Search Results (65)

This paper re-examines the problem of free will in light of both deterministic and indeterministic assumptions about the structure of the world. On the philosophical side, it analyzes van Inwagen’s arguments that free will is incompatible with determinism—because our actions would then be fixed by a remote past and the laws of nature—and with indeterminism, on the grounds that indeterministic outcomes reduce to mere chance. On the neuroscientific side, it revisits Libet-style experiments, often interpreted as showing that unconscious brain activity initiates voluntary actions before conscious intention, and critically reviews recent reinterpretations of the readiness potential and the limitations of such paradigms for assessing free will. The paper then diagnoses a shared structure in these challenges: they presuppose a strict dichotomy between Laplacean determinism and a thin, law-governed conception of chance that leaves no conceptual space for non-chance indeterminism or for agent-level causal contributions. A simple quantum thought experiment is used to show how microscopic indeterminism can have direct macroscopic effects, undermining the assumption that the macroworld is effectively deterministic. Finally, the implications of computational and dynamical models of cognition are considered, arguing that their built-in constraints should be read as limits of the models rather than as metaphysical results. The conclusion advocates a naturalistic agnosticism: current physics, neuroscience, and cognitive science neither establish nor refute free will, but underdetermine its status while still placing substantive constraints on any viable theory of it. Full article

Empathetic dialogue systems built upon large language models overwhelmingly adopt a monolithic inference paradigm that processes emotion perception, causal reasoning, memory retrieval, and response planning within a single forward pass without architecturally enforced intermediate representations, forfeiting intermediate-state transparency and long-horizon personalization. Drawing on neuroscientific and cognitive–psychological evidence that human empathy is functionally dissociable, we present MOSAIC (Multi-agent Orchestration with Structured Affective memory for Interpretable empathiC dialogue), a training-free framework that operationalizes empathetic dialogue as a four-stage cognitive pipeline: affective perception, causal appraisal, episodic memory retrieval, and response synthesis. Three innovations distinguish MOSAIC from prior work: (1) a cognitively motivated modular architecture whose functionally dissociable stages enable post hoc failure attribution through logged intermediate states; (2) a hierarchical three-tier emotional memory—perceptual, semantic, and episodic—coupled with adaptive three-dimensional retrieval over emotion, situation, and coping-strategy cues; and (3) a heterogeneous model orchestration strategy coordinating open-source and API-accessible models through role-specific chain-of-thought prompts, requiring no task-specific fine-tuning. We note that the EmpatheticDialogues evaluation pre-populates the memory store with 200 training-split episodes prior to test-set interaction, a data-access asymmetry relative to single-model baselines that must be borne in mind when interpreting comparative results. Experiments on EmpatheticDialogues and ESConv show that MOSAIC achieves a 76.4% weighted F1 and an empathy score of 3.87 (on a 1–5 Likert scale) and that it improves over single-model, training-free baselines on aggregate empathy and—most prominently—on human-rated personalization (3.67 vs. 3.24 against Claude-3.5 five-shot, $d=0.48$). We caution that the comparison against training-free baselines is not data access-controlled (see the cold-start discussion in Methods); the personalization advantage, supported by the ablation without the Event Agent, is the result we treat as the primary practical contribution of this work. Full article

Accurate and interpretable assessment of cognitive workload from EEG remains a central challenge in neuroergonomics and real-time human–machine interaction. To address the limitations of existing Empirical Mode Decomposition (EMD) and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) approaches, particularly their instability, limited neuroscientific grounding, and sensitivity to amplitude fluctuations, this paper introduces Scale-Stabilized Relative Intrinsic Mode Energy (SS-RIME), a theoretically motivated and physiologically informed feature extraction framework. SS-RIME integrates instantaneous frequency stabilization to enforce a consistent oscillatory hierarchy across subjects, delta (1–4 Hz) and theta (4–7.5 Hz) spectral weighting based on established frontal-midline activity, and cross-IMF energy normalization to reduce amplitude-driven variability. Applied to 64-channel EEG recorded during N-back tasks, the proposed framework achieved high performance, outperforming both classical machine-learning baselines and deep learning models such as EEGNet, DeepConvNet, and ShallowConvNet. SS-RIME yielded accuracies of $99.12\pm 0.41\%$ (0 vs. 2-back), $97.84\pm 0.63\%$ (0 vs. 3-back), and $92.31\pm 1.12\%$ (2 vs. 3-back), demonstrating strong cross-subject generalization. Theta-dominant IMFs over frontal midline regions emerged as the most discriminative components, supporting the neuroscientific validity of the stabilized and spectrally weighted Hilbert–Huang representation. With an inference time below 20 ms per epoch, SS-RIME is computationally efficient and suitable for real-time neuroergonomics applications, providing a robust, explainable, and physiologically grounded solution for EEG-based cognitive workload decoding while addressing key methodological gaps in prior EMD/CEEMDAN and deep learning approaches. Full article

Background/Objectives: Schizophrenia is one of the most extensively studied yet conceptually unstable disorders in the history of medicine and brain sciences. Since its formalization at the turn of the twentieth century, the disorder has been repeatedly redefined, reflecting changes in clinical observation, diagnostic philosophy, and neuroscientific models of brain function. The objective of this review is to critically examine the historical evolution of schizophrenia as a medical construct and to analyze how shifts in diagnostic systems have shaped the search for biological and molecular biomarkers. Methods: A narrative-historical review of the literature was conducted, integrating classical psychiatric texts, diagnostic manuals, and contemporary neuroscientific studies. Key milestones in the conceptualization of schizophrenia were analyzed alongside the development of biological hypotheses, including neurochemical, electrophysiological, neuroimaging, genetic, immunological, omics-based, and digital approaches. Emphasis was placed on identifying conceptual continuities, ruptures, and methodological limitations across historical periods. Results: The analysis reveals that the evolution of schizophrenia has been characterized by increasing diagnostic standardization accompanied by growing biological heterogeneity. While successive biological models have provided valuable insights into specific aspects of the disorder, none have yielded single, robust diagnostic biomarkers. Instead, findings consistently reflect partial overlaps between clinical phenotypes and biological signals, strongly influenced by historically derived diagnostic categories. Conclusions: The persistent absence of definitive diagnostic biomarkers for schizophrenia reflects not only technical limitations but also the historical construction of the disorder as a heterogeneous clinical category. Understanding this historical context is essential for interpreting current findings in brain sciences. Future research is likely to benefit from stratification-based, dimensional, and integrative frameworks that move beyond categorical diagnosis while preserving clinical relevance. Full article

Background/Objectives: This article is a narrative review that examines the development of attachment from intrauterine life to the first thousand days of a child’s life, integrating psychoanalytic, neuroscientific, genetic, and cross-cultural perspectives. Biological, relational, neurological, and cultural factors interact and shape individual differences in socio-emotional functioning. This paper aims to propose a reinterpretation of early attachment, describing it as both a clinical and relational phenomenon and an adaptive process inscribed in human evolutionary history, according to the Four-Domain Integrative Framework described herein. Methods: The review examined three main areas of evidence: early attachment characteristics, cross-cultural caregiving variations, and genetic and epigenetic mechanisms underlying environmental sensitivity. Results: The review first identified seven characteristics of early attachment (proximity seeking, emotional attunement, intrauterine experiences, maternal holding, security patterns, brain plasticity, and maternal stress) which represent developmental mechanisms that generate individual differences in trust, self-regulation, resilience, and psychopathological vulnerability. Second, cross-cultural variations in six distinct caregiving contexts were examined, demonstrating that secure attachment emerges through culturally specific pathways, differentially influencing motor development, sleep patterns, hypothalamic–pituitary–adrenal axis maturation, and social skills. Finally, the differential susceptibility model was provided through the analysis of five genetic and epigenetic systems (oxytocin receptor gene, serotonin transporter gene, dopamine receptor gene, glucocorticoid receptor methylation, and fetal programming) that modulate environmental sensitivity. Conclusions: Biological, relational, neurological, and cultural factors interact and shape individual differences in socio-emotional functioning. Full article

The recent paradigm shift within cognitive neuroscience toward predictive processing appears to align with many psychoanalytic conceptualizations of the mind. In this article, we argue that several psychoanalytic concepts, such as projection, transference, wish-fulfillment, and perceptual identity, are particularly compatible with the current neuroscientific conception of the brain as a prediction machine. Specifically, we propose that the concept of projection as used in modern psychoanalysis to explore subjective experience and fantasies is closely analogous to the concept of prediction as it is used to explain the fundamental cognitive functions of the brain. We discuss the implications of this parallelism for understanding the role of homeostasis in psychoanalysis and cognitive neuroscience, and we also discuss the parallels between insight and surprise in these two fields of mental science. Limitations in drawing parallels between projection and prediction are also addressed. By integrating these two fields, we envision the possibility of tackling subjectivity scientifically. Full article

Hypnosis has traditionally been conceptualized as a clinical technique for reducing physiological symptoms (e.g., pain, nausea) and psychological symptoms (e.g., anxiety, intrusive thoughts), yet emerging neuroscientific evidence suggests it operates through the fundamental mechanisms of emotional regulation and self-integration. This integrative review synthesizes research on clinical hypnosis from cognitive neuroscience, affective science, and clinical practice to examine how hypnotic phenomena modulate large-scale brain networks—particularly the default mode network (DMN), executive control network (ECN), and salience network (SaN)—to reorganize emotional experience and self-referential processing. We propose a formal mechanistic model in which hypnotic induction produces heightened experiential plasticity through coordinated network reconfiguration, enabling adaptive emotion regulation and reduced dissociative fragmentation. Central to this framework is the construct of Fundamental Peace (FP), operationalized as a dynamic neuro-experiential state characterized by: (1) flexible attentional control without effortful suppression; (2) emotional coherence across self-states; (3) reduced self-referential rigidity; (4) compassionate self-awareness. Unlike equanimity (affective neutrality) or well-being (positive evaluation), Fundamental Peace represents integrated regulatory capacity under changing conditions. Key findings from neuroimaging studies demonstrate that hypnotic states consistently reduce DMN activity, enhance ECN-SaN coupling, and modulate connectivity patterns associated with self-referential processing. Meta-analytic evidence from 85 controlled experimental trials shows robust pain reduction effects, while clinical studies document improvements in trauma-related dissociation and emotional dysregulation. We critically evaluate this framework against alternative theories (dissociated control, cold control, predictive processing, social-cognitive models), specify testable predictions, and assess evidence quality across neuroimaging and clinical domains. Implications for trauma treatment, clinical implementation, and future research integrating causal inference methods are discussed, alongside ethical and cultural considerations. Full article

Objective: Accurate odor classification from EEG signals requires informative and interpretable features. Although Local Binary Pattern (LBP) and variants such as the center-symmetric binary pattern are widely used, they lack sufficient explainability and tensor-level implementations. Additionally, neuroscientific understanding of odor processing remains limited. Methods: We propose Tensor Center-Symmetric Binary Pattern (TensorCSBP), a novel tensor-based feature extractor designed for EEG odor analysis. TensorCSBP is integrated into an explainable feature engineering (XFE) pipeline with four steps: (1) TensorCSBP for feature generation, (2) CWNCA for feature selection, (3) tkNN classifier for decision making, and (4) DLob method for symbolic interpretability. Results: TensorCSBP XFE was evaluated on a newly collected 32-channel EEG dataset for odor detection. It achieved 96.68% accuracy under 10-fold cross-validation. Conclusions: The information entropy of the DLob symbol sequence was 3.5675, demonstrating the richness of the interpretability output. Significance: This study presents a high-accuracy, explainable, and computationally efficient model for EEG-based odor classification. TensorCSBP bridges low-level signal patterns with symbolic neuroscience insights, offering real-time potential for BCI and clinical applications. Full article

Recent advancements in XAI have radically changed the way that AI systems are evaluated, as transparency and trustworthiness are now valued as highly as performance. This is especially true in medical applications, as, in order for such tools to be used in practical applications, interpretability is a key requirement for clinical adoption. Electroencephalography (EEG) analysis, in particular, has seen a significant rise in research, as the difficult and complex nature of EEG signals benefits from these methods, enabling researchers and practitioners to gain new insights from the vast amount of data that is now available. This survey presents a comprehensive analysis of the latest trends and advancements in XAI for EEG analysis. First, we provide a brief overview of fundamental EEG tasks, available datasets, and AI model approaches used for analysis. Then, we classify XAI methods using well-established taxonomies in XAI research, such as locality and generalization of explanations. By exploring all relevant XAI techniques in EEG analysis, our study offers researchers a clear perspective on the current state of the field and identifies potential research gaps. Our review indicates that current XAI approaches for EEG often face limitations in robustness, consistency, and neuroscientific grounding. These findings highlight the need for more reliable and domain-informed explainability methods to support trustworthy EEG analysis in research and clinical practice. 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

(1) Background: Mental health disparities persist across culturally diverse populations despite robust cognitive–behavioral therapy (CBT) efficacy evidence. Cultural neuroscience suggests that neurobiological processes underlying therapeutic mechanisms may exhibit culturally variable patterns, yet integration of neuroscientific frameworks into culturally adapted interventions remains limited. (2) Methods: Following PRISMA 2020 guidelines, we systematically searched PubMed/MEDLINE, PsycINFO, Scopus, and Web of Science (January 2014–December 2024) for peer-reviewed studies examining CBT interventions targeting depression, anxiety, PTSD, or psychological distress in culturally diverse populations. Ninety-four studies were synthesized using narrative methods; methodological heterogeneity precluded meta-analytic pooling. (3) Results: Culturally adapted CBT interventions consistently demonstrated superior outcomes compared to standard protocols across diverse populations. Group formats showed exceptional retention in collectivistic cultures, while hybrid technology-enhanced models achieved strong completion rates across contexts. Cultural adaptation enhanced engagement (e.g., 84% vs. 52% retention in refugee populations) and maintenance of treatment gains. Individual studies reported effect sizes ranging from d = 0.29 to d = 2.4; substantial within-group variability was observed, and identified patterns likely reflect learned cultural adaptations rather than inherent biological differences. Direct neuroimaging evidence within included studies remained limited (13.8%). (4) Conclusions: The evidence supports culturally adapted interventions as essential for equitable mental health outcomes. Cultural experiences may influence therapeutic processes, suggesting potential benefit from considering culturally variable processing patterns alongside universal mechanisms. However, conclusions regarding specific neural pathways remain preliminary, and individual assessment remains paramount, with cultural background representing one factor among many in treatment planning. Full article

Neuroscience adopts a multidimensional approach to decode thoughts and actions originating inside the brain, also called Brain Computer Interface (BCI). However, achieving high accuracy in the electroencephalography signal-based decoding remains a challenge and an open research topic in BCI research. This study aims to enhance the accuracy of signal classification for identifying human emotional states. We utilized the publicly available EEG–Audio–Video (EAV) dataset that comprises EEG recordings from 42 subjects across five emotional categories. Our key contribution is to exploit the two-dimensional contrast enhancement applied to the spectrogram for feature extraction, followed by classification using the EEGNet model. As a result, 12.5% improvement in classification accuracy over the baseline was achieved. This contribution demonstrates a potential advancement in BCI-based EEG signal processing in neuroscientific research. Full article

Background: Although the Shared Affective Movement Experience (SAME) model suggests the crucial role of imitation and synchronization in music-induced emotion, their application in teaching settings remains largely unexplored. Objectives: This study compared the “Body Rhythm Teaching Method,” based on the principle of mimicking musical elements through bodily movements, with traditional lecture-based instruction. It examined the effects of both teaching approaches on brain activation patterns, measured via functional Near-Infrared Spectroscopy (fNIRS) hyperscanning and instructional outcomes (assessed through musical emotion processing and teaching quality evaluations). The aim was to investigate their efficacy in enhancing students’ musical emotional processing abilities. Methods: A total of 3 teachers and 103 student participants were randomly assigned to the lecture teaching group (n = 35), the body rhythm teaching group (n = 35), or the control group (n = 33). The musical materials used across all three groups were identical, with only the teaching methods differing. fNIRS hyperscanning imaging was employed throughout the process to record brain activity. Results: Results indicate that the body rhythm group significantly outperformed other groups in both behavioral and neural metrics. Specifically, during the post-test music-listening phase, participants in this group not only reported higher emotional arousal but also exhibited stronger activation levels in the bilateral frontopolar cortex (FPC) associated with multisensory integration—both significantly higher than those in the lecture group and control group. Furthermore, during instruction, students in the body rhythm group rated teaching quality higher and exhibited significantly stronger teacher–student IBS across multiple brain regions involved in socio-emotional processing. These included the left orbitofrontal cortex (lOFC) for interoceptive emotion processing, the left frontopolar cortex (lFPC) for multisensory integration, and the right superior temporal gyrus (rSTG) for social interaction. In contrast, the lecture teaching group only showed significantly higher emotional valence ratings compared to the control group. Conclusions: This study confirms the role of imitation and synchronization mechanisms in the SAME model for music-induced emotional responses, providing a neuroscientific basis for teaching practice. Full article

Third-wave cognitive-behavioral therapies (CBT3) have progressively shifted the focus of psychotherapy from symptom reduction to process-based and transdiagnostic mechanisms of change, emphasizing self-identification as a core dimension. Within this evolution, the Self-Identification Program (SIP) represents a conceptual and clinical advancement particularly relevant to mood disorders, where maladaptive self-identification, rumination, and self-judgment play central roles. SIP directly targets dysfunctional self-identification—the reification of transient and maladaptive mental contents as defining features of a self—through a framework integrating the three levels of CBT3: mindfulness (CBT3.1), loving/kindness and compassion (CBT3.2), and deconstructive insight into the nature of a self (CBT3.3). Theoretically, SIP aligns with dimensional psychiatry (AMPD, HiTOP, RDoC) and recent advances in behavioral linguistics (Relational Frame Theory) and psychotherapy (Process-Based Behavioral Therapy). By integrating linguistic, affective, and neuroscientific perspectives, SIP bridges contextual behavioral science and contemplative practice, offering a unified, process-based model of identity transformation. Clinically, SIP extends CBT3 beyond mindfulness and loving/kindness and/or compassion training to specifically address the mechanism by which self-identification becomes a source of suffering—namely, the mistaken identification with an independent and permanent self. In doing so, SIP provides a novel, mechanistically grounded pathway toward enduring change in depressive and bipolar spectrum disorders. Full article

The concept of Brain Health is transforming the neuroscientific landscape, promoting an integrative and preventive approach to care under a unifying vision. This position paper, developed by Swiss junior societies in neurology and psychiatry, presents a trainee perspective on how Brain Health should be addressed from the earliest stages of postgraduate training. It explores current gaps in postgraduate training, including the continued separation of neurology, psychiatry and other specialties involved in brain disorder care, limited interdisciplinary and interprofessional exposure, and gaps in leadership, public health, and advocacy skills. We highlight promising models such as Switzerland’s integrated training components and the proposed “brain medicine” framework, inspired by internal medicine. Additionally, we examine innovative initiatives from trainee associations that promote collaborative learning, advocacy, and Brain Health awareness through academic and creative channels. The paper also stresses the importance of equitable global access to training, the integration of research into clinical education, and the urgent need to address burnout and working conditions among early-career professionals. By reframing trainees not as passive learners but as active agents of change, we call for systemic reforms that support their role in advancing Brain Health. Ultimately, we advocate for the development of international core competencies, adaptable curricula, and structured interdisciplinary pathways that embed Brain Health into every level of medical training. Only through this comprehensive approach can we equip the next generation of clinicians to promote lifelong Brain Health across specialties, systems, and populations. Full article