Search Results (5,816)

Interspecies communication is increasingly recognized as an affective–cognitive process co-created between humans and animals rather than a one-directional transmission of signals. This review integrates findings from ethology, neuroscience, welfare science, behavioral studies, and posthumanist ethics to examine how emotional expression, communicative intentionality, and relational engagement shape understanding across species. Research on primates, dogs, elephants, and marine mammals demonstrates that empathy, consolation, cooperative signaling, and multimodal perception rely on evolutionarily conserved mechanisms, including mirror systems, affective contagion, and oxytocin-mediated bonding. These biological insights intersect with ethical considerations concerning animal agency, methodological responsibility, and the interpretation of non-human communication. Emerging technological tools—bioacoustics, machine vision, and AI-assisted modeling—offer new opportunities to analyze complex vocal and behavioral patterns, yet they require careful contextualization to avoid anthropocentric misclassification. Synthesizing these perspectives, the review proposes a relational framework in which meaning arises through shared emotional engagement, embodied interaction, and ethically grounded interpretation. This approach highlights the importance of welfare-oriented, minimally invasive methodologies and supports a broader shift toward recognizing animals as communicative partners whose emotional lives contribute to scientific knowledge. This review primarily synthesizes empirical and theoretical research on primates and dogs, complemented by selected examples from elephants and marine mammals, which provide the most developed evidence base for the affective–cognitive and relational mechanisms discussed. Full article

The concept of neuroception of psychological safety, rooted in Polyvagal Theory, offers a framework for understanding how individuals perceive safety at a physiological and psychological level. Illness may disrupt this perception and affect bodily regulation, emotional resilience, social connection, and self-compassion. This study aims to explore how experiences of being unwell, across both acute and chronic contexts, affect individuals’ neuroception of psychological safety. Semi-structured interviews were conducted with eleven adult participants aged 20–79, including individuals with both acute and chronic illness experiences. Interview questions were informed by the Neuroception of Psychological Safety and Polyvagal Theory. Data were analysed using reflexive thematic analysis, following Braun and Clarke’s six-step process. Four key themes were identified: dysregulation and the narrowing window of tolerance (reduced emotional resilience and heightened bodily sensitivity); distrust and disappointment (a rupture in bodily and self-trust); responsibility and internalised guilt (moral and emotional burdens around illness and recovery); and illness demands attention and disrupts social connection (withdrawal, emotional depletion, and compromised compassion). Across these themes, participants described a diminished sense of psychological safety when unwell, shaped by both internal physiological changes and altered social dynamics. Illness can profoundly undermine psychological safety by disrupting neurobiological regulation, altering relational engagement, and eroding trust in one’s body and self. These findings highlight the importance of integrating psychological safety principles into models of care, particularly in how individuals experience and recover from illness. Full article

This article examines how the role of digital platforms is reshaping political communication and consensus-building in contemporary societies. It questions how algorithmic architectures are transforming the relationship between leadership, audiences, and power. Drawing on an empirical analysis of online interaction data, the study analyses Donald Trump’s political communication during the August 2025 summit with Putin in Alaska, presenting it as a paradigmatic example of networked leadership. The study focuses on the dynamics of mobilisation, polarisation, and identity construction within digital ecologies. The findings show that the leader’s centrality derives not only from traditional party structures, but also from the ability to coordinate heterogeneous communication flows as well as activate processes of affective and symbolic resonance. The article proposes a theoretical model that conceptualises Trump’s audience as a cognitive and emotional power device, highlighting the convergence of post-organisational populism, algorithmic mediatisation, and communicative governance. This leadership expresses forms of “algorithmic charisma” that redefine the modalities of political legitimacy. Methodologically, the study highlights the value of data-driven interpretive approaches, while also addressing their limitations related to algorithmic transparency and replicability. In conclusion, the article offers a critical reflection on emerging ecologies of consensus and the democratic implications of the ongoing “platformisation” of the public sphere. Full article

Background/Objectives: Family caregivers of individuals with dementia frequently experience substantial psychological distress, yet their emotional responses are heterogeneous. Depression, anxiety and psychological well-being may co-occur in distinct patterns, and socio-economic resources such as education and income are often hypothesized to buffer caregiver distress. This study aimed to identify latent psychological profiles among dementia caregivers and to examine whether education and income moderate the association between affective symptoms and well-being. Methods: A cross-sectional study was conducted with 73 family caregivers of dementia patients attending the Neurology–Psychiatry Department of C.F.2 Clinical Hospital, Bucharest (November 2023–April 2024). Participants completed the PHQ-9 (depression), the COVI Anxiety Scale and Ryff’s Psychological Well-Being Scales. Care recipients’ cognitive status was extracted from medical records using the MMSE. Gaussian Mixture Modeling was used for latent profile analysis (LPA). Between-profile differences were examined using one-way ANOVAs and Tukey post-hoc tests and Pearson correlations were used to assess associations between affective symptoms and psychological well-being, and examined whether education and income were associated with profile membership and psychological well-being. Results: LPA supported a three-profile solution: (1) lower depressive symptoms with moderate anxiety (33%), (2) severe combined depression and anxiety (18%) and (3) moderately severe depression with severe anxiety (49%). Profiles differed significantly in depressive symptom severity, whereas anxiety severity did not differ significantly across profiles. Caregivers in Profile 3 (moderately severe depression–severe anxiety) reported significantly higher overall psychological well-being than those in Profile 1 (moderate depression–moderate anxiety). In contrast, caregivers in Profile 2 (severe depression–severe anxiety), who exhibited the highest affective symptom burden, showed intermediate levels of overall well-being, with comparatively lower scores on specific dimensions such as purpose in life. Depressive symptoms were weakly but significantly associated with autonomy and self-acceptance, whereas anxiety symptoms showed no significant associations with psychological well-being. Education level and household income were not significantly associated with profile membership or psychological well-being. Conclusions: Family caregivers of individuals with dementia can be meaningfully described as forming three exploratory psychological profiles characterized by different configurations of depressive and anxiety symptoms. These findings indicate that caregiver distress does not follow a simple severity gradient and that psychological well-being is not solely determined by symptom burden. Socio-economic characteristics did not account for differences in caregiver adjustment, underscoring the importance of individualized psychological assessment and tailored interventions to support caregiver mental health. Full article

Background/Objectives: This study investigates whether psychological well-being moderates the relationship between burnout and depressive symptoms among mental health professionals, who are frequently exposed to high emotional labor. Methods: A sample of 607 professionals—including psychologists, guidance counselors, social workers, and psychiatrists—working in public and private institutions in Turkey participated in the study. Data were collected online using the Maslach Burnout Inventory, the Psychological Well-Being Scale, and the Beck Depression Inventory–II. A latent interaction term between burnout and psychological well-being was tested using Structural Equation Modeling (SEM). Results: Burnout was positively associated with depressive symptoms (β = 0.37, p < 0.001), and psychological well-being showed a significant main effect on depressive symptoms (β = 0.26, p < 0.001). Importantly, the interaction between burnout and psychological well-being was significant (β = 0.20, p < 0.001), indicating that psychological well-being significantly moderates the relationship between burnout and depressive symptoms. Specifically, the positive interaction suggests that the association between burnout and depressive symptoms becomes stronger at higher levels of psychological well-being. The model explained 27% of the variance in depressive symptoms. Conclusions: These findings highlight the protective role of psychological well-being and suggest that interventions aimed at enhancing well-being may help reduce depressive symptoms among mental health professionals in high-stress environments. Full article

Background/Objectives: Nurses are at high risk for burnout. Identification of biomarkers associated with early manifestations of distress is essential to support effective intervention efforts. Methods: Fifty nurses from a large hospital system participated in a 30-day study of biopsychosocial factors that may contribute to burnout. Nurses wore an Oura ring that collected behavioral data and they completed a self-report burnout questionnaire at baseline and the end of the study period. Machine learning models were developed to evaluate whether objective measures could predict burnout states and changes at the end of the study period. Analyses were exploratory and hypothesis-generating for future work. Results: Data for 45 participants were included in the analyses. Participants with burnout had significantly higher sleep variability. Sleep measures provided 75.75% accuracy in ability to discriminate between burnout states. Heart rate-based measures better modeled changes in symptomatic components of burnout (Emotional Exhaustion, Depersonalization) over time. Heart rate-based measures provided a R-squared value of 0.13 (p < 0.05) (RMSE of 7.41) in a regression model of changes in Emotional Exhaustion evaluated in a leave-one-participant-out cross-validation. Conclusions: Sleep measures’ association with a state of burnout may reflect the longer-term manifestations of chronic exposure to workplace stress. Short-term changes in burnout symptoms are associated with disturbances in heart rate measures. Wearable technology may support monitoring/early identification of those at risk for burnout. Full article

Detecting fake news is essential in natural language processing to verify news authenticity and prevent misinformation-driven social, political, and economic disruptions targeting specific groups. A major challenge in multimodal fake news detection is effectively integrating textual and visual modalities, as semantic gaps and contextual variations between images and text complicate alignment, interpretation, and the detection of subtle or blatant inconsistencies. To enhance accuracy in fake news detection, this article introduces an ensemble-based framework that integrates textual and visual data using ViLBERT’s two-stream architecture, incorporates VADER sentiment analysis to detect emotional language, and uses Image–Text Contextual Similarity to identify mismatches between visual and textual elements. These features are processed through the Bi-GRU classifier, Transformer-XL, DistilBERT, and XLNet, combined via a stacked ensemble method with soft voting, culminating in a T5 metaclassifier that predicts the outcome for robustness. Results on the Fakeddit and Weibo benchmarking datasets show that our method outperforms state-of-the-art models, achieving up to 96% and 94% accuracy in fake news detection, respectively. This study highlights the necessity for advanced multimodal fake news detection systems to address the increasing complexity of misinformation and offers a promising solution. Full article

AI Recommender Systems (RecSys) function as personalised forecasting engines, predicting user preferences to reduce information overload. However, the efficacy of these systems is often bottlenecked by the “Last Mile” of forecasting: the end-user’s willingness to adopt and rely on the prediction. While the existing literature often assumes that algorithmic accuracy (e.g., low RMSE) automatically drives utilisation, empirical evidence suggests that users frequently reject accurate forecasts due to a lack of trust or cognitive friction. This study challenges the utilitarian view that users adopt systems simply because they are useful, instead proposing that sustainable adoption requires a state of Cognitive Absorption—a psychological flow state enabled by the Cognitive Economy of trust. Grounded in the Motivation–Opportunity–Ability (MOA) framework, we developed the Trust–Absorption–Intention (TAI) model. We analysed data from 366 users of a major predictive platform using Partial Least Squares Structural Equation Modelling (PLS-SEM). The Disjoint Two-Stage Approach was employed to model the reflective–formative Higher-Order Constructs. The results demonstrate that Cognitive Trust (specifically the relational dimensions of Benevolence and Integrity) operates via a dual pathway. It drives adoption directly, serving as a mechanism of Cognitive Economy where users suspend vigilance to rely on the AI as a heuristic, while simultaneously freeing mental resources to enter a state of Cognitive Absorption. Affective Trust further drives this immersion by fostering curiosity. Crucially, Cognitive Absorption partially mediates the relationship between Cognitive Trust and adoption intention, whereas it fully mediates the impact of Affective Trust. This indicates that while Cognitive Trust can drive reliance directly as a rational shortcut, Affective Trust translates to adoption only when it successfully triggers a flow state. This study bridges the gap between algorithmic forecasting and behavioural adoption. It introduces the Cognitive Economy perspective: Trust reduces the cognitive cost of verifying predictions, allowing users to outsource decision-making to the AI and enter a state of effortless immersion. For designers of AI forecasting agents, the findings suggest that maximising accuracy may be less effective than minimising cognitive friction for sustaining long-term adoption. To solve the cold start problem, platforms should be designed for flow by building emotional rapport and explainability, thereby converting sporadic users into continuous data contributors. Full article

This study explores the factors that influence Gen Z consumers’ green purchasing behavior, addressing a gap in current research by combining the Theory of Planned Behavior, the Theory of Consumption Values, and Generational Theory, and applying them to the touristic accommodation context. Based on a quantitative survey of Gen Z tourists from Spain, Norway, and Lithuania, the study examined traditional constructs of the Theory of Planned Behavior alongside subjective knowledge, environmental consciousness, perceived value, and green trust. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), the results indicate a significant role for price-based functional value and emotional value in shaping Gen Z’s attitudes toward sustainable accommodation. Furthermore, subjective knowledge and environmental consciousness strengthen green trust, which in turn contributes to a more positive attitude toward sustainable touristic options. Attitude, subjective norms, and perceived behavioral control significantly predict behavioral intention, while both perceived behavioral control and behavioral intention directly influence actual purchasing behavior. The findings suggest that tourism marketers should focus on enhancing price-related and emotionally meaningful value propositions, while also fostering trust through clear communication of environmental performance. Overall, the study offers a comprehensive understanding of the drivers behind Gen Z’s sustainable accommodation choices and provides practical implications for promoting environmentally responsible tourism. Full article

Artificial intelligence is reshaping approaches to architectural heritage conservation by enabling a deeper understanding of how people perceive and experience historic built environments. This study employs deep learning and large language models (LLMs) to explore public perceptions of the Qinghefang Historical and Cultural District in Hangzhou, illustrating how AI-driven analytics can inform intelligent heritage management and architectural revitalization. Large-scale public online reviews were processed through BERTopic-based clustering to extract thematic structures of experience, while interpretive synthesis was refined using an LLM to identify core perceptual dimensions including Hangzhou Housing & Residential Choice, Hangzhou Urban Tourism & Culture, Hangzhou Food & Dining, and Qinghefang Culture & Creative. Sentiment polarity and emotional intensity were quantified using a fine-tuned BERT model, revealing distinct affective and perceptual patterns across the district’s architectural and cultural spaces. The results demonstrate that AI-based textual analytics can effectively decode human–heritage interactions, offering actionable insights for data-informed conservation, visitors’ experience optimization, and sustainable management of historic districts. This research contributes to the emerging field of AI-driven innovation in architectural heritage by bridging computational intelligence and heritage conservation practice. Full article

Background: Adolescents in residential care frequently face functional challenges, yet few interventions integrate functional-cognitive models with motivational theories to support their daily function. Methods: This mixed-methods feasibility study is an innovative conceptual integration that links motivational and metacognitive approaches with growth-mindset principles to address both beliefs about the ability to change and functional performance. Quantitative data were collected from staff (n = 44), alumnae (n = 5), and current residents (n = 3), assessing mindset and functional-skill confidence among three focus groups (n = 16). The qualitative insights highlighted the motivational processes, strategy use, and barriers each group faced. Results: The findings informed the development of structured intervention psychoeducation protocol to facilitate goal-setting and reflective questioning. The feasibility and acceptability were tested by a pilot study among five staff members. Before implementation, staff demonstrated lower confidence in promoting daily autonomy and providing strategy-focused feedback. Alumnae and residents reported high emotional support, yet persistent gaps in functional independence. Conclusions: The pilot findings may inform the development of structured staff practices for delivering functional guidance, pending further evaluation. This study offers a novel conceptual contribution by positioning growth mindset as an active mechanism that supports functional-cognitive processes to enhance autonomy among adolescents in residential care settings. Full article

Background and Objectives: This study aimed to examine the severity of misophonia symptoms in individuals diagnosed with obsessive–compulsive disorder (OCD) and to evaluate the pattern of the relationship between misophonia and OCD symptom severity in relation to anxiety sensitivity and mindfulness. Materials and Methods: This comparative and cross-sectional study included 108 patients diagnosed with OCD according to DSM-5 criteria and 81 healthy control subjects without any psychiatric diagnosis. Participants completed the Misophonia Symptom List (MSL), Anxiety Sensitivity Index-3 (ASI-3), Mindful Attention Awareness Scale (MAAS), Yale–Brown Obsessive Compulsive Scale (Y-BOCS), and Beck Anxiety Inventory (BAI). Statistical analyses included group comparisons, Pearson correlations, multiple linear regression, and mediation analyses using the PROCESS macro. Results: MSL scores were significantly higher in the OCD group compared to the control group (104.10 ± 33.00 vs. 87.56 ± 20.07, p < 0.001). ASI-3 (33.53 ± 18.72 vs. 18.12 ± 11.55, p < 0.001) and BAI scores (20.74 ± 13.14 vs. 11.04 ± 8.47, p < 0.001) were higher; MAAS scores were lower (53.23 ± 14.92 vs. 60.72 ± 12.70, p < 0.001). In the OCD group, MSL scores were positively correlated with anxiety sensitivity (r = 0.626, p < 0.001) and Beck anxiety (r = 0.515, p < 0.001) and negatively correlated with MAAS (r = −0.357, p < 0.001). In multiple regression analysis, anxiety sensitivity was identified as the only variable significantly predicting misophonia severity (β = 0.523, p < 0.001). Mediation analyses showed that anxiety sensitivity emerged as the dominant indirect pathway between OCD symptom severity and misophonia, whereas the contribution of mindfulness was not independent of anxiety sensitivity in the serial mediation model. Conclusions: The findings indicate that misophonia symptoms are significantly elevated in individuals diagnosed with OCD and that these symptoms are particularly associated with cognitive-emotional variables such as anxiety sensitivity and mindfulness. Given the cross-sectional design, the mediation findings should be interpreted as indirect associations rather than evidence of causal pathways. Considering these variables may be useful in assessing misophonia symptoms accompanying OCD and planning clinical approaches. Full article

This study explores how school principals evacuated from their schools in the wake of the 7 October 2023 war perceived their unique challenges, the strategies they adopted, and the ways in which their agency was shaped during the extreme crisis. Using semi-structured, in-depth interviews with 33 displaced principals from elementary, middle, secondary, and special education schools, we identified four interconnected dimensions of the principals’ agency during wartime. Intrapersonal agency reflected the principal’s inner identity as a foundation for action. Critical agency emerged from frustration with systemic failures and bureaucratic obstacles, motivating the pursuit of meaningful change. Collaborative agency was expressed in building and maintaining trust-based networks and partnerships that enabled effective solutions. Finally, proactive agency was driven by an internal desire for growth and influence, promoting innovative strategies and renewal processes at organizational, emotional, and community levels. This leadership framework for understanding principalship in wartime highlights agency as a holistic framework that enables principals not only to ensure the survival of their schools, but also to respond to chaotic realities. Practically, the findings inform the design of models for ensuring educational continuity in emergencies, and tailored support mechanisms for displaced educational communities. Full article

Rising rates of mental health concerns worldwide highlight the need for accessible and effective support. Music wellness, defined as “interacting with music in a way that contributes to holistic health and flourishing,” may help address this gap when integrated into education. In 2024, this research team conducted a Delphi study to establish this definition and propose a four-dimensional music wellness model (catalyst, act, outcome, and skill). The present study builds on that work by evaluating the model’s clarity, relevance, and real-world applicability through a mixed-methods validation study. Semi-structured interviews with five experts assessed conceptual coherence and clarity, and a survey of 160 adults examined whether the model reflected everyday uses of music for well-being. Experts confirmed the model’s logic and utility while suggesting refinements to improve clarity. Survey responses showed high overall agreement (mean = 85.6%) across items representing all four dimensions, and all participants reported daily engagement with music and positive emotional effects. These findings support the model’s validity and applicability, providing an evidence-based foundation for integrating music wellness into curricula that promote lifelong health and flourishing. Full article

This paper proposes E-MOTE (Emotion-aware Teacher Education Framework), an ethically grounded conceptual model aimed at enhancing teacher education through the integrated use of the Facial Action Coding System (FACS), Artificial Intelligence (AI), and Virtual Reality (VR). As a conceptual and design-oriented proposal, E-MOTE is presented as a structured blueprint for future development and empirical validation, not as an implemented or evaluated system. Grounded in neuroscientific and educational research, E-MOTE seeks to strengthen teachers’ emotional awareness, teacher noticing, and social–emotional learning competencies. Rather than reporting empirical findings, this article offers a theoretically structured framework and an operational blueprint for the design of emotion-aware teacher training environments, establishing a structured foundation for future empirical validation. E-MOTE articulates three core contributions: (1) it clarifies the multi-layered construct of emotion-aware teaching by distinguishing between emotion detection, perception, awareness, and regulation; (2) it proposes an integrated AI–FACS–VR architecture for real-time and post hoc feedback on teachers’ perceptual performance; and (3) it outlines a staged experimental blueprint for future empirical validation under ethically governed conditions. As a design-oriented proposal, E-MOTE provides a structured foundation for cultivating emotionally responsive pedagogy and inclusive classroom management, supporting the development of perceptual micro-skills in teacher practice. Its distinctive contribution lies in proposing a shift from predominantly macro-behavioral simulation toward the deliberate cultivation of perceptual micro-skills through FACS-informed analytics integrated with AI-driven simulations. Full article