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In the rapidly evolving field of artificial intelligence, the importance of multimodal sentiment analysis has never been more evident, especially amid the ongoing COVID-19 pandemic. Our research addresses the critical need to understand public sentiment across various dimensions of this crisis by integrating data from multiple modalities, such as text, images, audio, and videos sourced from platforms like Twitter. Conventional methods, which primarily focus on text analysis, often fall short in capturing the nuanced intricacies of emotional states, necessitating a more comprehensive approach. To tackle this challenge, our proposed framework introduces a novel hybrid model, IChOA-CNN-LSTM, which leverages Convolutional Neural Networks (CNNs) for precise image feature extraction, Long Short-Term Memory (LSTM) networks for sequential data analysis, and an Improved Chimp Optimization Algorithm for effective feature fusion. Remarkably, our model achieves an impressive accuracy rate of 97.8%, outperforming existing approaches in the field. Additionally, by integrating the GeoCoV19 dataset, we facilitate a comprehensive analysis that spans linguistic and geographical boundaries, enriching our understanding of global pandemic discourse and providing critical insights for informed decision-making in public health crises. Through this holistic approach and innovative techniques, our research significantly advances multimodal sentiment analysis, offering a robust framework for deciphering the complex interplay of emotions during unprecedented global challenges like the COVID-19 pandemic., (© 2024. The Author(s).)

Reinforcement learning (RL) is an effective method in training dialogue policies to steer the conversation towards successful task completion. However, most RL-based methods only rely on semantic inputs that lack empathy as they ignore the user emotional information. Moreover, these methods suffer from delayed rewards caused by the user simulator returning valuable results only at dialogue end. Recently, some methods have been proposed to learn the reward function together with user emotions, but they omit considering user emotion in each dialogue turn. In this paper, we proposed an emotion-sensitive dialogue policy model (ESDP), it incorporates user emotions information into dialogue policy and selects the optimal action by the combination of top-k actions with the user emotions. The user emotion information in each turn is used as an immediate reward for the current dialogue state to solve sparse rewards and the dependency on termination. Extensive experiments validate that our method outperforms the baseline approaches when combined with different Q-Learning algorithms, and also surpasses other popular existing dialog policies' performance., (© 2024. The Author(s).)

Contemporary theories propose that dysregulation of emotional perception is involved in the aetiology of psychosis. 298 healthy adolescents were assessed at age 14- and 19-years using fMRI while performing a facial emotion task. Psychotic-like experiences (PLEs) were assessed with the CAPE-42 questionnaire at age 19. The high PLEs group at age 19 years exhibited an enhanced response in right insular cortex and decreased response in right prefrontal, right parahippocampal and left striatal regions; also, a gradient of decreasing response to emotional faces with age, from 14 to 19 years, in the right parahippocampal region and left insular cortical area. The right insula demonstrated an increasing response to emotional faces with increasing age in the low PLEs group, and a decreasing response over time in the high PLEs group. The change in parahippocampal/amygdala and insula responses during the perception of emotional faces in adolescents with high PLEs between the ages of 14 and 19 suggests a potential 'aberrant' neurodevelopmental trajectory for critical limbic areas. Our findings emphasize the role of the frontal and limbic areas in the aetiology of psychotic symptoms, in subjects without the illness phenotype and the confounds introduced by antipsychotic medication.

Undercover videos have become a popular tool among NGOs to influence public opinion and generate engagement for the NGO's cause. These videos are seen as a powerful and cost-effective way of bringing about social change, as they provide first-hand evidence and generate a strong emotional response among those who see them. In this paper, we empirically assess the impact of undercover videos on support for the cause. We in addition analyze whether the increased engagement among viewers is driven by the negative emotional reactions produced by the video. To do so, we design an online experiment that enables us to estimate both the total and emotion-mediated treatment effects on engagement by randomly exposing participants to an undercover video (of animal abuse) and randomly introducing a cooling-off period. Using a representative sample of the French population (N=3,310), we find that the video successfully increases actions in favor of animals (i.e., donations to NGOs and petitions), but we fail to prove that this effect is due to the presence of primary emotions induced by the video. Last, we investigate whether activists correctly anticipate their undercover videos' (emotional) impact via a prediction study involving activists (exploratory analysis). Protocol registration: This manuscript is a Stage-2 working paper of a Registered Report that received In-Principle-Acceptance from Scientific Reports on November 20th, 2023 [Link to Stage-1]. The Stage-1 that received In-Principal-Acceptance can be found here: https://osf.io/8cg2d. [ABSTRACT FROM AUTHOR]

University learning and teaching involves contrasting and interacting emotional experiences. Even in disciplines considered as objective as Science and Engineering, emotion plays a significant role in catalysing and sustaining learning. Although emotions are individually felt, they are socially constructed between people. This is especially relevant in group-based learning evident in much contemporary higher education. This paper applies the concept of emotion socialization to illustrate how groups of students and teachers cope and succeed in cognitively and emotionally challenging learning settings. The study is based on qualitative data collected across six STEMM university departments, from 280 students via in-situ questionnaires and from 20 teachers via group and follow-up interviews. Two key findings offer insight into processes of reciprocal influence on cognition and emotion. The first relates to ways in which students' and teachers' differing knowledge-related goals and relationships with knowledge influenced cognition and emotion, uncovering previously unacknowledged connections. The second relates to how students' comparisons of progress towards academic goals with peers contributed considerably to their emotional experiences in cognitively and emotionally helpful and unhelpful ways. Practical implications are presented, including possibilities for capitalising on existing emotion socialization processes and enhancing how they influence cognition and emotional well-being., (© 2024. The Author(s).)

How stress affects functional hemispheric asymmetries is relevant because stress represents a risk factor for the development of mental disorders and various mental disorders are associated with atypical lateralization. Using three lateralization tasks, we investigated whether functional hemispheric asymmetries in the form of hemispheric dominance for language (verbal dichotic listening task), emotion processing (emotional dichotic listening task), and visuo-spatial attention (line bisection task) were affected by acute stress in healthy adults. One hundred twenty right-handed men and women performed these lateralization tasks in randomized order after exposure to a mild online stressor (i.e., an online variant of the Trier Social Stress Test (TSST), TSST-OL) and a non-stressful online control task (friendly TSST-OL, fTSST-OL) in a within-subjects design. Importantly, the verbal and the emotional dichotic listening tasks were presented online whereas the line bisection task was completed in paper-pencil form. During these tasks, we found the expected hemispheric asymmetries, indicating that online versions of both the verbal and the emotional dichotic listening task can be used to measure functional hemispheric asymmetries in language and emotion processing remotely. Even though subjective and physiological markers confirmed the success of the online stress manipulation, replicating previous studies, we found no stress-induced effect on functional hemispheric asymmetries. Thus, in healthy participants, functional hemispheric asymmetries do not seem to change flexibly in response to acute stress., (© 2024. The Author(s).)

Better communication with older people requires not only improving speech intelligibility but also understanding how well emotions can be conveyed and the effect of age and hearing loss (HL) on emotion perception. In this paper, emotion discrimination experiments were conducted using a vocal morphing method and an HL simulator in young normal hearing (YNH) and older participants. Speech sounds were morphed to represent intermediate emotions between all combinations of happiness, sadness, and anger. Discrimination performance was compared when the YNH listened to normal sounds, when the same YNH listened to HL simulated sounds, and when older people listened to the same normal sounds. The results showed that there was no significant difference between discrimination with and without HL simulation, suggesting that peripheral HL may not affect emotion perception. The discrimination performance of the older participants was significantly worse only for the anger-happiness pair than for the other emotion pairs and for the YNH. It was also found that the difficulty increases with age, not just with hearing level., (© 2024. The Author(s).)

Speech emotion recognition (SER) technology involves feature extraction and prediction models. However, recognition efficiency tends to decrease because of gender differences and the large number of extracted features. Consequently, this paper introduces a SER system based on gender. First, gender and emotion features are extracted from speech signals to develop gender recognition and emotion classification models. Second, according to gender differences, distinct emotion recognition models are established for male and female speakers. The gender of speakers is determined before executing the corresponding emotion model. Third, the accuracy of these emotion models is enhanced by utilizing an advanced differential evolution algorithm (ADE) to select optimal features. ADE incorporates new difference vectors, mutation operators, and position learning, which effectively balance global and local searches. A new position repairing method is proposed to address gender differences. Finally, experiments on four English datasets demonstrate that ADE is superior to comparison algorithms in recognition accuracy, recall, precision, F1-score, the number of used features and execution time. The findings highlight the significance of gender in refining emotion models, while mel-frequency cepstral coefficients are important factors in gender differences., (© 2024. The Author(s).)

Affect recognition in a real-world, less constrained environment is the principal prerequisite of the industrial-level usefulness of this technology. Monitoring the psychological profile using smart, wearable electroencephalogram (EEG) sensors during daily activities without external stimuli, such as memory-induced emotions, is a challenging research gap in emotion recognition. This paper proposed a deep learning framework for improved memory-induced emotion recognition leveraging a combination of 1D-CNN and LSTM as feature extractors integrated with an Extreme Learning Machine (ELM) classifier. The proposed deep learning architecture, combined with the EEG preprocessing, such as the removal of the average baseline signal from each sample and extraction of EEG rhythms (delta, theta, alpha, beta, and gamma), aims to capture repetitive and continuous patterns for memory-induced emotion recognition, underexplored with deep learning techniques. This work has analyzed EEG signals using a wearable, ultra-mobile sports cap while recalling autobiographical emotional memories evoked by affect-denoting words, with self-annotation on the scale of valence and arousal. With extensive experimentation using the same dataset, the proposed framework empirically outperforms existing techniques for the emerging area of memory-induced emotion recognition with an accuracy of 65.6%. The EEG rhythms analysis, such as delta, theta, alpha, beta, and gamma, achieved 65.5%, 52.1%, 65.1%, 64.6%, and 65.0% accuracies for classification with four quadrants of valence and arousal. These results underscore the significant advancement achieved by our proposed method for the real-world environment of memory-induced emotion recognition., (© 2024. The Author(s).)

Recent research has focused extensively on employing Deep Learning (DL) techniques, particularly Convolutional Neural Networks (CNN), for Speech Emotion Recognition (SER). This study addresses the burgeoning interest in leveraging DL for SER, specifically focusing on Punjabi language speakers. The paper presents a novel approach to constructing and preprocessing a labeled speech corpus using diverse social media sources. By utilizing spectrograms as the primary feature representation, the proposed algorithm effectively learns discriminative patterns for emotion recognition. The method is evaluated on a custom dataset derived from various Punjabi media sources, including films and web series. Results demonstrate that the proposed approach achieves an accuracy of 69%, surpassing traditional methods like decision trees, Naïve Bayes, and random forests, which achieved accuracies of 49%, 52%, and 61% respectively. Thus, the proposed method improves accuracy in recognizing emotions from Punjabi speech signals., (© 2024. The Author(s).)

Speech emotion recognition (SER) has gained an increased interest during the last decades as part of enriched affective computing. As a consequence, a variety of engineering approaches have been developed addressing the challenge of the SER problem, exploiting different features, learning algorithms, and datasets. In this paper, we propose the application of the graph theory for classifying emotionally-colored speech signals. Graph theory provides tools for extracting statistical as well as structural information from any time series. We propose to use the mentioned information as a novel feature set. Furthermore, we suggest setting a unique feature-based identity for each emotion belonging to each speaker. The emotion classification is performed by a Random Forest classifier in a Leave-One-Speaker-Out Cross Validation (LOSO-CV) scheme. The proposed method is compared with two state-of-the-art approaches involving well known hand-crafted features as well as deep learning architectures operating on mel-spectrograms. Experimental results on three datasets, EMODB (German, acted) and AESDD (Greek, acted), and DEMoS (Italian, in-the-wild), reveal that our proposed method outperforms the comparative methods in these datasets. Specifically, we observe an average UAR increase of almost [Formula: see text], [Formula: see text] and [Formula: see text], respectively., (© 2024. The Author(s).)

Humans express their emotions in various ways, such as through facial expressions and voices. In particular, emotions are directly expressed or indirectly implied in the text of utterance. Research on the technology to identify emotions included in human speech and generate utterances is being conducted in conversational artificial intelligence technology. Despite the importance of recognizing the factors of previously generated emotions to generate emotion-based utterances, most of the existing datasets only provide the classification of emotions in text and utterances. In addition, in the case of Korean datasets, the classification of emotions is not diverse, and it is mainly biased toward negative emotion classification. In this paper, we propose KEmoFact, a Korean emotion-factor dataset for extracting emotion and factors in Korean conversations. We also define two tasks for the KEmoFact dataset, EFE (Emotion Factor Extraction) and EFPE (Emotion-Factor Pair Extraction), and propose baseline models for the tasks. We contribute to the study of conversational artificial intelligence, especially in Korean, one of the low-resource languages, by proposing the KEmoFact dataset and suggesting baseline models for two tasks., (© 2023. The Author(s).)

The negative impact of used batteries of new energy vehicles on the environment has attracted global attention, and how to effectively deal with used batteries of new energy vehicles has become a hot issue. This paper combines the rank-dependent expected utility with the evolutionary game theory, constructs an evolutionary game model based on the interaction mechanism between decision makers' emotions and decision making, and studies the recycling strategy of new energy automobile trams under the heterogeneous combination of emotions. The study shows that: (1) In addition to the establishment of effective external norms, the subjective preference of decision makers can also positively affect the recycling strategy of new energy vehicle batteries. (2) Fairness preferences can have a significant nonlinear effect on new energy vehicle battery recycling strategies by changing the utility function of decision makers. (3) When new energy vehicle manufacturers remain optimistic and new energy vehicle demanders remain rational or pessimistic, the new energy vehicle battery recycling strategy can reach the optimal steady state. [ABSTRACT FROM AUTHOR]

Abstract Undercover videos have become a popular tool among NGOs to influence public opinion and generate engagement for the NGO’s cause. These videos are seen as a powerful and cost-effective way of bringing about social change, as they provide first-hand evidence and generate a strong emotional response among those who see them. In this paper, we empirically assess the impact of undercover videos on support for the cause. We in addition analyze whether the increased engagement among viewers is driven by the negative emotional reactions produced by the video. To do so, we design an online experiment that enables us to estimate both the total and emotion-mediated treatment effects on engagement by randomly exposing participants to an undercover video (of animal abuse) and randomly introducing a cooling-off period. Using a representative sample of the French population (N=3,310), we find that the video successfully increases actions in favor of animals (i.e., donations to NGOs and petitions), but we fail to prove that this effect is due to the presence of primary emotions induced by the video. Last, we investigate whether activists correctly anticipate their undercover videos’ (emotional) impact via a prediction study involving activists (exploratory analysis). Protocol registration This manuscript is a Stage-2 working paper of a Registered Report that received In-Principle-Acceptance from Scientific Reports on November 20th, 2023 [ Link to Stage-1 ]. The Stage-1 that received In-Principal-Acceptance can be found here: https://osf.io/8cg2d .

Affective states are psycho-physiological constructs connecting mental and physiological processes. They can be represented in terms of arousal and valence according to the Russel's model and can be extracted from physiological changes in human body. However, a well-established optimal feature set and a classification method effective in terms of accuracy and estimation time are not present in the literature. This paper aims at defining a reliable and efficient approach for real-time affective state estimation. To obtain this, the optimal physiological feature set and the most effective machine learning algorithm, to cope with binary as well as multi-class classification problems, were identified. ReliefF feature selection algorithm was implemented to define a reduced optimal feature set. Supervised learning algorithms, such as K-Nearest Neighbors (KNN), cubic and gaussian Support Vector Machine, and Linear Discriminant Analysis, were implemented to compare their effectiveness in affective state estimation. The developed approach was tested on physiological signals acquired on 20 healthy volunteers during the administration of images, belonging to the International Affective Picture System, conceived for inducing different affective states. ReliefF algorithm reduced the number of physiological features from 23 to 13. The performances of machine learning algorithms were compared and the experimental results showed that both accuracy and estimation time benefited from the optimal feature set use. Furthermore, the KNN algorithm resulted to be the most suitable for affective state estimation. The results of the assessment of arousal and valence states on 20 participants indicate that KNN classifier, adopted with the 13 identified optimal features, is the most effective approach for real-time affective state estimation., (© 2023. The Author(s).)

Social networks can provide insights into the emotions expressed by a society. However, the dynamic nature of emotions presents a significant challenge for policymakers, politicians, and communication professionals who seek to understand and respond to changes in emotions over time. To address this challenge, this paper investigates the frequency, duration, and transition of 24 distinct emotions over a 2-year period, analyzing more than 5 million tweets. The study shows that emotions with lower valence but higher dominance and/or arousal are more prevalent in online social networks. Emotions with higher valence and arousal tend to last longer, while dominant emotions tend to have shorter durations. Emotions occupying the conversations predominantly inhibit others with similar valence and dominance, and higher arousal. Over a month, emotions with similar valences tend to prevail in online social network conversations. [ABSTRACT FROM AUTHOR]

Emotional prosody results from the dynamic variation of language's acoustic non-verbal aspects that allow people to convey and recognize emotions. The goal of this paper is to understand how this recognition develops from childhood to adolescence. We also aim to investigate how the ability to perceive multiple emotions in the voice matures over time. We tested 133 children and adolescents, aged between 6 and 17 years old, exposed to 4 kinds of linguistically meaningless emotional (anger, fear, happiness, and sadness) and neutral stimuli. Participants were asked to judge the type and intensity of perceived emotion on continuous scales, without a forced choice task. As predicted, a general linear mixed model analysis revealed a significant interaction effect between age and emotion. The ability to recognize emotions significantly increased with age for both emotional and neutral vocalizations. Girls recognized anger better than boys, who instead confused fear with neutral prosody more than girls. Across all ages, only marginally significant differences were found between anger, happiness, and neutral compared to sadness, which was more difficult to recognize. Finally, as age increased, participants were significantly more likely to attribute multiple emotions to emotional prosody, showing that the representation of emotional content becomes increasingly complex. The ability to identify basic emotions in prosody from linguistically meaningless stimuli develops from childhood to adolescence. Interestingly, this maturation was not only evidenced in the accuracy of emotion detection, but also in a complexification of emotion attribution in prosody., (© 2022. The Author(s).)

The introduction of COVID-19 lockdown measures and an outlook on return to normality are demanding societal changes. Among the most pressing questions is how individuals adjust to the pandemic. This paper examines the emotional responses to the pandemic in a repeated-measures design. Data (n = 1698) were collected in April 2020 (during strict lockdown measures) and in April 2021 (when vaccination programmes gained traction). We asked participants to report their emotions and express these in text data. Statistical tests revealed an average trend towards better adjustment to the pandemic. However, clustering analyses suggested a more complex heterogeneous pattern with a well-coping and a resigning subgroup of participants. Linguistic computational analyses uncovered that topics and n-gram frequencies shifted towards attention to the vaccination programme and away from general worrying. Implications for public mental health efforts in identifying people at heightened risk are discussed. The dataset is made publicly available. [ABSTRACT FROM AUTHOR]

Bursts and collective emotion have been widely studied in social physics field where researchers use mathematical models to understand human social dynamics. However, few researches recognize and separately analyze the internal and external influence on burst behaviors. To bridge this gap, we introduce a non-parametric approach to classify an interevent time series into five scenarios: random arrival, endogenous burst, endogenous non-burst, exogenous burst and exogenous non-burst. In order to process large-scale social media data, we first segment the interevent time series into sections by detecting change points. Then we use the rule-based algorithm to classify the time series based on its distribution. To validate our model, we analyze 27.2 million COVID-19 related comments collected from Chinese social media between January to October 2020. We adopt the emotion category called Profile of Mood States which consists of six emotions: Anger, Depression, Fatigue, Vigor, Tension and Confusion. This enables us to compare the burst features of different collective emotions during the COVID-19 period. The burst detection and classification approach introduced in this paper can also be applied to analyzing other complex systems, including but not limited to social media, financial market and signal processing. [ABSTRACT FROM AUTHOR]

Despite the great potential of Virtual Reality (VR) to arouse emotions, there are no VR affective databases available as it happens for pictures, videos, and sounds. In this paper, we describe the validation of ten affective interactive Virtual Environments (VEs) designed to be used in Virtual Reality. These environments are related to five emotions. The testing phase included using two different experimental setups to deliver the overall experience. The setup did not include any immersive VR technology, because of the ongoing COVID-19 pandemic, but the VEs were designed to run on stereoscopic visual displays. We collected measures related to the participants' emotional experience based on six discrete emotional categories plus neutrality and we included an assessment of the sense of presence related to the different experiences. The results showed how the scenarios can be differentiated according to the emotion aroused. Finally, the comparison between the two experimental setups demonstrated high reliability of the experience and strong adaptability of the scenarios to different contexts of use., (© 2021. The Author(s).)

Due to the effect of emotions on interactions, interpretations, and decisions, automatic detection and analysis of human emotions based on EEG signals has an important role in the treatment of psychiatric diseases. However, the low spatial resolution of EEG recorders poses a challenge. In order to overcome this problem, in this paper we model each emotion by mapping from scalp sensors to brain sources using Bernoulli–Laplace-based Bayesian model. The standard low-resolution electromagnetic tomography (sLORETA) method is used to initialize the source signals in this algorithm. Finally, a dynamic graph convolutional neural network (DGCNN) is used to classify emotional EEG in which the sources of the proposed localization model are considered as the underlying graph nodes. In the proposed method, the relationships between the EEG source signals are encoded in the DGCNN adjacency matrix. Experiments on our EEG dataset recorded at the Brain-Computer Interface Research Laboratory, University of Tabriz as well as publicly available SEED and DEAP datasets show that brain source modeling by the proposed algorithm significantly improves the accuracy of emotion recognition, such that it achieve a classification accuracy of 99.25% during the classification of the two classes of positive and negative emotions. These results represent an absolute 1–2% improvement in terms of classification accuracy over subject-dependent and subject-independent scenarios over the existing approaches. [ABSTRACT FROM AUTHOR]

The acute non-image forming (NIF) effects of daytime light on momentary mood had been-although not always-established in the current literature. It still remains largely unknown whether short-time light exposure would modulate emotion perception in healthy adults. The current study (N = 48) was conducted to explore the effects of illuminance (100 lx vs. 1000 lx at eye level) and correlated color temperature (CCT, 2700 K vs. 6500 K) on explicit and implicit emotion perception that was assessed with emotional face judgment task and emotional oddball task respectively. Results showed that lower CCT significantly decreased negative response bias in the face judgment task, with labeling ambiguous faces less fearful under 2700 K vs. 6500 K condition. Moreover, participants responded slightly faster for emotional pictures under 6500 K vs. 2700 K condition, but no significant effect of illuminance or CCT on negativity bias was revealed in the emotional oddball task. These findings highlighted the differential role of illuminance and CCT in regulating instant emotion perception and suggested a task-dependent moderation of light spectrum on negativity bias., (© 2021. The Author(s).)

Emotion recognition is defined as identifying human emotion and is directly related to different fields such as human–computer interfaces, human emotional processing, irrational analysis, medical diagnostics, data-driven animation, human–robot communication, and many more. This paper proposes a new facial emotional recognition model using a convolutional neural network. Our proposed model, "ConvNet", detects seven specific emotions from image data including anger, disgust, fear, happiness, neutrality, sadness, and surprise. The features extracted by the Local Binary Pattern (LBP), region based Oriented FAST and rotated BRIEF (ORB) and Convolutional Neural network (CNN) from facial expressions images were fused to develop the classification model through training by our proposed CNN model (ConvNet). Our method can converge quickly and achieves good performance which the authors can develop a real-time schema that can easily fit the model and sense emotions. Furthermore, this study focuses on the mental or emotional stuff of a man or woman using the behavioral aspects. To complete the training of the CNN network model, we use the FER2013 databases at first, and then apply the generalization techniques to the JAFFE and CK+ datasets respectively in the testing stage to evaluate the performance of the model. In the generalization approach on the JAFFE dataset, we get a 92.05% accuracy, while on the CK+ dataset, we acquire a 98.13% accuracy which achieve the best performance among existing methods. We also test the system's success by identifying facial expressions in real-time. ConvNet consists of four layers of convolution together with two fully connected layers. The experimental results show that the ConvNet is able to achieve 96% training accuracy which is much better than current existing models. However, when compared to other validation methods, the suggested technique was more accurate. ConvNet also achieved validation accuracy of 91.01% for the FER2013 dataset. We also made all the materials publicly accessible for the research community at: https://github.com/Tanoy004/Emotion-recognition-through-CNN. [ABSTRACT FROM AUTHOR]

Fear, anxiety, and preference in fish are generally evaluated by video-based behavioural analyses. We previously proposed a system that can measure bioelectrical signals, called ventilatory signals, using a 126-electrode array placed at the bottom of an aquarium and achieved cameraless real-time analysis of motion and ventilation. In this paper, we propose a method to evaluate the emotional state of fish by combining the motion and ventilatory indices obtained with the proposed system. In the experiments, fear/anxiety and appetitive behaviour were induced using alarm pheromone and ethanol, respectively. We also found that the emotional state of the zebrafish can be expressed on the principal component (PC) space extracted from the defined indices. The three emotional states were discriminated using a model-based machine learning method by feeding the PCs. Based on discrimination performed every 5 s, the F-score between the three emotional states were as follows: 0.84 for the normal state, 0.76 for the fear/anxiety state, and 0.59 for the appetitive behaviour. These results indicate the effectiveness of combining physiological and motional indices to discriminate the emotional states of zebrafish.

Women were historically excluded from research participation partly due to the assumption that ovarian hormone fluctuations lead to variation, especially in emotion, that could not be experimentally controlled. Although challenged in principle and practice, relevant empirical data are limited by single measurement occasions. The current paper fills this knowledge gap using data from a 75-day intensive longitudinal study. Three indices of daily affective variability—volatility, emotional inertia, and cyclicity—were evaluated using Bayesian inferential methods in 142 men, naturally cycling women, and women using three different oral contraceptive formulations (that "stabilize" hormone fluctuations). Results provided more evidence for similarities between men and women—and between naturally cycling women and oral contraceptive users—than for differences. Even if differences exist, effects are likely small. Thus, there is little indication that ovarian hormones influence affective variability in women to a greater extent than the biopsychosocial factors that influence daily emotion in men. [ABSTRACT FROM AUTHOR]

Preliminary evidence concerning emotional intelligence (EI) and working memory (WM) showed that the relationship between them is dependent on the emotional content ('hot' or 'cool') of tasks involving WM. In this paper, we continue investigating the relationship between EI and WM, focusing on a crucial function of WM, i.e., the efficacy of updating its content. WM updating shows substantial correlations with general fluid intelligence (gF) and seems to be a significant predictor of cognitive performance and achievement. We assume that if updating is important for a wide range of higher-order processes, updating emotional content in WM could be essential for emotionally intelligent behavior. To test this hypothesis, we constructed two parallel versions of a task that requires WM updating: one with neutral and the other with emotional stimuli. In addition, performance-based measures of both gF and EI were used in the research. Using the structural equation approach, we sought to demonstrate that gF is dependent on the efficiency of WM updating for both emotional and neutral stimuli, whereas EI might depend only on the updating efficacy in the emotional context. The results are discussed in terms of the domain specificity of EI and the domain generality of gF. The main constraint of the study is its limited sample size (n = 123 for intelligence measures, n = 69 for WM updating tasks). Moreover, the study was based on a female sample; thus, the conclusions can be extrapolated only to women.

This paper presents a computational framework for providing affective labels to real-life situations, called A-Situ. We first define an affective situation, as a specific arrangement of affective entities relevant to emotion elicitation in a situation. Then, the affective situation is represented as a set of labels in the valence-arousal emotion space. Based on psychological behaviors in response to a situation, the proposed framework quantifies the expected emotion evoked by the interaction with a stimulus event. The accumulated result in a spatiotemporal situation is represented as a polynomial curve called the affective curve, which bridges the semantic gap between cognitive and affective perception in real-world situations. We show the efficacy of the curve for reliable emotion labeling in real-world experiments, respectively concerning (1) a comparison between the results from our system and existing explicit assessments for measuring emotion, (2) physiological distinctiveness in emotional states, and (3) physiological characteristics correlated to continuous labels. The efficiency of affective curves to discriminate emotional states is evaluated through subject-dependent classification performance using bicoherence features to represent discrete affective states in the valence-arousal space. Furthermore, electroencephalography-based statistical analysis revealed the physiological correlates of the affective curves.

This meta-analysis investigates the effect of classic psychedelic drugs on empathy and focuses on cognitive and emotional empathy measured using the Multifaceted Empathy Test (MET). Empathy entails the ability to understand and share the feelings of another and is a significant component of social interaction. Several studies have examined the effects of psychedelic drugs such as LSD, psilocybin and ayahuasca on empathy, yet their overall effect has not been studied so far. In this meta-analysis, we reviewed data from studies up to November 2023 with the aim of examining the effects of various psychedelic drugs on empathic abilities broadly. Our findings suggest that classical psychedelics significantly enhance explicit and implicit emotional empathy without affecting measures of cognitive empathy. The results emphasize the need to continue testing the therapeutic potential of classic psychedelic drugs. [ABSTRACT FROM AUTHOR]

Hope is a vital coping mechanism, enabling individuals to effectively confront life's challenges. This study proposes a technique employing Natural Language Processing (NLP) tools like Linguistic Inquiry and Word Count (LIWC), NRC-emotion-lexicon, and vaderSentiment to analyze social media posts, extracting psycholinguistic, emotional, and sentimental features from a hope speech dataset. The findings of this study reveal distinct cognitive, emotional, and communicative characteristics and psycholinguistic dimensions, emotions, and sentiments associated with different types of hope shared in social media. Furthermore, the study investigates the potential of leveraging this data to classify different types of hope using machine learning algorithms. Notably, models such as LightGBM and CatBoost demonstrate impressive performance, surpassing traditional methods and competing effectively with deep learning techniques. We employed hyperparameter tuning to optimize the models' parameters and compared their performance using both default and tuned settings. The results highlight the enhanced efficiency achieved through hyperparameter tuning for these models. [ABSTRACT FROM AUTHOR]

Mental health issues have increased substantially since the onset of the COVID-19 pandemic. However, health policymakers do not have adequate data and tools to predict mental health demand, especially amid a crisis. Using time-series data collected in Singapore, this study examines if and how algorithmically measured emotion indicators from Twitter posts can help forecast emergency mental health needs. We measured the mental health needs during 549 days from 1 July 2020 to 31 December 2021 using the public's daily visits to the emergency room of the country's largest psychiatric hospital and the number of users with "crisis" state assessed through a government-initiated online mental health self-help portal. Pairwise Granger-causality tests covering lag length from 1 day to 5 days indicated that forecast models using Twitter joy, anger and sadness emotions as predictors perform significantly better than baseline models using past mental health needs data alone (e.g., Joy Intensity on IMH Visits, χ2 = 14·9, P < ·001***; Sadness Count on Mindline Crisis, χ2 = 4·6, P = ·031*, with a one-day lag length). The findings highlight the potential of new early indicators for tracking emerging public mental health needs. [ABSTRACT FROM AUTHOR]

"Picture a Scientist," a documentary featuring stories and research about bias in STEM, reached a large international audience. Yet, the extent to which this type of engaging media can impact gender bias remains unclear. In a unique collaboration between film creators and researchers, the current large-scale field studies explored whether "Picture a Scientist" functioned as an intervention and persuasive message targeting sexism in STEM. Study 1 found viewers who indicated more knowledge and stronger emotions, perspective-taking, and transportation after the film were more inspired to continue learning sexism in STEM and combating unfair treatment, suggesting the documentary engaged both classic and narrative persuasion processes. Employing a quasi-experimental design, Study 2 demonstrated that compared to those who had not watched the film (but intended to), participants who had viewed the film indicated higher awareness of gender bias, stronger intentions to address this bias, and participants in leadership reported stronger intentions to enact inclusive policies (for example, making it easier to report mistreatment). Our findings suggest that the use of this documentary may be a relatively low-cost and easily scalable online intervention, particularly when organizations lack resources for in-person workshops. These studies can help inform organizational trainings using this or similar documentaries. [ABSTRACT FROM AUTHOR]

Humans express emotions through various modalities such as facial expressions and natural language. However, the relationships between emotions expressed through different modalities and their correlations with neural activities remain uncertain. Here, we aimed to unveil some of these uncertainties by investigating the similarity of emotion representations across modalities and brain regions. First, we represented various emotion categories as multi-dimensional vectors derived from visual (face), linguistic, and visio-linguistic data, and used representational similarity analysis to compare these modalities. Second, we examined the linear transferability of emotion representation from other modalities to the visual modality. Third, we compared the representational structure derived in the first step with those from brain activities across 360 regions. Our findings revealed that emotion representations share commonalities across modalities with modality-type dependent variations, and they can be linearly mapped from other modalities to the visual modality. Additionally, emotion representations in uni-modalities showed relatively higher similarity with specific brain regions, while multi-modal emotion representation was most similar to representations across the entire brain region. These findings suggest that emotional experiences are represented differently across various brain regions with varying degrees of similarity to different modality types, and that they may be multi-modally conveyable in visual and linguistic domains. [ABSTRACT FROM AUTHOR]

Harmony is one of the most fundamental elements of music that evokes emotional response. The inferior colliculus (IC) has been known to detect poor agreement of harmonics of sound, that is, dissonance. Electrophysiological evidence has implicated a relationship between a sustained auditory response mainly from the brainstem and unpleasant emotion induced by dissonant harmony. Interestingly, an individual's dislike of dissonant harmony of an individual correlated with a reduced sustained auditory response. In the current paper, we report novel evidence based on functional magnetic resonance imaging (fMRI) for such a relationship between individual variability in dislike of dissonance and the IC activation. Furthermore, for the first time, we show how dissonant harmony modulates functional connectivity of the IC and its association with behaviourally reported unpleasantness. The current findings support important contributions of low level auditory processing and corticofugal interaction in musical harmony preference.

Although, the quest to understand emotional processing in individuals with Autism Spectrum Disorders (ASD) has led to an impressive number of studies, the picture that emerges from this research remains inconsistent. Some studies find that Typically Developing (TD) individuals outperform those with ASD in emotion recognition tasks, others find no such difference. In this paper, we move beyond focusing on potential group differences in behaviour to answer what we believe is a more pressing question: do individuals with ASD use the same mechanisms to process emotional cues? To this end, we rely on model-based analyses of participants' accuracy during an emotion categorisation task in which displays of anger and fear are paired with direct vs. averted gaze. Behavioural data of 20 ASD and 20 TD adolescents revealed that the ASD group displayed lower overall performance. Yet, gaze direction had a similar impact on emotion categorisation in both groups, i.e. improved accuracy for salient combinations (anger-direct, fear-averted). Critically, computational modelling of participants' behaviour reveals that the same mechanism, i.e. increased perceptual sensitivity, underlies the contextual impact of gaze in both groups. We discuss the specific experimental conditions that may favour emotion processing and the automatic integration of contextual information in ASD.

The role of learner emotions in language learning has long been observed and researched. Of various emotions, foreign language anxiety, enjoyment and boredom have been the most often researched, while research on other emotions like pride and sadness is hardly available in the current literature. Research on emotions in students with special characteristics can be hardly found as well. Hence, this study collected interview data from 26 and survey data from 520 top students in two universities in China to examine their emotions in English language class. Analyses of the data revealed the following major findings: (a) The participants experienced a diversity of emotions in their English language class due to both learner-internal and external reasons, but the most often reported emotions were anxiety, enjoyment and boredom, (b) significant correlations existed among the students' English language classroom anxiety, enjoyment and boredom, (c) English language classroom anxiety and boredom significantly negatively while enjoyment significantly positively predicted the students' English test performance, and (d) English language classroom anxiety debilitated English learning but motivated students to study harder as well; enjoyment facilitated English learning but students might forget much of what had been learned after class; though boredom caused some students to be absent-minded in class, most students would study on their own when feeling bored. These findings further pinpoint the important role and complex nature of learner emotions in second/foreign language learning. Based on these findings, specific suggestions for language teachers and learners are discussed. [ABSTRACT FROM AUTHOR]

While alterations in both physiological responses to others' emotions as well as interoceptive abilities have been identified in autism, their relevance in altered emotion recognition is largely unknown. We here examined the role of interoceptive ability, facial mimicry, and autistic traits in facial emotion processing in non-autistic individuals. In an online Experiment 1, participants (N = 99) performed a facial emotion recognition task, including ratings of perceived emotional intensity and confidence in emotion recognition, and reported on trait interoceptive accuracy, interoceptive sensibility and autistic traits. In a follow-up lab Experiment 2 involving 100 participants, we replicated the online experiment and additionally investigated the relationship between facial mimicry (measured through electromyography), cardiac interoceptive accuracy (evaluated using a heartbeat discrimination task), and autistic traits in relation to emotion processing. Across experiments, neither interoception measures nor facial mimicry accounted for a reduced recognition of specific expressions with higher autistic traits. Higher trait interoceptive accuracy was rather associated with more confidence in correct recognition of some expressions, as well as with higher ratings of their perceived emotional intensity. Exploratory analyses indicated that those higher intensity ratings might result from a stronger integration of instant facial muscle activations, which seem to be less integrated in intensity ratings with higher autistic traits. Future studies should test whether facial muscle activity, and physiological signals in general, are correspondingly less predictive of perceiving emotionality in others in individuals on the autism spectrum, and whether training interoceptive abilities might facilitate the interpretation of emotional expressions. [ABSTRACT FROM AUTHOR]

Dogs can discriminate stressed from non-stressed human odour samples, but the effect on their cognition is unstudied. Using a cognitive bias task, we tested how human odours affect dogs' likelihood of approaching a food bowl placed at three ambiguous locations ("near-positive", "middle" and "near-negative") between trained "positive" (rewarded) and "negative" (unrewarded) locations. Using odour samples collected from three unfamiliar volunteers during stressful and relaxing activities, we tested eighteen dogs under three conditions: no odour, stress odour and relaxed odour, with the order of test odours counterbalanced across dogs. When exposed to stress odour during session three, dogs were significantly less likely to approach a bowl placed at one of the three ambiguous locations (near-negative) compared to no odour, indicating possible risk-reduction behaviours in response to the smell of human stress. Dogs' learning of trained positive and negative locations improved with repeated testing and was significant between sessions two and three only when exposed to stress odour during session three, suggesting odour influenced learning. This is the first study to show that without visual or auditory cues, olfactory cues of human stress may affect dogs' cognition and learning, which, if true, could have important consequences for dog welfare and working performance. [ABSTRACT FROM AUTHOR]

While perceiving the emotional state of others may be crucial for our behavior even when this information is present outside of central vision, emotion perception studies typically focus on central visual field. We have recently investigated emotional valence (pleasantness) perception across the parafovea (≤ 4°) and found that for briefly presented (200 ms) emotional face images (from the established KDEF image-set), positive (happy) valence was the least affected by eccentricity (distance from the central visual field) and negative (fearful) valence the most. Furthermore, we found that performance at 2° predicted performance at 4°. Here we tested (n = 37) whether these effects replicate with face stimuli of different identities from a different well-established image-set (NimStim). All our prior findings replicated and eccentricity-based modulation magnitude was smaller with NimStim (~ 16.6% accuracy reduction at 4°) than with KDEF stimuli (~ 27.3% reduction). Our current investigations support our earlier findings that for briefly presented parafoveal stimuli, positive and negative valence perception are differently affected by eccentricity and may be dissociated. Furthermore, our results highlight the importance of investigating emotions beyond central vision and demonstrate commonalities and differences across different image sets in the parafovea, emphasizing the contribution of replication studies to substantiate our knowledge about perceptual mechanisms. [ABSTRACT FROM AUTHOR]

Humans use pictures to model the world. The structure of a picture maps to mind space to form a concept. When an internal structure matches the corresponding external structure, an observation functions. Whether effective or not, the observation is self-consistent. In epistemology, people often differ from each other in terms of whether a concept is probabilistic or certain. Based on the effect of the presented IG and pull anti algorithm, we attempt to provide a comprehensive answer to this problem. Using the characters of hidden structures, we explain the difference between the macro and micro levels and the same difference between semantics and probability. In addition, the importance of attention is highlighted through the combination of symmetry and asymmetry included and the mechanism of chaos and collapse revealed in the presented model. Because the subject is involved in the expression of the object, representationalism is not complete. However, people undoubtedly reach a consensus based on the objectivity of the representation. Finally, we suggest that emotions could be used to regulate cognition. [ABSTRACT FROM AUTHOR]

This study delves into expressing primary emotions anger, happiness, sadness, and fear through drawings. Moving beyond the well-researched color-emotion link, it explores under-examined aspects like spatial concepts and drawing styles. Employing Python and OpenCV for objective analysis, we make a breakthrough by converting subjective perceptions into measurable data through 728 digital images from 182 university students. For the prominent color chosen for each emotion, the majority of participants chose red for anger (73.11%), yellow for happiness (17.8%), blue for sadness (51.1%), and black for fear (40.7%). Happiness led with the highest saturation (68.52%) and brightness (75.44%) percentages, while fear recorded the lowest in both categories (47.33% saturation, 48.78% brightness). Fear, however, topped in color fill percentage (35.49%), with happiness at the lowest (25.14%). Tangible imagery prevailed (71.43–83.52%), with abstract styles peaking in fear representations (28.57%). Facial expressions were a common element (41.76–49.45%). The study achieved an 81.3% predictive accuracy for anger, higher than the 71.3% overall average. Future research can build on these results by improving technological methods to quantify more aspects of drawing content. Investigating a more comprehensive array of emotions and examining factors influencing emotional drawing styles will further our understanding of visual-emotional communication. [ABSTRACT FROM AUTHOR]

Social decision-making is known to be influenced by predictive emotions or the perceived reciprocity of partners. However, the connection between emotion, decision-making, and contextual reciprocity remains less understood. Moreover, arguments suggest that emotional experiences within a social context can be better conceptualised as prosocial rather than basic emotions, necessitating the inclusion of two social dimensions: focus, the degree of an emotion's relevance to oneself or others, and dominance, the degree to which one feels in control of an emotion. For better representation, these dimensions should be considered alongside the interoceptive dimensions of valence and arousal. In an ultimatum game involving fair, moderate, and unfair offers, this online study measured the emotions of 476 participants using a multidimensional affective rating scale. Using unsupervised classification algorithms, we identified individual differences in decisions and emotional experiences. Certain individuals exhibited consistent levels of acceptance behaviours and emotions, while reciprocal individuals' acceptance behaviours and emotions followed external reward value structures. Furthermore, individuals with distinct emotional responses to partners exhibited unique economic responses to their emotions, with only the reciprocal group exhibiting sensitivity to dominance prediction errors. The study illustrates a context-specific model capable of subtyping populations engaged in social interaction and exhibiting heterogeneous mental states. [ABSTRACT FROM AUTHOR]

Trauma scientists have raised the alarm about the devastating consequences of the Ukraine war on mental health. We examined how higher education students—as indirect victims—coped with this conflict and how they emotionally reacted during 2022. We involved 2314 students from 16 countries through an online survey. A structural equation model indicated significant relations between war-related worry about military and macroeconomics domains and two coping strategies (opposition, support giving), in turn significantly linked with six emotions. The model was strongly invariant across gender, study field, and geographic area. The most frequent emotions were anger and anxiety, followed by two future-centred emotions (hopelessness and hope). Emotions were more frequent for females and students of the countries geographically close to the war region. Our findings call for evidence-based policy recommendations to be implemented by institutions to combat the negative short and long-term psychological sequelae of being witnesses of armed conflicts. [ABSTRACT FROM AUTHOR]

Music is ubiquitous in our everyday lives, and lyrics play an integral role when we listen to music. The complex relationships between lyrical content, its temporal evolution over the last decades, and genre-specific variations, however, are yet to be fully understood. In this work, we investigate the dynamics of English lyrics of Western, popular music over five decades and five genres, using a wide set of lyrics descriptors, including lyrical complexity, structure, emotion, and popularity. We find that pop music lyrics have become simpler and easier to comprehend over time: not only does the lexical complexity of lyrics decrease (for instance, captured by vocabulary richness or readability of lyrics), but we also observe that the structural complexity (for instance, the repetitiveness of lyrics) has decreased. In addition, we confirm previous analyses showing that the emotion described by lyrics has become more negative and that lyrics have become more personal over the last five decades. Finally, a comparison of lyrics view counts and listening counts shows that when it comes to the listeners' interest in lyrics, for instance, rock fans mostly enjoy lyrics from older songs; country fans are more interested in new songs' lyrics. [ABSTRACT FROM AUTHOR]

Among disabling post-traumatic stress symptoms (PTSS) are irritability, aggressive behavior, distressing memories and general impaired cognition and negative mood. Art therapy interventions, including mask-making, can potentially alleviate these symptoms. We tested the hypothesis that art conveys emotions and predicted that blinded viewers would be able to perceive changes in theoretically derived emotional profiles expressed in art made by military personnel with PTSS from the onset to the end of therapy. Five service members and veterans exhibiting PTSS were enrolled in an 8-session art therapy protocol, during which they artistically transformed papier-mâché masks at the beginning and end of the protocol. We found that blinded viewers without knowledge of the masks' creation stage (onset or end of therapy) read initial masks as conveying more negative emotions (e.g., angry, upset, and challenged) and later masks as conveying more positive emotions (calm and pleasure). Based on the assessments from the blinded evaluators, we infer the emotional transition experienced by the participants was expressed in the masks. In an exploratory arm of the study, we also found that viewers were better able to empathize with the negative emotions experienced by participants with PTSS when asked to explicitly take their perspective. [ABSTRACT FROM AUTHOR]

Developmental prosopagnosia (DP) is characterised by deficits in face identification. However, there is debate about whether these deficits are primarily perceptual, and whether they extend to other face processing tasks (e.g., identifying emotion, age, and gender; detecting faces in scenes). In this study, 30 participants with DP and 75 controls completed a battery of eight tasks assessing four domains of face perception (identity; emotion; age and gender; face detection). The DP group performed worse than the control group on both identity perception tasks, and one task from each other domain. Both identity perception tests uniquely predicted DP/control group membership, and performance on two measures of face memory. These findings suggest that deficits in DP may arise from issues with face perception. Some non-identity tasks also predicted DP/control group membership and face memory, even when face identity perception was accounted for. Gender perception and speed of face detection consistently predicted unique variance in group membership and face memory; several other tasks were only associated with some measures of face recognition ability. These findings indicate that face perception deficits in DP may extend beyond identity perception. However, the associations between tasks may also reflect subtle aspects of task demands or stimuli. [ABSTRACT FROM AUTHOR]

A person with impaired emotion recognition is not able to correctly identify facial expressions represented by other individuals. The aim of the present study is to assess eyes gaze and facial emotion recognition in a healthy population using dynamic avatars in immersive virtual reality (IVR). For the first time, the viewing of each area of interest of the face in IVR is studied by gender and age. This work in healthy people is conducted to assess the future usefulness of IVR in patients with deficits in the recognition of facial expressions. Seventy-four healthy volunteers participated in the study. The materials used were a laptop computer, a game controller, and a head-mounted display. Dynamic virtual faces randomly representing the six basic emotions plus neutral expression were used as stimuli. After the virtual human represented an emotion, a response panel was displayed with the seven possible options. Besides storing the hits and misses, the software program internally divided the faces into different areas of interest (AOIs) and recorded how long participants looked at each AOI. As regards the overall accuracy of the participants' responses, hits decreased from the youngest to the middle-aged and older adults. Also, all three groups spent the highest percentage of time looking at the eyes, but younger adults had the highest percentage. It is also noteworthy that attention to the face compared to the background decreased with age. Moreover, the hits between women and men were remarkably similar and, in fact, there were no statistically significant differences between them. In general, men paid more attention to the eyes than women, but women paid more attention to the forehead and mouth. In contrast to previous work, our study indicates that there are no differences between men and women in facial emotion recognition. Moreover, in line with previous work, the percentage of face viewing time for younger adults is higher than for older adults. However, contrary to earlier studies, older adults look more at the eyes than at the mouth.Consistent with other studies, the eyes are the AOI with the highest percentage of viewing time. For men the most viewed AOI is the eyes for all emotions in both hits and misses. Women look more at the eyes for all emotions, except for joy, fear, and anger on hits. On misses, they look more into the eyes for almost all emotions except surprise and fear. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

By focusing on colexification, we detected central emotions sharing semantic commonalities with many other emotions in terms of a semantic relationship of both similarity and associativity. In analysis, we created colexification networks from multiple languages by assigning a concept to a vertex and colexification to an edge. We identify concepts of emotions with a large weight in the colexification network and specify central emotions by finding hub emotions. Our resultant central emotions are four: "GOOD," "WANT," "BAD," and "LOVE." [ABSTRACT FROM AUTHOR]