congruency effects in interpersonal coordination

Journal of Experimental Psychology: HumanPerception and Performance

Congruency Effects in Interpersonal CoordinationJustin M. Fine, Cameron T. Gibbons, and Eric L. AmazeenOnline First Publication, March 4, 2013. doi: 10.1037/a0031953

CITATIONFine, J. M., Gibbons, C. T., & Amazeen, E. L. (2013, March 4). Congruency Effects inInterpersonal Coordination. Journal of Experimental Psychology: Human Perception andPerformance. Advance online publication. doi: 10.1037/a0031953

Congruency Effects in Interpersonal Coordination

Justin M. Fine, Cameron T. Gibbons, and Eric L. AmazeenArizona State University

Research on interpersonal coordination has demonstrated that incongruent tasks lead to unintendedmovements in the orthogonal plane. These effects have been interpreted using both an embodiedsimulation and coordination dynamics approach. To distinguish between these two perspectives, twoexperiments examined whether this congruency effect is best defined spatially or anatomically. In thefirst experiment, participants coordinated congruent and incongruent rhythmic arm movements with anactor. To dissociate spatial and anatomical congruency, the actor was rotated 90° in the coronal plane forhalf of the trials. In the second experiment, participants coordinated movements of different limbs (legand arm). Spatial and anatomical congruency was dissociated here by rotating the actor in the transverseplane. In both experiments, the unintended movements associated with the congruency effect emerged asa function of spatial congruency; there was no congruency effect associated with anatomical congruency.The data suggests that these unintended movements represent the recruitment of additional df necessaryto stabilize an unstable form of coordination.

Keywords: social coordination, dynamical systems, embodied, mirror neurons, simulation

In many social activities, such as team lifting (Asch, 1952),conversing (Shockley, Richardson, & Dale, 2009), or solving apuzzle (Richardson, Marsh, & Schmidt, 2005), one’s movementsare influenced by a counterpart (Marsh, Richardson, & Schmidt,2009). This suggests that there are mechanisms and processessupporting each person’s movements that are shared across per-formers. To capture this shared or social basis, such coordinationhas been termed interpersonal coordination, social coordination, orjoint action. In the present article, we examine the dynamics ofinterpersonal coordination while participants perform spatially andanatomically congruent or incongruent movements.

There are currently two major approaches to studying interper-sonal coordination (Knoblich, Butterfill, & Sebanz, 2011;Schmidt, Fitzpatrick, Caron, & Mergeche, 2011). These ap-proaches take different, but not necessarily opposing, perspectiveson the level of analysis and the mechanisms for interpersonalcoordination (Kelso, Decolle, & Schoner, 1990; Richardson,Marsh, & Schmidt, 2010). The first approach has adopted severaldifferent names such as the Embodied Simulation, Common Cod-ing, Emulator, or Cognitivist approach (Grush, 2004; Prinz, 1997;Schmidt et al., 2011). This line of research focuses on neural andrepresentational mechanisms in the individual (Richardson et al.,2010; Sebanz, Knoblich, & Prinz, 2003). The second approach, theCoordination Dynamics approach, focuses on emergent patterns ofcoordination based on the coupling of action and perception acrossindividuals (Schmidt & Richardson, 2008).

The current article aims to consider the contribution of bothapproaches to interpersonal coordination, while focusing on recent

findings addressed by both (Kilner, Pauligan, & Blakemore, 2003;Richardson, Campbell, & Schmidt, 2009). Two experiments ex-amined congruency effects on the interpersonal coordination ofarm and leg movements. Experiment 1 examined the roles ofanatomical (egocentric—relative to the body position) and spatial(exocentric—relative to the ground) congruency to assess whetherindividuals coordinate based on an anatomical or spatial frame ofreference. Experiment 2 examined the role of effector congruencyto assess whether interpersonal coordination is effector specific ora general property of interacting limbs (Schmidt & Richardson,2008).

Embodied Simulation Approach

Although researchers have long acknowledged a tendency to-ward interpersonal coordination (Condon & Ogston, 1967; Newt-son, Hairfield, Bloomingdale, & Cutino, 1987), it has recentlygenerated substantial interest (Knoblich et al., 2011; Sebanz &Knoblich, 2009), possibly due to its implications for motor coor-dination in individuals as well as pairs (Fine & Amazeen, 2011).Given appropriate information about others, people will coordinatemovements with (Schmidt, Carello, & Turvey, 1990) or mimic(Chartrand & Bargh, 1999) others’ postures (Varlet, Marin,Lagarde, & Bardy, 2011), leg swinging (Schmidt et al., 1990),breathing rates (McFarland, 2001), or chair rocking (Richardson,Marsh, Isenhower, Goodman, & Schmidt, 2007).

The Embodied Simulation approach proposes that such coordi-nation is driven by an overlap in how individuals represent per-ceived and performed action. Specifically, it posits that an ob-server watching an action being performed simulates—activatesthe representation that would be required if they were to produce—the observed action. Action simulation occurs as an automatic andcovert imitation (Wilson & Knoblich, 2005), below the level ofconscious motor planning (Prinz, 2002). A system of this form isargued to support individuals’ ability to understand other persons’

Justin M. Fine, Cameron T. Gibbons, and Eric L. Amazeen, Departmentof Psychology, Arizona State University.

Correspondence concerning this article should be addressed to Justin M.Fine, Department of Psychology, Box 871104, Arizona State University,Tempe, AZ 85287. E-mail: [email protected]

Journal of Experimental Psychology:Human Perception and Performance

© 2013 American Psychological Association

2013, Vol. 39, No. 2, 0000096-1523/13/$12.00 DOI: 10.1037/a0031953

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goals (Hamilton, Wolpert, & Frith, 2004; Kohler et al., 2002) andpredict others’ actions (Sebanz & Knoblich, 2009).

Physiological Support for Embodied Simulations:Mirror Neuron System (MNS)

Physiological support for the Embodied Simulation approach isbased on the discovery of mirror neurons, found mainly in the F5area of a Macaque monkey’s premotor cortex (Gallese, Fadiga,Fogassi, & Rizzolatti, 1996). These neurons activate similarlywhen a monkey produces an action and when observing anotherhominid producing a similar action. Data suggest that the humanpremotor and parietal cortex contains neurons analogous, but notidentical, to the monkey MNS (Iacoboni et al., 1999; Rizzolatti &Craighero, 2004). Furthermore, physiological correlates of mirror-ing activity such as somatotopic muscle activation (Fadiga, Fo-gassi, Pavesi, & Rizzolatti, 1995; Maeda, Kleiner-Fisman, &Pascual-Leone, 2001) and fMRI data from premotor areas (Buc-cino et al., 2001; Gazzola & Keysers, 2009; Iacoboni et al., 1999)have provided indirect evidence for a human mirror neuron sys-tem. However, the embodied simulations supported by this neuralsystem may be different for macaques and humans. Specifically,data with monkeys suggest that simulations induced by actionperformance or observation require that the movement is goal-directed and produced by another agent (Gallese et al., 1996).Human based data, on the other hand, is unclear (for a fulldiscussion, see Hickok, 2009) as to whether simulations requirethat a movement be produced by another agent (Kilner, Hamilton,& Blakemore, 2007; Kilner et al., 2003; Stanley, Gowen, & Miall,2007) or goal-directed (Fadiga et al., 1995).

Researchers have suggested that an individual observing anoth-er’s action simulates how they themselves would perform theaction, not how the external agent would. Calvo-Merino, Glaser,Grezes, Passingham, and Haggard (2005) showed that “mirrorneuron” activity was higher when participants watched videos ofmovements they were trained to perform, suggesting that theirunderstanding of another’s movements was based on their ownabilities. Furthermore, participants watching point-light displayshave been shown to be more sensitive to their own movementsthan the movements of another (Loula, Prasad, Harber, & Shiffrar,2005). This connection between observing another and simulatinghow you would perform that action has also been found in corticalbased measures of muscular activity (Gangitano, Mottaghy, &Pascual-Leone, 2001). Specifically, the cortical timing of motorevoked potentials (MEPs) recorded from the hands of the personobserving a reaching action are similar to the timing phase of amotor program underlying the perceived movement.

In keeping with the notion that individuals simulate how theywould perform an observed action, researchers have also suggestedthat motor commands underlying embodied simulations areeffector-specific (Boulenger, Hauk, & Pulvermuller, 2009; Gra-dinarova & Janyan, 2011; Pulvermuller, Harle, & Hummel, 2001).For example, fMRI data has shown that the human premotor cortexactivates differently in response to observing and hearing phrasesdepicting actions with different effectors (Aziz-Zadeh, Wilson,Rizzolatti, & Iacoboni, 2006). Further, topographical differencesin fMRIs are found when an individual listens to sentences de-scribing actions involving different effectors (Pulvermuller et al.,2001). This activation is primarily associated with activity in the

premotor area, where mirror neurons involved in perceptual pro-cessing of actions are proposed to reside. Data of this naturesuggests that embodied simulations and constituent motor com-mands activated in response to action observation are specific tothe effectors involved in the action.

The findings in humans, though, are not identical to those inmacaques (Hickok, 2009). Gallese, Fadiga, Fogassi, and Rizzolatti(1996) showed that, during observation of a hand motion, record-ings of macaque’s primary motor cortex (corresponding to thehand area; F1 or M1) were not active. EMG data was also recordedfrom hand muscles during observation and showed no activation inthe corresponding effector region. As previously discussed, thesefindings indicate that motor simulations and the involved MNS inhumans might differ from macaques.

Behavioral Support for Embodied Simulation:Action-Compatibility

Data in support of the Embodied Simulation approach are notrestricted to physiological recordings from areas connected to theMNS. In discrete movements, response and movement times arefacilitated (Edwards, Humphreys, & Castiello, 2003) or interferedwith, depending on the compatibility of stimulus and response(Bach & Tipper, 2007; Brass, Bekkering, & Prinz, 2001). Forexample, Bach and Tipper (2007) showed that watching a video ofa person type with the hands facilitated responses made with ahand, but delayed a response performed with the foot. Likewise,finger movement onset times are faster after watching a video of ahand performing the same instructed movement (Brass, Bekkering,Wohlschlager, & Prinz, 2000). Compatibility of movement kine-matics from an observed movement also affects participants’ phys-ical responses (Griffiths & Tipper, 2009). However, when move-ment is directionally incompatible (incongruent) with theinstructed response, movement onsets are slowed (Brass et al.,2001). In agreement with physiological data (Aziz-Zadeh et al.,2006; Boulenger et al., 2009) and a strict interpretation of anembodied simulation approach, these findings suggest two char-acteristics of simulations. First, simulations involve the executionof motor commands related to how a perceiver would perform anobserved action (Gallese, 2005; Gallese et al., 1996; Wilson &Knoblich, 2005). Second, the issued motor commands are specif-ically related to the effector and the postural state (e.g., its orien-tation) associated with the action.

In further support of the Embodied Simulation approach, thesecompatibility-based interference effects only seem to occur whenobserving a biological agent (Kilner et al., 2003). Kilner, Pauligan,and Blakemore (2003) had participants coordinate rhythmic verti-cal or horizontal arm movements with spatially congruent orincongruent movements of a confederate and a robotic arm. Duringincongruent coordination with the confederate, variance of partic-ipants’ movement in the uninstructed (orthogonal) plane increased,suggesting interference from the observed action. No such effectwas seen during coordination with the robotic arm; although thismight relate to the different velocity profiles of the confederatesand robot (Kilner et al., 2007).

Using a paradigm similar to Kilner et al. (2003), Stanley, Go-wen, and Miall (2007) showed that this congruency effect mightrely on whether the observer believes a biological agent created themovement. In this research, participants coordinated congruent

2 FINE, GIBBONS, AND AMAZEEN

and incongruent arm movements with a biologically or nonbio-logically based dot motion. Incongruent movements led to in-creased variance in the unintended plane for both motion types, butonly when the participant was told it was biologically created.What is important is that no actual external agent producing themovements was ever observed. The implication is that simulationsexecute without being strictly based on an external agent’s capa-bilities. Greater similarity of an observed movement to one’s ownmovement capabilities might underlie such observed interference(Calvo-Merino et al., 2005; Kilner et al., 2007; Stanley et al.,2007). This finding supports the notion that individuals seem torepresent or simulate other’s actions via their own motor system,without specifying an external agent (Gallese, 2005).

Simulation mechanisms of this type are, as discussed, thought toassist in the perceptual processing (e.g., prediction) of imitablestimuli (Wilson, 2001). From a strict interpretation of this view-point and its experimental support—such as compatibility effectsof hand posture (Brass et al., 2001) or coordination incongruency(Kilner et al., 2003)—it can be argued that these mechanismssimulate a movement trajectory using information derived frombiomechanical principles and concomitant parameters (e.g., joint-torque or rotation; Grush, 2004; Wilson, 2004; Wilson &Knoblich, 2005). This suggests that a perceiver understands themovements of another person by producing the motor commandsassociated with performing similar actions (Wilson, 2001; Wilson& Knoblich, 2005); moreover, these motor commands are basedon the perceiver’s biomechanics, current postural state, and ex-pected movement consequences (Grush, 2004; Reed & Farah,1995). This implies that stimuli congruency effects are based onwhether a stimulus is anatomically congruent with a perceiver’sstate or capabilities (Jacob & Shiffrar, 2005): A perceiver’s sim-ulation (or motor representation) of another agent’s movementwould be based on the present anatomical configuration and bio-mechanical abilities of the perceiver.

Coordination Dynamics Approach

In contrast to the Embodied Simulation emphasis on mecha-nisms and processes within the individual, the Coordination Dy-namics approach emphasizes an analysis at the level of the inter-action between components (Kugler, Kelso, & Turvey, 1980;Tuller, Kelso, & Harris, 1983). This approach to interpersonalcoordination follows directly from the Coordination Dynamicsapproach to coordination within an individual (Kelso, 1995) andseeks processes and mechanisms common to both. The emphasis ison the common principles that govern pattern formation, rangingfrom neural assemblies up to the behavioral level of analysis(Kelso, Decolle, & Schoner, 1990; Kugler & Turvey, 1987). Theseprinciples generally take the form of a neural or informational(e.g., vision) coupling between components (e.g., limbs or actors)that leads to coordinated behavior. The basis of coordination fromthis perspective, then, is the observation of another limb’s move-ments, not necessarily a mental simulation of producing thosemovements oneself.

Interpersonal Coordination Dynamics

Research has shown similar dynamical patterns in intrapersonal(bimanual) and interpersonal (social) coordination (Amazeen,

Schmidt, & Turvey, 1995; Fine & Amazeen, 2011; Richardson etal., 2007; Schmidt, Bienvenu, Fitzpatrick, & Amazeen, 1998;Schmidt et al., 1990). Schmidt, Carello, and Turvey (1990) hadpairs of participants each swing a leg, while seated in chairs,in-phase (relative phase (�) � 0°) and antiphase (� � 180°) whilevisually attending to each other. As has been shown repeatedly inthe intrapersonal case, in-phase coordination remained mostlystable as frequency increased, and antiphase yielded transitions toin-phase coordination with frequency increases. Further, evenwhen movement speed was decreased following an anti- to in-phase switch, the patterns did not switch back to antiphase. Al-though in most demonstrations of interpersonal coordination, par-ticipants are instructed to coordinate a priori, similar dynamicshave also been shown to govern unintentional coordination (Rich-ardson et al., 2007; Richardson et al., 2005; Schmidt & O’Brien,1997).

The tendency toward interpersonal coordination persists evenwhen participants are faced with unequal task demands. Fine andAmazeen (2011) demonstrated that—as in intrapersonal (biman-ual) target aiming—interpersonal coordination occurs spontane-ously and persists even when targets are of unequal difficulty.Under such conditions, Fitts’s law is violated for bimanual coor-dination (Fowler, Duck, Mosher, & Mathieson, 1991; Kelso,Southard, & Goodman, 1979; Marteniuk, MacKenzie, & Baba,1984). Fine and Amazeen (2011) found a similar violation whendyads performed a comparable aiming task. Despite the differingtask demands, there was a strong tendency toward in-phase coor-dination (necessitating a violation of Fitts’s law) in both intraper-sonal and interpersonal tasks.

Planned and unintentional coordination is not restricted to ef-fectors within or across agents, but also occurs in response to anenvironmental stimulus (Schmidt, Richardson, Arsenault, & Gal-antucci, 2007). Factors affecting intra- and interpersonal coordi-nation, such as differences in effector eigenfrequencies (Lopresti-Goodman, Richardson, Silva, & Schmidt, 2008; Schmidt et al.,1998) or information saliency (Richardson et al., 2007; Schmidt &O’Brien, 1997; Schmidt et al., 2007) also impact environmentalcoordination stability. The observed similarities between intraper-sonal, interpersonal, and environmental coordination (where thereis shared information regarding each effector’s movements, but nocentral controller) implies that coordination, in general, emergesfrom interacting components.

To address the fact that interpersonal coordination occurs with-out a single centralized controller, it been suggested that sharedmental models could support such coordination (Sebanz, Bekker-ing, & Knoblich, 2006; Wilson & Knoblich, 2005; Wolpert, Doya,& Kawato, 2003). It is reasonable to hypothesize that participantsinstructed to deliberately coordinate might be accessing a sharedmodel. In the case of the spontaneous interpersonal synchronydescribed above, though, it is less likely that participants wouldspontaneously access shared mental models. The likelihood of ashared mental model supporting coordination is further reducedwhen a global task (to coordinate) is not overtly assigned and eachperson has a different kinematic or spatial task requirement. Spon-taneous interpersonal coordination of this type would suggest thatthe dynamics governing the coordinated interactions of coupled-effectors do so by driving the system toward stable movementpatterns (Richardson et al., 2007; Richardson et al., 2005; Schmidt& O’Brien, 1997).

3CONGRUENCY AND COORDINATION

Stabilizing Coordination by Recruiting Degrees ofFreedom

Although most laboratory demonstrations of interpersonal co-ordination have participants perform identical tasks, the kinematicrequirements of the limbs are often not the same in motor coordi-nation in general. Consider, for example, a bimanual coordinationtask in which each hand is assigned a different amplitude. Themain result is a change in the movement amplitude of both hands,despite the assigned differences (Schwartz, Amazeen, & Turvey,1995); each effector’s amplitude is superimposed onto the other(Heuer, Spijkers, Kleinsorge, van der Loo, & Steglich, 1998).Similar results have been shown when participants are asked todraw a line with one hand and a circle with the other (Franz,Zelaznik, & McCabe, 1991). The drawings of one shape exhibitspatial deviations representative of the other, much like the inter-personal coordination results of Kilner et al. (2003).

The existence of coordination across unequal kinematic require-ments raises the question, how does the motor system control andremain flexibly adaptive under varying conditions that are subjectto difficulty constraints? One possible answer involves rethinkingthe status of variability as error or noise. Specifically, the motorsystem may be recruiting previously dormant motor degrees-of-freedom (df) in order to stabilize coordination (Buchanan & Kelso,1999; Fink, Kelso, Jirsa, & De Guzman, 2000; Richardson et al.,2009). When performance can no longer be stabilized, compensa-tory changes will inevitably occur. Examples include the transitionfrom antiphase to in-phase coordination (Kelso, 1984), superim-position of movement amplitude (Schwartz et al., 1995), and thetime to bimanually hit targets for two hands (Kelso et al., 1979), ortwo (Fine & Amazeen, 2011) people.

During speeded antiphase coordination, though, there is notalways a transition to in-phase. Recruiting inactive df (e.g., in-creasing movement dimensions) also serves the motor system’sneed to maintain a specified pattern (Kelso, Buchanan, de Guz-man, & Ding, 1993). Buchanan and Kelso (1999) found thatsuppressed phase-transitions during bimanual pendulum coordina-tion (with increasing frequency) were accompanied by changes inthe pendulums’ trajectories. Spatial trajectories shifted from two-dimensional (2D; planar) to three-dimensional (3D; elliptical)movements, accompanied by increased forearm movement. Simi-larly, Fink, Kelso, Jirsa, and De Guzman (2000) showed that initialmodes of antiphase led to increased movement in the orthogonalplane, greater spherical movements, and additional df recruitment.

Richardson, Campbell, and Schmidt’s (2009) findings regardingthe recruitment of df are particularly relevant to the comparison of theEmbodied Simulation and Coordination Dynamics approaches tointerpersonal coordination. Richardson et al. (2009) reexamined thecongruency effects found during spatially incongruent interpersonalcoordination—interpreted by Kilner et al. (2003) as interference or“error”—and showed that incongruent movements contained proper-ties indicative of a motor system making compensatory changes. Theyproposed that the supposed motor interference effect was indicative ofa coupled oscillator system, and the increased variability effect foundby Kilner et al. (2003) represented the recruitment of df, not error.This proposal was substantiated on two grounds. First, during incon-gruent coordination, participants’ movements in the unintended planewere oscillatory, containing frequency components specific to the task

requirements. Second, movements in the unintended plane were co-ordinated with the intended movements of the actor.

The suggestion that these unintended fluctuations do not reflecterror or interference presents a challenge for a strict interpretationof an Embodied Simulation theory. However, the data from Rich-ardson et al. (2009) alone do not rule out a role for motor simu-lations in interpersonal coordination because the participants mayhave engaged motor simulations that coordinated with the motorcommands for their own movement. Put differently, a strict inter-pretation of an embodied approach would predict coordinationcongruency effects to emerge due to anatomical differences inmovements between a perceiver and another person. However, thecoordinated movements in Richardson et al. (2009) were similarlyincongruent in spatially- and anatomically-defined planes. To fullydiscriminate between the Coordination Dynamics and EmbodiedSimulation perspectives, then, the participants need to be rotatedrelative to one another in order to separate spatial and anatomicalcongruency. Such a manipulation would also speak to the broaderquestion of whether the effects of asymmetrical performance areunderpinned by spatial or anatomical constraints (Amazeen,DaSilva, & Amazeen, 2008; Carson, Goodman, Kelso, & Elliot,1995; Jeka & Kelso, 1995; Li, Levin, Carson, & Swinnen, 2004;Park, Collins & Turvey, 2001).

Experiment 1

In this experiment, the participant and an actor performed aninterpersonal coordination task. Similar to the methods used byKilner et al. (2003) and Richardson et al. (2009), the pair coordi-nated rhythmic arm oscillations about the shoulder. This designserved the purpose of examining whether the coordination con-gruency effect is driven by anatomical or spatial congruency—specifically, to discern whether congruency effects found in paststudies (Kilner et al., 2003; Richardson et al., 2009) were based onindividuals utilizing motor representations derived from their an-atomical or spatial asymmetries. To distinguish between anatom-ical (postural states) and spatial congruency, participants stood andperformed movements in either the same or different spatial plane(horizontal or vertical) as the actor, while the actor was eitherstanding or lying on his side (see Figure 1).

Figure 1. In Experiment 1, participants coordinated vertical and horizon-tal arm movements with an actor. In order to manipulate anatomical andspatial congruency separately, the actor was standing for half of the trialsand rotated for half of the trials.


We predicted that participants’ movement in the unintended(orthogonal to the plane of intended motion) plane would indicatethe form of congruency driving the coordination. If the congruencyeffect were based on anatomical congruency, then we expectedvariability in the unintended plane to be greater when the actor wasmoving in the orthogonal anatomical plane. However, if this effectwere based on spatial congruency, then we expected variability inthe unintended plane to be greater when the actor was moving inthe orthogonal spatial plane, regardless of the actor’s bodily posi-tion. Following the proposal of Richardson et al. (2009), weexpected that any increased variability would emerge due to motordf recruitment, not error. This effect would be identified by in-creased coordination between the participants’ unintended move-ments and the actor’s intended movements under conditions ofincreased variability in the unintended plane—increased variabil-ity would be structured, not random.

Method

Participants. Eight dyads (10 men and six women; meanage � 20 years, range � 18–23 years) at Arizona State Universitywere recruited to participate in this experiment in exchange forcourse credit. All recruited participants were right-handed to main-tain a functional symmetry across dyads in handedness.

Design. Participants coordinated right-arm movements (withfingers extended) in the horizontal and vertical planes with hori-zontal or vertical right-arm movements of the actor. Conditionswere created so that anatomical and spatial congruence could beexamined independently. Anatomical congruence was defined bywhether both arms oscillated in the same plane, with respect to thebody. Spatial congruence was defined by whether both arms os-cillated in the same plane, with respect to the ground. In one set ofconditions, the actor stood facing the participant so that move-ments were both anatomically and spatially congruent or incon-gruent. In the other set (actor rotated 90° by laying on his side, seeFigure 1) coordination that was anatomically congruent was spa-tially incongruent, and vice versa. For example, when the actor’sorientation was rotated and he was moving vertically with respectto the ground and horizontally across the body, the standingparticipant moving vertically with respect to the ground and theirbody would retain spatial, but not anatomical, congruence. All ofthe conditions were performed at two different oscillation frequen-cies, .65 and 1.0 Hz. The complete design included 16 conditionsresulting from a factorial combination of Frequency (2) � ActorPosition (2) � Congruence (2, defined spatially) � ParticipantMovement Direction (2). There were two trials per condition dueto repetition, with a total of 32 trials overall.

Materials. Movement trajectories of the arms were recordedusing an Optotrak 3020 motion capture system and First Principlessoftware package (Northern Digital Inc., Waterloo, Canada). Thesystem was positioned to the side of the actor and participant (4.50m) and off of the ground (0.75 m). Cameras recorded the 3Dpositions (sampling rate � 100 Hz) from infrared light-emittingdiodes (IREDs; diameter � 1 mm). Diodes were attached withVelcro to the top and side of participants’ index fingers.

Procedure. The participant was positioned 1 m away from theactor (distance between their extended hands). They were in-structed to maintain in-phase coordination with the actor; a visualexplanation and demonstration was given to ensure that the par-

ticipant understood the intended pattern. Spatially congruent ver-tical movements were considered in-phase when both arms were inthe maximal upper or lower point at the same time. Spatiallyincongruent coordination was in-phase when the vertical move-ment was at the maximal upper point at the same time the hori-zontal movement was at the maximally right position. To preventinfluences of the participant’s movement on the actor, the actor’svision was occluded with a pair of blacked out goggles. The actorwas trained to produce the necessary movements with minimalspatial variability and deviation from target frequency.

To avoid startup transients, data recording began after an initial5 s of movement. Data was collected for 40 s. One experimentermonitored data collection and another monitored participants’ per-formance. Experimental sessions lasted approximately 1 hour.Participants were allowed to rest between trials if necessary. Theprocedures used in this experiment conform to the ethical guide-lines of the American Psychological Association and were ap-proved by the Institutional Review Board at Arizona State Uni-versity.

Data analysis. Before analysis, recorded movements werestandardized (centered around zero) and filtered using a second-order, low-pass Butterworth filter with a cutoff frequency of 12Hz. Several measures were used to assess coordination and move-ment in the intended and unintended planes. For movements in theintended planes, the relative phase was used to establish thedominant form of coordination between participant and actor. Thisvariable is calculated from the difference between the phase anglesof both individuals’ movements (� � � actor � � participant) acrosseach sample (for an example, see Schmidt & Turvey, 1994). It wasexpected that any entrainment would be depicted as a concentra-tion of mean relative phase at in-phase (0o). The standard deviationof relative phase (SD �) was also calculated to acquire a measureof coordination variability. Additionally, cross-spectral coherencewas used to calculate the correlation between the movements ofboth limbs at their shared dominant spectral peaks. Coherence istypically used as a means to index coordination strength (Fine &Amazeen, 2011; Richardson et al., 2009).

To inspect participant movements in the unintended plane, thestandard deviation (SD, in mm) of the trajectory was calculated.Following past research (Kilner et al., 2003; Richardson et al.,2009), it was anticipated that the SD would be higher for incon-gruent trials. It was also expected that orthogonal movement wouldexhibit oscillatory components by way of higher spectral power atthe target frequencies of the intended coordination (Richardson etal., 2009).

To identify coordination in the unintended plane, the relativephase and coherence between the actor’s instructed and partici-pants’ noninstructed plane movements were calculated. Instead ofphase plane and frequency based analyses, a wavelet approach wasutilized. Wavelet analyses are useful given the possibility of non-stationarities in the participants’ orthogonal plane movements. Thefollowing analyses all used a complex Morlet wavelet of order 6,with a frequency band ranging from .02 Hz to 2 Hz. First, thecross-wavelet transform (XWT) between both signals was used tocalculate relative phase. The XWT is a time-frequency basedanalysis, which can be used to capture the synchronization be-tween two signals across a frequency band. This method allowscalculation of the points in time-frequency space where bothsignals have high common power. After the transform, the circular


mean of the instantaneous relative phase was extracted at eachpoint falling in the frequency band corresponding to the instructedmovement frequency. A similar transform can be performed toassess the coherence of both signals across a range of frequencies.This method allows extraction of regions where both signalssignificantly covary. The result is time-localized correlation coef-ficients of both signals. Significant regions of common frequen-cies, with a cutoff level of 0.05, were established using the sig-nificance test of Torrence and Compo (1998). The average waveletcoherence was also calculated at the frequency specific to eachtrial. The utility of these methods has been examined in similarparadigms (Issartel, Gailot, & Cadopi, 2007; Varlet et al., 2011).

The aforementioned variables were each analyzed using 2 (Fre-quency: 0.65 Hz, 1.0 Hz) � 2 (Actor Position: standing, rotated) �2 (Congruence: congruent, incongruent; defined relative to theground) � 2 (Participant Movement Direction: horizontal, verti-cal) repeated-measures ANOVAs.

Results

Coordination of intended movements. To establish that par-ticipants were producing the intended in-phase coordination, mean� in the intended plane was examined. There were significantmain effects of Actor Position, F(1, 7) � 13.44, p � .05, �2 � .66,and Congruence, F(1, 7) � 5.8, p � .05, �2 � .46. Mean � washigher for the rotated (7°) compared with standing (5°) position.Similarly, mean � during incongruent (9°) coordination washigher than congruent (4°). Because Congruence is defined rela-tive to the ground, this result means that mean � was closer to 0°when the hands were moving in the same plane relative to theground, regardless of whether the actor was rotated; spatial con-straints seemed to predominate. All other effects were nonsignif-icant (p � .05) and there were no data to suggest that participantswere not producing in-phase coordination.

The stability of coordination was measured with SD �. Only thethree-way interaction of Frequency � Actor Position � ParticipantMovement Direction was significant, F(1, 7) � 9.67, p � .05,�2 �.58 (all other effects, p � .05). To examine this interaction, twoFrequency � Actor Position ANOVAs were run, one for eachParticipant Movement Direction. There was a significant Fre-quency � Actor Position interaction when the participant wasmoving horizontally (see Figure 2A), F(1, 7) � 9.2, p � .05, �2 �.56, but no interaction when the participant was moving vertically(Figure 2B), p � .05. When participants were moving vertically,mean SD � increased from slow to fast frequencies equally forboth the standing and rotated actor. This finding is in line with thenotion that stability of coordination decreases as frequency in-creases. When participants were moving horizontally, this sameeffect was observed when the actor was standing. However, SD �was unchanged across Frequency when the actor was rotated.

The strength of coordination was measured by the coherencebetween the participants’ and actor’s movements in their intendedplanes. Mean coherence decreased with increases in movementFrequency, F(1, 7) � 6.21, p � .05, �2 � .47. Coherence wasgreater at slow (.95) than fast (.87) frequencies. None of the othereffects of coherence were significant (p � .05).

Movement in the unintended plane. The SD (mm) of move-ment in the unintended plane has been used as an index ofinterference (Kilner et al., 2003; Richardson et al., 2009). Consis-

tent with past findings, there was an effect of Congruence, F(1,7) � 35.1, p � .05, �2 � .84, with incongruent trials exhibitinga higher SD than congruent trials (see Figure 3). Put differently,there was greater movement in the unintended plane when theactor was moving in a different spatial plane. The effect ofParticipant Movement Direction was also significant, F(1, 7) �12.3, p � .05,�2 � .31. An interaction of Congruence andParticipant Movement Direction supports these main effects,F(1, 7) � 5.1, p � .05,�2 � .22; similar to other studies,movement in the unintended plane was greater while perform-ing horizontal compared with vertical movements. All othereffects were nonsignificant (p � .05).

As was discussed previously, the increased variability in theunintended plane could be interpreted as either increased error orthe recruitment of additional df. An analysis of spectral power maydistinguish between these two options. If the increased variabilityresulted from increased error, then a spectral analysis should revealincreased noise. An increase in structured or coordinated variabil-ity, on the other hand, would suggest the recruitment of additionaldf. The spectral power at each condition’s specified target fre-quency was conducted to measure unintended movement period-icity (see Figure 4). There was an effect of Congruence, F(1, 7) �

Figure 2. SD � as a function of Frequency and Actor Position. Panel Adisplays the data for horizontal participant movement. Panel B displaysdata for vertical participant movement.


19.1, p � .05, �2 � .76. Mean spectral power was greatest forincongruent conditions, suggesting peaks were greater at the targetfrequency. There was also an effect of Frequency, F(1, 7) � 10.5,p � .05, �2 � .50, where spectral peaks were higher at the targetfrequency for the faster movements. A two-way interaction ofActor Position x Congruence was also significant, F(1, 7) � 6.4,p � .05, �2 � .52. Simple effects analysis revealed that spectralpower differences were greater between congruent and incongru-ent tasks when the actor’s position was rotated, F(1, 7) � 7.6, p �.05, �2 � .19. All other effects were nonsignificant (p � .05).

The relative phase of the actor’s intended and participants’unintended movements was calculated to establish whether pat-terned noninstructed coordination was elicited (see Figure 5). Aswith the spectral analysis above, coordination between the partic-ipants’ unintended movements and the actor’s intended move-ments during conditions of high variability in the unintended planewould suggest the recruitment of additional df. There were signif-icant effects of Congruence, F(1, 7) � 7.8, p � .05, �2 � .48, andFrequency, F(1, 7) � 6.1, p � .05, �2 � .25. Mean � was closerto in-phase for incongruent trials than for congruent trials. Simi-larly, mean � was closer to in-phase during slow compared withfast movements. All other effects were nonsignificant (p � .05).

Given the data suggesting that participants’ movements in theunintended plane were structured, it follows that there should begreater coherence with the actor’s intended movements duringincongruent trials. As expected, the effect of Congruence on co-herence was significant, F(1, 7) � 6.9, p � .05, �2 � .50. Themeans (see Figure 6) show that movements were weakly coordi-nated during congruent trials but strongly coordinated during in-congruent trials. A significant effect of Frequency, F(1, 7) � 13.7,p � .05, �2 � .67, also demonstrated that slower movements wereless coherent than faster movements. Plots of the average waveletcoherence for Congruence and Frequency are displayed in Figure7. All other effects were nonsignificant (p � .05).

Discussion

Anatomical and spatial congruence. In previous research oninterpersonal coordination, using similar methods, the two mem-

bers of a pair were always standing (Kilner et al., 2003; Richard-son et al., 2009; Stanley et al., 2007). In this configuration,anatomical and spatial definitions of congruence were identical.Because movements were identically congruent or incongruent,regardless of definition of congruence, it is impossible to discernthe effects of the performer’s postural state on the congruencyeffects. As discussed previously, some interpretations of an em-bodied simulation theory (Wilson, 2001; Wilson & Knoblich,2005) applied to social perception and coordination would predictthat information for simulations would be derived from the bio-mechanics underlying an observer’s postural state; the observer’sperception of another agent’s movements is derived from an ego-centric based motor representation. In this case, anatomical con-gruence would drive the increased variability effect. A Coordina-tion Dynamics explanation would predict that the observed spatialcongruence of trajectories between individuals would underlie theeffect. To distinguish between these two types of congruence, itwas necessary to rotate one member of the pair. In the rotatedposition, spatially congruent movements were anatomically incon-gruent and vice versa.

Figure 3. Mean movement SD as a function of Congruence for horizontaland vertical movements.

Figure 4. Normalized power spectral density in the unintended plane.Average power is shown across a range of frequencies (0.0�2.0 Hz) as afunction of Congruence and Actor Position. The 0.65 Hz target frequencyis displayed in Panel A, while the 1.00 Hz target frequency is displayed inPanel B.


In replication of work by Kilner et al. (2003) and Richardson etal. (2009), movements in the unintended plane were greatestduring incongruent coordination tasks. Because Congruence in thepresent experiment was arbitrarily defined spatially, this resultsuggests that coordination was influenced mainly by spatial con-gruence. If anatomical congruence dominated, then an effect ofPosition (or consistent interactions of Position and Congruence)would be expected. Specifically, the participant movement SDwould have exhibited a pronounced effect during congruent ro-tated trials. The lack of these effects suggests that movement in theunintended plane was not influenced by anatomical congruence. Inother words, the interference effect showed little variation withrespect to manipulating the actor’s postural state (anatomical con-gruency). Participants in the present experiment were influencedby the spatial congruence between their movements and the move-ments of the actor rather than on the basis of simulating postural-based motor representations. The main interference effect wasdriven by whether the movements between persons were per-formed in the same spatial plane. Moreover, these results suggestthat the interference effect might be better understood as a systemof coupled oscillators rather than competing simulations.

Recruitment of additional df. If the coordination betweenpersons operates like a coupled-oscillator system, then the unin-tended variability should exhibit structured oscillations. The ob-servation that the movements in the unintended plane were struc-tured during incongruent conditions suggests that thesemovements represent the recruitment of additional df, not error(see Richardson et al., 2009). This structure was seen both in themovement itself and in the coordination between those movementsand the intended movements of the observer. At the level of theindividual, incongruent conditions led to greater spectral peaks atthe target frequency with no effect of the actor’s position. At thelevel of the coordination between the participant and the actor,incongruent conditions led to stronger coordination between theunintended movements of the participant and the intended move-ments of the actor. Furthermore, faster trials were more coherent,suggesting that the strength of this coordination scaled withchanges in the task parameters. Orthogonal movements, during

spatially incongruent trials, represent organized changes in theoverall coordination pattern—the recruitment of additional df—tostabilize more difficult or less stable coordination patterns.

Embodied simulations. However, the results of Experiment 1don’t rule out the possibility that other biomechanically specificproperties (e.g., effectors) play a role in action observation andinterpersonal coordination. It has been argued that the mirrorneurons thought to play a role in motor interference (strictlycongruent; Gallese et al., 1996) are selectively sensitive to suchbiomechanical differences as, for example, perceiving the move-ment of an arm versus a leg (Aziz-Zadeh et al., 2006). Physiolog-ical and behavioral data discussed previously (Aziz-Zadeh et al.,2006; Bach & Tipper, 2007; Pulvermuller et al., 2001) support thishypothesis. If biomechanical information beyond the postural stateof the observer were involved in motor interference, the currentstudy would not have been able to show this. We only examinedthe coordination effects when the effector was the same. Experi-ment 2 was designed to test the possibility that constraints under-lying such unintended movements are constrained by effector-specific properties.

Experiment 2

In this experiment, we tested whether the unintended move-ments during spatially incongruent interpersonal coordination aresensitive to effector-specific properties. Considering the findingsof Aziz-Zadeh, Wilson, Rizzolatti, and Iacoboni (2006) and FadigaFogassi, Pavesi, and Rizzolatti (1995), it could be argued that thesensitivity of the premotor area to effector-specific actions impli-cates a role for biomechanical properties in action simulations. Ifmirror neuron activation and embodied simulations underpin themotor interference effect and discriminate between the effectorsinvolved in action observation, then the effect should not emergein the case of coordinating orthogonal movements with differenteffectors. In other words, the participant’s simulation of the actor’smovement should not interfere with the motor commands of theparticipant due to them involving different effectors (and motorcommands). Conversely, a Coordination Dynamics perspective

Figure 5. Mean � between the actor’s intended and participant’s unin-tended movements as a function of Congruence at each frequency.

Figure 6. Mean coherence between the actor’s intended and participant’sunintended movements as a function of Congruence and Frequency.


would predict that similar constraints are involved during thecoordination of different effectors (Baldissera, Cavallari, & Civ-aschi, 1982). Testing this possibility required evaluating coordi-nation between individuals using different effectors.

The methods in this experiment were similar to those of Exper-iment 1, except this time the pair used different effectors (i.e., armand leg). Spatial congruence was again manipulated by rotating theactor (see Figure 8.). However, in this experiment spatial andanatomical congruence were dissociated by rotating the actor inthe transverse plane (that is, turning 90° to the left; the actor inExperiment 1 was rotated in the coronal plane). If effector-specificmotor representations are activated as a result of action observa-

tion, then spatially incongruent coordination between differenteffectors should show a reduced or null congruency effect.

Method

Participants. A new set of seven dyads (nine men and fivewomen; mean age � 22 years, range � 18–23 years) was recruitedfrom Arizona State University. Participants were given credit forfulfillment of an introductory psychology class requirement. All ofthe participants were classified as right-hand dominant throughself-report.

Design. The design of this study was nearly identical to Ex-periment 1. Participants coordinated right arm or leg movementswith an actor’s movements, produced using the opposite limb. Themovements were made in one of two directions: front�back(sagittal plane) or left�right (coronal plane). To dissociate ana-tomical and spatial congruency, we again manipulated the orien-tation of the actor. In this experiment, the actor rotated in the trans-verse plane so that he was oriented to be either facing (anatomical andspatial congruency were identical) or 90° adjacent (spatial and ana-tomical congruency were dissociated) to the participant. A con-stant pace of 1.0 Hz was used throughout all trials. The designincluded 16 conditions resulting from a factorial combination of 2(Participant Effector) � 2 (Actor Position) � 2 (Congruence,defined spatially) � 2 (Participant Movement Direction). Therewere two trials per condition due to repetition, with a total of 32trials overall.

Materials and procedure. The same setup as Experiment 1was used to record arm and leg movement trajectories. Infrareddiodes were placed on the metatarsus region of the foot and distalsegment of the index finger. Participants were seated and in-structed to coordinate in-phase with the movements of the actor.

Figure 7. Averaged wavelet coherence between the actor’s intended and participant’s unintended movementsas a function of Congruence and Frequency. Regions falling within bold contour lines indicate significantmoments of coherence at a frequency scale (y-axis) over time (x-axis).

Figure 8. In Experiment 2, participants coordinated vertical and horizon-tal arm and leg movements with an actor. In order to manipulate anatomicaland spatial congruency separately, the actor was seated facing the partic-ipant for half of the trials and seated perpendicular for the other half of thetrials.


The same actor was trained to perform stable leg movements tominimize spatial and temporal variability. Each of the 32 trialslasted 45 s, while the first 5 s were removed from analysis due totransients. One experimenter collected data and another monitoredto ensure performance. Procedures used in this experiment con-form to the ethical guidelines of the American PsychologicalAssociation and were approved by the Institutional Review Boardat Arizona State University.

Data analysis. The analyses used were nearly identical toExperiment 1. The only differences were the elimination of Fre-quency and addition of Participant Effector (arm or leg) to theanalyses. Effector always referred to the limb being used by theparticipant; because the participant and actor used different effec-tors, whenever Effector was “arm,” the actor was using their leg,and vice versa. All variables were all analyzed using 2 (ParticipantEffector: arm, leg) � 2 (Actor Position: across, adjacent) � 2(Congruence: congruent, incongruent) � 2 (Participant MovementDirection: horizontal, vertical) repeated-measures ANOVAs.

Results

Coordination of intended movements. The analysis of mean� revealed significant main effects of Actor Position, F(1, 6) �24.72, p � .05, �2 � .82, and Congruence, F(1, 6) � 12.27, p �.05, �2 � .71. Mean � was greater when the actor and participantwere adjacent (5°) compared with when they were facing eachother (2°). Congruent conditions (4°) were closer to in-phase thanincongruent (8°). All other effects were nonsignificant (p � .05)and there were no data to suggest that participants were notproducing in-phase coordination.

Analysis of SD � revealed a significant main effect of Congru-ence, F(1, 6) � 8.49, p � .05, �2 � .63. Congruent conditionswere less variable than incongruent conditions. There was also asignificant two-way interaction of Congruence and ParticipantEffector, F(1, 6) � 10.94, p � .05, �2 � .69 (see Figure 9). Thesource of the interaction was examined using a simple effects testat each level of Congruence. The congruent and incongruentarrangements differed significantly when the participant used theirleg (and actor arm), F(1, 6) � 8.2, p � .05, �2 � .31. Considering

that the actor wore goggles, the participant was solely responsiblefor adjusting his or her behavior to achieve coordination. Accom-modating an incongruency would be more difficult with a largerlimb (the leg), whose trajectory is more difficult to alter. All othereffects were nonsignificant (p � .05).

The strength of coordination was measured by the coherencebetween the participants’ and actor’s movements in their intendedplanes. There was a main effect of Congruence, F(1, 6) � 3.13,p � .05,�2 � .28. Coordination was stronger in the congruenttrials (.95) than it was in the incongruent (.92). All other effectswere nonsignificant (p � .05). Importantly, the effect of Partici-pant Effector was also nonsignificant, suggesting that coordinationwas no stronger between identical effectors.

Movement in the unintended plane. Analysis of the SD(mm) of movement in the unintended plane identified a significanteffect of Congruence, F(1, 6) � 55.21, p � .05, �2 � .85, andParticipant Effector, F(1, 6) � 21.25, p � .05, �2 � .81 (seeFigure 10). Mean SD for congruent trials was less than that forincongruent trials. Mean SD when participants used their arm wasgreater than when they used their leg. Both of these effects indicatethat, although biomechanical differences can engage differing lev-els of variability, spatial incongruence dominated. No other effectsor interactions were significant (p � .05).

Examination of the average spectral power yielded a main effectof Congruence, F(1, 6) � 20.36, p � .05, �2 � .59. As predicted,the power of spectral peaks for incongruent trials was higher thancongruent trials. This indicates that unintended movements duringspatially incongruent trials were specific to the intended taskfrequency. There was also a significant main effect of ParticipantEffector, F(1, 6) � 11.85, p � .05, �2 � .52, indicating thatspectral power at the target frequency was higher when partici-pants were using their arm. However, there was also a signifi-cant two-way interaction of Congruence x Participant Effector,F(1, 6) � 10.64, p � .05, �2 � .63. As is shown in Figure 11,the effect of Participant Effector was only apparent duringcongruent trials, F(1, 6) � 4.09, p � .05, �2 � .49.

Mean � between the actor’s intended and participants’ unin-tended movements revealed main effects of Congruence, F(1, 6) �35.10, p � .05, �2 � .77, and Participant Direction, F(1, 6) �9.12, p � .05, �2 � .25. As in Experiment 1, mean � was closerto in-phase during incongruent trials compared with congruenttrials. The effect of Participant Movement Direction identified thatleft-right movements were closer to in-phase coordination thanfront�back movements. There was also a two-way interaction ofCongruence x Participant Movement Direction, F(1, 6) � 32.03,p � .05, �2 � .66. As can be seen in Figure 12, significantdifferences were found between horizontal and vertical move-ments only for congruent tasks, F(1, 6) � 19.01, p � .05, �2 �.34.

The analysis of average coherence identified a significant maineffect of Congruence, F(1, 6) � 40.01, p � .05, �2 � .78. As inExperiment 1, coherence was higher for incongruent than congru-ent trials. There was also a two-way interaction of Congruence andParticipant Effector, F(1, 6) � 13.06, p � .05, �2 � .65. As canbe seen in Figure 13, the effect of Participant Effector was onlysignificant in congruent trials, F(1, 6) � 12.21, p � .05, �2 � .33.Average wavelet coherence for both levels of Congruence andParticipant Effector are shown in Figure 14.Figure 9. SD � as a function of Congruence and Participant Effector.


Discussion

The primary results in this experiment were similar to those ofExperiment 1 and other research (Kilner et al., 2003; Richardson etal., 2009)—namely, congruence effects in interpersonal coordina-tion were based on a spatial form of congruence. In this experi-ment, congruency effects were also found during the coordinationof different effectors. Although premotor areas containing mirrorneurons respond differently for the perceived effector (Aziz-Zadehet al., 2006), this neural sensitivity might not affect coordination.If the motor commands associated with simulations were effector-sensitive, then the congruency effects should have been absent or

mitigated when the pair used different effectors. Even if motorpriming based on observing actions or sentences involving differ-ent effectors emerge as neural activity (Aziz-Zadeh et al., 2006) ordelays in behavioral responses (Bach & Tipper, 2007; Brass et al.,2001; Tausche, Springer, & Prinz, 2010), the results show that thiseffect doesn’t emerge at the behavioral level of coordination. Thissuggests that interference effects in interpersonal coordinationmight not be the product of embodied simulation interference.

What did emerge at the behavioral level, as it did in Experiment1, was a pattern of structured variability in the unintended planeduring incongruent coordination that could be associated with therecruitment of additional df. Again, these findings extend andsupport the findings of Richardson et al. (2009). Specifically,during periods of spatial incongruence, there were greater spectralpeaks at the target frequency and stronger coordination with theactor’s intended movements. Average wavelet coherence also

Figure 10. Mean participant movement SD as a function of Congruenceand Participant Effector.

Figure 11. Normalized power spectral density in the unintended plane.Average power is shown across a range of frequencies (0.0�2.0 Hz) as afunction of Congruence and Participant Effector. All trials used a targetfrequency of 1.0 Hz.

Figure 12. Mean � between the actor’s intended and participant’s unin-tended movements as a function of Participant Movement Direction andCongruence.

Figure 13. Mean coherence between the actor’s intended and partici-pant’s unintended movements as a function of Congruence and ParticipantEffector.


showed a strong signal in the narrow-band region surrounding thetask frequency (i.e., 1.0 Hz). This structured variability was notapparent during spatially congruent trials. Furthermore, these re-sults occurred despite the fact that participants were using differenteffectors. This finding is bolstered by the findings that individualssimilarly coordinate with environmentally derived stimuli(Schmidt et al., 2007; Stanley et al., 2007). As was concluded inExperiment 1, while performing a more difficult task, the partic-ipants’ overall movement patterns are modulated, through therecruitment of additional df, to maintain performance on the in-tended task.

General Discussion

Previous research has documented that participants produceincreased unintended movements during incongruent coordination(Kilner et al., 2003; Richardson et al., 2009). In Experiment 1, weshowed that this congruency effect was a function of spatialcongruency. The evidence did not suggest that this effect wasprimarily due to a mismatch between embodied simulations sup-ported by information regarding the postural state of actor andparticipant. Because both members of the pair in Experiment 1used their arms, it was still possible that a participant’s motorcommands for their action interfered with a simulation of theactor’s movement (see Aziz-Zadeh et al., 2006). Therefore, inExperiment 2, each member of the pair used a different effector.Consistent with Experiment 1, participants produced increasedunintended movements when coordination was spatially incongru-ent; manipulating the effectors did not alter this effect. In bothexperiments, the unintended movements of the participants werehighly structured and coordinated with the intended movements of

the actor under conditions of spatial incongruency, suggesting thatthe incongruency effect emerges from the recruitment of additionaldf, not error (Richardson et al., 2009).

Action-Compatibility: Neural and BehavioralConstraints on Coordination

The predominant role of spatial congruency in the presentexperiments should be considered alongside the fMRI data fromAziz-Zadeh et al. (2006) and discrete response studies (, e.g., seeGlenberg & Kaschak, 2002). Aziz-Zadeh et al. (2006) showedselective neural responses to hearing about and watching actionsinvolving different effectors; when listening to words or sentencesinvolving different effectors (e.g., “the ball was kicked”), partici-pants exhibited differential premotor cortex activation patternsbased mainly on differences in the involved effector. In addition,research has shown that response times are facilitated after watch-ing the instructed movement but inhibited after watching a differ-ent movement (Brass et al., 2001; Tausche, Springer, & Prinz,2010). These findings, however, don’t coincide with the currentfinding that changes in the actor’s postural state and differences ineffectors did not alter the increase in participants’ movement SD.In other words, the current findings suggest that the increasedmovement SD effect emerges from something other than a mis-match between the participant’s motor commands for their ownaction and an embodied simulation of the actor’s movement.

An alternative explanation is that simulations and compatibilitymay have different effects on interpersonal coordination at theneural and behavioral levels (see Richardson et al., 2009). Theeffect of hearing an action-based phrase (Aziz-Zadeh et al., 2006;Glenberg & Kaschak, 2002) might reveal neural correlates and

Figure 14. Averaged wavelet coherence between the actor’s intended and participant’s unintended movementsas a function of Congruence and Participant Effector. Regions falling within bold contour lines indicatesignificant moments of coherence at a frequency scale (y-axis) over time (x-axis).


initial motor inhibition. However, such neural correlates mighthave less of an effect on the governance of online coordination.Furthermore, motor simulations might only affect actions at theplanning stages of a response (Pratt, Adam, & Fischer, 2007); thiscould explain discrepancies between findings involving continu-ous motor coordination and discrete response paradigms. This isnot meant to propose that the central nervous system doesn’t playan active role in constraining coordination. In fact, EEG data hasshown that certain signals stemming from premotor areas (Tog-noli, Lagarde, De Guzman, & Kelso, 2007) thought to containmirror neurons change when the task changes from moving aloneto coordinating with another individual.

At the behavioral level, though, the systematic changes in co-ordination may be driven by the same dynamics that underlie othersystems of coupled oscillators. This possibility was supported hereand elsewhere (see Richardson et al., 2009) by demonstrating thatthe relative phase and coherence between the participants’ unin-tended and actor’s intended movements were coordinated in-phaseat the frequency specific to the task. Furthermore, this coordinationwas restricted to conditions when movements were spatially in-congruent and was found when the effectors differed. The latterresult is not surprising, given that similar dynamics govern lowerextremity (Schmidt et al., 1990), multieffector (Carson et al.,1995), and environmentally based coordination (Lopresti-Goodman et al., 2007; Schmidt et al., 2007). Simulation mecha-nisms may still play a role, but clearly work in service of thedynamical constraints governing a task such as motor coordination(Schmidt et al., 2011; Vesper, Butterfill, Knoblich, & Sebanz,2010).

Unintended Movements Reflect Intended Stability

Considering the possibility that the unintended movements as-sociated with congruency effects are governed by the CoordinationDynamics of coupled oscillators, a new explanation emerges as towhy these movements would occur. It has been proposed thatunintended variability in movement occurs for the purpose ofstabilizing a destabilized pattern (Fink et al., 2000; Richardson etal., 2009). The results of both experiments support this explana-tion. First, the participant’s unintended movements in the orthog-onal plane were only significantly coordinated (indexed by coher-ence) with the actor’s intended movements during spatiallyincongruent conditions. In Experiment 1, where frequency wasmanipulated, the strength of this unintended coordination in-creased with increasing frequency (see also, Fine & Amazeen,2011; Schmidt et al., 1990). As the intended task became increas-ingly difficult or spatially asymmetric—through changes in speedor spatial congruence—participants exhibited spatial changes topermit ongoing performance.

It is known that breaking the typical symmetry (e.g., in-phasepatterning) found in coordination through different timing, spatial,or amplitude requirements for each hand often leads to compen-sation and changes in both hands or effectors (Amazeen, Amazeen,& Turvey, 1998; Franz et al., 1991; Kelso & Jeka, 1992; Mulvey,Amazeen, & Riley, 2005; Schwartz et al., 1995). These findingsare analogous to coupled oscillator systems—individual oscillatorsexhibit a tendency to maintain their own pattern (“maintenancetendency”) and accommodate the movements of other oscillators(“magnet effect”; von Holst, 1939/1973). In the case of intraper-

sonal coordination, where anatomical and spatial coupling is avail-able (Amazeen et al., 2008; Park et al., 2001), manipulating thesymmetry of both muscular activation and external spatial trajec-tories of movements will affect stability (Baldissera et al., 1982;Carson et al., 1995; Park et al., 2001). For interpersonal coordi-nation, as in the current experiments, coupling is based primarilyon spatial information. This provides an explanation for whyspatial congruency dominated the increased movement SD effectin both experiments.

The suggestion that these unintended movements emerge due tochanges in task difficulty or symmetry (i.e., spatial congruence) isfurther supported by research on bimanual (Fink et al., 2000;Meesen, Wenderoth, Temprado, & Swinnen, 2008), multieffector(Baldissera, Cavallari, & Civaschi, 1982; Carson et al., 1995;Meesen, Wenderoth, & Swinnen, 2005) and interpersonal (Rich-ardson et al., 2009) coordination. One point of divergence is thatExperiment 1 did not exhibit prominent effects of frequency on SD�. This is likely due to the chosen range of frequencies (.65 and1.0 Hz) over which coordination was performed. Had individualsperformed similar tasks at a higher frequency, greater effects oncoordination variability might have appeared.

Conclusions

The dominance of spatial congruency in the interpersonal con-gruency effect could imply that stable coordination is drivenprimarily by perceptual factors. Research on intrapersonal biman-ual coordination has shown support for this view (Kelso & Jeka,1992; Mechsner, Kerzel, Knoblich, & Prinz, 2001). Other studies,however, have demonstrated either a predominant role for anatom-ical constraints (Li et al., 2004; Riek, Carson, & Byblow, 1992) orinteractive effects of the two (Amazeen et al., 2008; Mitra,Amazeen, & Turvey, 1997; Park et al., 2001). The question inthose studies, however, was whether movements are controlled atthe level of the effectors or the perceptual feedback from theeffectors. The focus of the present studies was whether congruencyeffects found during interpersonal coordination are best under-stood as a mismatch of an individual’s motor commands and anembodied simulation of an external agent’s movements, or thedynamics underlying systems of coupled oscillators. To answerthis question, the experiments examined whether the increasedorthogonal movements are best defined spatially or anatomically;the answer was predominantly spatial. The Coordination Dynam-ics of interpersonal coordination are likely spatially defined. Akinto the findings of Richardson et al. (2009), the increased move-ments represented the recruitment of motor df, not interference orerror (Kilner et al., 2003). If embodied simulations of another’smovements are involved, however, they are likely spatially definedas well.

References

Amazeen, E. L., Dasilva, F., & Amazeen, P. G. (2008). Visual-spatial andanatomical constraints interact in a bimanual coordination task withtransformed visual feedback. Experimental Brain Research, 191, 13–24.doi:10.1007/s00221-008-1490-x

Amazeen, P. G., Amazeen, E. L., & Turvey, M. T. (1998). Breaking thereflectional symmetry of interlimb coordination dynamics. Journal ofMotor Behavior, 30, 199–216. doi:10.1080/00222899809601337

Amazeen, P. G., Schmidt, R. C., & Turvey, M. T. (1995). Frequencydetuning of the phase entrainment dynamics of visually coupled rhyth-


mic movements. Biological Cybernetics, 72, 511–518. doi:10.1007/BF00199893

Asch, S. (1952). Social psychology. New York, NY: Prentice-Hall. doi:10.1037/10025-000

Aziz-Zadeh, L., Wilson, S. M., Rizzolatti, G., & Iacoboni, M. (2006).Congruent embodied representations for visually presented actions andlinguistic phrases describing actions. Current Biology, 16, 1818–1823.doi:10.1016/j.cub.2006.07.060

Bach, P., & Tipper, S. P. (2007). Implicit action encoding influencespersonal-trait judgments. Cognition, 102, 151–178. doi:10.1016/j.cognition.2005.11.003

Baldissera, F., Cavallari, P., & Civaschi, P. (1982). Preferential couplingbetween voluntary movements of ipsilateral limbs. Neuroscience Let-ters, 34, 95–100. doi:10.1016/0304-3940(82)90098-2

Boulenger, V., Hauk, O., & Pulvermuller, F. (2009). Grasping ideas withthe motor system: Semantic somatotopy in idiom comprehension. Ce-rebral Cortex, 19, 1905–1914. doi:10.1093/cercor/bhn217

Brass, M., Bekkering, H., & Prinz, W. (2001). Movement observationaffects movement execution in a simple response task. Acta Psycho-logica, 106, 3–22. doi:10.1016/S0001-6918(00)00024-X

Brass, M., Bekkering, H., Wohlschlager, A., & Prinz, W. (2000). Com-patibility between observed and executed finger movements: Comparingsymbolic, spatial, and imitative cues. Brain and Cognition, 44, 124–143.doi:10.1006/brcg.2000.1225

Buccino, G., Binkofski, F., Fink, G. R., Fadiga, L., Fogassi, L., Gallese, V.,Freund, H. J. (2001). Action observation activates premotor and parietalareas in a somatotopic manner: An fMRI study. European Journal ofNeuroscience, 13, 400–404.

Buchanan, J. J., & Kelso, J. A. (1999). To switch or not to switch:Recruitment of degrees of freedom stabilizes biological coordination.Journal of Motor Behavior, 31, 126 –144. doi:10.1080/00222899909600984

Calvo-Merino, B., Glaser, D. E., Grezes, J., Passingham, R. E., & Haggard,P. (2005). Action observation and acquired motor skills: An FMRI studywith expert dancers. Cerebral Cortex, 15, 1243–1249. doi:10.1093/cercor/bhi007

Carson, R. G., Goodman, D., Kelso, J. A., & Elliott, D. (1995). Phasetransitions and critical fluctuations in rhythmic coordination of ipsilat-eral hand and foot. Journal of Motor Behavior, 27, 211–224. doi:10.1080/00222895.1995.9941711

Chartrand, T. L., & Bargh, J. A. (1999). The chameleon effect: Theperception-behavior link and social interaction. Journal of Personalityand Social Psychology, 76, 893–910. doi:10.1037/0022-3514.76.6.893

Condon, W. S., & Ogston, W. D. (1967). A segmentation of behavior.Journal of Psychiatric Research, 5, 221–235. doi:10.1016/0022-3956(67)90004-0

Edwards, M. G., Humphreys, G. W., & Castiello, U. (2003). Motorfacilitation following action observation: A behavioural study in prehen-sile action. Brain and Cognition, 53, 495–502. doi:10.1016/S0278-2626(03)00210-0

Fadiga, L., Fogassi, L., Pavesi, G., & Rizzolatti, G. (1995). Motor facili-tation during action observation: A magnetic stimulation study. Journalof Neurophysiology, 73, 2608–2611.

Fine, J. M., & Amazeen, E. L. (2011). Interpersonal Fitts’s law: When twoperform as one. Experimental Brain Research, 211, 459–469. doi:10.1007/s00221-011-2707-y

Fink, P. W., Kelso, J. A., Jirsa, V. K., & De Guzman, G. (2000). Recruit-ment of degrees of freedom stabilizes coordination. Journal of Experi-mental Psychology: Human Perception and Performance, 26, 671–692.doi:10.1037/0096-1523.26.2.671

Fowler, B., Duck, T., Mosher, M., & Mathieson, B. (1991). The coordi-nation of bimanual aiming movements: Evidence for progressive desyn-chronization. The Quarterly Journal of Experimental Psychology, 43,205–221. doi:10.1080/14640749108400967

Franz, E. A., Zelaznik, H. N., & McCabe, G. (1991). Spatial topologicalconstraints in a bimanual task. Acta Psychologica, 77, 137–151. doi:10.1016/0001-6918(91)90028-X

Gallese, V. (2005). Embodied simulation: From neurons to phenomenalexperience. Phenomenology and the Cognitive Sciences, 4, 23–48. doi:10.1007/s11097-005-4737-z

Gallese, V., Fadiga, L., Fogassi, L., & Rizzolatti, G. (1996). Actionrecognition in the premotor cortex. Brain, 119, 593–609. doi:10.1093/brain/119.2.593

Gangitano, M., Mottaghy, F. M., & Pascual-Leone, A. (2001). Phase-specific modulation of cortical motor output during movement observa-tion. NeuroReport, 12, 1489–1492. doi:10.1097/00001756-200105250-00038

Gazzola, V., & Keysers, C. (2009). The observation and execution ofactions share motor and somatosensory voxels in all tested subjects:Single-subject analyses of unsmoothed fMRI data. Cerebral Cortex, 19,1239–1255. doi:10.1093/cercor/bhn181

Glenberg, A. M., & Kaschak, M. P. (2002). Grounding language in action.Psychonomic Bulletin & Review, 9, 558–565. doi:10.3758/BF03196313

Gradinarova, G. , & Janyan, A. (2011, May). Motor simulation and verbalassociation in idiom-idiom verification: Effects of imageability. Paperpresented at the Cognitive Science Society, Austin, TX.

Griffiths, D., & Tipper, S. P. (2009). Priming of reach trajectory whenobserving actions: Hand-centered effects. The Quarterly Journal ofExperimental Psychology, 62, 2450 –2470. doi:10.1080/17470210903103059

Grush, R. (2004). The emulation theory of representation: Motor Control,imagery, and perception. Behavioral and Brain Sciences, 27, 377–396.doi:10.1017/S0140525X04000093

Hamilton, A., Wolpert, D., & Frith, U. (2004). Your own action influenceshow you perceive another person’s action. Current Biology, 14, 493–498. doi:10.1016/j.cub.2004.03.007

Heuer, H., Spijkers, W., Kleinsorge, T., van der Loo, H., & Steglich, C.(1998). The time course of cross-talk during the simultaneous specifi-cation of bimanual movement amplitudes. Experimental Brain Re-search, 118, 381–392. doi:10.1007/s002210050292

Hickok, G. (2009). Eight problems for the mirror neuron theory of actionunderstanding in monkeys and humans. Journal of Cognitive Neurosci-ence, 21, 1229–1243. doi:10.1162/jocn.2009.21189

Iacoboni, M., Woods, R. P., Brass, M., Bekkering, H., Mazziotta, J. C., &Rizzolatti, G. (1999). Cortical mechanisms of human imitation. Science,286, 2526–2528. doi:10.1126/science.286.5449.2526

Issartel, J., Marin, L., & Cadopi, M. (2007). Unintended interpersonalco-ordination: “Can we march to the beat of our own drum?” Neurosci-ence Letters, 411, 174–179. doi:10.1016/j.neulet.2006.09.086

Jacobs, A., & Shiffrar, M. (2005). Walking perception by walking observ-ers. Journal of Experimental Psychology: Human Perception and Per-formance, 31, 157–169. doi:10.1037/0096-1523.31.1.157

Jeka, J. J., & Kelso, J. A. (1995). Manipulating symmetry in the coordi-nation dynamics of human movement. Journal of Experimental Psychol-ogy: Human Perception and Performance, 21, 360–374. doi:10.1037/0096-1523.21.2.360

Kelso, J. A. S. (1984). Phase transitions and critical behavior in humanbimanual coordination. American Journal of Physiology, 246, R1000–R1004.

Kelso, J. A. S. (1995). Dynamic patterns: The self-organization of brainand behavior (Complex adaptive systems). Cambridge, MA: MIT Press.

Kelso, J. A. S., Buchanan, J. J., de Guzman, G., & Ding, M. (1993).Spontaneous recruitment and annihilation of degrees of freedom inbiological coordination. Physics Letters A, 179, 364–371. doi:10.1016/0375-9601(93)90692-S

Kelso, J. A. S., Decolle, J. D., & Schoener, G. (1990). Action–perception


as a pattern formation process. In M. Jeannerod (Ed.), Attention andperformance XIII: Motor representation and control (pp. 139–169).Hillsdale, NJ: Erlbaum.

Kelso, J. A. S., & Jeka, J. J. (1992). Symmetry breaking dynamics ofhuman multilimb coordination. Journal of Experimental Psychology:Human Perception and Performance, 18, 645–668. doi:10.1037/0096-1523.18.3.645

Kelso, J. A. S., Southard, D. L., & Goodman, D. (1979). On the coordi-nation of two-handed movements. Journal of Experimental Psychology:Human Perception and Performance, 5, 229–238. doi:10.1037/0096-1523.5.2.229

Kilner, J. M., Hamilton, A. F., & Blakemore, S. J. (2007). Interferenceeffect of observed human movement on action is due to velocity profileof biological motion. Social Neuroscience, 2, 158–166. doi:10.1080/17470910701428190

Kilner, J. M., Paulignan, Y., & Blakemore, S. J. (2003). An interferenceeffect of observed biological movement on action. Current Biology, 13,522–525. doi:10.1016/S0960-9822(03)00165-9

Knoblich, G., Butterfill, S., & Sebanz, N. (Eds. ). (2011). Psychologicalresearch on joint action: Theory and data (Vol. 54). Burlington, VT:Academic Press.

Kohler, E., Keysers, C., Umilta, M. A., Fogassi, L., Gallese, V., &Rizzolatti, G. (2002). Hearing sounds, understanding actions: Actionrepresentation in mirror neurons. Science, 297, 846–848. doi:10.1126/science.1070311

Kugler, P. N., Kelso, J. A. S., & Turvey, M. T. (1980). On the concept ofcoordinative structures as dissipative structures: I. Theoretical lines ofconvergence. In G. E. Stelmach & J. Requin (Eds.), Tutorials in motorbehaviour (pp. 32–47). Amsterdam, The Netherlands: North Holland.

Kugler, P. N., & Turvey, M. T. (1987). Information, natural laws, andself-assembly of rhythmic movement. Hillsdale, NJ: Lawrence ErlbaumAssociates.

Li, Y., Levin, O., Carson, R. G., & Swinnen, S. P. (2004). Bimanualcoordination: Constraints imposed by the relative timing of homologousmuscle activation. Experimental Brain Research, 156, 27–38. doi:10.1007/s00221-003-1762-4

Lopresti-Goodman, S. M., Richardson, M. J., Silva, P. L., & Schmidt, R. C.(2008). Period basin of entrainment for unintentional visual coordina-tion. Journal of Motor Behavior, 40, 3–10. doi:10.3200/JMBR.40.1.3-10

Loula, F., Prasad, S., Harber, K., & Shiffrar, M. (2005). Recognizingpeople from their movement. Journal of Experimental Psychology:Human Perception and Performance, 31, 210–220. doi:10.1037/0096-1523.31.1.210

Maeda, F., Kleiner-Fisman, G., & Pascual-Leone, A. (2002). Motor facil-itation while observing hand actions: Specificity of the effect and role ofobserver’s orientation. Journal of Neurophysiology, 87, 1329–1335.

Marsh, K. L., Richardson, M. J., & Schmidt, R. C. (2009). Social connec-tion through joint action and interpersonal coordination. Topics in Cog-nitive Science, 1, 320–339. doi:10.1111/j.1756-8765.2009.01022.x

Marteniuk, R. G., MacKenzie, C. L., & Baba, D. M. (1984). Bimanualmovement control: Information processing and interaction effects. TheQuarterly Journal of Experimental Psychology, 36A,335–365. doi:10.1080/14640748408402163

McFarland, D. H. (2001). Respiratory markers of conversational interac-tion. Journal of Speech, Language, and Hearing Research, 44, 128–143.doi:10.1044/1092-4388(2001/012)

Mechsner, F., Kerzel, D., Knoblich, G., & Prinz, W. (2001). Perceptualbasis of bimanual coordination. Nature, 414, 69 –73. doi:10.1038/35102060

Meesen, R. L., Wenderoth, N., & Swinnen, S. P. (2005). The role ofdirectional compatibility in assembling coordination patterns involvingthe upper and lower limb girdles and the head. Behavioural BrainResearch, 165, 262–270. doi:10.1016/j.bbr.2005.07.029

Meesen, R. L., Wenderoth, N., Temprado, J. J., & Swinnen, S. P. (2008).Directional constraints during bimanual coordination: The interplay be-tween intrinsic and extrinsic directions as revealed by head motions.Behavioural Brain Research, 187, 361–370. doi:10.1016/j.bbr.2007.09.030

Mitra, S., Amazeen, P. G., & Turvey, M. T. (1997). Dynamics of rhythmicinterlimb coordination in the coronal plane. Motor Control, 1, 44–71.

Mulvey, G. M., Amazeen, P. G., & Riley, M. A. (2005). The use of(symmetry) group theory as a predictive tool for studying bimanualcoordination. Journal of Motor Behavior, 37, 295–309. doi:10.3200/JMBR.37.4.295-310

Newtson, D., Hairfield, J., Bloomingdale, J., & Cutino, S. (1987). Thestructure of action and interaction. Social Cognition, 5, 191–237. doi:10.1521/soco.1987.5.3.191

Park, H., Collins, D. R., & Turvey, M. T. (2001). Dissociation of muscularand spatial constraints on patterns of interlimb coordination. Journal ofExperimental Psychology: Human Perception and Performance, 27,32–47. doi:10.1037/0096-1523.27.1.32

Pratt, J., Adam, J. J., & Fischer, M. H. (2007). Visual layout modulatesFitts’s law: The importance of first and last positions. PsychonomicBulletin & Review, 14, 350–355. doi:10.3758/BF03194076

Prinz, W. (1997). Perception and action planning. European Journal ofCognitive Psychology, 9, 129–154. doi:10.1080/713752551

Prinz, W. (2002). Experimental approaches to imitation. In A. N. Meltzoff& W. Prinz (Eds.), The imitative mind: Development, evolution, andbrain bases (pp. 143–162). New York, NY: Cambridge University Press.doi:10.1017/CBO9780511489969.009

Pulvermuller, F., Harle, M., & Hummel, F. (2001). Walking or talking?Behavioral and neurophysiological correlates of action verb processing.Brain and Language, 78, 143–168. doi:10.1006/brln.2000.2390

Reed, C. L., & Farah, M. J. (1995). The psychological reality of the bodyschema: A test with normal participants. Journal of Experimental Psy-chology: Human Perception and Performance, 21, 334 –343. doi:10.1037/0096-1523.21.2.334

Richardson, M. J., Campbell, W. L., & Schmidt, R. C. (2009). Movementinterference during action observation as emergent coordination. Neu-roscience Letters, 449, 117–122. doi:10.1016/j.neulet.2008.10.092

Richardson, M. J., Marsh, K. L., Isenhower, R. W., Goodman, J. R., &Schmidt, R. C. (2007). Rocking together: Dynamics of intentional andunintentional interpersonal coordination. Human Movement Science, 26,867–891. doi:10.1016/j.humov.2007.07.002

Richardson, M. J., Marsh, K. L., & Schmidt, R. C. (2005). Effects of visualand verbal interaction on unintentional interpersonal coordination. Jour-nal of Experimental Psychology: Human Perception and Performance,31, 62–79. doi:10.1037/0096-1523.31.1.62

Richardson, M. J., Marsh, K. L., & Schmidt, R. C. (2010). Challenging theegocentric view of perceiving, acting, and knowing. In B. M. L. FeldmanBarrett & E. Smith (Ed.), The mind in context (pp. 307–333). New York,NY: Guilford Press.

Riek, S., Carson, R. G., & Byblow, W. D. (1992). Spatial and musculardependencies in bimanual coordination. Journal of Human MovementStudies, 23, 251–265.

Rizzolatti, G., & Craighero, L. (2004). The mirror-neuron system. AnnualReview of Neuroscience, 27, 169–192. doi:10.1146/annurev.neuro.27.070203.144230

Schmidt, R. C., Bienvenu, M., Fitzpatrick, P. A., & Amazeen, P. G. (1998).A comparison of intra- and interpersonal interlimb coordination: Coor-dination breakdowns and coupling strength. Journal of ExperimentalPsychology: Human Perception and Performance, 24, 884–900. doi:10.1037/0096-1523.24.3.884

Schmidt, R. C., Carello, C., & Turvey, M. T. (1990). Phase transitions andcritical fluctuations in the visual coordination of rhythmic movementsbetween people. Journal of Experimental Psychology: Human Percep-tion and Performance, 16, 227–247. doi:10.1037/0096-1523.16.2.227


Schmidt, R. C., Fitzpatrick, P., Caron, R., & Mergeche, J. (2011). Under-standing social motor coordination. Human Movement Science, 30,834–845. doi:10.1016/j.humov.2010.05.014

Schmidt, R. C., & O’Brien, B. (1997). Evaluating the dynamics of unin-tended interpersonal coordination. Ecological Psychology, 9, 189–206.doi:10.1207/s15326969eco0903_2

Schmidt, R. C., & Richardson, M. J. (2008). Dynamics of interpersonalcoordination. In A. Fuchs & V. Jirsa (Eds.), Coordination: Neural,behavioral and social dynamics (Vol. 17, pp. 281–308). Heidelberg,Germany: Springer.

Schmidt, R. C., Richardson, M. J., Arsenault, C., & Galantucci, B. (2007).Visual tracking and entrainment to an environmental rhythm. Journal ofExperimental Psychology: Human Perception and Performance, 33,860–870. doi:10.1037/0096-1523.33.4.860

Schmidt, R. C., & Turvey, M. T. (1994). Phase-entrainment dynamics ofvisually coupled rhythmic movements. Biological Cybernetics, 70, 369–376.

Schwartz, M., Amazeen, E. L., & Turvey, M. T. (1995). Superimpositionin interlimb rhythmic coordination. Human Movement Science, 14, 681–694. doi:10.1016/0167-9457(95)00033-X

Sebanz, N., Bekkering, H., & Knoblich, G. (2006). Joint action: Bodies andminds moving together. Trends in Cognitive Sciences, 10, 70–76. doi:10.1016/j.tics.2005.12.009

Sebanz, N., & Knoblich, G. (2009). Prediction in joint action: What, when,and where. Topics in Cognitive Science, 1, 353–367. doi:10.1111/j.1756-8765.2009.01024.x

Sebanz, N., Knoblich, G., & Prinz, W. (2003). Representing others’ ac-tions: Just like one’s own? Cognition, 88, B11–B21. doi:10.1016/S0010-0277(03)00043-X

Shockley, K., Richardson, D. C., & Dale, R. (2009). Conversation andcoordinative structures. Topics in Cognitive Science, 1, 305–319. doi:10.1111/j.1756-8765.2009.01021.x

Stanley, J., Gowen, E., & Miall, R. C. (2007). Effects of agency onmovement interference during observation of a moving dot stimulus.Journal of Experimental Psychology: Human Perception and Perfor-mance, 33, 915–926. doi:10.1037/0096-1523.33.4.915

Tausche, P., Springer, A., & Prinz, W. (2010). Effector-specific motorinterference in action simulation. In S. Ohlsson & R. Catrambone (Eds.),Proceedings of the 32nd annual conference of the cognitive sciencesociety (pp. 2698–2703). Austin, TX: Cognitive Science Society.

Tognoli, E., Lagarde, J., De Guzman, G. C., & Kelso, J. A. S. (2007). Thephi complex as a neuromarker of human social coordination. Proceed-ings of the National Academy of Sciences, 104, 8190–8195. doi:10.1073/pnas.0611453104

Torrence, C., & Compo, G. P. (1998). A practical guide to waveletanalysis. Bulletin of the American Meteorological Society, 79, 61–78.doi:10.1175/1520-0477(1998)079�0061:APGTWA�2.0.CO;2

Tuller, B., Kelso, J. A., & Harris, K. S. (1983). Converging evidence forthe role of relative timing in speech. Journal of Experimental Psychol-ogy: Human Perception and Performance, 9, 829–833. doi:10.1037/0096-1523.9.5.829

Varlet, M., Marin, L., Lagarde, J., & Bardy, B. G. (2011). Social posturalcoordination. Journal of Experimental Psychology: Human Perceptionand Performance, 37, 473–483. doi:10.1037/a0020552

Vesper, C., Butterfill, S., Knoblich, G., & Sebanz, N. (2010). A minimalarchitecture for joint action. Neural Networks, 23, 998–1003. doi:10.1016/j.neunet.2010.06.002

von Holst, E. (1939/1973). The behavioral physiology of man and animals.In R. Martin (Ed.), The collected papers of Erich von Holst (pp. 228–306). Coral Gables, FL: University of Miami.

Wilson, M. (2001). Perceiving imitatible stimuli: Consequences of isomor-phism between input and output. [Review]. Psychological Bulletin, 127,543–553. doi:10.1037/0033-2909.127.4.543

Wilson, M. (2004). Motoric emulation may contribute to perceiving imi-table stimuli. Behavioral and Brain Sciences, 27, 424. doi: 10.1017/S0140525X04510099

Wilson, M., & Knoblich, G. (2005). The case for motor involvement inperceiving conspecifics. Psychological Bulletin, 131, 460–473. doi:10.1037/0033-2909.131.3.460

Wolpert, D. M., Doya, K., & Kawato, M. (2003). A unifying computationalframework for motor control and social interaction. PhilosophicalTransactions of the Royal Society B: Biological Sciences, 358, 593–602.doi:10.1098/rstb.2002.1238

Received April 27, 2012Revision received January 15, 2013

Accepted January 22, 2013 �


congruency effects in interpersonal coordination

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