Analyzing the Direction of Emotional Influence in Nonverbal Dyadic Communication: A Facial-Expression Study

2021

Article

ps

Identifying the direction of emotional influence in a dyadic dialogue is of increasing interest in the psychological sciences with applications in psychotherapy, analysis of political interactions, or interpersonal conflict behavior. Facial expressions are widely described as being automatic and thus hard to be overtly influenced. As such, they are a perfect measure for a better understanding of unintentional behavior cues about socio-emotional cognitive processes. With this view, this study is concerned with the analysis of the direction of emotional influence in dyadic dialogues based on facial expressions only. We exploit computer vision capabilities along with causal inference theory for quantitative verification of hypotheses on the direction of emotional influence, i.e., cause-effect relationships, in dyadic dialogues. We address two main issues. First, in a dyadic dialogue, emotional influence occurs over transient time intervals and with intensity and direction that are variant over time. To this end, we propose a relevant interval selection approach that we use prior to causal inference to identify those transient intervals where causal inference should be applied. Second, we propose to use fine-grained facial expressions that are present when strong distinct facial emotions are not visible. To specify the direction of influence, we apply the concept of Granger causality to the time-series of facial expressions over selected relevant intervals. We tested our approach on newly, experimentally obtained data. Based on quantitative verification of hypotheses on the direction of emotional influence, we were able to show that the proposed approach is promising to reveal the cause-effect pattern in various instructed interaction conditions.

@article{LeaMueller_2021_CD,
  title = {Analyzing the Direction of Emotional Influence in Nonverbal Dyadic Communication: A Facial-Expression Study},
  author = {Shadaydeh, Maha and M{\"u}ller, Lea and Schneider, Dana and Thuemmel, Martin and Kessler, Thomas and Denzler, Joachim},
  journal = {IEEE Access},
  volume = {9},
  pages = {73780-73790},
  publisher = {IEEE},
  year = {2021},
  doi = {10.1109/ACCESS.2021.3078195}
}