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The present theory section deals with

Mimicry of appearance or of facial, vocal, or gestural expressions emerges frequently among members of different species. When such mimicry directly relates to affective aspects of an interaction, researchers talk about "emotional mimicry". Emotional mimicry has been amply documented but its functionality is still debated. Why and when do people mimic the expressions of others, who benefits, the mimicker or the mimicked, and how do they benefit? Which processes underlie emotional mimicry? Is it completely automatic and unconscious or can it be deliberate and conscious? The current Theory Section addresses these questions from different theoretical perspectives. The invited article by Hess and Fischer focused on the role of mimicry in social regulation and social bonding. The invited comment by Krets and Akyüz highlights information gathering and prediction in social interaction. The invited comment by Bernhold and Giles emphasizes vocal communication and its role in interpersonal accommodation. In this editorial, I propose the different theoretical perspectives may be integrated by assuming a multilevel appraisal and response generation mechanism. I also suggest that emotional mimicry research may be broadened by including social learning, vocal imitation, interspecies comparisons, and affective computing approaches.

Over the last century, emotion research has been beset by the problem of major disagreements with respect to the definition of the phenomenon and an abundance of different theories. Arguably, these divergences have had adverse effects on theory development, on the theoretical foundations of empirical research, and on knowledge accumulation in the study of emotion. Similar problems have been encountered in other areas of behavioural science. Increasingly, there have been calls to work towards some form of theory integration. In contrast, here an effort is made to show that a reasonable degree of

The concept of emotion disposition is proposed as an important dimension of individual differences. It refers to a stable tendency to experience certain emotions more or less frequently or intensely for similar classes of situations or events in daily experience than the majority of other people. In contrast to classic "trait affect" traditions, the theoretical framework described here proposes a specific mechanism based on the notion of appraisal bias, that is, the evaluation of events or situations in biased, often unrealistic, fashion. The bias toward internal versus external causal attribution is a classic example. It is suggested that such biases can affect virtually all appraisal criteria (e.g., novelty/expectedness, (un)pleasantness, goal conduciveness/obstruction, causation, control, power, and norm compliance), creating a disposition to experience specific emotions more frequently. In some cases, this process may lead to the development of affective disorders. Two studies are herein reported: (a) administering an emotion disposition assessment instrument to several thousand adults in personnel assessment contexts (N = 3,012), demonstrating the existence and intensity of emotion dispositions and identifying potential individual difference correlates; and (b) using an updated version of the instrument in a representative survey panel study (N = 190), assessing both emotion dispositions and appraisal biases, allowing analysis of their relationships (in addition to examining the effect of correlates). The results confirm the viability of the underlying theoretical assumptions as well as of the scenario method used for the assessment and provide leads for further research, particularly in the areas of emotional competence and affective disorders. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

It is widely recognised that emotions have important social functions, particularly with respect to the interaction between individual needs and preferences and socio-cultural norms and values. So far, however, a general theoretical framework has been missing for the social functions of emotion. In this issue of the special theory section of CognitionEmotion, an invited article by Keltner et al. proposes an expanded version of social functionalist theory entitled "How emotions, relationships, and culture constitute each other", which attempts to remedy this situation. Two invited commentaries, by Manstead and Von Scheve, welcome this attempt but enumerate issues in need of further elaboration, such as the interaction between relational needs and cultural norms, the necessity to consider situational context and cultural differences, and a more stringent specification of "functionality". The remainder of the current introductory article briefly outlines some areas in need of attention from social emotion theories, such as the effect of social and technological change on the elicitation and regulation of emotions. Here, earlier work is cited that evokes the danger of a potential waning of shame and guilt feelings due to changing values, norms, and self-ideals (for example, the growing importance of self-related values such as freedom and the weakening of prosocial values such as the responsibility for the common good). Another problem is group polarisation leading to negative emotions such as hate and potential violence taken together, the contributions to this theory section highlight the importance of creating a comprehensive theoretical framework for studying the social functions of emotions.

Starting from the assumption that computational models of emotion (CME) should produce tangible and useful results, I focus on their potential role in developing theoretical predictions and comparative testing of different theories. Concretely, I suggest a specific type of CME for this purpose, the prediction and data driven computational process (PDCP) model of emotion, as based on largely shared assumptions about the emotion process and the underlying components. After providing an overview of the wide variety of emotion theories and their suitability for computational modeling, the current version of the component process model (CPM) of emotion is described, including the specific predictions amenable to modeling. I then review the empirical data confirming many of these predictions and thus providing a solid basis for the development of CMEs. On this basis, I outline the skeleton of a realistic PDCP model that should allow testing competing theories and models. Specifically, I propose an incremental approach to concrete static and dynamic modeling efforts using recently developed advanced experimental procedures and assessment instruments as well as expert systems.

Appraisal theories suggest that valence appraisal should be differentiated into micro-valences, such as intrinsic pleasantness and goal-/need-related appraisals. In contrast to a macro-valence approach, this dissociation explains, among other things, the emergence of mixed or blended emotions. Here, we extend earlier research that showed that these valence types can be empirically dissociated. We examine the timing and the response patterns of these two micro-valences via measuring facial muscle activity changes (electromyography, EMG) over the brow and the cheek regions. In addition, we explore the effects of the sensory stimulus modality (vision, audition, and olfaction) on these patterns. The two micro-valences were manipulated in a social judgment task: first, intrinsic un/pleasantness (IP) was manipulated by exposing participants to appropriate stimuli presented in different sensory domains followed by a goal conduciveness/obstruction (GC) manipulation consisting of feedback on participants’ judgments that were congruent or incongruent with their task-related goal. The results show significantly different EMG responses and timing patterns for both types of micro-valence, confirming the prediction that they are independent, consecutive parts of the appraisal process. Moreover, the lack of interaction effects with the sensory stimulus modality suggests high generalizability of the underlying appraisal mechanisms across different perception channels. Electronic supplementary material The online version of this article (10.1007/s42761-020-00020-y) contains supplementary material, which is available to authorized users.

Principal Component Metrics is a novel theoretically-based and data-driven methodology that enables the evaluation of the internal structure at item level of maximum emotional intelligence tests. This method disentangles interindividual differences in emotional ability from acquiescent and extreme responding. Principal Component Metrics are applied to existing (Mayer-Salovey-Caruso Emotional Intelligence Test) and assembled (specifically, the Situational Test of Emotion Understanding, the Situational Test of Emotion Management, and the Geneva Emotion Recognition Test) emotional intelligence test batteries in an analysis of three samples (total N = 2,303 participants). In undertaking these analyses important aspects of the nomological network of emotional intelligence, acquiescent, and extreme responding are investigated. The current study adds a central piece of empirical validity evidence to the emotional intelligence domain. In the three different samples, theoretically predicted internal structures at item level were found using raw item scores. The validity of the indicators for emotional intelligence, acquiescent, and extreme responding was confirmed by their relationships across emotional intelligence tests and by their nomological networks. The current findings contribute to evaluating the efficacy of the emotional intelligence construct as well as the validity evidence surrounding the instruments that are currently designed for its assessment, in the process opening new perspectives for analyzing existing and constructing new emotional intelligence tests.

I consider the five contributions in this special section as evidence that the research area dealing with the vocal expression of emotion is advancing rapidly, both in terms of the number of pertinent empirical studies and with respect to an ever increasing sophistication of methodology. I provide some suggestions on promising areas for future interdisciplinary research, including work on emotion expression in singing and the potential of vocal symptoms of emotional disorder. As to the popular discussion of the respective role of universality versus language/culture differences, I suggest to move on from exclusively studying the accuracy of recognition in judgment studies to a more differentiated approach adding production aspects, taking into account the multiple vocal and acoustic features that interact to communicate emotion.

Research over the past decades has demonstrated the explanatory power of emotions, feelings, motivations, moods, and other affective processes when trying to understand and predict how we think and behave. In this consensus article, we ask: has the increasingly recognized impact of affective phenomena ushered in a new era, the era of affectivism? ispartof: Nature Human Behaviour vol:5 issue:7 pages:816-820 ispartof: location:England status: published

© 2018 American Psychological Association. Opera performances elicit strong emotional reactions in listeners. Yet, empirical demonstrations of these effects in situ are rare. Here we report a series of studies examining the emotional reactions of participants invited to the dress rehearsal of three different operas at the Geneva opera house before large audiences. Using a new affect checklist developed specifically for in situ studies of music performances, we asked participants to record (a) the intensity of 12 different types of affective reactions they experienced during selected scenes or (b) the frequency with which they experienced these emotions during specific acts or the opera as a whole. Results showed a high degree of specificity regarding the emotional impact of the operas as a whole and of individual scenes/acts. For one opera, Verdi's Macbeth, we also asked participants to rate both the emotions they actually felt during the opera scenes and the emotions expressed by the music or the singers' interpretation. Results confirm that spectators are clearly able to separate their own affective responses from what they perceive to be the emotions portrayed by the orchestra or on the stage. In addition, we evaluated the effect of different types of preperformance information sessions (on plot or music), as well as of participant personality and prior mood. Overall, the results demonstrate the feasibility of measuring highly differentiated emotional audience reactions to an opera performance with a brief validated checklist during actual performances in the opera house and the validity of self-reported emotions.

Individuals vary in emotion recognition ability (ERA), but the causes and correlates of this variability are not well understood. Previous studies have largely focused on unimodal facial or vocal expressions and a small number of emotion categories, which may not reflect how emotions are expressed in everyday interactions. We investigated individual differences in ERA using a brief test containing dynamic multimodal (facial and vocal) expressions of 5 positive and 7 negative emotions (the ERAM test). Study 1 (N = 593) showed that ERA was positively correlated with emotional understanding, empathy, and openness, and negatively correlated with alexithymia. Women also had higher ERA than men. Study 2 was conducted online and replicated the recognition rates from Study 1 (which was conducted in lab) in a different sample (N = 106). Study 2 also showed that participants who had higher ERA were more accurate in their meta-cognitive judgments about their own accuracy. Recognition rates for visual, auditory, and audio-visual expressions were substantially correlated in both studies. Results provide further clues about the underlying structure of ERA and its links to broader affective processes. The ERAM test can be used for both lab and online research, and is freely available for academic research.

Aesthetic emotions are elicited by different sensory impressions generated by music, visual arts, literature, theater, film, or nature scenes. Recently, the AESTHEMOS scale has been developed to facilitate the empirical assessment of such emotions. In this article we report a semantic profile analysis of aesthetic emotion terms that had been used for the development of this scale, using the GRID approach. This method consists of obtaining ratings of emotion terms on a set of meaning facets (features) which represent five components of the emotion process (appraisal, bodily reactions, action tendencies, expression, and feelings). The aims here were (a) to determine the dimensionality of the GRID features when applied to aesthetic emotions and compare it to published results for emotion terms in general, and (b) to examine the internal organization of the domain of aesthetic emotion terms in order to identify salient clusters of these items based on the similarity of their feature profiles on the GRID. Exploratory Principal Component Analyses suggest a four-dimensional structure of the semantic space consisting of valence, power, arousal, and novelty, converging with earlier GRID studies on large sets of standard emotion terms. Using cluster analyses, 15 clusters of aesthetic emotion terms with similar GRID feature profiles were identified, revealing the internal organization of the aesthetic emotion terms domain and meaningful subgroups of aesthetic emotions. While replication for further languages is required, these findings provide a solid basis for further research and methodological development in the realm of aesthetic emotions.

The development of a two-stage approach for appraisal inference from automatically detected Action Unit (AUs) intensities in recordings of human faces is described. AU intensity estimation is based on a hybrid approach fusing information from individually fitted mesh model of the faces and texture information. Evaluation results for two datasets and a comparison against a state of the art system are provided. In the second stage, the emotional appraisals novelty, valence and control are predicted from estimated AU intensities by linear regressions. Prediction performance is evaluated based on face recordings from a market research study, which were rated by human observers in terms of perceived appraisals. Predictions of valence and control from automatically estimated AU intensities closely match those obtained from manually coded AUs in terms of agreement with human observers, while novelty predictions lag somewhat behind. Overall, results highlight the flexibility and interpretability of a two-stage approach to emotion inference.

Background Acoustic aspects of emotional expressivity in speech have been analyzed extensively during recent decades. Emotional coloring is an important if not the most important property of sung performance, and therefore strictly controlled. Hence, emotional expressivity in singing may promote a deeper insight into vocal signaling of emotions. Furthermore, physiological voice source parameters can be assumed to facilitate the understanding of acoustical characteristics. Method Three highly experienced professional male singers sang scales on the vowel /ae/ or /a/ in 10 emotional colors (Neutral, Sadness, Tender, Calm, Joy, Contempt, Fear, Pride, Love, Arousal, and Anger). Sixteen voice experts classified the scales in a forced-choice listening test, and the result was compared with long-term-average spectrum (LTAS) parameters and with voice source parameters, derived from flow glottograms (FLOGG) that were obtained from inverse filtering the audio signal. Results On the basis of component analysis, the emotions could be grouped into four “families”, Anger-Contempt, Joy-Love-Pride, Calm-Tender-Neutral and Sad-Fear. Recognition of the intended emotion families by listeners reached accuracy levels far beyond chance level. For the LTAS and FLOGG parameters, vocal loudness had a paramount influence on all. Also after partialing out this factor, some significant correlations were found between FLOGG and LTAS parameters. These parameters could be sorted into groups that were associated with the emotion families. Conclusions (i) Both LTAS and FLOGG parameters varied significantly with the enactment intentions of the singers. (ii) Some aspects of the voice source are reflected in LTAS parameters. (iii) LTAS parameters affect listener judgment of the enacted emotions and the accuracy of the intended emotional coloring.

The automatic recognition of facial behaviours is usually achieved through the detection of particular FACS Action Unit (AU), which then makes it possible to analyse the affective behaviours expressed in the face. Despite the fact that advanced techniques have been proposed to extract relevant facial descriptors, the processing of real-life data, i. e., recorded in unconstrained environments, makes the automatic detection of FACS AU much more challenging compared to constrained recordings, such as posed faces, and even impossible when the corresponding parts of the face are masked or subject to low or no illumination. We present in this paper the very first attempt in using acoustic cues for the automatic detection of FACS AU, as an alternative way to obtain information from the face when such data are not available. Results show that features extracted from the voice can be effectively used to predict different types of FACS AU, and that the best performance are obtained for the prediction of the apex, in comparison to the prediction of onset, offset and occurrence.