Emotions as discrete patterns of systemic activity

Fingerprint patterns of human brain activity reveal a dynamic mix of emotional responses during virtual intergroup encounters

The Stereotype Content Model (SCM) states that different social groups elicit different emotions according to their perceived level of competence and warmth. Because of this relationship between stereotypes and emotional states and because emotions are highly predictive of intergroup behaviors, emotional evaluation is crucial for research on intergroup relations. However, emotional assessment heavily relies on self-reports, which are often compromised by social desirability and challenges in reporting immediate emotional appraisals. In this study, we used machine learning to identify emotional brain patterns using functional magnetic resonance imaging. Subsequently, those patterns were used to monitor emotional reactions during virtual intergroup encounters. Specifically, we showed Finnish majority group members 360-videos depicting members of their ethnic ingroup and immigrant outgroups approaching and entering participants' personal space. All the groups showed different levels of perceived competence and warmth. In alignment with the SCM, our results showed that the groups perceived as low in competence and warmth evoked contempt and discomfort. Moreover, the ambivalent low-competent/high-warm group elicited both happiness and discomfort. Additionally, upon the protagonists' approach into personal space, emotional reactions were modulated differently for each group. Taken together, our findings suggest that our method could be used to explore the temporal dynamics of emotional responses during intergroup encounters.

Multicomponent approach reveals differences in affective responses among children and adolescents

Investigating age-related shifts in affective responses to emotionally salient stimuli is key to comprehending emotional development during childhood and adolescence. Most of the research regarding emotional experiences has focused on adults, while the understanding of the development of emotional experiences across childhood remains elusive. To address this gap, we explored whether physiological and behavioural responses as well as self-reported emotions elicited in children and adolescents by naturalistic stimuli differ from those in adults. We developed a set of emotional videos to elicit different emotions - fear, joy, anger, sadness, amusement, and tenderness - and measured emotional intensity ratings, electrocardiography, and eye movements from 8-15-year-old children and adults during the viewing of the videos. We identified age-related changes in all measured responses. Emotional intensity and behavioural responses varied across emotion categories. Furthermore, specific emotions showed different maturation patterns. The study highlights the importance of a multicomponent approach to accurately discern and understand emotional states.

Aesthetic chills modulate reward learning in anhedonic depression

OBJECTIVE

This study aimed to examine the potential of experiencing aesthetic chills to enhance reward learning in individuals with elevated depressive symptoms, specifically anhedonia, by investigating the effect of chills on participants' ability to modulate behavior as a function of rewards.

METHODS

A total of 103 participants with elevated depressive symptoms took part in the experiment. Among them, 59 participants had depressive symptoms (BDI ≥ 20), with 26 classified as "High Anhedonic" (HA) and 33 as "Low Anhedonic" (LA). Additionally, 39 participants without elevated depressive symptoms (BDI < 20 and SHAPs <32) were included as the control group. We utilized ChillsDB, an open-source database of validated audiovisual stimuli known to elicit chills in the US population.

RESULTS

Anhedonic participants who experienced chills demonstrated a significant increase in response bias (p = .004) towards rewards compared to those who did not experience chills. Highlighting specificity, no significant difference in reward bias was observed among LA participants.

CONCLUSIONS

These findings suggest that the experience of chills has the potential to impact reward learning in anhedonic individuals, aligning with the known neurobiology of this phenomenon. These results highlight the potential of aesthetic chills as a novel approach to elicit and enhance positive affect in depressed populations.

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.

Neural Predictors of Fear Depend on the Situation

The extent to which neural representations of fear experience depend on or generalize across the situational context has remained unclear. We systematically manipulated variation within and across three distinct fear-evocative situations including fear of heights, spiders, and social threats. Participants (n = 21; 10 females and 11 males) viewed ∼20 s clips depicting spiders, heights, or social encounters and rated fear after each video. Searchlight multivoxel pattern analysis was used to identify whether and which brain regions carry information that predicts fear experience and the degree to which the fear-predictive neural codes in these areas depend on or generalize across the situations. The overwhelming majority of brain regions carrying information about fear did so in a situation-dependent manner. These findings suggest that local neural representations of fear experience are unlikely to involve a singular pattern but rather a collection of multiple heterogeneous brain states.

EDA-Graph: Graph Signal Processing of Electrodermal Activity for Emotional States Detection

The continuous detection of emotional states has many applications in mental health, marketing, human-computer interaction, and assistive robotics. Electrodermal activity (EDA), a signal modulated by sympathetic nervous system activity, provides continuous insight into emotional states. However, EDA possesses intricate nonstationary and nonlinear characteristics, making the extraction of emotion-relevant information challenging. We propose a novel graph signal processing (GSP) approach to model EDA signals as graphical networks, termed EDA-graph. The GSP leverages graph theory concepts to capture complex relationships in time-series data. To test the usefulness of EDA-graphs to detect emotions, we processed EDA recordings from the CASE emotion dataset using GSP by quantizing and linking values based on the Euclidean distance between the nearest neighbors. From these EDA-graphs, we computed the features of graph analysis, including total load centrality (TLC), total harmonic centrality (THC), number of cliques (GNC), diameter, and graph radius, and compared those features with features obtained using traditional EDA processing techniques. EDA-graph features encompassing TLC, THC, GNC, diameter, and radius demonstrated significant differences (p < 0.05) between five emotional states (Neutral, Amused, Bored, Relaxed, and Scared). Using machine learning models for classifying emotional states evaluated using leave-one-subject-out cross-validation, we achieved a five-class F1 score of up to 0.68.

A shared structure for emotion experiences from narratives, videos, and everyday life

Our knowledge of the diversity and psychological organization of emotion experiences is based primarily on studies that used a single type of stimulus with an often limited set of rating scales and analyses. Here we take a comprehensive data-driven approach. We surveyed 1,000+ participants on a diverse set of ratings of emotion experiences to a validated set of ca. 150 text narratives, a validated set of ca. 1,000 videos, and over 10,000 personal experiences sampled longitudinally in everyday life, permitting a unique comparison. All three types of emotion experiences were characterized by similar dimensional spaces that included valence and arousal, as well as dimensions related to generalizability. Emotion experiences were distributed along continuous gradients, with no clear clusters even for the so-called basic emotions. Individual differences in personality traits were associated with differences in everyday emotion experiences but not with emotions evoked by narratives or videos.

More than labels: neural representations of emotion words are widely distributed across the brain

Although emotion words such as "anger," "disgust," "happiness," or "pride" are often thought of as mere labels, increasing evidence points to language as being important for emotion perception and experience. Emotion words may be particularly important for facilitating access to the emotion concepts. Indeed, deficits in semantic processing or impaired access to emotion words interfere with emotion perception. Yet, it is unclear what these behavioral findings mean for affective neuroscience. Thus, we examined the brain areas that support processing of emotion words using representational similarity analysis of functional magnetic resonance imaging data (N = 25). In the task, participants saw 10 emotion words (e.g. "anger," "happiness") while in the scanner. Participants rated each word based on its valence on a continuous scale ranging from 0 (Pleasant/Good) to 1 (Unpleasant/Bad) scale to ensure they were processing the words. Our results revealed that a diverse range of brain areas including prefrontal, midline cortical, and sensorimotor regions contained information about emotion words. Notably, our results overlapped with many regions implicated in decoding emotion experience by prior studies. Our results raise questions about what processes are being supported by these regions during emotion experience.

Mapping the emotional homunculus with fMRI

Emotions are commonly associated with bodily sensations, e.g., boiling with anger when overwhelmed with rage. Studies have shown that emotions are related to specific body parts, suggesting that somatotopically organized cortical regions that commonly respond to somatosensory and motor experiences might be involved in the generation of emotions. We used functional magnetic resonance imaging to investigate whether the subjective feelings of emotion are accompanied by the activation of somatotopically defined sensorimotor brain regions, thus aiming to reconstruct an "emotional homunculus." By defining the convergence of the brain activation patterns evoked by self-generated emotions during scanning onto a sensorimotor map created on participants' tactile and motor brain activity, we showed that all the evoked emotions activated parts of this sensorimotor map, yet with considerable overlap among different emotions. Although we could not find a highly specific segmentation of discrete emotions over sensorimotor regions, our results support an embodied experience of emotions.

Dissociative Symptoms and Interoceptive Integration

Dissociative symptoms and disorders of dissociation are characterised by disturbances in the experience of the self and the surrounding world, manifesting as a breakdown in the normal integration of consciousness, memory, identity, emotion, and perception. This paper aims to provide insights into dissociative symptoms from the perspective of interoception, the sense of the body's internal physiological state, adopting a transdiagnostic framework.Dissociative symptoms are associated with a blunting of autonomic reactivity and a reduction in interoceptive precision. In addition to the central function of interoception in homeostasis, afferent visceral signals and their neural and mental representation have been shown to shape emotional feeling states, support memory encoding, and contribute to self-representation. Changes in interoceptive processing and disrupted integration of interoceptive signals into wider cognition may contribute to detachment from the body and the world, blunted emotional experience, and altered subjective recall, as experienced by individuals who suffer from dissociation.A better understanding of the role of altered interoceptive integration across the symptom areas of dissociation could thus provide insights into the neurophysiological mechanisms underlying dissociative disorders. As new therapeutic approaches targeting interoceptive processing emerge, recognising the significance of interoceptive mechanisms in dissociation holds potential implications for future treatment targets.

Neural patterns associated with mixed valence feelings differ in consistency and predictability throughout the brain

Mixed feelings, the simultaneous presence of feelings with positive and negative valence, remain an understudied topic. They pose a specific set of challenges due to individual variation, and their investigation requires analtyic approaches focusing on individually self-reported states. We used functional magnetic resonance imaging (fMRI) to scan 27 subjects watching an animated short film chosen to induce bittersweet mixed feelings. The same subjects labeled when they had experienced positive, negative, and mixed feelings. Using hidden-Markov models, we found that various brain regions could predict the onsets of new feeling states as determined by self-report. The ability of the models to identify these transitions suggests that these states may exhibit unique and consistent neural signatures. We next used the subjects' self-reports to evaluate the spatiotemporal consistency of neural patterns for positive, negative, and mixed states. The insula had unique and consistent neural signatures for univalent states, but not for mixed valence states. The anterior cingulate and ventral medial prefrontal cortex had consistent neural signatures for both univalent and mixed states. This study is the first to demonstrate that subjectively reported changes in feelings induced by naturalistic stimuli can be predicted from fMRI and the first to show direct evidence for a neurally consistent representation of mixed feelings.

Probing neurodynamics of experienced emotions-a Hitchhiker's guide to film fMRI

Film functional magnetic resonance imaging (fMRI) has gained tremendous popularity in many areas of neuroscience. However, affective neuroscience remains somewhat behind in embracing this approach, even though films lend themselves to study how brain function gives rise to complex, dynamic and multivariate emotions. Here, we discuss the unique capabilities of film fMRI for emotion research, while providing a general guide of conducting such research. We first give a brief overview of emotion theories as these inform important design choices. Next, we discuss films as experimental paradigms for emotion elicitation and address the process of annotating them. We then situate film fMRI in the context of other fMRI approaches, and present an overview of results from extant studies so far with regard to advantages of film fMRI. We also give an overview of state-of-the-art analysis techniques including methods that probe neurodynamics. Finally, we convey limitations of using film fMRI to study emotion. In sum, this review offers a practitioners' guide to the emerging field of film fMRI and underscores how it can advance affective neuroscience.

Effects of Affective Proximity and Treatment on Emotional Response toward People with Substance Use

BACKGROUND

Emotions act as mediators of the relationship between stereotypes and inclinations to discriminate against and isolate individuals with substance use disorders (SUD). Emotional responses toward people with SUD are more negative than toward those people with non-drug-related mental disorders. This study explored the effects of affective bonds with substance users and treatment on the type and frequency of emotions, valence, and interpersonal distance.

METHODS

A convenience sample of 1,195 individuals was included in this survey-based study. Participants responded to questions regarding their knowledge of psychoactive drugs and beliefs about substance use disorders and were requested to report the emotions they imagined having felt in four scenarios depicting a substance user whose characteristics varied according to two dimensions: the substance user was a relative or an unknown; the substance user was in treatment for SUD, or not.

RESULTS

Emotions toward relative drug users were more negative and expressed greater interpersonal distance. Treatment was associated with more positive valence and lower interpersonal distance, but emotions toward relatives in treatment were more negative than those not.

CONCLUSION

Specific interventions for relatives of people with SUD may be necessary because of the emotional burden caused by the courtesy stigma.

Are You Dominated by Your Affects? How and When Do Employees' Daily Affective States Impact Learning from Project Failure?

Given the enormous value that project failure brings to individuals and organizations, a large number of scholars have explored the antecedents that affect employees' learning from project failure. However, few scholars have paid attention to how individuals' affective states interact with cognition patterns to learn from failure. Based on cognitive behavioral theory, this paper explores the relationship between employees' different daily affective states and learning from project failure and incorporates the mediating role of error management strategy and the moderating role of project commitment. By using SPSS and Amos software, hierarchical regression analysis of questionnaire data collected from 774 employees in high-tech firms in China indicates that (1) positive/negative affective states positively/negatively affect learning from failure, respectively; (2) error management strategy mediates the relationship between daily affective states and learning from project failure; and (3) project commitment moderates the relationship between negative affective states and error management strategy; specifically, this relationship is weaker when project commitment is stronger. However, the moderating effect of project commitment on the relationship between positive affective states and error management strategy is not supported. The results further expand the research related to learning from failure and have practical implications for failure management in high-tech enterprises.

Pattern recognition reveals sex-dependent neural substrates of sexual perception

Sex differences in brain activity evoked by sexual stimuli remain elusive despite robust evidence for stronger enjoyment of and interest toward sexual stimuli in men than in women. To test whether visual sexual stimuli evoke different brain activity patterns in men and women, we measured hemodynamic brain activity induced by visual sexual stimuli in two experiments with 91 subjects (46 males). In one experiment, the subjects viewed sexual and nonsexual film clips, and dynamic annotations for nudity in the clips were used to predict hemodynamic activity. In the second experiment, the subjects viewed sexual and nonsexual pictures in an event-related design. Men showed stronger activation than women in the visual and prefrontal cortices and dorsal attention network in both experiments. Furthermore, using multivariate pattern classification we could accurately predict the sex of the subject on the basis of the brain activity elicited by the sexual stimuli. The classification generalized across the experiments indicating that the sex differences were task-independent. Eye tracking data obtained from an independent sample of subjects (N = 110) showed that men looked longer than women at the chest area of the nude female actors in the film clips. These results indicate that visual sexual stimuli evoke discernible brain activity patterns in men and women which may reflect stronger attentional engagement with sexual stimuli in men.

Mapping emotions on the body

Emotions are allostatic processes that transform the relationship between the environment and the desired bodily states into behaviour supporting homeostasis and well-being. Central emotion circuits are thus tightly coupled with the visceral signaling pathways and the autonomic nervous system (ANS). Although ANS activity patterns are not always emotion-specific, self-reported bodily sensations and pattern recognition analysis of functional magnetic resonance imaging data suggest discrete bodily and neural basis of emotions. The advent of total-body positron emission tomography (PET) systems allows simultaneous measurement of the central and peripheral axis of the emotional response. This provides a unique opportunity for quantifying the systems-level biology of the human emotion circuits.

Perspective-taking is associated with increased discriminability of affective states in the ventromedial prefrontal cortex

Recent work using multivariate-pattern analysis (MVPA) on functional magnetic resonance imaging (fMRI) data has found that distinct affective states produce correspondingly distinct patterns of neural activity in the cerebral cortex. However, it is unclear whether individual differences in the distinctiveness of neural patterns evoked by affective stimuli underlie empathic abilities such as perspective-taking (PT). Accordingly, we examined whether we could predict PT tendency from the classification of blood-oxygen-level-dependent (BOLD) fMRI activation patterns while participants (n = 57) imagined themselves in affectively charged scenarios. We used an MVPA searchlight analysis to map where in the brain activity patterns permitted the classification of four affective states: happiness, sadness, fear and disgust. Classification accuracy was significantly above chance levels in most of the prefrontal cortex and in the posterior medial cortices. Furthermore, participants' self-reported PT was positively associated with classification accuracy in the ventromedial prefrontal cortex and insula. This finding has implications for understanding affective processing in the prefrontal cortex and for interpreting the cognitive significance of classifiable affective brain states. Our multivariate approach suggests that PT ability may rely on the grain of internally simulated affective representations rather than simply the global strength.

Features and Extra-Striate Body Area Representations of Diagnostic Body Parts in Anger and Fear Perception

Social species perceive emotion via extracting diagnostic features of body movements. Although extensive studies have contributed to knowledge on how the entire body is used as context for decoding bodily expression, we know little about whether specific body parts (e.g., arms and legs) transmit enough information for body understanding. In this study, we performed behavioral experiments using the Bubbles paradigm on static body images to directly explore diagnostic body parts for categorizing angry, fearful and neutral expressions. Results showed that subjects recognized emotional bodies through diagnostic features from the torso with arms. We then conducted a follow-up functional magnetic resonance imaging (fMRI) experiment on body part images to examine whether diagnostic parts modulated body-related brain activity and corresponding neural representations. We found greater activations of the extra-striate body area (EBA) in response to both anger and fear than neutral for the torso and arms. Representational similarity analysis showed that neural patterns of the EBA distinguished different bodily expressions. Furthermore, the torso with arms and whole body had higher similarities in EBA representations relative to the legs and whole body, and to the head and whole body. Taken together, these results indicate that diagnostic body parts (i.e., torso with arms) can communicate bodily expression in a detectable manner.

Classification of emotion categories based on functional connectivity patterns of the human brain

Neurophysiological and psychological models posit that emotions depend on connections across wide-spread corticolimbic circuits. While previous studies using pattern recognition on neuroimaging data have shown differences between various discrete emotions in brain activity patterns, less is known about the differences in functional connectivity. Thus, we employed multivariate pattern analysis on functional magnetic resonance imaging data (i) to develop a pipeline for applying pattern recognition in functional connectivity data, and (ii) to test whether connectivity patterns differ across emotion categories. Six emotions (anger, fear, disgust, happiness, sadness, and surprise) and a neutral state were induced in 16 participants using one-minute-long emotional narratives with natural prosody while brain activity was measured with functional magnetic resonance imaging (fMRI). We computed emotion-wise connectivity matrices both for whole-brain connections and for 10 previously defined functionally connected brain subnetworks and trained an across-participant classifier to categorize the emotional states based on whole-brain data and for each subnetwork separately. The whole-brain classifier performed above chance level with all emotions except sadness, suggesting that different emotions are characterized by differences in large-scale connectivity patterns. When focusing on the connectivity within the 10 subnetworks, classification was successful within the default mode system and for all emotions. We thus show preliminary evidence for consistently different sustained functional connectivity patterns for instances of emotion categories particularly within the default mode system.

Current Advances in Assessment of Dog's Emotions, Facial Expressions, and Their Use for Clinical Recognition of Pain

Animals' facial expressions are involuntary responses that serve to communicate the emotions that individuals feel. Due to their close co-existence with humans, broad attention has been given to identifying these expressions in certain species, especially dogs. This review aims to analyze and discuss the advances in identifying the facial expressions of domestic dogs and their clinical utility in recognizing pain as a method to improve daily practice and, in an accessible and effective way, assess the health outcome of dogs. This study focuses on aspects related to the anatomy and physiology of facial expressions in dogs, their emotions, and evaluations of their eyebrows, eyes, lips, and ear positions as changes that reflect pain or nociception. In this regard, research has found that dogs have anatomical configurations that allow them to generate changes in their expressions that similar canids-wolves, for example-cannot produce. Additionally, dogs can perceive emotions similar to those of their human tutors due to close human-animal interaction. This phenomenon-called "emotional contagion"-is triggered precisely by the dog's capacity to identify their owners' gestures and then react by emitting responses with either similar or opposed expressions that correspond to positive or negative stimuli, respectively. In conclusion, facial expressions are essential to maintaining social interaction between dogs and other species, as in their bond with humans. Moreover, this provides valuable information on emotions and the perception of pain, so in dogs, they can serve as valuable elements for recognizing and evaluating pain in clinical settings.

Naturalistic Stimuli in Affective Neuroimaging: A Review

Naturalistic stimuli such as movies, music, and spoken and written stories elicit strong emotions and allow brain imaging of emotions in close-to-real-life conditions. Emotions are multi-component phenomena: relevant stimuli lead to automatic changes in multiple functional components including perception, physiology, behavior, and conscious experiences. Brain activity during naturalistic stimuli reflects all these changes, suggesting that parsing emotion-related processing during such complex stimulation is not a straightforward task. Here, I review affective neuroimaging studies that have employed naturalistic stimuli to study emotional processing, focusing especially on experienced emotions. I argue that to investigate emotions with naturalistic stimuli, we need to define and extract emotion features from both the stimulus and the observer.

Expressions of emotions across species

What are emotions and how should we study them? These questions give rise to ongoing controversy amongst scientists in the fields of neuroscience, psychology and philosophy, and have resulted in different views on emotions [1-6]. In this review, we define emotions as functional states that bear essential roles in promoting survival and thus have emerged through evolution. Emotions trigger behavioral, somatic, hormonal, and neurochemical reactions, referred to as expressions of emotion. We discuss recent studies on emotion expression across species and highlight emerging common principles. We argue that detailed and multidimensional analyses of emotion expressions are key to develop biology-based definitions of emotions and to reveal their neuronal underpinnings.

At the Neural Intersection Between Language and Emotion

What role does language play in emotion? Behavioral research shows that emotion words such as "anger" and "fear" alter emotion experience, but questions still remain about mechanism. Here, we review the neuroscience literature to examine whether neural processes associated with semantics are also involved in emotion. Our review suggests that brain regions involved in the semantic processing of words: (i) are engaged during experiences of emotion, (ii) coordinate with brain regions involved in affect to create emotions, (iii) hold representational content for emotion, and (iv) may be necessary for constructing emotional experience. We relate these findings with respect to four theoretical relationships between language and emotion, which we refer to as "non-interactive," "interactive," "constitutive," and "deterministic." We conclude that findings are most consistent with the interactive and constitutive views with initial evidence suggestive of a constitutive view, in particular. We close with several future directions that may help test hypotheses of the constitutive view.

The Embodied-Enactive-Interactive Brain: Bridging Neuroscience and Creative Arts Therapies

The recognition and incorporation of evidence-based neuroscientific concepts into creative arts therapeutic knowledge and practice seem valuable and advantageous for the purpose of integration and professional development. Moreover, exhilarating insights from the field of neuroscience coincide with the nature, conceptualization, goals, and methods of Creative Arts Therapies (CATs), enabling comprehensive understandings of the clinical landscape, from a translational perspective. This paper contextualizes and discusses dynamic brain functions that have been suggested to lie at the heart of intra- and inter-personal processes. Touching upon fundamental aspects of the self and self-other interaction, the state-of-the-art neuroscientific-informed views will shed light on mechanisms of the embodied, predictive and relational brain. The conceptual analysis introduces and interweaves the following contemporary perspectives of brain function: firstly, the grounding of mental activity in the lived, bodily experience will be delineated; secondly, the enactive account of internal models, or generative predictive representations, shaped by experience, will be defined and extensively deliberated; and thirdly, the interpersonal simulation and synchronization mechanisms that support empathy and mentalization will be thoroughly considered. Throughout the paper, the cross-talks between the brain and the body, within the brain through functionally connected neural networks and in the context of agent-environment dynamics, will be addressed. These communicative patterns will be elaborated on to unfold psychophysiological linkage, as well as psychopathological shifts, concluding with the neuroplastic change associated with the formulation of CATs. The manuscript suggests an integrative view of the brain-body-mind in contexts relevant to the therapeutic potential of the expressive creative arts and the main avenues by which neuroscience may ground, enlighten and enrich the clinical psychotherapeutic practice.

Responses to External Emotions or their Transitions at Central to Peripheral Nervous System Levels: A Methodological Contribution to Mental Health

Background:

Responses to external emotional-stimuli or their transitions might help to elucidate the scientific background and assist the clinical management of psychiatric problems, but pure emotional-materials and their utilization at different levels of neurophysiological processing are few.

Objective:

We aimed to describe the responses at central and peripheral levels in healthy volunteers and psychiatric patients when facing external emotions and their transitions.

Methods:

Using pictures and sounds with pure emotions of Disgust, Erotica, Fear, Happiness, Neutral, and Sadness or their transitions as stimuli, we have developed a series of non-invasive techniques, i.e., the event-related potentials, functional magnetic resonance imaging, excitatory and inhibitory brainstem reflexes, and polygraph, to assess different levels of neurophysiological responses in different populations.

Results:

Sample outcomes on various conditions were specific and distinguishable at cortical to peripheral levels in bipolar I and II disorder patients compared to healthy volunteers.

Conclusions:

Methodologically, designs with these pure emotions and their transitions are applicable, and results per se are specifically interpretable in patients with emotion-related problems.

Comparing supervised and unsupervised approaches to emotion categorization in the human brain, body, and subjective experience

Machine learning methods provide powerful tools to map physical measurements to scientific categories. But are such methods suitable for discovering the ground truth about psychological categories? We use the science of emotion as a test case to explore this question. In studies of emotion, researchers use supervised classifiers, guided by emotion labels, to attempt to discover biomarkers in the brain or body for the corresponding emotion categories. This practice relies on the assumption that the labels refer to objective categories that can be discovered. Here, we critically examine this approach across three distinct datasets collected during emotional episodes—measuring the human brain, body, and subjective experience—and compare supervised classification solutions with those from unsupervised clustering in which no labels are assigned to the data. We conclude with a set of recommendations to guide researchers towards meaningful, data-driven discoveries in the science of emotion and beyond.

Deep time-delay Markov network for prediction and modeling the stress and emotions state transition

To recognize stress and emotion, most of the existing methods only observe and analyze speech patterns from present-time features. However, an emotion (especially for stress) can change because it was triggered by an event while speaking. To address this issue, we propose a novel method for predicting stress and emotions by analyzing prior emotional states. We named this method the deep time-delay Markov network (DTMN). Structurally, the proposed DTMN contains a hidden Markov model (HMM) and a time-delay neural network (TDNN). We evaluated the effectiveness of the proposed DTMN by comparing it with several state transition methods in predicting an emotional state from time-series (sequences) speech data of the SUSAS dataset. The experimental results show that the proposed DTMN can accurately predict present emotional states by outperforming the baseline systems in terms of the prediction error rate (PER). We then modeled the emotional state transition using a finite Markov chain based on the prediction result. We also conducted an ablation experiment to observe the effect of different HMM values and TDNN parameters on the prediction result and the computational training time of the proposed DTMN.

Causal mapping of emotion networks in the human brain: Framework and initial findings

Emotions involve many cortical and subcortical regions, prominently including the amygdala. It remains unknown how these multiple network components interact, and it remains unknown how they cause the behavioral, autonomic, and experiential effects of emotions. Here we describe a framework for combining a novel technique, concurrent electrical stimulation with fMRI (es-fMRI), together with a novel analysis, inferring causal structure from fMRI data (causal discovery). We outline a research program for investigating human emotion with these new tools, and provide initial findings from two large resting-state datasets as well as case studies in neurosurgical patients with electrical stimulation of the amygdala. The overarching goal is to use causal discovery methods on fMRI data to infer causal graphical models of how brain regions interact, and then to further constrain these models with direct stimulation of specific brain regions and concurrent fMRI. We conclude by discussing limitations and future extensions. The approach could yield anatomical hypotheses about brain connectivity, motivate rational strategies for treating mood disorders with deep brain stimulation, and could be extended to animal studies that use combined optogenetic fMRI.

Fluctuation in Pupil Size and Spontaneous Blinks Reflect Story Transportation

Thirty-nine participants listened to 28 neutral and horror excerpts of Stephen King short stories while constantly tracking their emotional arousal. Pupil size was measured with an Eyelink 1000+, and participants rated valence and transportation after each story. In addition to computing mean pupil size across 1-sec intervals, we extracted blink count and used detrended fluctuation analysis (DFA) to obtain the scaling exponents of long-range temporal correlations (LRTCs) in pupil size time-series. Pupil size was expected to be sensitive also to emotional arousal, whereas blink count and LRTC's were expected to reflect cognitive engagement. The results showed that self-reported arousal increased, pupil size was overall greater, and the decreasing slope of pupil size was flatter for horror than for neutral stories. Horror stories induced higher transportation than neutral stories. High transportation was associated with a steeper increase in self-reported arousal across time, stronger LRTCs in pupil size fluctuations, and lower blink count. These results indicate that pupil size reflects emotional arousal induced by the text content, while LRTCs and blink count are sensitive to cognitive engagement associated with transportation, irrespective of the text type. The study demonstrates the utility of pupillometric measures and blink count to study literature reception.

Perception of Discrete Emotions in Others: Evidence for Distinct Facial Mimicry Patterns

Covert facial mimicry involves subtle facial muscle activation in observers when they perceive the facial emotional expressions of others. It remains uncertain whether prototypical facial features in emotional expressions are being covertly mimicked and also whether covert facial mimicry involves distinct facial muscle activation patterns across muscles per emotion category, or simply distinguishes positive versus negative valence in observed facial emotions. To test whether covert facial mimicry is emotion-specific, we measured facial electromyography (EMG) from five muscle sites (corrugator supercilii, levator labii, frontalis lateralis, depressor anguli oris, zygomaticus major) whilst participants watched videos of people expressing 9 different basic and complex emotions and a neutral expression. This study builds upon previous research by including a greater number of facial muscle measures and emotional expressions. It is the first study to investigate activation patterns across muscles during facial mimicry and to provide evidence for distinct patterns of facial muscle activation when viewing individual emotion categories, suggesting that facial mimicry is emotion-specific, rather than just valence-based.

Decoding dynamic affective responses to naturalistic videos with shared neural patterns

This study explored the feasibility of using shared neural patterns from brief affective episodes (viewing affective pictures) to decode extended, dynamic affective sequences in a naturalistic experience (watching movie-trailers). Twenty-eight participants viewed pictures from the International Affective Picture System (IAPS) and, in a separate session, watched various movie-trailers. We first located voxels at bilateral occipital cortex (LOC) responsive to affective picture categories by GLM analysis, then performed between-subject hyperalignment on the LOC voxels based on their responses during movie-trailer watching. After hyperalignment, we trained between-subject machine learning classifiers on the affective pictures, and used the classifiers to decode affective states of an out-of-sample participant both during picture viewing and during movie-trailer watching. Within participants, neural classifiers identified valence and arousal categories of pictures, and tracked self-reported valence and arousal during video watching. In aggregate, neural classifiers produced valence and arousal time series that tracked the dynamic ratings of the movie-trailers obtained from a separate sample. Our findings provide further support for the possibility of using pre-trained neural representations to decode dynamic affective responses during a naturalistic experience.

Dissociable neural systems for unconditioned acute and sustained fear

Fear protects organisms by increasing vigilance and preparedness, and by coordinating survival responses during life-threatening encounters. The fear circuit must thus operate on multiple timescales ranging from preparatory sustained alertness to acute fight-or-flight responses. Here we studied the brain basis of sustained and acute fear using naturalistic functional magnetic resonance imaging (fMRI) enabling analysis of different time-scales of fear responses. Subjects (N ​= ​37) watched feature-length horror movies while their hemodynamic brain activity was measured with fMRI. Time-variable intersubject correlation (ISC) was used to quantify the reliability of brain activity across participants, and seed-based phase synchronization was used for characterizing dynamic connectivity. Subjective ratings of fear were used to assess how synchronization and functional connectivity varied with emotional intensity. These data suggest that acute and sustained fear are supported by distinct neural pathways, with sustained fear amplifying mainly sensory responses, and acute fear increasing activity in brainstem, thalamus, amygdala and cingulate cortices. Sustained fear increased ISC in regions associated with acute fear, and also amplified functional connectivity within this network. The results were replicated in an independent experiment with a different subject sample and stimulus movie. The functional interplay between cortical networks involved in sustained anticipation of, and acute response to, threat involves a complex and dynamic interaction that depends on the proximity of threat, and the need to employ threat appraisals and vigilance for decision making and response selection.

Dynamic intersubject neural synchronization reflects affective responses to sad music

Psychological theories of emotion often highlight the dynamic quality of the affective experience, yet neuroimaging studies of affect have traditionally relied on static stimuli that lack ecological validity. Consequently, the brain regions that represent emotions and feelings as they unfold remain unclear. Recently, dynamic, model-free analytical techniques have been employed with naturalistic stimuli to better capture time-varying patterns of activity in the brain; yet, few studies have focused on relating these patterns to changes in subjective feelings. Here, we address this gap, using intersubject correlation and phase synchronization to assess how stimulus-driven changes in brain activity and connectivity are related to two aspects of emotional experience: emotional intensity and enjoyment. During fMRI scanning, healthy volunteers listened to a full-length piece of music selected to induce sadness. After scanning, participants listened to the piece twice while simultaneously rating the intensity of felt sadness or felt enjoyment. Activity in the auditory cortex, insula, and inferior frontal gyrus was significantly synchronized across participants. Synchronization in auditory, visual, and prefrontal regions was significantly greater in participants with higher measures of a subscale of trait empathy related to feeling emotions in response to music. When assessed dynamically, continuous enjoyment ratings positively predicted a moment-to-moment measure of intersubject synchronization in auditory, default mode, and striatal networks, as well as the orbitofrontal cortex, whereas sadness predicted intersubject synchronization in limbic and striatal networks. The results suggest that stimulus-driven patterns of neural communication in emotional processing and high-level cortical regions carry meaningful information with regards to our feeling in response to a naturalistic stimulus.

Deep Learning Classification of Neuro-Emotional Phase Domain Complexity Levels Induced by Affective Video Film Clips

In the present article, a novel emotional complexity marker is proposed for classification of discrete emotions induced by affective video film clips. Principal Component Analysis (PCA) is applied to full-band specific phase space trajectory matrix (PSTM) extracted from short emotional EEG segment of 6 s, then the first principal component is used to measure the level of local neuronal complexity. As well, Phase Locking Value (PLV) between right and left hemispheres is estimated for in order to observe the superiority of local neuronal complexity estimation to regional neuro-cortical connectivity measurements in clustering nine discrete emotions (fear, anger, happiness, sadness, amusement, surprise, excitement, calmness, disgust) by using Long-Short-Term-Memory Networks as deep learning applications. In tests, two groups (healthy females and males aged between 22 and 33 years old) are classified with the accuracy levels of [Formula: see text] and [Formula: see text] through the proposed emotional complexity markers and and connectivity levels in terms of PLV in amusement. The groups are found to be statistically different ( p << 0.5) in amusement with respect to both metrics, even if gender difference does not lead to different neuro-cortical functions in any of the other discrete emotional states. The high deep learning classification accuracy of [Formula: see text] is commonly obtained for discrimination of positive emotions from negative emotions through the proposed new complexity markers. Besides, considerable useful classification performance is obtained in discriminating mixed emotions from each other through full-band connectivity features. The results reveal that emotion formation is mostly influenced by individual experiences rather than gender. In detail, local neuronal complexity is mostly sensitive to the affective valance rating, while regional neuro-cortical connectivity levels are mostly sensitive to the affective arousal ratings.

Pure Emotion-loaded Materials in the International Affective Digitized Sounds (IADS): A Study on Intensity Ratings in Chinese University Students

Background:

Materials loaded with pure emotion are essential for basic and clinical research on sounds. The International Affective Digitized Sounds (IADS) is one of the widely-used emotional tools, but its materials are not clearly labeled with specific emotions. We have hypothesized that the IADS contains pure vectors of at least disgust, erotica (or erotism), fear, happiness, sadness and neutral emotions.

Methods:

We therefore selected 48 IADS sounds with saturate emotions, and invited 271 male and 353 female university students to rate the intensity of the emotions conveyed in each sound. The ratings were then analyzed with the exploratory and confirmatory factor analyses.

Results:

Five factors were observed, namely: erotica, fear-sadness, happiness, neutrality, and disgust. Later, as two facets, sounds of fear-sadness were separated. Thirty sounds under six facets were finally retained with good model-fit indices and satisfactory factor internal reliabilities. Moreover, males scored significantly higher on erotica than females did.

Conclusion:

Our study purified a series of emotion-loaded IADS sounds, which might help clarify the pure effects of sound emotion in future research and clinical management of affective disorders.

What the face displays: Mapping 28 emotions conveyed by naturalistic expression

What emotions do the face and body express? Guided by new conceptual and quantitative approaches (Cowen, Elfenbein, Laukka, & Keltner, 2018; Cowen & Keltner, 2017, 2018), we explore the taxonomy of emotion recognized in facial-bodily expression. Participants (N = 1,794; 940 female, ages 18-76 years) judged the emotions captured in 1,500 photographs of facial-bodily expression in terms of emotion categories, appraisals, free response, and ecological validity. We find that facial-bodily expressions can reliably signal at least 28 distinct categories of emotion that occur in everyday life. Emotion categories, more so than appraisals such as valence and arousal, organize emotion recognition. However, categories of emotion recognized in naturalistic facial and bodily behavior are not discrete but bridged by smooth gradients that correspond to continuous variations in meaning. Our results support a novel view that emotions occupy a high-dimensional space of categories bridged by smooth gradients of meaning. They offer an approximation of a taxonomy of facial-bodily expressions, visualized within an online interactive map. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Emotional Expression: Advances in Basic Emotion Theory

In this article, we review recent developments in the study of emotional expression within a basic emotion framework. Dozens of new studies find that upwards of 20 emotions are signaled in multimodal and dynamic patterns of expressive behavior. Moving beyond word to stimulus matching paradigms, new studies are detailing the more nuanced and complex processes involved in emotion recognition and the structure of how people perceive emotional expression. Finally, we consider new studies documenting contextual influences upon emotion recognition. We conclude by extending these recent findings to questions about emotion-related physiology and the mammalian precursors of human emotion.

What Basic Emotion Theory Really Says for the Twenty-First Century Study of Emotion

Basic emotion theory (BET) has been, perhaps, the central narrative in the science of emotion. As Crivelli and Fridlund (J Nonverbal Behav 125:1-34, 2019, this issue) would have it, however, BET is ready to be put to rest, facing "last stands" and "fatal" empirical failures. Nothing could be further from the truth. Crivelli and Fridlund's outdated treatment of BET, narrow focus on facial expressions of six emotions, inattention to robust empirical literatures, and overreliance on singular "critical tests" of a multifaceted theory, undermine their critique and belie the considerable advances guided by basic emotion theory.

The central extended amygdala in fear and anxiety: Closing the gap between mechanistic and neuroimaging research

Anxiety disorders impose a staggering burden on public health, underscoring the need to develop a deeper understanding of the distributed neural circuits underlying extreme fear and anxiety. Recent work highlights the importance of the central extended amygdala, including the central nucleus of the amygdala (Ce) and neighboring bed nucleus of the stria terminalis (BST). Anatomical data indicate that the Ce and BST form a tightly interconnected unit, where different kinds of threat-relevant information can be integrated to assemble states of fear and anxiety. Neuroimaging studies show that the Ce and BST are engaged by a broad spectrum of potentially threat-relevant cues. Mechanistic work demonstrates that the Ce and BST are critically involved in organizing defensive responses to a wide range of threats. Studies in rodents have begun to reveal the specific molecules, cells, and microcircuits within the central extended amygdala that underlie signs of fear and anxiety, but the relevance of these tantalizing discoveries to human experience and disease remains unclear. Using a combination of focal perturbations and whole-brain imaging, a new generation of nonhuman primate studies is beginning to close this gap. This work opens the door to discovering the mechanisms underlying neuroimaging measures linked to pathological fear and anxiety, to understanding how the Ce and BST interact with one another and with distal brain regions to govern defensive responses to threat, and to developing improved intervention strategies.

Distributed affective space represents multiple emotion categories across the human brain

The functional organization of human emotion systems as well as their neuroanatomical basis and segregation in the brain remains unresolved. Here, we used pattern classification and hierarchical clustering to characterize the organization of a wide array of emotion categories in the human brain. We induced 14 emotions (6 ‘basic’, e.g. fear and anger; and 8 ‘non-basic’, e.g. shame and gratitude) and a neutral state using guided mental imagery while participants' brain activity was measured with functional magnetic resonance imaging (fMRI). Twelve out of 14 emotions could be reliably classified from the haemodynamic signals. All emotions engaged a multitude of brain areas, primarily in midline cortices including anterior and posterior cingulate gyri and precuneus, in subcortical regions, and in motor regions including cerebellum and premotor cortex. Similarity of subjective emotional experiences was associated with similarity of the corresponding neural activation patterns. We conclude that different basic and non-basic emotions have distinguishable neural bases characterized by specific, distributed activation patterns in widespread cortical and subcortical circuits. Regionally differentiated engagement of these circuits defines the unique neural activity pattern and the corresponding subjective feeling associated with each emotion.

A New, Better BET: Rescuing and Revising Basic Emotion Theory

Basic Emotion Theory, or BET, has dominated the affective sciences for decades (Ekman, 1972, 1992, 1999; Ekman and Davidson, 1994; Griffiths, 2013; Scarantino and Griffiths, 2011). It has been highly influential, driving a number of empirical lines of research (e.g., in the context of facial expression detection, neuroimaging studies and evolutionary psychology). Nevertheless, BET has been criticized by philosophers, leading to calls for it to be jettisoned entirely (Colombetti, 2014; Hufendiek, 2016). This paper defuses those criticisms. In addition, it shows that we have good reason to retain BET. Finally, it reviews and puts to rest worries that BET's commitment to affect programs renders it outmoded. We propose that, with minor adjustments, BET can avoid such criticisms when conceived under a radically enactive account of emotions. Thus, rather than leaving BET behind, we show how its basic ideas can be revised, refashioned and preserved. Hence, we conclude, our new BET is still a good bet.

Intrinsic functional connectivity of the central extended amygdala

The central extended amygdala (EAc)-including the bed nucleus of the stria terminalis (BST) and central nucleus of the amygdala (Ce)-plays a critical role in triggering fear and anxiety and is implicated in the development of a range of debilitating neuropsychiatric disorders. Although it is widely believed that these disorders reflect the coordinated activity of distributed neural circuits, the functional architecture of the EAc network and the degree to which the BST and the Ce show distinct patterns of functional connectivity is unclear. Here, we used a novel combination of imaging approaches to trace the connectivity of the BST and the Ce in 130 healthy, racially diverse, community-dwelling adults. Multiband imaging, high-precision registration techniques, and spatially unsmoothed data maximized anatomical specificity. Using newly developed seed regions, whole-brain regression analyses revealed robust functional connectivity between the BST and Ce via the sublenticular extended amygdala, the ribbon of subcortical gray matter encompassing the ventral amygdalofugal pathway. Both regions displayed coupling with the ventromedial prefrontal cortex (vmPFC), midcingulate cortex (MCC), insula, and anterior hippocampus. The BST showed stronger connectivity with the thalamus, striatum, periaqueductal gray, and several prefrontal territories. The only regions showing stronger functional connectivity with the Ce were neighboring regions of the dorsal amygdala, amygdalohippocampal area, and anterior hippocampus. These observations provide a baseline against which to compare a range of special populations, inform our understanding of the role of the EAc in normal and pathological fear and anxiety, and showcase image registration techniques that are likely to be useful for researchers working with "deidentified" neuroimaging data.