Discrete Emotion Profiles in Old Age: Stability and Change for Better or Worse

Although discrete emotions can change in salience across adulthood, little is known about developmental shifts in the co-occurrence of multiple discrete emotions. The present study (n=389, Mage=73) adopted a person-centered approach to identify stability and change in commonly-occurring profiles of calmness, excitement, sadness, and anger. Daily emotions were assessed over 1-week periods at baseline and two years later. Latent class analyses yielded consistent 3-profile solutions at both waves: a positive emotion (high calmness-moderate excitement-low sadness and anger), a mixed emotion (moderate/high calmness-moderate excitement, sadness, and anger), and an apathetic emotion profile (low calmness, excitement, sadness, and anger). Latent transition analyses revealed both stability (82% remained in the same profile) and change (18% changed profiles) in profile membership. Higher baseline optimism and fewer chronic conditions were associated with adaptive changes in profile membership. Findings point to the importance of considering the co-occurrence of distinct emotions in studying emotional aging.

Understanding the Link Between Discrete Negative Emotions and Empathic Accuracy

Extant theories have suggested that negative emotions generally harm cognitive processes. However, adopting a discrete emotion perspective, in this study, we predicted that only anger and fear but not sadness should be negatively associated with empathic accuracy, a process that has been shown to be cognitively highly demanding. Over 100 participants (Mage = 66.66 years, SDage = 1.00) reported their emotional reactions in response to a negative film in the laboratory, documented their everyday momentary emotions six times a day over seven consecutive days, and completed a film-based empathic accuracy test. Initial findings suggest that only fear but not anger or sadness was related to empathic accuracy. More specifically, high levels of fear both in the laboratory and in everyday life predicted low empathic accuracy. This pattern of findings will be discussed in the context of discrete emotions theories.

Real Time Facial Gesture Recognition using Deep Learning

The human faces have vibrant frequency of characteristics, which makes it difficult to analyze the facial expression. Automated real time emotions recognition with the help of facial expressions is a work in computer vision. This environment is an important and interesting tool between the humans and computers. In this investigation an environment is created which is capable of analyzing the person’s emotions using the real time facial gestures with the help of Deep Neural Network. It can detect the facial expression from any image either real or animated after facial extraction (muscle position, eye expression and lips position). This system is setup to classify images of human faces into seven discrete emotion categories using Convolutional Neural Networks (CNNs). This type of environment is important for social interaction.

Perceiving Social-Emotional Volatility and Triggered Causes of COVID-19

Health support has been sought by the public from online social media after the outbreak of novel coronavirus disease 2019 (COVID-19). In addition to the physical symptoms caused by the virus, there are adverse impacts on psychological responses. Therefore, precisely capturing the public emotions becomes crucial to providing adequate support. By constructing a domain-specific COVID-19 public health emergency discrete emotion lexicon, we utilized one million COVID-19 theme texts from the Chinese online social platform Weibo to analyze social-emotional volatility. Based on computed emotional valence, we proposed a public emotional perception model that achieves: (1) targeting of public emotion abrupt time points using an LSTM-based attention encoder-decoder (LAED) mechanism for emotional time-series, and (2) backtracking of specific triggered causes of abnormal volatility in a cognitive emotional arousal path. Experimental results prove that our model provides a solid research basis for enhancing social-emotional security outcomes.

Antecedents and Appraisals of Triumph across Four Countries

This study examined cross-cultural similarities and differences in antecedents and appraisals of triumph. Participants in the U.S., Serbia, Russia, and Japan provided open-ended descriptions of previous antecedent events that elicited experiences of triumph, and completed a standard appraisal questionnaire about those events. Events that elicited pride were also included for comparison. The open-ended responses were coded using a framework that delineated theoretical characteristics of triumph based on previous research. Findings indicated cross-cultural similarities in the antecedents and appraisals of triumph-eliciting events. Cultural variations were also found, especially between Japan and the other cultural groups and with regard to self-evaluations, which suggested the role of culture in triggering and appraising emotion-eliciting events. These findings extended empirical evidence about these important components of triumph, further contributing to its possibility as a discrete emotion.

Emoji as Affective Symbols: Affective Judgments of Emoji, Emoticons, and Human Faces Varying in Emotional Content

An important function of emoji as communicative symbols is to convey emotional content from sender to receiver in computer-mediated communication, e. g., WhatsApp. However, compared with real faces, pictures or words, many emoji are ambiguous because they do not symbolize a discrete emotion or feeling state. Thus, their meaning relies on the context of the message in which they are embedded. Previous studies investigated affective judgments of pictures, faces, and words suggesting that these stimuli show a typical distribution along the big two emotion dimensions of valence and arousal. Also, emoji and emoticons have been investigated recently for their affective significance. The present study extends previous research by investigating affective ratings of emoji, emoticons and human faces and by direct comparison between them. In total, 60 stimuli have been rated by 83 participants (eight males, age: 18–49 years), using the non-verbal Self-Assessment Manikin Scales for valence and arousal. The emotionality of the stimuli was measured on a 9-point Likert scale. The results show significant main effects of the factors “stimulus category” and “discrete emotion” including emotionality, valence and arousal. Also, the interaction between these two main factors was significant. Emoji elicited highest arousal, whereas stimuli related to happiness were rated highest in valence across stimulus categories. Angry emoji were rated highest in emotionality. Also, the discrete emotion was best recognized in emoji, followed by human face stimuli and lastly emoticons.

Emotions Induced by Recalling Memories About Interpersonal Stress

The emotions that people experience in day-to-day social situations are often mixed emotions. Although autobiographical recall is useful as an emotion induction procedure, it often involves recalling memories associated with a specific discrete emotion (e.g., sadness). However, real-life emotions occur freely and spontaneously, without such constraints. To understand real-life emotions, the present study examined characteristics of emotions that were elicited by recalling “stressful interpersonal events in daily life” without the targeted evocation of a specific discrete emotion. Assuming generation of mixed and complex emotions, emotional groups with relatively strong correlation of multiple emotions according to surprise, fear, anger, disgust, sadness, and happiness were expected. Seventy-two university students (35 males, mean age: 19.69 ± 1.91 years; 37 females, 20.03 ± 2.42) participated in the study. In the emotion induction procedure, participants freely recalled memories as per the instructions on a monitor, and then responded silently to a series of questions concerning any one recalled incident. Assessments of emotional states using emotion scales and another item indicated that validated emotional changes had occurred during the task. Inter-correlations between six emotions demonstrated an emotional group consisting of disgust and anger, which frequently occur as negative interpersonal feelings, and that of fear and sadness. This indicated generation of mixed and complex emotions as experienced in social life. Future studies concerning relationships between these emotions and other factors, including neurophysiological responses, may facilitate further understanding about relationships between mental and physiological processes occurring in daily life.