Using emotional noise to uncloud audio-visual emotion perceptual evaluation
Emotion perception underlies communication and social interaction, shaping how we interpret our world. However, there are many aspects of this process that we still do not fully understand. Notably, we have not yet identified how audio and video information are integrated during the perception of emotion. In this work we present an approach to enhance our understanding of this process using the McGurk effect paradigm, a framework in which stimuli composed of mismatched audio and video cues are presented to human evaluators. Our stimuli set contain sentence-level emotional stimuli with either the same emotion on each channel (“matched”) or different emotions on each channel (“mismatched”, for example, an angry face with a happy voice). We obtain dimensional evaluations (valence and activation) of these emotionally consistent and noisy stimuli using crowd sourcing via Amazon Mechanical Turk. We use these data to investigate the audio-visual feature bias that underlies the evaluation process. We demonstrate that both audio and video information individually contribute to the perception of these dimensional properties. We further demonstrate that the change in perception from the emotionally matched to emotionally mismatched stimuli can be modeled using only unimodal feature variation. These results provide insight into the nature of audio-visual feature integration in emotion perception.