Search Results (62)

Journaling can support mental health and self-reflection, but traditional journaling requires users to act simultaneously as reflector, facilitator, and recorder, which may increase cognitive load and potentially hinder sustained practice or contribute to rumination. This study proposes the Reflective Conversational Journal (RCJ), an AI-based system in which AI supports facilitation and recording while users focus on reflection. Grounded in cognitive load theory, Rogers’ person-centered counseling principles, and Socratic questioning, RCJ was designed around three principles: contextual connectivity, structured recording, and empathy and questioning. A prototype integrating an AI agent, a template engine, and a client application was developed as a context-aware human–AI interaction system. Four experts in journaling and psychological counseling evaluated RCJ over one week and completed a post-use evaluation comprising Likert-scale items and open-ended questions. The mean score across the nine design-validity and implementation-fidelity items was 4.67/5 (SD = 0.48). Experts perceived contextual linking as useful for recognizing behavioral patterns and automatic structuring as helpful for reducing recording burden. However, limited depth in questions and interaction fatigue from frequent questioning were identified as areas for improvement. The findings provide preliminary evidence of design validity and implementation fidelity rather than objective evidence of cognitive-load reduction or clinical effectiveness. RCJ operationalizes a complementary human–AI role structure in which AI supports facilitation and recording while the user retains the reflector role. Full article

Dementia with Lewy bodies (DLB) is increasingly conceptualised not merely as a disorder of neuronal death but as a primary synaptopathy in which the functional collapse of synaptic transmission and plasticity precedes, and predicts, neurodegeneration and clinical decline. Two genetic determinants dominate the heritable risk architecture of DLB: the α-synuclein gene SNCA, in which both copy-number variation and missense mutations exert dose- and conformation-dependent effects, and GBA1, encoding the lysosomal hydrolase glucocerebrosidase (GCase), the single most influential genetic risk factor for the disease. Here we synthesise evidence that these loci converge on a shared pathogenic endpoint—the impairment of activity-dependent synaptic plasticity. We argue that GBA1 loss-of-function and the resulting accumulation of glucosylceramide stabilise specific neurotoxic α-synuclein proteoforms, including soluble oligomers and self-templating conformational strains bearing defined post-translational modifications. These proteoforms are trafficked to, and enriched within, presynaptic terminals, where they disrupt SNARE-complex assembly and synaptic-vesicle dynamics, while postsynaptically they perturb NMDA and AMPA receptor trafficking, dysregulate dendritic calcium, and compromise synaptic mitochondrial bioenergetics. The net consequence is a metaplastic shift away from long-term potentiation (LTP) and toward aberrant long-term depression (LTD), a signature of synaptic failure detectable before frank pathology. We map these molecular events onto disease-relevant circuits—particularly the cholinergic basal forebrain and hippocampal–cortical and thalamocortical networks—and relate them to the defining neuropsychiatric features of DLB, including cognitive fluctuations and recurrent visual hallucinations. Finally, we evaluate emerging therapeutic strategies that target the GBA1–α-synuclein axis and that aim to restore synaptic plasticity directly. Positioning DLB within the framework of genetically determined plasticity deficits clarifies its kinship with other neuropsychiatric disorders and identifies the synapse as the most tractable node for early, disease-modifying intervention. We further examine how GBA1 allele severity and zygosity grade the phenotype, which genetic and environmental factors modify penetrance in carriers, and what distinguishes this synaptopathy from those driven by PSEN1/PSEN2, MAPT, or HTT, and we summarise the therapeutic pipeline—including enzyme augmentation and adeno-associated viral GBA1 gene therapy—that targets it. Full article

In psychotherapy, case formulation can organize clinically relevant information into a coherent account of how psychological difficulties emerge, persist, and may change. The Personal Meaning System Fuzzy Cognitive Map (PMS-FCM) represents the client’s bipolar construct system as a weighted directed graph, whereas the FCM-FRHP (Fuzzy Cognitive Map of Human Problem Formation and Resolution) provides a professional functional reference model for problem formation and resolution. This paper proposes a semantically constrained method for identifying systemic archetype-inspired configurations in PMS-FCM representations enriched with FCM-FRHP semantics. Rather than importing classical systemic archetypes directly, the method reformulates them as configurable graph templates adapted to intrapersonal bipolar construct systems. Detection combines FCM-FRHP functional roles, predefined semantic-affinity rules and PB-based structural criteria, edge-weight thresholds, and ranking criteria. The goal is to support the traceable identification of static structures that may inform the examination of clinically relevant systemic hypotheses, without treating them as diagnoses or evidence of observed temporal dynamics. The pipeline combines property-graph querying, RDF/SHACL conformance checking, ranked materialization, and rule-based trace generation. A local language model is used only after detection and conformance checking, as a constrained graph-to-text layer grounded in graph evidence and FCM-FRHP semantics. The approach offers a formally specified and reproducible method for conducting explicit, auditable pattern-level analysis of psychological case formulations. Full article

This article proposes a formal epistemological and methodological framework for the analysis of lithic technology, moving beyond traditional optimization-oriented models and normative typologies. Grounded in Mario Bunge’s socio-natural science and Herbert Simon’s theory of bounded rationality, we introduce the concept of legaliform (law-like) principles. These principles are defined as stable, necessary relationships between technical procedures and morphological outcomes, dictated by the physics of brittle fracture and enacted through procedural memory. To enhance analytical precision and cross-cultural comparability, we develop a semi-formal symbolic notation that identifies the core “techno-scientific” operators underlying lithic reduction. We argue that stone tool morphology is not a static reflection of a “mental template,” but a dynamic sequence of technically adequate states achieved through satisficing behavior. By framing these legaliform regularities as epistemological meta-standards, this article establishes a standardized framework for data reproducibility, verification, and quality control in technological analysis. The framework identifies eight primary legaliform principles—including bifacial reduction (PBR), thermal alteration (PTT), and maintenance and reactivation (PMR)—illustrating how they structure technical action under material and cognitive constraints. The utility of this approach is demonstrated through a case study of Paleoindian Fell projectile points in South America. By operationalizing the Maintenance and Reactivation Principle (PMR), we explain the high morphological variability of these points as a predictable result of life-history and functional restoration rather than cultural “drift” or failed design. This perspective provides a rigorous basis for identifying Guided Convergence in the archaeological record, offering a scientific alternative to diffusionist explanations and purely morphological comparisons that often overlook the underlying techno-scientific regularities of lithic practice. Full article

Recent generative art outputs across music, literature, painting and moving-image media have attracted extensive scholarly and public interest, yet evaluations of their creative capacities are mostly limited to informal observational accounts. Drawing on neuroaesthetic reasoning, this paper puts forward a dual-layer analytical framework that differentiates two distinct modes of aesthetic reception: Alignment, defined as statistical template matching, and evocation, referring to the novel association of scattered embodied memory fragments. Building on this binary categorization, the study introduces the tentative Ring Scale taxonomy—a figurative target-shooting metaphor rather than quantitative metric—as a purely descriptive tool for stratifying relative aesthetic evocation intensity. This framework further unpacks the neurocognitive underpinnings of auditory, visual and textual aesthetic pathways, alongside their combined multimodal interactions within film and television works. It tentatively accounts for why generative systems tend to deliver more cohesive aesthetic outcomes within the auditory domain, and hypothesises a present functional limitation of current large models: these systems perform comparatively well within Alignment-driven aesthetic effects, while layered high-order evocation remains constrained by inherent structural limitations of statistical training architectures. From this diagnostic observation, three directional paradigm shifts for human–AI collaborative creation are outlined: shifting from human substitution to human–machine complementarity, shifting from exhaustive template imagery generation to targeted latent fragment elicitation, and shifting from optimising figurative Ring-tier descriptive labels to pursuing transformative aesthetic fission effects. The study frames imaginative cognition as the central driving force behind fruitful human–AI co-creation, and positions prompt engineering as the actionable operational bridge connecting human imaginative thought to machine-executable generative parameters. Three tentative prompt design tactics are then elaborated: physiological arousal framing, multisensory scenario simulation prompts, and intentional strategic blank-leaving. Additionally, this work discusses the plausible constructive functions of model hallucination phenomena when viewed through the lens of high-tier aesthetic evocation, rather than merely framing such outputs as technical errors. All judgments and tier comparisons raised throughout the paper are framed as unvalidated observational hypotheses open to empirical testing. To facilitate follow-up empirical scrutiny, the paper collates a full set of testable hypotheses derived from its theoretical reasoning and outlines feasible experimental validation pipelines, with an open call for controlled empirical research to corroborate or refine the proposed qualitative framework. Full article

Real-world IoT network security generates traffic at big-data scale with extreme class imbalance, temporal non-stationarity, and continuously evolving attack strategies that overwhelm static supervised classifiers. This paper presents a cognitive computing framework for network intrusion detection: a CNN–LSTM–DQN architecture with Prioritized Experience Replay (PER) evaluated on a 5,000,000-flow naturalistic sample of the TON_IoT Processed_Network dataset (4,000,000 training/1,000,000 temporally held-out test flows; 94.5% attack ratio) under a strict temporal split. The cognitive agent optimizes detection decisions using an Alerts per Million Flows (ARMF)-aware reward function that encodes both alert-fatigue cost and missed-attack penalty. We conduct a cross-attack-family generalization study: the methodology—architecture template, reward design, and hyperparameter calibration—is inherited from a framework previously validated on CSE-CIC-IDS2018, re-instantiated and retrained on the structurally different TON_IoT environment, and compared against the previously published benchmark. Initialization sensitivity is characterized across five independent random seeds using paired Wilcoxon signed-rank and t-tests. Across the five seeds, the proposed X2 model attains recall 0.833 ± 0.306 and F1 0.874 ± 0.241 (mean ± sample SD), versus the supervised X1 baseline at 0.858 ± 0.178 and 0.912 ± 0.116; the best-performing seed (42) achieves 97.52% accuracy, 98.02% attack recall, 99.46% precision, and 98.73% F1-score on 1,000,000 held-out XSS flows—an attack family entirely absent from training—with temporal stability variances of 4.63 × 10⁻⁷ (recall) and 1.38 × 10⁻⁷ (F1). The X2 advantage observed among the four stable seeds is not statistically demonstrated at n = 5 (statistical power ≈ 5.1%); the initialization-sensitivity finding itself, including one degenerate alert-suppression seed, is reported as a primary contribution. A formal, exactly additive ARMF decomposition distinguishes the detected-attack (structural) component (99.46%) from the model-induced false-positive component (0.54%), and we report a multi-seed, ARMF-aware cognitive IDS evaluation on naturalistic TON_IoT traffic under an unseen-attack-family test condition that, to the best of our knowledge, has not been reported in the surveyed RL-based NIDS literature. Full article

Although GenAI has been increasingly applied in pre-service teacher education, limited evidence is available on how permitted but unscaffolded GenAI use affects pre-service teachers’ learning and professional development in authentic course contexts. Grounded in cognitive load theory and the zone of proximal development, this quasi-experimental study examined the effects of unscaffolded GenAI use in an 11-week instructional design course. Two intact sophomore classes at a normal university participated, with one class permitted to use GenAI without prompt templates or instructional guidance and the other not permitted to use GenAI. Data were analyzed using paired-samples t-tests and a one-way analysis of covariance (ANCOVA). After controlling for pretest scores, no significant group differences were found in AI readiness, self-regulated learning, or critical thinking, whereas the control group showed stronger instructional design performance. Within-group comparisons showed that both groups improved in AI readiness and instructional design performance, but not in self-regulated learning or critical thinking. These findings suggest that, in this course context, unscaffolded GenAI access alone may be insufficient to support pre-service teachers’ professional learning and may be less favorable for their instructional design performance. Full article

Artificial intelligence (AI) has reshaped higher education over six decades, evolving from drill-and-practice programs to adaptive cognitive tutors and, most recently, transformer-based generative models. This article presents a critical review of publisher-built generative AI assistants, adopting an explicitly socio-technical perspective that combines a technological lens with a pedagogical one. It makes three contributions. First, it synthesizes the technical and algorithmic evolution of educational AI, from rule-based and expert systems through knowledge tracing and learning analytics to large language models and retrieval-augmented generation, and organizes these mechanisms into a taxonomy. Second, it introduces a reproducible evaluation framework that couples the TRIAD rubric (Trust, Relevance, Impact, Adoption, and Design) with a Jobs-to-Be-Done (JTBD) lens, complete with anchored scoring criteria, an evidence-and-confidence grading scheme, and reported inter-rater reliability. Third, it applies the framework to eleven assistants released by U.S. publishers, distinguishing peer-reviewed evidence from institutional reports and commercial claims. The analysis reflects a mid-2025 snapshot and is presented as a reusable template rather than a static ranking. Findings reveal substantial variation in privacy safeguards, curricular alignment, documented impact, adoption, and usability. The review identifies application scenarios and recommendations for researchers and institutional leaders seeking to guide the responsible integration of AI in higher education. Full article

Prime numbers are central to mathematics, yet popular discourse often treats particular primes as if they carried intrinsic messages, personalities, or moral charge. This study asks how that shift from legitimate curiosity to superstition-adjacent pattern making occurs and why it feels persuasive. Using qualitative content analysis of three widely circulated media examples, this paper maps how culturally specific number meanings are produced and transmitted, and how predictable cognitive biases support their plausibility. The analysis pairs anthropological mechanisms of symbolic association, prestige borrowing, community boundary marking, and meme-based diffusion with psychological mechanisms that include Type I error, apophenia, confirmation bias, availability, narrative fallacy, selection effects, survivorship, cultural priming, and authority or celebrity cueing. Across the cases, the results show a recurrent coupling: cultural schemas supply ready-made interpretive templates, while cognitive biases turn salience and coincidence into perceived significance, concentrating attention on narratively convenient primes and obscuring the many alternative patterns that could have been selected. This paper concludes that meanings such as 666 as evil are culture dependent rather than mathematical properties, and that improving public communication about primes requires making selection processes and interpretive frames explicit while preserving legitimate mathematical wonder. Full article

The rapid integration of artificial intelligence into private and public-sector decision-making has outpaced the development of standards governing the interaction between human judgment and machine intelligence. Existing frameworks—the EU AI Act Regulation, the NIST AI Risk Management Framework, and ISO/IEC 42001—regulate AI systems as discrete technical artifacts but do not standardize the hybrid intelligence configurations in which human cognition and algorithmic outputs jointly produce governance decisions. This paper proposes a three-layer standards framework comprising technical interoperability standards governing how AI outputs are communicated to human decision-makers, procedural standards governing human-AI task allocation and escalation protocols, and accountability standards governing responsibility attribution in distributed decision configurations. The framework is grounded in the Quadruple Bottom Line (QBL), which adds governance as a fourth sustainability dimension. To move beyond a purely conceptual contribution, the paper provides operationalization tools—including a role allocation matrix, confidence calibration thresholds, an accountability mapping template, and a domain classification schema—and proposes a three-tier conformity assessment methodology for evaluating framework implementation. By establishing the hybrid human–AI decision configuration as the unit of standardization, the paper introduces a governance architecture that enables operational, auditable, and comparable hybrid intelligence systems. Full article

Generative artificial intelligence (AI) is increasingly used in higher education as an interactive tutoring partner rather than a passive information tool. While AI offers opportunities to support learning, concerns remain regarding cognitive offloading, reduced engagement, and unreflective use. Although instructional scaffolding is a well-established design principle for supporting complex learning, its role in shaping cognitive and metacognitive processes in AI-supported settings remains underexplored. This quasi-experimental pre–post study examined how varying levels of scaffolding influence learning outcomes and motivational, cognitive and metacognitive processes during AI-tutored learning. A total of 175 first-semester students from two faculties and diverse academic backgrounds completed the same academic task within a four-hour university session under one of three conditions: (1) full scaffolding, including a structured prompting template based on the Goal–Context–Constraints (GCC) strategy, iterative refinement, and reflective guidance; (2) light scaffolding, including the GCC prompting template; or (3) no scaffolding template as the control condition. Measures included knowledge gain, motivation, cognitive load, critical thinking, and reflective use. Data were analysed using ANOVAs, ANCOVAs, regression models, and PROCESS moderation and mediation analyses. Across the conditions, students showed significant gains in knowledge, critical thinking, and reflective use, while motivation remained stable and intrinsic and extraneous cognitive load decreased; no significant differences between scaffolding conditions were observed. The scaffolding conditions did not produce significant interaction effects, although descriptive trends suggested higher gains in higher-order knowledge under scaffolded conditions. Overall, the findings suggest that short-term learning gains in AI-supported settings may not depend on scaffolding intensity alone, but rather on how learners engage with AI during the learning process. Full article

Evaluating emotional understanding in Large Language Models (LLMs) is challenging because assessments are subjective, ambiguous, multidimensional, and sensitive to controllable generation parameters. We developed a unified mathematical framework for characterizing LLM “emotional individuality” that integrates softmax sampling–temperature control (the decoding-time temperature parameter exposed by the API and typically used to modulate output randomness during token generation), fuzzy set theory with Shannon-type fuzzy entropy, and persona-based cognitive diversity analysis. We evaluated 36 API-accessible LLMs from seven major vendors on Japanese literary texts, using four personas each assigned a sampling temperature ($T\in \{0.1,0.4,0.7,0.9\}$), yielding 4227/4320 trial responses (97.8% coverage), of which 4067/4227 contained valid numeric emotion scores (96.2%). Temperature controllability varied approximately 25-fold (${\kappa }_M\in [0.039,0.982]$) with both positive and negative temperature–variance relationships across models. Because each sampling temperature is deterministically assigned to a persona in our design, ${\kappa }_M$ should be interpreted as an operational temperature–variance association across persona conditions rather than an isolated causal temperature effect. The model-level mean fuzzy entropy ranged from approximately 0.40 to 0.66, and the numerical stability consistency scores ranged from approximately 0.548 to 0.780. We also observed text-dependent structure, including genre-specific variation in the Interest–Sadness relationship. For practitioners, the framework is most directly useful as a benchmark-design and model-screening template for structured emotion-scoring tasks; its empirical conclusions remain limited to the present Japanese literary, text-only setting. Full article

In today’s AI-driven world, nurturing students’ capacity for independent, self-reflective learning is vital. They must build lifelong learning skills and develop personalized strategies through ongoing self-regulation. In this study, we employed a learning journal template to support self-regulated physics learning, highlighting the role of homework assignments designed to target different levels of cognitive domains. Our learning journal-supported approach aims to facilitate students’ preparation for lessons at home. Guided questions help students review the content covered in previous classes and reflect on the effectiveness of the instructional methods applied. The intervention focused specifically on the physics topic of dynamics, examining how students’ conceptual understanding and performance developed within this domain. The efficacy of this approach was tested among 7th- and 9th-grade students. Results indicate that the learning journal-based method, combined with structured homework, had a positive impact on students’ performance within the topic of dynamics. Full article

Pareidolia is often framed as a viewer-side illusion: a tendency to perceive meaningful forms—especially faces—in ambiguous inputs. This Concept Paper argues that pareidolia can also be deliberately engineered and therefore provides a tractable entry point into the neurophysiology of visual creativity. We propose a unifying construct in which engineered pareidolia functions as externally scaffolded mental imagery: minimal visual constraints recruit internally generated templates and top-down inference while remaining anchored to sensory input. To strengthen theoretical rigor, we define necessary and sufficient features that distinguish this construct from adjacent accounts (scaffolded cognition; perceptual scaffolding; bistable perception). Using Arcimboldo’s composite portraits and Dürer’s embedded face in View of the Arco Valley, plus a canonical Renaissance example (Leonardo’s Bacchus/Saint John the Baptist), we outline distinct “design regimes” that modulate cue validity, attentional release, and interpretive switching. We then connect engineered pareidolia to creativity research by linking pareidolia design and detection to measurable constructs in divergent/creative perception, including but not limited to Torrance-style domains, and we propose feasible behavioral and neurophysiological paradigms that control for artistic skill and clinical status. Finally, we distinguish benign pareidolia from hallucination, discuss clinical resonance in dementia with Lewy bodies where pareidolia can be quantified, and outline an empirically testable research program that reframes pareidolia as a bridge between imagination, perception, and creativity. Full article

This paper introduces a biomimetic framework and novel brain-inspired AI (BIAI) models based on spiking neural networks (SNNs) for emotional state recognition from audio (speech), visual (face), and integrated multimodal audio–visual data. The developed framework, named eXCube2, uses a three-dimensional SNN architecture NeuCube that is spatially structured according to a human brain template. The BIAI models developed in eXCube2 are trainable on spatio- and spectro-temporal data using brain-inspired learning rules. Such models are explainable in terms of revealing patterns in data and are adaptable to new data. The eXCube2 models are implemented as software systems and tested on speech and video data of subjects expressing emotional states. The use of a brain template for the SNN structure enables brain-inspired tonotopic and stereo mapping of audio inputs, topographic mapping of visual data, and the combined use of both modalities. This novel approach brings AI-based emotional state recognition closer to human perception, provides a better explainability and adaptability than existing AI systems. It also results in a higher or competitive accuracy, even though this was not the main goal here. This is demonstrated through experiments on benchmark datasets, achieving classification accuracy above 80% on single-modality data and 88.9% when multimodal audio–visual data are used, and a “don’t know” output is introduced. The paper further discusses possible applications of the proposed eXCube2 framework to other audio, visual, and audio–visual data for solving challenging problems, such as recognizing emotional states of people from different origins; brain state diagnosis (e.g., Parkinson’s disease, Alzheimer’s disease, ADHD, dementia); measuring response to treatment over time; evaluating satisfaction responses from online clients; cognitive robotics; human–robot interaction; chatbots; and interactive computer games. The SNN-based implementation of BIAI also enables the use of neuromorphic chips and platforms, leading to reduced power consumption, smaller device size, higher performance accuracy, and improved adaptability and explainability. This research shows a step toward building brain-inspired AI systems. Full article