Recognition Profile of Emotions in Natural and Virtual Faces

BACKGROUND: Computer-generated virtual faces become increasingly realistic including the simulation of emotional expressions. These faces can be used as well-controlled, realistic and dynamic stimuli in emotion research. However, the validity of virtual facial expressions in comparison to natural emotion displays still needs to be shown for the different emotions and different age groups. METHODOLOGY/PRINCIPAL FINDINGS: Thirty-two healthy volunteers between the age of 20 and 60 rated pictures of natural human faces and faces of virtual characters (avatars) with respect to the expressed emotions: happiness, sadness, anger, fear, disgust, and neutral. Results indicate that virtual emotions were recognized comparable to natural ones. Recognition differences in virtual and natural faces depended on specific emotions: whereas disgust was difficult to convey with the current avatar technology, virtual sadness and fear achieved better recognition results than natural faces. Furthermore, emotion recognition rates decreased for virtual but not natural faces in participants over the age of 40. This specific age effect suggests that media exposure has an influence on emotion recognition. CONCLUSIONS/SIGNIFICANCE: Virtual and natural facial displays of emotion may be equally effective. Improved technology (e.g. better modelling of the naso-labial area) may lead to even better results as compared to trained actors. Due to the ease with which virtual human faces can be animated and manipulated, validated artificial emotional expressions will be of major relevance in future research and therapeutic applications

The perception of emotion in artificial agents

Given recent technological developments in robotics, artificial intelligence and virtual reality, it is perhaps unsurprising that the arrival of emotionally expressive and reactive artificial agents is imminent. However, if such agents are to become integrated into our social milieu, it is imperative to establish an understanding of whether and how humans perceive emotion in artificial agents. In this review, we incorporate recent findings from social robotics, virtual reality, psychology, and neuroscience to examine how people recognize and respond to emotions displayed by artificial agents. First, we review how people perceive emotions expressed by an artificial agent, such as facial and bodily expressions and vocal tone. Second, we evaluate the similarities and differences in the consequences of perceived emotions in artificial compared to human agents. Besides accurately recognizing the emotional state of an artificial agent, it is critical to understand how humans respond to those emotions. Does interacting with an angry robot induce the same responses in people as interacting with an angry person? Similarly, does watching a robot rejoice when it wins a game elicit similar feelings of elation in the human observer? Here we provide an overview of the current state of emotion expression and perception in social robotics, as well as a clear articulation of the challenges and guiding principles to be addressed as we move ever closer to truly emotional artificial agents

EEVEE : the Empathy-Enhancing Virtual Evolving Environment

Empathy is a multifaceted emotional and mental faculty that is often found to be affected in a great number of psychopathologies, such as schizophrenia, yet it remains very difficult to measure in an ecological context. The challenge stems partly from the complexity and fluidity of this social process, but also from its covert nature. One powerful tool to enhance experimental control over such dynamic social interactions has been the use of avatars in virtual reality (VR); information about an individual in such an interaction can be collected through the analysis of his or her neurophysiological and behavioral responses. We have developed a unique platform, the Empathy-Enhancing Virtual Evolving Environment (EEVEE), which is built around three main components: (1) different avatars capable of expressing feelings and emotions at various levels based on the Facial Action Coding System (FACS); (2) systems for measuring the physiological responses of the observer (heart and respiration rate, skin conductance, gaze and eye movements, facial expression); and (3) a multimodal interface linking the avatar's behavior to the observer's neurophysiological response. In this article, we provide a detailed description of the components of this innovative platform and validation data from the first phases of development. Our data show that healthy adults can discriminate different negative emotions, including pain, expressed by avatars at varying intensities. We also provide evidence that masking part of an avatar's face (top or bottom half) does not prevent the detection of different levels of pain. This innovative and flexible platform provides a unique tool to study and even modulate empathy in a comprehensive and ecological manner in various populations, notably individuals suffering from neurological or psychiatric disorders.Canadian Foundation for Innovation Natural Sciences and Engineering Research Council of Canada to PLJ Canadian Institutes of Health Research Fonds de recherche du Québec – Sant

Is it the real deal? Perception of virtual characters versus humans: an affective cognitive neuroscience perspective

Recent developments in neuroimaging research support the increased use of naturalistic stimulus material such as film, animations, or androids. These stimuli allow for a better understanding of how the brain processes information in complex situations while maintaining experimental control. While avatars and androids are well suited to study human cognition, they should not be equated to human stimuli. For example, the Uncanny Valley hypothesis theorizes that artificial agents with high human-likeness may evoke feelings of eeriness in the human observer. Here we review if, when, and how the perception of human-like avatars and androids differs from the perception of humans and consider how this influences their utilization as stimulus material in social and affective neuroimaging studies. First, we discuss how the appearance of virtual characters affects perception. When stimuli are morphed across categories from non-human to human, the most ambiguous stimuli, rather than the most human-like stimuli, show prolonged classification times and increased eeriness. Human-like to human stimuli show a positive linear relationship with familiarity. Secondly, we show that expressions of emotions in human-like avatars can be perceived similarly to human emotions, with corresponding behavioral, physiological and neuronal activations, with exception of physical dissimilarities. Subsequently, we consider if and when one perceives differences in action representation by artificial agents versus humans. Motor resonance and predictive coding models may account for empirical findings, such as an interference effect on action for observed human-like, natural moving characters. However, the expansion of these models to explain more complex behavior, such as empathy, still needs to be investigated in more detail. Finally, we broaden our outlook to social interaction, where virtual reality stimuli can be utilized to imitate complex social situations

The influence of dynamics and speech on understanding humanoid facial expressions

Human communication relies mostly on nonverbal signals expressed through body language. Facial expressions, in particular, convey emotional information that allows people involved in social interactions to mutually judge the emotional states and to adjust its behavior appropriately. First studies aimed at investigating the recognition of facial expressions were based on static stimuli. However, facial expressions are rarely static, especially in everyday social interactions. Therefore, it has been hypothesized that the dynamics inherent in a facial expression could be fundamental in understanding its meaning. In addition, it has been demonstrated that nonlinguistic and linguistic information can contribute to reinforce the meaning of a facial expression making it easier to be recognized. Nevertheless, few studies have been performed on realistic humanoid robots. This experimental work aimed at demonstrating the human-like expressive capability of a humanoid robot by examining whether the effect of motion and vocal content influenced the perception of its facial expressions. The first part of the experiment aimed at studying the recognition capability of two kinds of stimuli related to the six basic expressions (i.e. anger, disgust, fear, happiness, sadness, and surprise): static stimuli, that is, photographs, and dynamic stimuli, that is, video recordings. The second and third parts were focused on comparing the same six basic expressions performed by a virtual avatar and by a physical robot under three different conditions: (1) muted facial expressions, (2) facial expressions with nonlinguistic vocalizations, and (3) facial expressions with an emotionally neutral verbal sentence. The results show that static stimuli performed by a human being and by the robot were more ambiguous than the corresponding dynamic stimuli on which motion and vocalization were associated. This hypothesis has been also investigated with a 3-dimensional replica of the physical robot demonstrating that even in case of a virtual avatar, dynamic and vocalization improve the emotional conveying capability

Dynamic Interactive Social Cognition Training in Virtual Reality (DiSCoVR) for adults with Autism Spectrum Disorder:A feasibility study

Background: Social cognitive difficulties in Autism Spectrum Disorder (ASD) can affect the daily lives of people with ASD profoundly, impacting the development and maintenance of meaningful social relations. Social cognition training (SCT) is commonly used for improving social functioning, but lacks ecological validity and the ability to effectively mimic social situations. Development of virtual reality (VR) interventions, focusing on enhancing social cognition, could add to the effectiveness of SCT within ASD care, by offering a safe, interactive and practical training setting, where generalization of knowledge and skills to the real-world are promoted. In this paper, our primary aim is to evaluate the feasibility and acceptance by participants and therapists of the Dynamic Interactive Social Cognition Method: Training in Virtual Reality (DiSCoVR) protocol as developed for adults with schizophrenic spectrum disorder (SSD), adapted for ASD (DiSCoVR-A). 26 participants, aged 18-63, took part in a pilot study. 22 participants completed baseline and post-assessment, including primary outcome evaluation assessment through a semi-structured interview. Secondary measures focused on social cognition, emotion recognition, mental flexibility, social anxiety, empathy and social responsiveness and were assessed at baseline (T0), post-treatment (T1), and at follow-up (T2) sixteen weeks after completion of the intervention. Results: Our results show that the majority of participant and therapists found the VR intervention acceptable and feasible, as reported in evaluation questionnaires and interviews. Conclusion: These preliminary findings are promising; however, controlled research is needed to further investigate the effectiveness of VR within social cognition training for adults with ASD