On the Importance of Both Dimensional and Discrete Models of Emotion

Fun with Frustration? TikTok Influencers' Emotional Expression Predicts User Engagement with COVID-19 Vaccination Messages

This study examined what kinds of TikTok video and message features are associated with user engagement in the context of COVID-19 vaccination. Content analysis was applied to study a sample of 223 COVID-19 vaccination-related videos from creators with at least 10,000 followers. The content analysis involved coding themes, video formats, the valence of attitude toward vaccination, and emotional expressions from the influencers. A majority of videos showcased personal vaccination experiences, followed by fictitious dramas and instructional information. More fictitious dramas expressed unclear attitudes, neither explicitly supporting nor opposing the COVID-19 vaccine, compared to personal vaccination stories and instructional videos. Some imaginative and dramatic scenes, such as zombie transformation or dramatic spasms after taking the vaccines, were widely imitated across influencers, perhaps humorously, and raised concerns about diminishing positive images of vaccine uptake. Videos with simultaneous expression of humor and frustration significantly predicted engagement when the video content opposed or was uncertain about taking the vaccine, implying the effectiveness of mixed emotional attributes within a message. This study provides insight into how social context and message choices by creators interact to influence audience engagement.

The Social Contagion Potential of Pro-Vaccine Messages on Black Twitter

Black Americans in the US not only suffered from disproportionately high hospitalization and death rates throughout the pandemic but also from the consequences of low COVID-19 vaccination rates. This pattern of disparity is linked to distrust of public health systems that originates from a history of medical atrocities committed against Black people. For that reason, mitigation of race-based inequity in COVID-19 impacts might find more success in grassroots information contagion than official public health campaigns. While Black Twitter is well-positioned as a conduit for such information contagion, little is known about message characteristics that would afford it. Here, we tested the impact of four different message frames (personalization, interactive, fear appeal, neutral) on the social contagion potential of bi-modal social media messages promoting COVID-19 vaccinations and finding personalized messages to be the most shareable. Wary of recommending personalization as the blueprint for setting a social contagion health campaign in motion, we probed further to understand the influence of individual-level variables on the communicability of personalized messages. Subsequently, regression models and focus group data were consulted, revealing that thinking styles, vaccine confidence levels, and attitudes toward social media were significant factors of influence on the contagion potential of personalized messages. We discussed the implications of these results for health campaigns.

The standardization of a new Explicit Pornographic Picture Set (EPPS)

Pictures with affective content have been extensively used in scientific studies of emotion and sexuality. However, only a few standardized picture sets have been developed that offer explicit images, with most lacking pornographic pictures depicting diverse sexual practices. This study aimed to fill this gap through developing a standardized affective set of diverse pornographic pictures (masturbation, oral sex, vaginal sex, anal sex, group sex, paraphilia) of same-sex and opposite-sex content, offering dimensional affective ratings of valence, arousal, and dominance, as well as co-elicited discrete emotions (disgust, moral and ethical acceptance). In total, 192 pornographic pictures acquired from online pornography platforms and 24 control IAPS images have been rated by 319 participants (Mage = 22.66, SDage = 4.66) with self-reported same- and opposite-sex sexual attraction. Stimuli were representative of the entire affective space, including positively and negatively perceived pictures. Participants showed differential affective perception of pornographic pictures according to gender and sexual attraction. Differences in affective ratings related to participants' gender and sexual attraction, as well as stimuli content (depicted sexual practices and sexes). From the stimuli set, researchers can select explicit pornographic pictures based on the obtained affective ratings and technical parameters (i.e., pixel size, luminosity, color space, contrast, chromatic complexity, spatial frequency, entropy). The stimuli set may be considered a valid tool of diverse explicit pornographic pictures covering the affective space, in particular, for women and men with same- and opposite-sex sexual attraction. This new explicit pornographic picture set (EPPS) is available to the scientific community for non-commercial use.

Evaluation of emotion classification schemes in social media text: an annotation-based approach

BACKGROUND

Emotion analysis of social media texts is an innovative method for gaining insight into the mental state of the public and understanding social phenomena. However, emotion is a complex psychological phenomenon, and there are various emotion classification schemes. Which one is suitable for textual emotion analysis?

METHODS

We proposed a framework for evaluating emotion classification schemes based on manual annotation experiments. Considering both the quality and efficiency of emotion analysis, we identified five criteria, which are solidity, coverage, agreement, compactness, and distinction. Qualitative and quantitative factors were synthesized using the AHP, where quantitative metrics were derived from annotation experiments. Applying this framework, 2848 Sina Weibo posts related to public events were used to evaluate the five emotion schemes: SemEval's four emotions, Ekman's six basic emotions, ancient China's Seven Emotions, Plutchik's eight primary emotions, and GoEmotions' 27 emotions.

RESULTS

The AHP evaluation result shows that Ekman's scheme had the highest score. The multi-dimensional scaling (MDS) analysis shows that Ekman, Plutchik, and the Seven Emotions are relatively similar. We analyzed Ekman's six basic emotions in relation to the emotion categories of the other schemes. The correspondence analysis shows that the Seven Emotions' joy aligns with Ekman's happiness, love demonstrates a significant correlation with happiness, but desire is not significantly correlated with any emotion. Compared to Ekman, Plutchik has two more positive emotions: trust and anticipation. Trust is somewhat associated with happiness, but anticipation is weakly associated with happiness. Each emotion of Ekman's corresponds to several similar emotions in GoEmotions. However, some emotions in GoEmotions are not clearly related to Ekman's, such as approval, love, pride, amusement, etc. CONCLUSION: Ekman's scheme performs best under the evaluation framework. However, it lacks sufficient positive emotion categories for the corpus.

SunoCaps: A novel dataset of text-prompt based AI-generated music with emotion annotations

The SunoCaps dataset aims to provide an innovative contribution to music data. Expert description of human-made musical pieces, from the widely used MusicCaps dataset, are used as prompts for generating complete songs for this dataset. This Automatic Music Generation is done with the state-of-the-art Suno generator of audio-based music. A subset of 64 pieces from MusicCaps is currently included, with a total of 256 generated entries. This total stems from generating four different variations for each human piece; two versions based on the original caption and two versions based on the original aspect description. As an AI-generated music dataset, SunoCaps also includes expert-based information on prompt alignment, with the main differences between prompt and final generation annotated. Furthermore, annotations describing the main discrete emotions induced by the piece. This dataset can have an array of implementations, such as creating and improving music generation validation tools, training systems for multi-layered architectures and the optimization of music emotion estimation systems.

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.

Using subjective emotion, facial expression, and gaze direction to evaluate user affective experience and predict preference when playing single-player games

The affective experience generated when users play computer games can influence their attitude and preference towards the game. Existing evaluation means mainly depend on subjective scales and physiological signals. However, some limitations should not be ignored (e.g. subjective scales are not objective, and physiological signals are complicated). In this paper, we 1) propose a novel method to assess user affective experience when playing single-player games based on pleasure-arousal-dominance (PAD) emotions, facial expressions, and gaze directions, and 2) build an artificial intelligence model to identify user preference. Fifty-four subjects participated in a basketball experiment with three difficulty levels. Their expressions, gaze directions, and subjective PAD emotions were collected and analysed. Experimental results showed that the expression intensities of angry, sad, and neutral, yaw angle degrees of gaze direction, and PAD emotions varied significantly under different difficulties. Besides, the proposed model achieved better performance than other machine-learning algorithms on the collected dataset.

Negative affect related to door-in-the-face strategy

A full 46 years after the first study of the door-in-the-face strategy (DITF), there is still a debate about the processes behind its effect. One relatively unexplored interpretation is the presence of negative affect related to large request refusal. We explored negative affect after large request refusal both explicitly (Experiment 1) and implicitly (Experiments 1 and 2). Participants were in a negative state after large request refusal (Experiment 1), and target request acceptance was a function of their emotional state (Experiment 2). Negative affect appears to play a role in acceptance of the target request in the door-in-the-face strategy. However, this pattern of results was only observed when affect was measured implicitly. The findings shed new light on the DIFT, by taking into account the complexity of the interaction with emotion. The reasons why negative affect occurs after large request refusal are discussed.

Development and validation of the Emotional Climate Change Stories (ECCS) stimuli set

Climate change is widely recognised as an urgent issue, and the number of people concerned about it is increasing. While emotions are among the strongest predictors of behaviour change in the face of climate change, researchers have only recently begun to investigate this topic experimentally. This may be due to the lack of standardised, validated stimuli that would make studying such a topic in experimental settings possible. Here, we introduce a novel Emotional Climate Change Stories (ECCS) stimuli set. ECCS consists of 180 realistic short stories about climate change, designed to evoke five distinct emotions-anger, anxiety, compassion, guilt and hope-in addition to neutral stories. The stories were created based on qualitative data collected in two independent studies: one conducted among individuals highly concerned about climate change, and another one conducted in the general population. The stories were rated on the scales of valence, arousal, anger, anxiety, compassion, guilt and hope in the course of three independent studies. First, we explored the underlying structure of ratings (Study 1; n = 601). Then we investigated the replicability (Study 2; n = 307) and cross-cultural validity (Study 3; n = 346) of ECCS. The collected ratings were highly consistent across the studies. Furthermore, we found that the level of climate change concern explained the intensity of elicited emotions. The ECCS dataset is available in Polish, Norwegian and English and can be employed for experimental research on climate communication, environmental attitudes, climate action-taking, or mental health and wellbeing.

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

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

EESCN: A novel spiking neural network method for EEG-based emotion recognition

BACKGROUND AND OBJECTIVE

Although existing artificial neural networks have achieved good results in electroencephalograph (EEG) emotion recognition, further improvements are needed in terms of bio-interpretability and robustness. In this research, we aim to develop a highly efficient and high-performance method for emotion recognition based on EEG.

METHODS

We propose an Emo-EEGSpikeConvNet (EESCN), a novel emotion recognition method based on spiking neural network (SNN). It consists of a neuromorphic data generation module and a NeuroSpiking framework. The neuromorphic data generation module converts EEG data into 2D frame format as input to the NeuroSpiking framework, while the NeuroSpiking framework is used to extract spatio-temporal features of EEG for classification.

RESULTS

EESCN achieves high emotion recognition accuracies on DEAP and SEED-IV datasets, ranging from 94.56% to 94.81% on DEAP and a mean accuracy of 79.65% on SEED-IV. Compared to existing SNN methods, EESCN significantly improves EEG emotion recognition performance. In addition, it also has the advantages of faster running speed and less memory footprint.

CONCLUSIONS

EESCN has shown excellent performance and efficiency in EEG-based emotion recognition with potential for practical applications requiring portability and resource constraints.

Accuracy and Response Time for Modus Ponens Syllogisms Vary by Controversial Topic and Categorical Emotion

Researchers have documented differential effects of emotion on cognitive processes, debating whether emotion may increase or decrease the response time and accuracy of logical thinking. The current study proposed that differences may be due to variability occurring across topic and categorical emotions, such that assessment of several basic emotional responses in the context of performing logical reasoning tasks may provide an initial indication of these differences. Utilizing modus ponens syllogisms composed of controversial statements, the current study evoked a variety of emotional responses and tested the accuracy of participants' basic logical thinking. Results indicated that logical skills were largely preserved despite the topic and emotion, nonetheless, accuracy varied across syllogism type (controversial vs. control), with increased accuracy on controversial syllogisms. Syllogisms rated as evoking no emotion were answered more accurately than those that evoked any emotion, with disgust and anger associated with less accuracy than no emotion and gladness associated with increased accuracy. Response times also differed across syllogism type, emotion, and emotion intensity.

Episodes of Atrial Fibrillation and Symptoms: A Temporal Analysis

Background

Data on the relationship between symptoms and atrial fibrillation (AF) episodes are limited.

Objective

The objective of this study was to determine the strength of temporal association between AF episodes and symptoms.

Methods

This cross-sectional ambulatory assessment study was performed in a tertiary care center between June 2018 and December 2021. Patients with paroxysmal AF (1 episode of AF, burden not exceeding 95%) who used a mobile application and continuous wearable electrocardiogram monitor for 21 days were enrolled. The primary outcome was worse symptoms (symptoms above the mean score) over the study period. The association between worse symptoms and the presence of AF was evaluated for different time epochs. Multilevel mixed-effects models were used to quantify associations after accounting for confounders.

Results

Worse symptoms were more likely to be associated with the presence of AF episodes 15 minutes prior to the reporting of palpitations (OR, 2.8 [95% CI, 1.6-5.0]; P < .001), shortness of breath (OR, 2.2 [95% CI, 1.3-3.7]; P = .003), dizziness/lightheadedness (OR, 2.0 [95% CI, 1.0-3.7]; P = .04), and fatigue (OR, 1.7 [95% CI, 1.0-2.9]; P = .03). The correlation between the severity of symptoms and AF lessened as the time interval from AF events to symptoms increased.

Conclusion

There is a significant relationship between onset of AF episodes and reporting of symptoms. This association diminishes over time and varies across different symptoms. If confirmed in larger studies, these findings may inform AF interventions that target symptoms just in time prior to a clinical visit.

The influence of selective attention to specific emotions on the processing of faces as revealed by event-related brain potentials

Event-related potential studies using affective words have indicated that selective attention to valence can increase affective discrimination at early perceptual stages. This effect most likely relies on neural associations between perceptual features of a stimulus and its affective value. Similar to words, emotional expressions in human faces are linked to specific visual elements. Therefore, selectively attending to a given emotion should allow for the preactivation of neural networks coding for the emotion and associated first-order visual elements, leading to enhanced early processing of faces expressing the attended emotion. To investigate this, we employed an expression detection task (N = 65). Fearful, happy, and neutral faces were randomly presented in three blocks while participants were instructed to respond only to one predefined target level of expression in each block. Reaction times were the fastest for happy target faces, which was accompanied by an increased occipital P1 for happy compared with fearful faces. The N170 yielded an arousal effect (emotional > neutral) while both components were not modulated by target status. In contrast, the early posterior negativity (EPN) arousal effect tended to be larger for target compared with nontarget faces. The late positive potential (LPP) revealed large effects of status and expression as well as an interaction driven by an increased LPP specifically for nontarget fearful faces. These findings tentatively indicate that selective attention to facial affect may enhance early emotional processing (EPN) even though further research is needed. Moreover, late controlled processing of facial emotions appears to involve a negativity bias.

Decoding auditory-evoked response in affective states using wearable around-ear EEG system

Objective.In this paper, an around-ear EEG system is investigated as an alternative methodology to conventional scalp-EEG-based systems in classifying human affective states in the arousal-valence domain evoked in response to auditory stimuli.Approach.EEG recorded from around the ears is compared to EEG collected according to the international 10-20 system in terms of efficacy in an affective state classification task. A wearable device with eight dry EEG channels is designed for ear-EEG acquisition in this study. Twenty-one subjects participated in an experiment consisting of six sessions over three days using both ear and scalp-EEG acquisition methods. Experimental tasks consisted of listening to an auditory stimulus and self-reporting the elicited emotion in response to the said stimulus. Various features were used in tandem with asymmetry methods to evaluate binary classification performances of arousal and valence states using ear-EEG signals in comparison to scalp-EEG.Main results.We achieve an average accuracy of 67.09% ± 6.14 for arousal and 66.61% ± 6.14 for valence after training a multi-layer extreme learning machine with ear-EEG signals in a subject-dependent context in comparison to scalp-EEG approach which achieves an average accuracy of 68.59% ± 6.26 for arousal and 67.10% ± 4.99 for valence. In a subject-independent context, the ear-EEG approach achieves 63.74% ± 3.84 for arousal and 64.32% ± 6.38 for valence while the scalp-EEG approach achieves 64.67% ± 6.91 for arousal and 64.86% ± 5.95 for valence. The best results show no significant differences between ear-EEG and scalp-EEG signals for classifications of affective states.Significance.To the best of our knowledge, this paper is the first work to explore the use of around-ear EEG signals in emotion monitoring. Our results demonstrate the potential use of around-ear EEG systems for the development of emotional monitoring setups that are more suitable for use in daily affective life log systems compared to conventional scalp-EEG setups.

Happiness feels light and sadness feels heavy: introducing valence-related bodily sensation maps of emotions

Bodily sensation mapping (BSM) is a recently developed self-report tool for the assessment of emotions in which people draw their sensations of activation in a body silhouette. Following the circumplex model of affect, activity and valence are the underling dimensions of every emotional experience. The aim of this study was to introduce the neglected valence dimension in BSM. We found that participants systematically report valence-related sensations of bodily lightness for positive emotions (happiness, love, pride), and sensations of bodily heaviness in response to negative emotions (e.g., anger, fear, sadness, depression) with specific body topography (Experiment 1). Further experiments showed that both computers (using a machine learning approach) and humans recognize emotions better when classification is based on the combined activity- and valence-related BSMs compared to either type of BSM alone (Experiments 2 and 3), suggesting that both types of bodily sensations reflect distinct parts of emotion knowledge. Importantly, participants found it clearer to indicate their bodily sensations induced by sadness and depression in terms of bodily weight than bodily activity (Experiment 2 and 4), suggesting that the added value of valence-related BSMs is particularly relevant for the assessment of emotions at the negative end of the valence spectrum.

Searching, Navigating, and Recommending Movies through Emotions: A Scoping Review

Movies offer viewers a broad range of emotional experiences, providing entertainment, and meaning. Following the PRISMA-ScR guidelines, we reviewed the literature on digital systems designed to help users search and browse movie libraries and offer recommendations based on emotional content. Our search yielded 83 eligible documents (published between 2000 and 2021). We identified 22 case studies, 34 empirical studies, 26 proof of concept, and one theoretical paper. User transactions (e.g., ratings, tags) were the preferred source of information. The documents examined approached emotions from both a categorical ( $n=35$ ) and dimensional ( $n=18$ ) perspectives, and nine documents offer a combination of both approaches. Although there are several authors mentioned, the references used are frequently dated, and 12 documents do not mention author or model used. We identified 61 words related to emotion or affect. Documents presented on average 1.36 positive terms and 2.64 negative terms. Sentiment analysis ( $n=31$ ) is frequently used for emotion identification, followed by subjective evaluations ( $n=15$ ), movie low-level audio and visual features (n = 11), and face recognition technologies ( $n=8$ ). We discuss limitations and offer a brief review of current emotion models and research.

The Asymmetric Influence of Emotion in the Sharing of COVID-19 Science on Social Media: Observational Study

BACKGROUND

Unlike past pandemics, COVID-19 is different to the extent that there is an unprecedented surge in both peer-reviewed and preprint research publications, and important scientific conversations about it are rampant on online social networks, even among laypeople. Clearly, this new phenomenon of scientific discourse is not well understood in that we do not know the diffusion patterns of peer-reviewed publications vis-à-vis preprints and what makes them viral.

OBJECTIVE

This paper aimed to examine how the emotionality of messages about preprint and peer-reviewed publications shapes their diffusion through online social networks in order to inform health science communicators' and policy makers' decisions on how to promote reliable sharing of crucial pandemic science on social media.

METHODS

We collected a large sample of Twitter discussions of early (January to May 2020) COVID-19 medical research outputs, which were tracked by Altmetric, in both preprint servers and peer-reviewed journals, and conducted statistical analyses to examine emotional valence, specific emotions, and the role of scientists as content creators in influencing the retweet rate.

RESULTS

Our large-scale analyses (n=243,567) revealed that scientific publication tweets with positive emotions were transmitted faster than those with negative emotions, especially for messages about preprints. Our results also showed that scientists' participation in social media as content creators could accentuate the positive emotion effects on the sharing of peer-reviewed publications.

CONCLUSIONS

Clear communication of critical science is crucial in the nascent stage of a pandemic. By revealing the emotional dynamics in the social media sharing of COVID-19 scientific outputs, our study offers scientists and policy makers an avenue to shape the discussion and diffusion of emerging scientific publications through manipulation of the emotionality of tweets. Scientists could use emotional language to promote the diffusion of more reliable peer-reviewed articles, while avoiding using too much positive emotional language in social media messages about preprints if they think that it is too early to widely communicate the preprint (not peer reviewed) data to the public.

Choice Hygiene for "Consumer Neuroscientists"? Ethical Considerations and Proposals for Future Endeavours

Is the use of psychological and neuroscientific methods for neuromarketing research always aligned with the principles of ethical research practice? Some neuromarketing endeavours have passed from informing consumers about available options, to helping to market as many products to consumers as possible. Needs are being engineered, using knowledge about the human brain to increase consumption further, regardless of individual, societal and environmental needs and capacities. In principle, the ground ethical principle of any scientist is to further individual, societal and environmental health and well-being with their work. If their findings can be used for the opposite, this must be part of the scientist's considerations before engaging in such research and to make sure that the risks for misuse are minimised. Against this backdrop, we provide a series of real-life examples and a non-exhaustive literature review, to discuss in what way some practices in the neuromarketing domain may violate the Helsinki Declaration of Experimentation with Human Subjects. This declaration was set out to regulate biomedical research, but has since its inception been applied internationally also to behavioural and social research. We illustrate, point by point, how these ground ethical principles should be applied also to the neuromarketing domain. Indisputably, the growth in consumption is required due to current prevalent economical models. Thus, in the final part of the paper, we discuss how alternative models may be promotable to a larger public, aided by more ethical marketing endeavours, based on neuroscientific discoveries about the human brain. We propose this as a philosophical question, a point of discussion for the future, to make neuromarketing as a discipline, fit for the future, respecting the ethical implications of this research.

Affective State Recognition in Livestock—Artificial Intelligence Approaches

Simple Summary

Emotions or affective states recognition in farm animals is an underexplored research domain. Despite significant advances in animal welfare research, animal affective state computing through the development and application of devices and platforms that can not only recognize but interpret and process the emotions, are in a nascent stage. The analysis and measurement of unique behavioural, physical, and biological characteristics offered by biometric sensor technologies and the affiliated complex and large data sets, opens the pathway for novel and realistic identification of individual animals amongst a herd or a flock. By capitalizing on the immense potential of biometric sensors, artificial intelligence enabled big data methods offer substantial advancement of animal welfare standards and meet the urgent needs of caretakers to respond effectively to maintain the wellbeing of their animals.

Abstract

Farm animals, numbering over 70 billion worldwide, are increasingly managed in large-scale, intensive farms. With both public awareness and scientific evidence growing that farm animals experience suffering, as well as affective states such as fear, frustration and distress, there is an urgent need to develop efficient and accurate methods for monitoring their welfare. At present, there are not scientifically validated ‘benchmarks’ for quantifying transient emotional (affective) states in farm animals, and no established measures of good welfare, only indicators of poor welfare, such as injury, pain and fear. Conventional approaches to monitoring livestock welfare are time-consuming, interrupt farming processes and involve subjective judgments. Biometric sensor data enabled by artificial intelligence is an emerging smart solution to unobtrusively monitoring livestock, but its potential for quantifying affective states and ground-breaking solutions in their application are yet to be realized. This review provides innovative methods for collecting big data on farm animal emotions, which can be used to train artificial intelligence models to classify, quantify and predict affective states in individual pigs and cows. Extending this to the group level, social network analysis can be applied to model emotional dynamics and contagion among animals. Finally, ‘digital twins’ of animals capable of simulating and predicting their affective states and behaviour in real time are a near-term possibility.

Gamma Oscillations in the Temporal Pole Reflect the Contribution of Approach and Avoidance Motivational Systems to the Processing of Fear and Anger Words

Prior reports suggest that affective effects in visual word processing cannot be fully explained by a dimensional perspective of emotions based on valence and arousal. In the current study, we focused on the contribution of approach and avoidance motivational systems that are related to different action components to the processing of emotional words. To this aim, we compared frontal alpha asymmetries and brain oscillations elicited by anger words associated with approach (fighting) motivational tendencies, and fear words that may trigger either avoidance (escaping), approach (fighting) or no (freezing) action tendencies. The participants’ task was to make decisions about approaching or distancing from the concepts represented by words. The results of cluster-based and beamforming analyses revealed increased gamma power band synchronization for fear words relative to anger words between 725 and 750 ms, with an estimated neural origin in the temporal pole. These findings were interpreted to reflect a conflict between different action tendencies underlying the representation of fear words in semantic and emotional memories, when trying to achieve task requirements. These results are in line with the predictions made by the fear-hinders-action hypothesis. Additionally, current data highlights the contribution of motivational features to the representation and processing of emotional words.

The Paths to Connectedness: A Review of the Antecedents of Connectedness to Nature

Although many philosophers and environmental psychologists agree that progress toward a more ecologically conscious society depends upon individuals developing a sense of connectedness to nature, such agreement is of limited use if we do not understand how connectedness forms. The purpose of this review is to delineate the state of the psychological literature concerning the antecedents of connectedness to nature. The literature review is organized into three main sections: (1) situational contexts that influence connectedness; (2) individual difference predictors, such as demographic group membership, personality, or beliefs; and (3) internal psychological states that may explain psychological processes that result in connectedness. Major critiques of the extant literature and future directions are presented in a discussion following the body of the review. The primary implications highlighted by the review are a greater need for theories delineating the formation of connectedness, a greater focus on process, and increased differentiation between similar antecedents of connectedness.

Understanding the Emotional Impact of GIFs on Instagram through Consumer Neuroscience

The ability of GIFs to generate emotionality in social media marketing strategies is analyzed. The aim of this work is to show how neuroscience research techniques can be integrated into the analysis of emotions, improving the results and helping to guide actions in social networks. This research is structured in two phases: an experimental study using automated biometric analysis (facial coding, GSR and eye tracking) and an analysis of declared feelings in the comments of Instagram users. Explicit valence, type of emotion, length of comment and proportion of emojis are extracted. The results indicate that the explicit measure of emotional valence shows a higher and more positive emotional level than the implicit one. This difference is influenced differently by the engagement and the proportion of emojis in the comment. A further step has been taken in the measurement of user emotionality in social media campaigns, including not only content analysis, but also providing new insights thanks to neuromarketing.

Electrocardiogram-Based Emotion Recognition Systems and Their Applications in Healthcare-A Review

Affective computing is a field of study that integrates human affects and emotions with artificial intelligence into systems or devices. A system or device with affective computing is beneficial for the mental health and wellbeing of individuals that are stressed, anguished, or depressed. Emotion recognition systems are an important technology that enables affective computing. Currently, there are a lot of ways to build an emotion recognition system using various techniques and algorithms. This review paper focuses on emotion recognition research that adopted electrocardiograms (ECGs) as a unimodal approach as well as part of a multimodal approach for emotion recognition systems. Critical observations of data collection, pre-processing, feature extraction, feature selection and dimensionality reduction, classification, and validation are conducted. This paper also highlights the architectures with accuracy of above 90%. The available ECG-inclusive affective databases are also reviewed, and a popularity analysis is presented. Additionally, the benefit of emotion recognition systems towards healthcare systems is also reviewed here. Based on the literature reviewed, a thorough discussion on the subject matter and future works is suggested and concluded. The findings presented here are beneficial for prospective researchers to look into the summary of previous works conducted in the field of ECG-based emotion recognition systems, and for identifying gaps in the area, as well as in developing and designing future applications of emotion recognition systems, especially in improving healthcare.

The Joyful Life: An Existential-Humanistic Approach to Positive Psychology in the Time of a Pandemic

In the midst of a global pandemic, psychology has a duty to identify dispositional or character traits that can be cultivated in citizens in order to create resiliency in the face of profound losses, suffering and distress. Dispositional joy holds some promise as such a trait that could be especially important for well-being during the current pandemic and its consequences. The concept of the Joyful Life may operate as bridge between positive psychology and humanistic, existential, and spiritual views of the good life, by integrating hedonic, prudential, eudaimonic and chaironic visions of the good life. Previous phenomenological research on state joy suggests that momentary states of joy may have features that overlap with happiness but go beyond mere hedonic interests, and point to the experience of a life oriented toward virtue and a sense of the transcendent or the sacred. However, qualitative research on the Joyful Life, or dispositional joy, is sorely lacking. This study utilized a dialogical phenomenological analysis to conduct a group-based analysis of 17 volunteer students, who produced 51 autobiographical narrative descriptions of the joyful life. The dialogical analyses were assisted by integration of the Imagery in Movement Method, which incorporated expressive drawing and psychodrama as an aid to explicate implicit themes in the experiences of the participants. The analyses yielded ten invariant themes found across the autobiographical narrative descriptions: Being broken, being grounded, being centered, breaking open, being uplifted, being supertemporal, being open to the mystery, being grateful, opening up and out, and being together. The descriptions of a Joyful Life were consistent with a meaning orientation to happiness, due to their emphasis on the cultivation of virtue in the service of a higher calling, the realization of which was felt to be a gift or blessing. The discussion examines implications for future research, including the current relevance of a joyful disposition during a global pandemic. Due to the joyful disposition's tendency to transform suffering and tragedy into meaning, and its theme of an orientation to prosocial motivations, the Joyful Life may occupy a central place in the study of resiliency and personal growth in response to personal and collective trauma such as COVID-19.

WeDea: A New EEG-based Framework for Emotion Recognition

With the development of sensing technologies and machine learning, techniques that can identify emotions and inner states of a human through physiological signals, known as electroencephalography (EEG), have been actively developed and applied to various domains, such as automobiles, robotics, healthcare, and customer-support services. Thus, the demand for acquiring and analyzing EEG signals in real-time is increasing. In this paper, we aimed to acquire a new EEG dataset based on the discrete emotion theory, termed as WeDea (Wireless-based eeg Data for emotion analysis), and propose a new combination for WeDea analysis. For the collected WeDea dataset, we used video clips as emotional stimulants that were selected by 15 volunteers. Consequently, WeDea is a multi-way dataset measured while 30 subjects are watching the selected 79 video clips under five different emotional states using a convenient portable headset device. Furthermore, we designed a framework for recognizing human emotional state using this new database. The practical results for different types of emotions have proven that WeDea is a promising resource for emotion analysis and can be applied to the field of neuroscience.

Emotion elicitation during music listening: Subjective self-reports, facial expression, and autonomic reactivity

The use of music as emotional stimuli in experimental studies has grown in recent years. However, prior studies have mainly focused on self-reports and central measures, with a few works exploring the time course of psychophysiological correlates. Moreover, most of the previous research has been carried out either from the dimensional or categorical model but not combining both approaches to emotions. This study aimed to investigate subjective and physiological correlates of emotion elicitation through music, following the three-dimensional and the discrete emotion model. A sample of 50 healthy volunteers (25 women) took part in this experiment by listening to 42 film music excerpts (14 pleasant, 14 unpleasant, 14 neutral) presented during 8 s, while peripheral measures were continuously recorded. After music offset, affective dimensions (valence, energy arousal, and tension arousal) as well as discrete emotions (happiness, sadness, tenderness, fear, and anger) were collected using a 9-point scale. Results showed an effect of the music category on subjective and psychophysiological measures. In peripheral physiology, greater electrodermal activity, heart rate acceleration, and zygomatic responses, besides lower corrugator amplitude, were observed for pleasant excerpts in comparison to neutral and unpleasant music, from 2 s after stimulus onset until the end of its duration. Overall, our results add evidence for the efficacy of standardized film music excerpts to evoke powerful emotions in laboratory settings; thus, opening a path to explore interventions based on music in pathologies with underlying emotion deregulatory processes.

Dimensional and Discrete Emotional Reactivity in Alzheimer's Disease: Film Clips as a Research Tool in Dementia

BACKGROUND

No studies have been conducted to date on the dimensional and discrete classification of emotions to study the emotional reactivity of older adults with Alzheimer's disease (AD). Additionally, the presentation of film clips with affective content is currently one of the most effective and widely used Mood Induction Procedures (MIPS). However, it has been scarcely used in AD patients.

OBJECTIVE

Based on the dimensional and discrete models of emotion, this study examines the emotional reactivity of older adults with AD, using a popular set of film clips to induce emotions.

METHODS

We compared the responses of older adults aged over 65-years with mild to moderate AD (n = 15) and a healthy comparison group (n = 17) to six target emotions: disgust, fear, anger, sadness, amusement, and tenderness.

RESULTS

The results showed significant differences in the reactivity of fear, anger, and sadness between AD patients and healthy comparison group. However, the responses of the two groups to positive film clips were similar. Only in the amusement clip did the AD participants show a higher intensity response.

CONCLUSION

These findings suggest that the characteristic loss of cognitive abilities in AD is related to a reduction in the ability to react to emotional stimuli, especially negative ones. However, these abilities seem to be preserved when it comes to positive emotions. Future research is necessary to investigate whether the positivity effect is present in AD patients.

How does daily family-supportive supervisor behavior relieve subordinates' job stress? The effect of ethical leadership and positive emotions

Purpose

Relying on a multilevel approach, this paper investigates the day-to-day variations in family-supportive supervisor behaviors influencing subordinates' job stress, as well as the mediating role of positive emotions and the moderating role of ethical leadership.

Design/methodology/approach

Using the experience-sampling methodology, the study collected the data from 137 civil servants in China who responded to one daily survey for 10 working days.

Findings

With a total of 1,370 surveys, results supported the hypothesized model linking daily family-supportive supervisor behaviors to daily job stress via subordinates' daily positive emotions. In addition, the study found a moderating effect for ethical leadership positively in the indirect relationship between family-supportive supervisor behaviors and job stress.

Practical implications

The findings in this study serve practitioners in organizational and leadership development. For one thing, this study contributes to raising awareness about the importance of improving family-related support in the workplace in generating subordinates' positive emotions and relieving their job stress. For another, the findings highlight the necessity of cultivating ethical leadership for leaders.

Originality/value

This study fulfills an identified need to clarify how and when daily family supportive supervisor behaviors influence subordinates' daily job stress. This study moves beyond previous research by adopting the experience sampling method and demonstrating important cross-level effects of ethical leadership on the within-individual relationship between family supportive supervisor behaviors and job stress.

The early processing of fearful and happy facial expressions is independent of task demands - Support from mass univariate analyses

Most ERP studies on facial expressions of emotion have yielded inconsistent results regarding the time course of emotion effects and their possible modulation by task demands. Most studies have used classical statistical methods with a high likelihood of type I and type II errors, which can be limited with Mass Univariate statistics. FMUT and LIMO are currently the only two available toolboxes for Mass Univariate analysis of ERP data and use different fundamental statistics. Yet, no direct comparison of their output has been performed on the same dataset. Given the current push to transition to robust statistics to increase results replicability, here we compared the output of these toolboxes on data previously analyzed using classic approaches (Itier & Neath-Tavares, 2017). The early (0-352 ms) processing of fearful, happy, and neutral faces was investigated under three tasks in a within-subject design that also controlled gaze fixation location. Both toolboxes revealed main effects of emotion and task but neither yielded an interaction between the two, confirming the early processing of fear and happy expressions is largely independent of task demands. Both toolboxes found virtually no difference between neutral and happy expressions, while fearful (compared to neutral and happy) expressions modulated the N170 and EPN but elicited maximum effects after the N170 peak, around 190 ms. Similarities and differences in the spatial and temporal extent of these effects are discussed in comparison to the published classical analysis and the rest of the ERP literature.

Dissociating disorders of depression, anxiety, and their comorbidity with measures of emotional processing: A joint analysis of visual brain potentials and auditory perceptual asymmetries

In a multigenerational study of families at risk for depression, individuals with a lifetime history of depression had: 1) abnormal perceptual asymmetry (PA; smaller left ear/right hemisphere [RH] advantage) in a dichotic emotion recognition task, and 2) reduced RH late positive potential (P3_RH) during an emotional hemifield task. We used standardized difference scores for processing auditory (PA sad-neutral) and visual (P3_RH negative-neutral) stimuli for 112 participants (52 men) in a logistic regression to predict history of depression, anxiety or comorbidity of both. Whereas comorbidity was separately predicted by reduced PA (OR = 0.527, p = .042) or P3_RH (OR = 0.457, p = .013) alone, an interaction between PA and P3_RH (OR = 2.499, p = .011) predicted depressive disorder. Follow-up analyses revealed increased probability of depression at low (lack of emotional differentiation) and high (heightened reactivity to negative stimuli) levels of both predictors. Findings suggest that reduced or heightened right-lateralized emotional responsivity to negative stimuli may be uniquely associated with depression.

Differences between young and older adults in physiological and subjective responses to emotion induction using films

Emotional response in aging is typically studied using the dimensional or the discrete models of emotion. Moreover, it is typically studied using subjective or physiological variables but not using both perspectives simultaneously. Additionally, tenderness is neglected in emotion induction procedures with older adults, with the present work being the first to include the study of physiological tenderness using film clips. This study integrated two separate approaches to emotion research, comparing 68 younger and 39 older adults and using a popular set of film clips to induce tenderness, amusement, anger, fear, sadness and disgust emotions. The direction of subjective emotional patterns was evaluated with self-reports and that of physiological emotional patterns was evaluated with a wearable emotion detection system. The findings suggest a dual-process framework between subjective and physiological responses, manifested differently in young and older adults. In terms of arousal, the older adults exhibited higher levels of subjective arousal in negative emotions and tenderness while young adults showed higher levels of physiological arousal in these emotions. These findings yield information on the multidirectionality of positive and negative emotions, corroborating that emotional changes in the adult lifespan appear to be subject to the relevance of the emotion elicitor to each age group.

Cognitive Dissonance in Technology Adoption: A Study of Smart Home Users

This study aims to address a research gap related to the outcomes of the use of technology when the performance falls short of initial expectations, and the coping mechanisms that users may deploy in such circumstances. By adopting Cognitive Dissonance Theory, the objectives of the study are a) to examine how dissonance, caused by the negative disconfirmation of expectations, may translate into a positive outcome and b) study how negative emotions, such as anger, guilt and regret, determine the selection of the mechanism to reduce dissonance. The theorised model was tested using a cross-sectional research design and a sample of 387 smart home users. The focus on smart home users fitted the objectives of the study due to the high expectations that users form and the challenges that the utilisation of technology sometimes causes. The collected data was analysed using structural equation modelling. Findings indicate that post-disconfirmation dissonance induces feelings of anger, guilt and regret, correlating with dissonance reduction mechanisms, which in turn have a distinctive effect on satisfaction and wellbeing. The findings of the study contribute to the discussion on expectation-disconfirmation and cognitive dissonance, by illustrating the interrelationship between emotional, cognitive and behavioural factors following the evaluation of technology performance and confirming that negative disconfirmation may result in satisfaction.

On the Characteristics of the Cognitive Dissonance State: Exploration Within the Pleasure Arousal Dominance Model

Little is actually known about the nature and characteristics of the cognitive dissonance state. In this paper, we review the actual knowledge and the main limitations of past studies. Then, we present two studies that investigate the characteristics of the cognitive dissonance state from the perspective the Pleasure Arousal Dominance model of emotion. Study 1 (N = 102) used the hypocrisy paradigm and Study 2 (N = 130) used a counterattitudinal essay. In Study 1, participants in the Dissonance condition reported less Pleasure with each inconsistent behaviour remembered. In Study 2, participants in the Dissonance condition reported less Pleasure than participants in the Control Condition. In both studies, no significant difference was found on the Arousal and Dominance indexes. These results are among the first to link cognitive dissonance to a general model of emotions, an approach that should be pursued further.

Keeping in Touch with Mental Health: The Orienting Reflex and Behavioral Outcomes from Calatonia

Physical and psychological therapy based on touch has been gradually integrated into broader mental health settings in the past two decades, evolving from a variety of psychodynamic, neurobiological and trauma-based approaches, as well as Eastern and spiritual philosophies and other integrative and converging systems. Nevertheless, with the exception of a limited number of well-known massage therapy techniques, only a few structured protocols of touch therapy have been standardized and researched to date. This article describes a well-defined protocol of touch therapy in the context of psychotherapy-the Calatonia technique-which engages the orienting reflex. The orienting reflex hypothesis is explored here as one of the elements of this technique that helps to decrease states of hypervigilance and chronic startle reactivity (startle and defensive reflexes) and restore positive motivational and appetitive states.

Ten challenges for EEG-based affective computing

The emerging field of affective computing focuses on enhancing computers’ ability to understand and appropriately respond to people’s affective states in human-computer interactions, and has revealed significant potential for a wide spectrum of applications. Recently, the electroencephalography (EEG) based affective computing has gained increasing interest for its good balance between mechanistic exploration and real-world practical application. The present work reviewed ten theoretical and operational challenges for the existing affective computing researches from an interdisciplinary perspective of information technology, psychology, and neuroscience. On the theoretical side, we suggest that researchers should be well aware of the limitations of the commonly used emotion models, and be cautious about the widely accepted assumptions on EEG-emotion relationships as well as the transferability of findings based on different research paradigms. On the practical side, we propose several operational recommendations for the challenges about data collection, feature extraction, model implementation, online system design, as well as the potential ethical issues. The present review is expected to contribute to an improved understanding of EEG-based affective computing and promote further applications.

Did you really need to ask? Cultural variation in emotional responses to providing solicited social support

Most prior research on culture and the dynamics of social support has focused on the emotional outcomes for social support recipients. Though an existing body of research has identified cross-cultural differences in the emotional correlates of receiving different types of social support, researchers have seldom examined possible cultural differences in the experience of social support providers. This study used the Day Reconstruction Method to examine cultural differences in the emotional correlates of the provision of solicited and unsolicited and emotional and informational social support in the daily lives of Singaporean (n = 79) and American (n = 88) participants. Singaporean participants reported providing more social support overall. Regardless of culture, participants reported more positive emotion (affection, happiness) and less negative emotion (anger, anxiety) when they provided emotional social support. Also, multilevel modeling analyses revealed a 3-way interaction between culture, social support provision, and social support solicitation, indicating cultural differences in negative emotional responses to providing solicited social support. Specifically, results suggest that attempts to provide more solicited social support were associated with more negative emotions in the U.S. In contrast, provider negative emotions were highest in Singapore when the provider did not meet the recipient’s request for support. Patterns of cultural differences in social support provision are dissimilar to—rather than simply mirroring—those found in published research on social support receipt, highlighting the importance of studying social support provision as a distinct phenomenon.

A standardised database of Chinese emotional film clips

Film clips are widely used in emotion research due to their relatively high ecological validity. Although researchers have established various film clip sets for different cultures, the few that exist related to Chinese culture do not adequately address positive emotions. The main purposes of the present study were to establish a standardised database of Chinese emotional film clips that could elicit more categories of reported positive emotions compared to the existing databases and to expand the available film clips that can be used as neutral materials. Two experiments were conducted to construct the database. In experiment 1, 111 film clips were selected from more than one thousand Chinese movies for preliminary screening. After 315 participants viewed and evaluated these film clips, 39 excerpts were selected for further validation. In experiment 2, 147 participants watched and rated these 39 film clips, as well as another 8 excerpts chosen from the existing databases, to compare their validity. Eventually, 22 film excerpts that successfully evoked three positive emotions (joy, amusement, and tenderness), four negative emotions (moral disgust, anger, fear, and sadness), and neutrality formed the standardised database of Chinese emotional film clips.