Dependence of the perception of emotional body movements on concurrent social motor behavior.

UNLABELLED: Embodiment theories hypothesize that the perception of emotions from body movements involves an activation of brain structures that are involved in motor execution during social interaction [1,2]. This predicts that, for identical visual stimulation, bodily emotions should be perceived as more expressive when the observers are involved in social motor behavior. We tested this hypothesis, exploiting advanced VR technology, requiring participants to judge the emotions of an avatar that reacted to their own motor behavior.

METHODS: Based on motion capture data from four human actors, we learned generative models for the body motion during emotional pair interactions, exploiting a framework based on Gaussian Process Latent Variable Models [3]. Using a head mounted display, participants observed ten angry and ten fearful emotional reactions, each with eight repetitions, of a human-sized virtual agent, who turned towards the subject after being tipped on the shoulder. As control conditions, participants watched exactly the same stimuli without doing any movements. The emotion information of the avatars was controlled using a motion morphing method based on the GP model, using 5 emotional strength levels, which were carefully adjusted based on a pre-experiment for each emotion and actor. Participants had to rate the emotional expressiveness of the stimuli on a Likert scale.

RESULTS: Initial data indicates that emotional expressiveness of the stimuli was rated higher when the participants initiate the emotional reaction of the avatar in the VR setup by their own behavior, as compared to pure observation (F > 6.2 and p < 0.03). This confirms an involvement of representations for the execution of social interactive behaviors in the processing of emotional body expressions, consistent with embodiment theories. [1] Wolpert et al., Science 269, 1995. [2] Wicker et al., Neuropsychologia 41, 2003. [3] Lawrence, ND, Advances in neural information processing, 2004. Meeting abstract presented at VSS 2015.