Emotional Representations Research Articles

Emotion-Based Reinforcement Attention Network for Depression Detection on Social Media: Algorithm Development and Validation.

BackgroundDepression detection has recently received attention in the field of natural language processing. The task aims to detect users with depression based on their historical posts on social media. However, existing studies in this area use the entire historical posts of the users and select depression indicator posts. Moreover, these methods fail to effectively extract deep emotional semantic features or simply concatenate emotional representation. To solve this problem, we propose a model to extract deep emotional semantic features and select depression indicator posts based on the emotional states.ObjectiveThis study aims to develop an emotion-based reinforcement attention network for depression detection of users on social media.MethodsThe proposed model is composed of 2 components: the emotion extraction network, which is used to capture deep emotional semantic information, and the reinforcement learning (RL) attention network, which is used to select depression indicator posts based on the emotional states. Finally, we concatenated the output of these 2 parts and send them to the classification layer for depression detection.ResultsExperimental results of our model on the multimodal depression data set outperform the state-of-the-art baselines. Specifically, the proposed model achieved accuracy, precision, recall, and F1-score of 90.6%, 91.2%, 89.7%, and 90.4%, respectively.ConclusionsThe proposed model utilizes historical posts of users to effectively identify users’ depression tendencies. The experimental results show that the emotion extraction network and the RL selection layer based on emotional states can effectively improve the accuracy of detection. In addition, sentence-level attention layer can capture core posts.

ConGNN: Context-consistent cross-graph neural network for group emotion recognition in the wild

Group-level emotion recognition (GER) is challenging since it significantly relies on different individual facial expressions, complex group relationships, and contextual scene information. Due to complicated emotion interactions and emotion bias among multiple emotion cues, current techniques still fall short when it comes to detecting complex group emotion. In this study, we propose a context-consistent cross-graph neural network (ConGNN) for accurate GER in the wild. It can model multi-cue emotional relations and alleviate emotion bias among different cues, thus obtaining the robust and consistent group emotion representation. In ConGNN, we first extract the facial, local object, and global scene features to form multi-cue emotion features. Then, we develop a cross-graph neural network (C-GNN) for modeling inter- and intra-branch emotion relations, obtaining a comprehensive cross-branch emotion representation. To alleviate the effect of emotion bias during C-GNN training, we propose an emotion context-consistent learning mechanism with an emotion bias penalty to help obtain context-consistent group emotion, and then achieve robust GER. Furthermore, we create a new, more realistic benchmark, SiteGroEmo, and use it to evaluate ConGNN. Extensive experiments on two challenging GER datasets (GroupEmoW and SiteGroEmo) demonstrate that our ConGNN outperforms state-of-the-art techniques, with relative accuracy gains of 3.35% and 4.32%, respectively.

Lower emotional complexity as a prospective predictor of psychopathology in adolescents from the general population.

Emotional complexity (EC) involves the ability to distinguish between distinct emotions (differentiation) and the experience of a large range of emotions (diversity). Lower EC has been related to psychopathology in cross-sectional studies. This study aimed to investigate (a) whether EC prospectively predicts psychopathology and (b) whether this effect is contingent on stressful life events. To further explore EC, we compared the effects of differentiation and diversity. Adolescents from the general population (N = 401) rated 8 negatively valenced emotions 10 times a day for 6 consecutive days. Further, they completed the Symptom Checklist-90 (baseline and 1-year follow-up) and a questionnaire on past year's life events at follow-up. Logistic regression analyses tested whether EC-reflected by emotion differentiation (intraclass correlation coefficient [ICC]) and diversity (diversity index [DI])-predicted prognosis (good: remitting or lacking symptoms vs. bad: worsening or persisting symptoms). EC predicted prognoses but only when based on the ICC (OREC.ICC = 1.42, p = .02). An ECICC 1 SD above average increased the probability of good prognosis from .67 to .74. This effect was not related to stressful life events (OREC × Life events = 1.03, p = .86) and disappeared when emotion intensity (mean level) was taken into account (OREC = 1.20, p = .20). Predicting future prognosis does not necessitate complex measures of emotional experience (ICC, DI) but rather might be achieved through simpler indices (mean). The discrepant effects of the ICC and DI on prognosis suggest that impaired emotion representation (ICC) plays a more important role in vulnerability to mental ill health than does low diversity of emotions (DI). (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Acceptance and Adherence to COVID-19 Vaccination-The Role of Cognitive and Emotional Representations.

Vaccine hesitation is a topic of utmost importance, with the COVID-19 pandemic serving as a clear reminder of its timeliness. Besides evaluating COVID-19 vaccine acceptance in a sample of Portuguese people, this study aims at understanding cognitive and emotional representations related to vaccination, and their influence on vaccination hesitation. A cross-sectional online survey was conducted between 27 December 2020 and 27 January 2021. It assessed cognitive and emotional COVID-19 representations; vaccination status; cognitive and emotional representations of vaccination and perceived necessity and concerns about vaccines. Of 31 × 58 participants, 91% accepted taking a COVID-19 vaccine. Among several other significant findings, women (71.3%) more often considered that the pandemic affected their lives (p < 0.001) and were more often concerned with being infected (p < 0.001). Likewise, there were significantly more female participants concerned about taking a COVID-19 vaccine and its possible effects, when compared to the number of male participants (p < 0.001). The number of participants with a higher education level that were more worried about becoming infected was greater (p = 0.001), when compared with those less educated. Regarding age groups, people aged 18 to 24 had fewer concerned participants (9.6%), while the number of individuals aged 55 to 64 had the most (p < 0.001). Somewhat surprisingly, perceiving oneself as extremely informed about COVID-19 was not associated with greater vaccine acceptance (OR = 1.534 [1.160–2.029]; (p = 0.003)). Moreover, people aged 25 to 64 years old and with lower education level were more likely not to accept vaccination (OR = 2.799 [1.085–7.221]; (p = 0.033)). Finally, being more concerned about taking a vaccine lowers its acceptance (OR = 4.001 [2.518–6.356]; (p < 0.001)). Cognitive and emotional representations have a great impact and are reliable predictors of vaccine acceptance. Thus, it is of extreme importance that public health messages be adapted to the different characteristics of the population.

Illness perceptions in long-COVID: A cross-sectional analysis in adults

Symptoms may persist after an acute COVID-19 infection, a phenomenon termed long-COVID. Increasing attention is now directed toward these long-term effects, as many seem to be affected. So far, long-COVID was primarily discussed from a medical perspective, leaving psychological factors on health-related outcomes understudied. Thus, the present study contributes to the current literature by examining Leventhal’s common-sense model of self-regulation, investigating patients’ cognitive and emotional illness perceptions and their association with different health-related outcomes in the context of long-COVID. We examined 246 long-COVID patients (89.0% female, Mage = 45.29 ± 12.12 years), assessing illness perceptions, fatigue, depressive symptoms, anxiety, and quality of life in a cross-sectional survey study. Linear regression analyses were applied to model the unique effects of the illness perception dimension on health-related outcomes. More than half of the sample had elevated levels of depressive symptoms, anxiety, and fatigue. Illness perception dimensions explained between 28% and 37% of the variance across the analyzed health-related outcomes. More symptoms, higher perceived consequences, and higher emotional representation were related to worse health-related outcomes. In general, long-COVID was poorly understood (i.e., low coherence) and perceived as difficult to control (i.e., personal & treatment control). Health professionals involved in counseling should pay close attention to both cognitive and emotional illness perceptions to address possible ways of coping with long-COVID.

Social context effects on emotional language: The influence of the COVID-19 pandemic on the emotional evaluation of words

The COVID-19 pandemic has altered our routines, our conversations, the specific social contexts in which we hear or use certain words, and potentially, the representation of the words related to the disease and its consequences. Here we investigated whether the effects of the pandemic have changed the representation of the affective features of COVID-19-related words. To this aim, we collected new ratings of valence (from unpleasant to pleasant) and arousal (from calm to activated) dimensions for COVID-19-related words (e.g., hospital) and COVID-19-unrelated words (e.g., whale). Subsequently, we compared these scores with those from databases that reported ratings for the same pool of words before the pandemic. Our results showed significant changes in arousal for COVID-19-related words but not unrelated words, thus revealing that the pandemic social context modified their affective representation. These findings support the flexibility of emotional representations and the malleability and dynamicity of the mental lexicon as a function of contextual factors.

Different stages of emotional prosody processing in healthy ageing-evidence from behavioural responses, ERPs, tDCS, and tRNS.

Past research suggests that the ability to recognise the emotional intent of a speaker decreases as a function of age. Yet, few studies have looked at the underlying cause for this effect in a systematic way. This paper builds on the view that emotional prosody perception is a multi-stage process and explores which step of the recognition processing line is impaired in healthy ageing using time-sensitive event-related brain potentials (ERPs). Results suggest that early processes linked to salience detection as reflected in the P200 component and initial build-up of emotional representation as linked to a subsequent negative ERP component are largely unaffected in healthy ageing. The two groups show, however, emotional prosody recognition differences: older participants recognise emotional intentions of speakers less well than younger participants do. These findings were followed up by two neuro-stimulation studies specifically targeting the inferior frontal cortex to test if recognition improves during active stimulation relative to sham. Overall, results suggests that neither tDCS nor high-frequency tRNS stimulation at 2mA for 30 minutes facilitates emotional prosody recognition rates in healthy older adults.

Cognitive and emotional illness representation of people with chronic hepatitis B in Ghana: A qualitative inquiry.

Hepatitis B remains a public health concern in Ghana, with an estimated prevalence of 8.4% in adults and 14.3% in adolescents. This study explored the cognitive and emotional illness representations of persons with chronic hepatitis B who sought care at a tertiary hospital's liver clinic in Ghana. A qualitative exploratory descriptive design was adopted, and 15 participants were purposefully chosen for in-person interviews. The data was analyzed using the content analysis procedure. The findings revealed that participants' cognitive representations of hepatitis B infection varied. Some believed they got the infection through unsafe sexual practices, while others thought it was from their mother or spiritual sources. Participants' predominant emotional representations included shock, confusion, and fear. Education about the causes and management of hepatitis B should be addressed as part of patient care.

Emotion expression through spoken language in Huntington disease

Patients with Huntington's disease suffer from disturbances in the perception of emotions; they do not correctly read the body, vocal and facial expressions of others. With regard to the expression of emotions, it has been shown that they are impaired in expressing emotions through face but up until now, little research has been conducted about their ability to express emotions through spoken language.To better understand emotion production in both voice and language in Huntington's Disease (HD), we tested 115 individuals: 68 patients (HD), 22 participants carrying the mutant HD gene without any motor symptoms (pre-manifest HD), and 25 controls in a single-centre prospective observational follow-up study. Participants were recorded in interviews in which they were asked to recall sad, angry, happy, and neutral stories. Emotion expression through voice and language was investigated by comparing the identifiability of emotions expressed by controls, preHD and HD patients in these interviews. To assess separately vocal and linguistic expression of emotions in a blind design, we used machine learning models instead of a human jury performing a forced-choice recognition test. Results from this study showed that patients with HD had difficulty expressing emotions through both voice and language compared to preHD participants and controls, who behaved similarly and above chance. In addition, we did not find any differences in expression of emotions between preHD and healthy controls. We further validated our newly proposed methodology with a human jury on the speech produced by the controls. These results are consistent with the hypothesis that emotional deficits in HD are caused by impaired sensori-motor representations of emotions, in line with embodied cognition theories. This study also shows how machine learning models can be leveraged to assess emotion expression in a blind and reproducible way.

The Mathematical Analysis Model of Educational System in Music Courses in Colleges and Universities

Abstract This article first adopts a fuzzy mathematical analysis model and proposes a mathematical model of fuzzy characteristics of music categories. Second, the article extracts audio data characteristics and conducts music classification experiments. The article establishes a DNN-Bottleneck feature framework, extracts robust perceptual features from audio data, and then encodes to achieve fast audio perceptual hashing. Experiments verify the effect of different feature selections on the semantic space representation of music emotions and the impact on retrieval performance.

Analysis of Emotional Color Representation in Oil Painting Based on Deep Learning Model Evaluation

When an artist creates an oil painting, it is rich in emotion. Color is the main way to express emotion in oil painting. As far as the art field is concerned, color is an objective phenomenon that people can really feel its unique richness and brilliance, and different people will have different emotions when facing the same picture. Color itself is not emotional, but people have different psychological feelings by looking at different colors. This is the power of color. In the same way, color is also a carrier for painters to convey emotions in oil paintings. The study of emotional color expression in oil painting is more helpful for people to understand the emotion conveyed by oil painting. Color has an important role and an irreplaceable position in oil painting creation. In the creation of oil painting, color expression is combined with emotional expression. The creator conveys his experience and feelings in the form of color. In this paper, we look at the relationship between color and emotion, the emotional expression of color, and the expression of emotion in painting. The creators of oil paintings convey their feelings and experiences in the form of color, which plays a unique and important role in portraying the image of their works, expressing emotions, and creating an atmosphere, and we analyze and reveal how to use paint to express emotions in oil painting.

Emotional representation of music in multi-source data by the Internet of Things and deep learning

Emotional representation of music in multi-source data by the Internet of Things and deep learning

The Achievement Motive in the Brain: BOLD Responses to Pictures of Challenging Activities Predicted by Implicit Versus Explicit Achievement Motives.

The achievement motive refers to a preference for mastering challenges and competing with some standard of excellence. Along with affiliation and power motives, the achievement motive is typically considered to occur on the level of implicit versus explicit representations. Specifically, whereas implicit motives involve pictorial, emotional goal representations and facilitate corresponding action effortlessly, explicit motives involve propositional (“verbalized”) goal representations but need some effort to translate into action (McClelland et al., 1989). We used functional magnetic resonance imaging (fMRI) to investigate whether and to which degree the implicit and explicit achievement motives differentially predict blood-oxygen-level-dependent (BOLD) responses to pictures of individuals engaging in challenging activities. Whereas the implicit AM predicted activity in areas associated with emotion (orbitofrontal cortex) and visual processing (right dorsolateral prefrontal cortex, premotor and occipital cortices), the explicit AM predicted activity in areas associated with cognitive self-control or verbal goal processing (dorsal anterior cingulate cortex, left dorsolateral prefrontal cortex). The findings support the commonly assumed distinction between implicit and explicit motives with neuronal data. They also suggest that explicit motives require cognitive self-control to overcome potential lacks of motivation.

Knowledge Bridging for Empathetic Dialogue Generation

Lack of external knowledge makes empathetic dialogue systems difficult to perceive implicit emotions and learn emotional interactions from limited dialogue history. To address the above problems, we propose to leverage external knowledge, including commonsense knowledge and emotional lexical knowledge, to explicitly understand and express emotions in empathetic dialogue generation. We first enrich the dialogue history by jointly interacting with external knowledge and construct an emotional context graph. Then we learn emotional context representations from the knowledge-enriched emotional context graph and distill emotional signals, which are the prerequisites to predicate emotions expressed in responses. Finally, to generate the empathetic response, we propose an emotional cross-attention mechanism to learn the emotional dependencies from the emotional context graph. Extensive experiments conducted on a benchmark dataset verify the effectiveness of the proposed method. In addition, we find the performance of our method can be further improved by integrating with a pre-trained model that works orthogonally.

Lost in the Past: Emotions, History and International Relations

Lost in the Past: Emotions, History and International Relations

Region Dual Attention-Based Video Emotion Recognition.

To solve the emotional differences between different regions of the video frame and make use of the interrelationship between different regions, a region dual attention-based video emotion recognition method (RDAM) is proposed. RDAM takes as input video frame sequences and learns a discriminatory video emotion representation that can make full use of the emotional differences of different regions and the interrelationship between regions. Specifically, we construct two parallel attention modules: one is the regional location attention module, which generates a weight value for each feature region to identify the relative importance of different regions. Based on the weight, the emotion feature that can perceive the emotional sensitive region is generated. The other is the regional relationship attention module, which generates a region relation matrix that represents the interrelationship of different regions of a video frame. Based on the region relation matrix, the emotion feature that can perceive interrelationship between different regions is generated. The outputs of these two attention modules are fused to produce the emotional features of video frames. Then, the features of video frame sequences are fused by attention-based fusion network, and the final emotion feature of the video is produced. The experimental results on the video emotion recognition data sets show that the proposed method outperforms the other related works.

Would Illness Representations of COVID-19 and COVID-19 Fears During Clinic Visits Promote or Reduce Behavioral Intention to Seek Medical Consultations for Flu Symptoms? A Random Telephone Survey in Hong Kong, China.

BackgroundThe study investigated the level of behavioral intention to consult doctors for flu symptoms (BICDFS) during the Coronavirus Disease 2019 (COVID-19) pandemic and examined its associations with illness representations of COVID-19 and fear of COVID-19 during clinic visits in a general Chinese adult population.MethodsA random telephone survey was conducted among 300 residents in Hong Kong, China in April 2020 when the second wave of COVID-19 was just ended in the region. The participants were asked about their intention to consult doctors if they had mild or severe flu symptoms in the next week (from 1 = definitely no to 5 = definitely yes). Illness representations of COVID-19 were measured by the Chinese version of the Brief Illness Perception Questionnaire (BIPQ). The fear of COVID-19 during clinic visits were assessed by two single items. Linear regression adjusted for background variables and hierarchical strategies were employed.ResultsOf the participants, 52.3 and 92.0% showed an intention to consult doctors for mild and severe flu symptoms, respectively. Adjusted for background factors, COVID-19-related cognitive representations (consequences: standardized b = 0.15, p = 0.010; understanding: standardized b = 0.21, p = 0.001) and emotional representations (concern: standardized b = 0.17, p = 0.001; negative emotions: standardized b = 0.19, p = 0.001) were positively associated with BICDFS. In the hierarchical model, independent significant factors of BICDFS included understanding (standardized b = 0.16, p = 0.013) and negative emotions (standardized b = 0.17, p = 0.008). The fear-related variables showed non-significant associations with the BICDFS.ConclusionsPromotion of care-seeking behaviors for flu symptoms during the COVID-19 pandemic should consider improving people's understanding of COVID-19 and providing advice on related coping strategies for emotional responses to COVID-19.

Illness and tretment representation in onological patients undergoing chemotherapy: relationship with subjective well-being

IntroductionThe knowledge, expectations, fears that a patient has about the oncological disease and treatment can affect the quality of life of patients (Colagiuri et al., 2013; Whitford, 2012).ObjectivesThe aim was to reveal the relationship between well-being of patients with cancer undergoing chemotherapy and their illness and treatment representation.Methods110 patients undergoing chemotherapy in Medsi Clinical Hospital filled Chemotherapy Attitudes Questionnaire (Zinchenko et al., 2020), Life Satisfaction Scale (Diener et al., 1985), Scale of Positive and Negative Experience (Diener et al., 2009), Quality of Life Questionary C30 (Aaronson N. K. et al., 1994), Illness Perception Questionnaire (Moss-Morris et al., 2002), Self-Regulation Questionnaire in the Rehabilitation Process (Kovyazina M. et al, 2019), Hospital Anxiety and Depression Scale (Zigmond, Snaith, 1983).ResultsCorrelation analysis revealed that patients with severe difficulties in physical functioning had a lower level of life satisfaction (R = -0.23, p &lt;.05) and quality of life (R = -0.35, p &lt;.001), perceived disease as long-term (R = 0.34; p &lt;0.001), cyclical (R = 0.33; p &lt;0.001) and carrying significant negative consequences for life (R = 0.55; p &lt;0.001), also these patients were characterized by anxiety about health during treatment (R = 0.37; p &lt;0.001).Perception of illness duration, personal control, emotional representations, self-efficacy, confidence in the effectiveness of treatment can predict the level of satisfaction with life of cancer patients undergoing chemotherapy (R2 increased from 0.05 to 0.37, p&lt;0.001).ConclusionsHealth anxiety, illness duration, personal control, self-efficacy could be targets for interventions in patients undergoing chemotherapy.DisclosureNo significant relationships.

The relationship between the recognition of specific basic emotions and negative symptom domains in patients with schizophrenia spectrum disorders

IntroductionCurrent research suggests emotion recognition to be significantly impaired in individuals with schizophrenia spectrum disorders (SSD), whereby negative symptoms are theorised to play a crucial role. Emotion recognition deficits are assumed to be predictors of transition from clinical high risk to schizophrenia. So far, little attention has been given hereby to the subdomains of negative symptoms and recognizing the individual basic emotions.ObjectivesOur study aimed to explore the relationship between the recognition of the basic emotions and each negative symptom domain.Methods66 patients with a SSD diagnosis were recruited at the Charité – Universitätsmedizin Berlin. Correlational and regression analyses to control for the covariates (age, education, sex) were conducted between the recognition of the six basic emotions (anger, disgust, fear, happiness, sadness, surprise) using the Emotion Recognition Task of the Cambridge Neuropsychological Test Automated Battery (CANTAB) and the seven different subdomains of negative symptoms of the Positive and Negative Syndrome Scale (PANSS).Results revealed significantly negative correlations of blunted affect with the recognition of happiness, fear, and disgust. Difficulties in abstract thinking, also correlated positively with the recognition of fear. Additionally, we found a significant positive correlation between stereotyped thinking and difficulties in abstract thinking with the response latency in emotion recognition.ConclusionsIndividuals with SSD and domains of negative symptoms showed specific impairments in recognizing the representation of basic emotions. A longitudinal design to make causality statements would be useful for future research. Moreover, emotion recognition should be considered for early detection and individualized treatment.DisclosureNo significant relationships.

A Unifying Perspective on Perception and Cognition Through Linguistic Representations of Emotion.

This article will provide a unifying perspective on perception and cognition via the route of linguistic representations of emotion. Linguistic representations of emotions provide a fertile ground for explorations into the nature and form of integration of perception and cognition because emotion has facets of both perceptual and cognitive processes. In particular, this article shows that certain types of linguistic representations of emotion allow for the integration of perception and cognition through a series of steps and operations in cognitive systems, whereas certain other linguistic representations of emotion are not so representationally structured as to permit the unity of perception and cognition. It turns out that the types of linguistic representations of emotion that readily permit the desired unity of perception and cognition are exactly those that are linguistically encoded emotive representations of everyday objects, events, and things around us. It is these ordinary objects, events and things that provide the scaffolding for task-dependent or goal-oriented activities of cognitive systems including autonomous systems. In this way, cognitive systems can be saliently tuned to the outer world by being motivated and also subtly governed by emotion-driven representations. This helps not only tie together perceptual and cognitive processes via the interface between language and emotive representations, but also reveal the limits of emotive representations in amalgamating perceptual and cognitive processes in cognitive systems.