The nonverbal expression of guilt in healthy adults.

Guilt is a negative emotion elicited by realizing one has caused actual or perceived harm to another person. One of guilt's primary functions is to signal that one is aware of the harm that was caused and regrets it, an indication that the harm will not be repeated. Verbal expressions of guilt are often deemed insufficient by observers when not accompanied by nonverbal signals such as facial expression, gesture, posture, or gaze. Some research has investigated isolated nonverbal expressions in guilt, however none to date has explored multiple nonverbal channels simultaneously. This study explored facial expression, gesture, posture, and gaze during the real-time experience of guilt when response demands are minimal. Healthy adults completed a novel task involving watching videos designed to elicit guilt, as well as comparison emotions. During the video task, participants were continuously recorded to capture nonverbal behaviour, which was then analyzed via automated facial expression software. We found that while feeling guilt, individuals engaged less in several nonverbal behaviours than they did while experiencing the comparison emotions. This may reflect the highly social aspect of guilt, suggesting that an audience is required to prompt a guilt display, or may suggest that guilt does not have clear nonverbal correlates.

A systematic review of workplace triggers of emotions in the healthcare environment, the emotions experienced, and the impact on patient safety.

BACKGROUND: Healthcare staff deliver patient care in emotionally charged settings and experience a wide range of emotions as part of their work. These emotions and emotional contexts can impact the quality and safety of care. Despite the growing acknowledgement of the important role of emotion, we know very little about what triggers emotion within healthcare environments or the impact this has on patient safety. OBJECTIVE: To systematically review studies to explore the workplace triggers of emotions within the healthcare environment, the emotions experienced in response to these triggers, and the impact of triggers and emotions on patient safety. METHODS: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, four electronic databases were searched (MEDLINE, PsychInfo, Scopus, and CINAHL) to identify relevant literature. Studies were then selected and data synthesized in two stages. A quality assessment of the included studies at stage 2 was undertaken. RESULTS: In stage 1, 90 studies were included from which seven categories of triggers of emotions in the healthcare work environment were identified, namely: patient and family factors, patient safety events and their repercussions, workplace toxicity, traumatic events, work overload, team working and lack of supervisory support. Specific emotions experienced in response to these triggers (e.g., frustration, guilt, anxiety) were then categorised into four types: immediate, feeling states, reflective, and longer-term emotional sequelae. In stage 2, 13 studies that explored the impact of triggers or emotions on patient safety processes/outcomes were included. CONCLUSION: The various triggers of emotion and the types of emotion experienced that have been identified in this review can be used as a framework for further work examining the role of emotion in patient safety. The findings from this review suggest that certain types of emotions (including fear, anger, and guilt) were more frequently experienced in response to particular categories of triggers and that healthcare staff's experiences of negative emotions can have negative effects on patient care, and ultimately, patient safety. This provides a basis for developing and tailoring strategies, interventions, and support mechanisms for dealing with and regulating emotions in the healthcare work environment.

An appraisal-based chain-of-emotion architecture for affective language model game agents.

The development of believable, natural, and interactive digital artificial agents is a field of growing interest. Theoretical uncertainties and technical barriers present considerable challenges to the field, particularly with regards to developing agents that effectively simulate human emotions. Large language models (LLMs) might address these issues by tapping common patterns in situational appraisal. In three empirical experiments, this study tests the capabilities of LLMs to solve emotional intelligence tasks and to simulate emotions. It presents and evaluates a new Chain-of-Emotion architecture for emotion simulation within video games, based on psychological appraisal research. Results show that it outperforms control LLM architectures on a range of user experience and content analysis metrics. This study therefore provides early evidence of how to construct and test affective agents based on cognitive processes represented in language models.

Autonomous Sensory Meridian Response (ASMR) and the Functions of Consciousness.

"Autonomous Sensory Meridian Response" (ASMR) refers to a sensory-emotional experience that was first explicitly identified and named within the past two decades in online discussion boards. Since then, there has been mounting psychological and neural evidence of a clustering of properties common to the phenomenon of ASMR, including convergence on the set of stimuli that trigger the experience, the properties of the experience itself, and its downstream effects. Moreover, psychological instruments have begun to be developed and employed in an attempt to measure it. Based on this empirical work, we make the case that despite its nonscientific origins, ASMR is a good candidate for being a real kind in the cognitive sciences. The phenomenon appears to have a robust causal profile and may also have an adaptive evolutionary history. We also argue that a more thorough understanding of the distinctive type of phenomenal experience involved in an ASMR episode can shed light on the functions of consciousness, and ultimately undermine certain "cognitive" theories of consciousness. We conclude that ASMR should be the subject of more extensive scientific investigation, particularly since it may also have the potential for therapeutic applications.

Cerebellum function: The chronometry of social perception.

The posterior cerebellum is emerging as a key structure for social cognition. A new study causally demonstrates its early involvement during emotion perception and functional connectivity with the posterior superior temporal sulcus, a cortical hub of the social brain.

The influence of progeny-Parents family travel on the well-being of the elderly in filial piety culture.

Grounded in the cultural context of Chinese filial piety, this study employs structural equation model to analyze survey data from elderly participants. It explores the effect and path of progeny-parents family travel on the elderly's sense of well-being and examines the mediating roles of generational interaction, optimistic emotion, and psychological resilience. The findings indicate that progeny-parents family travel positively influences the well-being of the elderly, with generational interaction, optimistic emotion, and psychological resilience serving as intermediary roles. Theoretically, this study enriches the localized perspective of family travel's psychological and behavioral impact on the elderly. It elucidates the spillover effects of family travel within the framework of filial piety culture, delineates the mechanisms by which family travel enhances elderly well-being, and offers theoretical insights for businesses to develop customized family travel products and services.

Domestication constrains the ability of dogs to convey emotions via facial expressions in comparison to their wolf ancestors.

Dogs (Canis lupus familiaris) are the domestically bred descendant of wolves (Canis lupus). However, selective breeding has profoundly altered facial morphologies of dogs compared to their wolf ancestors. We demonstrate that these morphological differences limit the abilities of dogs to successfully produce the same affective facial expressions as wolves. We decoded facial movements of captive wolves during social interactions involving nine separate affective states. We used linear discriminant analyses to predict affective states based on combinations of facial movements. The resulting confusion matrix demonstrates that specific combinations of facial movements predict nine distinct affective states in wolves; the first assessment of this many affective facial expressions in wolves. However, comparative analyses with kennelled rescue dogs revealed reduced ability to predict affective states. Critically, there was a very low predictive power for specific affective states, with confusion occurring between negative and positive states, such as Friendly and Fear. We show that the varying facial morphologies of dogs (specifically non-wolf-like morphologies) limit their ability to produce the same range of affective facial expressions as wolves. Confusion among positive and negative states could be detrimental to human-dog interactions, although our analyses also suggest dogs likely use vocalisations to compensate for limitations in facial communication.

Detecting emotions through EEG signals based on modified convolutional fuzzy neural network.

Emotion is a human sense that can influence an individual's life quality in both positive and negative ways. The ability to distinguish different types of emotion can lead researchers to estimate the current situation of patients or the probability of future disease. Recognizing emotions from images have problems concealing their feeling by modifying their facial expressions. This led researchers to consider Electroencephalography (EEG) signals for more accurate emotion detection. However, the complexity of EEG recordings and data analysis using conventional machine learning algorithms caused inconsistent emotion recognition. Therefore, utilizing hybrid deep learning models and other techniques has become common due to their ability to analyze complicated data and achieve higher performance by integrating diverse features of the models. However, researchers prioritize models with fewer parameters to achieve the highest average accuracy. This study improves the Convolutional Fuzzy Neural Network (CFNN) for emotion recognition using EEG signals to achieve a reliable detection system. Initially, the pre-processing and feature extraction phases are implemented to obtain noiseless and informative data. Then, the CFNN with modified architecture is trained to classify emotions. Several parametric and comparative experiments are performed. The proposed model achieved reliable performance for emotion recognition with an average accuracy of 98.21% and 98.08% for valence (pleasantness) and arousal (intensity), respectively, and outperformed state-of-the-art methods.

Violent and prosocial music: Evidence for the impact of lyrics and musical tone on aggressive thoughts, feelings, and behaviors.

Although there is a large research base on the psychological impacts of violent and prosocial visual media, there is little research addressing the impacts of violent and prosocial music, and which facets of the music have the greatest impact. Four experiments tested the impact of lyrics and/or musical tone on aggressive and prosocial behavior, and on underlying psychological processes, using purpose-built songs to avoid the effect of music-related confounds. In study one, where mildly aggressive, overtly aggressive and violent lyrics were compared to neutral lyrics, any level of lyrical aggression caused an increase in behavioral aggression, which plateaued for all three aggression conditions. Violent lyrics were better recalled than other lyrics one week later. In studies two-three no significant effects of lyrics, or of aggressive versus nonaggressive musical tone, were found on aggressive or prosocial behavior. In terms of internal states, violent lyrics increased hostility/hostile cognitions in all studies, and negatively impacted affective state in three studies. Prosocial lyrics decreased hostility/hostile cognitions in three studies, but always in tandem with another factor. Aggressive musical tone increased physiological arousal in two studies and increased negative affect in one. In study four those who listened to violent lyrics drove more aggressively on a simulated drive that included triggers for aggression. Overall, violent lyrics consistently elicited hostility/hostile cognitions and negative affect, but these did not always translate to aggressive behavior. Violent music seems more likely to elicit behavioral aggression when there are aggression triggers and a clear way to aggress. Implications are discussed.

Roles of negative emotions and personality traits in psoriasis vulgaris: A mendelian randomization study.

BACKGROUND: Many studies have indicated that negative emotions and personality traits are related to psoriasis, though few have provided causal evidence. METHODS: Our analysis utilized 15 genome-wide association study datasets to identify instrumental variables associated with negative emotions, personality traits and psoriasis vulgaris. Two-sample Mendelian randomization was conducted to identify the causal associations of negative emotions and personality traits with psoriasis vulgaris. To mitigate bias from multiple tests, we adjusted p-values using the Benjamini-Hochberg method. RESULTS: Our study revealed causal links between negative emotions and psoriasis vulgaris, including depressed affect, worry too long, feeling hurt, guilty feelings, mood swings, unenthusiasm, miserableness, fed-up feelings. However, there was no significant evidence of a causal relationship between feeling lonely and psoriasis vulgaris. Additionally, personality traits including neuroticism and openness to experience were found to have causal effects on psoriasis vulgaris. However, no significant evidence supported a causal relationship between agreeableness, conscientiousness, and extraversion with psoriasis vulgaris. CONCLUSION: Our findings suggest that experiencing negative emotions including depressed affect, worrying excessively, feeling hurt, guilty feelings, mood swings, lack of enthusiasm, miserableness and fed-up feelings may pose risks for psoriasis vulgaris. Additionally, neuroticism is associated with a risk of psoriasis vulgaris. Conversely, the openness trait may serve a protective role against psoriasis vulgaris.

Susceptibility to positive versus negative emotional contagion: First evidence on their distinction using a balanced self-report measure.

Susceptibility to emotional contagion is defined as the disposition of how susceptible someone is to catch others' emotions and it has long been studied in research on mental health, well-being, and social interaction. Given that existing self-report measures of susceptibility to emotional contagion have focused almost exclusively on negative emotions, we developed a self-report measure to assess the susceptibility to emotional contagion of both positive and negative emotions (2 scales). In two studies, we examined their factor structure, validity, and reliability using exploratory factor analysis (Study 1, N = 257), confirmatory factor analysis (Study 2, N = 247) and correlations. Our results confirmed the two-factor structure and demonstrated good internal consistencies. Regarding external validity, our scales showed diverging correlational patterns: While susceptibility to negative emotional contagion was linked to mental health problems and negative emotions, susceptibility to positive emotional contagion was linked to interpersonal functioning and prosocial tendencies. In conclusion, our scales appear to be internally/externally valid and a promising tool for future research.

FetchEEG: a hybrid approach combining feature extraction and temporal-channel joint attention for EEG-based emotion classification.

Objective. Electroencephalogram (EEG) analysis has always been an important tool in neural engineering, and the recognition and classification of human emotions are one of the important tasks in neural engineering. EEG data, obtained from electrodes placed on the scalp, represent a valuable resource of information for brain activity analysis and emotion recognition. Feature extraction methods have shown promising results, but recent trends have shifted toward end-to-end methods based on deep learning. However, these approaches often overlook channel representations, and their complex structures pose certain challenges to model fitting.Approach. To address these challenges, this paper proposes a hybrid approach named FetchEEG that combines feature extraction and temporal-channel joint attention. Leveraging the advantages of both traditional feature extraction and deep learning, the FetchEEG adopts a multi-head self-attention mechanism to extract representations between different time moments and channels simultaneously. The joint representations are then concatenated and classified using fully-connected layers for emotion recognition. The performance of the FetchEEG is verified by comparison experiments on a self-developed dataset and two public datasets.Main results. In both subject-dependent and subject-independent experiments, the FetchEEG demonstrates better performance and stronger generalization ability than the state-of-the-art methods on all datasets. Moreover, the performance of the FetchEEG is analyzed for different sliding window sizes and overlap rates in the feature extraction module. The sensitivity of emotion recognition is investigated for three- and five-frequency-band scenarios.Significance. FetchEEG is a novel hybrid method based on EEG for emotion classification, which combines EEG feature extraction with Transformer neural networks. It has achieved state-of-the-art performance on both self-developed datasets and multiple public datasets, with significantly higher training efficiency compared to end-to-end methods, demonstrating its effectiveness and feasibility.

The big five factors as differential predictors of self-regulation, achievement emotions, coping and health behavior in undergraduate students.

BACKGROUND: The aim of this research was to analyze whether the personality factors included in the Big Five model differentially predict the self-regulation and affective states of university students and health. METHODS: A total of 637 students completed validated self-report questionnaires. Using an ex post facto design, we conducted linear regression and structural prediction analyses. RESULTS: The findings showed that model factors were differential predictors of both self-regulation and affective states. Self-regulation and affective states, in turn, jointly predict emotional performance while learning and even student health. These results allow us to understand, through a holistic predictive model, the differential predictive relationships of all the factors: conscientiousness and extraversion were predictors regulating positive emotionality and health; the openness to experience factor was non-regulating; nonregulating; and agreeableness and neuroticism were dysregulating, hence precursors of negative emotionality and poorer student health. CONCLUSIONS: These results are important because they allow us to infer implications for guidance and psychological health at university.

Patterns of cognitive-emotional change after cognitive-behavioural treatment in emotional disorders: A 12-month longitudinal cluster analysis.

INTRODUCTION: The aim of this study was to use cluster analysis based on the trajectory of five cognitive-emotional processes (worry, rumination, metacognition, cognitive reappraisal and expressive suppression) over time to explore differences in clinical and performance variables in primary care patients with emotional symptoms. METHODS: We compared the effect of adding transdiagnostic cognitive-behavioural therapy (TD-CBT) to treatment as usual (TAU) according to cluster membership and sought to determine the variables that predicted cluster membership. 732 participants completed scales about cognitive-emotional processes, anxiety and depressive symptoms, functioning, and quality of life (QoL) at baseline, posttreatment, and at 12 months. Longitudinal cluster analysis and logistic regression analyses were carried out. RESULTS: A two-cluster solution was chosen as the best fit, named as "less" or "more" improvement in cognitive-emotional processes. Individuals who achieved more improvement in cognitive-emotional processes showed lower emotional symptoms and better QoL and functioning at all three time points. TAU+TD-CBT, income level, QoL and anxiety symptoms were significant predictors of cluster membership. CONCLUSIONS: These results underscore the value of adding TD-CBT to reduce maladaptive cognitive-emotional regulation strategies. These findings highlight the importance of the processes of change in therapy and demonstrate the relevance of the patient's cognitive-emotional profile in improving treatment outcomes.

Maternal childhood emotional abuse increases cardiovascular responses to children's emotional facial expressions.

Parents with a history of childhood maltreatment may be more likely to respond inadequately to their child's emotional cues, such as crying or screaming, due to previous exposure to prolonged stress. While studies have investigated parents' physiological reactions to their children's vocal expressions of emotions, less attention has been given to their responses when perceiving children's facial expressions of emotions. The present study aimed to determine if viewing facial expressions of emotions in children induces cardiovascular changes in mothers (hypo- or hyper-arousal) and whether these differ as a function of childhood maltreatment. A total of 104 mothers took part in this study. Their experiences of childhood maltreatment were measured using the Childhood Trauma Questionnaire (CTQ). Participants' electrocardiogram signals were recorded during a task in which they viewed a landscape video (baseline) and images of children's faces expressing different intensities of emotion. Heart rate variability (HRV) was extracted from the recordings as an indicator of parasympathetic reactivity. Participants presented two profiles: one group of mothers had a decreased HRV when presented with images of children's facial expressions of emotions, while the other group's HRV increased. However, HRV change was not significantly different between the two groups. The interaction between HRV groups and the severity of maltreatment experienced was marginal. Results suggested that experiences of childhood emotional abuse were more common in mothers whose HRV increased during the task. Therefore, more severe childhood experiences of emotional abuse could be associated with mothers' cardiovascular hyperreactivity. Maladaptive cardiovascular responses could have a ripple effect, influencing how mothers react to their children's facial expressions of emotions. That reaction could affect the quality of their interaction with their child. Providing interventions that help parents regulate their physiological and behavioral responses to stress might be helpful, especially if they have experienced childhood maltreatment.

Seeing the impossible: the impact of watching magic on positive emotions, optimism, and wellbeing.

Watching a magic trick is a unique experience in which seemingly impossible events appear possible but without any suspension of disbelief. Unfortunately, relatively little work has examined the psychological impact of this fascinating experience. In the current study, participants first completed a measure of the degree to which they disliked magic (Loathing of Legerdemain Scale: LOLS) and then watched a video that either contained a series of magic tricks (magic video) or carefully matched non-magic tricks (control video). Participants then rated the degree to which they experienced positive epistemic emotions (Epistemically Related Emotion Scale: ERES), their belief about impossible events being possible in the future (Modal Judgment Task: MJT), general optimism (State Optimism Measure: SOM) and subjective wellbeing (Satisfaction With Life Scale: SWLS). Compared to participants who watched the control video, those who saw the magic video reported more positive epistemic emotions on the ERES. There were no significant differences on the MJT, SOM and SWLS. Participants' LOLS scores were negatively correlated with the ERES, SOM and SWLS, suggesting that those who like magic are more likely to experience positive epistemic emotions, have higher levels of general optimism, and express greater satisfaction with their lives. These findings are discussed within the context of short-term and long-term exposure to magic, along with recommendations for future work.

Default mode network shows distinct emotional and contextual responses yet common effects of retrieval demands across tasks.

The default mode network (DMN) lies towards the heteromodal end of the principal gradient of intrinsic connectivity, maximally separated from the sensory-motor cortex. It supports memory-based cognition, including the capacity to retrieve conceptual and evaluative information from sensory inputs, and to generate meaningful states internally; however, the functional organisation of DMN that can support these distinct modes of retrieval remains unclear. We used fMRI to examine whether activation within subsystems of DMN differed as a function of retrieval demands, or the type of association to be retrieved, or both. In a picture association task, participants retrieved semantic associations that were either contextual or emotional in nature. Participants were asked to avoid generating episodic associations. In the generate phase, these associations were retrieved from a novel picture, while in the switch phase, participants retrieved a new association for the same image. Semantic context and emotion trials were associated with dissociable DMN subnetworks, indicating that a key dimension of DMN organisation relates to the type of association being accessed. The frontotemporal and medial temporal DMN showed a preference for emotional and semantic contextual associations, respectively. Relative to the generate phase, the switch phase recruited clusters closer to the heteromodal apex of the principal gradient-a cortical hierarchy separating unimodal and heteromodal regions. There were no differences in this effect between association types. Instead, memory switching was associated with a distinct subnetwork associated with controlled internal cognition. These findings delineate distinct patterns of DMN recruitment for different kinds of associations yet common responses across tasks that reflect retrieval demands.

Understanding empathy deficits and emotion dysregulation in psychopathy: The mediating role of alexithymia.

Psychopathy is a severe personality disorder marked by a wide range of emotional deficits, including a lack of empathy, emotion dysregulation, and alexithymia. Previous research has largely examined these emotional impairments in isolation, ignoring their influence on each other. Thus, we examined the concurrent interrelationship between emotional impairments in psychopathy, with a particular focus on the mediating role of alexithymia. Using path analyses with cross-sectional data from a community sample (N = 315) and a forensic sample (N = 50), our results yielded a statistically significant mediating effect of alexithymia on the relationship between psychopathy and empathy (community and forensic) and between psychopathy and emotion dysregulation (community). Moreover, replacing psychopathy with its three dimensions (i.e., meanness, disinhibition, and boldness) in the community sample revealed that boldness may function as an adaptive trait, with lower levels of alexithymia counteracting deficits in empathy and emotion dysregulation. Overall, our findings indicate that psychopathic individuals' limited understanding of their own emotions contributes to their lack of empathy and emotion dysregulation. This underscores the potential benefits of improving emotional awareness in the treatment of individuals with psychopathy.

Charitable crowdfunding donation-intention estimation depending on emotional project images using fNIRS-based functional connectivity.

Charitable fundraising increasingly relies on online crowdfunding platforms. Project images of charitable crowdfunding use emotional appeals to promote helping behavior. Negative emotions are commonly used to motivate helping behavior because the image of a happy child may not motivate donors to donate as willingly. However, some research has found that happy images can be more beneficial. These contradictory results suggest that the emotional valence of project imagery and how fundraisers frame project images effectively remain debatable. Thus, we compared and analyzed brain activation differences in the prefrontal cortex governing human emotions depending on donation decisions using functional near-infrared spectroscopy, a neuroimaging device. We advance existing theory on charitable behavior by demonstrating that little correlation exists in donation intentions and brain activity between negative and positive project images, which is consistent with survey results on donation intentions by victim image. We also discovered quantitative brain hemodynamic signal variations between donors and nondonors, which can predict and detect donor mental brain functioning using functional connectivity, that is, the statistical dependence between the time series of electrophysiological activity and oxygenated hemodynamic levels in the prefrontal cortex. These findings are critical in developing future marketing strategies for online charitable crowdfunding platforms, especially project images.