Semantic Space Theory: A Computational Approach to Emotion

Lights, Camera, Emotion: REELMO's 1060 Hours of Affective Reports to Explore Emotions in Naturalistic Contexts

Emotions are central to human experience, yet their complexity and context-dependent nature challenge traditional laboratory studies. We present REELMO (REal-time EmotionaL responses to MOvies), a novel dataset bridging controlled experiments and naturalistic affective experiences. REELMO includes 1,060 hours of moment-by-moment emotional reports across 20 affective states collected during the viewing of 60 full-length movies, along with additional measures of personality traits, empathy, movie synopses, and overall liking from 161 participants. It also features fMRI data from 20 volunteers recorded while watching the full-length movie Jojo Rabbit. Complemented by visual and acoustic features as well as semantic content derived from deep-learning models, REELMO provides a comprehensive platform for advancing emotion research. Its high temporal resolution, rich annotations, and integration with fMRI data enable investigations into the interplay between sensory information, narrative structures, and contextual factors in shaping emotional experiences, as well as the study of affective chronometry, mixed-valence states, psychological trait influences, and machine learning applications in affective (neuro)science.

How mood-related physiological states bias economic decisions

When making decisions, humans are susceptible to all sorts of biases, relative to rational norms. An important factor is incidental changes in affective states, such as variations in mood between happiness and sadness. We previously developed a computational model, in which mood affects choice by forming a predisposition to face costs and seek more rewards. Here, we generalized this theory to account for how specific inductions of happiness and sadness affect different types of economic decisions involving a tradeoff between costs (risk, delay, effort) and benefits (financial rewards). Across exploratory and confirmatory studies (N = 94), we observed a consistent bias exerted by transitory mood states, whether they were assessed through self-reports (rated happiness minus sadness) or inferred from physiological measures (valence of facial expression times intensity of autonomous arousal). This choice bias was best explained by our computational model, with a mood-scaled bonus added to the value of the more rewarded but more costly option, irrespective of the cost type (risk, delay or effort). Additionally, gaze tracking during decision making confirmed that the choice bias was driven by an early preference for the mood-congruent option. Together, these results demonstrate the feasibility of predicting irrational choices from objective measures of affective states.

Blocking lower facial features reduces emotion identification accuracy in static faces and full body dynamic expressions

During COVID, much of the world wore masks covering their lower faces to prevent the spread of disease. These masks cover lower facial features, but how vital are these lower facial features to the recognition of facial expressions of emotion? Going beyond the Ekman 6 emotions, in Study 1 (N = 372), we used a multilevel logistic regression to examine how artificially rendered masks influence emotion recognition from static photos of facial muscle configurations for many commonly experienced positive and negative emotions. On average, masks reduced emotion recognition accuracy by 17% percent for negative emotions and 23% for positive emotions. In Study 2 (N = 338), we asked whether these results generalised to multimodal full-body expressions of emotions, accompanied by vocal expressions. Participants viewed videos from a previously validated set, where the lower facial features were blurred from the nose down. Here, though the decreases in emotion recognition were noticeably less pronounced, highlighting the power of multimodal information, we did see important decreases for certain specific emotions and for positive emotions overall. Results are discussed in the context of the social and emotional consequences of compromised emotion recognition, as well as the unique facial features which accompany certain emotions.

PERSONALIZED PROFILES OF ORAL HEALTH AND DISEASE: USING HIGH-DIMENSIONAL VECTOR MODELS FOR GUIDING PRECISION DENTAL CARE

Operationalizing the oral health experience is an ongoing effort with various clinical and patient-reported outcomes contributing to such conceptualizations. Computational technology has afforded advances in the ability to model complex interactions between various phenomena and provides an opportunity to reconsider the way oral health is conceptualized. High-dimensional vector space modeling is introduced and discussed as a theoretical way to incorporate all relative features associated with understanding oral health, including clinical, patient-reported, and demographic information. Specifically, a novel application of high-dimensional vector space models is proposed as a vehicle to operationalize the 3P model of oral health. Additionally, this paper outlines how this approach can 1) create more precise, person-level characterizations of oral health; 2) track oral health over time, offering greater opportunities for behavioral interventions to prevent, mitigate, or treat the negative impacts of dental, oral, and craniofacial diseases; and 3) offer comparisons to dynamically tuned comparison vectors which can define "good" oral health and quantify disparities and features on which to intervene to mitigate them.

Possible Worlds Theory: How the Imagination Transcends and Recreates Reality

The imagination is central to human social life but undervalued worldwide and underexplored in psychology. Here, we offer Possible Worlds Theory as a synthetic theory of the imagination. We first define the imagination, mapping the mental states it touches, from dreams and hallucinations to satire and fiction. The conditions that prompt people to imagine range from trauma to physical and social deprivation, and they challenge the sense of reality, stirring a need to create possible worlds. We theorize about four cognitive operations underlying the structure of the mental states of the imagination. We then show how people embody the imagination in social behaviors such as pretense and ritual, which give rise to experiences of a special class of feelings defined by their freedom from reality. We extend Possible Worlds Theory to four domains-play, spirituality, morality, and art-and show how in flights of the imagination people create new social realities shared with others.

The Confrustion Constellation: A New Way of Looking at Confusion and Frustration

There has been considerable research on confusion and frustration that has treated them as two unitary constructs, distinct from each other. In this article, we argue that there is instead a constellation of different types of confusion and frustration, with different antecedents, manifestations, and impacts, and that the commonalities between many types of confusion and frustration justify thinking of them as part of the same constellation of affect, distinct from other prominent affective categories. We discuss how these types of affect have been considered historically and in key models. We then discuss unusual manifestations of each form of affect that have been documented in the literature, and what light they shed on the broader constructs. We conclude with a discussion of a new theoretical framing that treats confusion and frustration as a confrustion constellation, and the opportunities and open questions that this perspective presents.

Naturalistic multimodal emotion data with deep learning can advance the theoretical understanding of emotion

What are emotions? Despite being a century-old question, emotion scientists have yet to agree on what emotions exactly are. Emotions are diversely conceptualised as innate responses (evolutionary view), mental constructs (constructivist view), cognitive evaluations (appraisal view), or self-organising states (dynamical systems view). This enduring fragmentation likely stems from the limitations of traditional research methods, which often adopt narrow methodological approaches. Methods from artificial intelligence (AI), particularly those leveraging big data and deep learning, offer promising approaches for overcoming these limitations. By integrating data from multimodal markers of emotion, including subjective experiences, contextual factors, brain-bodily physiological signals and expressive behaviours, deep learning algorithms can uncover and map their complex relationships within multidimensional spaces. This multimodal emotion framework has the potential to provide novel, nuanced insights into long-standing questions, such as whether emotion categories are innate or learned and whether emotions exhibit coherence or degeneracy, thereby refining emotion theories. Significant challenges remain, particularly in obtaining comprehensive naturalistic multimodal emotion data, highlighting the need for advances in synchronous measurement of naturalistic multimodal emotion.

Language-specific representation of emotion-concept knowledge causally supports emotion inference

Humans no doubt use language to communicate about their emotional experiences, but does language in turn help humans understand emotions, or is language just a vehicle of communication? This study used a form of artificial intelligence (AI) known as large language models (LLMs) to assess whether language-based representations of emotion causally contribute to the AI's ability to generate inferences about the emotional meaning of novel situations. Fourteen attributes of human emotion concept representation were found to be represented by the LLM's distinct artificial neuron populations. By manipulating these attribute-related neurons, we in turn demonstrated the role of emotion concept knowledge in generative emotion inference. The attribute-specific performance deterioration was related to the importance of different attributes in human mental space. Our findings provide a proof-in-concept that even an LLM can learn about emotions in the absence of sensory-motor representations and highlight the contribution of language-derived emotion-concept knowledge for emotion inference.

Considering distinct positive emotions in psychedelic science

In this review, we discuss psychedelics' acute subjective and persisting therapeutic effects, outline the science of positive emotions, and highlight the value in considering distinct positive emotions in psychedelic science. Psychedelics produce a wide variety of acute subjective effects (i.e. the 'trip'), including positive emotions and affective states such as awe and joy. However, despite a rich literature on distinct emotions and their different correlates and sequelae, distinct emotions in psychedelic science remain understudied. Insofar as psychedelics' acute subjective effects may play a role in their downstream therapeutic effects (e.g. decreased depression, anxiety, and substance misuse), considering the role of distinct positive emotions in psychedelic experiences has the potential to yield more precise statements about psychedelic-related subjective processes and outcomes. We propose here that understanding the role of positive emotions within the context of psychedelic experiences could help elucidate the connection between psychedelics' acute subjective effects and therapeutic outcomes.

Emotion specificity, coherence, and cultural variation in conceptualizations of positive emotions: a study of body sensations and emotion recognition

The present study examines the association between people's interoceptive representation of physical sensations and the recognition of vocal and facial expressions of emotion. We used body maps to study the granularity of the interoceptive conceptualisation of 11 positive emotions (amusement, awe, compassion, contentment, desire, love, joy, interest, pride, relief, and triumph) and a new emotion recognition test (Emotion Expression Understanding Test) to assess the ability to recognise emotions from vocal and facial behaviour. Overall, we found evidence for distinct interoceptive conceptualizations of 11 positive emotions across Asian American, European American, and Latino/a American cultures, as well as the reliable identification of emotion in facial and vocal expressions. Central to new theorising about emotion-related representation, the granularity of physical sensations did not covary with emotion recognition accuracy, suggesting that two kinds of emotion conceptualisation processes might be distinct.

Nature lifts when feeling low: Daily high and low awe nature clips decrease repetitive negative thinking and dampening and increase subjective happiness in adults

Exposure to nature can enhance mental well-being, making nature-based interventions promising for the treatment and prevention of mental health problems like depression. Given the decreased self-focus and sense of self-diminishment associated with awe, the present study investigated the impact of exposure to awe-evoking nature on two key risk and maintenance factors of depression-repetitive negative thinking (RNT) and dampening of positive feelings-and on subjective happiness. In a randomized controlled trial, we tested the effects of exposure to awe-evoking nature clips through a 1-week intervention, consisting of watching a 1-min clip on a daily basis of either awe-evoking (n high awe = 108) or more mundane nature scenes (n low awe = 105). Before, immediately after (post-intervention) and 1 week after the intervention (follow-up), participants completed self-report scales probing RNT, dampening, and subjective happiness. Results indicated significant decreases in these outcomes at post-intervention and follow-up in both groups. We discuss study limitations, touch upon future research ideas, and reflect upon the role of nature for clinical applications.

Emotional palette: a computational mapping of aesthetic experiences evoked by visual art

Despite the evolutionary history and cultural significance of visual art, the structure of aesthetic experiences it evokes has only attracted recent scientific attention. What kinds of experience does visual art evoke? Guided by Semantic Space Theory, we identify the concepts that most precisely describe people's aesthetic experiences using new computational techniques. Participants viewed 1457 artworks sampled from diverse cultural and historical traditions and reported on the emotions they felt and their perceived artwork qualities. Results show that aesthetic experiences are high-dimensional, comprising 25 categories of feeling states. Extending well beyond hedonism and broad evaluative judgments (e.g., pleasant/unpleasant), aesthetic experiences involve emotions of daily social living (e.g., "sad", "joy"), the imagination (e.g., "psychedelic", "mysterious"), profundity (e.g., "disgust", "awe"), and perceptual qualities attributed to the artwork (e.g., "whimsical", "disorienting"). Aesthetic emotions and perceptual qualities jointly predict viewers' liking of the artworks, indicating that we conceptualize aesthetic experiences in terms of the emotions we feel but also the qualities we perceive in the artwork. Aesthetic experiences are often mixed and lie along continuous gradients between categories rather than within discrete clusters. Our collection of artworks is visualized within an interactive map ( https://barradeau.com/2021/emotions-map/ ), revealing the high-dimensional space of aesthetic experiences associated with visual art.

Interaction between the prefrontal and visual cortices supports subjective fear

It has been reported that threatening and non-threatening visual stimuli can be distinguished based on the multi-voxel patterns of haemodynamic activity in the human ventral visual stream. Do these findings mean that there may be evolutionarily hardwired mechanisms within early perception, for the fast and automatic detection of threat, and maybe even for the generation of the subjective experience of fear? In this human neuroimaging study, we presented participants ('fear' group: N = 30; 'no fear' group: N = 30) with 2700 images of animals that could trigger subjective fear or not as a function of the individual's idiosyncratic 'fear profiles' (i.e. fear ratings of animals reported by a given participant). We provide evidence that the ventral visual stream may represent affectively neutral visual features that are statistically associated with fear ratings of participants, without representing the subjective experience of fear itself. More specifically, we show that patterns of haemodynamic activity predictive of a specific 'fear profile' can be observed in the ventral visual stream whether a participant reports being afraid of the stimuli or not. Further, we found that the multivariate information synchronization between ventral visual areas and prefrontal regions distinguished participants who reported being subjectively afraid of the stimuli from those who did not. Together, these findings support the view that the subjective experience of fear may depend on the relevant visual information triggering implicit metacognitive mechanisms in the prefrontal cortex. This article is part of the theme issue 'Sensing and feeling: an integrative approach to sensory processing and emotional experience'.

Social media emotions annotation guide (SMEmo): Development and initial validity

The proper measurement of emotion is vital to understanding the relationship between emotional expression in social media and other factors, such as online information sharing. This work develops a standardized annotation scheme for quantifying emotions in social media using recent emotion theory and research. Human annotators assessed both social media posts and their own reactions to the posts' content on scales of 0 to 100 for each of 20 (Study 1) and 23 (Study 2) emotions. For Study 1, we analyzed English-language posts from Twitter (N = 244) and YouTube (N = 50). Associations between emotion ratings and text-based measures (LIWC, VADER, EmoLex, NRC-EIL, Emotionality) demonstrated convergent and discriminant validity. In Study 2, we tested an expanded version of the scheme in-country, in-language, on Polish (N = 3648) and Lithuanian (N = 1934) multimedia Facebook posts. While the correlations were lower than with English, patterns of convergent and discriminant validity with EmoLex and NRC-EIL still held. Coder reliability was strong across samples, with intraclass correlations of .80 or higher for 10 different emotions in Study 1 and 16 different emotions in Study 2. This research improves the measurement of emotions in social media to include more dimensions, multimedia, and context compared to prior schemes.

How emotion is experienced and expressed in multiple cultures: a large-scale experiment across North America, Europe, and Japan

Core to understanding emotion are subjective experiences and their expression in facial behavior. Past studies have largely focused on six emotions and prototypical facial poses, reflecting limitations in scale and narrow assumptions about the variety of emotions and their patterns of expression. We examine 45,231 facial reactions to 2,185 evocative videos, largely in North America, Europe, and Japan, collecting participants' self-reported experiences in English or Japanese and manual and automated annotations of facial movement. Guided by Semantic Space Theory, we uncover 21 dimensions of emotion in the self-reported experiences of participants in Japan, the United States, and Western Europe, and considerable cross-cultural similarities in experience. Facial expressions predict at least 12 dimensions of experience, despite massive individual differences in experience. We find considerable cross-cultural convergence in the facial actions involved in the expression of emotion, and culture-specific display tendencies-many facial movements differ in intensity in Japan compared to the U.S./Canada and Europe but represent similar experiences. These results quantitatively detail that people in dramatically different cultures experience and express emotion in a high-dimensional, categorical, and similar but complex fashion.

Refuting Six Misconceptions about Romantic Love

Scientific research on romantic love has been relatively sparse but is becoming more prevalent, as it should. Unfortunately, several misconceptions about romantic love are becoming entrenched in the popular media and/or the scientific community, which hampers progress. Therefore, I refute six misconceptions about romantic love in this article. I explain why (1) romantic love is not necessarily dyadic, social, or interpersonal, (2) love is not an emotion, (3) romantic love does not just have positive effects, (4) romantic love is not uncontrollable, (5) there is no dedicated love brain region, neurotransmitter, or hormone, and (6) pharmacological manipulation of romantic love is not near. To increase progress in our scientific understanding of romantic love, I recommend that we study the intrapersonal aspects of romantic love including the intensity of love, that we focus our research questions and designs using a component process model of romantic love, and that we distinguish hypotheses and suggestions from empirical findings when citing previous work.

How self-states help: Observing the embodiment of self-states through nonverbal behavior

The concept of self-states is a recurring theme in various psychotherapeutic and counseling methodologies. However, the predominantly unconscious nature of these self-states presents two challenges. Firstly, it renders the process of working with them susceptible to biases and therapeutic suggestions. Secondly, there is skepticism regarding the observability and differentiation of self-states beyond subjective experiences. In this study, we demonstrate the feasibility of eliciting self-states from clients and objectively distinguishing these evoked self-states through the lens of neutral observers. The self-state constellation method, utilized as an embodied approach, facilitated the activation of diverse self-states. External observers then assessed the nonverbal manifestations of affect along three primary dimensions: emotional valence, arousal, and dominance. Our findings indicate that external observers could reliably discern and differentiate individual self-states based on the bodily displayed valence and dominance. However, the ability to distinguish states based on displayed arousal was not evident. Importantly, this distinctiveness of various self-states was not limited to specific individuals but extended across the entire recording sample. Therefore, within the framework of the self-state constellation method, it is evident that individual self-states can be intentionally evoked, and these states can be objectively differentiated beyond the subjective experiences of the client.

Deep learning reveals what facial expressions mean to people in different cultures

Cross-cultural studies of the meaning of facial expressions have largely focused on judgments of small sets of stereotypical images by small numbers of people. Here, we used large-scale data collection and machine learning to map what facial expressions convey in six countries. Using a mimicry paradigm, 5,833 participants formed facial expressions found in 4,659 naturalistic images, resulting in 423,193 participant-generated facial expressions. In their own language, participants also rated each expression in terms of 48 emotions and mental states. A deep neural network tasked with predicting the culture-specific meanings people attributed to facial movements while ignoring physical appearance and context discovered 28 distinct dimensions of facial expression, with 21 dimensions showing strong evidence of universality and the remainder showing varying degrees of cultural specificity. These results capture the underlying dimensions of the meanings of facial expressions within and across cultures in unprecedented detail.

Dissecting abstract, modality-specific and experience-dependent coding of affect in the human brain

Emotion and perception are tightly intertwined, as affective experiences often arise from the appraisal of sensory information. Nonetheless, whether the brain encodes emotional instances using a sensory-specific code or in a more abstract manner is unclear. Here, we answer this question by measuring the association between emotion ratings collected during a unisensory or multisensory presentation of a full-length movie and brain activity recorded in typically developed, congenitally blind and congenitally deaf participants. Emotional instances are encoded in a vast network encompassing sensory, prefrontal, and temporal cortices. Within this network, the ventromedial prefrontal cortex stores a categorical representation of emotion independent of modality and previous sensory experience, and the posterior superior temporal cortex maps the valence dimension using an abstract code. Sensory experience more than modality affects how the brain organizes emotional information outside supramodal regions, suggesting the existence of a scaffold for the representation of emotional states where sensory inputs during development shape its functioning.

Emotionally intelligent school leadership predicts educator well-being before and during a crisis

We examined the role of educator perceptions of school leader emotion regulation (ER) and emotional support (ES) in educator well-being during a typical year and during the COVID-19 pandemic. Based on emotion contagion theory, leaders' (in)ability to regulate their own emotions may trigger ripple effects of positive or negative emotions throughout their organizations, impacting staff well-being. Additionally, based on conservation of resources theory, when experiencing psychologically taxing events, skillful emotional support provided by leaders may help to replenish staff's depleted psychological resources, promoting staff well-being. In two national studies, a cross-sectional (NStudy 1 = 4,847) and a two-wave study (NStudy 2 = 2,749), we tested the association between United States preK-12 educator perceptions of school leaders' ER and ES with educator well-being before and during the COVID-19 pandemic, employing structural equation modeling and multilevel modeling. In Studies 1 and 2, educator reports of their leaders' ER and ES skills predicted greater educator well-being, including higher positive affect and job satisfaction and lower emotional exhaustion and turnover intentions. In moderation analyses, perceived leader ER predicted well-being about equally among educators facing severe versus mild health impacts from COVID-19. In contrast, perceived leader ES was more strongly associated with educator well-being for some outcomes in those severely versus mildly impacted by COVID-19 illness and death. Leader ER played a role in the well-being of everyone, whereas leader ES was more predictive of well-being for those severely impacted by a crisis. Regarding implications for policy and practice, efforts to promote well-being among educators may be enhanced when combined with efforts to develop school leaders' ER and ES skills, especially in times of crisis. Accordingly, school districts should consider the value of investing in systematic, evidence-based emotion skills training for their leaders.

The Sound of Emotional Prosody: Nearly 3 Decades of Research and Future Directions

Emotional voices attract considerable attention. A search on any browser using "emotional prosody" as a key phrase leads to more than a million entries. Such interest is evident in the scientific literature as well; readers are reminded in the introductory paragraphs of countless articles of the great importance of prosody and that listeners easily infer the emotional state of speakers through acoustic information. However, despite decades of research on this topic and important achievements, the mapping between acoustics and emotional states is still unclear. In this article, we chart the rich literature on emotional prosody for both newcomers to the field and researchers seeking updates. We also summarize problems revealed by a sample of the literature of the last decades and propose concrete research directions for addressing them, ultimately to satisfy the need for more mechanistic knowledge of emotional prosody.

Habitual Expressive Suppression of Positive, but not Negative, Emotions Consistently Predicts Lower Well-being across Two Culturally Distinct Regions

Habitual expressive suppression (i.e., a tendency to inhibit the outward display of one's emotions; hereafter suppression) is often conceptualized as a maladaptive emotion regulation strategy. Yet, is this equally true for suppression of positive and of negative emotions? Across three studies and seven samples (total N > 1300 people) collected in two culturally distinct regions (i.e., Taiwan and the US), we examined the separability and distinct well-being effects of suppressing positive vs. negative emotions. Results consistently showed that (a) people suppressed their positive (vs. negative) emotions less, (b) the construct of suppression of positive (vs. negative) emotions was conceptually farther away from that of suppression of emotions in general, (c) suppression of positive and of negative emotions were only moderately correlated, and (d) only suppression of positive, but not negative, emotions, predicted lower well-being. An internal meta-analysis (k = 52 effect sizes) showed that these associations were robust to the inclusion of age, gender, and region as covariates. Future research may further probe the respective links between suppression of positive and of negative emotions and well-being across more cultural regions and across the life-span.

Infrequent facial expressions of emotion do not bias attention

Despite the obvious importance of facial expressions of emotion, most studies have found that they do not bias attention. A critical limitation, however, is that these studies generally present face distractors on all trials of the experiment. For other kinds of emotional stimuli, such as emotional scenes, infrequently presented stimuli elicit greater attentional bias than frequently presented stimuli, perhaps due to suppression or habituation. The goal of the current study then was to test whether such modulation of attentional bias by distractor frequency generalizes to facial expressions of emotion. In Experiment 1, both angry and happy faces were unable to bias attention, despite being infrequently presented. Even when the location of these face cues were more unpredictable-presented in one of two possible locations-still no attentional bias was observed (Experiment 2). Moreover, there was no bottom-up influence for angry and happy faces shown under high or low perceptual load (Experiment 3). We conclude that task-irrelevant posed facial expressions of emotion cannot bias attention even when presented infrequently.

Development and Validation of an Ability Measure of Emotion Understanding: The Core Relational Themes of Emotion (CORE) Test

Emotion understanding (EU) ability is associated with healthy social functioning and psychological well-being. Across three studies, we develop and present validity evidence for the Core Relational Themes of Emotions (CORE) Test. The test measures people's ability to identify relational themes underlying 19 positive and negative emotions. Relational themes are consistencies in the meaning people assign to emotional experiences. In Study 1, we developed and refined the test items employing a literature review, expert panel, and confusion matrix with a demographically diverse sample. Correctness criteria were determined using theory and prior research, and a progressive (degrees of correctness) paradigm was utilized to score the test. In Study 2, the CORE demonstrated high internal consistency and a confirmatory factor analysis supported the unidimensional factor structure. The CORE showed evidence of convergence with established EU ability measures and divergent relationships with verbal intelligence and demographic characteristics, supporting its construct validity. Also, the CORE was associated with less relational conflict. In Study 3, the CORE was associated with more adaptive and less maladaptive coping and higher well-being on multiple indicators. A set of effects remained, accounting for variance from a widely used EU test, supporting the CORE's incremental validity. Theoretical and methodological contributions are discussed.

Emotional content and sharing on Facebook: A theory cage match

While emotional content predicts social media post sharing, competing theories of emotion imply different predictions about how emotional content will influence the virality of social media posts. We tested and compared these theoretical frameworks. Teams of annotators assessed more than 4000 multimedia posts from Polish and Lithuanian Facebook for more than 20 emotions. We found that, drawing on semantic space theory, modeling discrete emotions independently was superior to models examining valence (positive or negative), activation/arousal (high or low), or clusters of emotions and was on par with but had more explanatory power than a seven basic emotion model. Certain discrete emotions were associated with post sharing, including both positive and negative and relatively lower and higher activation/arousal emotions (e.g., amusement, cute/kama muta, anger, and sadness) even when controlling for number of followers, time up, topic, and Facebook angry reactions. These results provide key insights into better understanding of social media post virality.

Advancing Naturalistic Affective Science with Deep Learning

People express their own emotions and perceive others' emotions via a variety of channels, including facial movements, body gestures, vocal prosody, and language. Studying these channels of affective behavior offers insight into both the experience and perception of emotion. Prior research has predominantly focused on studying individual channels of affective behavior in isolation using tightly controlled, non-naturalistic experiments. This approach limits our understanding of emotion in more naturalistic contexts where different channels of information tend to interact. Traditional methods struggle to address this limitation: manually annotating behavior is time-consuming, making it infeasible to do at large scale; manually selecting and manipulating stimuli based on hypotheses may neglect unanticipated features, potentially generating biased conclusions; and common linear modeling approaches cannot fully capture the complex, nonlinear, and interactive nature of real-life affective processes. In this methodology review, we describe how deep learning can be applied to address these challenges to advance a more naturalistic affective science. First, we describe current practices in affective research and explain why existing methods face challenges in revealing a more naturalistic understanding of emotion. Second, we introduce deep learning approaches and explain how they can be applied to tackle three main challenges: quantifying naturalistic behaviors, selecting and manipulating naturalistic stimuli, and modeling naturalistic affective processes. Finally, we describe the limitations of these deep learning methods, and how these limitations might be avoided or mitigated. By detailing the promise and the peril of deep learning, this review aims to pave the way for a more naturalistic affective science.

Processing emotions from faces and words measured by event-related brain potentials

Affective aspects of a stimulus can be processed rapidly and before cognitive attribution, acting much earlier for verbal stimuli than previously considered. Aimed for specific mechanisms, event-related brain potentials (ERPs), expressed in facial expressions or word meaning and evoked by six basic emotions - anger, disgust, fear, happy, sad, and surprise - relative to emotionally neutral stimuli were analysed in a sample of 116 participants. Brain responses in the occipital and left temporal regions elicited by the sadness in facial expressions or words were indistinguishable from responses evoked by neutral faces or words. Confirming previous findings, facial fear elicited an early and strong posterior negativity. Instead of expected parietal positivity, both the happy faces and words produced significantly more negative responses compared to neutral. Surprise in facial expressions and words elicited a strong early response in the left temporal cortex, which could be a signature of appraisal. The results of this study are consistent with the view that both types of affective stimuli, facial emotions and word meaning, set off rapid processing and responses occur very early in the processing stage.

The nature and neurobiology of fear and anxiety: State of the science and opportunities for accelerating discovery

Fear and anxiety play a central role in mammalian life, and there is considerable interest in clarifying their nature, identifying their biological underpinnings, and determining their consequences for health and disease. Here we provide a roundtable discussion on the nature and biological bases of fear- and anxiety-related states, traits, and disorders. The discussants include scientists familiar with a wide variety of populations and a broad spectrum of techniques. The goal of the roundtable was to take stock of the state of the science and provide a roadmap to the next generation of fear and anxiety research. Much of the discussion centered on the key challenges facing the field, the most fruitful avenues for future research, and emerging opportunities for accelerating discovery, with implications for scientists, funders, and other stakeholders. Understanding fear and anxiety is a matter of practical importance. Anxiety disorders are a leading burden on public health and existing treatments are far from curative, underscoring the urgency of developing a deeper understanding of the factors governing threat-related emotions.

The Meso-Expression Test (MET): A Novel Assessment of Emotion Perception

Emotion perception is a primary facet of Emotional Intelligence (EI) and the underpinning of interpersonal communication. In this study, we examined meso-expressions-the everyday, moderate-intensity emotions communicated through the face, voice, and body. We theoretically distinguished meso-expressions from other well-known emotion research paradigms (i.e., macro-expression and micro-expressions). In Study 1, we demonstrated that people can reliably discriminate between meso-expressions, and we created a corpus of 914 unique video displays of meso-expressions across a race- and gender-diverse set of expressors. In Study 2, we developed a novel video-based assessment of emotion perception ability: The Meso-Expression Test (MET). In this study, we found that the MET is psychometrically valid and demonstrated measurement equivalence across Asian, Black, Hispanic, and White perceiver groups and across men and women. In Study 3, we examined the construct validity of the MET and showed that it converged with other well-known measures of emotion perception and diverged from cognitive ability. Finally, in Study 4, we showed that the MET is positively related to important psychosocial outcomes, including social well-being, social connectedness, and empathic concern and is negatively related to alexithymia, stress, depression, anxiety, and adverse social interactions. We conclude with a discussion focused on the implications of our findings for EI ability research and the practical applications of the MET.

Dynamic autonomic nervous system states arise during emotions and manifest in basal physiology

The outflow of the autonomic nervous system (ANS) is continuous and dynamic, but its functional organization is not well understood. Whether ANS patterns accompany emotions, or arise in basal physiology, remain unsettled questions in the field. Here, we searched for brief ANS patterns amidst continuous, multichannel physiological recordings in 45 healthy older adults. Participants completed an emotional reactivity task in which they viewed video clips that elicited a target emotion (awe, sadness, amusement, disgust, or nurturant love); each video clip was preceded by a pre-trial baseline period and followed by a post-trial recovery period. Participants also sat quietly for a separate 2-min resting period to assess basal physiology. Using principal components analysis and unsupervised clustering algorithms to reduce the second-by-second physiological data during the emotional reactivity task, we uncovered five ANS states. Each ANS state was characterized by a unique constellation of patterned physiological changes that differentiated among the trials of the emotional reactivity task. These ANS states emerged and dissipated over time, with each instance lasting several seconds on average. ANS states with similar structures were also detectable in the resting period but were intermittent and of smaller magnitude. Our results offer new insights into the functional organization of the ANS. By assembling short-lived, patterned changes, the ANS is equipped to generate a wide range of physiological states that accompany emotions and that contribute to the architecture of basal physiology.

Awe as a Pathway to Mental and Physical Health

How do experiences in nature or in spiritual contemplation or in being moved by music or with psychedelics promote mental and physical health? Our proposal in this article is awe. To make this argument, we first review recent advances in the scientific study of awe, an emotion often considered ineffable and beyond measurement. Awe engages five processes-shifts in neurophysiology, a diminished focus on the self, increased prosocial relationality, greater social integration, and a heightened sense of meaning-that benefit well-being. We then apply this model to illuminate how experiences of awe that arise in nature, spirituality, music, collective movement, and psychedelics strengthen the mind and body.

Emotional Well-Being: What It Is and Why It Matters

Psychological aspects of well-being are increasingly recognized and studied as fundamental components of healthy human functioning. However, this body of work is fragmented, with many different conceptualizations and terms being used (e.g., subjective well-being, psychological well-being). We describe the development of a provisional conceptualization of this form of well-being, here termed emotional well-being (EWB), leveraging prior conceptual and theoretical approaches. Our developmental process included review of related concepts and definitions from multiple disciplines, engagement with subject matter experts, consideration of essential properties across definitions, and concept mapping. Our conceptualization provides insight into key strengths and gaps in existing perspectives on this form of well-being, setting a foundation for evaluating assessment approaches, enhancing our understanding of the causes and consequences of EWB, and, ultimately, developing effective intervention strategies that promote EWB. We argue that this foundation is essential for developing a more cohesive and informative body of work on EWB.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42761-022-00163-0.

Social-functional characteristics of Chinese terms translated as "shame" or "guilt": a cross-referencing approach

Previous research has found a rich lexicon of shame and guilt terms in Chinese, but how comparable these terms are to "shame" or "guilt" in English remains a question. We identified eight commonly used Chinese terms translated as "shame" and "guilt". Study 1 assessed the Chinese terms' intensities, social characteristics, and action tendencies among 40 Chinese speakers. Testing term production in the reverse direction, Study 2 asked another Chinese-speaking sample (N = 85) to endorse emotion terms in response to eight eliciting scenarios generated using each term's social characteristics from Study 1. A native English-speaking sample (N = 83) was also included to examine the production of English emotion terms and compare motivational tendencies cross-culturally. Using this cross-referencing method, we found that some of the Chinese terms shared similar social-functional characteristics to their English translation, but some had distinct profiles. The two large shame-like and guilt-like term categories yielded in Study 1 were replicated in Study 2's Chinese term-production task where larger-scale correspondences between categorised elicitors and term clusters were found. Meanwhile, English speakers' term use provided further evidence for the equivalence between some Chinese terms and "shame" or "guilt" both in terms of their social and motivational characteristics.

Deep learning reveals what vocal bursts express in different cultures

Human social life is rich with sighs, chuckles, shrieks and other emotional vocalizations, called 'vocal bursts'. Nevertheless, the meaning of vocal bursts across cultures is only beginning to be understood. Here, we combined large-scale experimental data collection with deep learning to reveal the shared and culture-specific meanings of vocal bursts. A total of n = 4,031 participants in China, India, South Africa, the USA and Venezuela mimicked vocal bursts drawn from 2,756 seed recordings. Participants also judged the emotional meaning of each vocal burst. A deep neural network tasked with predicting the culture-specific meanings people attributed to vocal bursts while disregarding context and speaker identity discovered 24 acoustic dimensions, or kinds, of vocal expression with distinct emotion-related meanings. The meanings attributed to these complex vocal modulations were 79% preserved across the five countries and three languages. These results reveal the underlying dimensions of human emotional vocalization in remarkable detail.

The construct of cuteness: A validity study for measuring content and evoked emotions on social media

Social media users are often exposed to cute content that evokes emotional reactions and influences them to feel or behave certain ways. The cuteness phenomenon in social media has been scarcely studied despite its prevalence and potential to spread quickly and affect large audiences. The main framework for understanding cuteness and emotions related to cuteness outside of social media is baby schema (having juvenile characteristics), which triggers parental instincts. We propose that baby schema is a necessary but not sufficient component of explaining what constitutes cuteness and how people react to it in the social media context. Cute social media content may also have characteristics that evoke approach motivations (a desire to interact with an entity, generally with the expectation of having a positive experience) that can manifest behaviorally in sharing and other prosocial online behaviors. We developed and performed initial validation for measures in social media contexts of: (1) cute attributes that encompass both baby schema and other proposed cuteness characteristics (the Cuteness Attributes Taxonomy, CAT) and (2) the emotional reactions they trigger (Heartwarming Social Media, HSM). We used the Kama Muta Multiplex Scale (KAMMUS Two), as previously validated measure of kama muta (an emotion akin to tenderness; from Sanskrit, "moved by love") as a measure of emotional reaction to cute stimuli and the dimension Cute Content of the Social Media Emotions Annotation Guide (SMEmo-Cute Content) as a developed measure of gestalt cute content to help validate our newly developed measures. Using 1,875 Polish tweets, our results confirmed that cute social media content predicted a kama muta response, but not all KAMMUS Two subscales were sensitive to cute content, and that the HSM measure was a better indicator of the presence of cute content. Further, the CAT measure is an effective means of categorizing cute attributes of social media content. These results suggest potential differences between in-person, online, and social media experiences evoking cute emotional reactions, and the need for metrics that are developed and validated for use in social media contexts.

Recovering Individual Emotional States from Sparse Ratings Using Collaborative Filtering

A fundamental challenge in emotion research is measuring feeling states with high granularity and temporal precision without disrupting the emotion generation process. Here we introduce and validate a new approach in which responses are sparsely sampled and the missing data are recovered using a computational technique known as collaborative filtering (CF). This approach leverages structured covariation across individual experiences and is available in Neighbors, an open-source Python toolbox. We validate our approach across three different experimental contexts by recovering dense individual ratings using only a small subset of the original data. In dataset 1, participants (n=316) separately rated 112 emotional images on 6 different discrete emotions. In dataset 2, participants (n=203) watched 8 short emotionally engaging autobiographical stories while simultaneously providing moment-by-moment ratings of the intensity of their affective experience. In dataset 3, participants (n=60) with distinct social preferences made 76 decisions about how much money to return in a hidden multiplier trust game. Across all experimental contexts, CF was able to accurately recover missing data and importantly outperformed mean and multivariate imputation, particularly in contexts with greater individual variability. This approach will enable new avenues for affective science research by allowing researchers to acquire high dimensional ratings from emotional experiences with minimal disruption to the emotion-generation process.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42761-022-00161-2.

Context reconsidered: Complex signal ensembles, relational meaning, and population thinking in psychological science

This article considers the status and study of "context" in psychological science through the lens of research on emotional expressions. The article begins by updating three well-trod methodological debates on the role of context in emotional expressions to reconsider several fundamental assumptions lurking within the field's dominant methodological tradition: namely, that certain expressive movements have biologically prepared, inherent emotional meanings that issue from singular, universal processes which are independent of but interact with contextual influences. The second part of this article considers the scientific opportunities that await if we set aside this traditional understanding of "context" as a moderator of signals with inherent psychological meaning and instead consider the possibility that psychological events emerge in ecosystems of signal ensembles, such that the psychological meaning of any individual signal is entirely relational. Such a fundamental shift has radical implications not only for the science of emotion but for psychological science more generally. It offers opportunities to improve the validity and trustworthiness of psychological science beyond what can be achieved with improvements to methodological rigor alone. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

How emotions, relationships, and culture constitute each other: advances in social functionalist theory

Social Functionalist Theory (SFT) emerged 20 years ago to orient emotion science to the social nature of emotion. Here we expand upon SFT and make the case for how emotions, relationships, and culture constitute one another. First, we posit that emotions enable the individual to meet six "relational needs" within social interactions: security, commitment, status, trust, fairness, and belongingness. Building upon this new theorising, we detail four principles concerning emotional experience, cognition, expression, and the cultural archiving of emotion. We conclude by considering the bidirectional influences between culture, relationships, and emotion, outlining areas of future inquiry.

Emotion norms for 6000 Polish word meanings with a direct mapping to the Polish wordnet

Emotion lexicons are useful in research across various disciplines, but the availability of such resources remains limited for most languages. While existing emotion lexicons typically comprise words, it is a particular meaning of a word (rather than the word itself) that conveys emotion. To mitigate this issue, we present the Emotion Meanings dataset, a novel dataset of 6000 Polish word meanings. The word meanings are derived from the Polish wordnet (plWordNet), a large semantic network interlinking words by means of lexical and conceptual relations. The word meanings were manually rated for valence and arousal, along with a variety of basic emotion categories (anger, disgust, fear, sadness, anticipation, happiness, surprise, and trust). The annotations were found to be highly reliable, as demonstrated by the similarity between data collected in two independent samples: unsupervised (n = 21,317) and supervised (n = 561). Although we found the annotations to be relatively stable for female, male, younger, and older participants, we share both summary data and individual data to enable emotion research on different demographically specific subgroups. The word meanings are further accompanied by the relevant metadata, derived from open-source linguistic resources. Direct mapping to Princeton WordNet makes the dataset suitable for research on multiple languages. Altogether, this dataset provides a versatile resource that can be employed for emotion research in psychology, cognitive science, psycholinguistics, computational linguistics, and natural language processing.

The representation of emotion knowledge across development

The present study examined how children spontaneously represent facial cues associated with emotion. 106 three‐ to six‐year‐old children (48 male, 58 female; 9.4% Asian, 84.0% White, 6.6% more than one race) and 40 adults (10 male, 30 female; 10% Hispanic, 30% Asian, 2.5% Black, 57.5% White) were recruited from a Midwestern city (2019–2020), and sorted emotion cues in a spatial arrangement method that assesses emotion knowledge without reliance on emotion vocabulary. Using supervised and unsupervised analyses, the study found evidence for continuities and gradual changes in children's emotion knowledge compared to adults. Emotion knowledge develops through an incremental learning process in which children change their representations using combinations of factors—particularly valence—that are weighted differently across development.

Are All "Basic Emotions" Emotions? A Problem for the (Basic) Emotions Construct

Despite decades of challenges to the idea that a small number of emotions enjoys the special status of "basic emotions," the idea continues to have considerable influence in psychology and beyond. However, different theorists have proposed substantially different lists of basic emotions, which suggests that there exists no stable criterion of basicness. To some extent, the basic-emotions enterprise is bedeviled by an overreliance on English affective terms, but there also lurks a more serious problem-the lack of agreement as to what emotions are. To address this problem, three necessary conditions are proposed as a minimal requirement for a mental state to be an emotion. A detailed analysis of surprise, a widely accepted basic emotion, reveals that surprise violates even this minimal test, raising the possibility that it and perhaps other would-be basic emotions might not be emotions at all. An approach that combines ideas such as undifferentiated affect and cognitive appraisal is briefly proposed as a way of theorizing about emotions that is less dependent on the vagaries of language and incoherent notions of basic emotions. Finally, it is suggested that the perennial question of what an emotion is should be given more serious attention.