Strong evidence for universals in facial expressions: A reply to Russell's mistaken critique.

Cited by (193)

Items 101–193 of 193

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5. Reconocimiento de la expresión facial de la emoción en mexicanos universitarios
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11. Efficient Facial Emotion Recognition Using An Optimized Deep Learning Model Based On Quantum Gazelle Optimization Algorithm
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14. La inteligencia emocional rasgo, la autoeficacia para el liderazgo y su vinculación a procesos afectivos grupales, cognitivos y de desempeño
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15. Il ruolo delle emozioni nell’esperienza estetica
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16. The effects of personal relationships on facial displays
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17. Automatic local shape spectrum analysis for 3D facial expression recognition
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18. Automated Real Time Emotion Recognition Using Facial Expression Analysis
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19. A Computational Model for Managing Emotions and Affections in Emotional Learning Platforms and Learning Experience in Emotional Computing Context
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21. Who Would Argue?
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22. 认知与情绪的交互作用
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23. Commentaries on "Caregiving: The Forgotten Element in Attachment"
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24. PREMOC: Plataforma de reconocimiento multimodal de emociones
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25. Basic Emotion Theory
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26. Facial Expressions and Non Verbal Comunication
    Mihai Valentin Ciolacu · 2014 · Procedia - Social and Behavioral Sciences
27. Facial expression recognition by applying multi-step integral projection and SVMs
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28. On the relative impact of male and female sellers’ profile image and its facial expressions upon peer users’ behavior on AirbnbTM
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29. Rehearsals on Faciality Diagrams of AI Avatars
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30. Combining integral projection and Gabor transformation for automatic facial feature detection and extraction
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31. Risk Aversion and Emotions
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32. Do People Agree on How Positive Emotions are Expressed? A Survey of Four Emotions and Five Modalities across 11 Cultures
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33. A multiscale and multilevel fusion network based on ResNet and MobileFaceNet for facial expression recognition
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34. Designing a Music Content Retrieval System Supporting Korean
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35. InFER++: Real-World Indian Facial Expression Dataset
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36. Missing Value Imputation Method by Using Bayesian Network with Weighted Learning
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37. Gibt es ein Lachen der Schadenfreude?
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38. Multi-Granularity Feature Fusion Network for Dynamic Sequential Facial Expression Recognition
    Guo Xiao-Ying, Mao Xin-Chen, Wenshu Li, Bai Ru-Yi · 2023 · China Automation Congress (CAC)
39. EXPLORING THE PHENOMENON OF SEXUAL HARASSMENT IN CONTEXT OF DRESSING AND NON-VERBAL EMOTIONAL CUES IN PAKISTANI CULTURE
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40. What Eye Movements and Facial Expressions Tell Us about User-Friendliness: Testing a Tool for Communicators and Journalists
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41. Emotion Universals: The Foundation from which Cultural Variability of Emotion Emerges
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42. Positive and negative facial valence perception are modulated differently by eccentricity in the parafovea: Replication from KDEF to NimStim
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43. Reflexes and Shared Intentionality in the Origins of Emotions Development: A Scoping Review of Studies on Blinking in Infants
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44. New Findings for Face Processing Deficits in the Mental Disorder of Schizophrenia
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45. Kansei Engineering in Gigakuman Character Design
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46. Recognizing human emotional state via SRC in Fractional Fourier Domain
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47. Application of the 2D-FrFT combined with fuzzy fusion classification algorithm in human emotional state recognition
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48. Importancia de las emociones en la adquisición de nuevos aprendizajes mediatizados por aspectos relacionales
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50. Facial expression recognition using Eigen face approach
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51. Facial Emotion Recognition with AI
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52. Deterioro de las habilidades emocinales en la aleximia nición y la cuestión de las emociones básicas. El primer problema no discute la participación de factores biológicos y cognitivos, sino que lo que plantea es si las emociones constituyen un proceso biológico o cognitivo, es decir, cuál tiene primacía en las emociones (Lazarus, 1984; Zajonc, 1984). Por un lado, Zajonc e Izard, que dan primacía a lo biológico, consideran que las emociones son activadas a través de las expresiones faciales (feedback facial), las tasas de descarga neuronal o la influencia de las vías neuronales límbicas. Estos fenómenos surgen del procesamiento subcortical, incluyendo o no la actividad cortical. Por el contrario, Lazarus y James plantean una primacía de lo cognitivo, ya que consideran las emociones como fenómenos postcognitivos que surgen de influencias cognitivas y requieren de procesos cognitivos antecedentes. Lazarus resalta la importancia de lo que denominó evaluación perceptiva primitiva, que consiste en cómo el sujeto, para poder tener una respuesta emocional, tiene que realizar una evaluación cognitiva en función de lo relevante que sea. Entre ambas posiciones, se formulan distintas posturas intermedias, de entre las cuales destacamos la de Plutchik (1980a) que considera las emociones como un proceso dinámico en el que se integran tanto los aspectos cognitivos como los biológicos. Por otra parte, Buck (1985) admite la existencia de dos sistemas emocionales paralelos que activan y regulan las emociones simultáneamente. Según este autor el biológico es innato y automático, mientras que el cognitivo se adquiere mediante la historia social y cultural de la persona. El segundo problema teórico en este ámbito plantea si existe un número determinado de emociones básicas o un número ilimitado de ellas. La perspectiva biológica/evolucionista afirma la existencia de unas emociones innatas como la rabia o el miedo, a partir de las cuales se derivan el resto de las reacciones afectivas. De acuerdo a este planteamiento se pone de manifiesto la universalidad de la expresión facial y el reconocimiento de estas emociones básicas en las diferentes culturas. No obstante, frente a esta postura de la universalidad de las emociones y su reconocimiento innato, otros autores abogan por el relativismo de las manifestaciones emocionales y de su reconocimiento (Russell, 1994). A pesar de estas controversias, no existe acuerdo en el número e identidad de estas emociones consideradas básicas. Así, para Plutchik (1980b), estas emociones básicas son el miedo, la ira, la alegría, la tristeza, la aceptación, el asco, la anticipación y la sorpresa. En cambio, Izard (1991) identifica como tales el interés, el placer, la sorpresa, la tristeza, la ira, el asco, el desprecio y el miedo. Mientras que para Ekman (1993) son el miedo, la ira, la alegría, la sorpresa, el asco, la tristeza y el desprecio. Esta última clasificación es la más utilizada en los diferentes estudios sobre emoción. 153
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53. Personalidade e expressão facial da emoção: investigando padrões em um caso de assassinato em massa
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54. Emotion Recognition using Facial Expression
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55. Subject Know Thyself? Comparing Self-Reported and Observed Emotions and Their Influence on Political Attitudes
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56. Navigation Key Using Face Expression in Endless Game
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57. Positive and negative facial valence perception are modulated differently by eccentricity in the parafovea
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58. Consumer Evaluation of Cheese: Linking Hedonics, Emotions and Perception of Product Attributes
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59. Emotion recognition : from physiological signals to affective states
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60. Thresholding Approach based on GPU for Facial Expression Recognition
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61. Robust representation and recognition of facial emotions
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62. Meta-cognitive learning algorithm for neuro-fuzzy inference systems
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63. The twin taboos of discussing religion and politics : a study of six "basic" emotions and interpersonal relationships in response to Rick Perry's "Strong"
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64. Conclusion
    Berit “Brit” Brogaard · 2020 · Oxford University Press eBooks
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66. The Influence of Spatial Position on Affect
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67. Methods to assess food-evoked emotions across cultures
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68. The Influence of Spatial Position on Affect
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69. Prosodic cues to emotion : perceptual and acoustic analyses
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70. Book Review
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71. The Organizational Neuroscience of Emotions
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72. References
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73. Cognitive and Affective Factors in Relation to Learning
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74. The Allan McNeill Postgraduate Poster Prize Award: Angry, furious, mad as hell! The effect of satiating different emotion words on emotion perception
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75. PREDICTION OF REAL-TIME FACIAL EMOTION BASED ON MACHINE LEARNING ALGORITHM
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76. References
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77. Facial expression recognition based scoring system for restaurants by using deep learning concepts
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78. Facial expression recognition for a seven-class small and medium-sized dataset based on transfer learning CNNs
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79. Newton Makes Me Happy: Cycling Emotions during Science Text Reading
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80. Emotions in archetypal media content
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81. A predição da preferência por fachadas de edifícios residenciais multifamiliares sob a ótica da percepção ambiental e da experiência do usuário
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82. Realtime Facial Emotion Detection
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83. Facial Expression Based Restaurant Scoring System
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84. Copyright Page
    Berit “Brit” Brogaard · 2020 · Oxford University Press eBooks
85. Dedication
    Berit “Brit” Brogaard · 2020 · Oxford University Press eBooks
86. Prologue
    Berit “Brit” Brogaard · 2020 · Oxford University Press eBooks
87. Notes
    Berit “Brit” Brogaard · 2020 · Oxford University Press eBooks
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93. Üniversite Öğrencilerinde Gelişmeleri Kaçırma Korkusu (FoMO): Sosyal Ağ Bağımlılığı, Bağlanma Stilleri ve Duygu Düzenleme Becerilerinin Yordayıcı Rolü
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