Causal Inference in Nonverbal Dyadic Communication with Relevant Interval Selection and Granger Causality
This work addresses the challenge of causal inference in human emotional communication for psychology and AI researchers, but it is incremental as it builds on existing methods like Granger causality.
The paper tackled the problem of identifying cause-effect relationships in nonverbal dyadic communication by using facial expressions and relevant interval selection to handle transient influences, showing that this approach effectively reveals patterns in various interaction conditions.
Human nonverbal emotional communication in dyadic dialogs is a process of mutual influence and adaptation. Identifying the direction of influence, or cause-effect relation between participants is a challenging task, due to two main obstacles. First, distinct emotions might not be clearly visible. Second, participants cause-effect relation is transient and variant over time. In this paper, we address these difficulties by using facial expressions that can be present even when strong distinct facial emotions are not visible. We also propose to apply a relevant interval selection approach prior to causal inference to identify those transient intervals where adaptation process occurs. To identify the direction of influence, we apply the concept of Granger causality to the time series of facial expressions on the set of relevant intervals. We tested our approach on synthetic data and then applied it to newly, experimentally obtained data. Here, we were able to show that a more sensitive facial expression detection algorithm and a relevant interval detection approach is most promising to reveal the cause-effect pattern for dyadic communication in various instructed interaction conditions.