Computer Animated Movement and Person Perception: Methodological Advances in Nonverbal Behavior Research

Journal of Nonverbal Behavior 25(3), Fall 2001 � 2001 Human Sciences Press, Inc. 151

COMPUTER ANIMATED MOVEMENTAND PERSON PERCEPTION:METHODOLOGICAL ADVANCES IN NONVERBALBEHAVIOR RESEARCH

Gary Bente, Nicole C. Kramer, Anita Petersen,and Jan Peter de Ruiter

ABSTRACT: Impression effects of videotaped dyadic interactions were comparedwith 3D-computer animations based on movement transcripts of the same interac-tions to determine whether similar effects could be obtained. One minute se-quences of movement behavior taken from three different dyadic interactions weretranscribed using the Bernese Coding System (BCS). Descriptive data were con-verted into animation scripts for professional animation software. Original videodocuments and computer animations were shown to separate groups of observersand their socio-emotional impressions were assessed on a standard adjective check-list. Only marginal differences were found between the two presentation modes.On the contrary, the data point to remarkable similarities in the impression ratingsin both conditions, indicating that most of the relevant social information availableto observers in the video recordings was also conveyed by computer animations.Overall, the data suggest that the systematic use of computer animation techniquesin nonverbal research deserves further scientific attention.

KEY WORDS: artificial behavior; computer animation; nonverbal behavior; personperception; new methodology.

Although gestures, movements, and postures are supposed to add a

meaningful dimension to human communication (Argyle, 1972; Davis,

1972; Mehrabian, 1969), much remains to be learned about the implicit

Gary Bente, Nicole C. Kramer, Anita Petersen, Jan Peter de Ruiter, University of Cologne,Germany.;

This research was supported by grant BE 1745/2-1 from the Deutsche Forschungsge-meinschaft (DFG, German Research Association). We also thank two anonymous reviewersfor helpful comments on earlier versions of this article.

Address correspondence to Prof. Dr. Gary Bente, Department of Psychology, Universityof Cologne, Bernhard-Feilchenfeld-Strasse 11, 50969 Cologne, Germany; e-mail: bente�uni-koeln.de.

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JOURNAL OF NONVERBAL BEHAVIOR

information and the psychological impact of this ‘silent language’ (Hall,

1959). For many years, a recurrent problem of movement analysis in the

context of nonverbal communication research has been the lack of ade-

quate description methods (see, e.g., Duncan & Fiske, 1979; Duncan,

Kanki, Makros, & Fiske, 1984; Ellsworth & Ludwig, 1972; Grammer, Fil-

ova, & Fieder, 1997). Some research from the last two decades has signifi-

cantly advanced methodological knowledge in this area (Donaghy, 1989).

Based on standard video technology a series of transcription procedures

and coding strategies have been developed that provide detailed and accu-

rate protocols for both facial behavior and body movement (Ekman &

Friesen, 1978; Frey, Hirsbrunner, Florin, Daw, & Crawford, 1983). More-

over, powerful tools for automatic data acquisition have been introduced

to the field recently, using sophisticated measurement devices such as mo-

tion-capture devices, infra-red and ultra-sonic movement sensors, or video

based pattern recognition techniques (Altorfer, Jossen, & Wurmle, 1997;

Bers, 1996; Cassell et al., 1999; Essa, 1995; Essa & Pentland, 1995; Gram-

mer, Filova, & Fieder, 1997; Thorisson, 1996).

The strength of this purely descriptive methodology, i.e., the exclusion

of observers’ evaluations from the measurement process, becomes a weak-

ness when the analysis of interpersonal effects and the psychological

meaning of nonverbal behavior are the focus of attention. When the as-

signment of meaning is not included in the coding process, it must be

reconstructed either from structural aspects inherent in the behavior proto-

cols or by adding a semantic dimension to the data base. The first ap-

proach was suggested by Grammer, Filova, and Fieder (1997) following a

“radical empiricism,“ which relies on the identification of recurrent patterns

in the stream of nonverbal behavior (Grammer, Kruck, & Magnusson, 1998).

A similar idea was introduced by Altorfer (1988, 1989), namely reducing

the question of meaning to the problem of isolating behaviors preceding

systematical changes in the course of the interaction; e.g., the content of

any verbal message that follows. Examination of these procedures reveals

that meaningful findings cannot be complete without a verbal translation of

descriptive data into psychological categories, which carry a semantic sur-

plus that may have little or no empirical foundation.

On the other hand, it has also been difficult to study the meanings and

interpersonal effects of nonverbal behavior using a systematic experimental

approach, especially the control of the independent variables. Such experi-

ments, i.e. the manipulation of particular nonverbal behaviors, caused se-

vere problems because of the many interactions between different nonver-

bal channels and between features of appearance (e.g., physical

attractiveness) and those nonverbal channels. For example, Lewis, Derlega,

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GARY BENTE, NICOLE C. KRAMER, ANITA PETERSEN, JAN PETER DE RUITER

Shankar, Cochard, and Finkel (1997) could show that the experimental

variation of touch behavior was confounded by simultaneous variations in

other nonverbal channels. They concluded that “in spite of specific instruc-

tions to keep nonverbal behavior consistent, confederates in the touch

versus no touch condition displayed different behaviors. Confederates who

touched used more nervous gestures and fewer expressive hand gestures

compared to those who did not touch” (Lewis et al., 1997, p. 821). Other

investigators tried to solve such problems by using images or puppets that

could be controlled more easily and precisely than actors. For example,

Frey, Hirsbrunner, Florin, Daw, and Crawford (1983) systematically varied

head positions by means of photo-retouch techniques to investigate the

socio-emotional impact of lateral head tilt, a subtle cue used in art paint-

ings. Similarly, Trautner (1991) manipulated wooden manikins to study the

perception of sex-stereotyped postures from a developmental perspective.

Schouwstra and Hoogstraten (1995) used hand drawings in cartoon style to

study the impression effects of different positions of head and body. De-

spite some seemingly encouraging results, all these studies have been re-

stricted to the investigation of static and easily manipulated features of

nonverbal behavior such as postures or positions of specific body parts.

One of the first attempts to study the perception of dynamic features of

bodily behavior was pursued by Johansson (1973, 1976), who introduced

the so-called ‘point light display’ technique. Berry, Kean, Misovich, and

Baron (1991) pointed out that this method: “. . . is a potentially valuable

tool for researchers in the area of nonverbal behavior, as it permits the

study of human movement in the absence of potentially contaminating

cues such as physical appearance” (p. 82). However, the point light display

method has the inherent disadvantage that light sources or light-reflecting

materials must be applied to the subjects, which can obstruct movements

and also increase self-awareness and evaluation apprehension.

Against this background, Berry et al. (1991) proposed an alternative

method that can be used with common video documents. Based on a tech-

nique referred to as ‘quantization’, (see Harmon, 1973; Morrone, Burr, &

Ross, 1983) standard videotapes are electronically distorted in a post pro-

duction process to eliminate the recognition of most features of physical

appearance. With this procedure the stimulus person can be filmed under

natural, non-restrictive conditions and, when required, even without

awareness of the video recording. While this method is to some degree

useful in analyzing dynamic aspects of movement behavior independently

of an actor’s physical appearance, it still lacks the important dimension of

systematic experimental stimulus control. As Berry (1990) already pointed

out, in order to solve this problem: “. . . the precise nature of the stimulus

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information communicating a given characteristic needs to be described.

And that stimulus quality of interest needs to be manipulated in some fash-

ion in order to demonstrate that this is indeed the effective stimulus under-

lying a given percept” (p. 150). The proper way to describe and manipulate

the particular ‘stimulus quality of interest’ remained an open question for

future research. Earlier, Cutting and Profitt (1981) suggested computer-

simulation as a possible solution to the problem. However, deficiencies in

affordable computer hardware and software did not permit its systematic

use in nonverbal research until recently (see Berry, 1990). A first software

implementation for the experimental computer animation based on so-

called position time-series protocols of movement was described by Bente

(1989). The computer program allows for the animation of a simple wire-

frame model of a human head that could be attached to static images of

various body models. This method has been evaluated in a study on sex-

stereotyped person perception (Bente, Feist, & Elder, 1996). Another com-

puter program for the 3D-animation of body movement was introduced by

Kempter (in press). Although allowing for full body animation, the program

is restricted to the use of low resolution 3D-models. Further developments

in technology have facilitated the application of more sophisticated com-

puter animations in this research area. Based on these advancements, a

new research tool was introduced recently, linking position time series pro-

tocols to a professional 3D-computer animation platform, thus allowing for

the interactive editing of the movement data and the generation of smooth

animations performed by realistic 3D-models (see Bente, Petersen, &

Kramer, 1999; Bente, 2000).

The major concern of the present approach was to determine whether

such computer animations are capable of producing realistic person per-

ception effects in neutral observers. This test is crucial for any future appli-

cation of this methodology in person perception research: before system-

atic variations of computer animations can be applied in experimental

research into specific nonverbal behaviors, it has to be demonstrated that

animated movement evokes socio-emotional effects similar to those of

real-life or videotaped behavior and is not merely perceived as ‘artificial’

or ‘strange.’

Although we were interested in demonstrating (near-)similarity, we fol-

lowed the traditional statistical approach of rejecting the null hypothesis.

Indeed the assumption that differences will be found bears some plaus-

ibility, as the available computer model still lacks some details of nonver-

bal behavior. In particular the virtual actors perform neither facial activity

nor finger movements. Also, the physical appearance of the computer ac-

tors is standardized and thus different from all the actors on the video (see

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figure 1). The anatomical differences between stimulus persons and the

computer model (e.g., length of arms and legs) could also lead to slight

differences in the angles or the positions of the extremities. Thus, if crucial

information gets lost on the way from the video to the computer anima-

tions the stimulus should provoke different judgements or at least more

ambiguous ones when compared to the video judgements. As a lack of

significance cannot be interpreted as a direct proof of similarity we used

further analyses such as correlation-based comparisons of judgement pro-

files. The problems of demonstrating similarity that become salient in this

context will be discussed below.

Method

Stimulus Material

Stimulus material was selected from three dyadic interaction se-

quences between six male students involved in casual chats with no spe-

cific topic. One minute of movement behavior in the initial phase of the

conversation was transcribed for each stimulus person using the ‘Bernese

Coding System’ (BCS; Frey, Hirsbrunner, Florin, Daw, & Crawford, 1983;

Hirsbrunner, Frey, & Crawford, 1987). The BCS is based on the principle of

so-called position-time-series-notation. Assigning numerical codes to the

spatial deviations of the various body parts from predefined base positions,

the BCS allows for a detailed and reliable transcription of video-recorded

movement behavior into high resolution data protocols. As could be shown,

these phenotypical codes can be converted into generic rotation angles

(Euler angles) that can be used as input for 3D-animation programs (Bente,

1989). A special software tool was developed for the current study to per-

form this conversion in real time (Bente, 2000; Leuschner, 1999). The pro-

gram SoftImage3D� was used as front end animation tool because it comes

together with an elaborated human body model with a skeleton that could

be easily adopted to our needs and also provides a special interface for

external animation data. Combining SoftImage3D� with our own con-

verter module, we were able to produce accurate computer animated rep-

lications of video-recorded movement and at the same time retain full ac-

cess to descriptive BCS-data for the purpose of analysis and experimental

variation. Restrictions of the computer animation concern finger move-

ments, facial activity and the display of realistic clothes. Thus, parts of the

computer models were colored in black producing the impression of a

pantomime’s suit (see figure 1). Video transcription was done with a tem-

poral resolution of 2 Hz. Movement protocols were then interpolated to 25

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Figure 1. Presentation modes.

Hz and rendered by means of SoftImage3D�. Original video and com-

puter animations were converted to digital video (AVI) and stored on hard-

disk for experimental presentation. Figure 1 shows screen representations

of both experimental conditions.

Participants

Students from different disciplines (economics, computer science,

chemistry, art and psychology) at the University of Cologne participated in

the study (N � 104). The age of the participants ranged from 19 to 57

years with a mean age of 26.11 (SD � 5.75) years. Matching for gender,

the participants were assigned to one of the experimental groups. In the

experiment 53 observers (26 male/27 female) were shown the video re-

cordings and 51 observers (25 male/26 female) saw the animations. Devia-

tions from the intended equal distribution over the three conditions were

caused by participant drop outs. The participants were paid for their assis-

tance.

Dependent Measures

The Positive-Negative-Affect-Schedule (PANAS, Krohne, Egloff, Kohl-

mann, & Tausch, 1996; Watson, Clark, & Tellegen, 1988) was used to mea-

sure the observers’ attributions of emotional states and interpersonal

attitudes towards both actors on the screen. Although developed for self-

assessment of emotional states, the system provides an item list that can be

used for the judgement of other social stimuli as well. The following

PANAS items were used with a five-point scale (1 � not at all; 5 � ex-

157


tremely): active, interested, excited, strong, inspired, proud, enthusiastic,

alert, determined, attentive, distressed, upset, guilty, scared, hostile, irrita-

ble, ashamed, nervous, jittery and afraid. Additionally, we included seven

questionnaire items asking for a direct comparison of the actors on the

screen. Three of these items are related to the basic socio-emotional di-

mensions ‘evaluation,’ ‘activity’ and ‘potency’ (Which person do you con-

sider more sympathetic? . . . more active? . . . more dominant?) (see Mehra-

bian, 1969, 1970). Four further items were used to assess the personal

preferences of the observer for either one of the actors (Which person did

you pay more attention to? Which person’s perspective could you take

more easily? Which person did you identify with? Which person would

you prefer to talk to?).

Procedure

Groups of up to 12 participants were seated in the small cinema-like

presentation room and asked to watch three different sequences of silent

one-minute-interactions presented via a large screen LCD projector. The

groups were shown the stimulus material in either one of the presentation

modes (video or computer animation). Each interaction sequence was

shown twice, and after each presentation the participants were asked to

write down their PANAS rating for one of the interactors. Potential memory

effects were counterbalanced by exchanging the order of judgement for left

and right person on the screen for half of the observers in each experimen-

tal group. To prevent serial effects, the order of the three different interac-

tions was rotated systematically for all groups. Also, questionnaire items for

comparative evaluation of the actors were alternatingly combined with the

judgment of either one of the actors.

Results

Because the aim of our study is to investigate whether computer anima-

tions will evoke similar responses from subjects as the original video, we

want to make the argument that there are few differences between the two

modes. This means that we are trying to show that two sets of PANAS

profiles are effectively similar. Therefore, we need to take care that those

items of the PANAS that are unreliably scored in the video modus are

removed from the analysis. Items that are unreliably scored in the video

mode will probably also be unreliable scored in the computer animation,

which will lead to nonsignificant differences in the comparison, not be-

158


cause these items are scored similarly, but because the variance in both

data sets will be large and unsystematic. Therefore, we computed the inter-

rater reliability (intraclass correlation) between the different subjects for all

items of the video PANAS data. We set the rejection criterion at .6 since

the reliability for values higher than that are generally conceived to be

‘substantial’ (cf. Landis & Koch, 1977; Fleiss, 1981). There were four items

that did not reach the criterion (the items hostile, irritable, nervous and

proud) and were therefore excluded from the subsequent analysis.

T-tests were used on the remaining 16 PANAS items to detect signifi-

cant judgement discrepancies between the two presentation modes. Differ-

ences in dichotomous questionnaire data were analyzed by means of chi-

square-tests. The alpha level for all tests was set to .01 (two-tailed).

Impression Formation Based on Video vs. Computer Animated Stimuli

Significant differences could be found for a small number of PANAS

items in the comparison between video and computer animated stimuli.

Table 1 summarizes the significant results for the 6 stimulus persons. As

can be seen, the results are unsystematic and thus difficult to interpret as

specific effects of the experimental variation: overall, 8 different items of

the PANAS reveal significant differences.

TABLE 1

Significant Judgement Differences (p � .01) in the PANAS-Items for 6Different Stimulus Persons

Video Animation t-test

Items / IDa M SD M SD t df p

Excited / 1 2.34 1.02 1.59 1.00 3.79 102 .000

Ashamed / 2 1.30 .61 1.76 .97 �2.9 83.34 .005

Ashamed / 3 1.47 .98 2.20 1.30 �3.17 92.97 .002

Afraid / 3 1.55 1.00 2.44 1.28 �3.85 92.48 .000

Determined / 4 2.35 1.17 2.96 1.09 �2.75 101 .007

Upset / 5 1.91 .97 1.32 .59 3.74 86.59 .000

Alert / 6 2.34 .94 3.08 .89 �4.11 102 .000

Attentive / 6 2.70 .85 3.27 .90 �3.38 102 .001

Interested / 6 2.36 .96 3.10 .85 �4.14 102 .000

aID of stimulus person: dyad 1: 1 and 2; dyad 2: 3 and 4; dyad 3: 5 and 6).

159


The number of significant differences is quite low for all stimulus per-

sons but varies between one (persons 1, 2, 4, 5), two (person 3) and three

differences (person 6) per person. As an illustration, the judgement profiles

for the third dyad are represented in figure 2. These were selected since

they include the judgement for the person showing the largest number (per-

son 6: right person in dyad 3) and the person showing the smallest number

of significant differences (person 5: left person in dyad 3), respectively. The

figure demonstrates that despite the named discrepancies the profiles show

remarkable overall correspondence.

Interestingly, the small number of significant differences between the

Figure 2. Impression rating profiles for stimulus person 1 and 2 in dyad 3.

160


video and the animation data are interpretable with the help of the dimen-

sion ‘activity’ of Mehrabian (1969, 1970). The items upset and excited are

rated higher in the video data (which corresponds to a high degree of

Mehrabian’s concept ‘activated’), whereas the items ashamed and afraid

are rated higher in the animation data (corresponding to a low level of

Mehrabian’s ‘activitated’ dimension). Possibly, this is due to the general

lack of facial activity in the computer animations. Two explanations seem

to be worth considering. First, the animation models’ lack of facial dy-

namics could lead to weaker perceptual effects with respect to the acti-

vated states upset and excited, relative to the video data. Second, the fact

that the facial expression of the animation models are static could also be a

discriminative socio-emotional cue in itself, which could be perceived as a

‘frozen face’ and thus lead to higher ratings on the PANAS scales ashamed

and afraid.

Another difference between the two conditions concerns the data from

person 6, who is consistently rated as more alert, attentive and interested

when presented as an animated character. This may be due to the lack of

eye movements in the animation condition. For example, a person who is

rotated towards but not precisely looking at the interaction partner could

be perceived as more attentive when presented as an animated character

because in the animation the eyes are consistently pointing in the same

direction as the head.

From the fact that the majority of the t-tests performed over PANAS

items are not significant, it can of course not be concluded that there are

no differences between the groups. Therefore, it is important to inspect the

statistical power of the experiment (e.g., Cohen, 1988, 1992; Faul &

Erdfelder, 1992). Given the number of subjects for each comparison

(n � 51, 53), an alpha level of .01 (two-tailed), and the relatively low

variance (SD appr. 1), the power for detecting an effect size (delta) of

� � 0.75 is 1–� � 0.88, which is larger than the conventional lower limit

of 0.8. The specified effect size of � � 0.75 corresponds with an average

scoring difference of less than one point on the five-point scale. The anal-

ysis of the statistical power of this experiment thus indicates that the low

number of significant differences is not due to the insensitivity of the statis-

tical test that was used.

Unfortunately, performing t-tests on individual items of the PANAS

scale does not allow us to evaluate the similarity of the profiles as a whole.

For this purpose, we computed Hofstatter’s Q value (Hofstatter, 1959), es-

sentially a correlation coefficient based on Pearson’s product-moment cor-

relation measure, indicating the correlation between two profiles as a

whole. The correlations are based on N � 16 (number of items taken into

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account) and range from Q � .75 (for stimulus person 3) to Q � .97 (for

stimulus person 5), all significant at the .001 level.

These high correlations indicate a very high degree of similarity be-

tween the profiles obtained with video presentation on the one hand, and

computer animation on the other. Because Q coefficients are based on the

average response of all subjects, we still need to investigate whether

switching from video to computer animation will not result in a loss of

interrater reliability. This was done by comparing the average interrater

reliability over all 16 remaining PANAS items for video (r � .815), com-

puter animation (r � .751) and video and computer animation taken to-

gether (r � .850). (The fact that it is higher for the two data sets together is

related to the fact that the number of data points is twice as large in that

dataset). Overall, this analysis shows that using computer animations in-

stead of video does not result in a loss of interrater reliability. Moreover,

the fact that the interrater-reliability does not decrease if judgements based

on computer animation are included in the analysis again shows that the

evaluations in the two groups are highly similar.

Comparative Evaluation of Stimulus Persons in Video and Computer

Animation Sequences

Chi-square tests were run for the comparative evaluation items to test

differences in right person/left person preferences between video and com-

puter animation mode. Preference choices in the video condition were

treated as expected frequencies and the preference choices in the com-

puter animation mode were treated as observed frequencies. Expected fre-

quencies were corrected for the number of observers in the experimental

group. In only one of 21 tests (7 Items per dyad) a significant difference in

preference choices was detected: the video based rating of ‘dominance’ in

dyad 2 (fleft � 39; fright � 12) is inverted in the computer animation condi-

tion (fleft � 22; fright � 29; �2 (1, N � 102) � 7.41; p � 0.006). This dif-

ference could also be due to the fact that gaze direction, which is strongly

correlated with dominance (see Dovidio, Ellyson, Keating, Heltman, &

Brown, 1988), is not recognizable in the computer animation condition.

Therefore, the left person who is perceived as more dominant in the video

condition, is perhaps no longer perceived to be dominant in the animation

condition since he is displaying ‘power’ mainly by his gaze behavior. Be-

yond this particular difference, however, the inspection of the frequency

distributions reveals a remarkable correspondence in the direction of social

preferences when comparing computer animations to video.

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Discussion

A method for the 3D-computer animation of body movement has been

introduced as a new tool for nonverbal communication research. In con-

trast to previous approaches, that were either limited with respect to the

range of simulated behavior or with respect to the realism of the computer

animations, the new instrument allows for sophisticated 3D-animations of

whole body movements using realistic 3D-polygon-models with skeleton

and skin-like envelope. By means of a special converter program it has

been made possible to translate position time-series protocols of video-

recorded movement behavior into animation-scripts of a professional com-

puter animation platform. The new method was evaluated in an experi-

ment comparing person perception effects of video recorded nonverbal

interactions with a computer animation of the same behavior to find out

whether similar impression effects could be obtained. The results indicate a

remarkable correspondence between impression ratings based on standard

video recordings and those based on computer animation—as can be

shown by the lack of significant differences on the judgement item level,

the overall correlations of judgement profiles and the correspondence in

direction of social preferences when comparing computer animations to

video. Even though some aspects of nonverbal behavior such as facial ac-

tivity or finger movements could not be displayed by the available com-

puter models, the animated movements lead to nearly the same judge-

ments as in the video condition.

This again indicates (see Bente, 1989; Bente, Feist & Elder, 1996) that

the detailed behavior protocols coded by means of the Bernese System

capture movements faithfully and are especially valuable to describe

movements. Although the coding procedure is a time consuming process,

its employment will also be necessary in the future. The alternative of using

motion capture devices seriously decreases the ecological validity of the

experimental situation due to its intrusiveness. Moreover observers com-

paring video with animation would also be disturbed by the devices the

stimulus persons are wearing. Video based analysis techniques on the other

hand can not be used for this purpose in the near future since the methods

available at the moment are not sufficiently detailed. Especially their spa-

tial resolution is not sufficient to determine the exact positions of the rele-

vant body parts. As mentioned earlier, another important advantage of the

Bernese Coding System is the availability of phenotypical codes that can

be edited by hand to create variations of the nonverbal behavior of interest

(see Bente, 1989).

The remarkable correspondence in person perception effects of video

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and computer animations does not only provide a strong argument for the

capacity of our simulations to accurately convey the relevant movement

cues but also leads to the assumption that a considerable part of impres-

sion variance is explained by the observed movement behavior. This is

consistent with the findings of Kempter (in press) who also found that the

absence of facial cues makes no difference in interpersonal evaluations.

Since the lack of facial activity does not lead to different evaluations we

suggest that body movements and positions are more crucial for impression

formation and person perception processes than has been supposed before

(e.g., Ekman & Friesen, 1969). Whether body movement, postures and ges-

tures are the predominant or even sole channels for person perception or

whether the relevant information is to some degree redundantly coded in

facial and bodily activities however, can only be answered on the basis of

experiments designed to answer this question.

Against this background our data make a clear point for the systematic

use of this new technology in future experimental nonverbal research. As

the data used for 3D-animation can be analyzed quantitatively and also

modified according to specific hypotheses, this platform creates unique

possibilities for both data-driven and theory-driven research strategies. For

example, in data driven approaches differences in the impression ratings

based on specific simulations can be traced back to structural differences

in the behavior protocols. Specific aspects of behavior, like postures,

movements of head, body or hands or general movement qualities such as

speed, acceleration or complexity (see Bente, Donaghy & Suwelack, 1998)

can be systematically controlled modifying the relevant numerical entries

in the raw data. This can be done by adding a certain constant value to a

specific column in the BCS data matrix in order to change the position of

the head without changing the original dynamics. Another possibility is

multiplying a column with a certain (small) value in order to intensify

movements. Hereby subtle changes in otherwise natural behavior can be

produced. Their specific impression effects can then be tested in subse-

quent experiments. As our knowledge grows, algorithms for behavior gen-

eration can be formulated within theory driven approaches. These algo-

rithms can then be used for rule based generation of behavior or certain

aspects of behavior, that can also be evaluated in person perception exper-

iments.

The application of our data driven methodology, however, is not re-

stricted to fundamental research. It is also relevant in applied research.

Especially, the field of ‘anthropomorphic interfaces’ or ‘embodied com-

puter agents’ (i.e., autonomously directed, virtual agents of human-like ap-

pearance) can profit from findings about the effects of specific nonverbal

164


cues when shown by animated characters. Against this background graphi-

cal computer animation of nonverbal behavior will not only be a fascinat-

ing experimental tool, but also one of the most fruitful applied areas of

nonverbal research.

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Documents

Computer Animated Movement and Person Perception: Methodological Advances in Nonverbal Behavior Research