Perception of emotional gaits using avatar animation of real and artificially synthesized gaits

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1 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction Perception of emotional gaits using avatar animation of real and artificially synthesized gaits Halim Hicheur, Hideki Kadone, Julie Grèzes and Alain Berthoz Department of Medicine, University of Fribourg, 90 boulevard de Pérolles, Fribourg, Switzerland. Center for Cybernics Research, University of Tsukuba, Tsukuba, Japan. Laboratory of Cognitive Neuroscience, INSERM U960, Ecole Normale Superieure, Paris, France. Laboratoire de Physiologie de la Perception et de l Action, Collège de France, Paris, France. Abstract The emotional component of human walking patterns can be characterized by a limited set of kinematic cues [8]. Here, we tested whether artificial synthesis of emotional gaits based on these cues can facilitate emotion perception in human observers. To this purpose, we recorded neutral gaits and artificially modified the walking speed, the upperbody posture or some combination thereof. Nave observers had to judge emotion conveyed by these animated avatars or by real emotional gaits recorded in professional actors. We found that the recognition rates of both animated and real movies were comparable and particularly high (e.g. > 85 %): all emotions but fear were unambiguously perceived in walking avatars thanks to a particular combination of walking speed and head/trunk posture. This reveals a strong coupling between motor and perceptual processes underlying emotion expression and recognition. The potential applications of these findings in the fields of animated motion picture and humanoid robotics are discussed. Keywords-emotion, locomotion, posture, avatar animation, gait analysis I. INTRODUCTION Emotional body language (EBL) provides reliable cues to recognize emotions even when viewed from distance and when facial expression is not visible. As revealed by few studies in the literature, EBL can readily be recognized in static postures [13], [4], whole body movements [15], [1] or even in simple dynamic point-light displays [5]. At the methodological level, previous investigations on the neural basis of EBL perception stressed the relevance of using stimuli consisting of whole-body emotional expressions as well as those including biological movements [7], [4], [6], [11] in addition to using static facial expressions of emotions. While these studies extended the question of the expression of emotions to the whole body posture, kinematic properties of the emotional whole body movement received little attention. In the case of arm movements, Pollick and colleagues [12] showed that humans perceive particularly well different emotions conveyed by arm movements using kinematic cues like the peaks of wrist velocities and accelerations. Atkinson and colleagues [1],[2] extended these findings to the case of body gestures. In their study, nave observers had to judge the emotions conveyed in grey-scale movies of people expressing different emotional actions (including walking). Inverting movies orientations or playing them backwards impaired emotion recognition significantly more for patch-light displays (where the body was not visible as a whole) compared to the identical but fully illuminated displays, but the recognition performance was still above chance level for patch-light displays, providing evidence for distinct contributions of form-related and motion-related cues to emotions recognition from whole body movements. Troje [14] proposed a computational approach for analyzing and synthesizing human gait patterns, successfully applied in the case of gender recognition during the production of locomotor behavior. A comparable approach, based on ICAbased algorithms [10] was recently applied to the recognition of emotional gaits. Here, we aimed at investigating whether a minimum set of behaviorally-relevant variables can be used to generate realistic emotional gaits. For instance, how emotions affect the production of walking behaviors was recently described [8] as a specific combination of walking speed and upperbody changes. We tested here the hypothesis that these combined motion-related and posture-related changes also determine the processes underlying emotion perception. We believe that such bio-inspired approach could be beneficial to the implementation of emotional gaits of bipedal robots as well as for the animation of artificial walkers in arts entertainment and multimedia. II. METHODS: SYNTHESIS AND PERCEPTION OF REAL AND ANIMATED MOTION A. Kinematic features of emotional gaits Actors walked in five emotional states (neutral NE, joy JO, anger AN, sadness SA and fear FE). Their wholebody movements were recorded using a VICON motion capture system equipped with 24 cameras. We used a total of 41 markers placed on the body using the Vicon Plug in Gait model (VICON; Oxford Metrics Limited, Oxford, UK). Importantly, the actors were instructed to feel the emotion before starting walking. They were then free to interpret and express the emotions with a minimal guidance from their (professional) instructor. In the case of fear, a scenario of /13 $ IEEE DOI /ACII

2 walking forward in a dark and dangerous room was specified to some actors in order to have them walking (and not running, as did some actors who spontaneously expressed fearful escape behaviours). The comparison between emotional and neutral gait patterns (performed at different speeds by nave observers - normal/natural, slow and fast speeds) allowed discriminating between speed-related and emotion-specific changes. The kinematic analysis was performed at both global (i.e. walking speed, step length, duration and width as well as stance/swing phases durations) and local (angular motion of the head, trunk, arms and legs across step cycles) levels. The discriminative power of each of these motion components revealed that almost all emotional gaits were induced by a particular combination of speed and head-trunk orientation in space [8]. In what follows, we describe how we integrated these findings in order to simulate emotions in virtual characters (avatars) walking at different speeds. B. Animation of avatars from real motion We followed a procedure of combining motion capture and character animation techniques to create avatar animation. Two models of avatars were created from the motion capture of two actors (one male and one female). We first created the skeleton of each actor using the markers coordinates (Autodesk Motion BuilderTM 7.5). We then selected a particular character (Plastic Man, Motion BuilderTM software, see figure 1) and created a dynamic calibration of this character by mapping the body segments of the character onto the (motion captured) skeleton of the actor: this was done after a recording of specific movements which aimed at acquiring information at every joint level. The dynamic calibration information was then used to establish a correspondence between joints of the experimentally recorded skeleton and those of the animated character. Once this manual phase of animation was completed for both actors, we obtained avatar models of the actors which we then used to animate all recorded gaits. This phase resulted in animations of 50 emotional gaits. NEUTRAL ANGER REAL MOVIES ANIMATED MOVIES Figure 1. Snapshots of real and animated movies illustrating here the neutral and angry gaits. Animated movies were generated either using real motion-capture data recorded in two actors or through artificial modulations of gait patterns recorded in the neutral condition (see text for details). C. Animation of avatars from artificially synthesized motion The animated gaits recorded in the neutral condition were used to generate artificial gaits by manipulating 2 parameters, the walking speed and the upper body orientation (either the head orientation alone or the head and trunk orientations). In order to test whether a gait could convey emotional information, three types of manipulations were applied at the level of the walking speed only, the upper body orientation only or some combination thereof. This led to a large range of configurations, as detailed below. 1) Speed changes: The walking speed was either decreased (by 60%, 50%, 40%, 30% or 20%, corresponding to the S40, S50, S60, S70 and S80 conditions, respectively) or increased (by 20%, 30%, 40%, 50% or 60%, corresponding to the S120, S130, S140, S150 or S160 conditions, respectively), compared to the neutral condition. The choice of these speed values was based on the observations of emotional gaits reported in [8]. Speed changes were performed by scaling the markers coordinate in the direction of heading as well as extending the time length of the neutral movies (this last possibility generated unrealistic steps). To change speed to S times, marker coordinate scaling generated S 1/2 times speed change, and then time length change adjusted to S times speed change. The scaling procedure provided changes coherent with step length changes reported in [8]. It was achieved as follows. For every marker M i, we applied a single scaling parameter (which was adjusted as a function of the desired speed) to the coordinate (x), which corresponded to the direction of locomotion, of all markers using the following equation: X i (t) =α(x i (t) x i (0)) + x i (0) (1) where X i is the new coordinate of the marker M i at the instant t, after scaling the original coordinate x i (t) with the coefficient α. This was applied for real and virtual gaits. The speed was modified by changing the play speed of the movie. 2) Orientation changes: Changes in upper body orientation were performed at the head (H), the trunk (T) or at both head and trunk (HT) levels. For each segment, 4 types of increments /decrements were performed, resulting in 12 changes. A rotation of the segment (H, T or HT) was performed either in the downwards (D) direction (by -8 or -16 degrees, leading to the HD08, HD16, TD08, TD16, HTD08 and HTD16 head HD, trunk TD and head-trunk HTD postures, respectively) or in the upwards (U) direction (by 8 or 16 degrees, leading to the HU08, HU16, TU08, TU16, HTU08 and HTU16 postures). These rotations were directly applied on head and trunk joint angles computed from the modeled skeleton of each actor. The choice of this range of rotation (from -16 to 16 degrees) was based on the range of changes reported in the emotional gaits described in [8]. 461

3 3) Speed and orientation changes: Finally, we also combined speed and orientation changes similar to that mentioned above. Note here that the speed was reduced or increased by 30% or 60%. This yielded a total of 48 different speed-orientation (3 segments x 4 speeds x 4 angles) combinations for each actor. The whole 50 real emotional gaits were converted in a movie format with a black background (figure 1). The artificially generated gaits represented a total of 140 movies also converted in this format. A total of 95 movies per actor (25 recorded gaits, 10 accelerated or decelerated gaits, 12 gaits with orientation changes and 48 gaits with both speed and orientation changes) was generated (a total of 95 x 2 actors = 190 movies, was thus generated, including 140 artificially generated movies). The visual height of avatars on the screen was 8.6 centimetres for the actress and 9.2 centimetres for the actor. Real and animated movies were mixed and presented in a randomized manner. D. Control condition Since the artificial 3D environment and the absence of facial expression could have biased the perception of animated gaits, we recorded real movies using a numeric camera (HDR-SR7E Sony Handycam Camera - 50 Hz sampling frequency) in 3 actors (2 males and 1 female) who already participated to the initial study [8]. They wore black clothes and were filmed from profile with a white background (figure 1). The distance between the observer and the camera was equal to 7.38 meters and the actors walked for 5 meters (5 repetitions were performed for each condition). Movies resolution was reduced so that facial expression could not be perceived. The visual height of the actor on the screen was equal to 7.53 ± 1.15 centimetres. These settings were chosen to fit the visual height of our animated avatars. A total of 75 movies was generated following this procedure and used as control stimuli. E. Emotion discrimination task Naïve observers had to judge the emotions conveyed in real or animated movies. The video-clips were presented using the Presentation software v12.0. For each movie, observers were first asked to judge the emotion conveyed by the gait as quick as possible and then to rate its intensity. For the first task, observers had to choose among six buttons displayed on the computer screen (neutral, fear, anger, joy, sad, unknown) using the keyboard. The unknown button was used to avoid having observers spend too much time on a movie for which a particular emotion was hardly identifiable. To avoid any learning effect, stimuli were presented in a random order. After this first choice, observers judged the intensity of the perceived emotion (if any) by rating it on a scale which extremities were labeled Low and High. They had to slide a mouse cursor along this scale and collected scores ranged from 0 to 500. For all stimuli (20 observers x 75-3 actors x 5 emotions x 5 repetitions - tested trials), we computed the recognition rate, the response time for both correct and incorrect responses and the perceived intensity. Since the perceived intensity is a subjective measurement, we normalized intensity scores for each observer into percentage representation, using the maximal and minimal intensity scores delivered by a particular observer. The absolute response time was expressed in seconds and the normalized response time was expressed in percentage of the movies duration (for purpose of comparison between the tested emotions which were characterized by different speeds). We also calculated the duration of the intensity response. Two types of movies (real and animated) were used as stimuli in two separate groups, as detailed below. 1) Control condition (real movies): Twenty (20) naïve observers (12 males and 8 females, 29.9 ± 5.9 years old) participated in this experimental session. 2) Simulated emotional gaits (real-motion or artificiallygenerated animated movies): Thirty-two (32) nave observers (18 males and 14 females, 26.8 ± 6.5 years old, different from those tested in 1) participated in this session. They were randomly assigned to two groups (N=16) for which movies were generated from actor or actress movements. This was done to avoid any confusion as avatars had the same appearance. Indeed, given that tested actors had different natural walking speeds, shifting from one avatar to another during movie presentation might have considerably biased observers perception. Note that animated movies based on real motion-capture or artificially-modified neutral gaits were mixed and presented in a randomized manner. F. Statistical analysis We performed repeated measurements analysis of variance (ANOVA) and t tests with the Statistica 5.1 software package (Statsoft) in order to compare the mean recognition rates, response times, emotional intensities as well as intensity response times. As observers performances for the actor and the actress movies did not significantly differ (ANOVA tests performed on the recognition rates of all tested gaits between the two groups of observers F(1,15) p > 0.05), we consequently pooled together all movies for the analysis. III. RESULTS A. Real movies (Control condition) 1) Recognition rates: The distribution of observers responses to real movies is presented in figure 2a. On average, the emotion recognition rate was superior to 90% except for the JO condition where it was around 75%. Interestingly, observers answered anger instead of joy in more than 10% of the cases. 2) Response times: The response times were longer for fear and sadness compared to the other conditions (F (1, 222) = , p < 0.01, see figure 2b). The response times observed for neutral, joy and anger conditions did 462

4 a b Figure 2. Perception of emotion from real movies : a- Distribution of the observers responses across emotional gaits (fear FE, sadness SA, neutral NE, joy JO, anger AN and unknown UN) b- Response times (in seconds or in % of movie duration m.d.), perceived emotional intensity (in % of maximal intensity M.I.) and intensity response time duration. (Mean ± SD) not significantly differ (F (2, 444) = 3.87, p > 0.01). Observers delivered their answers after 8 seconds for the fear condition and around 7 seconds for sadness (this difference was statistically significantly different, F (1, 222) =22.21, p < 0.01) and after 6 seconds in the other conditions. In contrast, the response times computed after normalization (after dividing by the movie duration) revealed that fear and sadness were recognized faster than anger and joy (F (1, 222) = , p < 0.01). Indeed, observers answered at 55% of the movie duration while they waited after the end of the movie (around 110%) for anger and joy. The response time for the neutral condition was intermediate (around 83%) and was significantly different from fear-sadness and joyanger groups, respectively (F (1, 222) = , p < 0.01 and F (1, 222) = , p < 0.01, respectively). Observers answered significantly faster for fear compared to sadness (F (1, 222) = 7.32, p < 0.01) although response times of these two emotions were close one to another. Joy was also recognized faster than anger (F (1, 222) = 27.23, p < 0.01). Since actors walked a same distance for the different emotions, the observed differences between the absolute response times and the normalized ones were a function of the movie duration only. 3) Perceived emotional intensity: The perceived emotional intensity was comparable across emotions and was around 65% of the maximal intensity. However, statistical comparisons revealed that only sadness, joy and anger scores are not significantly different (p > 0.05) and that the perceived intensity for fear was significantly higher than for joy, anger and sadness (F (1, 222) =12.89, p < 0.01). As expected, the neutral condition score was close to 0. The intensity response time was comparable across emotions (p > 0.05) and close to 2 seconds: it was significantly longer (F (1, 222) = 80.26, p < 0.01) than for neutral gaits (around 1.2 seconds). These results showed that the expressiveness of actors gaits is good: recognition rates are around 90% (with the exception of joy) and intensity scores are significantly higher for emotional vs neutral gaits. B. Animated movies 1) Real-motion animated movies 1a) Recognition rate The distribution of observers responses to avatar movies is presented in figure 3a. On average, the emotion recognition rate was superior to 90% except for the JO condition for which it was around 65%. Interestingly here, observers answered anger instead of joy in about 20% of the movies. The same observation was reported for the control condition. 1b) Response times The response times (figure 3b) significantly differed across emotions (F (4, 412) = 4.96, p < 0.01). They were equal to 5 seconds for sadness, joy and anger, and were not found to significantly differ from the neutral (p > 0.05) condition. However, they were significantly longer (around 7 seconds) for fear compared to other conditions (F (1, 103) = 4.62, p = 0.03). They were 1 to 2 seconds shorter than those observed in real movies for fear and sadness conditions. The response times computed after normalization (figure 3b) revealed that fear and sadness were recognized faster than anger and joy, as observed for real movies (F (1, 103) = , p < 0.01). Indeed, observers answered at 55% of movie duration for fear and sadness while they waited after the end of the movie (around 150% of movie duration) for anger and joy. The response time for the neutral condition (around 135%) was close to but significantly lower than that of the joy-anger group (F (1, 103) = 17.08, p < 0.01). Observers answered significantly faster to fear compared to sadness (F (1, 103) = 11.60, p < 0.01) although normalized response times were close one to another. Joy and anger were recognized after the same duration (p > 0.05). Since actors walked a same distance for all emotions, the difference observed between absolute response times and normalized ones were, here also, a function of movie duration only. 1c) Perceived emotional intensity The perceived emotional intensity was systematically higher in animated compared to real movies. While the 463

5 a b Figure 3. Perception of emotion from avatar movies animated using real motion capture data: Same legend as figure 2. Note that the recognition rates are similar to that observed in real movies. intensity was around 65% for the real movies, the perceived intensity of the emotion ranged between 60 (for joy) and 80% (for anger and fear) for the avatars (figure 3a). Statistical comparisons revealed that fear, sadness and anger scores (around 80%) were not significantly different (p>0.05) and that the perceived intensity of joy was significantly lower (around 55%) than the fear-anger-sadness group (F (1, 103) = 44.70, p < 0.01). As expected, the neutral condition score is close to 0 here also. The intensity response time was around 1,5 seconds (figure 3b) and was comparable across emotions (p > 0.05) while it was significantly longer (F (1, 103) = 58.78, p < 0.01) than that of neutral gaits (around 0.9 seconds). The intensity response time was around 500 milliseconds shorter for animated compared to real movies. These results showed that the expressiveness of avatars gaits is good as the recognition rates were around 90% (with the exception of joy): intensity scores were higher for emotional gaits and the duration for choosing a particular intensity was shorter compared to real movies. 2) Artificially-generated animated movies In this section, we examined the respective and combined effects of speed and upper body postural changes artificially introduced into neutral gaits. 2a) Speed changes The effects of decelerating or accelerating the neutral gaits on observers responses are illustrated in figure 4a1. For speeds ranged between 60 and 130% of the original speed, no emotion was dominantly perceived and observers answered neutral in more than 40% of the cases. For decelerated gaits, fear and sadness progressively reached significant scores with speeds S40 and S50 where they were recognized in 60% of observers responses. The opposite was observed for accelerated gaits where joy and anger were reported for S150 and S160 in more than 60% of the cases. The only situation where a single emotion was perceived above the chance level (50%) was the speed 160: here anger was perceived in more than 60% of the cases. Thus, speed changes provided a first cue for discrimination emotions, yet insufficient alone to reach significant emotion recognition rates. 2b) Head and trunk postural changes The effects of inclining the upper body downwards or upwards on observers responses are illustrated in figure 4 a2. A change in the head/trunk orientation by up to 16 degrees yielded significant emotion recognition rates. An orientation changed in the downwards direction (for both head and trunk segments) resulted in 40% of the cases either in sadness or in anger recognition while a change in the upwards direction resulted in joy recognition in more than 50% of the cases. Fear was the less frequently perceived response (rates < 5%). Orientation changes provided emotional cues (except for fear), yet insufficient to reach significant and systematic recognition rates. 2c) Combined speed and postural changes The effects of changing speed and upper body posture are illustrated in figure 4a3. These combined changes yielded significant emotions recognition (except for fear). 2c1) Recognition rate A decelerated gait (S40 or S70) combined with a headtrunk downwards inclination resulted in sadness perception. An accelerated gait (Speed160) combined with a similar downwards inclination resulted in anger perception and eventually, an accelerated gait (Speed130) combined with an upwards orientation change of the upper boy resulted in joy recognition. More specifically the highest scores were obtained for the conditions Speed40HTD16, Speed40TU16 and Speed160TD16 (figure 4b) where the recognition rates were around 95%, 70% and 90%, respectively for the sadness, joy and anger responses. The recognition rate of fear was not increased by adding orientation changes (as expected from results reported in [8]) and a decelerated gait by up to 50% (S50) resulted in 50% of fear perception. 2c2) Response times Observers answered after 8, 6, 6 and 4 seconds for the S50 (fear-like), Speed40HTD16 (sadness-like), Speed40TU16 (joy-like) and Speed160TD16 (anger-like), respectively (not shown). These delays in responses were 1 to 2 seconds 464

6 % total responses' number a1 a3 SPEED + ORIENTATION SPEED a2 ORIENTATION b Figure 4. Perception of emotions from artificially-generated animated movies. a 1 -a 2 -a 3 : Distribution of the observers responses across artificial gaits synthesized by modifying, the walking speed (a 1 ), the upper-body posture (a 2 ) or a combination thereof (a 3 ), of neutral gaits. The colored arrows in a 1 -a 2 -a 3 correspond to the most perceived emotions in b. longer than for real emotional gaits except for joy or anger recognition for which the delay was shorter for the Speed160TD16 condition compared to the real anger. When normalized with respect to movie duration, this longer response time becomes even more evident for fear (150% of the movie duration for condition Speed50 instead of 50% for fear) and sadness (100% of the movie duration for Speed40HTD16 instead of 60% for sadness). 2c3) Perceived emotional intensity The perceived emotional intensity is also higher for real emotional gaits compared with artificially generated emotional gaits. This was particularly true for the fear condition (80% versus 20%) but also hold for other conditions (75% versus 60%, 60% versus 40% and 75% versus 55% respectively for artificial and real sadness, joy and anger conditions). The intensity response times were comparable between real and animated movies (they were even shorter for animated movies in fear and anger conditions, by up to 500 ms). Taken together, these results indicated that emotions were recognized (yet with a lower intensity) with scores comparable to those observed in real movies) except for fear. These speed and orientation changes matched the emotion-specific values reported in [8]. IV. DISCUSSION We observed that real and animated emotional gaits were recognized with similar scores. This was mainly explained by a particular combination of two kinematic cues (namely the walking speed and the upper body orientation), as evidenced by the strong emotional percepts (with the exception of fear) provided by an artificial modulation of these parameters in animated neutral gaits. However, the perceived intensity was weaker in the artificial vs emotional animated gaits, suggesting a role of other cues in the generation of emotional percepts. The underlying motor-perceptual interactions as well as the potential impact of these findings are discussed below. A. Perception of the emotional walking behaviour Human ability to recognize a human gait from point-light displays [9], gender [14] or emotions from different arm or whole body gestures [12], [1] depend upon the visual availability of kinematic and form-related cues [2]. Based on the kinematic changes occurring during emotional vs neutral gaits [8], we hypothesized that emotions can be inferred from the visual processing of walking speed and upper body orientation. According to this hypothesis, we created artificial gaits by manipulating these parameters in neutral gaits. It should be noted that that observers answered unknown in few cases in our study, meaning that both visual appearance of avatars and the animated motion fairly reproduced natural gaits produced by actors. We observed that neither walking speed changes alone, nor postural changes alone, could correctly produce the perception of emotional gaits. In contrast, artificial gaits with specific combinations of speed and postural changes (Speed130TU16, Speed40HTD16 and Speed160HTD16) were unambiguously recognized as joy, anger and sadness, respectively. These quantitative modulations of neutral gaits strikingly match the kinematic variations observed across emotional gait patterns [8], revealing a strong coupling between motor and perceptual processes involved in emotion production and recognition, respectively. The nature of these sensorimotor interactions was investigated. Shared motor representations used to predict the sensory consequences of ones own actions were shown to be also used to predict anothers behavior and associated predicted somatosensory consequences (involving parietal and premotor cortices [16][17]). This kind of approach may be useful in identifying the cortical structures involved in the processing of the real and artificial stimuli tested in our study. Our approach proved to be particularly helpful for all emotions except for fear. Fearful gaits are also characterized by very weak discriminative features at the behavioural level 465

7 [8]. One possible explanation might be that fear requires additional motion features (like specific arm movements) that are more context-dependent compared to other emotions (see Methods section). As the walking speed and postural changes were sufficient to induce emotion perception for other emotions, we can only speculate that the fearful behaviour tested in the present study requires taking into account additional and probably discrete features which still remain to be identified. The present study, at least, clearly confirms that emotions can be inferred from the whole body movements, independently from facial expressions. B. Potential applications The ability of humanoid robots to adequately interact with humans can be substantially improved by taking into account variables informing about their emotional states. While these cues greatly rely on the analysis of facial expression at close distances, whole-body motion cues also convey information about more distant humans. Comparatively to the pattern recognition algorithms required to interpret facial expressions, the efficient perception of emotions from human gait can be based on a limited set of behaviorally-relevant cues: the walking speed and the upper-body orientation. The same variables could also be integrated into the motion controller of walking robots. This is particularly interesting if one consider the high dimensionality of any bipedal system. One could also imagine the implementation of such bioinspired approach in the animation of artificial walkers in arts entertainment and multimedia. Indeed, the realistic animation of virtual characters often relies on computation of huge motion capture databases: the use of such simple bioinspired variables can contribute to reduce the computational cost associated with the generation of such artificial emotional gaits. While further studies are required to provide an exhaustive understanding of the factors determining emotion recognition from gaits (including fear, for instance), our findings clearly show that a limited set of kinematic cues are sufficient to generate strong emotional percepts during whole-body movements. ACKNOWLEDGMENT This work was supported by EU-COBOL and French Locanthrope projects. REFERENCES [1] Atkinson, A. P., Dittrich, W. H., Gemmell, A. J., & Young, A. W. (2004). Emotion perception from dynamic and static body expressions in point-light and full-light displays. Perception, 33, [2] Atkinson, A. P., Tunstall, M. L., & Dittrich, W. H. (2007). Evidence for distinct contributions of form and motion information to the recognition of emotions from body gestures. Cognition, 104, [3] Bonda, E., Petrides, M., Ostry, D., & Evans, A. (1996). Specific involvement of human parietal systems and the amygdala in the perception of biological motion. 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