Natural human postural oscillations enhance the empathic response to a facial pain expression in a virtual character

Abstract

Virtual reality platforms producing interactive and highly realistic characters are being used more and more as a research tool in social and affective neuroscience to better capture both the dynamics of emotion communication and the unintentional and automatic nature of emotional processes. While idle motion (i.e., non-communicative movements) is commonly used to create behavioural realism, its use to enhance the perception of emotion expressed by a virtual character is critically lacking. This study examined the influence of naturalistic (i.e., based on human motion capture) idle motion on two aspects (the perception of other's pain and affective reaction) of an empathic response towards pain expressed by a virtual character. In two experiments, 32 and 34 healthy young adults were presented video clips of a virtual character displaying a facial expression of pain while its body was either static (still condition) or animated with natural postural oscillations (idle condition). The participants in Experiment 1 rated the facial pain expression of the virtual human as more intense, and those in Experiment 2 reported being more touched by its pain expression in the idle condition compared to the still condition, indicating a greater empathic response towards the virtual human's pain in the presence of natural postural oscillations. These findings are discussed in relation to the models of empathy and biological motion processing. Future investigations will help determine to what extent such naturalistic idle motion could be a key ingredient in enhancing the anthropomorphism of a virtual human and making its emotion appear more genuine.

Figure 1

Left side: virtual character at the beginning of the clip (neutral expression). Right side: virtual character expressing pain ( AUs at 60% of maximum contraction) (Fig. 1 was drawn using Blender 2.79 http://www.blender.org).

Figure 2

Logistic modelling of “Very Intense Pain” responses according to facial pain expression intensity in the still and idle conditions. PSE and JND correspond to the mean of PSE and JND obtained from the modelling of each participant.

Figure 3

Mean of PSE and JND obtained from the logistic modelling of each participant’s “Very Intense Pain” responses in the still and idle conditions. Error bars correspond to confidence intervals at 95%.

Figure 4

Logistic modelling of “Touched” responses according to facial pain expression intensity in the still and idle conditions. PSE and JND correspond to the mean of PSE and JND obtained from the modelling of each participant.

Figure 5

Mean of PSE and JND obtained from the logistic modelling of “Touched” responses of each participant in the still and idle conditions. Error bars correspond to the confidence intervals at 95%.