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Introduction: This study explores the graduated perception of apparent social traits in virtual characters by experimental manipulation of perceived affiliation with the aim to validate an existing predictive model in animated whole-body avatars.

Methods: We created a set of 210 animated virtual characters, for which facial features were generated according to a predictive statistical model originally developed for 2D faces. In a first online study, participants (N = 34) rated mute video clips of the characters on the dimensions of trustworthiness, valence, and arousal. In a second study (N = 49), vocal expressions were added to the avatars, with voice recordings manipulated on the dimension of trustworthiness by their speakers.

Results: In study one, as predicted, we found a significant positive linear (p < 0.001) as well as quadratic (p < 0.001) trend in trustworthiness ratings. We found a significant negative correlation between mean trustworthiness and arousal (τ = −.37, p < 0.001), and a positive correlation with valence (τ = 0.88, p < 0.001). In study two, wefound a significant linear (p < 0.001), quadratic (p < 0.001), cubic (p < 0.001), quartic (p < 0.001) and quintic (p = 0.001) trend in trustworthiness ratings. Similarly, to study one, we found a significant negative correlation between mean trustworthiness and arousal (τ = −0.42, p < 0.001) and a positive correlation with valence (τ = 0.76, p < 0.001).

Discussion: We successfully showed that a multisensory graduation of apparent social traits, originally developed for 2D stimuli, can be applied to virtually animated characters, to create a battery of animated virtual humanoid male characters. These virtual …