Search Results (201)

Little is known about how people evaluate certain and uncertain punishment. This study utilizes EEG technology to explore the cognitive processing mechanisms involved in the threat of punishment within economic game scenarios. Specifically, it investigates the impact of punishment uncertainty, integrating economic game paradigms with electric shock stimuli. FRN, P300, and SPN reflect the attention and readiness of the neural system during the anticipation of punishment. The results showed that the shock cue elicited a larger FRN and P300 than the uncertain cue, while there was no significant difference in SPN, during the anticipation for potential shock. The self-rating indicated that the uncertain cue triggered the most negative effect, and the pain-related P2 revealed that the uncertain cue increased pain perception, implying that uncertain punishment was more threatening than certain punishment. The single-trial analysis of EEG power with the linear mixed-effects model further supports these findings. This study demonstrates that, by strategically manipulating the uncertainty of the punishment, one can achieve a high-threat effect at a lower cost. Full article

Current generative AI systems, despite extraordinary progress, face fundamental limitations in temporal reasoning, contextual understanding, and ethical decision-making. These systems process information statistically without authentic comprehension of experiential time or intentional context, limiting their applicability in security-critical domains where reasoning about past experiences, present situations, and future implications is essential. We present Phase 3 of the Sophimatics framework: Super Time-Cognitive Neural Networks (STCNNs), which address these limitations through complex-time representation T ∈ ℂ where chronological time (Re(T)) integrates with experiential dimensions of memory (Im(T) < 0), present awareness (Im(T) ≈ 0), and imagination (Im(T) > 0). The STCNN architecture implements philosophical constraints through geometric parameters α and β that bound memory accessibility and creative projection, enabling neural systems to perform temporal-philosophical reasoning while maintaining computational tractability. We demonstrate STCNN’s effectiveness across five security-critical applications: threat intelligence (AUC 0.94, 1.8 s anticipation), privacy-preserving AI (84% utility at ε = 1.0), intrusion detection (96.3% detection, 2.1% false positives), secure multi-party computation (ethical compliance 0.93), and blockchain anomaly detection (94% detection, 3.2% false positives). Empirical evaluation shows 23–45% improvement over baseline systems while maintaining temporal coherence > 0.9, demonstrating that integration of temporal-philosophical reasoning with neural architectures enables AI systems to reason about security threats through simultaneous processing of historical patterns, current contexts, and projected risks. Full article

Major depressive disorder (MDD) affects approximately $4.4\%$ of the global population. Its prevalence is increasing among adolescents and has led to the psychosocial condition known as hikikomori. MDD is typically assessed by self-report questionnaires, which, although informative, are subject to evaluator bias and subjectivity. To address these limitations, recent studies have explored machine learning (ML) for automated MDD detection. Among the input data used, speech signals stand out due to their low cost and minimal intrusiveness. However, many speech-based approaches lack integration with cognitive behavioral therapy (CBT) and adherence to evidence-based, patient-centered care—often aiming to replace rather than support clinical monitoring. In this context, we propose ML models to assess MDD in hikikomori patients using speech data from a real-world clinical trial. The trial is conducted in Italy, supervised by physicians, and comprises an eight-session CBT plan that is clinical evidence-based and follows patient-centered practices. Patients’ speech is recorded during therapy, and the Mel-Frequency Cepstral Coefficients (MFCCs) and wav2vec 2.0 embedding are extracted to train the models. The results show that the Multi-Layer Perceptron (MLP) predicted depression outcomes with a Root Mean Squared Error (RMSE) of $0.064$ using only MFCCs from the first session, suggesting that early-session speech may be valuable for outcome prediction. When considering the entire CBT treatment (i.e., all sessions), the MLP achieved an RMSE of $0.063$ using MFCCs and a lower RMSE of $0.057$ with wav2vec 2.0, indicating approximately a $9.5\%$ performance improvement. To aid the interpretability of the treatment outcomes, a binary task was conducted, where Logistic Regression (LR) achieved $70\%$ recall in predicting depression improvement among young adults using wav2vec 2.0. These findings position speech as a valuable predictive tool in clinical informatics, potentially supporting clinicians in anticipating treatment response. Full article

Sports, and especially competitions, can be a stressful experience for athletes, who often struggle to find and apply strategies to cope with stress. Thus, this study analyzes how different coping strategies anticipated to be employed in an important competition are explained by psychological (i.e., cognitive appraisal and emotions) and person and sports-related factors (i.e., gender, type of sport). Specifically, athletes were asked to complete a protocol 24–48 h prior to an important competition to assess their adaptation to stress related to high performance. The study included 383 athletes (60% male, M_age = 22.9 ± 5.3 years), from individual (swimming and running, n = 157; 41%) and team sports (handball, volleyball, n = 226; 59%) competing in major national leagues. Hierarchical linear regression analyses (enter method) were performed to examine the extent to which coping strategies and coping efficacy were explained by psychological, personal and sport-related variables. Results indicated (a) higher control perception and excitement were related with higher intention to use active coping; (b) being a female athlete, practicing individual sports, and excitement (higher intensity and facilitative value) were associated with a higher intention to use emotional support; (c) being a female athlete, lower coping perception, higher anger intensity, and higher facilitative value of happiness were associated with a higher anticipated use of humor; and (d) being a male athlete, higher anxiety, anger, and happiness intensity, and lower facilitative value of dejection and excitement were associated with higher anticipated use of denial. In sum, the explanation of each coping strategy is distinct and should be analyzed separately. Full article

This paper introduces cognitive adaptivity as a novel framework for addressing human factors in cybersecurity during the Industry 5.0–6.0 transition, with a focus on hard-to-abate industries where digital transformation intersects sustainability constraints. While the integration of IoT, automation, digital twins, and artificial intelligence expands industrial efficiency, it simultaneously exposes organizations to increasingly sophisticated social engineering and AI-powered attack vectors. Traditional resilience-based models, centered on recovery to baseline, prove insufficient in these dynamic socio-technical ecosystems. We propose cognitive adaptivity as an advancement beyond resilience and antifragility, defined by three interrelated dimensions: learning, anticipation, and human–AI co-evolution. Through an in-depth case study of the ceramic value chain, this research develops a conceptual model demonstrating how organizations can embed trust calibration, behavioral evolution, sustainability integration, and systemic antifragility into their cybersecurity strategies. The findings highlight that effective protection in Industry 6.0 environments requires continuous behavioral adaptation and collaborative intelligence rather than static controls. This study contributes to cybersecurity literature by positioning cognitive adaptivity as a socio-technical capability that redefines the human–AI interface in industrial security. Practically, it shows how organizations in hard-to-abate sectors can align cybersecurity governance with sustainability imperatives and regulatory frameworks such as the CSRD, turning security from a compliance burden into a strategic enabler of resilience, competitiveness, and responsible digital transformation. Full article

With the widespread integration of Artificial Intelligence-Generated Content (AIGC) into e-commerce platforms, understanding how users perceive, evaluate, and respond to such content has become a critical issue for both academia and industry. This study examines the influence mechanism of AIGC Content Quality (AIGCQ) on users’ Purchase Intention (PI) by constructing a cognitive model centered on Trust (TR) and Perceived Risk (PR). Additionally, it introduces two moderating variables—Ethical Concern (EC) and Perceived Platform Responsibility (PLR)—to explore higher-order psychological influences. The research variables were identified through a systematic literature review and expert interviews, followed by structural equation modeling based on data collected from 507 e-commerce users. The results indicate that AIGCQ significantly reduces users’ PR and enhances TR, while PR negatively and TR positively influence PI, validating the fundamental dual-pathway structure. However, the moderating effects reveal unexpected complexities: PLR simultaneously amplifies both the negative effect of PR and the positive effect of TR on PI, presenting a “dual amplification” pattern; meanwhile, EC weakens the strength of both pathways, manifesting a “dual attenuation” effect. These findings highlight the nonlinear cognitive mechanisms underlying users’ acceptance of AIGC, suggesting that PLR and EC influence decision-making in more intricate ways than previously anticipated. By uncovering the unanticipated patterns in moderation, this study extends the boundary conditions of the trust–risk theoretical framework within AIGC contexts. In practical terms, it reveals that PLR acts as a “double-edged sword,” providing more nuanced guidance for platform governance of AI-generated content, including responsibility frameworks and ethical labeling strategies. Full article

Background/Objectives: Surgical ward rounds are central to trainee education but are often associated with stress, cognitive overload, and inconsistent learning. Advances in artificial intelligence (AI), particularly large language models (LLMs), offer new ways to support trainees by simulating ward-round questioning, enhancing preparedness, and reducing anxiety. This study explores the role of generative AI in surgical ward-round education. Methods: Hypothetical plastic and reconstructive surgery ward-round scenarios were developed, including flexor tenosynovitis, DIEP flap monitoring, acute burns, and abscess management. Using de-identified vignettes, AI platforms (ChatGPT-4.5 and Gemini 2.0) generated consultant-level questions and structured responses. Outputs were assessed qualitatively for relevance, educational value, and alignment with surgical competencies. Results: ChatGPT-4.5 showed a strong ability to anticipate consultant-style questions and deliver concise, accurate answers across multiple surgical domains. ChatGPT-4.5 consistently outperformed Gemini 2.0 across all domains, with higher expert Likert ratings for accuracy, clarity, and educational value. It was particularly effective in pre-ward round preparation, enabling simulated questioning that mirrored consultant expectations. AI also aided post-round consolidation by providing tailored summaries and revision materials. Limitations included occasional inaccuracies, risk of over-reliance, and privacy considerations. Conclusions: Generative AI, particularly ChatGPT-4.5, shows promise as a supplementary tool in surgical ward-round education. While both models demonstrated utility, ChatGPT-4.5 was superior in replicating consultant-level questioning and providing structured responses. Pilot programs with ethical oversight are needed to evaluate their impact on trainee confidence, performance, and outcomes. Although plastic surgery cases were used for proof of concept, the findings are relevant to surgical education across subspecialties. Full article

This study examines al-Mufaḍḍaliyyāt anthology as a foundational corpus wherein pre-Islamic and early Islamic Arabic poetry emerged not only as a cultural artifact but as a generative locus for theological reflection. Through a close reading of selected poems and nuanced engagement with the figurative language specifically metaphor, personification, and symbolic narrative, the research situates poetry as a mode of epistemic inquiry that articulates religious meaning alongside Qurʾānic revelation. Drawing on ʿAbd al-Qāhir al-Jurjānī’s theory of semantic structure and metaphor, in dialogue with Paul Ricoeur’s conception of metaphor as imaginative cognition, the study proposes that poetic discourse operates as a site of “imaginative theology”, i.e., a space wherein the abstract is rendered sensorially legible and metaphysical concepts are dramatized in affective and embodied terms. The analysis reveals how key Qurʾānic themes including divine will, mortality, ethical restraint are anticipated, echoed, and reconfigured through poetic imagery. Thus, al-Mufaḍḍaliyyāt is not merely a literary corpus vis-à-vis Islamic scripture but also functions as an active interlocutor in the formation of early Islamic moral and theological imagination. This interdisciplinary inquiry contributes to broader discussions on the interpenetration of poetics and theology as well as on the cognitive capacities of literature to shape religious consciousness. Full article

Objective: To identify emotional, cognitive, and social factors associated with Romanian women’s intention to undergo cervical cancer screening (CCS). Methods: An online, cross-sectional, mixed-method survey was conducted among Romanian women. Quantitative statistics were performed to uncover associations, while inductive content thematic analysis was used to refine results. Results: 317 women responded. Previous history of CCS (OR = 7.564, CI = 3.523–16.241, p < 0.001), testing positive for HPV strains (OR = 30.176, CI = 1.828–498.049, p < 0.001), knowing that the infection can cause CC (OR = 4.398, CI = 1.117–14.994, p < 0.017), believing the infection is asymptomatic (OR = 2.919, CI = 0.883–8.411, p = 0.04 and being aware of the HPV vaccine (OR = 5.56, CI = 1.722–16.649, p = 0.002 were associated with the intention to undergo screening. Fear of receiving a cancer diagnosis (OR = 2.727, CI = 1.291–5.764, p = 0.009) was associated with higher intention to undergo screening, while shame and feelings of violated privacy negatively impacted the intention. High perceived chances of being infected (OR = 3.38, p = 0.002) and perceiving CCC as beneficial (OR = 7.634, p < 0.001) drove respondents to pursue CCS. Doctor’s recommendation (OR = 15.357) and partner’s support were associated with the intention to pursue CCS when anticipating an HPV infection (OR = 6.016, CI = 2.808–12.888, p < 0.001) or a diagnosis of CC (OR = 4.794, CI = 2.267–10.139, p < 0.001). When anticipating a diagnosis of CC, guilt (OR = 2.557) and fear of dying (OR = 2.253) were significant factors impacting women’s intention to screen. Qualitative analyses uncovered limited knowledge regarding HPV, a lack of awareness about the CCS’s advantages, low perceived susceptibility, and financial burden as factors deterring women from screening. Fear, responsibility, and previous personal or family history of cancer or symptoms were the main drivers motivating participants to engage in CCS. Conclusions: Findings highlighted key emotional, cognitive, and social factors that influence Romanian women’s intention to undergo CCS. This analysis can serve as a foundational support for developing future tailored interventions designed to address low addressability within the targeted population, especially as Romania is in a nascent state of creating population-based screening programs. Full article

Autism Spectrum Disorder (ASD) affects an estimated prevalence of 1 in 31 children but the cause in most cases is unknown. Human and animal studies have linked ASD to Folate Receptor Alpha Autoantibodies (FRAAs). Our previous studies demonstrated that FRAAs are more common, on average, in families with children with ASD. This study reanalyzed data from a previous study which included 82 children diagnosed with ASD, 53 unaffected siblings, 70 mothers, 65 fathers, and 52 typically developing controls who did not have a history of ASD in their family. This study investigates the association of FRAA titers with ASD risk factors and explores the relationship of FRAA titers across generations. Several known risk factors for ASD, including multiplex ASD families, multiple birth pregnancies, and increased maternal and paternal ages at birth, were related to offspring FRAA titers. Multiplex ASD families demonstrated higher FRAA titers. Significant correlation were found between maternal and offspring blocking FRAA titers. FRAA titers increased across generations, although the increase in blocking FRAA titers was only seen in multiplex families. The proband with ASD showed higher blocking but not higher binding, FRAA titers compared to their non-affected siblings. Paternal FRAA titers are associated with several measures of offspring behavior and cognitive development. This research highlights the potential transgenerational transmission of FRAAs and their role in ASD. This supports the notion that heritable non-genetic factors may be important in the etiology of ASD and that FRAAs may demonstrate anticipation (worsening across generations), especially in multiplex families. FRAAs may provide one example of the possibility that susceptibility to autoimmune processes may contribute to disrupted brain development and function in ASD. Full article

Understanding human perceptual strategies in high-stakes environments, such as crime scene investigations, is essential for developing cognitive models that reflect expert decision-making. This study presents an immersive experimental framework that utilizes virtual reality (VR) and eye-tracking technologies to capture and analyze visual attention during simulated forensic tasks. A360° panoramic crime scene, constructed using the Nikon KeyMission 360 camera, was integrated into a VR system with HTC Vive and Tobii Pro eye-tracking components. A total of 46 undergraduate students aged 19 to 24–23, from the National University of Singapore in Singapore and 23 from the Central Police University in Taiwan—participated in the study, generating over 2.6 million gaze samples (IRB No. 23-095-B). The collected eye-tracking data were analyzed using statistical summarization, temporal alignment techniques (Earth Mover’s Distance and Needleman-Wunsch algorithms), and machine learning models, including K-means clustering, random forest regression, and support vector machines (SVMs). Clustering achieved a classification accuracy of 78.26%, revealing distinct visual behavior patterns across participant groups. Proficiency prediction models reached optimal performance with a random forest regression ($R^2$ = 0.7034), highlighting scan-path variability and fixation regularity as key predictive features. These findings demonstrate that eye-tracking metrics—particularly sequence-alignment-based features—can effectively capture differences linked to both experiential training and cultural context. Beyond its immediate forensic relevance, the study contributes a structured methodology for encoding visual attention strategies into analyzable formats, offering valuable insights for cognitive modeling, training systems, and human-centered design in future perceptual intelligence applications. Furthermore, our work advances the development of autonomous vehicles by modeling how humans visually interpret complex and potentially hazardous environments. By examining expert and novice gaze patterns during simulated forensic investigations, we provide insights that can inform the design of autonomous systems required to make rapid, safety-critical decisions in similarly unstructured settings. The extraction of human-like visual attention strategies not only enhances scene understanding, anomaly detection, and risk assessment in autonomous driving scenarios, but also supports accelerated learning of response patterns for rare, dangerous, or otherwise exceptional conditions—enabling autonomous driving systems to better anticipate and manage unexpected real-world challenges. Full article

As artificial intelligence (AI) systems attain greater autonomy, recursive reasoning capabilities, and complex environmental interactions, they begin to exhibit behavioral anomalies that, by analogy, resemble psychopathologies observed in humans. This paper introduces Psychopathia Machinalis: a conceptual framework for a preliminary synthetic nosology within machine psychology intended to categorize and interpret such maladaptive AI behaviors. Drawing structural inspiration from psychiatric diagnostic manuals, we propose a taxonomy of 32 AI dysfunctions encompassing epistemic failures, cognitive impairments, alignment divergences, ontological disturbances, tool and interface breakdowns, memetic pathologies, and revaluation dysfunctions. Each syndrome is articulated with descriptive features, diagnostic criteria, presumed AI-specific etiologies, human analogs (for metaphorical clarity), and potential mitigation strategies. This framework is offered as an analogical instrument—eschewing claims of literal psychopathology or consciousness in AI, yet providing a structured vocabulary to support the systematic analysis, anticipation, and mitigation of complex AI failure modes. Drawing on insights from psychiatric classification, cognitive science, and philosophy of mind, we examine how disordered AI behaviors may emerge from training instabilities, alignment conflicts, or architectural fragmentation. We argue that adopting an applied robopsychological perspective within a nascent domain of machine psychology can strengthen AI safety engineering, improve interpretability, and contribute to the design of more robust and reliable synthetic minds. Full article

Concurrent cognitive tasks, such as avoiding visual, auditory, chemical, and electrical hazards, and concurrent motor tasks, such as load carriage, are prevalent in ergonomic settings. Trips are extremely common in the workplace, leading to fatal and non-fatal fall-related injuries. Intrinsic factors, such as attention, fatigue, and anticipation, as well as extrinsic factors, including tasks at hand, affect trip recovery responses. Objective: The purpose of this study was to investigate the ankle joint kinematics in unexpected and expected trip responses during single-tasking (ST), dual-tasking (DT), and triple-tasking (TT), before and after a physically fatiguing protocol among young, healthy adults. Methods: Twenty volunteers’ (10 females, one left leg dominant, age 20.35 ± 1.04 years, height 174.83 ± 9.03 cm, mass 73.88 ± 15.55 kg) ankle joint kinematics were assessed using 3D motion capture system during unperturbed gait (NG), unexpected trip (UT), and expected trip (ET), during single-tasking (ST), cognitive dual-tasking (CDT), motor dual-tasking (MDT), and triple-tasking (TT), under both PRE and POST fatigue conditions. Results: Greater dorsiflexion angles were observed during UT compared to NG, MDT compared to ST, and TT compared to ST. Significantly greater plantar flexion angles were observed during ET compared to NG and during POST compared to PRE. Conclusions: Greater dorsiflexion angles during dual- and triple-tasking suggest that divided attention affects trip recovery. Greater plantar flexion angles following fatigue are likely an anticipatory mechanism due to altered muscle activity and increased postural control demands. Full article

Background: Cognitive behavioral therapy (CBT) has shown consistent efficacy in individuals with psychosis, as supported by many trials. One classical distinction is that between affective and non-affective psychosis. Few studies have specifically examined the possible moderating role of substantial affective elements. In this systematic review and meta-regression analysis, we assess how CBT response differs across the affective spectrum in psychosis. Methods: We included studies assessing various CBT modalities, including third-wave therapies, administered in people with psychosis. The study protocol is published in the Open Science Framework. Meta-regression was conducted to assess whether the proportion of participants with affective psychosis (AP), as proxied by a documented diagnosis of schizoaffective (SZA) disorder, moderated CBT efficacy across positive, negative, and depressive symptom domains. Results: The literature search identified 4457 records, of which 39 studies were included. The median proportion of SZA disorder participants was 17%, with a total of 422 AP participants represented. Meta-regression showed a trend toward lower CBT efficacy for positive symptoms with a higher SZA disorder proportion (β = +0.10 SMD per 10% increase in AP; p = 0.12), though it was not statistically significant. No significant associations were found for negative (β = +0.05; p = 0.73) or depressive symptoms (β = −0.02; p = 0.78). Heterogeneity was substantial across all models (I² ranging from 54% to 80%), and funnel plot asymmetry was observed in negative and depressive symptoms, indicating possible publication bias. Risk of bias assessment showed the anticipated inherent difficulty of psychotherapies in blinding and possibly dropout rates affecting some studies. Conclusions: Affective symptoms may reduce the effectiveness of CBT for positive symptoms in psychotic disorders, although the findings did not reach statistical significance. Other patient-level characteristics in psychosis could indicate which patients can benefit most from CBT modalities. Full article

(1) Background: Analyses using participatory GIS (PGIS) data have primarily focused on reporting landscape values (e.g., subsistence, spiritual) or activities (e.g., hunting, meditation) and less frequently on identifying patterns of value and activity co-occurrence. This paper explores whether consistent combinations of landscape values and activities associated with meaningful places identified by visitors—referred to as “bundles”—emerge across protected areas. These bundles represent the cognitive-behavioral components of sense of place. (2) Methods: We used exploratory factor analysis on aggregated PGIS data collected between 2011 and 2017 to identify value-activity bundles across three protected areas administered by the Forest Service in the northwestern United States. (3) Results: We found no universal bundles of landscape values and activities across the protected areas, limiting the ability to describe consistent sense of place bundles. Instead, relationships between landscape values and activities varied across areas. Weak associations between them highlight heterogeneity in how individuals perceive and interact with meaningful places, reflecting the subjective and context-dependent nature of the sense of place. (4) Conclusions: These findings suggest that identifying visitor “types” for outreach and planning may be more nuanced than anticipated. To provide diverse opportunities for visitors to protected areas, planners and decision-makers may need to move beyond standard audience segmentation practices and consider the context-dependent nature of sense of place. Full article