Causal Inference in Nonverbal Dyadic Communication with Relevant Interval Selection and Granger Causality

TL;DR

This paper introduces a method combining relevant interval selection and Granger causality to identify influence directions in nonverbal dyadic communication, effectively capturing transient emotional interactions.

Contribution

It proposes a novel approach that uses facial expressions and interval selection to improve causal inference in dyadic emotional communication.

Findings

Effective detection of influence direction in dyadic interactions.

Relevance of interval selection for transient emotional cues.

Validation on synthetic and experimental data.

Abstract

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.