62,944 research outputs found

    Towards the improvement of self-service systems via emotional virtual agents

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    Affective computing and emotional agents have been found to have a positive effect on human-computer interactions. In order to develop an acceptable emotional agent for use in a self-service interaction, two stages of research were identified and carried out; the first to determine which facial expressions are present in such an interaction and the second to determine which emotional agent behaviours are perceived as appropriate during a problematic self-service shopping task. In the first stage, facial expressions associated with negative affect were found to occur during self-service shopping interactions, indicating that facial expression detection is suitable for detecting negative affective states during self-service interactions. In the second stage, user perceptions of the emotional facial expressions displayed by an emotional agent during a problematic self-service interaction were gathered. Overall, the expression of disgust was found to be perceived as inappropriate while emotionally neutral behaviour was perceived as appropriate, however gender differences suggested that females perceived surprise as inappropriate. Results suggest that agents should change their behaviour and appearance based on user characteristics such as gender

    A Motivational Determinant of Facial Emotion Recognition : Regulatory Focus Affects Recognition of Emotions in Faces

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    Funding: The research was supported by The Netherlands Organization for Scientific Research (NWO, project 452-07-006). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

    Gender Specific Disruptions in Emotion Processing in Younger Adults with Depression

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    Background: One of the principal theories regarding the biological basis of major depressive disorder (MDD) implicates a dysregulation of emotion-processing circuitry. Gender differences in how emotions are processed and relative experience with emotion processing might help to explain some of the disparities in the prevalence of MDD between women and men. This study sought to explore how gender and depression status relate to emotion processing. Methods: This study employed a 2 (MDD status) × 2 (gender) factorial design to explore differences in classifications of posed facial emotional expressions (N=151). Results: For errors, there was an interaction between gender and depression status. Women with MDD made more errors than did nondepressed women and men with MDD, particularly for fearful and sad stimuli (Ps Ps P=.01). Men with MDD, conversely, performed similarly to control men (P=.61). Conclusions: These results provide novel and intriguing evidence that depression in younger adults (years) differentially disrupts emotion processing in women as compared to men. This interaction could be driven by neurobiological and social learning mechanisms, or interactions between them, and may underlie differences in the prevalence of depression in women and men. Depression and Anxiety, 2009. Published 2008 Wiley-Liss, Inc

    First impressions: A survey on vision-based apparent personality trait analysis

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    © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.Peer ReviewedPostprint (author's final draft

    The perception of emotion in artificial agents

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    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

    The Effects of Parental Behavior on Infants' Neural Processing of Emotion Expressions

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    Infants become sensitive to emotion expressions early in the 1st year and such sensitivity is likely crucial for social development and adaptation. Social interactions with primary caregivers may play a key role in the development of this complex ability. This study aimed to investigate how variations in parenting behavior affect infants' neural responses to emotional faces. Event-related potentials (ERPs) to emotional faces were recorded from 40 healthy 7-month-old infants (24 males). Parental behavior was assessed and coded using the Emotional Availability Scales during free-play interaction. Sensitive parenting was associated with increased amplitudes to positive facial expressions on the face-sensitive ERP component, the negative central. Findings are discussed in relation to the interactive mechanisms influencing how infants neurally encode positive emotions

    The Curvilinear Relationship between Age and Emotional Aperture : The Moderating Role of Agreeableness

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    The capability to correctly recognize collective emotion expressions (i.e., emotional aperture) is crucial for effective social and work-related interactions. Yet, little remains known about the antecedents of this ability. The present study therefore aims to shed new light onto key aspects that may promote or diminish an individual’s emotional aperture. We examine the role of age for this ability in an online sample of 181 participants (with an age range of 18 to 72 years, located in Germany), and we investigate agreeableness as a key contingency factor. Among individuals with lower agreeableness, on the one hand, our results indicate a curvilinear relationship between age and emotional aperture, such that emotional aperture remains at a relatively high level until these individuals’ middle adulthood (with a slight increase until their late 30s) and declines afterwards. Individuals with higher agreeableness, on the other hand, exhibit relatively high emotional aperture irrespective of their age. Together, these findings offer new insights for the emerging literature on emotional aperture, illustrating that specific demographic and personality characteristics may jointly shape such collective emotion recognition
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