Mapping discrete and dimensional emotions onto the brain: controversies and consensus

Children and adolescents' neural response to emotional faces and voices: Age-related changes in common regions of activation

The perception of facial and vocal emotional expressions engages overlapping regions of the brain. However, at a behavioral level, the ability to recognize the intended emotion in both types of nonverbal cues follows a divergent developmental trajectory throughout childhood and adolescence. The current study a) identified regions of common neural activation to facial and vocal stimuli in 8- to 19-year-old typically-developing adolescents, and b) examined age-related changes in blood-oxygen-level dependent (BOLD) response within these areas. Both modalities elicited activation in an overlapping network of subcortical regions (insula, thalamus, dorsal striatum), visual-motor association areas, prefrontal regions (inferior frontal cortex, dorsomedial prefrontal cortex), and the right superior temporal gyrus. Within these regions, increased age was associated with greater frontal activation to voices, but not faces. Results suggest that processing facial and vocal stimuli elicits activation in common areas of the brain in adolescents, but that age-related changes in response within these regions may vary by modality.

EEG-Based BCI Emotion Recognition: A Survey

Affecting computing is an artificial intelligence area of study that recognizes, interprets, processes, and simulates human affects. The user's emotional states can be sensed through electroencephalography (EEG)-based Brain Computer Interfaces (BCI) devices. Research in emotion recognition using these tools is a rapidly growing field with multiple inter-disciplinary applications. This article performs a survey of the pertinent scientific literature from 2015 to 2020. It presents trends and a comparative analysis of algorithm applications in new implementations from a computer science perspective. Our survey gives an overview of datasets, emotion elicitation methods, feature extraction and selection, classification algorithms, and performance evaluation. Lastly, we provide insights for future developments.

Recognize basic emotional statesin speech by machine learning techniques using mel-frequency cepstral coefficient features

Speech Emotion Recognition (SER) has been widely used in many fields, such as smart home assistants commonly found in the market. Smart home assistants that could detect the user’s emotion would improve the communication between a user and the assistant enabling the assistant to offer more productive feedback. Thus, the aim of this work is to analyze emotional states in speech and propose a suitable algorithm considering performance verses complexity for deployment in smart home devices. The four emotional speech sets were selected from the Berlin Emotional Database (EMO-DB) as experimental data, 26 MFCC features were extracted from each type of emotional speech to identify the emotions of happiness, anger, sadness and neutrality. Then, speaker-independent experiments for our Speech emotion Recognition (SER) were conducted by using the Back Propagation Neural Network (BPNN), Extreme Learning Machine (ELM), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM). Synthesizing the recognition accuracy and processing time, this work shows that the performance of SVM was the best among the four methods as a good candidate to be deployed for SER in smart home devices. SVM achieved an overall accuracy of 92.4% while offering low computational requirements when training and testing. We conclude that the MFCC features and the SVM classification models used in speaker-independent experiments are highly effective in the automatic prediction of emotion.

Affective Cortical Asymmetry at the Early Developmental Emergence of Emotional Expression

Emotions have an important survival function. Vast amounts of research have demonstrated how affect-related changes in physiology promote survival by effecting short-term and long-term changes in adaptive behavior. However, if emotions truly serve such an inherent function, they should be pervasive across species and be established early in life. Here, using electroencephalographic (EEG) brain activity we sought to characterize core neurophysiological features underlying affective function at the emergence of emotional expression [i.e., at the developmental age when human infants start to show reliable stimulus-elicited emotional states (4–6 months)]. Using an approach that eschews traditional EEG frequency band delineations (like theta, alpha), we demonstrate that negative emotional states induce a strong right hemispheric increase in the prominence of the resonant frequency (∼5–6 Hz) in the infant frontal EEG. Increased rightward asymmetry was strongly correlated with increased heart rate responses to emotionally negative states compared with neutral states. We conclude that functional frontal asymmetry is a key component of emotional processing and suggest that the rightward asymmetry in prominence of the resonant frequency during negative emotional states might reflect functional asymmetry in the central representation of anatomically driven asymmetry in the autonomic nervous system. Our findings indicate that the specific mode hallmarking emotional processing in the frontal cortex is established in parallel with the emergence of stable emotional states very early during development, despite the well known protracted maturation of frontal cortex.

Redundancy, isomorphism, and propagative mechanisms between emotional and amodal representations of words: A computational study

Some proposals claim that language acts as a link to propagate emotional and other modal information. Thus, there is an eminently amodal path of emotional propagation in the mental lexicon. Following these proposals, we present a computational model that emulates a linking mechanism (mapping function) between emotional and amodal representations of words using vector space models, emotional feature-based models, and neural networks. We analyzed three central concepts within the embodiment debate (redundancy, isomorphism, and propagative mechanisms) comparing two alternative hypotheses: semantic neighborhood hypothesis versus specific dimensionality hypothesis. Univariate and multivariate neural networks were trained for dimensional (N = 11,357) and discrete emotions (N = 2,266), and later we analyzed its predictions in a test set (N = 4,167 and N = 875, respectively). We showed how this computational model could propagate emotional responses to words without a direct emotional experience via amodal propagation, but no direct relations were found between emotional rates and amodal distances. Thereby, we found that there were clear redundancy and propagative mechanisms, but no isomorphism should be assumed. Results suggested that it was necessary to establish complex links to go beyond amodal distances of vector spaces. In this way, although the emotional rates of semantic neighborhoods could predict the emotional rates of target words, the mapping function of specific amodal features seemed to simulate emotional responses better. Thus, both hypotheses would not be mutually exclusive. We also showed that discrete emotions could have simpler relations between modal and amodal representations than dimensional emotions. All these results and their theoretical implications are discussed.

Effects of Subthalamic Nucleus Deep Brain Stimulation on Facial Emotion Recognition in Parkinson's Disease: A Critical Literature Review

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective therapy for Parkinson's disease (PD). Nevertheless, DBS has been associated with certain nonmotor, neuropsychiatric effects such as worsening of emotion recognition from facial expressions. In order to investigate facial emotion recognition (FER) after STN DBS, we conducted a literature search of the electronic databases MEDLINE and Web of science. In this review, we analyze studies assessing FER after STN DBS in PD patients and summarize the current knowledge of the effects of STN DBS on FER. The majority of studies, which had clinical and methodological heterogeneity, showed that FER is worsening after STN DBS in PD patients, particularly for negative emotions (sadness, fear, anger, and tendency for disgust). FER worsening after STN DBS can be attributed to the functional role of the STN in limbic circuits and the interference of STN stimulation with neural networks involved in FER, including the connections of the STN with the limbic part of the basal ganglia and pre- and frontal areas. These outcomes improve our understanding of the role of the STN in the integration of motor, cognitive, and emotional aspects of behaviour in the growing field of affective neuroscience. Further studies using standardized neuropsychological measures of FER assessment and including larger cohorts are needed, in order to draw definite conclusions about the effect of STN DBS on emotional recognition and its impact on patients' quality of life.

Negative content enhances stimulus-specific cerebral activity during free viewing of pictures, faces, and words

Negative visual stimuli have been found to elicit stronger brain activation than do neutral stimuli. Such emotion effects have been shown for pictures, faces, and words alike, but the literature suggests stimulus-specific differences regarding locus and lateralization of the activity. In the current functional magnetic resonance imaging study, we directly compared brain responses to passively viewed negative and neutral pictures of complex scenes, faces, and words (nouns) in 43 healthy participants (21 males) varying in age and demographic background. Both negative pictures and faces activated the extrastriate visual cortices of both hemispheres more strongly than neutral ones, but effects were larger and extended more dorsally for pictures, whereas negative faces additionally activated the superior temporal sulci. Negative words differentially activated typical higher-level language processing areas such as the left inferior frontal and angular gyrus. There were small emotion effects in the amygdala for faces and words, which were both lateralized to the left hemisphere. Although pictures elicited overall the strongest amygdala activity, amygdala response to negative pictures was not significantly stronger than to neutral ones. Across stimulus types, emotion effects converged in the left anterior insula. No gender effects were apparent, but age had a small, stimulus-specific impact on emotion processing. Our study specifies similarities and differences in effects of negative emotional content on the processing of different types of stimuli, indicating that brain response to negative stimuli is specifically enhanced in areas involved in processing of the respective stimulus type in general and converges across stimuli in the left anterior insula.

EEG-Based Functional Connectivity Representation using Phase Locking Value for Brain Network Based Applications

In more recent times, the network perspective study of the human brain has expanded enormously due to the advancements in the field of network neuroscience. Existing methods to form the functional connectivity from the multichannel EEG leads to a fully connected network. Representation of a fully connected functional network with a significant functional network (SFN) can help to characterize and quantify the complex brain networks. Further, it can also provide novel insight into the brain cognition analysis and is crucial in several brain network-based applications. This paper presents a framework to find the SFN corresponding to any event from its fully connected network. Using the phase-locking value (PLV) in EEG we first identify the difference PLV of an event to the rest. Based on the difference PLV, we then identify the reactive band and event-associated most reactive pairs (MRPs). The SFNs corresponding to their events is then formed with the identified MRPs. The proposed method is employed on 'database for emotion analysis using physiological signals (DEAP)' data set to find the SFNs associated with emotions. Comparable state-of-the-art multiple emotion classification accuracies are obtained with the identified SFNs. Results show that the proposed methods can be used as a general thresholding technique to identify the event-related SFNs which are crucial in brain network-based applications.

Emotional Networked maps from EEG signals. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020;2020:34-37. [PMID: 33017924 DOI: 10.1109/embc44109.2020.9175935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

The EEG has showed that contains relevant information about recognition of emotional states. It is important to analyze the EEG signals to understand the emotional states not only from a time series approach but also determining the importance of the generating process of these signals, the location of electrodes and the relationship between the EEG signals. From the EEG signals of each emotional state, a functional connectivity measurement was used to construct adjacency matrices: lagged phase synchronization (LPS), averaging adjacency matrices we built a prototype network for each emotion. Based on these networks, we extracted a set node features seeking to understand their behavior and the relationship between them. We found through the strength and degree, the group of representative electrodes for each emotional state, finding differences from intensity of measurement and the spatial location of these electrodes. In addition, analyzing the cluster coefficient, degree, and strength, we find differences between the networks from the spatial patterns associated with the electrodes with the highest coefficient. This analysis can also gain evidence from the connectivity elements shared between emotional states, allowing to cluster emotions and concluding about the relationship of emotions from EEG perspective.

Quantification of anticipation of excitement with a three-axial model of emotion with EEG

OBJECTIVE

Multiple facets of human emotion underlie diverse and sparse neural mechanisms. Among the many existing models of emotion, the two-dimensional circumplex model of emotion is an important theory. The use of the circumplex model allows us to model variable aspects of emotion; however, such momentary expressions of one's internal mental state still lacks a notion of the third dimension of time. Here, we report an exploratory attempt to build a three-axis model of human emotion to model our sense of anticipatory excitement, 'Waku-Waku' (in Japanese), in which people predictively code upcoming emotional events.

APPROACH

Electroencephalography (EEG) data were recorded from 28 young adult participants while they mentalized upcoming emotional pictures. Three auditory tones were used as indicative cues, predicting the likelihood of the valence of an upcoming picture: positive, negative, or unknown. While seeing an image, the participants judged its emotional valence during the task and subsequently rated their subjective experiences on valence, arousal, expectation, and Waku-Waku immediately after the experiment. The collected EEG data were then analyzed to identify contributory neural signatures for each of the three axes.

MAIN RESULTS

A three-axis model was built to quantify Waku-Waku. As expected, this model revealed the considerable contribution of the third dimension over the classical two-dimensional model. Distinctive EEG components were identified. Furthermore, a novel brain-emotion interface was proposed and validated within the scope of limitations.

SIGNIFICANCE

The proposed notion may shed new light on the theories of emotion and support multiplex dimensions of emotion. With the introduction of the cognitive domain for a brain-computer interface, we propose a novel brain-emotion interface. Limitations of the study and potential applications of this interface are discussed.

The Neural Representation of Visually Evoked Emotion Is High-Dimensional, Categorical, and Distributed across Transmodal Brain Regions

Central to our subjective lives is the experience of different emotions. Recent behavioral work mapping emotional responses to 2,185 videos found that people experience upward of 27 distinct emotions occupying a high-dimensional space, and that emotion categories, more so than affective dimensions (e.g., valence), organize self-reports of subjective experience. Here, we sought to identify the neural substrates of this high-dimensional space of emotional experience using fMRI responses to all 2,185 videos. Our analyses demonstrated that (1) dozens of video-evoked emotions were accurately predicted from fMRI patterns in multiple brain regions with different regional configurations for individual emotions; (2) emotion categories better predicted cortical and subcortical responses than affective dimensions, outperforming visual and semantic covariates in transmodal regions; and (3) emotion-related fMRI responses had a cluster-like organization efficiently characterized by distinct categories. These results support an emerging theory of the high-dimensional emotion space, illuminating its neural foundations distributed across transmodal regions.

Linking Personality Traits to Individual Differences in Affective Spaces

Different individuals respond differently to emotional stimuli in their environment. Therefore, to understand how emotions are represented mentally will ultimately require investigations into individual-level information. Here we tasked participants with freely arranging emotionally charged images on a computer screen according to their subjective emotional similarity (yielding a unique affective space for each participant) and subsequently sought external validity of the layout of the individuals’ affective spaces through the five-factor personality model (Neuroticism, Extraversion, Openness to Experience, Agreeableness, Conscientiousness) assessed via the NEO Five-Factor Inventory. Applying agglomerative hierarchical clustering to the group-level affective space revealed a set of underlying affective clusters whose within-cluster dissimilarity, per individual, was then correlated with individuals’ personality scores. These cluster-based analyses predominantly revealed that the dispersion of the negative cluster showed a positive relationship with Neuroticism and a negative relationship with Conscientiousness, a finding that would be predicted by prior work. Such results demonstrate the non-spurious structure of individualized emotion information revealed by data-driven analyses of a behavioral task (and validated by incorporating psychological measures of personality) and corroborate prior knowledge of the interaction between affect and personality. Future investigations can similarly combine hypothesis- and data-driven methods to extend such findings, potentially yielding new perspectives on underlying cognitive processes, disease susceptibility, or even diagnostic/prognostic markers for mental disorders involving emotion dysregulation.

Time-dependent effects of discrete post-encoding emotions on item memory and source memory

This study examined time-dependent effects of discrete emotions on item and source memory. In Experiment 1, after encoding, participants watched a comic, disgust-inducing, anger-inducing or neutral video at different delays. Positive emotion did not affect item memory but enhanced source memory (only in the 5 min delay). Anger impaired recognition in all delays, but a trend occurred for anger to impair source memory only in the 50 min delay. Disgust did not affect item memory, but a trend emerged for it to enhance and impair source memory in the 5 and 35 min delays, respectively. Experiment 2 showed that positive emotion and disgust had no effect on recognition, and consistent with Experiment 1, positive emotion, not anger or disgust, enhanced source memory. A trend occurred for positive emotion and disgust to impair source memory in the 0 min delay but not in the other delays. Consistent with Experiment 1, Experiment 3 showed that anger impaired both recognition and source memory (for males). Taken together, these findings suggest that the effect of emotion does vary depending on nature of memory tasks, category of emotion, and delay in emotion elicitation.

Behavioral and Neurobiological Convergence of Odor, Mood and Emotion: A Review

The affective state is the combination of emotion and mood, with mood reflecting a running average of sequential emotional events together with an underlying internal affective state. There is now extensive evidence that odors can overtly or subliminally modulate mood and emotion. Relying primarily on neurobiological literature, here we review what is known about how odors can affect emotions/moods and how emotions/moods may affect odor perception. We take the approach that form can provide insight into function by reviewing major brain regions and neural circuits underlying emotion and mood, and then reviewing the olfactory pathway in the context of that emotion/mood network. We highlight the extensive neuroanatomical opportunities for odor-emotion/mood convergence, as well as functional data demonstrating reciprocal interactions between these processes. Finally, we explore how the odor- emotion/mood interplay is, or could be, used in medical and/or commercial applications.

A Generalizable Multivariate Brain Pattern for Interpersonal Guilt

Feeling guilty when we have wronged another is a crucial aspect of prosociality, but its neurobiological bases are elusive. Although multivariate patterns of brain activity show promise for developing brain measures linked to specific emotions, it is less clear whether brain activity can be trained to detect more complex social emotional states such as guilt. Here, we identified a distributed guilt-related brain signature (GRBS) across two independent neuroimaging datasets that used interpersonal interactions to evoke guilt. This signature discriminated conditions associated with interpersonal guilt from closely matched control conditions in a cross-validated training sample (N = 24; Chinese population) and in an independent test sample (N = 19; Swiss population). However, it did not respond to observed or experienced pain, or recalled guilt. Moreover, the GRBS only exhibited weak spatial similarity with other brain signatures of social-affective processes, further indicating the specificity of the brain state it represents. These findings provide a step toward developing biological markers of social emotions, which could serve as important tools to investigate guilt-related brain processes in both healthy and clinical populations.

The Default Mode Network's Role in Discrete Emotion

Emotions are often assumed to manifest in subcortical limbic and brainstem structures. While these areas are clearly important for representing affect (e.g., valence and arousal), we propose that the default mode network (DMN) is additionally important for constructing discrete emotional experiences (of anger, fear, disgust, etc.). Findings from neuroimaging studies, invasive electrical stimulation studies, and lesion studies support this proposal. Importantly, our framework builds on a constructionist theory of emotion to explain how instances involving diverse physiological and behavioral patterns can be conceptualized as belonging to the same emotion category. We argue that this ability requires abstraction (from concrete features to broad mental categories), which the DMN is well positioned to support, and we make novel predictions from our proposed framework.

Age-related differences in neural activation and functional connectivity during the processing of vocal prosody in adolescence

The ability to recognize others' emotions based on vocal emotional prosody follows a protracted developmental trajectory during adolescence. However, little is known about the neural mechanisms supporting this maturation. The current study investigated age-related differences in neural activation during a vocal emotion recognition (ER) task. Listeners aged 8 to 19 years old completed the vocal ER task while undergoing functional magnetic resonance imaging. The task of categorizing vocal emotional prosody elicited activation primarily in temporal and frontal areas. Age was associated with a) greater activation in regions in the superior, middle, and inferior frontal gyri, b) greater functional connectivity between the left precentral and inferior frontal gyri and regions in the bilateral insula and temporo-parietal junction, and c) greater fractional anisotropy in the superior longitudinal fasciculus, which connects frontal areas to posterior temporo-parietal regions. Many of these age-related differences in brain activation and connectivity were associated with better performance on the ER task. Increased activation in, and connectivity between, areas typically involved in language processing and social cognition may facilitate the development of vocal ER skills in adolescence.

Towards emotion recognition in texts: A sound-symbolic experiment

The purpose of this study is to investigate the relationship between the phonetic content of prose texts in English and the emotion that the texts inspire, namely - the effect of vowel-consonant bi-phones on subjects’ evaluation of positive or negative emotional valence when reading. The methodology is based on data from an experiment where the participants, native speakers of three different languages, evaluated the valence invoked in them by one-page texts from English books. The sub-lexical level of the texts was obtained using phonetic transcriptions of the words and their further decomposition into vowel-consonant bi-phones. The statistical investigation relies on density-measures of the investigated bi-phones over each text as a whole. The result shows that there exists a correlation between the obtained sub-lexical representation and the valence perceived by the readers. Concerning the type of the consonants in the bi-phones (abrupt or sonorant), the influence of the abrupt bi-phones is stronger. However, sub-sets of both types of bi-phones showed relatedness with the emotional valence conveyed by the texts. In conclusion, the speech, expressed in written form, is laden with emotional valence even when the words’ lexicological meaning is not taken into consideration and the words are apprehended as mere phonetic constructs. This prompts hypothesizing that words’ semantics itself is partly underpinned by some mental emotion-related level of conceptualization, influenced by sounds. For practical purposes, the result suggests that based on the syllabic content of a text it should be possible to predict the valence that the text would inspire in its readers.

Mapping the Passions: Toward a High-Dimensional Taxonomy of Emotional Experience and Expression

What would a comprehensive atlas of human emotions include? For 50 years, scientists have sought to map emotion-related experience, expression, physiology, and recognition in terms of the "basic six"-anger, disgust, fear, happiness, sadness, and surprise. Claims about the relationships between these six emotions and prototypical facial configurations have provided the basis for a long-standing debate over the diagnostic value of expression (for review and latest installment in this debate, see Barrett et al., p. 1). Building on recent empirical findings and methodologies, we offer an alternative conceptual and methodological approach that reveals a richer taxonomy of emotion. Dozens of distinct varieties of emotion are reliably distinguished by language, evoked in distinct circumstances, and perceived in distinct expressions of the face, body, and voice. Traditional models-both the basic six and affective-circumplex model (valence and arousal)-capture a fraction of the systematic variability in emotional response. In contrast, emotion-related responses (e.g., the smile of embarrassment, triumphant postures, sympathetic vocalizations, blends of distinct expressions) can be explained by richer models of emotion. Given these developments, we discuss why tests of a basic-six model of emotion are not tests of the diagnostic value of facial expression more generally. Determining the full extent of what facial expressions can tell us, marginally and in conjunction with other behavioral and contextual cues, will require mapping the high-dimensional, continuous space of facial, bodily, and vocal signals onto richly multifaceted experiences using large-scale statistical modeling and machine-learning methods.

An Integrative Way for Studying Neural Basis of Basic Emotions With fMRI

How emotions are represented in the nervous system is a crucial unsolved problem in the affective neuroscience. Many studies are striving to find the localization of basic emotions in the brain but failed. Thus, many psychologists suspect the specific neural loci for basic emotions, but instead, some proposed that there are specific neural structures for the core affects, such as arousal and hedonic value. The reason for this widespread difference might be that basic emotions used previously can be further divided into more “basic” emotions. Here we review brain imaging data and neuropsychological data, and try to address this question with an integrative model. In this model, we argue that basic emotions are not contrary to the dimensional studies of emotions (core affects). We propose that basic emotion should locate on the axis in the dimensions of emotion, and only represent one typical core affect (arousal or valence). Therefore, we propose four basic emotions: joy-on positive axis of hedonic dimension, sadness-on negative axis of hedonic dimension, fear, and anger-on the top of vertical dimensions. This new model about basic emotions and construction model of emotions is promising to improve and reformulate neurobiological models of basic emotions.

fNIRS Evidence for Recognizably Different Positive Emotions

The behavioral differentiation of positive emotions has recently been studied in terms of their discrete adaptive functions or appraising profiles. Some preliminary neurophysiological evidences have been found with electroencephalography or autonomic nervous system measurements such as heart rate, skin conductance, etc. However, the brain's hemodynamic responses to different positive emotions remain largely unknown. In the present study, the functional near-infrared spectroscopy (fNIRS) technique was employed. With this tool, we for the first time reported recognizable discrete positive emotions using fNIRS signals. Thirteen participants watched 30 emotional video clips to elicit 10 typical kinds of positive emotions (joy, gratitude, serenity, interest, hope, pride, amusement, inspiration, awe, and love), and their frontal neural activities were simultaneously recorded with a 24-channel fNIRS system. The multidimensional scaling analysis of participants' subjective ratings on these 10 positive emotions revealed three distinct clusters, which could be interpreted as "playfulness" for amusement, joy, interest, "encouragement" for awe, gratitude, hope, inspiration, pride, and "harmony" for love, serenity. Hemodynamic responses to these three positive emotion clusters showed distinct patterns, and HbO-based individual-level binary classifications between them achieved an averaged accuracy of 73.79 ± 11.49% (77.56 ± 7.39% for encouragement vs. harmony, 73.29 ± 11.87% for playfulness vs. harmony, 70.51 ± 13.96% for encouragement vs. harmony). Benefited from fNIRS's high portability, low running cost and the relative robustness against motion and electrical artifacts, our findings provided support for implementing a more fine-grained emotion recognition system with subdivided positive emotion categories.

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.

EEG spectral powers and source localization in depressing, sad, and fun music videos focusing on gender differences

Previously, gender-specific affective responses have been shown using neurophysiological signals. The present study intended to compare the differences in electroencephalographic (EEG) power spectra and EEG brain sources between men and women during the exposure of affective music video stimuli. The multi-channel EEG signals of 15 males and 15 females available in the database for emotion analysis using physiological signals were studied, while subjects were watching sad, depressing, and fun music videos. Seven EEG frequency bands were computed using average Fourier cross-spectral matrices. Then, standardized low-resolution electromagnetic tomography (sLORETA) was used to localize regions involved specifically in these emotional responses. To evaluate gender differences, independent sample t test was calculated for the sLORETA source powers. Our results showed that (1) the mean EEG power for all frequency bands in the women's group was significantly higher than that of the men's group; (2) spatial distribution differentiation between men and women was detected in all EEG frequency bands. (3) This difference has been related to the emotional stimuli, which was more evident for negative emotions. Taken together, our results showed that men and women recruited dissimilar brain networks for processing sad, depressing, and fun audio-visual stimuli.

Frontal EEG Asymmetry and Middle Line Power Difference in Discrete Emotions

A traditional model of emotion cannot explain the differences in brain activities between two discrete emotions that are similar in the valence-arousal coordinate space. The current study elicited two positive emotions (amusement and tenderness) and two negative emotions (anger and fear) that are similar in both valence and arousal dimensions to examine the differences in brain activities in these emotional states. Frontal electroencephalographic (EEG) asymmetry and midline power in three bands (theta, alpha and beta) were measured when participants watched affective film excerpts. Significant differences were detected between tenderness and amusement on FP1/FP2 theta asymmetry, F3/F4 theta and alpha asymmetry. Significant differences between anger and fear on FP1/FP2 theta asymmetry and F3/F4 alpha asymmetry were also observed. For midline power, midline theta power could distinguish two negative emotions, while midline alpha and beta power could effectively differentiate two positive emotions. Liking and dominance were also related to EEG features. Stepwise multiple linear regression results revealed that frontal alpha and theta asymmetry could predict the subjective feelings of two positive and two negative emotions in different patterns. The binary classification accuracy, which used EEG frontal asymmetry and midline power as features and support vector machine (SVM) as classifiers, was as high as 64.52% for tenderness and amusement and 78.79% for anger and fear. The classification accuracy was improved after adding these features to other features extracted across the scalp. These findings indicate that frontal EEG asymmetry and midline power might have the potential to recognize discrete emotions that are similar in the valence-arousal coordinate space.

Neurophysiological Profile of Antismoking Campaigns

Over the past few decades, antismoking public service announcements (PSAs) have been used by governments to promote healthy behaviours in citizens, for instance, against drinking before the drive and against smoke. Effectiveness of such PSAs has been suggested especially for young persons. By now, PSAs efficacy is still mainly assessed through traditional methods (questionnaires and metrics) and could be performed only after the PSAs broadcasting, leading to waste of economic resources and time in the case of Ineffective PSAs. One possible countermeasure to such ineffective use of PSAs could be promoted by the evaluation of the cerebral reaction to the PSA of particular segments of population (e.g., old, young, and heavy smokers). In addition, it is crucial to gather such cerebral activity in front of PSAs that have been assessed to be effective against smoke (Effective PSAs), comparing results to the cerebral reactions to PSAs that have been certified to be not effective (Ineffective PSAs). The eventual differences between the cerebral responses toward the two PSA groups will provide crucial information about the possible outcome of new PSAs before to its broadcasting. This study focused on adult population, by investigating the cerebral reaction to the vision of different PSA images, which have already been shown to be Effective and Ineffective for the promotion of an antismoking behaviour. Results showed how variables as gender and smoking habits can influence the perception of PSA images, and how different communication styles of the antismoking campaigns could facilitate the comprehension of PSA's message and then enhance the related impact.

The past thirty years of emotion research: appraisal and beyond

For this Special Issue, I highlight the past and present importance of appraisal theory as well as the challenges to its status as a total theory of emotions from the other functions of emotions: associative learning, self-regulation and social communication. This theoretical view applies both to emotion research in general and the specific fields of my interest in the emotions of moral judgment and intergroup processes. Methodologically, developments in analyses of large and more naturally occurring data sets will give an opportunity to square psychology's structural models of discrete emotions with the more complicated reality that exists. Both for the field and for individual researchers picking up the study of emotions, my advice is to pay special attention to measures, their assumptions and their context.

Neurophysiological Measures of the Perception of Antismoking Public Service Announcements Among Young Population

Tobacco constitutes a global emergency with totally preventable millions of deaths per year and smoking-related illnesses. Public service announcements (PSAs) are the main tool against smoking and by now their efficacy is still assessed through questionnaires and metrics, only months after their circulation. The present study focused on the young population, because at higher risk of developing tobacco addiction, investigating the reaction to the vision of Effective, Ineffective and Awarded antismoking PSAs through: electroencephalography (EEG), autonomic activity variation (Galvanic skin response—GSR- and Heart Rate—HR-) and Eye-Tracking (ET). The employed indices were: the EEG frontal alpha band asymmetry and the frontal theta; the Emotional Index (EI), deriving from the GSR and HR signals matching; the ET Visual Attention (VA) index, based on the ratio between the total time spent fixating an area of interest (AOI) and its area. Smokers expressed higher frontal alpha asymmetry values in comparison to non-smokers. Concerning frontal theta, Awarded PSAs reported the highest values in comparison to both Effective and Ineffective PSAs. EI results highlighted that lowest values were expressed by Heavy Smokers (HS), and Effective PSAs obtained the highest EI values. Finally, concerning the Effective PSAs, regression analysis highlighted a correlation between the number of cigarettes smoked by participants (independent variable) and frontal alpha asymmetry, frontal theta and EI values. ET results suggested that for the Ineffective PSAs the main focus were texts, while for the Effective and Awarded PSAs were the visual elements. Results support the use of methods aimed at assessing the physiological reaction for the evaluation of PSAs images, in particular when considering the smoking habits of target populations.

An Appraisal-Driven Componential Approach to the Emotional Brain

This article suggests that methodological and conceptual advancements in affective sciences militate in favor of adopting an appraisal-driven componential approach to further investigate the emotional brain. Here we propose to operationalize this approach by distinguishing five functional networks of the emotional brain: (a) the elicitation network, (b) the expression network, (c) the autonomic reaction network, (d) the action tendency network, and (e) the feeling network, and discuss these networks in the context of the affective neuroscience literature. We also propose that further investigating the “appraising brain” is the royal road to better understand the elicitation network, and may be key to revealing the neural causal mechanisms underlying the emotion process as a whole.

Integrating Perspectives on Affective Neuroscience: Introduction to the Special Section on the Brain and Emotion

In this special section, three target articles present three different perspectives on emotion and how it is implemented in the human brain. Fundamental issues are discussed such as the nature and organization of emotion’s representation in the brain and the best approaches for elucidating emotion’s neural basis. Comments and author replies further discuss these issues and explore their interconnections. A common theme of the target articles and commentaries is that multiple approaches and perspectives must be integrated across all levels of analysis to understand the neural basis of emotion.

Brain States That Encode Perceived Emotion Are Reproducible but Their Classification Accuracy Is Stimulus-Dependent

The brain state hypothesis of image-induced affect processing, which posits that a one-to-one mapping exists between each image stimulus and its induced functional magnetic resonance imaging (fMRI)-derived neural activation pattern (i.e., brain state), has recently received support from several multivariate pattern analysis (MVPA) studies. Critically, however, classification accuracy differences across these studies, which largely share experimental designs and analyses, suggest that there exist one or more unaccounted sources of variance within MVPA studies of affect processing. To explore this possibility, we directly demonstrated strong inter-study correlations between image-induced affective brain states acquired 4 years apart on the same MRI scanner using near-identical methodology with studies differing only by the specific image stimuli and subjects. We subsequently developed a plausible explanation for inter-study differences in affective valence and arousal classification accuracies based on the spatial distribution of the perceived affective properties of the stimuli. Controlling for this distribution improved valence classification accuracy from 56% to 85% and arousal classification accuracy from 61% to 78%, which mirrored the full range of classification accuracy across studies within the existing literature. Finally, we validated the predictive fidelity of our image-related brain states according to an independent measurement, autonomic arousal, captured via skin conductance response (SCR). Brain states significantly but weakly (r = 0.08) predicted the SCRs that accompanied individual image stimulations. More importantly, the effect size of brain state predictions of SCR increased more than threefold (r = 0.25) when the stimulus set was restricted to those images having group-level significantly classifiable arousal properties.

Functional connections between activated and deactivated brain regions mediate emotional interference during externally directed cognition

Recent evidence shows that task-deactivations are functionally relevant for cognitive performance. Indeed, higher cognitive engagement has been associated with higher suppression of activity in task-deactivated brain regions - usually ascribed to the Default Mode Network (DMN). Moreover, a negative correlation between these regions and areas actively engaged by the task is associated with better performance. DMN regions show positive modulation during autobiographical, social, and emotional tasks. However, it is not clear how processing of emotional stimuli affects the interplay between the DMN and executive brain regions. We studied this interplay in an fMRI experiment using emotional negative stimuli as distractors. Activity modulations induced by the emotional interference of negative stimuli were found in frontal, parietal, and visual areas, and were associated with modulations of functional connectivity between these task-activated areas and DMN regions. A worse performance was predicted both by lower activity in the superior parietal cortex and higher connectivity between visual areas and frontal DMN regions. Connectivity between right inferior frontal gyrus and several DMN regions in the left hemisphere was related to the behavioral performance. This relation was weaker in the negative than in the neutral condition, likely suggesting less functional inhibitions of DMN regions during emotional processing. These results show that both executive and DMN regions are crucial for the emotional interference process and suggest that DMN connections are related to the interplay between externally-directed and internally-focused processes. Among DMN regions, superior frontal gyrus may be a key node in regulating the interference triggered by emotional stimuli.

Emotion fingerprints or emotion populations? A meta-analytic investigation of autonomic features of emotion categories

The classical view of emotion hypothesizes that certain emotion categories have a specific autonomic nervous system (ANS) "fingerprint" that is distinct from other categories. Substantial ANS variation within a category is presumed to be epiphenomenal. The theory of constructed emotion hypothesizes that an emotion category is a population of context-specific, highly variable instances that need not share an ANS fingerprint. Instead, ANS variation within a category is a meaningful part of the nature of emotion. We present a meta-analysis of 202 studies measuring ANS reactivity during lab-based inductions of emotion in nonclinical samples of adults, using a random effects, multilevel meta-analysis and multivariate pattern classification analysis to test our hypotheses. We found increases in mean effect size for 59.4% of ANS variables across emotion categories, but the pattern of effect sizes did not clearly distinguish 1 emotion category from another. We also observed significant variation within emotion categories; heterogeneity accounted for a moderate to substantial percentage (i.e., I2 ≥ 30%) of variability in 54% of these effect sizes. Experimental moderators epiphenomenal to emotion, such as induction type (e.g., films vs. imagery), did not explain a large portion of the variability. Correction for publication bias reduced estimated effect sizes even further, increasing heterogeneity of effect sizes for certain emotion categories. These findings, when considered in the broader empirical literature, are more consistent with population thinking and other principles from evolutionary biology found within the theory of constructed emotion, and offer insights for developing new hypotheses to understand the nature of emotion. (PsycINFO Database Record

Human amygdala stimulation effects on emotion physiology and emotional experience

The amygdala is a key structure mediating emotional processing. Few studies have used direct electrical stimulation of the amygdala in humans to examine stimulation-elicited physiological and emotional responses, and the nature of such effects remains unclear. Determining the effects of electrical stimulation of the amygdala has important theoretical implications for current discrete and dimensional neurobiological theories of emotion, which differ substantially in their predictions about the emotional effects of such stimulation. To examine the effects of amygdala stimulation on physiological and subjective emotional responses we examined epilepsy patients undergoing intracranial EEG monitoring in which depth electrodes were implanted unilaterally or bilaterally in the amygdala. Nine subjects underwent both sham and acute monopolar electrical stimulation at various parameters in electrode contacts located in amygdala and within lateral temporal cortex control locations. Stimulation was applied at either 50 Hz or 130 Hz, while amplitudes were increased stepwise from 1 to 12 V, with subjects blinded to stimulation condition. Electrodermal activity (EDA), heart rate (HR), and respiratory rate (RR) were simultaneously recorded and subjective emotional response was probed after each stimulation period. Amygdala stimulation (but not lateral control or sham stimulation) elicited immediate and substantial dose-dependent increases in EDA and decelerations of HR, generally without affecting RR. Stimulation elicited subjective emotional responses only rarely, and did not elicit clinical seizures in any subject. These physiological results parallel stimulation findings with animals and are consistent with orienting/defensive responses observed with aversive visual stimuli in humans. In summary, these findings suggest that acute amygdala stimulation in humans can be safe and can reliably elicit changes in emotion physiology without significantly affecting subjective emotional experience, providing a useful approach for investigation of amygdala-mediated modulatory effects on cognition.

Dissociable meta-analytic brain networks contribute to coordinated emotional processing

Meta-analytic techniques for mining the neuroimaging literature continue to exert an impact on our conceptualization of functional brain networks contributing to human emotion and cognition. Traditional theories regarding the neurobiological substrates contributing to affective processing are shifting from regional- towards more network-based heuristic frameworks. To elucidate differential brain network involvement linked to distinct aspects of emotion processing, we applied an emergent meta-analytic clustering approach to the extensive body of affective neuroimaging results archived in the BrainMap database. Specifically, we performed hierarchical clustering on the modeled activation maps from 1,747 experiments in the affective processing domain, resulting in five meta-analytic groupings of experiments demonstrating whole-brain recruitment. Behavioral inference analyses conducted for each of these groupings suggested dissociable networks supporting: (1) visual perception within primary and associative visual cortices, (2) auditory perception within primary auditory cortices, (3) attention to emotionally salient information within insular, anterior cingulate, and subcortical regions, (4) appraisal and prediction of emotional events within medial prefrontal and posterior cingulate cortices, and (5) induction of emotional responses within amygdala and fusiform gyri. These meta-analytic outcomes are consistent with a contemporary psychological model of affective processing in which emotionally salient information from perceived stimuli are integrated with previous experiences to engender a subjective affective response. This study highlights the utility of using emergent meta-analytic methods to inform and extend psychological theories and suggests that emotions are manifest as the eventual consequence of interactions between large-scale brain networks.

Emotion recognition from multichannel EEG signals using K-nearest neighbor classification

BACKGROUND

Many studies have been done on the emotion recognition based on multi-channel electroencephalogram (EEG) signals.

OBJECTIVE

This paper explores the influence of the emotion recognition accuracy of EEG signals in different frequency bands and different number of channels.

METHODS

We classified the emotional states in the valence and arousal dimensions using different combinations of EEG channels. Firstly, DEAP default preprocessed data were normalized. Next, EEG signals were divided into four frequency bands using discrete wavelet transform, and entropy and energy were calculated as features of K-nearest neighbor Classifier.

RESULTS

The classification accuracies of the 10, 14, 18 and 32 EEG channels based on the Gamma frequency band were 89.54%, 92.28%, 93.72% and 95.70% in the valence dimension and 89.81%, 92.24%, 93.69% and 95.69% in the arousal dimension. As the number of channels increases, the classification accuracy of emotional states also increases, the classification accuracy of the gamma frequency band is greater than that of the beta frequency band followed by the alpha and theta frequency bands.

CONCLUSIONS

This paper provided better frequency bands and channels reference for emotion recognition based on EEG.

Advances in neuroscience imply that harmful experiments in dogs are unethical

Functional MRI (fMRI) of fully awake and unrestrained dog 'volunteers' has been proven an effective tool to understand the neural circuitry and functioning of the canine brain. Although every dog owner would vouch that dogs are perceptive, cognitive, intuitive and capable of positive emotions/empathy, as indeed substantiated by ethological studies for some time, neurological investigations now corroborate this. These studies show that there exists a striking similarity between dogs and humans in the functioning of the caudate nucleus (associated with pleasure and emotion), and dogs experience positive emotions, empathic-like responses and demonstrate human bonding which, some scientists claim, may be at least comparable with human children. There exists an area analogous to the 'voice area' in the canine brain, enabling dogs to comprehend and respond to emotional cues/valence in human voices, and evidence of a region in the temporal cortex of dogs involved in the processing of faces, as also observed in humans and monkeys. We therefore contend that using dogs in invasive and/or harmful research, and toxicity testing, cannot be ethically justifiable.

Direct electrical stimulation of the amygdala enhances declarative memory in humans

Emotional events are often remembered better than neutral events, a benefit that many studies have hypothesized to depend on the amygdala's interactions with memory systems. These studies have indicated that the amygdala can modulate memory-consolidation processes in other brain regions such as the hippocampus and perirhinal cortex. Indeed, rodent studies have demonstrated that direct activation of the amygdala can enhance memory consolidation even during nonemotional events. However, the premise that the amygdala causally enhances declarative memory has not been directly tested in humans. Here we tested whether brief electrical stimulation to the amygdala could enhance declarative memory for specific images of neutral objects without eliciting a subjective emotional response. Fourteen epilepsy patients undergoing monitoring of seizures via intracranial depth electrodes viewed a series of neutral object images, half of which were immediately followed by brief, low-amplitude electrical stimulation to the amygdala. Amygdala stimulation elicited no subjective emotional response but led to reliably improved memory compared with control images when patients were given a recognition-memory test the next day. Neuronal oscillations in the amygdala, hippocampus, and perirhinal cortex during this next-day memory test indicated that a neural correlate of the memory enhancement was increased theta and gamma oscillatory interactions between these regions, consistent with the idea that the amygdala prioritizes consolidation by engaging other memory regions. These results show that the amygdala can initiate endogenous memory prioritization processes in the absence of emotional input, addressing a fundamental question and opening a path to future therapies.

A lesion model of envy and Schadenfreude: legal, deservingness and moral dimensions as revealed by neurodegeneration

The study of moral emotions (i.e. Schadenfreude and envy) is critical to understand the ecological complexity of everyday interactions between cognitive, affective, and social cognition processes. Most previous studies in this area have used correlational imaging techniques and framed Schadenfreude and envy as unified and monolithic emotional domains. Here, we profit from a relevant neurodegeneration model to disentangle the brain regions engaged in three dimensions of Schadenfreude and envy: deservingness, morality, and legality. We tested a group of patients with behavioural variant frontotemporal dementia (bvFTD), patients with Alzheimer’s disease, as a contrastive neurodegeneration model, and healthy controls on a novel task highlighting each of these dimensions in scenarios eliciting Schadenfreude and envy. Compared with the Alzheimer’s disease and control groups, patients with bvFTD obtained significantly higher scores on all dimensions for both emotions. Correlational analyses revealed an association between envy and Schadenfreude scores and greater deficits in social cognition, inhibitory control, and behaviour disturbances in bvFTD patients. Brain anatomy findings (restricted to bvFTD and controls) confirmed the partially dissociable nature of the moral emotions’ experiences and highlighted the importance of socio-moral brain areas in processing those emotions. In all subjects, an association emerged between Schadenfreude and the ventral striatum, and between envy and the anterior cingulate cortex. In addition, the results supported an association between scores for moral and legal transgression and the morphology of areas implicated in emotional appraisal, including the amygdala and the parahippocampus. By contrast, bvFTD patients exhibited a negative association between increased Schadenfreude and envy across dimensions and critical regions supporting social-value rewards and social-moral processes (dorsolateral prefrontal cortex, angular gyrus and precuneus). Together, this study provides lesion-based evidence for the multidimensional nature of the emotional experiences of envy and Schadenfreude. Our results offer new insights into the mechanisms subsuming complex emotions and moral cognition in neurodegeneration. Moreover, this study presents the exacerbation of envy and Schadenfreude as a new potential hallmark of bvFTD that could impact in diagnosis and progression.

Basic emotions and adaptation. A computational and evolutionary model

The core principles of the evolutionary theories of emotions declare that affective states represent crucial drives for action selection in the environment and regulated the behavior and adaptation of natural agents in ancestrally recurrent situations. While many different studies used autonomous artificial agents to simulate emotional responses and the way these patterns can affect decision-making, few are the approaches that tried to analyze the evolutionary emergence of affective behaviors directly from the specific adaptive problems posed by the ancestral environment. A model of the evolution of affective behaviors is presented using simulated artificial agents equipped with neural networks and physically inspired on the architecture of the iCub humanoid robot. We use genetic algorithms to train populations of virtual robots across generations, and investigate the spontaneous emergence of basic emotional behaviors in different experimental conditions. In particular, we focus on studying the emotion of fear, therefore the environment explored by the artificial agents can contain stimuli that are safe or dangerous to pick. The simulated task is based on classical conditioning and the agents are asked to learn a strategy to recognize whether the environment is safe or represents a threat to their lives and select the correct action to perform in absence of any visual cues. The simulated agents have special input units in their neural structure whose activation keep track of their actual "sensations" based on the outcome of past behavior. We train five different neural network architectures and then test the best ranked individuals comparing their performances and analyzing the unit activations in each individual's life cycle. We show that the agents, regardless of the presence of recurrent connections, spontaneously evolved the ability to cope with potentially dangerous environment by collecting information about the environment and then switching their behavior to a genetically selected pattern in order to maximize the possible reward. We also prove the determinant presence of an internal time perception unit for the robots to achieve the highest performance and survivability across all conditions.

Neural correlates of deception: lying about past events and personal beliefs

Although a growing body of literature suggests that cognitive control processes are involved in deception, much about the neural correlates of lying remains unknown. In this study, we tested whether brain activation associated with deception, as measured by functional magnetic resonance imaging (fMRI), can be detected either in preparation for or during the execution of a lie, and whether they depend on the content of the lie. We scanned participants while they lied or told the truth about either their personal experiences (episodic memories) or personal beliefs. Regions in the frontal and parietal cortex showed higher activation when participants lied compared with when they were telling the truth, regardless of whether they were asked about their past experiences or opinions. In contrast, lie-related activation in the right temporal pole, precuneus and the right amygdala differed by the content of the lie. Preparing to lie activated parietal and frontal brain regions that were distinct from those activated while participants executed lies. Our findings concur with previous reports on the involvement of frontal and parietal regions in deception, but specify brain regions involved in the preparation vs execution of deception, and those involved in deceiving about experiences vs opinions.

Cardiac vagal control as a marker of emotion regulation in healthy adults: A review

In the last two decades, a growing body of theory and research has targeted the role of cardiac vagal control (CVC) in emotional responding. This research has either focused on resting CVC (also denoted as cardiac vagal tone) or phasic changes in CVC (also denoted as vagal reactivity) in response to affective stimuli. The present paper is aimed at reporting a review of the papers published between 1996 and 2016, and focused on the results of 135 papers examining cardiac vagal control as a physiological marker of emotion regulation in healthy adults. The review shows that studies have employed a wide array of methodologies and measures, often leading to conflicting results. High resting CVC has been associated with better down-regulation of negative affect, use of adaptive regulatory strategies, and more flexible emotional responding. Concerning phasic changes, research has consistently found decreased CVC in response to stress, while CVC increases have been shown to reflect either self-regulatory efforts or recovery from stress. Despite conflicting results, we conclude that existing literature supports the use of CVC as a noninvasive, objective marker of emotion regulation.

Neural signatures of dynamic emotion constructs in the human brain

How is emotion represented in the brain: is it categorical or along dimensions? In the present study, we applied multivariate pattern analysis (MVPA) to magnetoencephalography (MEG) to study the brain's temporally unfolding representations of different emotion constructs. First, participants rated 525 images on the dimensions of valence and arousal and by intensity of discrete emotion categories (happiness, sadness, fear, disgust, and sadness). Thirteen new participants then viewed subsets of these images within an MEG scanner. We used Representational Similarity Analysis (RSA) to compare behavioral ratings to the unfolding neural representation of the stimuli in the brain. Ratings of valence and arousal explained significant proportions of the MEG data, even after corrections for low-level image properties. Additionally, behavioral ratings of the discrete emotions fear, disgust, and happiness significantly predicted early neural representations, whereas rating models of anger and sadness did not. Different emotion constructs also showed unique temporal signatures. Fear and disgust - both highly arousing and negative - were rapidly discriminated by the brain, but disgust was represented for an extended period of time relative to fear. Overall, our findings suggest that 1) dimensions of valence and arousal are quickly represented by the brain, as are some discrete emotions, and 2) different emotion constructs exhibit unique temporal dynamics. We discuss implications of these findings for theoretical understanding of emotion and for the interplay of discrete and dimensional aspects of emotional experience.

Distributed Neural Processing Predictors of Multi-dimensional Properties of Affect

Recent evidence suggests that emotions have a distributed neural representation, which has significant implications for our understanding of the mechanisms underlying emotion regulation and dysregulation as well as the potential targets available for neuromodulation-based emotion therapeutics. This work adds to this evidence by testing the distribution of neural representations underlying the affective dimensions of valence and arousal using representational models that vary in both the degree and the nature of their distribution. We used multi-voxel pattern classification (MVPC) to identify whole-brain patterns of functional magnetic resonance imaging (fMRI)-derived neural activations that reliably predicted dimensional properties of affect (valence and arousal) for visual stimuli viewed by a normative sample (n = 32) of demographically diverse, healthy adults. Inter-subject leave-one-out cross-validation showed whole-brain MVPC significantly predicted (p < 0.001) binarized normative ratings of valence (positive vs. negative, 59% accuracy) and arousal (high vs. low, 56% accuracy). We also conducted group-level univariate general linear modeling (GLM) analyses to identify brain regions whose response significantly differed for the contrasts of positive versus negative valence or high versus low arousal. Multivoxel pattern classifiers using voxels drawn from all identified regions of interest (all-ROIs) exhibited mixed performance; arousal was predicted significantly better than chance but worse than the whole-brain classifier, whereas valence was not predicted significantly better than chance. Multivoxel classifiers derived using individual ROIs generally performed no better than chance. Although performance of the all-ROI classifier improved with larger ROIs (generated by relaxing the clustering threshold), performance was still poorer than the whole-brain classifier. These findings support a highly distributed model of neural processing for the affective dimensions of valence and arousal. Finally, joint error analyses of the MVPC hyperplanes encoding valence and arousal identified regions within the dimensional affect space where multivoxel classifiers exhibited the greatest difficulty encoding brain states – specifically, stimuli of moderate arousal and high or low valence. In conclusion, we highlight new directions for characterizing affective processing for mechanistic and therapeutic applications in affective neuroscience.

Self-report captures 27 distinct categories of emotion bridged by continuous gradients

Emotions are centered in subjective experiences that people represent, in part, with hundreds, if not thousands, of semantic terms. Claims about the distribution of reported emotional states and the boundaries between emotion categories-that is, the geometric organization of the semantic space of emotion-have sparked intense debate. Here we introduce a conceptual framework to analyze reported emotional states elicited by 2,185 short videos, examining the richest array of reported emotional experiences studied to date and the extent to which reported experiences of emotion are structured by discrete and dimensional geometries. Across self-report methods, we find that the videos reliably elicit 27 distinct varieties of reported emotional experience. Further analyses revealed that categorical labels such as amusement better capture reports of subjective experience than commonly measured affective dimensions (e.g., valence and arousal). Although reported emotional experiences are represented within a semantic space best captured by categorical labels, the boundaries between categories of emotion are fuzzy rather than discrete. By analyzing the distribution of reported emotional states we uncover gradients of emotion-from anxiety to fear to horror to disgust, calmness to aesthetic appreciation to awe, and others-that correspond to smooth variation in affective dimensions such as valence and dominance. Reported emotional states occupy a complex, high-dimensional categorical space. In addition, our library of videos and an interactive map of the emotional states they elicit (https://s3-us-west-1.amazonaws.com/emogifs/map.html) are made available to advance the science of emotion.

Effects of emotional valence and arousal on the voice perception network

Several theories conceptualise emotions along two main dimensions: valence (a continuum from negative to positive) and arousal (a continuum that varies from low to high). These dimensions are typically treated as independent in many neuroimaging experiments, yet recent behavioural findings suggest that they are actually interdependent. This result has impact on neuroimaging design, analysis and theoretical development. We were interested in determining the extent of this interdependence both behaviourally and neuroanatomically, as well as teasing apart any activation that is specific to each dimension. While we found extensive overlap in activation for each dimension in traditional emotion areas (bilateral insulae, orbitofrontal cortex, amygdalae), we also found activation specific to each dimension with characteristic relationships between modulations of these dimensions and BOLD signal change. Increases in arousal ratings were related to increased activations predominantly in voice-sensitive cortices after variance explained by valence had been removed. In contrast, emotions of extreme valence were related to increased activations in bilateral voice-sensitive cortices, hippocampi, anterior and midcingulum and medial orbito- and superior frontal regions after variance explained by arousal had been accounted for. Our results therefore do not support a complete segregation of brain structures underpinning the processing of affective dimensions.