  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 overtly influence. As such, they are a perfect measure for a better
understanding of unintentional behavior cues about social-emotional cognitive
processes. With this view, this study is concerned with the analysis of the
direction of emotional influence in dyadic dialogue 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., causal 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 the quantitative verification of hypotheses on the
direction of emotional influence, we were able to show that the proposed
approach is most promising to reveal the causal effect pattern in various
instructed interaction conditions.
