Experimental results of affective valence and arousal to avatar's facial expressions

Cyberpsychol Behav. 2005 Oct;8(5):493-503. doi: 10.1089/cpb.2005.8.493.

Abstract

The objectives of this study were to propose a method of presenting dynamic facial expressions to experimental subjects, in order to investigate human perception of avatar's facial expressions of different levels of emotional intensity. The investigation concerned how perception varies according to the strength of facial expression, as well as according to an avatar's gender. To accomplish these goals, we generated a male and a female virtual avatar with five levels of intensity of happiness and anger using a morphing technique. We then recruited 16 normal healthy subjects and measured each subject's emotional reaction by scoring affective arousal and valence after showing them the avatar's face. Through this study, we were able to investigate human perceptual characteristics evoked by male and female avatars' graduated facial expressions of happiness and anger. In addition, we were able to identify that a virtual avatar's facial expression could affect human emotion in different ways according to the avatar's gender and the intensity of its facial expressions. However, we could also see that virtual faces have some limitations because they are not real, so subjects recognized the expressions well, but were not influenced to the same extent. Although a virtual avatar has some limitations in conveying its emotion using facial expressions, this study is significant in that it shows that a new potential exists to use or manipulate emotional intensity by controlling a virtual avatar's facial expression linearly using a morphing technique. Therefore, it is predicted that this technique may be used for assessing emotional characteristics of humans, and may be of particular benefit for work with people with emotional disorders through a presentation of dynamic expression of various emotional intensities.

MeSH terms

  • Adult
  • Affect*
  • Arousal*
  • Discrimination Learning
  • Facial Expression*
  • Female
  • Humans
  • Internal-External Control
  • Linear Models
  • Male
  • Nonverbal Communication*
  • Pattern Recognition, Visual*
  • Sex Factors
  • User-Computer Interface*