Exp Brain Res (2011) 211:547556DOI 10.1007/s00221-011-2649-4RESEARCH ARTICLE

Cooperation or competition? Discriminating between social intentions by observing prehensile movements

Valeria Manera Cristina Becchio Andrea Cavallo Luisa Sartori Umberto Castiello

Received: 10 November 2010 / Accepted: 20 March 2011 / Published online: 5 April 2011 Springer-Verlag 2011

Abstract Body movement provides a rich source ofinformation about other peoples goals and intentions. Inthe present study, we examined a particular aspect con-cerned with the interpretation of bodily movementhowwell people can distinguish between diVerent social inten-tions by observing a reach-to-grasp movement. To ascertainto what extent intention-from-motion judgements rely onvisual kinematics, we compared prediction performance ona typical temporal-occlusion video task with prediction per-formance on a temporal-occlusion point-light task. In thevideo task, participants observed videos showing a modelreaching towards and grasping a wooden block with diVer-ent intents: to cooperate with a partner in building a tower,compete with an opponent to be the Wrst to put the object inthe middle of the working surface, or perform an individualaction. In the point-light task, participants observed point-light displays of the same movements. Although predic-tions were more accurate for the video task, prediction per-formance was not disrupted for the point-light task,suggesting that essential kinematic information available inpoint-light displays was indeed suYcient for intention-from-motion judgement. Importantly, the same kinematiclandmarks were used to discriminate between social inten-

tions for the video and the point-light task. This suggeststhat observers not only have the ability to use kinematicinformation when no other information is available, butthey use kinematic information to discriminate betweenintentions when watching the motion of others under fulllight conditions.

Keywords Cooperation Competition Kinematics Intention Point-light Discrimination of intention Prehensile movement

Introduction

As social animals, humans behave largely on the basis oftheir interpretations and predictions about the actions andintentions of others. On encountering others, the ability todetermine what the other person is like and what she isgoing to do next is essential for successful social interaction(Frith and Frith 2006). Who goes there? What are the otherpersons intentions? Does she intend to help me or harmme?

An important source of information for answering thesequestions is bodily movement. When presented with bodymovements, people can not only judge the type of actionsperformed (Dittrich 1993; Vanrie and Verfaillie 2004), butalso the associated emotions (Atkinson et al. 2004; Clarkeet al. 2005; Dittrich et al. 1996; Pollick et al. 2001, 2002).Bodily movements can also provide information regardingthe future course of ongoing actions. For example, byobserving a person aiming a dart at a target board, observ-ers can predict the landing position of the dart on the board(Knoblich and Flach 2001). Depending on their level ofmotor skill, they might be able to anticipate the directionand depth of a badminton or a tennis stroke (Abernethy

V. Manera C. Becchio A. CavalloCentro di Scienza Cognitiva, Dipartimento di Psicologia, Universit di Torino, Turin, Italy

V. ManeraLaboratory of Experimental Psychology, K.U. Leuven, Leuven, Belgium

L. Sartori U. Castiello (&)Dipartimento di Psicologia Generale, Universit di Padova, 35131 Padova, Italye-mail: umberto.castiello@unipd.it123

548 Exp Brain Res (2011) 211:547556et al. 2001, 2008 Abernethy and Zawi 2007; Shim et al.2005) or to determine whether a basketball player is aboutto throw a ball or mimic a throw (Sebanz and ShiVrar2009). Similarly, people viewing everyday actions such aslifting a box might be able to judge from the actors motionwhether he is trying to deceive them concerning the realweight of the box (Grezes et al. 2004; Runeson andFrykholm 1983).

An important tenet stemming from this body of researchis that, in some circumstances, the movement of a humanbody or body part is suYcient to make judgements not onlyregarding fundamental movement patterns, but also in rela-tion to the actors intention. In the present study, we exam-ined a particular aspect of intention-from-motionjudgementhow well people can distinguish betweendiVerent social intentions by observing a reach-to-graspmovement.

The way we reach towards an object and grasp it variesdepending on how we want to use that object (Ansuini et al.2006; Becchio et al. 2008a). Grasping kinematics for thesame objecta bottle, for examplevaries depending onwhat the actor intends to do with the object: throw it, poursomething from it, or pass it to another person (Ansuiniet al. 2008). Similarly, the kinematics of reach-to-graspmovements depends on the actors social intention: tocooperate with a partner, compete against an opponent, orperform an individual action (Georgiou et al. 2007; see alsoBecchio et al. 2008b).

An important, but so far poorly investigated question iswhether observers are sensitive to the kinematic propertiesof prehensile movements, and can use these properties todiscriminate between movements performed with diVerentintentions. Experimentally, one approach to this issue is topresent temporally occluded video clips of arm move-ments (Abernethy and Zawi 2007; Sartori et al. 2011). Sar-tori and colleagues (2011) adopted this approach toinvestigate how well people can distinguish betweendiVerent social intentions by observing prehensile move-ments. Participants observed a model reaching towardsand grasping a wooden block with the intent to cooperatewith a partner, compete against an opponent, or performan individual action. The results revealed that observerscould readily judge the intention of the model by observ-ing the initial reach-to-grasp phase of the movement. Sim-ilar results were obtained for full-body video clips andpartially masked video clips, displaying only the arm andforearm of the model.

In the present study, we aimed to isolate the speciWc rolethat visual kinematics might play in discriminating betweenintentions. Video clips have the advantage of capturing thenormal visual input that is available when watching themotion of others. However, because movement informationis provided in conjunction with other sources of informa-

tion, one limitation of video clips is that they do not providea direct means for determining the speciWc contribution ofkinematics. To ascertain to what extent intention-from-movement judgements rely on visual kinematics, we com-pared prediction performance on a typical temporal-occlu-sion video task with prediction performance on a temporal-occlusion point-light task. In the video task, participantsobserved videos showing a model reaching towards andgrasping a wooden block with diVerent intents: to cooperatewith a partner in building a tower, compete with an oppo-nent to be the Wrst to put the object in the middle of theworking surface, or perform an individual action. In thepoint-light task, participants observed point-light displaysof the same movements consisting of disconnected movingdots representing the location of the wrist, the index Wnger,and the thumb of the models right hand. Point-light dis-plays are devoid of all the contours, textures, shapes, col-ours and Wgural cues that exist in Wlm displays, but preservethe essential kinematic information characterizing theobserved movement pattern. We reasoned that, if observersrely on kinematic information to discriminate betweenintentions in grasping an object, then: (i) observing prehen-sile movements under point-light conditions should not dis-rupt prediction performance; (ii) prediction performanceshould be related to the kinematic properties of theobserved movements.

Method

Participants

Twenty undergraduate and graduate students from theUniversity of Padova (12 women and 8 men; meanage = 24.1 years, age range = 1831 years) took part in theexperiment. All had normal or corrected to normal visionand were naive as to the purpose of the experiment. Thisresearch was approved by the local Ethical Committee inline with the Declaration of Helsinki.

Materials

Video recording and motion capture

To create the stimulus material, we Wlmed four types ofaction sequences:

Single-agent: natural speed. The model was asked toreach and grasp the stimulus positioned in front of his/her right hand, at natural speed, and move it to the mid-dle of the working surface.Single-agent: fast speed. The model was asked to reachand grasp the stimulus positioned in front of his/her123

Exp Brain Res (2011) 211:547556 549right hand, as fast as possible, and move it quickly tothe middle of the working surface.Cooperation. The model was seated opposite an inter-acting partner. The model and the partner were asked toreach towards and grasp their respective objects, andcooperate to build a tower in the middle of the workingsurface.Competition. This action sequence was similar to thecooperative sequence except that the model and theinteracting partner had to compete to be the Wrst to putWrst the respective object in the middle of the workingsurface.

We recorded the actions of eight right-handed models (4women and 4 men, aged 2025 years). To optimize theview of both the reaching and grasping components of theaction, the actions were Wlmed from a lateral perspectiveusing a digital video camera, and recorded using aSMART-D motion analysis system (Bioengineering Tech-nology and Systems, B|T|S|). ReXective passive markers(diameter: .25 cm) were attached to the wrist, index Wngerand thumb of the models right hand. The wrist marker wasused to measure the reaching component of the action. Themarkers positioned on the index Wnger and thumb wereused to measure the grasp component of the action. Sixinfrared cameras (sampling rate = 140 Hz) placed aroundthe table captured the movement of the markers in 3Dspace. Each model performed 10 trials for each type ofaction. This resulted in a total of 80 trials per type of action.Kinematic analysis was restricted to the reach-to-graspmovement phase, which was common to all actionsequences. The statistical analysis considered key reach-to-grasp kinematic landmarks, which are known to varydepending on movement speed and the type of social atti-tude (for details see Georgiou et al. 2007; Becchio et al.2008a, b; Sartori et al. 2009). In line with previous studies(e.g. Becchio et al. 2008b), we found statistically signiW-cant diVerences among the four types of actions for ninekinematic parameters concerned with both the reaching andgrasping components of the action (see Table 1). Out of320 trials, 30 trials were randomly selected for each type of

action (excluding trials with large amounts of missingdata). To uncover the structure of the possible diVerencesrelated to the kinematics underlying the selected reach-to-grasp actions, we submitted the nine kinematic parametersto a principal components analysis (PCA). The results indi-cated that the Wrst three components accounted for 79% ofthe variance (54, 15, and 10%, respectively). As they pro-vided a good characterisation of the data, they wereretained and subjected to oblique rotation (direct oblimin).

Weights of the kinematic parameters for the Wrst threecomponents are reported in Table 1. The three componentswere positively correlated with each other (Rs ranging from.21 to .32). The Wrst component had positive weights(.30) for movement time, the time of peak wrist decelera-tion and the amplitude of peak grip closing velocity, andnegative weights for the amplitude of peak wrist velocity,the amplitude of maximum grip aperture, and the amplitudeof peak grip opening velocity. This suggests that such com-ponent can be interpreted as a global descriptor of com-bined reaching and grasping kinematics. The peak gripopening velocity and the time of peak grip closing velocityweighted substantially on the second component, suggest-ing that this can be interpreted as a grip timing component.Finally, the third component showed only one large weightrelated to the amplitude of peak wrist deceleration.

Univariate ANOVAs (followed by Tukey HSD post-hoctests) were used to compare the diVerent types of actions(cooperative, competitive, natural speed and fastspeed) with respect to the three kinematic components.The eVect of the type of action was signiWcant for all threecomponents (see Table 2). For the global component, allpair-wise comparisons were signiWcant. For the grip timingcomponent, post-hoc comparisons showed signiWcantdiVerences between the natural-speed and the fast-speedmovements, and between the cooperative and the competi-tive movements. This indicates that this component has theability to discriminate between movements performed atdiVerent speeds. Finally, for the wrist deceleration com-ponent, pair-wise comparisons revealed signiWcant diVer-ences between the fast-speed movements and both the

Table 1 Weights of the kine-matic parameters for the Wrst three components

Component 1 Component 2 Component 3

Movement time (ms) .721 .286 .203Max wrist velocity (mm/s) .776 .113 .132Max wrist deceleration (mm/s2) .016 .100 .977Time of max wrist deceleration (ms) .707 .276 .212Max aperture (mm) .889 .161 .292Max opening velocity (mm/s) .810 .122 .155Max closing velocity (mm/s) .869 .109 .025Time of max opening velocity (ms) .187 .722 .097Time of max closing velocity (%) .071 .930 .137123

550 Exp Brain Res (2011) 211:547556competitive and the natural-speed movements. The factthat only the global kinematic component discriminatedbetween the cooperative and the natural-speed movementsmight suggest that the kinematic proWles for cooperativeversus natural-speed movements were more similar com-pared to those for both cooperative versus competitivemovements (discriminated by the global component and thegrip timing component) and competitive versus fast-speedmovements (discriminated by the global component and thewrist deceleration component).

Experimental stimuli

The videos and motion capture data corresponding to theselected trials (30 for each condition) provided the materialused to prepare the video and point-light displays.

Video stimuli. 120 unique video clips, 30 for each typeof action sequence (.avi format, disabled audio, 25 frames/s, resolution 720 576 pixel, duration three s, subtendedregion 22.62 33.40), were edited using a video editing

software (Adobe Premiere pro). Each video clip startedwith the models resting their right hand on a starting padand ended right after the model had placed the object in itsWnal position. To reduce movement onset predictability, thehand action started randomly 10, 18 or 25 frames after thevideo clip onset. Digital video editing was used so that onlythe region of the models body comprising the shoulders toarm and forearm was visually available (see Fig. 1a). Toensure that only advanced sources of information weremade available to participants in order to judge the modelsintention, the video clips were temporally occluded at thepoint of contact between the Wngers and the object, so thatthe hand disappeared behind a black screen after the reach-to-grasp movement. Neither the second part of the move-ment nor the interacting model was made visually avail-able.

Point-light stimuli. To create point-light stimuli, 3Dcoordinates of the three markers used for motion acquisi-tion (indicating the wrist, index Wnger, and the thumb of themodels right hand) were extracted for each trial. Data were

Table 2 Kinematic diVerences across types of actions (average component scores)

* P < .05; ** P < .001

Omnibus ANOVA Natural speed versus Fast speed

Cooperative versus competitive

Competitive versus fast speed

Cooperative versus natural-speed

Component 1 global F(3,101) = 118.764 1.203 versus .651 .564 versus 1.008 1.008 versus .651 .564 versus 1.203P < .001 P < .001** P < .001** P = .035* P < .001**

p2

= .77Component 2 grip timing F(3,101) = 5.629 .320 versus .380 .442 versus .345 .345 versus .380 .442 versus .320

P = .001 P = .040* P = .017* P = .999 P = .967p

2= .118

Component wrist deceleration F(3,101) = 48.052 .364 versus 1.210 .311 versus .718 .718 versus 1.210 .311 versus .364P < .001 P < .001** P = .117 P < .001** P = .992p

2= .576

Fig. 1 Examples of stimuli used in the experiment. a Single frames extracted from a video clip representing a cooperative action sequence. b Single frames extracted from a point-light clip representing the same coopera-tive action sequence123

Exp Brain Res (2011) 211:547556 551resampled to 25 frames/s, and coordinates were imported in3D Studio MAX (Autodesk 2008) as moving brightspheres. Some manual smoothing was performed to avoidany jumpy dot movements. To create the Wnal point-lightstimuli, all the frames of each action were rendered as.aviWles (resolution 720 576 pixel, duration three s, sub-tended region 22.62 33.40), with the white markersdisplayed against a black background. As in the video stim-uli, the actions were displayed from a lateral perspective.An orthographic projection was used, and there was noocclusion, thus no explicit depth cues were available. Eachvideo clip started with the model resting her hand on a start-ing pad and ended right after the model had placed theobject in its Wnal position. The action started randomly 10,18 or 25 frames after the video clip onset. To ensure thatthe same amount of movement was available in the videoand point-light stimuli, hand markers disappeared behind agrey screen at the end of the reach-to-grasp movement. Theobject grasped by the actor was not visible in the point-lightanimations (see Fig. 1b).

Procedure

Testing was carried out in a dimly lit room. Participants satin front of a 17-inch computer screen, at a viewing distanceof 60 cm. Stimuli presentation, timing and randomisationprocedures were controlled using E-Prime V2.0 software(Psychology Software Tools, Pittsburgh, PA).

Each trial started with the presentation of a Wxation point(1,800 ms), followed by the video (or point-light) clipdepicting the reach-to-grasp phase of the action sequence(3,000 ms). The task consisted of predicting the type ofaction by pressing a key with the right or left index Wnger.Response keys were randomised across participants. Theparticipants were instructed to respond correctly as quicklyas possible and within a maximum of 6,000 ms. Feedbackwas given in case of a missed response (i.e. a responsegiven after 6,000 ms). The inter-trial interval was 1,800 ms.Participants were tested in four experimental conditions forboth the video and point-light tasks:

1. Natural speed versus fast speed. In this condition, par-ticipants were asked to judge whether the observedreach-to-grasp movement prepared for an individualaction performed at either natural or fast speed.

2. Cooperative versus competitive. In this condition, par-ticipants were asked to judge whether the observedreach-to-grasp movement prepared for a cooperative ora competitive action.

3. Competitive versus fast speed. In this condition, partic-ipants were asked to judge whether the observed reach-to-grasp movement prepared for a competitive or indi-vidual action at fast speed.

4. Cooperative versus natural speed. In this condition,participants were asked to judge whether the observedreach-to-grasp movement prepared for a cooperative oran individual action at natural speed.

To avoid the possibility of intention-from-motion judge-ment under full light conditions inXuencing judgementunder point-light conditions, video stimuli and point-lightstimuli were shown in two separate sessions, with a 10-minpause in between. The order of presentation of the sessionswas counterbalanced across participants. Sixty trials werepresented for each of the four conditions (for both the videoand the point-light sessions), for a total of 480 trials. Theorder of presentation of the four conditions and the type oftrial within each condition were randomised across partici-pants. The experiment lasted about 50 min.

Data analysis

Missed responses accounted for less than .1% and weretherefore not analysed. Participants performance wasassessed by means of response times (RTs) and proportionof correct responses (accuracy). Response times were onlyanalysed for correct responses. Since in yesno tasks, theproportion of correct responses represents a biased measureof accuracy (i.e. it does not consider systematic errors inperformance), we also extracted Signal Detection Theoryparameters (Heeger 1997; Macmillan and Creelman 2005).The proportions of hits (i.e. the proportion of signalresponses on signal trials) and false alarms (i.e. the propor-tion of signal responses on no-signal trials) were used tocalculate the location of the criterion c (i.e. the general ten-dency to respond signal or no signal; e.g. a value of zeroindicates no bias) and the d, an unbiased sensitivity indexindependent of the criterion adopted by the participant (e.g.a value of zero indicates an inability to discriminatebetween signal and no signal, whereas larger values indi-cate a correspondingly greater ability to discriminatebetween signal and no signal). Hits and false alarm propor-tions of zero were replaced with .5/N, and proportions of 1were replaced with (N .5)/N (where N is the number ofsignal and no-signal trials; Macmillan and Kaplan 1985).By convention, during data analysis we labelled the follow-ing categories as signal: natural speed in the naturalspeed versus fast speed condition; cooperative in thecooperative versus competitive condition; competitivein the competitive versus fast speed condition; and coop-erative in the cooperative versus natural speed condition.

RTs, accuracy, d values and c values were submitted toseparate ANOVAs with intention (natural speed versus fastspeed; cooperative versus competitive; competitive versusfast speed; cooperative versus natural speed) and displaytype (video versus point-light) as within-subjects factors.123

552 Exp Brain Res (2011) 211:547556Bonniferroni corrections were applied (alpha level .05).The levels of c and d were calculated for each conditionand one-sample t-tests were performed to ascertain theparticipants ability to distinguish between signal and nosignal.

Results

The repeated-measure ANOVA on RTs yielded a statisti-cally signiWcant eVect of intention (F(3,57) = 94.21,P < .001, p2 = .83 see Fig. 2a). Post-hoc comparisonsrevealed that RTs for the cooperative versus natural-speedcondition were longer than those for the natural speed ver-sus fast speed, the cooperative versus competitive andthe competitive versus fast speed conditions (Ps < .001).Furthermore, RTs for the competitive versus fast speedcondition were longer than those for the natural speed ver-sus fast speed condition (P = .026). No eVect of type ofdisplay was found.

The ANOVA on the proportion of correct responsesyielded a statistically signiWcant eVect of intention (F(3,57) =177.27, P < .001, p2 = .90) and display type (F(1,19) =25.95, P < .001, p2 = .58; see Fig. 2b). Post-hoc compari-sons indicated lower accuracy values for the competitiveversus fast speed and the cooperative versus naturalspeed conditions compared to the natural speed versusfast speed and the cooperative versus competitive condi-tions (Ps < .001). Accuracy values were lower for thepoint-light displays than for the video displays (P < .001).

c parameter values ranged from .28 to .23 (M = .04,SD = .25) in the video condition and from .14 to .17(M = .03, SD = .25) in the point-light condition. TheANOVA on c revealed a main eVect of intention(F(3,57) = 26.03, P < .001, p2 = .58). Post-hoc comparisonsindicated that the c values were higher for the competitiveversus fast speed and the cooperative versus naturalspeed conditions compared to the natural speed versusfast speed and the cooperative versus competitive condi-tions (Ps < .001), thus suggesting that participants adopteda more conservative criterion (i.e. reluctance to report sig-nal) when asked to discriminate between intentions associ-ated with similar proWles of movements (see Table 1).Accordingly, mean c values for the cooperative versus nat-ural speed and the competitive versus fast speed condi-tions were signiWcantly above zero (Ps ranging from .02to < .001) for both the video and point-light displays (alsowhen corrected for multiple comparisons using the False

Fig. 2 Mean RTs (a), accuracy (b) and d values (c) across experi-mental conditions for the point-light (PL) and video displays. Barsrepresent standard errors

123

Exp Brain Res (2011) 211:547556 553Discovery Rate method). For the natural speed versus fastspeed and the cooperative versus competitive conditions,c values were signiWcantly lower than zero for the videodisplays (P < .01), thus suggesting a bias towards reportinga signal (natural speed and cooperative, respectively).For the point-light displays, c values did not diVer fromzero (P > .05).

The ANOVA on d yielded a signiWcant main eVect ofintention (F(3,57) = 213.37, P < .001, p2 = .93) and dis-play type (F(1,19) = 29.43, P < .001, p2 = .61; seeFig. 2c). Post-hoc comparisons indicated that the d waslower for the competitive versus fast speed and thecooperative versus natural speed conditions comparedto the natural speed versus fast speed and the coopera-tive versus competitive conditions (Ps < .001). Further-more, d values were lower for the point-light displaysthan for the video displays (P < .001; see Fig. 2b). Addi-tional t-tests revealed that, for the video displays, d val-ues were lower for the competitive versus fast speedand the cooperative versus natural-speed conditionscompared to the natural speed versus fast speed and thecooperative versus competitive conditions (Ps < .001).Moreover, d values were lower for the cooperative ver-sus natural speed than for the competitive versus fastspeed condition (P = .026). This result was mainly dueto the rate of false alarms, which was higher for thecooperative versus natural speed than for the competi-tive versus fast speed condition (P = .03). For the point-light displays, d values were lower for the competitiveversus fast speed and the cooperative versus natural-speed conditions compared to the natural speed versusfast speed and the cooperative versus competitive con-ditions (Ps < .001). No signiWcant diVerence was foundbetween the cooperative versus natural-speed andcompetitive versus fast speed conditions (P = 1.000).Mean d values were signiWcantly greater than zero (Psranging from .01 to < .001) for all the experimental con-ditions (also when corrected for multiple comparisonsusing the False Discovery Rate method), except for thecooperative versus natural speed point-light condition(t(19) = 1.86, P = .079).

Overall, these Wndings demonstrate that for both thevideo and point-light tasks, discrimination performancewas most accurate for the natural speed versus fastspeed and cooperative versus competitive conditions,less accurate for the competitive versus fast speedcondition, and least accurate for the cooperative versusnatural speed condition. For the point-light task, in par-ticular, discrimination between cooperative and naturalspeed actions was at chance level. In line with resultsfrom the PCA (see Methods section), this suggests thatthe similarity of the kinematic proWles made it more diY-cult to discriminate between cooperative and individual

natural-speed movements than between the other types ofmovements.

The relationship between accuracy of discrimination and kinematic properties for video and point-light displays

To investigate whether the same kinematic information pre-dicted performance in the video and the point-light dis-plays, we examined the relationship between accuracy ofdiscrimination and the kinematic properties of the observedreach-to-grasp movements for the two types of displays. Todo this, we Wrst calculated the mean proportion of correctresponses (accuracy) for each type of observed action (nat-ural speed, fast speed, cooperative, competitive), in eachtrial. This was done separately for the video task and thepoint-light task. Then, we Wtted four repeated measuresGLMs, predicting accuracy scores (one for each type ofobserved action), with display-type (video vs. point-light)as a repeated measures factor and kinematic components(global component, grip timing component and wrist decel-eration component) as covariates. If participants used thesame kinematic information to discriminate between inten-tions in the two types of displays, we would expect nointeraction between display type and the three kinematiccomponents.

In line with this prediction, our results revealed no sig-niWcant interaction eVect for the global component and thegrip timing component (Ps ranging from .130 to .858). AsigniWcant interaction eVect was only found for the gripdeceleration component in the competitive actions(F(1,21) = 7.87, P = .011). To explore this result further, weexamined the correlations between accuracy scores andwrist deceleration for the video and the point-light displaysseparately. A signiWcant correlation between wrist deceler-ation and accuracy was found for the video display(r(26) = .418, P = .037), but not for the point-light display(r(26) = .003, P = .990). This may suggest that for competi-tive actions, participants were able to exploit the informa-tion conveyed by the wrist deceleration component in thevideo task, but not in the point-light task.

Taken together, these results indicate that in discriminat-ing between intentions from motion participants relied onalmost the same kinematic information for the video andthe point-light tasks.

Discussion

Observers are attuned to advance movement informationand can use this information to predict others actions.Here, we demonstrate how kinematic information from pre-hensile movements is suYcient to discriminate betweenmovements performed with diVerent social intentions.123

554 Exp Brain Res (2011) 211:547556Kinematic cues in discrimination between intentions

Social context shapes action planning in such a way that,although the object to be grasped remains the same, diVer-ent kinematical patterns for individual actions and actionspreparing for a subsequent social interaction are observed(Becchio et al. 2008a; b; Sartori et al. 2009; for a reviewsee Becchio et al. 2010). Our results provide direct evi-dence that these diVerences, occurring at the early stage ofaction performance, might constitute signiWcant cues fordiscriminating between intentions. By simply observinganother person reaching towards an object and grasping it,participants were able to anticipate whether the object wasgrasped with the intent to cooperate, compete, or performan individual action. Although predictions were more accu-rate for the video task, prediction performance was not dis-rupted for the point-light task, suggesting that essentialkinematic information available in point-light displays wasindeed suYcient for intention-from-motion judgement.These Wndings indicate that observers are able to infersocial intention from visual kinematics of prehensile move-ments. Most importantly, they demonstrate that participantsrelied on the same landmarks to discriminate betweensocial intentions under either full light or point-light condi-tions. This suggests that observers not only have the abilityto use kinematic information when no other information isavailable, but actually use kinematic information to dis-criminate between intentions when watching the motion ofothers under full light conditions.

The role of kinematic information in intention-from-motion judgement has been shown in a number of previouspoint-light studies conducted in sports settings (Abernethyet al. 2001; Abernethy and Zawi 2007; Abernethy et al.2008; Shim et al. 2005). Critically, the present study is theWrst to demonstrate that social intentions can be inferredfrom isolated prehensile movements under point-lightconditions. This Wnding concurs with previous results bySartori and colleagues (2011) who also suggest that otherpeoples social intentions can be inferred from prehensilemovements under full light conditions.

Cooperative intentions in video and point displays

For both types of displays, discrimination was most accu-rate for the natural speed versus fast speed condition andfor the cooperative versus competitive condition; lessaccurate for the competitive versus fast speed conditionand for the cooperative versus natural speed condition.Whereas these Wndings suggest that the pattern of advanceinformation pick-up across conditions is similar for bothtypes of displays, we observed an interesting discrepancybetween the video and point-light tasks for discriminationbetween cooperative and natural-speed movements. For the

video task, prediction of accuracy was lower for the coop-erative versus natural speed than for the competitive ver-sus fast speed condition. In contrast, for the point-lighttask, no signiWcant diVerence was observed between thecooperative versus natural speed and competitive versusfast speed condition. This result was mainly due to thehigh rate of false alarms, which was higher for the videotask in the cooperative versus natural speed conditioncompared to the competitive versus fast speed condition(P < .05). A similar pattern of results was reported by Sar-tori and colleagues (2011) for judgements of intention invideo displays under diVerent temporal and spatial occlu-sion conditions. These authors interpreted such Wnding asevidence of a cooperative bias: People are naturallyinclined to cooperate with others and, apparently, tend tosee cooperation even when no cooperation exists.

In the present study, over-attribution of cooperativeintention was evident in the video task, but not in the point-light task. This might signify that perceiving actions per-formed with a non-cooperative intent as cooperative ismodulated by the type of display. Evidence that the appear-ance of the character used to render the motion inXuencesaction perception has been provided by adding anthropo-morphic features to point-light characters (Chaminade et al.2007). Whereas sensitivity was not aVected by therendering style, response bias towards perceiving a motionas biological decreased as a function of characters anthro-pomorphism. Future studies using variations of animatedcharacters appearance may help to clarify whether anthro-pomorphism and stimulus complexity aVect the tendency toperceive individual actions as cooperative.

A further issue is whether changing the instructionsmight aVect accuracy. In the present study, participantswere requested to respond as quickly and accurately as pos-sible. This might have determined possible diVerences inspeed-accuracy trade-oV across conditions and participants.An interesting question to be addressed in future research iswhether performance in point-light tasks would be improvedin the absence of time constraints.

Intentionmovement judgements: the role of motor simulation

The central advance of this study is the demonstration thatobservers are attuned to kinematic information from prehen-sile movements and use this information to distinguish reach-to-grasp movements performed with diVerent social intents.Because, prehensile movements have diVerent motion signa-tures depending on the actors social intention, monitoringthe kinematic properties of prehensile movements seemssuYcient for observers to decide whether the object isgrasped with the intent to cooperate with a partner, competeagainst an opponent or perform an individual action.123

Exp Brain Res (2011) 211:547556 555What kind of mechanisms do observers rely upon? It hasbeen proposed that an important function of the motor sys-tem lies in the prediction of others actions (Blakemore andFrith 2005; Prinz 2006; Wilson and Knoblich 2005).Observing others actions activates corresponding repre-sentations in the observers motor system. And these repre-sentations might be used to generate predictions by runninginternal simulations. In this perspective, perceptual andmotor systems share representations for actions (common-coding hypothesis; Prinz 1997) and the same predictivemechanism used to anticipate the sensory consequences ofones own movement may be employed to predict whatothers will do next (Wolpert and Flanagan 2001).

Several studies point to a predictive function of themotor system in action observation. First, observing motoracts that are within the observers motor repertoire activatessome of the same motor regions activated during the execu-tion of the same actions (e.g. Calvo-Merino et al. 2005; fora review see Rizzolatti and Sinigaglia 2010). The fact thatthis motor activation also occurs prior to a predicted move-ment suggests that the observers motor system anticipates,rather than merely reacts to others actions (Kilner et al.2004; see also Umilt et al. 2001). Second, observers whoare expert in producing a given action are also more accu-rate in predicting the outcome of the same action performedby another person (Abernethy et al. 2001; Abernethy andZawi 2007; Abernethy et al. 2008; Sebanz and ShiVrar2009). For example, expert badminton players are better atanticipating the direction and depth of a stroke compared tonon-experts (Abernethy and Zawi 2007; Abernethy et al.2008). Similarly, basketball athletes are able to predict shotoutcome (i.e. in or out) more accurately and earlier thannovices and visual experts, i.e. coaches and sport journal-ists, who are presumed to have no motor but comparablevisual experience (Aglioti et al. 2008; see also, Wllner andCaalBruland 2010). Moreover, in accordance with a cor-ollary hypothesis derived from the common coding theory,a high degree of overlap between perceptual and action rep-resentations facilitates action perception (Schtz-Bosbachand Prinz 2007). Observers demonstrate the greatest visualsensitivity to their own actions, with which they have thegreatest motor experience (Knoblich and Flach 2001). Thissuggests that participants use their own motor experience toperceive and anticipate others actions.

One possibility is thus that in the present study, partici-pants relied on simulation processes occurring within theirown motor system to anticipate the actors social intentionin grasping the object. Vingerhoets and colleagues (2010)recently demonstrated, that discriminating between anactors intention to move or use an object based on thevisual properties of the movement involves multifocalintraparietal activations, including the anterior, middle andcaudal segments of the intraparietal sulcus. Because these

parietal regions are strongly associated with motor behav-iour, this Wnding supports the motor simulation hypothesisfor intention-from-movement judgements. Future neuroim-aging studies employing paradigms such as those used heremight be needed to test whether the same mechanism ofmotor simulation extends to discrimination between socialintentions.

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Cooperation or competition? Discriminating between social intentions by observing prehensile movementsAbstractIntroductionMethodParticipantsMaterialsVideo recording and motion captureExperimental stimuli

ProcedureData analysis

ResultsThe relationship between accuracy of discrimination and kinematic properties for video and point-light displays

DiscussionKinematic cues in discrimination between intentionsCooperative intentions in video and point displaysIntention-movement judgements: the role of motor simulation

References

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