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  1. Discrete Emotions or Dimensions? The Role of Valence Focus and Arousal Focus.L. Feldman Barrett - 1998 - Cognition and Emotion 12 (4):579-599.
    The present study provides evidence that valence focus and arousal focus are important processes in determining whether a dimensional or a discrete emotion model best captures how people label their affective states. Individuals high in valence focus and low in arousal focus fit a dimensional model better in that they reported more co-occurrences among like-valenced affective states, whereas those lower in valence focus and higher in arousal focus fit a discrete model better in that they reported fewer co-occurrences between like-valenced (...)
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  • (1 other version)Brief report the dynamic aspects of emotional facial expressions.Wataru Sato & Sakiko Yoshikawa - 2004 - Cognition and Emotion 18 (5):701-710.
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  • Smiling reflects different emotions in men and women.Simine Vazire, Laura P. Naumann, Peter J. Rentfrow & Samuel D. Gosling - 2009 - Behavioral and Brain Sciences 32 (5):403-405.
    We present evidence that smiling is positively associated with positive affect in women and negatively associated with negative affect in men. In line with Vigil's model, we propose that, in women, smiling signals warmth (trustworthiness cues), which attracts fewer and more intimate relationships, whereas in men, smiling signals confidence and lack of self-doubt (capacity cues), which attracts numerous, less-intimate relationships.
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  • Sensitivity to genuine versus posed emotion specified in facial displays.Tracey McLellan, Lucy Johnston, John Dalrymple-Alford & Richard Porter - 2010 - Cognition and Emotion 24 (8):1277-1292.
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  • Can perceivers recognise emotions from spontaneous expressions?Disa A. Sauter & Agneta H. Fischer - 2017 - Cognition and Emotion 32 (3):504-515.
    ABSTRACTPosed stimuli dominate the study of nonverbal communication of emotion, but concerns have been raised that the use of posed stimuli may inflate recognition accuracy relative to spontaneous expressions. Here, we compare recognition of emotions from spontaneous expressions with that of matched posed stimuli. Participants made forced-choice judgments about the expressed emotion and whether the expression was spontaneous, and rated expressions on intensity and prototypicality. Listeners were able to accurately infer emotions from both posed and spontaneous expressions, from auditory, visual, (...)
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  • A Review of Dynamic Datasets for Facial Expression Research. [REVIEW]Eva G. Krumhuber, Lina Skora, Dennis Küster & Linyun Fou - 2017 - Emotion Review 9 (3):280-292.
    Temporal dynamics have been increasingly recognized as an important component of facial expressions. With the need for appropriate stimuli in research and application, a range of databases of dynamic facial stimuli has been developed. The present article reviews the existing corpora and describes the key dimensions and properties of the available sets. This includes a discussion of conceptual features in terms of thematic issues in dataset construction as well as practical features which are of applied interest to stimulus usage. To (...)
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  • Universality Revisited.Nicole L. Nelson & James A. Russell - 2013 - Emotion Review 5 (1):8-15.
    Evidence does not support the claim that observers universally recognize basic emotions from signals on the face. The percentage of observers who matched the face with the predicted emotion (matching score) is not universal, but varies with culture and language. Matching scores are also inflated by the commonly used methods: within-subject design; posed, exaggerated facial expressions (devoid of context); multiple examples of each type of expression; and a response format that funnels a variety of interpretations into one word specified by (...)
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  • The Autonomic Nervous System and Emotion.Robert W. Levenson - 2014 - Emotion Review 6 (2):100-112.
    In many evolutionary/functionalist theories, emotions organize the activity of the autonomic nervous system and other physiological systems. Two kinds of patterned activity are discussed: coherence, and specificity. For each kind of patterning, significant methodological obstacles are considered that need to be overcome before empirical studies can adequately test theories and resolve controversies. Finally, links that coherence and specificity have with health and well-being are considered.
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  • Effects of Dynamic Aspects of Facial Expressions: A Review.Eva G. Krumhuber, Arvid Kappas & Antony S. R. Manstead - 2013 - Emotion Review 5 (1):41-46.
    A key feature of facial behavior is its dynamic quality. However, most previous research has been limited to the use of static images of prototypical expressive patterns. This article explores the role of facial dynamics in the perception of emotions, reviewing relevant empirical evidence demonstrating that dynamic information improves coherence in the identification of affect (particularly for degraded and subtle stimuli), leads to higher emotion judgments (i.e., intensity and arousal), and helps to differentiate between genuine and fake expressions. The findings (...)
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  • Perceptual and affective mechanisms in facial expression recognition: An integrative review.Manuel G. Calvo & Lauri Nummenmaa - 2016 - Cognition and Emotion 30 (6).
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  • Caricaturing facial expressions.Andrew J. Calder, Duncan Rowland, Andrew W. Young, Ian Nimmo-Smith, Jill Keane & David I. Perrett - 2000 - Cognition 76 (2):105-146.
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  • Discrete Emotions or Dimensions? The Role of Valence Focus and Arousal Focus.Lisa Feldman Barrett - 1998 - Cognition and Emotion 12 (4):579-599.
    The present study provides evidence that valence focus and arousal focus are important processes in determining whether a dimensional or a discrete emotion model best captures how people label their affective states. Individuals high in valence focus and low in arousal focus fit a dimensional model better in that they reported more co-occurrences among like-valenced affective states, whereas those lower in valence focus and higher in arousal focus fit a discrete model better in that they reported fewer co-occurrences between like-valenced (...)
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  • Introducing the Geneva Multimodal Emotion Portrayal (GEMEP) corpus. Bänziger, T. & Scherer & Kr - 2010 - In Klaus R. Scherer, Tanja Bänziger & Etienne Roesch (eds.), A Blueprint for Affective Computing: A Sourcebook and Manual. Oxford University Press.
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