BACKGROUND Significant information exchange occurs between a doctor and patient through nonverbal communication such as gestures, body position, and eye gaze. In addition, empathy is an important trust-building element in a physician: patient relationship. Previous work validates the use of virtual patients (VP) to teach and assess content items related to history-taking and basic communication...

The three-dimensional models used to embody intelligent agents are becoming increasingly realistic. We discuss two studies in which we embodied intelligently behaving virtual agents with photographically realistic models of human subjects' heads and faces. We then immersed those subjects with agents embodied with their virtual selves and compared their interactions and nonverbal behaviors to se...

Virtual agents are autonomous software characters that support social interactions with human users. With the emergence of better graphical representation and control over the virtual agent’s embodiment, communication through nonverbal behaviors has become an active research area. Researchers have taken different approaches to author the behaviors of virtual agents. In this work, we present our...

This paper describes the addition of nonverbal affective behaviors to a humanoid robot, as well as recognition of these behaviors based on an online survey. The expressive behaviors were implemented in the context of a framework for affective robot behavior (TAME) and span across three types of affective phenomena: traits, moods and emotions.

Learners experience a wide array of cognitive and affective states during tutoring. Detecting and responding to these states is a core problem of adaptive learning environments that aim to foster motivation and increase learning. Recognizing learner affect through nonverbal behavior is particularly challenging, as students display affect across numerous modalities. This study utilizes an automa...

In this report, we present a system that employs computer vision and machine learning methods to automatically detect and annotate human nonverbal cues in video data. Our system uses face tracking to extract low-level facial motion parameters, and a set of support vector machines to detect and classify higher-level nonverbal behavior cues. For all behaviors detected, time-series annotations are...

A persistent question in the deception literature has been the extent to which nonverbal behaviors can reliably distinguish between truth and deception. It has been argued that deception instigates cognitive load and arousal that are betrayed through visible nonverbal indicators. Yet, empirical evidence has often failed to find statistically significant or strong relationships. Given that inter...

Virtual human research has often modeled nonverbal behaviors based on the findings of psychological research. In recent years, however, there have been growing efforts to use automated, data-driven approaches to find patterns of nonverbal behaviors in video corpora and even thereby discover new factors that have not been previously documented. However, there have been few studies that compare h...

We investigate the verbal and nonverbal means for grounding, and propose a design for embodied conversational agents that relies on both kinds of signals to establish common ground in human-computer interaction. We analyzed eye gaze, head nods and attentional focus in the context of a direction-giving task. The distribution of nonverbal behaviors differed depending on the type of dialogue move ...