Emotional and cognitive representation of COVID-19 among Polish adults during the peak of the second wave of the pandemic

Introduction: This paper presents the results of cognitive and emotional representation of COVID-19 in the sample of adult Poles during the peak of the second wave of the pandemic (November–December 2020). Aims: The study was designed to investigate the mental and emotional representation of COVID-19 in adult Poles. It was hypothesised that the representation would have a different structure depending on gender, age, education as well as personal experience of COVID-19 or other medical conditions. Methods: The survey was carried out in November and December 2020, and involved two hundred Polish adults aged 17 to 58 years (Mage = 32.59, SD = 10.19). The subjects were surveyed via the Google Forms web survey platform. A link to the survey was sent to the participants on Facebook. Results: Three in four respondents were found to believe that COVID-19 indeed existed, and that a virus was the most important cause of the problem. According to nearly one in two respondents, the effects of the disease were exaggerated by the mass media. On average, the respondents tend to believe that the severity of the disease may be controlled by one’s behaviour. The emotional representation of COVID-19 reflected predominantly negative emotions. The respondents were convinced that the disease led to significant consequences affecting the domains of personal life and work. Discussion: According to many participants, the effects of the disease are overestimated in media reports. The lack of knowledge about neurological and neuropsychological complications suggests that this aspect of the disease is insufficiently emphasised in the mass media during the second wave of the pandemic. Conclusions: Individual variables and experience of COVID-19 affect one’s cognitive and emotional representation of the disease and one’s beliefs concerning the mitigation of risks. This means that any future information related to COVID-19, and the promotion of knowledge concerning the possible mechanisms of disease development, must be conveyed in a way adjusted to gender and age as well as the level of education.

Does Weight-Cycling Influence Illness Beliefs in Obesity? A Gender-Sensitive Approach

Obesity is classified as a chronic disease. Women and men seem to face different obstacles in their attempts to overcome one of the most challenging tasks in the treatment of this disease, namely, weight reduction maintenance. The Common-Sense-Model (CSM) is mainly used to improve the understanding of self-regulation and health behaviour in chronic diseases but has yet to be explored for obesity. This paper applies the CSM to obesity, focussing on the construct of illness representations, which is the basis of health behaviour according to the CSM. A sample of n = 356 women and n = 77 men with obesity was investigated to assess the extent that illness representations in obesity are shaped by experiences of weight-cycling and the extent that gender influences their quality. Our results show that the representations of timeline and consequences as well as the emotional representation are particularly influenced by weight-cycling, especially in men. On average, women showed more maladaptive illness representations than men. These findings not only contribute to a better applicability of the CSM in obesity, but also emphasize the importance of gender in obesity research and interventions.

Emotional representations of space vary as a function of peoples’ affect and interoceptive sensibility

Most research on people’s representation of space has focused on spatial appraisal and navigation. But there is more to space besides navigation and assessment: people have different emotional experiences at different places, which create emotionally tinged representations of space. Little is known about the emotional representation of space and the factors that shape it. The purpose of this study was to develop a graphic methodology to study the emotional representation of space and some of the environmental features (non-natural vs. natural) and personal features (affective state and interoceptive sensibility) that modulate it. We gave participants blank maps of the region where they lived and asked them to apply shade where they had happy/sad memories, and where they wanted to go after Covid-19 lockdown. Participants also completed self-reports on affective state and interoceptive sensibility. By adapting methods for analyzing neuroimaging data, we examined shaded pixels to quantify where and how strong emotions are represented in space. The results revealed that happy memories were consistently associated with similar spatial locations. Yet, this mapping response varied as a function of participants’ affective state and interoceptive sensibility. Certain regions were associated with happier memories in participants whose affective state was more positive and interoceptive sensibility was higher. The maps of happy memories, desired locations to visit after lockdown, and regions where participants recalled happier memories as a function of positive affect and interoceptive sensibility overlayed significantly with natural environments. These results suggest that people’s emotional representations of their environment are shaped by the naturalness of places, and by their affective state and interoceptive sensibility.

One’s Interoception Affects the Representation of Seeing Others’ Pain: A Randomized Controlled qEEG Study

Objective. This research demonstrates that interoceptive attentiveness (IA) can modulate cortical oscillations related to the emotional and cognitive representations of observing pain in others. Methods. Twenty participants were required to observe painful/nonpainful stimuli in an individual versus the interactive condition during the recording of the electroencephalogram. The sample was divided into experimental (EXP) and control (CTR) groups, and the EXP group was explicitly required to direct the attention on its interoceptive correlates while observing the stimuli. Results. Mixed repeated measures, analyses of variance, were applied to each EEG frequency band. Significant findings were obtained mainly for theta and beta bands for the two groups. A hemispheric lateralisation effect was found, with right lateralisation of the theta band for the EXP group when observing painful stimuli and enhanced left activation of theta and beta bands for the CTR group when observing nonpainful stimuli. For both groups, frontal cortical regions were significantly sensitive to social scenarios, while posterior parietal activation was found for stimuli depicting the individual condition. Conclusions. The results suggest that IA might enhance the emotional representation of painful stimuli, highlighting their negative and unpleasant features in the EXP group, while the attention of the CTR group was mainly drawn to nonpainful stimuli in social and individual conditions, with a positive valence. The role of frontal regions in the processing of social stimuli through social cognition, inducing emotional mirroring and requiring deeper analysis of the social context, was underlined. We propose that IA could be trained for promoting emotion regulation and empathic response.

Multi-Level Context Pyramid Network for Visual Sentiment Analysis

Sharing our feelings through content with images and short videos is one main way of expression on social networks. Visual content can affect people’s emotions, which makes the task of analyzing the sentimental information of visual content more and more concerned. Most of the current methods focus on how to improve the local emotional representations to get better performance of sentiment analysis and ignore the problem of how to perceive objects of different scales and different emotional intensity in complex scenes. In this paper, based on the alterable scale and multi-level local regional emotional affinity analysis under the global perspective, we propose a multi-level context pyramid network (MCPNet) for visual sentiment analysis by combining local and global representations to improve the classification performance. Firstly, Resnet101 is employed as backbone to obtain multi-level emotional representation representing different degrees of semantic information and detailed information. Next, the multi-scale adaptive context modules (MACM) are proposed to learn the sentiment correlation degree of different regions for different scale in the image, and to extract the multi-scale context features for each level deep representation. Finally, different levels of context features are combined to obtain the multi-cue sentimental feature for image sentiment classification. Extensive experimental results on seven commonly used visual sentiment datasets illustrate that our method outperforms the state-of-the-art methods, especially the accuracy on the FI dataset exceeds 90%.

Developmental Differences in Emotional Representation Between Subcortical Structures and Ventromedial Prefrontal Cortex

Behavioral data has found differences in how adults and children express affective experiences and information. However, the exact mechanisms which drive these differences have yet to be elucidated. One possible reason why adults and children may demonstrate observable differences is the representation of affect in key neurological structures. Fifty-seven participants (36 children; 21 adults) passively viewed positive, negative, and neutral clips from popular films while undergoing functional magnetic resonance imaging. Using representational similarity analysis to measure variability in neural pattern similarity, we found developmental differences between children and adults in the amygdala, nucleus accumbens, and ventromedial prefrontal cortex, such that children generated contrasting patterns between subcortical structures and the ventromedial prefrontal cortex; a phenomenon not replicated among their older counterparts. Furthermore, children generated valence-specific differences in representational patterns across regions while adults failed to demonstrate similar valence-specific responses. These results may suggest that affective representations grow increasingly dissimilar over development as individuals mature from visceral emotional responses to more evaluative analyses. Further research is required to determine whether these differences influence affective expression and behavior.

Emotional representations of space vary as a function of peoples’ mental health and interoceptive awareness

As people interact in extensive environments, their space becomes intertwined with emotions. Yet, beyond the study of spatial appraisal and navigation1–3, the emotional representation of space remains elusive. Here we developed a method that, even without mobility (during Covid-19 lockdown), allows examining participants’ emotional representation of space and psychophysiological correlates. We gave participants blank maps of the region where they lived and asked them to apply shade where they had happy/sad memories, and where they wanted to go after the lockdown. They also completed self-reports on mental health and interoceptive awareness (appraisal of inner bodily sensations). By adapting neuroimaging methods, we examined shaded pixels instead of brain voxels to quantify where and how strong emotions are represented in space. The results revealed that happy memories were consistently associated with similar spatial locations. Yet, this mapping response varied as a function of participants’ mental health and interoceptive awareness. Interestingly, maps of happy memories and desired locations after lockdown overlay significantly with natural environments (vs. non-natural). These results suggest that our relationship with the environment relates to how we feel and appraise bodily sensations (i.e., allostasis in space). Our method may provide a spatially ecological marker for physical and mental disorders.

Hierarchical Network with Label Embedding for Contextual Emotion Recognition

Emotion recognition has been used widely in various applications such as mental health monitoring and emotional management. Usually, emotion recognition is regarded as a text classification task. Emotion recognition is a more complex problem, and the relations of emotions expressed in a text are nonnegligible. In this paper, a hierarchical model with label embedding is proposed for contextual emotion recognition. Especially, a hierarchical model is utilized to learn the emotional representation of a given sentence based on its contextual information. To give emotion correlation-based recognition, a label embedding matrix is trained by joint learning, which contributes to the final prediction. Comparison experiments are conducted on Chinese emotional corpus RenCECps, and the experimental results indicate that our approach has a satisfying performance in textual emotion recognition task.