Search Results (948)

Background: Functional Neurological Disorder (FND) encompasses conditions with neurological symptoms inconsistent with structural pathology, arising instead from complex interactions between psychological, biological, and social factors. Despite growing research, the etiological and risk factor landscape remains only partially understood, complicating diagnosis and treatment. Objective: This systematic review maps risk factors for major FND subtypes such as functional seizures (psychogenic non-epileptic seizures or PNES), functional cognitive disorder (FCD), functional movement disorders (FMD), functional weakness and sensory disturbances, functional visual symptoms, and functional gait abnormalities by categorizing predisposing, precipitating, and perpetuating influences. Methods: A systematic search of PubMed, PsycINFO, Scopus, and Web of Science initially identified 245 records. After removal of 64 duplicates, 181 studies were screened by title and abstract. Of these, 96 full texts were examined in detail, and finally 23 studies met the predefined inclusion criteria. Data were extracted and analyzed thematically within a biopsychosocial framework, with results summarized in subtype-specific profiles. Results: Childhood adversity, especially emotional, physical, or sexual abuse, emerged as a robust and consistent predisposing factor across PNES cohorts. Psychiatric history (notably anxiety, depression, and PTSD), neurodevelopmental traits (more frequent in FCD), and personality patterns such as alexithymia and somatization also contributed to vulnerability. Precipitating influences included acute psychological stress, intrapersonal conflict, or concurrent medical illness. Perpetuating factors comprise maladaptive illness beliefs, avoidance behaviors, insufficient explanation or validation by healthcare providers, and secondary gains related to disability. While several risk factors were shared across subtypes, others appeared subtype-specific (trauma was especially associated with PNES, whereas neurodevelopmental traits were more characteristic of FCD). Conclusions: FND arises from a dynamic interplay of predisposing, precipitating, and perpetuating factors, with both shared and subtype-specific influences. Recognizing this heterogeneity can enhance diagnostic precision, guide tailored intervention, and inform future research into the neurobiological and psychosocial mechanisms underlying FND. Full article

Background: Loneliness and social isolation among older people are currently widespread and recognized as the foremost public health problems globally and locally. Hong Kong, which exhibits a rapid aging trend and an expanding elderly population, is inevitably facing these issues. This study explored the lived experiences of loneliness among older adults in Hong Kong. Methods: Qualitative interviews were conducted among older adults in the community aged 60 or above who were cared for by migrant domestic workers and presented varying levels of loneliness. Purposive sampling was used to select subjects for face-to-face, semi-structured individual interviews, with consent for audio recording, which led to the inclusion of 19 older adults, among whom five were male, nine lived with a spouse, and three lived with their children. Interpretative phenomenological analysis was adopted. Results: We identified a core theme, “Should I stay at home alone?”, and the following four interrelated themes: (1) experience of inadequate social support and networks, (2) altered family dynamics and support, (3) deterioration in physical functions and mobility limitations, and (4) experience of negative and complex emotions. Conclusions: Based on our investigation into the lived experience of loneliness among older adults locally, we recommend that the government, non-governmental organizations, and healthcare institutions establish appropriate strategies and integrated services to address the social, physical, familial, and emotional issues in this population to foster healthy aging, improve their quality of life, and encourage support from families and communities. Full article

Drawing on in-depth interviews and thematic analysis, this study explores the therapeutic benefits of outdoor experiences through the lived experiences of 24 outdoor practitioners, including educators, environmentalists, therapists, and program leaders. Three core themes emerged: (a) nature as an emotional regulator and reflective space; (b) therapeutic benefits of human–nature relationships; and (c) decolonial, bioregional, and cultural healing. Although practitioners facilitated physical challenges and skill-building for their participants, they primarily described outdoor experiences as relational, somatic, and culturally rooted practices that foster emotional regulation, grief processing, identity integration, and social inclusion. Healing emerged through solitude, silence, ancestral connections, sacred landscapes, inclusive dynamics, and the restoration of cultural knowledge. This study’s results challenge Western-centric outdoor education models by foregrounding Indigenous and postcolonial perspectives embedded in Indian ecological traditions. The results contribute to global discussions on decolonizing outdoor fields and offer implications for culturally responsive, emotionally safe, and ecologically grounded practices. Full article

In the digital age, social media significantly shapes public attitudes and emotional responses towards health interventions, such as HPV vaccination, which is critical in developing countries. This study employed a deep learning model to identify fine-grained emotions of 38,615 HPV-related tweets from 2016 to 2024, revealing significant shifts in public emotions. Notably, skepticism about vaccine commercialization motives heightened anger, while university outreach initiatives fostered positive emotions. Structural entropy analysis highlighted polarized emotional communication networks: the network of joy exhibited lower entropy with centralized information flow, whereas other emotions displayed higher entropy, fragmented dissemination, and enhanced cross-community communication efficiency. New communicators, such as campus accounts and music bloggers, played pivotal roles in spreading positive emotions, while individual bloggers in specific fields amplified negative emotions like anger, particularly in closed networks. This research underscores the intricate dynamics of online health communication and the need for targeted interventions to address stigma and enhance public awareness of HPV vaccination, providing valuable insights for future public health policy. Full article

Background: In recent years, complexity analysis has attracted considerable attention in the field of neural mechanism exploration due to its nonlinear characteristics, providing a new perspective for revealing the complex information processing mechanisms of the brain. In precision sports such as shooting, complexity analysis can quantify the complexity of activity in different areas of the brain and dynamic changes. Methods: This study extracted multiple complexity indicators based on microstate and traceability analysis and examined brain complexity during the shooting preparation stage and the brain’s reaction mechanisms under audiovisual limitations. Results: Microstate Lempel-Ziv complexity and microstate fluctuation complexity in low-light environment were significantly higher than those in normal environment. The complexity of the brain increases and then decreases during shooting. In low-light conditions, nine brain regions—insula R’, posterior cingulate R’, entorhinal, superior frontal L’, caudal anterior cingulate L’, rostral anterior cingulate L’, posterior cingulate R’, medial orbitofrontal L’ and rostral middle frontal R’—exhibited differential results. SSV-R_PHC-COG and SSV-R_LOF-SCORE showed strong negative correlations with behavioral indicators. Conclusions: First, during shooting, the processing of visual information mainly relies on the secondary cortex and visual connection functions, rather than the primary cortex. Furthermore, there are automated processes based on experience in shooting sports. Second, noise has little effect on shooting, but low light has a multifaceted impact on shooting. This is mainly reflected in difficulties in integrating sensorimotor information, excessive memory retrieval, reduced movement stability, triggering of negative emotions, and changes in shooting strategies. Full article

Facial attractiveness plays a significant role in job search evaluations, with recruiters often rating candidates with higher levels of attractiveness more favorably. This paper investigates how physical appearance and employability jointly influence applicant evaluations during resume screening. Using event-related potential (ERP) techniques, the study observes dynamic brain changes during the experiment. The findings reveal that: (1) Employability significantly enhances P200 amplitudes (reflecting early attentional allocation), while its effects on N170 and LPP components are contingent upon attractiveness levels; (2) These employability effects are selectively modulated by facial attractiveness: under high-attractiveness conditions, high employability potentiates both P200 and LPP responses (suggesting enhanced motivational engagement and emotional arousal); low employability leads to more negative N170 amplitudes (indicating early conflict detection to stereotype-incongruent cues). Conversely, no such effects emerge under low-attractiveness conditions, demonstrating that facial attractiveness gates the neural prioritization of qualification information. These results provide valuable insights into job search evaluations and highlight the neural mechanisms involved in facial perception and processing during resume screening. Full article

Automatic Facial Expression Recognition (AFER) is a key component of affective computing, enabling machines to recognize and interpret human emotions across various applications such as human–computer interaction, healthcare, entertainment, and social robotics. Dynamic AFER systems, which exploit image sequences, can capture the temporal evolution of facial expressions but often suffer from high computational costs, limiting their suitability for real-time use. In this paper, we propose an efficient dynamic AFER approach based on a novel spatio-temporal representation. Facial landmarks are extracted, and all possible Euclidean distances are computed to model the spatial structure. To capture temporal variations, three statistical metrics are applied to each distance sequence. A feature selection stage based on the Extremely Randomized Trees (ExtRa-Trees) algorithm is then performed to reduce dimensionality and enhance classification performance. Finally, the emotions are classified using a linear multi-class Support Vector Machine (SVM) and compared against the k-Nearest Neighbors (k-NN) method. The proposed approach is evaluated on three benchmark datasets: CK+, MUG, and MMI, achieving recognition rates of 94.65%, 93.98%, and 75.59%, respectively. Our results demonstrate that the proposed method achieves a strong balance between accuracy and computational efficiency, making it well-suited for real-time facial expression recognition applications. Full article

The inherent randomness and concealment of rumors in social networks exacerbate their spread, leading to significant societal instability. To explore the mechanisms of rumor propagation for more effective control and mitigation of harm, we propose a novel fractional-order Susceptible-Infected-Negative-Positive-Removed (SINPR) rumor propagation model, which simultaneously incorporates emotional mechanisms by distinguishing between positive and negative emotion spreaders, as well as memory effects through fractional-order derivatives. The proposed model extends traditional frameworks by jointly capturing the bidirectional influence of emotions and the anomalous, history-dependent dynamics often overlooked by integer-order models. First, we calculate the equilibrium points and thresholds of the model, and analyze the stability of the equilibrium, along with the sensitivity and transcritical bifurcation associated with the basic reproduction number. Next, we validate the theoretical results through numerical simulations and analyze the individual effects of fractional-order derivatives and emotional mechanisms. Finally, we predict the rumor propagation process using real datasets. Comparative experiments with other models demonstrate that the fractional-order SINPR model achieves R-squared values of 0.9712 and 0.9801 on two different real datasets, underscoring its effectiveness in predicting trends in rumor propagation. Full article

Background/Objectives: Negative emotional inertia describes the extent to which a prior emotional state can predict the subsequent one, and it is considered a significant indicator of psychological maladjustment that has several negative consequences in the workplace. The current study tested a theoretical model in which the inertia of negative emotions is moderated by regulatory emotional self-efficacy beliefs (RESE) in managing negative affects across workers of different ages. Specifically, we hypothesized that RESE moderates the relation between negative emotions at consecutive time points, reducing their persistence, and that age would influence this relation, with older workers relying less on this resource than younger ones. Methods: Participants were 221 workers (57.8% females) exposed to social work stressors who reported their affectivity every evening for 31 consecutive days. We analyzed the data using dynamic structural equation models (DSEM), which enable examining within-person time series trends while estimating individual differences therein. Results/Conclusions: In line with our predictions, results suggest that emotional self-efficacy is a key personal resource that might be able to buffer individuals from emotional stasis, a resource primarily useful for younger workers who rely less on actual emotional regulation expertise than older adults. Full article

Contemporary societies are undergoing rapid and profound transformations—economic, technological, social, and environmental—increasingly challenging the capacity of public governance to effectively manage social and structural complexity. In response, new governance paradigms promoting inclusive and participatory approaches are emerging with the aim of increasing the capability of public policy-making to effectively grasp social demands. This paper aims to foster the potential synergies between participatory policy-making and semiotic psychology, building on the constructivist and psychoanalytical frameworks. Moving beyond the traditional, medicalized, and normalizing stances characterizing mainstream psychological approaches, we advocate for a framework capable of addressing the symbolic and emotional foundations of the social reality driving individual and collective behaviors. This is expected to foster the debate about the intersection between psychology and public policy-making, emphasizing the critical role of semiotic dynamics in the structural and political transformations. Full article

With the increasing complexity of human–computer interaction scenarios, conventional digital human facial expression systems show notable limitations in handling multi-emotion co-occurrence, dynamic expression, and semantic responsiveness. This paper proposes a digital human system framework that integrates multimodal emotion recognition and compound facial expression generation. The system establishes a complete pipeline for real-time interaction and compound emotional expression, following a sequence of “speech semantic parsing—multimodal emotion recognition—Action Unit (AU)-level 3D facial expression control.” First, a ResNet18-based model is employed for robust emotion classification using the AffectNet dataset. Then, an AU motion curve driving module is constructed on the Unreal Engine platform, where dynamic synthesis of basic emotions is achieved via a state-machine mechanism. Finally, Generative Pre-trained Transformer (GPT) is utilized for semantic analysis, generating structured emotional weight vectors that are mapped to the AU layer to enable language-driven facial responses. Experimental results demonstrate that the proposed system significantly improves facial animation quality, with naturalness increasing from 3.54 to 3.94 and semantic congruence from 3.44 to 3.80. These results validate the system’s capability to generate realistic and emotionally coherent expressions in real time. This research provides a complete technical framework and practical foundation for high-fidelity digital humans with affective interaction capabilities. Full article

Green food serves as a bridge connecting healthy lifestyles with environmental values, particularly in the context of sustainable consumption transitions. However, existing research lacks a systematic understanding of how consumers negotiate cognitive evaluations and emotional responses when forming green food purchase intentions. This study addresses that gap by exploring the cognitive–affective negotiation process underlying consumers’ green food choices. Based on 26 semi-structured interviews with Chinese consumers across diverse socio-economic backgrounds, the grounded theory methodology was employed to inductively construct a conceptual model. The coding process achieved theoretical saturation, while sentiment analysis was integrated to trace the emotional valence of key behavioral drivers. Findings reveal that external factors—including price sensitivity, label ambiguity, access limitations, social influence, and health beliefs—shape behavioral intentions indirectly through three core affective mediators: green trust, perceived value, and lifestyle congruence. These internal constructs translate contextual stimuli into evaluative and motivational responses, highlighting the dynamic interplay between rational judgments and symbolic–emotional interpretations. Sentiment analysis confirmed that emotional trust and psychological reassurance are pivotal in facilitating consumption intention, while price concerns and skepticism act as affective inhibitors. The proposed model extends the Theory of Planned Behavior by embedding affective mediation pathways and structural constraint dynamics, offering a more context-sensitive framework for understanding sustainable consumption behaviors. Given China’s certification-centered trust environment, these findings underscore the cultural specificity of institutional trust mechanisms, with implications for adapting the model in different market contexts. Practically, this study offers actionable insights for policymakers and marketers to enhance eco-label transparency, reduce structural barriers, and design emotionally resonant brand narratives that align with consumers’ identity aspirations. Full article

(1) Background: Dog-assisted therapy (DAT) integrates dogs into therapeutic sessions to enhance participants’ physical, emotional, and social well-being. Despite its growing popularity, little is known about the interaction dynamics between the dog, participant, and therapist during sessions. (2) Methods: This study examined these dynamics, focusing on active participation, focus direction, joint focus, and physical contact. Video data from sessions 1, 5, and 9 of 10 individual therapy sessions with five participants were analysed using behavioural observations and an ethogram. (3) Results: Results indicated that therapists’ active participation increased over time while participants’ activity levels remained stable. Dogs were most active during the initial and final sessions. Participants’ focus on therapists remained consistent, but their focus on the dog stabilised after an initial decline. Dogs are primarily focused on their surroundings. The joint focus between participants and therapists increased, and physical contact with dogs varied significantly among participants and dogs. (4) Conclusions: The findings partially support the “icebreaker” theory, whereby dogs help establish initial rapport. However, the trend was not consistent across all participants. Therapist–dog interactions remained low and stable. Differences in dog characteristics (e.g., breed and fur type) and participant needs may explain variation in physical contact. These findings underline the complexity of DAT and highlight the need for further research into interaction patterns relate to participants and dog characteristics. Full article

Autism Spectrum Disorder (ASD) presents unique challenges in social interaction, communication and emotional regulation. Recent research has explored the potential influence of anime consumption among children with ASD, and the current findings suggest both beneficial and adverse effects. This review examines the role of anime in fostering social learning, emotional resilience, and cognitive engagement while also addressing concerns regarding its cultivation of social withdrawal, unrealistic expectations, and over-reliance on fictional narratives. By analyzing existing literature, this paper provides insights into the nuanced relationship between anime and ASD, highlighting the possibility that patterns of engagement may be associated with both positive and negative outcomes. Understanding these dynamics is crucial for parents, educators, and clinicians seeking to support the well-being and development of children with ASD. Full article

Facial emotion recognition has become increasingly important in healthcare, where understanding delicate cues like pain, discomfort, or unconsciousness can support more timely and responsive care. Yet, recognizing facial expressions in real-world settings remains challenging due to varying lighting, facial occlusions, and hardware limitations in clinical environments. To address this, we propose TriViT-Lite, a lightweight yet powerful model that blends three complementary components: MobileNet, for capturing fine-grained local features efficiently; Vision Transformers (ViT), for modeling global facial patterns; and handcrafted texture descriptors, such as Local Binary Patterns (LBP) and Histograms of Oriented Gradients (HOG), for added robustness. These multi-scale features are brought together through a texture-aware cross-attention fusion mechanism that helps the model focus on the most relevant facial regions dynamically. TriViT-Lite is evaluated on both benchmark datasets (FER2013, AffectNet) and a custom healthcare-oriented dataset covering seven critical emotional states, including pain and unconsciousness. It achieves a competitive accuracy of 91.8% on FER2013 and of 87.5% on the custom dataset while maintaining real-time performance (~15 FPS) on resource-constrained edge devices. Our results show that TriViT-Lite offers a practical and accurate solution for real-time emotion recognition, particularly in healthcare settings. It strikes a balance between performance, interpretability, and efficiency, making it a strong candidate for machine-learning-driven pattern recognition in patient-monitoring applications. Full article