Embodied conversational agents (ECAs) are key social interaction partners in various virtual reality (VR) applications, with their perceived social presence significantly influencing the quality and effectiveness of user-ECA interactions. This article investigates the potential of a novel indirect objective proxy for evaluating social presence, which is traditionally assessed through subjective questionnaires. As proxy we use the Heard Text Recall (HTR) paradigm, originally designed to assess memory performance in listening tasks. In the task participants have to recall information from family stories being told. When combined with a secondary task in a dual-task paradigm, the HTR can also be used to evaluate cognitive spare capacity, which is then correlated with subjectively rated social presence. As a prerequisite for this investigation, we introduce various co-verbal gesture modification techniques and assess their impact on the perceived naturalness of the presenting ECA, a crucial aspect fostering social presence. The main study then explores the applicability of HTR as a proxy for social presence by examining its effectiveness under different multimodal degraders of ECA behavior, including degraded co-verbal gestures, omitted lip synchronization, and the use of synthetic voices. The findings suggest that while HTR shows potential as an objective measure of social presence, its effectiveness is primarily evident in response to substantial changes in ECA behavior. Additionally, the study also highlights the negative effects of synthetic voices and inadequate lip synchronization on perceived social presence, emphasizing the need for careful consideration of these elements in ECA design.

Taking turns in a conversation is a delicate interplay of various signals, which we as humans can easily decipher. Embodied conversational agents (ECAs) communicating with humans should leverage this ability for smooth and enjoyable conversations. Extensive research has analyzed human turn-taking cues, and attempts have been made to predict turn-taking based on observed cues. These cues vary from prosodic, semantic, and syntactic modulation over adapted gesture and gaze behavior to actively used respiration. However, when generating such behavior for social robots or ECAs, often only single modalities were considered, e.g., gazing. We strive to design a comprehensive system that produces cues for all non-verbal modalities: gestures, gaze, and breathing. The system provides valuable cues without requiring speech content adaptation. We evaluated our system in a VR-based user study with N = 32 participants executing two subsequent tasks. First, we asked them to listen to two ECAs taking turns in several conversations. Second, participants engaged in taking turns with one of the ECAs directly. We examined the system's usability and the perceived social presence of the ECAs' turn-taking behavior, both with respect to each individual non-verbal modality and their interplay. While we found effects of gesture manipulation in interactions with the ECAs, no effects on social presence were found.