Beyond Stereotypes: Analyzing Gender and Cultural Differences in Nonverbal Rapport

Abstract

The current paper addresses two methodological problems pertinent to the analysis of observer studies in non-verbal rapport and beyond. These problems concern: (1) the production of standardized stimulus materials that allow for unbiased observer ratings and (2) the objective measurement of non-verbal behaviors to identify the dyadic patterns underlying the observer impressions. We suggest motion capture and character animation as possible solutions to these problems and exemplarily apply the novel methodology to the study of gender and cultural differences in non-verbal rapport. We compared a Western, individualistic culture with an egalitarian gender-role conception (Germany) and a collectivistic culture with a more traditional gender role conceptions (Middle East, Gulf States). Motion capture data were collected for five male and five female dyadic interactions in each culture. Character animations based on the motion capture data served as stimuli in the observation study. Female and male observers from both cultures rated the perceived rapport continuously while watching the 1 min sequences and guessed gender and cultural background of the dyads after each clip. Results show that masking of gender and culture in the stimuli was successful, as hit rates for both aspects remained at chance level. Further the results revealed high levels of agreement in the rapport ratings across gender and culture, pointing to universal judgment policies. A 2 × 2 × 2 × 2 ANOVA for gender and culture of stimuli and observers showed that female dyads were rated significantly higher on rapport across the board and that the contrast between female and male dyads was more pronounced in the Arab sample as compared to the German sample. Non-verbal parameters extracted from the motion capture protocols were submitted to a series of algorithms to identify dyadic activity levels and coordination patterns relevant to the perception of rapport. The results are critically discussed with regard to the role of non-verbal coordination as a constituent of rapport.

Keywords: character animation; culture; gender; motion capture; non-verbal behavior; rapport.

FIGURE 1

Posed interaction showing the setup and the interface of the capture software as projection in the background (180° rotated).

FIGURE 2

Screenshot of the user interface for the continuous judgment of rapport (description in the text).

FIGURE 3

Correlation graph for German and Arab observers, differentiated for male (green squares) and female (blue circles) stimulus dyads.

FIGURE 4

Continuous RTR rapport ratings. (A) Averaged for each of the 20 stimulus clips across 88 observers. (B) Averaged for 7 high, 6 neutral, and 7 low rapport dyads.

FIGURE 5

Interaction graph for rapport ratings (estimated marginal means) depending on gender and culture of the stimulus dyads. The y-axis scale is set from –1 to +1 here to showcase the effect (the participants rated from –4 to +4).

FIGURE 6

Rationale for measuring rotational head orientation and interpersonal proximity. Invisible virtual markers between the characters’ eyes were used as anchor for distance measurement and as references for the assessment of rotational deviations from the direct line of view. The angle was calculated as orthogonal to the line between two virtual ear markers projected onto the x/z plane. Distance was calibrated as percentage of both partners’ arm lengths.

FIGURE 7

Rationale for multidimensional comparisons: Both skeletons’ hips were constrained to the position as well as the y-rotation of a static object in the coordinate system origin, thus always pointing to the front. Postural differences were calculated as Euclidian distances between the corresponding joints of both partners for each timepoint.

FIGURE 8

Results of RWTLCC for the head rotation dynamics of 2 dyads differing in the peak correlation offset. The upper graphs show the low pass filtered, standardized head rotations. The mid graphs show the time lagged correlations with lags of ±5 s (±125 data points) and a moving window with a step size of 0.2 s (5 data points). The lower graphs show the mean correlations for the time lags from –125 to +125 datapoints.