlund et al., 2008; Kokal and Keysers, 2010; Konvalinka et al., 2014; Ménoret et al., 2014; Sacheli et al., 2015b). These studies brought to light the recruitment of fronto-parietal networks during interactive contexts where MN are thought to play a role in the internal simulation, action prediction and understanding. However, interpersonal coordination requires both perception and understanding of our partner's movements while controlling our own movements. To explain neural processes in bidirectional interactions, Hari and Kujala (2009) proposed the "interactiveloop" model. This model is that a coupling of perceptive and motor systems of the individuals is necessarily involved during joint actions in order to form common internal representations. By their actions and intentions, a person influences and changes not only their environment but also the movements that the interactional partner needs to perform in order to interact smoothly. These modifications modulate the perception of the environment the other person has had up to this point. That person reciprocates by influencing the environment in return by changing external representations on their partner's brain. The progressive construction of these "action-perception" loops is constantly changing and seems to be essential for encoding social actions and building up a common representation of the action for all the protagonists involved (Hari and Kujala, 2009). The enrolment of fronto-parietal networks might therefore allow the construction of an "interactive loop" to build a common representation of an action in both protagonists, allowing successful interactions.

In line with this model, it is our opinion that building experimental paradigms allowing bidirectional and either synchronic or diachronic adaption (Tognoli and Kelso, 2015) involving at least two interacting subjects (Tognoli et al., 2007; Noy et al., 2011; Konvalinka et al., 2014; Sacheli et al., 2015a; Moreau and Candidi, 2016), it should be possible to highlight a common adaptation between participants at a behavioral level. In our study, we focused on behavior changes during a bidirectional diachronic joint-action, involving one participant putting a base in the middle of a table and another participant placing a parallelepiped into the base's slot. Crucially, the action of placing the parallelepiped is more or less facilitated depending on how the base is oriented on the table (for an equivalent solo action, see Allami et al., 2008). Our paradigm therefore allows space for adaptation and gives us objective measures to define the success of the interaction and the installation of the common representation. The purpose of the study, set on a cognitivist approach, is to highlight that full comprehension of a joint action is not automatic and that the installation of the common representation is progressive.

We attempted to reveal a behavioral change (placing the base in a more optimal position) when both individuals had physically experienced the other partner's motor task difficulty. Results indicate that the adaptation to the new task was not automatic and required a common experience from both participants. We wish to point out a limit of the maximalist interpretations of the MNS, according to which this system serves the understanding of others action and provide empirical data supporting the minimalist approach of the representational content of MN (Pacherie and Dokic, 2006). Although we agree the MNS is part of basic constituents of action understanding, this system appeared not sufficient to fully understand actions and apprehend movement difficulties encountered by the other.

# MATERIALS AND METHODS

fpsyg-07-02016 December 24, 2016 Time: 11:26 # 3

# Subjects

Twenty healthy right-handed subjects (6 males, 14 females; mean age: 22.9, range : 18–50 years) participated in the Main experiment and 10 healthy right-handed subjects participated in the Control Experiment (5 males, 5 females; mean age : 21.2, range : 16–28 years). None of them had any history of neurological disorder. All participants gave written consent before the experiment. Participants were familiarized with the methods prior to the experiment. and an explanation of the purpose of the study was given at the end of the experiment. The study was approved by the Ethical Committee CPP Sud-Est II.

# Procedure

### Main Experiment

The experiment involved a pair of participants: subject 1 (S1) and subject 2 (S2). In the following, participants will be referred as S1 or S2 depending on their original role in the experiment (Condition 1). Subjects were seated face to face on opposite sides of a 50 cm × 70 cm table. Both subjects were instructed to use only their thumbs and index fingers to grip and displace the experimental objects, which were placed on predefined spots: the cylindrical base located to the right of S1 and the parallelepiped to the right of S2. The slot of the base and the parallelepiped were both oriented parallel to the sagittal axis (**Figure 1**). The subjects were asked to carry out a simple task: S1 was to move the base to the center of the table and S2 was to place the parallelepiped in the slot on the base. Note that the action performed by S2 is more or less facilitated depending on how S1 place the base on the table: difficult when the slot in the base remains parallel to the sagittal axis; easy when it is slightly tilted to the left. The experiment was divided into three conditions with instructions given prior to each condition. In Condition 1, the joint action was repeated 20 times with no other instruction other than completing the task after a vocal "Go" signal from the experimenter, which was the same for every trial. In Condition 2, subject's roles were interchanged so S1 was in charge of moving the parallelepiped and S2 the base for 3 trials. Because this second condition was designed to give the opportunity for participants to experience the other's action, data from Condition 2 was not analyzed. In Condition 3, subjects reverted to their initial roles (so they were carrying out the same task as in Condition 1) for another 20 trials. In this final condition, participants were further instructed to perform the task as fast as possible. During the whole experiment, participants were not allowed to communicate their feelings either verbally or by explicit non-verbal utterance. To ensure the accuracy and well-being of the experiment, an experimenter continuously stayed next to the table in order to both control inter-subjects communication and to replace objects at their correct initial location when required. At no time were participants encouraged to change their motor behavior either explicitly or implicitly; they believed the goal was a simple kinematic study until the end of the experiment.

# Control Experiment

In the Control Experiment, the task was explained as in the Main Experiment but subjects only completed Condition 3 (where the task was to perform the joint action as fast as possible). This control served to ascertain that verbal instruction to perform the task as fast as possible was not sufficient to provoke potential behavioral changes. A critical point of the Control experiment was that participants were only assigned to one role; they did not have the opportunity to experience the other participant's task.

# Movements Recording

The movements of the right arm and hand of both subjects were recorded by means of an Optotrak Certus camera (manufactured by Northern Digital Inc, in Waterloo, Ontario, Canada). The camera was fixed 2 m away from the table. Five markers were placed on each subject: marker 1 was on the distal extremity of the thumb, marker 2 on the distal extremity of the index, marker 3 on the metacarpophalangeal joint of the index, marker 4 on the radial styloid and marker 5 was 3 cm over the radial styloid. The spatial position of active markers was sampled at 250 Hz with a spatial precision of 0.1 mm.

Raw data was pre-processed using a second order Butterworth dual-pass filter (cut-off frequency, 10 Hz). Kinematic parameters were assessed for each individual movement using Optodisp software (Optodisp copyright INSERM-CNRS-UCBL, Thévenet et al., 2001). The movement duration was analyzed and the velocity peak of the two sub-phases of the movement recorded (Reach-to-Grasp and Displace). Sub-movements onset and offset were determined by a sequence of at least eleven increasing or decreasing points of the wrist marker velocity profiles. Velocity peak was determined as the maximal value in wrist marker velocity profiles. The workspace was defined by the X, Y, Z axes defined by the table surface. The angle between the fingers markers 1 and 2 of S2 and the sagittal axis was also analyzed. This angle corresponded to the opposition axis (Paulignan et al., 1997) an index of movement difficulty (Frak et al., 2001). Note that the opposition axis is directly linked to the behavior of the subject that places the slot (S1) because the opposition axis changes with the orientation of slot.

# Statistical Analyses

Kinematic parameters were determined for each individual trial and averaged for each participant and condition. Statistical analysis data was analyzed by using Statsoft Statistica 8. General Linear Model (GLM) and the Greenhouse–Geisser correction for non-sphericity was applied when appropriate (Keselman and Rogan, 1980). Post hoc comparisons were performed using the Newman–Keuls correction for multiple comparisons (significance threshold was fixed at p < 0.05).

Execution time and Velocity peaks were both analyzed each by separated 2×2×2 (Condition × Movement × Subject) ANOVAs and final angles by a one-way ANOVA.

# RESULTS

# Kinematics Results

### Execution Times

The 2 × 2 × 2 (Condition × Movement × Subject) ANOVA showed a significant main effect of Condition [F(1,8) = 70,824, p < 0,001], indicating a smaller execution time during Condition 3 compared to Condition 1. There was a significant main effect of movement [F(1,8) = 111,06, p < 0,001], showing that Reachto-Grasp movement was realized faster than Displace and a significant main effect of subject [F(1,8) = 59,335, p < 0,001] that showed that S1's action were executed faster than S2's. Post hoc test indicated that S1 execution times were smaller in Condition 3 compared to Condition 1 for both Reach-to-Grasp (p < 0,001) and Displace (p < 0,001) (see **Figure 2A**) and that S2 execution times were smaller in Condition 3 compared to Condition 1 for both Reach-to-Grasp (p < 0,001) and Displace (p < 0,001) (see **Figure 2B**).

### Velocity Peaks

The 2 × 2 × 2 (Condition × Movement × Subject) ANOVA showed a significant main effect of Condition [F(1,8) = 49,339, p < 0,001], indicating a greater execution speed during Condition 3 compared to Condition 1. A significant main effect of movement [F(1,8) = 114,47, p < 0,001] showed that Reachto-Grasp movement was realized with a smaller speed than Displace. Post hoc test indicated that S1 velocity peaks were greater in Condition 3 compared to Condition 1 for both Reachto-Grasp (p = 0,006) and Displace (p < 0,001) (see **Figure 2C**) and S2 velocity peaks were greater in Condition 3 compared to Condition 1 for both Reach-to-Grasp (p = 0,002) and Displace (p < 0,001) (see **Figure 2D**).

# Final Angle

In order to compare the final angles of Condition 1, Condition 2, and Control, we performed a one way ANOVA that showed a main effect [F(2,6) = 7,7206, p = 0,02]. Post hoc test revealed that the final angle in Condition 3 was significantly smaller compared to Condition 1 (p = 0,03) and compared to Control (p = 0,02) (see **Figure 3**).

# DISCUSSION

In this paper, we developed a diachronic joint action task that induced behavioral adjustments made by one of the participants in order to make the task of their partner easier and consequently improving the common realization of the task for both participants. The results revealed better performance (execution time and velocity) in both sub-movements of the task, Reach-to-Grasp and Displace, for both participants when common representation of the task was achieved.

Our measurements also highlight a significant change in the final angle of the cylindrical base between the Condition 1 (before the subjects switched roles) and Condition 3 (after the subjects switched their roles). According to Allami et al. (2008), this orientation's change from the sagittal axis facilitates the partner Displace sub-movement: the articular tension in the arm and wrist joints are less extreme in Condition 3 than in

Condition 1. This behavioral measurement indicates that the participant placing the base changed their previous behavior, after experiencing the partner's contribution to the joint action.

The results from our statistical analysis reveal an increase in the performance of the subjects for both sub-movements during Condition 3. In Condition 3, both subjects were asked to perform the action as fast as possible. Due to this, one can assess that the instruction (to perform the task as fast as possible) is the main contributor to the increase in performance, rather than changes in the orientation. Because of this, we were also able to calculate the effect size (Cohen's d) according to Fitts's (1954) law , where the level of difficulty of a movement is proportional to the amplitude of kinematics values (Ferri et al., 2011). Thus we compared Cohen's d values calculated for velocity peaks for each sub-movement in Condition 1 and Condition 3 for S2 (the participant placing the parallelepiped). Cohen's d value for Reach-to-Grasp sub-movement between Condition 1 and Condition 3 is 0.76, corresponding to a medium effect, while Cohen's d for Displace Sub-movement between Condition 1 and Condition 3 is 1.38, corresponding to a large effect (Cohen, 1992). The discrepancy between Cohen's d values during the two movements' phases highlights the facilitation of S2 Displace sub-movement when S1 (participants placing the base) changed the orientation of the base. On the one hand, imposed constrain on movement speed in Condition 3 improves the kinematics parameters in both submovements but on the other hand, we observe a greater orientation-specific effect only on the Displace sub-movement. This result is consistent with previous data from Allami et al. (2008).

Our aim, based on Hari and Kujala's (2009) model, was to demonstrate the establishment of a common representation during a joint action. Through the first condition, the two subjects were performing the task freely. Even if Condition 1 was uncomfortable for the participant placing the parallelepiped (S2), the participant placing the base (S1) didn't change the base's orientation to facilitate S2 contribution. Nonetheless, after both subjects experienced each other's role in the action (in Condition 2), a significant change in the base's orientation has been measured, revealing a change in the behavior of S1 during Condition 3. This change also improved the task performance in Displace sub-movement of S2. Changes in opposition axis orientation are close to those obtained in previous studies describing easy movements (Frak et al., 2001; Allami et al., 2008).

As our Control experiment showed, the orientation change is not due to the requested high speed during Condition 3; indeed, no change in angle was measured when the roles of both subjects were not interchanged (Control condition). This change in the instructions was only to maintain participants focus and avoid boredom.

From the neural perceptive, parietal and premotor regions are thought to form the action observation network, also known as the MNS (Rizzolatti and Craighero, 2004). Although action mirroring has become a popular way to explain joint action effects (Frischen et al., 2009), if we adhere to the original description of this neural substrate, both action and observation should activate the same neurons. However, in our experiment, observation of S2's action did not seem enough for S1 to fully understand their difficulty. In fact, S1 kept putting the base in a difficult position for S2 through all Condition 1. However, when S1 had experience of both parts of the joint-action, S1 adapted and changed their initial behavior to facilitate S2's movement. We can agree here that MN were certainly activated to understand globally the action but not the subtleties such as extreme joint angles. The critical behavioral change only appeared in Condition 3, when both subjects shared a common knowledge of the two individual actions composing the joint action. In any case, our experiment stirs up a debate about MNS role in joint-actions. While we do not deny the possible role of MN for joint action, or the fact that they are involved in action comprehension, it seems that observation of the partner's movements (and thus activity in the MNS) was not enough to fully understand the action of others (Kokal and Keysers, 2010) during our protocol. In fact, we highlighted a delayed installation of a common understanding between the two subjects participating in a joint action. Our interpretation of behavioral data is in line with Pacherie and Dokic (2006) minimalist theory.

Over the last few decades, scientific research has improved our understanding of how perception and action are linked. It still remains unclear whether the processing of relevant information during joint actions emerges from the physical and informational constraints (Rigoli et al., 2015) or whether it is supported by high-level representations (social-specific) mechanisms (Sebanz et al., 2006) or through lower perceptive mechanisms (Dolk et al., 2011). In other words, is the mental representation of a partner necessary, and if it is, to what extent does one interacting individual need to mentally represent the actions of others to interact with them?

One of the most studied joint tasks is derived from the Simon task (Simon, 1969) where participants have to respond to the color of stimuli with both their hands while ignoring their spatial location (e.g., red with left hand, blue with right hand). The so-called Simon effect (SE) describes the fact that participants are faster to answer when the stimulus and the response are congruent (e.g., use the left hand when the stimulus is presented of the left of the screen). The SE disappears when participants are asked to only answer to one stimulus (a go/no-go task) but the joint Simon effect arises when performed by a pair of participants (where each participant has a go/no-go task). The joint Simon task has been used to highlight co-representations during joint tasks, suggesting the existence of a specific neural mechanism facilitating social interactions with conspecifics (Tsai and Brass, 2007; Welsh, 2009). An alternative interpretation was proposed by Dolk et al. (2013), suggested that "social" effects from the Joint Simon task can be explained by the Theory of Event Coding (Hommel et al., 2001; Hommel, 2009) and further claimed that generic sensorimotor regions were the substrate of joint actions without the need of a co-representation. Another framework challenging the notion of co-representation during joint action is the Coordination Dynamics approach where social effects are

due to motor coordination rather than mental stimulation of a complementary action (Doneva and Cole, 2014). The findings of our experiment seem to be contesting the latter theories (both Theory of Event Coding and Coordination Dynamics), rather they agree that the common representation of the action during social interaction is through mental representation. Our results appear to have a better fit in a top-down explanation: our behavioral change did not appear automatically through action observation nor motor coordination between subjects but only when knowledge of task-specific features was shared between the two participants. Although our conclusions are only based on kinematics, the change in the behaviors of participants seems to fit in the theory of co-representation. Here we argue that participants lacked knowledge of the others' specific action, resulting in a misrepresentation during Condition 1. It is our belief that, only thanks to the shared experience of the task during Condition 2, was co-representation achieved, resulting in an adaptation of interacting subjects' behaviors in

# REFERENCES

Condition 3. In our task, the common understanding required both action perception and self-experienced execution of the subtasks in order to reach a common understanding through corepresentation. Further experiments focusing on neural activity will be required in order to highlight the installation of the co-representation, such as reported modulations of the alpha rhythm during joint actions (Naeem et al., 2012; Novembre et al., 2016).

# AUTHOR CONTRIBUTIONS

All authors provided substantial contributions to the conception or design of the work. QM and LG were in charge of the acquisition of data, QM, LG, and YP were in charge of the analysis, all authors were involved in the interpretation of data for the work. QM and YP drafted the work and TN revised it critically and all authors gave final approval of the version to be published.

**Conflict of Interest Statement:** 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.

Copyright © 2016 Moreau, Galvan, Nazir and Paulignan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Verbal Synchrony and Action Dynamics in Large Groups

#### Jorina von Zimmermann\* and Daniel C. Richardson

Department of Experimental Psychology, University College London, London, England

While synchronized movement has been shown to increase liking and feelings of togetherness between people, we investigated whether collective speaking in time would change the way that larger groups played a video game together. Anthropologists have speculated that the function of interpersonal coordination in dance, chants, and singing is not just to produce warm, affiliative feelings, but also to improve group action. The group that chants and dances together hunts well together. Direct evidence for this is sparse, as research so far has mainly studied pairs, the effects of coordinated physical movement, and measured cooperation and affiliative decisions. In our experiment, large groups of people were given response handsets to play a computer game together, in which only joint coordinative efforts lead to success. Before playing, the synchrony of their verbal behavior was manipulated. After the game, we measured group members' affiliation toward their group, their performance on a memory task, and the way in which they played the group action task. We found that verbal synchrony in large groups produced affiliation, enhanced memory performance, and increased group members' coordinative efforts. Our evidence suggests that the effects of synchrony are stable across modalities, can be generalized to larger groups and have consequences for action coordination.

Edited by:

Michael J. Richardson, University of Cincinnati, USA

#### Reviewed by:

Lynden K. Miles, University of Aberdeen, UK Nicola Yuill, University of Sussex, UK

> \*Correspondence: Jorina von Zimmermann<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Cognitive Science, a section of the journal Frontiers in Psychology

Received: 30 September 2016 Accepted: 15 December 2016 Published: 26 December 2016

#### Citation:

von Zimmermann J and Richardson DC (2016) Verbal Synchrony and Action Dynamics in Large Groups. Front. Psychol. 7:2034. doi: 10.3389/fpsyg.2016.02034 Keywords: synchrony, behavioral coordination, affiliation, joint action, cooperation

# INTRODUCTION

Thirty strong legs are rhythmically thrilling the ground. Chests, thighs, and arms become drums and strong voices are forcefully chanting together. Eyes are rolled and tongues are poked out. Before every match, the New Zealand rugby team performs the haka, a traditional Maori war dance composed of rigorous, synchronized movements and fierce, rhythmical chants. Amongst other things, the haka was performed before a battle to demonstrate strength and power and to intimidate the opponent. However, anthropologists and historians have argued for a long time that 'keeping together in time' (McNeill, 1995) induces emotional bonding among human groups with significant consequences for interaction and cooperation (von Zimmermann and Richardson, 2015). The haka might scare the enemy on the battlefield or rugby pitch, but it might also strengthen intragroup bonds and have a significant impact on the group's performance.

Rhythmic and coordinated actions such as marching, dancing, singing, or playing music together have been part of human rituals across all cultures in the world (McNeill, 1995; Codrons et al., 2014), but synchrony is not only a human phenomenon. It can be found everywhere in the natural world as well. For example, cardiac cells fire in synchrony and fireflies flash in unison (Strogatz, 2003; Cabeza et al., 2010). Metronomes automatically synchronize if they are put on

a freely moving base (Pantaleone, 2002), and neurons synchronize their activity to allow for coherent percepts and actions (Singer, 1993). Human beings coordinate their postural sway during conversation (Shockley et al., 2009), and their movements during a pendulum swinging task, or while rocking in a chair when visually coupled (Richardson et al., 2005; Richardson M. J.et al., 2007). There seems to be a compelling drive for systems to self-organize in synchrony (Strogatz, 2003), and it has been suggested that human beings possess a fundamental drive to coordinate their actions with the actions of others, as this forms the basis for social connectedness (Marsh et al., 2009).

Social scientists have started to collect empirical evidence for the effects of synchronized human activity and a growing body of research supports the idea that coordinated action can function as 'social glue' that binds people together and enhances their willingness to cooperate (Valdesolo et al., 2010). For example, observing synchronous movement increases perceived rapport and interpersonal connectedness between people (Miles et al., 2009; Lakens and Stel, 2011); exposure to synchronous stimulation enhances the degree of self-other merging (Paladino et al., 2010); and active engagement in synchronized physical and verbal activities boosts actual liking and cooperation (Hove and Risen, 2009; Wiltermuth and Heath, 2009; Reddish et al., 2013; Launay et al., 2014), as well as pro-social behavior toward an interaction partner (Valdesolo and DeSteno, 2011).

To date, an impressive breadth and variety of studies investigating behavioral coordination has been published. However, there are several fundamental questions about the phenomena, which are currently unanswered. For instance, do the effects of coordination scale up from pairs of people to small and then large groups? With a few exceptions (e.g., Wiltermuth and Heath, 2009; Reddish et al., 2013; Codrons et al., 2014; Tarr et al., 2015), behavioral coordination has mostly been studied in pairs, which makes it difficult to generalize from two people to large groups of people. These studies have also mostly studied the effects of coordinated movement. So one might wonder, does it matter which aspect of behavior is coordinated – speech, posture or gesture – in order to produce particular psychological effects? Finally, are the benefits of coordination restricted to social judgments – attitudes and opinions about other people – or does it also affect cognition and joint action, such as the ability of people to perform a dynamic task together?

First, we will briefly review the current answers we have to these questions with a focus on synchrony as a particular form of behavioral coordination. Then, we present an experiment combining verbal synchrony and group action that attempts to answer some of the unresolved issues. Finally, we will discuss how the results of our study fit in with existing research and which future research directions could be taken to clarify the subject further.

# Synchrony in Groups

Most experimental demonstrations of coordinated behavior focus on pairs of participants, or more commonly, a participant and a confederate who has been instructed to mimic body motions (e.g., Chartrand and Bargh, 1999). Most of the findings reported in the synchrony literature also stem from either experienced or observed dyad interaction. While the findings reported significantly advance our understanding about the circumstances under which synchrony emerges and the effects it has, generalizations from pairs to groups can be problematic. It is therefore crucial to also study synchrony experienced in a group context, as coordinated behavior has played an important role throughout history and cultures (Haidt et al., 2008), and has lost none of its significance. Even today, soldiers are still drilled to march in synchrony during their education and parades all over the world, synchrony is frequent in dance and sports, and collective chants take place during rituals, demonstrations, and religious ceremonies to name but a few examples.

One of the reasons for the lack of group studies in relation to synchrony and behavioral coordination more generally is that it is almost certainly difficult to get more than one or two participants into the lab at the same time, or having to coordinate multiple confederates simultaneously. A second possible reason is that group data is often very noisy and challenging to make sense of. In spite of these difficulties, a few studies have been published, which have looked at the effects of synchrony experienced in bigger groups. These studies report that synchrony increases aggressive behavior toward an outgroup and obedience to a leader (Wiltermuth, 2012a,b), while at the same time it increases ingroup affiliation (Tarr et al., 2015), and cooperation (Reddish et al., 2013). Similarly, in a recent study we found that the amount of distributed coordination naturally emerging over time in a choreographic task, which facilitated synchrony without instructing it, predicted how much group members liked each other and the group as a whole, and how much they conformed to each others opinions (von Zimmermann et al., under review). While these studies suggest that synchrony at the group level has similar effects as it has at the pair level, the evidence is still sparse.

# Synchrony across Different Behaviors

Does it matter which kind of behavior is synchronized, or simply that the same action happens at the same time between two or more people? The literature is not clear on this point, as the many skeins of behavioral coordination that have been discovered are isolated in different disciplines, different tasks and types of interaction, different measures and means of analysis. Social psychologists may study mimicry between gestures, ecological psychologists the rhythmic entrainment of body sway, and psycholinguists the repetition of grammatical forms. These differences are important to the scientists, but are they important to the psychological outcome of behavioral coordination?

We wanted to investigate whether the effects of movement coordination reported in the literature would also result from verbal coordination alone. Speakers have been found to possess a remarkable ability to speak in synchrony with one another, without any practice or detailed instructions (Cummins, 2011). Perhaps unsurprisingly then, chanting, or joint speech, can be observed in every human culture, and as a means of storing and passing on information, it predates the written word (Cummins, 2013). It has been speculated that when a group moves and chants together, this will help to increase group affiliation and improve the group's coordination (McNeill, 1995). However, it is not clear

if verbal behavior alone will produce positive effects that will spread over to forms of movement coordination.

# Effects of Synchrony on Cognition and Action

While some studies have investigated how social and cognitive influences, such as the socially undesirable actions of others (Miles et al., 2010a), a cooperative versus a competitive context (Schmidt and Richardson, 2008), or a pro-social mindset in comparison to a pro-self focus (Lumsden et al., 2012), affect the emergence and stability of synchrony, the majority of empirical measures of behavioral coordination are concerned with the positive feelings that an individual will have toward the person or group with whom they are coordinating (von Zimmermann and Richardson, 2015). Sometimes these effects are measured by ratings and judgments the individual makes about the joint performance or likeability of an interaction partner or group, or the degree of similarity and closeness they feel toward them. At other times the effects are measured by decisions the individual makes about sharing resources or opting to cooperate with the group even if that means to personally sacrifice.

In addition to social outcomes, it is possible that behavioral coordination leads directly to changes in cognition and action. Discussions about the evolution of behavioral coordination often focus less on the advantages of liking and positive feelings in a group, and more on the adaptive value of being able to act as a coherent group, planning and executing a hunt, for example. Performance benefits from behavioral coordination are rarely studied, however, with one exception that we are aware of. Valdesolo et al. (2010) found that synchronous rocking in a chair increased the perceptual sensitivity of participants, which helped them perform better on a subsequent joint action task, in which they had to coordinate their movements with those of an interaction partner. Their findings suggest that there is indeed a synchrony-action as well as a synchrony-cognition link and that sharing the specific skill of synchronization might influence the execution of other joint tasks by enhancing cooperative and collaborative skills. Yet, the empirical evidence for the idea that synchronizing behavior at one time improves future action coordination is still sparse and calls for more extensive scientific investigations.

Furthermore, even though there is some evidence that hand movements performed in synchrony enhanced participant's memories for an interaction partner's utterances and facial appearance (Macrae et al., 2008), the benefits of synchronized activity on memory are not well-established, yet. More specifically, the possible benefits of collective speech on memory seem to have been overlooked entirely (von Zimmermann and Richardson, 2015). This is interesting since collective speech is employed in educational settings in which remembering the spoken word is important such as in schools or churches. On top of that, one could speculate that national anthems, songs sung at sport events, or slogans shouted during demonstrations are remembered not only because people are exposed to them frequently, or because they are memorable, but also because they are almost exclusively associated and performed with the collective.

# Verbal Coordination, Groups, and Action

In our experiment, groups of 20–30 participants either read a list of words out loud together or individually. Participants reading single words in unison is quite different to the coordinated, spontaneous joint speech that one finds during demonstrations or at a football game. However, it is a first approximation, and allowed a close comparison with participants in the asynchronous speech condition. Those people read the same words out loud, but started at different places in the list, and so spoke out of time with each other.

After reading for around 2 min, participants played a group video game in which they used audience response handsets to jointly control a tightrope walker and keep him upright (Richardson et al., 2011). Following the game (**Figure 1**),

participants were asked to recall as many words as possible from the list, and rate their feelings toward their group. Our hypotheses were that those in the synchronized reading condition would perform better as a group in the action task, they would remember more words from the list, and have increased feelings of group affiliation.

# MATERIALS AND METHODS

# Participants

In exchange for course credit, 215 participants from UCL participated in this study (M age = 18.85, SD age = 0.90, Number of Males = 35). They were run in eight groups of between 23 and 34 people as part of a lab demonstration course. The participants were informed that this was research on the 'effects of memory retrieval' and were unaware of the true research hypothesis until after the experiment was complete.

# Ethics Statement

Ethical approval was obtained from the UCL Research Ethics Committee. All participants consented to taking part in this experiment and were fully debriefed upon completion of the study.

# Apparatus and Stimuli

Each participant was given a Turning Technologies audience response handset. Button presses were sent to a USB receiver plugged into a MacBook. These responses were sent to the tightrope game, developed by Delosis. The MacBook was connected to a projector, which displayed the game on a large screen that everyone could see.

In the game, participants saw a man holding a pole, balancing on a rope (**Figure 1**). Each time one of the participants pressed either 1 or 3 on their handset, it sent a very small nudge to the tightrope walker, sending him to the left or right. The size of individual nudges depended on the number of people playing, such that the strength of all nudges added together would be the same across games with different numbers of people. The game was made harder by tomatoes that were fired from the sides of the screen, destabilizing the tightrope walker. They appeared at random and their frequency varied to change the difficulty of the game. The movements of the tightrope walker and the appearance of the tomatoes were governed by a physics engine that accounted for the size and position and momentum of the objects.

A game ended when the tightrope walker fell off the rope, or participants successfully kept him upright for 30 s. **Figure 2** shows the tightrope walker's angle and the net response from the audience across 20 s of one of the games in our experiment.

# Procedure

Participants were randomly allocated to groups, and each group was assigned to the synchronous or asynchronous speech condition. Participants were given a list of 54 words, split into three columns. They were told to read them out loud, completing two cycles of the entire list. In the synchronous condition, participants were instructed to start at the top of the page with the first word and read the words at the same time as each other. In the asynchronous condition, participants were first given a number between 1 and 3. They were told to start reading at the top of the first, second, or third column, respectively. Since participants were numbered consecutively where they sat, participants sat next to each other always started in different places.

Once participants had read through the list twice (which typically took around 100 s) they were introduced to the tightrope game. They were allowed a practice session with no tomatoes being fired as we explained how they could control the tightrope walker. Then they played five games with monotonically increasing rates of tomatoes being fired at them. If the tightrope walker fell off before 30 s, the game was restarted, until participants were able to complete a total of 30 s.

After playing the game, participants filled in a worksheet. In 60 s they wrote down as many of the words as they could remember from the list that they had read out previously. Then they responded on a 7-point Likert scale from 'strongly disagree' to 'strongly agree' to the following statements, designed to assess participants' positive feelings toward their group:

# RESULTS

# Memory and Affiliation

Participants in the synchronous conditions scored better on the memory test and felt more affiliation toward their groups, as shown in the two distributions plotted in **Figure 3**. For a memory score, we counted the number of words that participants correctly recalled minus the number that they incorrectly recalled. For every participant, the averages of the four affiliation items were calculated. Affiliation ratings for the synchronous groups (M = 25.22, SE = 0.39) were higher than for the asynchronous groups (M = 22.20, SE = 0.51), and memory scores for the synchronous groups (M = 6.96, SE = 0.93) were also higher than for the asynchronous groups (M = 4.15, SE = 0.70). Conventional t-tests found significant differences between conditions for the memory scores [t(212) = −2.20, p = 0.029] and affiliation ratings [t(212) = −5.88, p < 0001]. The BayesFactor package (Morey and Rouder, 2015) in R was used to estimate the odds of differences between the conditions, plotted on the right of **Figure 3**. For both, memory score and rated group affiliation, an estimated difference of zero between the conditions lay outside the 95% credibility interval (Kruschke, 2010), giving strong evidence in favor of an effect of condition. Participants in the synchronous speech condition remembered more words than

participants in the asynchronous speech condition and they also expressed higher levels of liking for their group.

line shows the net left or right nudge from a group of participants as they try to keep him upright.

# Tightrope Game Performance

We analyzed performance on the tightrope game at three levels, as shown in **Figure 4**. At the broadest level, groups in the two chanting conditions succeeded at the game to roughly equivalent degree, measured by how close to upright they kept the tightrope walker. At the lowest difficultly level, all groups managed the task without having to restart, whereas at the highest level there were 1.3 restarts on average. However, there was not a significant effect on the number of restarts by difficulty level, condition, nor an interaction (all Fs < 1). Yet, looking in more detail at how they played the game, participants in the synchronous chanting condition tended to make a response more readily when the tightrope walker was closer to the vertical, and at each moment in time, their responses tended to be more homogenous within the group.

For each game, we calculated the average distance of the tightrope walker from the vertical in degrees. We ran an ANOVA with difficulty level and chanting conditions as factors, but there was no main effect of condition [F(1,6) = 1.1], only a marginally significant effect of difficulty level [F(1,59) = 3.51, p = 0.08], and no significant interaction [F(1,6) = 0.83]. To analyze individual participants' behavior, we calculated the average distance of the tightrope walker from the vertical at each moment the participant made a response. Participants in the synchronous condition made responses when he was approximately 5◦ closer to vertical. Bayesian analysis showed that the 95% credibility interval for this difference was above zero, which was also reflected by a significant t-test on the condition means [t(192) = 6.43, p < 0.0001].

Finally, we analyzed individual responses, calculating the proportion of identical responses that occurred 250 ms before and after each one. For each chanting condition, we plotted this measure of group similarity against the distance of the tightrope walker from the vertical. As can be seen in the final plot of **Figure 4**, when he was close to vertical, group similarity in responses was low, as participants were nudging him to both the left and right to keep him balanced. As he veered away from the upright, groups responses increasingly became more similar, as it was more apparent which direction he needed to be nudged in

order to right him. However, the two chanting conditions differed in this regard. As shown by the non-overlapping confidence intervals, from around 10◦ onward, responses in the synchronous group were more similar to each other moment by moment. A Bayesian analysis confirmed that between 10 and 70◦ , the 95% credibility interval for this difference between conditions was above zero.

In summary, there is evidence that reading out the list of words together had an effect on participants' behavior in a task of group coordination. When the results were analyzed at the level of games and groups there was only a marginally significant effect of chanting conditions. However, when individuals' responses were analyzed, we found that those in the synchronous condition more readily made responses as the tightrope walker deviated from the vertical, and once he passed 10◦ from the vertical, responses amongst the synchronous group were more similar to each others.

# DISCUSSION

With our experiment we wanted to expand on already existing synchrony and behavioral coordination literature in three ways. First, we wanted to see if the affiliative effects generally reported in pair studies scale up to larger groups. While some studies have reported that synchronized movement in small groups increased liking amongst group members (e.g., Reddish et al., 2013; Tarr et al., 2015), we also found that members of large groups reported to feel closer to each other after they had chanted together in synchrony. The finding that behavioral synchrony

can lead to interpersonal liking and rapport seems to therefore hold true also for much larger groups than previously reported on. This might not come as a surprise since human beings have engaged in synchronous movement and collective speech as part of rituals for centuries with important social consequences: Participation in collective rituals promotes social cohesion and thereby strengthens individuals' attachments to each other and the group, making effective group action possible (Whitehouse and Lanman, 2014). Respectively, research has shown that rituals not only significantly increase ingroup affiliation in comparison to non-ritualistic group activities (Wen et al., 2016), but those rituals, which include synchronous behavior, lead to increased liking and cooperation within a group (Fischer et al., 2013).

Second, we wanted to investigate if verbal synchrony alone is sufficient to induce the affiliative effects of behavioral coordination generally observed. Our groups were only instructed in relation to their verbal coordination, but no statements were made with reference to movement. This means that in theory, through the coordination of their verbal articulations, group members might have also spontaneously coordinated their postural movements (Shockley et al., 2009), and possibly even started sharing physiological dynamics such as heart rate (Fusaroli et al., 2016). While we cannot completely exclude this as a potential alternative explanation of, or at least mediating influence on our findings, we do not believe that any kind of physical coordination, which might have occurred, would have been strong enough to explain our results. In contrast to other experiments, which reported spontaneous coordination of movement or physiological functions, our participants were seated in rows next to and behind each other and did not have direct eye contact with one another. Except for chanting together they also did not interact with each other in any other way before moving on to playing the tightrope game. We are thus confident that verbal synchrony – as the prevalent form of coordination in the experiment – was the main mechanism, which lead to significant changes in our participants. Respectively, individuals' ratings of their perceived affiliation with the group and their groups performance increased in the synchronous condition. Joint speech, like joint movement, allows interaction partners to construe a shared representation of the world, in which intentions become aligned and common ground is established (Cummins, 2014). Like protestors chanting the same slogan together, demonstrating an extreme form of alignment with respect to the world (Cummins, 2014), the participants in our synchronous speech condition probably experienced higher levels of alignment than those participants, who were reading the words out asynchronously. Through coupling their actions during the joint speech task, participants established a common goal with affiliative, cognitive and coordinative consequences.

Third, we were curious to see if synchronous behavior would also affect action and cognition in addition to the social effects often observed. In other words, we wanted to find empirical evidence for the hypothesis that there is a synchrony-action link, that group members, who have previously synchronized with one another, will be better coordinated in a subsequent task. Our evidence supports this idea. Groups overall seem to do better on a coordination task after their members have engaged in synchronous behavior, at least at the harder levels of task difficulty. Why might this be the case? To successfully coordinate behavior and synchronize, people need to anticipate each other's behaviors (Sebanz et al., 2006; Konvalinka et al., 2010). In this respect, it has been argued that perceiving another's movements, for example, activates one's own action system for that same movement, which increases the likelihood for a matched action to occur (Brass et al., 2001). This suggests a tight neural link between perception and action, which could extend to the development of shared representations of a joint action task and of self and other (Hurley, 2008; Kirschner and Tomasello, 2009). While an increase in self-other overlap is said to foster social bonds (Galinsky et al., 2005), one could speculate that participants in our synchrony condition were able to develop a shared representation of the chanting task and each other, which then influenced not only their feelings for each other, but also improved their coordinative skills in the tightrope game.

In this study, however, not only did we find a synchronyaction link, but also a synchrony-cognition link: Participants

who had chanted words collectively, rather than reading them out loud by themselves, remembered more of these words at the end of the experiment. With the present data, of course, we can't judge whether the reason for the memory improvement in the synchronous condition was because the asynchronous chanting was a distraction to participants, and this caused them to encode fewer words in the first place, or because of motivational benefits from higher perceived affiliation with the synchronous group, or because of a general performance boost that mirrored the improved performance in the balancing task. In spite of this limitation, our results seem to be in line with the findings from two other studies, which looked at the relationship between synchrony and memory, albeit in relation to social information. Synchronous movement was reported to enhance people's attention for each other during a social exchange, enhancing memory for another's verbalizations as well as their facial appearance (Macrae et al., 2008). Comparing the memory performance of participants, who listened to words over headphones, while engaging in arm curls together with a confederate either in-phase or in the less stable anti-phase coordination, produced a memory advantage for self-related in comparison to other-related information in the anti-phase coordination, whereas this effect was eliminated when participants had moved in-phase with the confederate (Miles et al., 2010b). The findings from our study suggest that synchronous actions might not only influence memory in relation to social information, but more generally as well. This, however, needs to be tested more rigorously in the future.

A diverse set of researchers have come to the realization that perception, action and cognition cannot be fully understood by investigating single individuals (e.g., Sebanz et al., 2006; Barsalou et al., 2007; Robbins and Aydede, 2009). Studies of situated cognition show that cognition 'in the wild' is intimately linked not only to representations of the external world, but also to the cognitive processes of others. For example, Hutchins (1995) observed the ways that navy navigators would distribute cognitive processes between themselves by using external tools and representations, such as maps and notations. In the past few years, experimental methods have also started to reveal the cognitive mechanisms involved in the joint activity of two people engaged in parallel tasks (Sebanz et al., 2006), talking to each other (Richardson D. C.et al., 2007), or just silently looking at pictures, changing their gaze patterns because of the knowledge that someone is looking at the same thing (Richardson et al., 2012). Knoblich and Jordan (2003) gave a detailed analysis of the way that two people coordinate their actions: To be successful, participants had to anticipate both the movements of the objects in the game and the actions of their partner. It is possible that chanting together in our experiment helped participants to anticipate each other's actions and thereby facilitated coordination in the tightrope walker game. However, it becomes clear that no explanation at this point goes beyond speculation. It will therefore be an interesting task in the future to study how perception, cognition and action are linked in social situations, which involve more than two people, and what the exact mechanisms are, which could explain a synchrony-action link.

Behavioral coordination is often portrayed as something that binds people together, evoking positive and pro-social feelings toward interaction partners. However, there is more to coordinated joint action than hugs. For example, while synchrony, like mimicry (Chartrand and Bargh, 1999) often increases rapport and cooperation, sometimes it has quite different results. In two studies, Wiltermuth (2012a,b) showed that synchrony can lead to aggressive behavior and destructive obedience. People who had just bonded with one another through synchronous action were more likely to comply with each other's requests, even if this entailed to engage in aggressive behavior toward others, such as administering a noise blast to another group of participants, or killing sow bugs at a leader's request (Wiltermuth, 2012a,b). These studies support the idea that physical synchrony does not exclusively lead to pro-social, but also to anti-social and destructive behavior. There seems to be a dark side to the phenomenon, and verbal synchrony seems to have comparable effects. Spectators at a football game who had engaged in collective chanting during the game reported higher levels of aggression than those who had not chanted (Bensimon and Bodner, 2011).

# CONCLUSION

Anthropologists and historians have long argued that acting together in time influences group cohesion and group action. In our experiment, large groups of people, who had engaged in collective speech, acted better together in a subsequent task, displayed improved cognitive functions, and liked each other more. Although we were able to explore the scope of behavioral coordination in our experiment, there is one significant question about the directionality of the effects we found, which we cannot answer with our findings. Does synchrony increase group affiliation and thereby improve cognition and action, or does synchrony increase group performance and this improvement increases the attraction to the group? No matter what the answer to this question is, the New Zealand rugby team should keep performing the haka prior to important games, as it might be an important part of their success strategy.

# AUTHOR CONTRIBUTIONS

JvZ developed the initial experimental design. Both authors performed the testing and data collection together. DR performed the data analysis. Both authors contributed equally to the writing of this manuscript and approved the final version for submission.

# ACKNOWLEDGMENTS

We would like to thank the PALS lab demonstrators and C. Aichelburg at University College London, who helped us with the organisation of this experiment.

# REFERENCES

Wiltermuth, S. S., and Heath, C. (2009). Synchrony and Cooperation. Psychol. Sci. 20, 1–5. doi: 10.1111/j.1467-9280.2008.02253.x

**Conflict of Interest Statement:** 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.

Copyright © 2016 von Zimmermann and Richardson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# How Moving Together Brings Us Together: When Coordinated Rhythmic Movement Affects Cooperation

#### Liam Cross 1 , Andrew D. Wilson2 and Sabrina Golonka2 \*

*1 Department of Psychology, Lancaster University, Lancaster, UK, 2 Psychology, School of Social Sciences, Leeds Beckett University, Leeds, UK*

Although it is well established that rhythmically coordinating with a social partner can increase cooperation, it is as yet unclear when and why intentional coordination has such effects. We distinguish three dimensions along which explanations might vary. First, pro-social effects might require in-phase synchrony or simply coordination. Second, the effects of rhythmic movements on cooperation might be direct or mediated by an intervening variable. Third, the pro-social effects might occur in proportion to the quality of the coordination, or occur once some threshold amount of coordination has occurred. We report an experiment and two follow-ups which sought to identify which classes of models are required to account for the positive effects of coordinated rhythmic movement on cooperation. Across the studies, we found evidence (1) that coordination, and not just synchrony, can have pro-social consequences (so long as the social nature of the task is perceived), (2) that the effects of intentional coordination are direct, not mediated, and (3) that the degree of the coordination did not predict the degree of cooperation. The fact of inter-personal coordination (moving together in time and in a social context) is all that's required for pro-social effects. We suggest that future research should use the kind of carefully controllable experimental task used here to continue to develop explanations for when and why coordination affects pro-social behaviors.

Keywords: coordinated rhythmic movement, interpersonal entrainment, interpersonal synchrony, interpersonal coordination, rhythmic entrainment, joint action, social cognition, cooperation

# INTRODUCTION

It is well-established that moving in time with other people can increase cooperation between coactors (Anshel and Kipper, 1988; Wiltermuth and Heath, 2009; Kirschner and Tomasello, 2010; Reddish et al., 2013, 2014; but see Kirschner and Ilari, 2014), though, it is still unclear what it is about these Coordinated Rhythmic Movement (CRM) tasks that makes people more cooperative. Previous work has identified a number of interesting effects and it is now time to begin trying to explain why these effects occur. At present, this work is complicated by the sheer variety of paradigms employed to generate and measure these effects. The purpose of this paper is to try to lay the groundwork for developing an explanation of the pro-social effects of coordination. We do this by tackling a number of basic questions about the effect using a single, well-understood, CRM paradigm.

#### Edited by:

*Michael J. Richardson, University of Cincinnati, USA*

#### Reviewed by:

*Lynden K. Miles, University of Aberdeen, UK Kerry Marsh, University of Connecticut, USA*

\*Correspondence: *Sabrina Golonka<EMAIL_ADDRESS>
#### Specialty section:

*This article was submitted to Cognitive Science, a section of the journal Frontiers in Psychology*

Received: *26 September 2016* Accepted: *05 December 2016* Published: *22 December 2016*

#### Citation:

*Cross L, Wilson AD and Golonka S (2016) How Moving Together Brings Us Together: When Coordinated Rhythmic Movement Affects Cooperation. Front. Psychol. 7:1983. doi: 10.3389/fpsyg.2016.01983*

In this paper we consider some classes of model that could characterize how coordination impacts cooperation. These models vary along three dimensions: (1) whether increased cooperation depends on in-phase synchrony (S+) or coordination, more generally (S−), (2) whether the relationship between social coordination and cooperation is direct (D+) or mediated (D−), and (3) whether cooperation varies in proportion to coordination at the individual level (P+), or whether there is a threshold effect (P−). The first dimension relates to whether synchronous (in-phase) movements are necessary to impact cooperation, or whether other coordinations (e.g., anti-phase) can also affect cooperation. The second dimension concerns whether there is a direct path between social coordination and cooperation, or whether this relationship is mediated by other factors, such as group cohesion (e.g., Wiltermuth and Heath, 2009). The third dimension concerns whether there is a linear relationship between coordination and cooperation at the level of individual participants, or whether pro-social benefits obtain (and then remain more or less constant) after a certain threshold in coordination is reached. These models are, themselves, descriptive rather than explanatory. However, this work moves us further down the road toward explanation by explicitly identifying the features that any future explanatory model must possess. We first discuss the dimensions of interest in more detail below with reference to the existing evidence from the literature in favor of particular classes of models. We then summarize our choice of movement task and explain how it enables us to test the dimensions of interest, thereby helping us home in on essential features that an explanatory model of the pro-social effects of intentional coordination must possess.

# IN-PHASE SYNCHRONY VS. COORDINATION (S+ VS. S−)

Movements are coordinated when two rhythmically moving limbs (oscillators) move so as to maintain some relative phase with respect to one another. Movements are synchronous when those limbs move in-phase (i.e., at 0◦ relative phase). During inphase movements, the two oscillators move in the same direction at the same time. During anti-phase (180◦ relative phase) movements, each oscillator moves in the opposite direction as its partner at the same time. Throughout this work the term synchrony is used to refer to in-phase movements only (in line with the general literature on coordination, e.g., Kelso, 1995), although elsewhere anti-phase has sometimes been treated as an example of synchrony (e.g., Miles et al., 2010). Our definition of synchrony was chosen in order to allow us to easily discriminate between strict in-phase synchronization and other forms of coordination (i.e., anti-phase). Technically, successfully moving so as to maintain any relative phase (from 0 to 360◦ ) is an instance of a coordinated rhythmic movement (although there are well known limits to the coordinations humans can produce without extensive training; Kelso, 1995). The question is whether the pro-social effects of coordination reported in the literature, are actually restricted to cases where the coordination is in-phase (synchronous movements).

If coordination, generally, and not just in-phase synchrony, has positive consequences on cooperation, then the effects should be obtained following coordination at any relative phase. We currently lack evidence to support this idea because the majority of tasks used to test the pro-social effects of coordination rely exclusively on in-phase coordination (and, to our knowledge, our experiment is the first work to address the effects of anti-phase coordination on cooperation, specifically). Those that have employed anti-phase conditions have found mixed evidence concerning whether anything besides in-phase synchrony impacts social variables (e.g., Miles et al., 2010; Cirelli et al., 2014; Sullivan et al., 2014). To begin disambiguating the effects of in-phase synchrony from the effects of coordination more generally, Experiment 1 explicitly compares the effects of in-phase and anti-phase coordination on post-task cooperation.

# DIRECT VS. INDIRECT EFFECT (D+ VS. D−)

The effect of coordination on pro-social variables is indirect if coordination must impact an intervening variable (e.g., group cohesion) or coincide with a causally relevant variable (e.g., social context) in order to affect cooperation. If this is the case, then coordination only has positive consequences for pro-social variables by virtue of its effect on something like group cohesion or by providing the opportunity to engage in a certain type of social context. In contrast, the effect of coordination on prosocial variables could be direct. If the relationship is direct then coordination would not need to impact an intervening variable or coincide with another causally relevant variable to influence cooperation.

The literature, to date, is conflicted concerning directness. We first consider evidence for a mediating variable between coordination and cooperation. Research has focused exclusively on two potential mediators—group cohesion and self-otheroverlap. Group cohesion is the feeling of being on the same team and being emotionally connected with other group members. Wiltermuth and Heath (2009), Wiltermuth (2012) found that levels of post-task group cohesion were related to the social effects of coordination, though others (e.g., Reddish et al., 2013; Lumsden et al., 2014; Dong et al., 2015) found no such relationship. The discrepancy in results may be, at least, partially explained by differences in how group cohesion was conceptualized and measured. Reddish et al. (2013) grouped emotional connection, trust and self/other overlap (the extent of self-rated overlap between oneself and others) into a single construct, which was termed group cohesion, after factor analysis suggested they all tap a similar construct. Wiltermuth (2012), on the other hand, measured group integrators only (i.e., perceived closeness, connectedness and similarity to the group) and labeled the construct emotional connection (see also Wiltermuth and Heath, 2009; Lumsden et al., 2014; Dong et al., 2015).

Others have investigated self-other-overlap as a potential mediator of the relationship between coordination and cooperation; again, evidence for the mediated model is inconclusive. Lumsden et al. (2014) and Reddish et al. (2013) found evidence in favor of a mediating relationship, while Reddish et al. (2014) found no evidence for such a relationship. As before, it is difficult to draw conclusions from the literature given the plurality of methods and measures.

Another way the effect of coordination on pro-social variables could be thought of as in/direct depends on whether a coordination task, in and of itself, (i.e., absent a particular social context), is sufficient to impact coordination. If it is direct in this way then coordinating movements with, say, a metronome or a computer display rather than a co-actor, would be sufficient to lead to social consequences. If it is indirect in this way, then coordination must be accompanied by some kind of social context to impact pro-sociality; i.e., effect would not be due to coordination "per se"—coordination itself and/or coordination by itself. There is considerable evidence that some kind of social context is an important element to obtain positive social effects following coordination tasks (Hove and Risen, 2009; Kirschner and Tomasello, 2009; Wu et al., 2013; Launay et al., 2014), however, questions remain about how much social context is necessary and whether this relationship is one of mediation or moderation.

In sum, the evidence from previous research is inconclusive about whether coordination must impact an intervening variable in order to have positive consequences on cooperation. Evidence is stronger for the idea that coordination must coincide with a social context in order to affect cooperation. The studies reported below provide the strongest evidence to date for D+ vs. D− models by testing a variety of potential mediators (i.e., group cohesion, self-other overlap, trust, self-rated success at coordination, self-rated task difficulty, task difficulty, and mood) within subjects at both pre- and post-coordination. In line with the substantial existing evidence that social context is important, all of the studies below involve pairs of participants completing an intentional coordination task together; however Followup 1 manipulates whether the information participants use to coordinate is social or non-social.

# INDIVIDUAL VS. GROUP LEVEL EFFECTS (P+ VS. P−)

Whether the effect of intentional coordination on cooperation is direct or indirect, there are two main types of relationship we might observe between these variables. The first possibility is that individual measures of coordination success predict individual levels of cooperation. That is, changes in cooperation occur in proportion to changes in coordination success. The second possibility is that there is a threshold relationship between coordination and cooperation. In this case, coordination would positively influence cooperation as long as some minimum threshold of coordination success was achieved.

Previous research paints a mixed picture in terms of what to expect on this dimension. The only work focusing on cooperation to take actual measures of coordination found that coordination did not predict cooperation (Kirschner and Ilari, 2014), but this result is limited by the fact that they found no effect of coordination on cooperation anyway. Looking beyond cooperation to other social variables does little to clarify the picture. On the one hand, there is evidence that tightness in movement coupling predicts likability between co-actors (Hove and Risen, 2009). On the other hand, coordination success is not a good predictor of post-task trust (Launay et al., 2013). The studies reported below compare P+ vs. P− models by testing whether individual level success at coordination predicts subsequent individual level cooperation behavior.

# Our Coordination Task

Researchers have used a variety of tasks to investigate the effect of intentional coordination on pro-sociality (e.g., waving cups and singing: Wiltermuth and Heath, 2009; flexing and extending arms: Miles et al., 2010). It is difficult to lay the groundwork for an explanatory model using results from such a variety of complex tasks. It would be preferable to identify a coordination task that is simple enough to study but that is complex enough to allow all the necessary manipulations required to investigate when and how coordination affects social behavior. We believe we have found such a task and this is described below, though, first we explain in more detail the basic structure of CRM tasks, generally.

CRM tasks are essentially perception-action tasks, and have typically been studied as such in the experimental literature (e.g., Kelso, 1995; Bingham, 2001, 2004). They involve the continuous control and matching of rhythmic movements via perceptual information about the coordination between those movements. The rhythm of a CRM is defined by the relative phase between the oscillating movements. Movements are coordinated when a particular relative phase is maintained within some error band. As discussed earlier, in-phase coordination occurs when the movements are in the same direction at the same time, while anti-phase coordination occurs when the movements are in the opposite direction at the same time. The remaining range of coordinated movements is generally described as "out-of-phase." The basic phenomena of a CRM task are that movements are stable at in- and anti-phase, while movements at any other phase are difficult to maintain and highly variable. In-phase movements are more stable than anti-phase movements and, if the frequency of anti-phase movements is increased to around 3–4 Hz they transition to in-phase. These effects persist when the coordination is enacted between two people (Schmidt et al., 1990) and between a person and a point light display (e.g., Wilson et al., 2005a,b). This indicates that the ability to maintain rhythmic coordination depends on a perceptual coupling of information specifying relative phase between oscillators.

Bingham et al. (Bingham, 2001, 2004; Snapp-Childs et al., 2011) have developed a model of CRM (the Bingham model) using a task where participants move joysticks from side to side at some relative phase to coordinate the motions of two dots on a computer screen. The screen shows a point light display representing the limbs' motions (see also Wilson et al., 2005a,b). This task contains all the critical elements of a CRM task: voluntary control of limbs, coordination of limbs with a coactor and perceptual control of the coordination. The Bingham model explains the above phenomena by explicitly modeling the perception-action components involved in the task. Several papers have empirically validated the main predictions of the model (Wilson and Bingham, 2008; Wilson et al., 2010; Snapp-Childs et al., 2011).

The studies below are based on the task used by Bingham and colleagues to develop an explanatory model of CRM. This CRM task is particularly well-suited to the job of discriminating S+ and S− models, as it's possible to run the task with any target relative phase, and of discriminating P+ and P− models, as it allows us to compute precise and sensitive measures of coordination that can be used to determine how much actual coordination predicts post-task measures, if at all. We can then combine data from this task and other measures to discriminate D+ and D− models as well.

This choice of task is also ideal for constructing an appropriate control task, which has proven a major challenge in the literature. A good control task must be comparable to the CRM task, involving co-actors making comparable movements (though ones that are not rhythmically coordinated with their co-actors). However, control tasks in the six papers looking at how CRM affects cooperation varied considerably in how closely they match the experimental task (see **Table 1**). Some previous work has even used anti-phase movements as a control condition. However, as noted above, moving anti-phase (or even out-of-phase) with someone is still a type of CRM. People can and do entrain at antiphase, and similar social effects might also be fostered by antiphase interpersonal entrainment (see Cirelli et al., 2014). Tasks involving completely disparate activities such as doing a jigsaw (Reddish et al., 2014) or watching a documentary (Anshel and Kipper, 1988) may also not be appropriate controls, as they are too different from the experimental tasks at hand. For example, tapping one's foot in time to a metronome with two other people is not very similar to doing a jigsaw with two other people (Reddish et al., 2014), as these tasks vary in multiple ways (i.e., one includes music and one does not, one includes coordinating your moments with the other person in a certain way while one does not employ movement coordination at all). This makes interpreting findings between conditions as the result of CRM difficult, if not impossible.

Our CRM task is amenable to a straight forward, wellmatched control task whereby participants are instructed to move their joysticks at different frequencies while performing different movements. This control condition is minimally different from coordinated conditions (both involve rhythmically moving a joystick at a specified frequency), while breaking the coordination between partners.

# The Current Studies

The goal of the studies that follow is to begin homing in on the class of model that best captures the relationship between intentional coordination and cooperation. This work will place specific, empirically-driven constraints on future work concerning the mechanism by which coordination influences cooperation. Experiment 1 was designed to discriminate between S+ and S− models (in-phase synchrony or coordination), between D+ and D− models (direct or mediated), and between P+ and P− models (group or individual level effect). Based on the results of this experiment we conducted two follow-ups. The first further explores the S+/S− distinction by investigating the consequences of coordinating via social and non-social information. The second probes the necessary features of a coordination task by testing two control tasks.

# EXPERIMENT 1

Experiment 1 tested whether in-phase synchrony is necessary to the effect of coordination on cooperation or whether the effect obtains with other coordinations as well (S+ or S−). Since our task allows a kinematic record of each participant's movements, we also tested whether cooperation varies in proportion to coordination, allowing us to discriminate between P+ and P− models. Finally, we measured several potential mediators suggested from previous research, which provides some evidence for D+ vs. D− models.

# METHODS

# Participants

Sixty-six undergraduate students at Leeds Beckett University volunteered to participate (19 males and 47 females Mage = 19.17 year, SDage = 2.77). All participants were naive to the aims of the study. The experiment was approved by the Leeds Beckett University Psychology Ethics Review Board.

# Design

The study employed an experimental design with one betweensubjects factor: Movement Phase. This had three levels: in-phase (0◦ ), anti-phase (180◦ ), or no coordination (control).

# Tasks and Measures Movement

In both experimental conditions, pairs of participants, sitting side by side moved one joystick each (Logitech Pro joysticks with force feedback disabled) horizontally at 0.75 Hz using a point light display (PLD) to monitor their and their partner's movements.  The dots were 40 × 40 pixels, and separated by a visual angle of 0.14◦ , one above the other, positioned in the center of the screen (Wilson et al., 2005a,b, 2010; Snapp-Childs et al., 2011). In the in-phase condition, participants moved so as to maintain 0◦ relative motion between their and their partner's dots. In the anti-phase condition, participants moved so as to maintain 180◦ relative motion between their and their partner's dots.

For the control task, participants made uncoordinated movements at different frequencies. One participant always moved their joystick at 0.6 Hz and the other always moved at 0.9 Hz (0.75 ± 0.15 Hz). Participants alternated moving their joysticks vertically and in clockwise circles, so that partners never performed the same movement during a trial. Participants switched movements every trial (e.g., person 1 moved vertically on one trial, in circles on the next etc.; person 2 in the pair did the opposite).

Participants in all conditions first saw two 15 s demonstrations of dots moving at the desired phase and frequency. In the experimental conditions both dots moved at 0.75 Hz (at either 0 or 180◦ relative to each other). In the control condition one dot moved at 0.6 Hz and the other at 0.9 Hz. After each demo participants had 30 s practice time to acquaint themselves with the required movements. Following this brief initial practice, participants completed six 60 s trials. Each trial was preceded by a four second version of the demonstration pacing them to the required phase and frequency of movements. This experiment was run on a MacBook Pro with a custom Matlab toolbox programmed by the second author and incorporating the Psychtoolbox (Brainard, 1997; Pelli, 1997; Kleiner et al., 2007).

# Social Mediators

#### **Self/other overlap**

Self/other overlap was measured using the Inclusion of the Other in Self (IOS) scale (Aron et al., 1992). Participants were asked to indicate how much overlap they felt between themselves and the other participant by choosing from one of seven different diagrams. The diagrams consist of increasingly overlapping circles, one representing the self and one representing the other (see Data Sheet 1).

### **Cohesion scale**

Five questions were used to measure mood, trust and cohesion (see Data Sheet 2). Question 1 measured participants' mood. Question 5 measured how much participants trusted each other. Questions 2–4 measured participants' cohesion to each other (closeness, connectedness and similarity). These were the same questions as have previously been used to measure cohesion in Wiltermuth and Heath (2009). Participants recorded their responses to each of these questions by marking a 185 mm continuum. This response scale was used to make it more likely to detect any changes after the movement manipulation and has been successfully used in a similar context by Lumsden et al. (2014).

# Dependent Variables

# **Economic game**

This included both a Public Goods Game (PGG) and an investment game (see Data Sheet 3). The PGG was identical to that used by Wiltermuth and Heath (2009) except token values were changed from dollar amounts to points. Participants were given a response booklet containing instructions and response sheets for each of five rounds of play. The aim of the game was to collect as many points as possible. In order to encourage competition between participants, the person who collected the most points won £40 of vouchers. For each of the five rounds participants had ten tokens to allocate between two accounts, a private account and a public account. Each token in the public account was worth three points to each of the players, while each token in the private account was worth five points only to the player who allocated that token. In each round participants privately recorded how many tokens they wished to allocate to each of the two accounts.

#### **Investment game**

After Round 5 of the PGG, participants played an investment game (adapted from Berg et al., 1995) to measure trust and reciprocity. Participants had the chance to transfer/invest the points (none, a quarter, half, or all) that they had earned in the public goods game. Any points that were invested were automatically doubled but it was up to the other player how many of these points to return to them (none, only the original amount invested, the original investment plus half of the earned bonus, or all of the original investment and the earned bonus). Each participant acted as both investor and banker simultaneously by confidentially marking their choices on a separate sheet without any discussion.

# Procedure

This study was conducted in pairs. Sessions lasted approximately 25 min. Participants completed the IOS and the cohesion scale (pre-test measures of potential mediators, and mood item) followed by the movement task. Participants then rated their perceived success at the coordination task as well as task difficulty and enjoyment using four-point Likert scales. Next, participants completed a second copy of the IOS and cohesion scale (posttest measures of potential mediators, and mood item). Finally, participants took part in the Economic (public goods and investment) Game.

# RESULTS

We checked whether mood, task difficulty, task enjoyment, and perceived success differed between in-phase, anti-phase, and control tasks. The distribution of scores on each of these variables was found non-normal from Shapiro-Wilkes tests (SW tests of normality used throughout) (p's < 0.05). Kruskal-Wallis tests confirmed that scores on these variables did not differ between movement tasks (all p's > 0.05). It was therefore concluded that mood, task enjoyment, perceived task difficulty or perceived success did not contribute to the effects described below.

# Coordination

All movement trials except for the first two practice rounds were analyzed. A low-pass Butterworth filter with a cut-off frequency of 10 Hz filtered each dot's position time series. A 60 Hz time series of the relative phase between the two dots was computed as the difference between the arctangent of each dot's velocity over position at each sample.

Mean vector length (MVL) is the circular equivalent of the standard deviation (Batschelet, 1981; see Wilson et al., 2005a,b for more detail). It is the normalized length of the resultant vector obtained by summing the relative phase vectors from each time step and measures coordination stability. MVL ranges from 0 (indicating minimum stability, a uniform circular distribution) to 1 (indicating maximum stability, no variability).

The distribution of MVL scores of those who moved in-, antiphase and those who did not coordinate all differed significantly from normality (p's < 0.05). An independent samples Kruskal-Wallis test identified a significant effect of phase on coordination scores [H(2) = 47.29, p < 0.001]. Bonferonni post-hoc tests with adjusted p-values (for 3 pairwise comparisons) showed more stable coordination for those moving in- and anti-phase than in the control condition (see **Figure 1** for mean MVL scores), p's < 0.001. Coordination at anti-phase did not significantly differ from coordination at in-phase (p > 0.05)1 .

# Cooperation

Next we examined whether participants in the in- and antiphase conditions were more cooperative post movement task than those in the control condition. A univariate ANOVA found a significant effect of phase on the mean public account donation [F(2, 63) = 3.62, p < 0.05, N 2 = 0.10]. Bonferroni post-hoc tests indicated that the only significant difference lay between those who moved in-phase and the control (p < 0.05), no other comparison was significant (p's > 0.05). Post-coordination cooperation was greater for participants in the in-phase group compared to the control group (**Figure 2**).

Next we conducted a simple linear regression with each pair's MVL scores and each pair's average public goods donation to determine if the degree of coordination success predicts

the degree of cooperation. A pair's coordination score did not significantly predict their average cooperation score [F(1, 31) = 3.19, p > 0.05, r 2 = 0.093].

# Trust and Reciprocity

Trust was measured using the first part of the investment game (choosing what to invest with the other player: investing nothing, a quarter, half, or all). The distributions of those who moved inphase, anti-phase, and those who did not coordinate all deviated significantly from normality (p's < 0.05). A Kruskall-Wallis test showed no significant difference in trust between those who moved at in-, anti-phase and those who did not coordinate [H(2) = 4.48, p > 0.05].

As a further check that coordination had no effect on trust, we compared self-reported measures of trust across the coordination conditions. Change scores for the self-reported trust measure were first calculated by subtracting each person's "before" score from their "after" score. The distributions for those who moved in-phase, anti-phase, and those who did not coordinate all deviated significantly from normality (p's < 0.05). Consistent with the measure of trust based on the investment game, a Kruskall-Wallis test showed no significant change in selfreported trust between those who moved at in-, anti-phase, and those who did not coordinate [H(2) = 3.87, p > 0.05].

1Anti-phase is typically less stable than in-phase; this is one of the hallmarks of coordinated rhythmic movement. The lack of a difference here is a common issue with the MVL measure because it does not account for what relative phase people are actually performing. Anti-phase coordination can show an elevated MVL if people end up switching to in-phase coordination, and do that well (Wilson et al., 2005a,b; Snapp-Childs et al., 2011). We address this in detail in the Discussion section.

Reciprocity was measured using the option chosen in the second part of the investment game (choosing to return nothing, return only the original investment, return the original investment plus half of the bonus, or, return the original investment plus all of the bonus). Reciprocity scores for those who moved in-, anti-phase and those who did not coordinate all deviated significantly from normality (p's < 0.05). A Kruskall-Wallis test showed no significant difference in reciprocity between those who moved at in-, anti- phase, and those who did not coordinate [H(2) = 4.11, p > 0.05].

# Potential Mediators (Group Cohesion and Self/Other Overlap)

Change in group cohesion was measured as the sum of the difference between the three cohesion change questions (how similar/close/connected they felt to each other). A univariate ANOVA with phase (in-, anti-phase, no coordination) showed no significant effect of phase on group cohesion [F(2, 63) = 1, p > 0.05].

Change in self-other overlap was measured as the difference in self-other overlap before and after engaging in the coordination task (post-coordination—pre-manipulation). The distribution of overlap change scores for those who moved at in-, anti-phase, and those who did not coordinate all deviated significantly from normality (p's < 0.05). An independent samples Kruskal-Wallis test showed no significant effect of phase on changes in overlap between the three conditions [H(2) = 0.262, p > 0.05].

Analysis previously reported also confirmed that self-report measures of trust, mood, task difficulty, task enjoyment and perceived success did not differ between movement conditions.

# DISCUSSION

# Overview

The results showed that participants who moved in-phase with one another were more cooperative than those who moved in an uncoordinated manner. None of the measured candidate mediators were related to cooperation, and cooperation was not predicted by the level of coordination between partners. The results of Experiment 1 lend support to S+, D+, and P− models of how intentional coordination affects cooperation.

# Coordination Success (P+ vs. P− Models)

MVL scores suggested participants coordinated equally well at both in- and anti-phase. Coordination in both of these experimental conditions was better than in the control condition. MVL scores did not significantly predict cooperation, which suggests that the social effects seen post-entrainment do not vary linearly at an individual level with coordination. This is consistent with Kirschner and Ilari (2014) and Launay et al. (2013) and rules in favor of P- style models.

MVL is a measure of coordination (i.e., the extent to which people are doing something together) but it is not a measure of success at performing the target coordination. For example, people trying to move in anti-phase might fail to do so and spend their time moving in-phase. MVL might still be high because the partners were coordinating, even though they had failed at the target task (see Snapp-Childs et al., 2011 and Wilson et al., 2005a for detailed analyses of this problem). A better measure of coordination for this purpose is the proportion-time-on-target. This is the proportion of time people spent coordinating at the required phase (within an error bandwidth, typically set to 20◦ ). Proportion-time-on target, therefore, indicates how successful participants are at coordinating at the required relative phase (Wilson et al., 2010; Snapp-Childs et al., 2011, 2015). This measure was not used in our primary analysis because our control task has no target relative phase (meaning it is not possible to compute proportion-time-on-target for the control condition). However, the proportion-time-on-target can be calculated for the experimental conditions.

Further analyses of the proportion-time-on-target scores revealed that those who were instructed to move in-phase were more successful than those that were instructed to move anti-phase (See **Figure 3** for mean proportion-on-target-scores). Scores for those who moved at anti-phase were not normally distributed (p < 0.05). Because of this, an independent samples Mann-Whitney U-test was performed, which showed that there was a significant effect of phase on coordination (U = 140 p < 0.05), with those moving at in-phase performing significantly better than those moving anti-phase. However, coordination measured with proportion-time-on-target still did not significantly predict cooperation. A simple linear regression was run with each pair's proportion-time-on target scores and each pair's average public goods donation, to determine if coordination success predicts cooperation. A pair's coordination score did not significantly predict a pair's average cooperation score [F(1, 42) = 0.54, p > 0.05, r 2 = −0.011]. With the improved measure, we could identify the expected difference in performance between in- and anti-phase but the degree of coordination still did not predict the degree of cooperation. The data therefore still come down in favor of P− models; once some threshold amount of coordination has occurred, cooperation is positively affected.

# Potential Mediators (D+ vs. D− Models)

Against predictions, changes in trust, group cohesion and self/other overlap did not differ between conditions, suggesting

that these factors do not mediate CRM's effect on cooperation (supporting D+ models). The finding that increases in group cohesion do not mediate these effects supports the work of Dong et al. (2015), Lumsden et al. (2014), and Reddish et al. (2014). However, it did not support the work of Reddish et al. (2013), Wiltermuth and Heath (2009), and Wiltermuth (2012), which found that cohesion partially mediates the relationship between CRM and its social consequences. The finding that self/other overlap does not mediate these effects contradicts studies reported by Lumsden et al. (2014) and Reddish et al. (2013).

One reason for the inconsistencies in findings could be that the present study is the first to take "before and after" coordination measures of possible mediators. It may be the case that CRM does not actually foster changes in the given variables and that previous studies simply found group differences across these variables as opposed to actual increases in mediators as a result of CRM. Alternatively it could be that the measures used here are not sensitive enough to be used as a before and after measure. Completion of the pre-test measures may have restricted participant's answers to post-test measures, therefore leaving participants unable or unwilling to give more natural responses which may have otherwise led to us finding increases in potential mediators. For the cohesion measure we saw a mean change score of 2.27 with a standard deviation of 5.63. For the overlap measure we saw a mean change score of 0.45 with a standard deviation of 1.3. Considering we find considerable variation in individual change scores, we do not believe this interpretation alone can explain our findings.

# Synchrony vs. Coordination (S+ vs. S-Models)

This experiment did not provide conclusive evidence that cooperation was improved by coordination more generally. Significantly greater cooperation was only seen after in-phase coordination compared to control. Anti-phase coordination did not promote greater cooperation than after control, however cooperation levels following anti-phase coordination did not significantly differ from cooperation levels following in-phase coordination either. While this might initially lend some support to the S+ class of models (synchrony, rather than coordination being required). Findings lead us to further question whether inphase synchrony is crucial? Anti-phase coordination is a stable form of coordination (Kelso, 1995), that has been shown to affect other pro-social variables (see Cirelli et al., 2014).

The findings of Kokal et al. (2011) might shed light on the conditions necessary for different coordinations to affect prosociality. They provide evidence that, only when a coordination is relatively easy to perform can we attend to the social nature of the task, which is crucial to the pro-social consequences which follow. Anti-phase coordination is known to be harder and more demanding than in-phase (Kelso, 1995), as was supported by the proportion-time-on-target results in this Experiment (See **Figure 3**).

One potential limitation of our task was the use of simple PLDs to transmit movement information. These displays are informative about the dynamics of a person's action (Johansson, 1950; Bingham, 1987) and the success of coordinated movements in particular (Wilson and Bingham, 2008; Wilson et al., 2010) However, with their attention focused on the PLDs instead of on their partner, the social context of the coordination task might have been attenuated. In other words, using the PLD's to coordinate might dilute the social context of the coordination task.

The fact that relevant social information may be harder to detect during anti-phase coordination might explain why antiphase coordination did not significantly differ from control. A follow up explores this possibility by having participants coordinate at both relative phases using direct visual information of each other's movements. This set up makes the social nature of the task more salient. If post task cooperation is higher following anti-phase coordination given this change, it would add further support for D− models, where an additional causally relevant factor (e.g., social context) is necessary for coordination to affect cooperation.

# FOLLOW UP 1

In this follow up, we used a modified version of the CRM task in which co-actors coordinated by looking at each other in a fulllength mirror instead of using PLDs. Only the two experimental conditions (in- and anti-phase) were run in order to test whether increased social information would allow cooperation following the anti-phase condition to reach the level seen after in-phase coordination in Experiment 1. It was hypothesized that coordinating via a mirror would allow anti-phase CRM to affect cooperation similarly to in-phase CRM.

# METHODS

# Participants

Forty-four psychology students at Leeds Beckett University volunteered to participate (8 males and 36 females, Mage = 19.86 year, SDage = 1.79). All participants were naive to the aims of the study. This study was approved by the Leeds Beckett University Psychology Ethics Review Board.

# Design, Measures, and Procedure

The design was identical to the in-phase and anti-phase conditions from Experiment 1 except that participants watched each other using a 6 ft mirror placed horizontally 1m in front of them, below the laptop screen so that they could each view both of their upper bodies. These data were compared to the corresponding conditions from Experiment 1 to see whether enriched visual social information influenced cooperation. This follow up employed an experimental design with one between-subjects factor: Movement Phase, with two levels inand anti-phase. This enabled us to analyse the coordination data using the superior proportion time-on-target measure. The remaining measures and procedure were identical to Experiment 1.

#### Cross et al. Moving Together Brings Us Together

# RESULTS

We first examined mood, task difficulty, task enjoyment and perceived success measures for these two new conditions to see whether these varied across conditions, using a series of Kruskal-Wallis tests (all data distributions non-normal, p's < 0.05). None of these variables differed between the in-phase and anti-phase groups (all p's > 0.05). It was therefore concluded that mood, task enjoyment, perceived task difficulty or perceived success did not contribute to the effects described below.

# Coordination

We investigated differences in coordination scores across conditions using proportion-time-on target as a measure of coordination. The distributions of those who coordinated using the PLD and mirror at both in- and anti-phase (p's < 0.05) all differed significantly from normality, and Levene's test indicated unequal variances (F = 15.95, p < 0.001). Transforming the data did not allow it to meet the normality or homogeneity assumptions. Since no non-parametric alternative to a 2-way ANOVA could be performed and Field (2013) advises that homogeneity violations are irrelevant if sample sizes amongst conditions are roughly equal (sample sizes per condition here are identical, n = 22), a univariate ANOVA was still used. There was only a significant effect of Movement Phase [F(1, 87) = 14.78, p < 0.001], with those who moved in-phase showing greater coordination (M = 0.591, SD = 0.016) than those who moved anti-phase (M = 0.507, SD = 0.016). The effect of Coordination Information [F(1, 87) = 2.45, p > 0.05] and the interaction [F(1, 87) = 0.73, p > 0.05] were not significant (see **Figure 4** for mean proportion-time-on-target scores). It was therefore concluded that only Movement Phase had a significant effect on coordination, with those coordinating in-phase performing more accurately than those coordinating at anti-phase. The type of available Coordination Information had no effect on coordination scores.

# Cooperation

We then explored how rhythmically coordinating at different relative phases via differing Coordination Information affected cooperation using a 2 way ANOVA. There was no main effect of either Coordination Information or Movement Phase (p's > 0.05). However, there was a significant interaction between the phase people moved at and the information they used to coordinate their movements [F(1, 84) = 4.18, p < 0.05, N 2 = 0.04]. People who coordinated anti-phase via a mirror cooperated more than people who coordinated anti-phase via PLDs. There was no effect of Coordination Information on cooperation when people coordinated in-phase (see **Figure 5** for the mean public account donations for each condition).

Next we conducted a simple linear regression with each pair's proportion-time-on target scores and each pair's average public goods donation, to determine if coordination success predicts cooperation. A pair's coordination score did not significantly predict a pair's average cooperation score [F(1, 86) = 0.16, p > 0.05, r 2 = 0.01].

# Potential Mediators (Group Cohesion and Self/Other Overlap)

Separate 2 Way ANOVA's were conducted for each of the potential mediators as reported in Experiment 1, no significant main effects of either Movement Phase or Coordination Information and no significant interactions were found in any of these analyses (all p's > 0.05).

# DISCUSSION

Participants coordinating at anti-phase were more cooperative if they coordinated via direct visual information of their partner's movements rather than via PLDs. In fact, those coordinating at anti-phase using the mirror saw cooperation levels comparable to participants in the in-phase condition. There was no such increase in effect for those coordinating in-phase using direct visual info. This supports the claim of Kokal et al. (2011) that the social nature of the task is an important element in why CRM has pro-social consequences (supporting a D− model), which can be obscured in more demanding tasks. This suggests that both inand anti-phase movements are capable of affecting cooperation under the right circumstances, favoring a S− model.

Coordination scores (proportion-time-spent-on-target) again did not significantly predict cooperation scores (supporting a P− model). There is still no evidence that coordination success is driving CRM's effect on cooperation, replicating the result from Experiment 1 and supporting work by Kirschner and Ilari (2014) and Launay et al. (2013).

Greater cooperation can therefore follow either in- and anti-phase CRM compared with uncoordinated movements. However, analyses of coordination scores have shown that actual coordination does not seem to be driving this effect. The degree of coordination does not successfully predict the degree of cooperation. So what is it about the CRM task that is driving differences in cooperation? What are the critical differences between the coordinated and uncoordinated versions of this task?

# FOLLOW UP 2

In the CRM task people make the same (horizontal) movements at a shared frequency (0.75 Hz), while in the control task people make different movements (circular and vertical) at different frequencies (0.6 or 0.9 Hz). This means there are two potential differences between the CRM task and the control, type of movement and frequency of movement. Having participants perform different movements is essential to break coordination in the control task, since research shows people will end up falling into one of the two stable phases of coordination when performing the same kinds of movement unless they are trained to achieve out-of-phase coordination (Kelso, 1995).

When engaging in CRM in everyday life (e.g., when dancing), people often coordinate different movements to the same overall rhythm. What is more, Lakens (2010) has shown that people judge coordinated rhythmically moving co-actors as more entitative (seeing each other more as a unified group than as disparate individuals) regardless of whether they are coordinating exactly the same movements or not. Therefore, in order to investigate whether coordinating different movements to the same rhythm could also affect cooperation, a further followup condition was run in which participants coordinated different movements but to the same frequency. This is compared with the original control and the original in-phase CRM conditions from Experiment 1. It was hypothesized that coordinating different movements to the same overall frequency would foster greater cooperation than performing uncoordinated movements.

# METHODS

# Participants

Twenty-two undergraduate students at Leeds Beckett University volunteered to participate (4 males and 18 females, Mage = 18.73 year, SDage = 4.32). All participants were naive to the aims of the study. This study was approved by the Leeds Beckett University Psychology Ethics Review Board.

# Design, Measures, and Procedure **Movement task**

Participants made different movements but at the same frequency (0.75 Hz). One participant moved the joystick vertically and the other in clockwise circles. Participants switched movements each trial. Otherwise the structure of the movement task was identical to the Control in Experiment 1. This condition (Coordinated) was then compared with the original in-phase (In-phase) and control condition (Control) from Experiment 1. With no defined target relative phase we analyzed coordination using MVL. The remaining measures and procedure were identical to those reported in Experiments 1.

# RESULTS

We first examined mood, task difficulty, task enjoyment and perceived success measures to see whether these varied across conditions using a series of Kruskal-Wallis tests (All data's distributions not normal, p's < 0.05). There was no significant effect of any of the above variables (all p's > 0.05). It was therefore concluded that mood, task enjoyment, perceived task difficulty or perceived success did not contribute to the effects described below.

# Coordination

We then investigated whether coordination scores differed across conditions using an independent samples Kruskal-Wallis test (recall coordination data previously failed normality tests). There was a significant effect of Movement Type on coordination scores [H(2) = 57.83, p < 0.001]. Pair-wise comparisons with adjusted p-values showed that those who moved In-phase coordinated significantly more than those in the Coordinated condition (U = 3.8, p < 0.001) and those in the Control (U = 7.60, p < 0.001). Those in the Coordinated condition coordinated significantly more than those in the Control (U = 3.8, p < 0.001). See **Figure 6** for the mean MVL scores.

# Cooperation

Next we examined the cooperation scores of those in the Coordinated compared with the original In-phase and Control conditions from Experiment 1. A univariate ANOVA was performed to see whether cooperation (mean public account donation) differed across the three movement conditions (Inphase, Coordinated and Control). There was a significant effect of

Movement Type [F(2, 63) = 5.69, p < 0.01 N 2 = 0.15]. Bonferroni post-hoc tests indicated that those who moved In-phase (M = 6.19, SD = 2.24) showed more post-coordination cooperation than those in the Control (M = 4.2, SD = 2.81, p < 0.05). Those in the Coordinated condition (M = 6.72, SD = 2.74) also showed more cooperation than those in the Control (p < 0.01). There was no difference in cooperation between those in the Coordinated condition and those who moved In-phase (p > 0.05). See **Figure 7** for the mean public account donations for each condition.

# Potential Mediators (Group Cohesion and Self/Other Overlap)

A univariate ANOVA and Kruskal Wallis test (recall previous normality scores) again confirmed that there were no significant differences in any of the candidate mediators between conditions (all p's > 0.05).

# DISCUSSION

The results of this follow up show that similar levels of cooperation are seen after coordinating different movements to a common frequency as are seen after in-phase coordination, despite levels of actual coordination being significantly lower. MVL scores show that coordinating different movements to a common frequency produced significantly less tight coordination than coordinating at in-phase but significantly tighter coordination than in the original control. This was not the pattern observed in cooperation, however. The Coordinated and In-phase conditions produced comparable levels of cooperation, and both showed higher cooperation than the Control condition.

These results suggest that people do not need to perform the same type of movements for coordination to have cooperative social consequences and emphasize again that tightness of coordination is not directly linked to the magnitude of cooperation (P− model). The important factor appears to be that they coordinate to a common rhythm. Verbal reports from participants in this new condition also indicated that participants felt they were coordinating their actions. Multiple participants in this condition reported that they were trying to coordinate one full cycle of their movements to a full cycle of the other's

movements (i.e., trying to complete one full up-down-up cycle on the time it took the other to complete a full circle).