Search Results (3,316)

Background: While stable dietary patterns and hedonic responses in food preferences are associated with depression, whether neurobiological mechanisms mediate this relationship remains unknown. This study therefore investigates the food preference–depression associations and their potential neurostructural mediation. Methods: Over 140,000 participants were included in a longitudinal study and cross-sectional. We employed Cox regression models to examine the relationships between food preferences and depression. Half-longitudinal mediation analysis and cross-lagged models estimated the mediating roles of brain grey matter and prospective relationships with depressive symptoms. Results: We found protective effects of moderate salty, bitter and spicy preferences against depression, especially in overweight and obese participants. Further, brain structure demonstrated widespread positive correlations with both food preferences and depressive symptoms. The mediation analysis identified volumes of peripheral cortical grey matter, ventricular cerebrospinal fluid, and grey and white matter as potential mediators in the relationships between salty preferences and depression. Of these, cross-lagged models revealed the distinct directional relationships between the volume of grey matter in the VI cerebellum (vermis)/lateral occipital cortex inferior division (right) and depression, indicating that their mutual influences are mediated through different neural mechanisms. Conclusions: Moderate, but not extreme, liking of specific tastes (spicy, bitter, or salty) reflects healthier mental states. The structure of brain grey matter may mediate the relationship between salty preference and depression; also, the cerebellum together with lateral occipital regions may potentially serve as potential emotional correlates. Further neurobiological investigations are needed to confirm this pathway and inform novel therapeutic strategies for depressive disorders. Full article

Background: Population aging has increased the prevalence of cognitive impairment and dementia, highlighting the need for personalized non-pharmacological interventions. Although socially assistive robots have shown therapeutic benefits, most rely on predefined interactions with limited adaptability. This study proposes an AI-enabled framework integrating continuous self-monitoring and explainable multicriteria decision-making to personalize robot-assisted interventions. Methods: A 12-week longitudinal quasi-experimental study was conducted involving 78 older adults with mild-to-moderate cognitive impairment allocated to three groups: an adaptive AI-based robot (n = 26), a sensor-based robot (n = 26), and a control group receiving conventional care (n = 26). The proposed framework combined continous self-monitoring, AI-based emotional-state estimation, and an Analytic Hierarchy Process (AHP) model to adapt robot behaviour according to participants’ clinical and behavioural profiles. Results: The AI-based robot achieved the greatest improvements in emotional status, social interaction, and functional performance. Depressive symptoms decreased by 42.9%, anxiety decreased by 39.1%, social interaction increased by 60.7%, and functional independence improved by 20.1%. Although the sensor-based robot showed slightly higher adherence (97.2% vs. 95.6%), the AI-based intervention achieved the highest overall effectiveness (AHP global score = 0.90). Conclusions: Integrating continuous self-monitoring, AI-based emotional-state estimation, and explainable multicriteria decision-making enables personalized robot-assisted interventions that improve emotional well-being, social engagement, and functional independence. These findings support the potential of adaptive socially assistive robots as AI-driven clinical decision-support systems for dementia care. Full article

The Fei Qian (飞箝) Argumentation Scheme, one of the core rhetorical schemes of the School of Diplomacy (Zonghengjia, 纵横家) aimed at controlling others, has yet to receive systematic theoretical analysis, and its significance within the history of Chinese logic remains underexplored. Drawing primarily on the Guiguzi (《鬼谷子》) and related pre-Qin period sources, this article examines the Fei Qian Argumentation Scheme across three dimensions: its structural logic, its intended targets, and its practical application. The analysis reveals that the Fei Qian Argumentation Scheme exhibits a fundamental asymmetry in evaluative standards—it demands factual reliability of the premises while judging the conclusion solely by the criterion of audience acceptability. Operationally, the scheme proceeds by deploying external flattery (fei, 飞) to draw upon captivating words (Gou Qian zhi ci, 钩箝之辞), supplemented where necessary by inward emotional manipulation (Gou Qian, 钩箝). Its application is further stratified by social hierarchy: the scheme is directed upward at rulers (zhi tian xia, 制天下) and laterally at peers (zhi ren, 制人), while subordinates may be directed without recourse to it. In practice, the Fei Qian Scheme presupposes a careful assessment of power and capacity (duo quan liang neng, 度权量能), pursues the goal of winning willing compliance (cong hua, 从化), and resorts to the compound strategy of “encumber responsibilities and reveal weaknesses” (chong lei–zi hui, 重累—訾毁) when the primary scheme fails. The emergence of the Fei Qian scheme form and the broader rise in the School of Diplomacy were inseparable from the specific socio-cultural and historical context of the Spring and Autumn and Warring States periods. To a significant degree, the argumentation theories and practices of the School of Diplomacy advanced the development of bianxue (辩学, the art of disputation) and of Chinese logic as a whole. Full article

Mothers of preterm infants (PT; <37 weeks gestational age) face economic barriers to mother’s own milk (MOM) provision, including lack of paid maternity leave and unpaid workload that may prevent sustained MOM provision. The objective of this study was to understand how mothers of PT infants navigate decisions about MOM provision and paid and unpaid work. Semi-structured interviews were conducted with mothers of PT infants between 5 days and 10 weeks postpartum. Interviews included questions about responsibilities in the home, pre-delivery work experience and plans to provide MOM, and postpartum work experience and MOM provision. Data were analyzed using reflexive thematic analysis, following Braun and Clarke’s method. This study included 18 mothers, who were predominantly non-White (79%) and covered by Medicaid (72%) with infants born at a median gestational age of 32 weeks. Three themes emerged: (1) unexpected prenatal events create postpartum job uncertainty; (2) breastfeeding intentions are often derailed after PT delivery; and (3) the unpredictable reality of having a PT infant shifts maternal priorities and obligations related to paid and unpaid workload. PT delivery creates emotional and financial stress and uncertainty for new mothers. Although federal and employment-based paid leave may alleviate some financial stress for mothers employed prior to delivery in the United States, mothers who leave the workforce before delivery will not benefit from these policies. Policies are needed to support all mothers of PT infants in facilitating long-duration MOM provision, independent of employment status. Full article

With the development of smart classrooms and digital learning platforms, multimodal learning behavior data provide a new sensing basis for understanding students’ cognitive states and psychological risk warnings. However, existing educational data mining methods mainly focus on performance prediction, dropout warning, or surface-level emotion recognition, while continuous and interpretable modeling of deeper cognitive biases and related psychological risks remains insufficient. To address this issue, we propose MLBS-Net, a multimodal learning behavior sensing network for teaching feedback that jointly models students’ textual expressions, behavioral sequences, classroom interactions, and psychological auxiliary signals. MLBS-Net integrates theory-guided textual cognitive bias encoding, temporal behavioral state modeling, and reliability-aware cross-modal fusion to capture psychologically interpretable cognitive patterns, characterize dynamic learning-state changes, and adaptively integrate multimodal information according to data quality and task contribution while providing interpretable feedback for teachers. Experimental results show that MLBS-Net achieves a Macro-F1 of 0.855 for cognitive bias recognition and an AUC of 0.891 for psychological risk warning, outperforming traditional machine learning, unimodal deep learning, and standard multimodal methods. Ablation results further support the effectiveness of theory-guided semantic encoding, temporal behavioral modeling, reliability estimation, and multitask learning. These findings demonstrate that MLBS-Net can jointly characterize cognitive biases and potential psychological risks from multisource learning behaviors, providing a feasible approach for learning-state sensing, risk warning, and interpretable teaching support in smart education. Full article

Background: The risk of violence is elevated in patients with Schizophrenia Spectrum Disorders (SSD). However, current violence risk assessment approaches rely predominantly on clinical and historical factors and remain limited in predictive accuracy, highlighting the need for more objective complementary markers. Methods: The present study examined the neurobiological differences between violent (n = 66) and non-violent (n = 166) SSD patients, and compared the predictive utility of these neurobiological markers and clinical risk factors, including childhood trauma and psychopathology, in distinguishing violent from non-violent individuals. We further aimed to construct a multimodal predictive model using the strongest predictors from both domains, evaluating whether combining neural and clinical variables improves predictive performance beyond either single domain. Structural and resting-state MRI scans were acquired for patients and healthy controls (n = 504), who were used to calibrate the normative model. Neuroimaging data were analyzed within a normative modelling framework, and univariate associations between neurobiological and clinical factors and violence were examined. Results: A multimodal model integrating neurobiological and clinical measures achieved moderate classification performance (74.7% balanced accuracy), outperforming all individual predictors. The strongest predictors included the medial orbitofrontal cortex, caudal middle frontal gyrus, limbic-default mode network functional connectivity, and childhood sexual abuse. Conclusions: Our findings suggest that alterations in fronto-limbic systems involved in goal-directed decision-making and emotion regulation may contribute to violent behaviour in SSD. Furthermore, the improved performance of the multimodal model supports the potential added utility of integrating neuroimaging markers with established clinical risk factors in violence risk assessment. Full article

Objective: Depressive symptoms exist on a continuum in the general population, yet the underlying neurobiological mechanisms, particularly the interplay between resting-state networks and task-evoked social cognitive responses, remain elusive. Methods: Leveraging the Human Connectome Project (HCP) dataset, we included 867 participants. With depression scores as the independent variable and age/sex as covariates, we systematically examined associations with sleep quality, negative emotions, sensory scores, gray matter volume (GMV), fractional amplitude of low-frequency fluctuations (fALFF), multi-seed resting-state functional connectivity (rsFC), as well as brain activation and behavioral performance during working memory, emotion recognition, social cognition, relational reasoning, language comprehension, and gambling tasks. The statistical threshold was set at voxel-level p < 0.001 (uncorrected) combined with cluster-level FWE correction at p < 0.05. Results: (1) Depression scores were positively correlated with sleep disturbances, negative emotions (anger/fear), and pain. (2) In resting-state, depression scores negatively correlated with ventral striatum (VS)–cerebellum/parahippocampal gyrus/fusiform rsFC, yet positively correlated with pregenual anterior cingulate cortex (preACC)–supplementary motor area (SMA) rsFC. (3) In task-fMRI, only the social task showed a positive association with task accuracy and regional activation in bilateral pre/postcentral gyri, superior temporal gyri, left middle frontal gyrus, and SMA/paracentral lobule. Conclusions: Elevated depression scores are linked to a pattern that may reflect relative decoupling between reward and perceptual systems, along with enhanced connectivity in cognitive control circuits. Socially, high scorers exhibit a pattern suggestive of compensatory hypervigilance, accompanied by enhanced behavioral performance. This study provides multidimensional evidence for the dimensional neural representation of depressive symptoms. Full article

Background: Elite soccer performance emerges from the interplay of cognitive, emotional, psychophysiological, and tactical processes that operate in real time during matches. Advances in wearable sensors and artificial intelligence (AI) now allow continuous monitoring of physiological and psychological states. They also allow modeling of how these states relate to tactical and physical performance. Existing reviews have examined machine learning in soccer, heart rate variability (HRV) monitoring, and psychological determinants of performance separately. No scoping review has mapped the intersection of AI analytics, wearable psychophysiological monitoring, and psychological performance constructs as one integrated decision-support framework in elite soccer. Aim: The aim of this study was to map the available evidence on the integration of AI and machine learning with psychophysiological monitoring for performance modeling in elite soccer, to identify the psychological constructs already used as model inputs, to describe the wearable technologies and AI methods applied, and to set out the translational challenges and evidence gaps that need priority attention. Methods: The review followed the PRISMA extension for Scoping Reviews (PRISMA-ScR) and the updated Joanna Briggs Institute (JBI) methodology. The protocol was registered on the Open Science Framework (OSF). Six databases (PubMed/MEDLINE, Scopus, Web of Science, SPORTDiscus, IEEE Xplore, and PsycINFO) were searched from January 2000 to March 2026 using the Population–Concept–Context (PCC) framework. Two reviewers independently screened titles, abstracts, and full texts (Cohen’s kappa = 0.82). Results: Thirty-six sources met the eligibility criteria after screening of 3104 records. AI and machine learning have been applied widely to predict physical and tactical performance in soccer, yet they rarely include psychological constructs. Reported models (decision trees, gradient boosting, and artificial neural networks) reach high accuracy for physical outcomes in internal validation, for example, above 66% for injury risk. Multi-modal models that add physiological and psychological inputs report stronger prediction. These figures come mostly from internal validation, and external validation and overfitting controls are seldom reported, so they should be read as optimistic upper bounds. Psychological and psychophysiological inputs remain under-represented. Explainable AI (XAI) methods, in particular Shapley Addictive exPlanations (SHAP) values, are appearing, but validation with domain experts is scarce. HRV has been reviewed as a psychophysiological marker in soccer, yet its use within AI decision-support tools for real-time psychological readiness has not been mapped. Three translational challenges stand out: the ecological validity gap between laboratory cognitive tests and match-embedded psychophysiology; the interpretability problem of opaque AI in high-stakes decisions; and the data fragmentation problem created by disconnected physical, tactical, and psychological data streams. Conclusions: Integrating AI with wearable psychophysiological monitoring offers a credible route toward integrated performance modeling in elite soccer. Closing this gap calls for multi-modal frameworks that combine psychological constructs, physiological markers, and tactical data within explainable AI. Research priorities include ecologically valid psychophysiological assessment protocols, position-specific psychological profiling, and practitioner-validated tools that turn AI outputs into usable coaching recommendations. Full article

Just-in-time adaptive interventions require timely and low-burden state estimation, while olfaction offers a programmable output channel with limited attentional demand. We describe a prototype architecture that links electroencephalography (EEG)-based emotion estimation to a six-channel odorant device and evaluate only the EEG sensing and decoding module. Forty EEG sessions from 39 adults were recorded with a 14-channel Emotiv EPOC X headset (128 Hz) during six standardized emotion-induction conditions. No odor was administered. Band-power, frontal alpha asymmetry (FAA) and global field power (GFP) were analyzed with rank-based repeated-measures tests and explicit multiple-comparison correction. Emotion decoding used subject-aware cross-validation. Frontal beta power, the beta/alpha ratio and GFP differed across conditions after false-discovery-rate correction, although effect sizes were small (Kendall’s W = 0.089–0.155). On-line affective metrics showed larger effects (W = 0.130–0.365). Six-class accuracy was 45.1% ± 13.2% within participants (n = 29; chance 16.7%; p < 10⁻⁸) and 23.1% across participants after per-subject normalization (macro-F1 = 0.23; permutation p = 0.005). FAA did not differ. Consumer-headset EEG contained person-specific information about laboratory-induced states, but performance was not sufficient to establish a clinically usable regulator. The results validate neither a complete closed loop nor olfactory efficacy; end-to-end latency, artifact and temporal robustness, chemical characterization and controlled odor-regulation effects require prospective evaluation. Full article

Empathy is essential to humanized nursing care, yet its relationship with emotional competence before professional entry remains insufficiently specified. This single-school cross-sectional study examined whether distinct dimensions of emotional competence were differentially associated with emotional empathy among 337 secondary-level vocational nursing students enrolled in a five-year Croatian program for general care nurses/medical technicians. Students completed the Emotional Skills and Competence Questionnaire (ESCQ-45) and the Emotional Empathy Scale from the E-Questionnaire, which primarily captures affective responsiveness to others’ distress and other negative-valence states. Spearman correlations, Kruskal–Wallis tests with Dunn–Holm post hoc comparisons, Welch tests, and hierarchical linear regression with heteroscedasticity-consistent type 3 robust standard errors were used. Emotional empathy correlated with emotion perception and understanding (ρ = 0.35, p < 0.001) and emotion management (ρ = 0.28, p < 0.001), but not with emotion expression and naming (ρ = 0.10, p = 0.070). In the fully adjusted regression model, emotion perception and understanding (β = 0.254, p < 0.001), emotion management (β = 0.254, p = 0.003), and female versus male reported sex (β = 0.277, p < 0.001) were statistically significant predictors, whereas clinical exposure was not (p = 0.429). Emotion expression and naming had a negative adjusted coefficient (β = −0.158, p = 0.040) after simultaneous control for the remaining predictors. The findings support a dimensional interpretation of emotional competence and indicate that negative-valence emotional empathy is more closely associated with recognizing, understanding, and managing emotions than with expressive emotional skills considered in isolation. Full article

Background/Objectives. Human voices convey critical socio-affective information, including emotional states. Although autism has frequently been associated with difficulties in processing vocal emotional cues, findings remain inconsistent, particularly in adults. This study investigated neural and behavioral sensitivity to vocal emotion expressions in autistic adults using an objective auditory frequency-tagging EEG paradigm. Methods. Twenty-five autistic adult men and 25 age- and IQ-matched non-autistic men completed an auditory frequency-tagging EEG task and an auditory and multimodal emotion-recognition assessment. During EEG recording, neutral vocal utterances were presented at 4 Hz, with emotional utterances (fear, anger, happiness, or sadness) inserted every third stimulus, generating an oddball frequency of 1.333 Hz indexing vocal emotion discrimination. Results. No significant group differences were observed in neural or behavioral measures of emotion processing. Robust oddball EEG responses were present in both groups, indicating automatic discrimination of emotional from neutral vocalizations. Fearful and angry vocalizations elicited the strongest neural responses. On the behavioral task, autistic and non-autistic participants showed comparable performance in the auditory modality as well as in the visual and audiovisual modalities, with auditory emotion recognition being the most challenging condition for both groups. Conclusions. These findings provide converging neural and behavioral evidence for intact vocal emotion processing in autistic adult men and are consistent with the view that socio-affective processing differences may attenuate across development. Auditory frequency-tagging EEG shows promise as a sensitive tool for studying individual differences in socio-affective processing. Full article

The use of artificial intelligence chatbots that mirror human interaction and emotional closeness has given rise to new forms of crime. Traditional online grooming is generally conceptualized as an offence in which a human perpetrator plans, initiates, and executes criminal conduct. However, the increasing involvement of artificial intelligence has introduced novel and complex scenarios. AI systems may either autonomously engage in conduct that facilitates the sexual exploitation of children or serve as tools that enhance, automate, or scale offenders’ activities. These developments challenge the traditional understanding of the offence and expose significant gaps in existing legal frameworks. Consequently, current regulatory approaches may prove inadequate to address the evolving nature of AI-assisted online grooming and associated forms of child sexual exploitation. This study investigates the case of grooming via social media using AI chatbots and discusses whether the current criminal legislation is sufficient to address this offence. Through a legal comparative method, this study examines the legal rules in the European Union, the United Kingdom, the United States, and China, focusing on the elements of criminal acts and criminal intent and the consideration of the liability of platform operators, developers, and deployers of AI systems. The study also discusses the problem of intermediary liability rules and less mature AI governance policies to tackle the fragmented and hidden nature of algorithmic actions. The study concludes that existing criminal law frameworks face significant challenges in addressing AI-assisted grooming, particularly regarding criminal intent, foreseeability, and liability allocation. The fragmentation of responsibility among offenders, platforms, and AI developers creates regulatory and enforcement gaps in the law. Accordingly, this study advocates for a risk-based liability framework, enhanced platform accountability, greater algorithmic transparency, and stronger child-centered safeguards. Full article

Women with endometriosis experience chronic cyclic pelvic pain (CCPP) or chronic persistent pelvic pain (CPPP), both of which may be incapacitating even after treatment. Emotional dysregulation in endometriosis impedes patient recovery. This study evaluated the relationships among emotional state, pain perception, and inflammatory biomarkers (IL-1β, IL-6, and TNF-α) in women with endometriosis presenting with CCPP or CPPP. An exploratory, observational, descriptive, cross-sectional, comparative with repeated sampling study was conducted with 52 women diagnosed with endometriosis and experiencing either CPPP or CCPP. Participants completed a psychometric battery including the State-Trait Anxiety Inventory (STAI), Beck Depression Inventory (BDI-II), Goldberg General Health Questionnaire (GHQ-30), Hospital Anxiety and Depression Scale (HADS), and Mini-Mental State Examination (MMSE). Pain perception was assessed using the Wong–Baker Pain Rating Scale (FACES). Saliva samples were collected at baseline, during stressor and recovery phases, and concentrations of IL-1β, IL-6, and TNF-α were determined by ELISA. Fifty-two women with endometriosis were included (CPPP: n = 33; CCPP: n = 19). No significant between group differences were observed in emotional state (HADS: p = 0.682; BDI: p = 0.842), anxiety (STAI-State: p = 0.086; STAI-Trait: p = 0.615), general distress (GHQ-30: p = 0.730), or pain intensity (FACES: p = 0.705). The prevalence of depressive symptoms did not differ between groups (CPPP: 69.7% vs. CCPP: 73.7%; χ² = 0.093, p = 0.760). Salivary cytokine levels (IL-1β, IL-6, TNF-α) were comparable between groups across all measurement conditions. Spearman correlations revealed uncorrected significance between TNF-α and emotional distress: basal TNF-α correlated inversely with HADS (ρ = −0.292, p = 0.031) and BDI (ρ = −0.278, p = 0.041); TNF-α under stress correlated with HADS (ρ = −0.329, p = 0.014) and GHQ-30 (ρ = −0.271, p = 0.046); and TNF-α during recovery correlated with GHQ-30 (ρ = −0.363, p = 0.006). These findings indicate that CPPP and CCPP were not associated with statistically significant differences in emotional states or salivary cytokine profiles at the group level. Exploratory pos hoc analyses suggested that pain pattern may moderate the association between TNF-α and psychological burden, particularly in the CCPP subgroup; however, these findings require confirmation in larger studies with prespecified analyses. Exploratory analyses suggested patterns between salivary inflammation and psychological burden; however, these findings should be interpreted cautiously because of the small subgroup size, multiple comparisons, and lack of a matched healthy control group. Full article

Companion animals have increasingly become central to human emotional life, yet the literature linking living with companion animals, human–animal interaction, and mental health remains fragmented. This study mapped global research trends on companion animals and mental health from 2000 to 2024 using bibliographic records retrieved from the Web of Science Core Collection (WoSCC). A final dataset of 930 peer-reviewed articles was analyzed with Bibliometrix (RStudio), CiteSpace, and VOSviewer to characterize publication growth, national and institutional collaboration, influential authors and journals, co-cited references, keyword clusters, and citation bursts. Results show a strong nonlinear increase in output after 2015, with the United States as the dominant contributor and Australia and the WoSCC country node labelled England (distinct from Scotland) as important international bridges. Core knowledge clusters center on human–animal interaction, animal-assisted intervention, older adults, child/adolescent development, loneliness, attachment, and post-traumatic stress. Recent burst terms and references indicate a shift from general companion-animal caregiving and physiological stress buffering toward intervention design, vulnerable populations, and pandemic-era mental health. Overall, the field has matured into an interdisciplinary research area with growing methodological sophistication, but future work should strengthen cross-cultural evidence, longitudinal designs, standardized outcome measures, and explicit assessment of animal welfare. Full article

Dynamic facial expression recognition (DFER) is a crucial field in computer vision that aims to automatically detect and analyze human emotions from video sequences. Despite recent advances, current DFER methods face critical limitations. Existing approaches struggle to effectively integrate global facial features with detailed local information, temporal modeling techniques inadequately capture subtle expression changes, and most methods are vulnerable to noisy annotations from crowdsourced datasets. This study proposes a novel framework, an attention-driven temporal–spatial learning architecture for DFER (ATSLA-DFER), designed to address these critical issues in DFER tasks. The proposed approach leverages a multi-component architecture that effectively combines spatial and temporal learning mechanisms. It first incorporates a dual-stream feature extraction process utilizing pre-trained IR50 and MobileFaceNet backbones for micro- and macro-spatial feature processing. The global local attention module (GLAM) further enhances the macro features for the feature-enriching process. Moreover, a cross-adaptive feature fusion (CAFF) is proposed for effective multi-scale feature integration, and a custom-designed temporal convolutional transformer (TCT) is introduced for capturing complex temporal dynamics. To further optimize the model’s performance, we propose a novel temporal self-reference regularization (TSRR) loss to enhance temporal consistency and mitigate emotion ambiguity. Extensive evaluations demonstrate ATSLA-DFER achieves state-of-the-art (SOTA) performance on DFEW (69.48% UAR and 79.62% WAR) and FERV39k (44.89% UAR and 55.87% WAR), while achieving competitive performance on the challenging MAFW dataset (43.10% UAR and 56.24% WAR). ATSLA-DFER’s ability to effectively learn and integrate temporal–spatial features through its attention-driven architecture represents a significant step forward in advancing DFER capabilities for a wide range of practical applications in unconstrained environments. Full article